Influence of Various Processing Parameters on the Microbial Community Dynamics, Metabolomic Profiles, and Cup Quality During Wet Coffee Processing

Abstract

Post-harvest wet coffee processing is a commonly applied method to transform coffee cherries into green coffee beans through depulping or demucilaging, fermentation, washing, soaking, drying, and dehulling. Multiple processing parameters can be modified and thus influence the coffee quality (green coffee beans and cup quality). The present study aimed to explore the impacts of these parameters, including processing type (depulping or demucilaging), fermentation duration, and application of soaking, on the microbial community dynamics, metabolite compositions of processing waters (fermentation and soaking) and coffee beans, and resulting cup quality through a multiphasic approach. A large-scale wet coffee processing experiment was conducted with Coffea arabica var. Catimor in Yunnan (China) in duplicate. The fermentation steps presented a dynamic interaction between constant nutrient release (mainly from the cherry mucilage) into the surrounding water and active microbial activities led by lactic acid bacteria, especially Leuconostoc and Lactococcus. The microbial communities were affected by both the processing type and fermentation duration. At the same time, the endogenous coffee bean metabolism remained active at different stages along the processing, as could be seen through changes in the concentrations of carbohydrates, organic acids, and free amino acids. Among all the processing variants tested, the fermentation duration had the greatest impact on the green coffee bean compositions and the cup quality. A long fermentation duration resulted in a fruitier and more acidic cup. As an ecological alternative for the depulped processing, the demucilaged processing produced a beverage quality comparable to the depulped one. The application of soaking, however, tempered the positive fermentation effects and standardized the green coffee bean quality, regardless of the preceding processing practices applied. Lastly, the impact strength of each processing parameter would also depend on the coffee variety used and the local geographical conditions. All these findings provide a considerable margin of opportunities for future coffee research.

Full text

fmicb-10-02621 November 11, 2019 Time: 14:8 # 1
ORIGINAL RESEARCH
published: 13 November 2019
doi: 10.3389/fmicb.2019.02621
Edited by:
Teresa Zotta,
Italian National Research Council
(CNR), Italy
Reviewed by:
Carlo Giuseppe Rizzello,
University of Bari Aldo Moro, Italy
Maria Aponte,
University of Naples Federico II, Italy
*Correspondence:
Luc De Vuyst
luc.de.vuyst@vub.be
†These authors have contributed
equally to this work
Specialty section:
This article was submitted to
Food Microbiology,
a section of the journal
Frontiers in Microbiology
Received: 04 September 2019
Accepted: 28 October 2019
Published: 13 November 2019
Citation:
Zhang SJ, De Bruyn F,
Pothakos V, Contreras GF, Cai Z,
Moccand C, Weckx S and De Vuyst L
(2019) Influence of Various Processing
Parameters on the Microbial
Community Dynamics, Metabolomic
Profiles, and Cup Quality During Wet
Coffee Processing.
Front. Microbiol. 10:2621.
doi: 10.3389/fmicb.2019.02621
Influence of Various Processing
Parameters on the Microbial
Community Dynamics, Metabolomic
Profiles, and Cup Quality During Wet
Coffee Processing
Sophia Jiyuan Zhang1†, Florac De Bruyn1†, Vasileios Pothakos1, Gonzalo F. Contreras2,
Zhiying Cai3, Cyril Moccand4, Stefan Weckx1and Luc De Vuyst1*
1Research Group of Industrial Microbiology and Food Biotechnology, Faculty of Sciences and Bioengineering Sciences, Vrije
Universiteit Brussel, Brussels, Belgium, 2Nestlé Coffee Agriculture Service, Yunnan, China, 3Yunnan Institute of Tropical
Crops, Kunming, China, 4Nestlé Research, Vers-chez-les-Blanc, Switzerland
Post-harvest wet coffee processing is a commonly applied method to transform coffee
cherries into green coffee beans through depulping or demucilaging, fermentation,
washing, soaking, drying, and dehulling. Multiple processing parameters can be
modified and thus influence the coffee quality (green coffee beans and cup quality). The
present study aimed to explore the impacts of these parameters, including processing
type (depulping or demucilaging), fermentation duration, and application of soaking,
on the microbial community dynamics, metabolite compositions of processing waters
(fermentation and soaking) and coffee beans, and resulting cup quality through a
multiphasic approach. A large-scale wet coffee processing experiment was conducted
with Coffea arabica var. Catimor in Yunnan (China) in duplicate. The fermentation steps
presented a dynamic interaction between constant nutrient release (mainly from the
cherry mucilage) into the surrounding water and active microbial activities led by lactic
acid bacteria, especially Leuconostoc and Lactococcus. The microbial communities
were affected by both the processing type and fermentation duration. At the same
time, the endogenous coffee bean metabolism remained active at different stages
along the processing, as could be seen through changes in the concentrations of
carbohydrates, organic acids, and free amino acids. Among all the processing variants
tested, the fermentation duration had the greatest impact on the green coffee bean
compositions and the cup quality. A long fermentation duration resulted in a fruitier
and more acidic cup. As an ecological alternative for the depulped processing, the
demucilaged processing produced a beverage quality comparable to the depulped
one. The application of soaking, however, tempered the positive fermentation effects
and standardized the green coffee bean quality, regardless of the preceding processing
practices applied. Lastly, the impact strength of each processing parameter would also
depend on the coffee variety used and the local geographical conditions. All these
findings provide a considerable margin of opportunities for future coffee research.
Keywords: coffee bean fermentation, wet processing, Coffea arabica, amplicon sequencing, shotgun
metagenomics, metabolomics, lactic acid bacteria
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Zhang et al. Post-harvest Processing Affects Coffee Quality
INTRODUCTION
It has only been 500 years since coffee acquired its worldwide
popularity (Yilmaz et al., 2017). Today, coffee has become one
of the most important commercial crops, on which millions of
people depend for their livelihood (Lambot et al., 2017). To
transform the coffee cherries into a cup of coffee, a complex chain
of post-harvest events is needed, during which the cherries are
first picked from the coffee trees, processed to green coffee beans,
subsequently roasted, and finally brewed to generate a coffee
beverage with pleasant aroma and taste (Batista and Chalfoun,
2014;Brando and Brando, 2014;Silva, 2014;Poisson et al., 2017;
Waters et al., 2017). Each step of this processing chain plays
a significant role in the coffee quality, which can be evaluated
by the quality of the green coffee beans as well as the sensory
experience of the brewed coffees (i.e., cup quality) (Batista and
Chalfoun, 2014). Hence, post-harvest processing offers a margin
for improvement of the coffee quality.
Whereas many recent studies have investigated mainly the
roasting (Bustos-Vanegas et al., 2018;Gloess et al., 2018) and
brewing steps of coffee production (Parenti et al., 2014;Labbe
et al., 2016), the impacts of the post-harvest processing of the
coffee cherries and beans, besides genetic attributes of coffee
varieties and farming practices, on the coffee quality still remain
an open field of research (Lee et al., 2015;Vaughan et al., 2015;
De Bruyn et al., 2017;Waters et al., 2017;Pereira et al., 2019;
Zhang et al., 2019). Among numerous post-harvest processing
practices, wet processing is commonly applied for Coffea arabica
to generate high-quality Arabica coffee. During wet processing,
harvested mature coffee cherries are first depulped (i.e., squeezed
mechanically to remove the skin and pulp) and then fermented
underwater until the mucilage (a carbohydrate-rich layer) is
removed, which usually takes 12–72 h (Brando and Brando, 2014;
Silva, 2014). After washing the fermented beans, they are dried
until their moisture content is below 12% (m/m), and finally
dehulled to yield the green coffee beans. The duration of the
fermentation step has an impact on the microbial activities that
take place and, hence, on the chemical composition of the green
coffee beans and the resulting cup quality (De Bruyn et al., 2017;
Zhang et al., 2019). Sometimes, an extra soaking step, during
which the washed beans are submerged in clean water, is applied
to improve the visual appearance of the green coffee beans and to
obtain a clean taste in the final cup (Murthy and Naidu, 2012).
Consequently, the wet processing method is time-demanding
and resource-intensive and requires a high water usage and
extra treatments of the fermentation and washing waters, due to
their high contents of organic pollutants (Bonilla-Hermosa et al.,
2014). An alternative method is to use a demucilager, a machine
that can scrape off the mucilage from the depulped beans
mechanically (Murthy and Naidu, 2012;Brando and Brando,
2014). In this case, the demucilaged beans are usually dried either
immediately or after a short fermentation step (usually 12 h;
Sanz-Uribe et al., 2017). With the help of a demucilager, the
water usage and the processing time can be reduced (Chapagain
and Hoekstra, 2007). Further, the waste products are more easily
processed and discarded (Murthy and Naidu, 2012;Borém et al.,
2014). However, the added values of demucilaging and soaking
are controversial, since their impacts on the green coffee beans
and cup quality are still unclear, as they have never been studied
in great detail.
On top of possible variations of the processing parameters, wet
processing is an interplay of microbial activities and endogenous
bean metabolism, especially at the stage of fermentation (Kramer
et al., 2010;Lee et al., 2015;De Bruyn et al., 2017;Waters et al.,
2017;Zhang et al., 2019). Bacteria [especially lactic acid bacteria
(LAB) and enterobacteria, but also acetic acid bacteria (AAB) and
bacilli] and yeasts, which originate from the environment (cherry
surfaces, plantation surroundings, and/or processing equipment)
and are highly variable and difficult to predict, are prevalent
during the fermentation step (Silva, 2014;Evangelista et al.,
2015;Lee et al., 2015;Vaughan et al., 2015;De Bruyn et al.,
2017;Pereira et al., 2019;Zhang et al., 2019). Their metabolites
can accumulate onto the coffee beans, resulting in an indirect
impact on the cup quality. As intermediate seeds, the coffee beans
remain active throughout the post-harvest processing chain
and respond to various abiotic stress factors, such as hypoxia
during fermentation and drought stress during drying (Selmar
et al., 2006;Kramer et al., 2010;De Bruyn et al., 2017;Zhang
et al., 2019). Detailed monitoring of the microbial community
dynamics and diversity as well as the metabolomics of both
pulp and beans along the whole post-harvest processing chain
has shown that on-plantation practices and fermentation under
carefully controlled conditions regarding the ripeness stage of the
coffee cherries and the fermentation duration selects for specific
LAB species (De Bruyn et al., 2017;Zhang et al., 2019). Moreover,
a long fermentation duration results in not only a microbial
shift (from leuconostocs to acid-tolerant lactobacilli) but also
chemical compositional changes in the green coffee beans and
distinct sensory attributes in the brewed cup (De Bruyn et al.,
2017;Zhang et al., 2019). As the latter studies were performed
with C. arabica var. Typica in Ecuador, it was valuable to verify
if the same effects would be obtained with a different coffee
variety in a distinct geographical region. Furthermore, there are
no scientific data available about how demucilaging affects the
microbial ecology or metabolite compositions of beans during
fermentation, which could be a compelling way of modulating
the coffee flavor by inferring specific changes in the microbial
consortium during fermentation.
The present study aimed to investigate the impacts of
multiple processing parameters (demucilaging and depulping,
fermentation duration, and application of soaking) on the
dynamics of the microbial communities, the metabolomic
profiles of the coffee beans and processing waters, the metabolite
compositions of the green coffee beans, and the sensory quality of
the coffee brews upon post-harvest processing of C. arabica var.
Catimor cherries and beans in China.
MATERIALS AND METHODS
Wet Processing Field Experiments
Wet processing field experiments were carried out at a coffee
plantation of the Experimental and Demonstration farm near
Jinghong in Yunnan, China (latitude and longitude coordinates
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Zhang et al. Post-harvest Processing Affects Coffee Quality
21◦5805700 N and 101◦702300 E, respectively; altitude, 1,300 m) in
December 2015 to January 2016. During this period, cold weather
conditions occurred and the daily coffee harvesting yield was
4000–5000 kg of matured coffee cherries per day. Two biological
replicates were performed (1 week apart), further referred to as
the first and second processing trials. Each replicate included
two processing types, namely demucilaged processing (further
referred to as DM1 and DM2 processes) and depulped processing
(DP1 and DP2 processes) (Figure 1). For each replicate, 2000 kg
of mature cherries of C. arabica var. Catimor were handpicked by
local farmers and split evenly for the DM and DP processes. In
the DP processes, the cherries were depulped with a mechanical
depulper (UCBE 500; Penagos, Bucaramanga, Colombia) and the
beans were subsequently submerged in clean water to ferment
in a concrete tank (4.0 m ×2.5 m ×2.5 m). Removal of the
mucilage determined the standard fermentation time (36 h for
DP1 and 48 h for DP2). When the mucilage was removed,
half of the beans were withdrawn, representing a standard
fermentation, while the other half remained in the tank until
84 h of fermentation, representing an extended fermentation. In
the DM processes, the cherries were depulped and demucilaged
by the same machine used for the DP processes. The mucilage-
free beans were then submerged in clean water to ferment in
a concrete tank (same dimensions as those mentioned above).
Half of the beans were withdrawn after a fixed duration of
12 h (according to local practices), representing a standard
fermentation, while the other half remained in the tank until
72 h of fermentation, representing an extended fermentation.
After all fermentations, the beans were washed thoroughly by
passing them through a curved water channel. Part of the washed
beans (approximately 125 kg) were dried immediately, while
the rest of the beans (approximately 875 kg) were placed in a
plastic bucket and soaked for 24 h in clean water before drying.
All beans were dried on a concrete patio with frequent mixing
until the moisture content was below 12% (m/m). Together
with each replicate, a negative control was implemented, for
which the demucilaged beans (approximately 125 kg) were dried
immediately, without fermentation or soaking, further referred to
as control processes (C).
The temperature and pH of the fermenting masses were
monitored on-line by means of a WTW pH 3110 data logger
(Xylem Analytics, Weilheim, Germany). For off-line analyses,
multiple samples were taken along the processing duration,
encompassing fresh coffee cherries (DX1CB for the first trial
and DX2CB for the second trial), beans and fermentation
waters during the fermentation step (FB and FW, respectively),
beans and soaking waters during the soaking step (SB and
SW, respectively), and the final green coffee beans (GB).
Microbiological plating for culture-dependent analysis was
performed on-site. The bean and processing water samples
were frozen immediately at −20◦C for culture-independent and
metabolomic analysis after shipping to Belgium. The GB of each
processing variant were shipped to Nestlé Research (NR, Vers-
chez-les-Blanc, Switzerland) at room temperature and used there
for coffee brewing and sensory analysis.
In an additional experiment, a prolonged submersion of beans
and pulps was conducted to investigate the potential diffusion of
FIGURE 1 | Overview of the Arabica coffee wet processing experiments, with
multiple variations in processing practices, including processing type
(demucilaged processing and depulped processing, referred to as DM and DP
processes, respectively), fermentation duration (standard and extended), and
application of soaking (with and without soaking; S0 and S24, respectively).
Control processes (C), without fermentation or soaking, were also conducted.
The experiments were conducted in duplicate, indicated as first (1) and
second (2) trials. DX1CB and DX2CB, fresh coffee fruits; GB, green coffee
beans.
coffee substrates into the surrounding waters (further referred
to as bean water and pulp water). Freshly demucilaged beans
and pulp were collected from a same harvest of coffee cherries
(80 kg). The demucilaged beans were thoroughly rinsed and any
pulp present was removed manually. Subsequently, the pulp-free
demucilaged beans (B) and the fresh pulps from the depulper
(P) were submersed separately in buckets with clean water
for 72 h. The bean and pulp waters, beans, and pulps at the
beginning and the end of submersion were sampled and frozen
immediately at −20◦C for metabolomic analysis after shipping
to Belgium. The beginning and the end of submersion were
denoted as D0 and D72.
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Zhang et al. Post-harvest Processing Affects Coffee Quality
Culture-Dependent Microbiological
Analysis
Microbial counts were monitored by plating on selective agar
media, as described previously (Zhang et al., 2019). The
total aerobic microbiota was targeted on plate count agar
(PCA; Oxoid, Basingstoke, Hampshire, United Kingdom), LAB
were targeted on de Man-Rogosa-Sharpe agar supplemented
with sorbic acid (1%, m/v) (MRS-S agar; Pothakos et al.,
2014), AAB on modified deoxycholate-mannitol-sorbitol agar
(mDMS agar; Papalexandratou et al., 2013), and yeasts and
filamentous fungi on yeast-glucose agar (YG agar; Laureys and
De Vuyst, 2014). Enterobacteria were targeted by plating on
Rapid’Enterobacteriaceae agar (BioRad Laboratories; Hercules,
CA, United States). Counts are represented as triplicate averages
with their standard deviations. Isolate recovery (10–15 colonies
per agar medium and per time point corresponding with the
highest dilutions) and dereplication and identification were
performed for LAB, yeasts, and AAB, as described previously
(Zhang et al., 2019). Concisely, genomic DNA of pure cultures
was extracted and specific loci were sequenced to allocate species
level identity for each isolate. LAB were identified by sequencing
the 16S rRNA gene, AAB by sequencing the 16S rRNA gene and
dnaK gene, and yeasts by sequencing the internal transcribed
spacer (ITS) region of the fungal ribosomal RNA transcribed
unit. The accession numbers of the reference sequences used for
identification of the isolates are represented in Table 1.
Culture-Independent Microbiological
Analysis
Metagenetics
Total microbial DNA extraction and amplicon sequencing were
done as described previously (Zhang et al., 2019). Concisely,
total microbial DNA was extracted from each sample, based
on multiple cell lysis steps (targeting bacteria and fungi),
phenol/chloroform/isoamyl alcohol extraction, and on-column
purification. The V4 hypervariable region of the bacterial 16S
rRNA gene and the ITS1 region of the fungal 26S rRNA gene
were amplified selectively. These amplicons were sequenced on
the MiSeq platform (Illumina; San Diego, CA, United States)
of the Brussels Interuniversity Genomics High Throughput core
facility BRIGHTcore (Jette, Belgium). For the bioinformatic
analysis, the resulting sequences were converted into relative
abundances of amplicon sequence variants (ASVs) with the
R-package DADA2 (Callahan et al., 2017). The sequences are
available at the European Nucleotide Archive under accession
numbers PRJEB30537 for the V4 sequences1and PRJEB30538 for
the ITS1 sequences2.
Shotgun Metagenomics
Shotgun metagenomic sequencing and quality processing
Shotgun metagenomic sequencing was applied on six well-chosen
underwater fermentation samples, namely those corresponding
with fermentation time point 72 h of both DM processes
1http://www.ebi.ac.uk/ena/data/view/PRJEB30537
2http://www.ebi.ac.uk/ena/data/view/PRJEB30538
TABLE 1 | Accession numbers of the reference sequences used for species level
identification of the isolates obtained from the Arabica coffee post-harvest
processing experiments.
Species identification Accession number
(NCBI nucleotide database)
Yeasts ITS sequences
Candida humilis JQ726600
Candida quercitrusa KF728800
Candida solani KY102402
Cordyceps brongniartii JN941759
Hanseniaspora uvarum KJ706285
Hanseniaspora vineae HQ909094
Lachancea lanzarotensis KY076618
Papiliotrema terrestris KY104479
Pichia kluyveri LT714692
Saccharomyces cerevisiae KJ781352
Starmerella bacillaris KY076623
Torulaspora delbrueckii KM384545
Wickerhamomyces anomalus KC597821
Lactic acid bacteria 16S rRNA gene sequences
Lactobacillus coryniformis EU626008
Lactobacillus plantarum LT593850
Lactococcus hircilactis NR_136465
Lactococcus lactis MF429271
Leuconostoc citreum NR_041727
Leuconostoc holzapfelii NR_042620
Leuconostoc mesenteroides KC108669
Leuconostoc pseudomesenteroides GQ351323
Weissella soli NR_025642
NCBI, National Center for Biotechnology Information; ITS, internal
transcribed spacer.
(DM1_F72 and DM2_F72), and the time points that represented
the end of the standard fermentation duration for the two
DP processes (DP1_F36 and DP2_F48) and the end of the
extended fermentation duration for the DP processes (DP1_F84
and DP2_F84). A DNA-based metagenomic analysis was chosen
to perform both a taxonomic assignment of metagenomic
reads and a functional analysis based on contigs of the same
metagenomic reads. Hereto, the total microbial DNA, extracted
as described above, was fragmented into pieces with an average
size of 550 base pairs (bp), using a Covaris M220 device
(Covaris, Brighton, United Kingdom). Barcoded libraries were
created using a KAPA Hyper Prep Kit (KAPA Biosystems,
Wilmington, MA, United States), according to the manufacturer’s
instructions. After size selection using Agencourt AMPure XP
magnetic beads (Beckman Coulter, Brea, IN, United States),
the size of the fragments in the final libraries was controlled
using an Agilent 2100 Bioanalyzer (Agilent Technologies, Santa
Clara, CA, United States). The concentrations were measured
using a fluorescence-based method (Qubit 2.0; Thermo Fisher
Scientific, Wilmington, DE, United States). All libraries with a
concentration above 1.0 ng/µL and a concordant size range were
quantified based on qPCR using a LightCycler 480 II (Roche
Diagnostics, Basel, Switzerland) and the KAPA Illumina Library
Quantification Kit (KAPA Biosystems). Subsequently, all libraries
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were diluted to a concentration of 2 nM. Per sequencing run,
three libraries were pooled equimolarly prior to denaturation
with NaOH, followed by a dilution to 7 pM and final paired-
end (PE) sequencing by means of a MiSeq sequencer (Illumina)
and MiSeq Reagent Kit v3 (600-Cycle). Library preparation
and sequencing were performed by BRIGHTcore. The sequence
data obtained was demultiplexed and quality-processed using
Trimmomatic-0.36 (Bolger et al., 2014). Only the paired forward
and reverse metagenomic sequence reads were retained, which
were merged into contigs, further referred to as metagenomic
sequences, using PANDAseq v2.7 (Masella et al., 2012) with the
minimum overlap set to 10 nucleotides (nt).
To remove metagenomic sequences that were derived from
the coffee plant, the metagenomic sequences were aligned to the
genome sequence of Coffea canephora (RefSeq accession number
PRJEB4211). Although the experiments were performed using
C. arabica, an allotetraploid plant and a hybrid between the
diploid C. canephora and Coffea eugenoides, its genome was
not publicly available at the time of analysis. Alignment was
performed using the BLAST algorithm blastn (Altschul et al.,
1990) with the word size set to 25 nt, the minimum alignment
identity to 80%, the maximum alignment hits per read to 1, and
the query coverage to 70%.
Taxonomic assignment of metagenomic reads
For taxonomic assignment, the metagenomic reads were
aligned using the blastn algorithm, with the same settings
as mentioned above, to a customized database containing all
available genomes of those genera that were present during
coffee processing, a strategy also known as metagenomic
recruitment plotting (Vermote et al., 2018). Hereto, a total of
561 genome sequences were downloaded from the National
Center for Biotechnology Information (NCBI) RefSeq and
Whole Genome Shotgun (WGS) databases, comprising genome
sequences from Acetobacteraceae (n= 44), Lactobacillaceae
(n= 175), Leuconostocaceae (n= 35), Enterococcaceae (n= 26),
Streptococcaceae (n= 32), Enterobacterales (n= 99), fungi
(n= 56), and miscellanea (n= 94). Genera that were
represented by less than 0.1% of all metagenomic reads were not
further considered.
Functional analysis
Assembly of the metagenomic reads into contigs was done
using the MEGAHIT assembler (Li et al., 2015), with the
pre-set parameter set to “meta sensitive” for each time point
independently. Only contigs with a length higher than 1 kbp were
retained. The metagenomic contigs assembled were annotated
with Prokka (Seemann, 2014). The annotations obtained were
filtered for genes encoding known proteins and designated
with an EC number, reflecting the reactions catalyzed by the
enzymes encoded. Subsequently, per sample, the occurrence of
each EC number was quantified, and the list of EC numbers was
condensed to the sub-subclass level.
The metagenomic shotgun reads are available at the European
Nucleotide Archive under accession number PRJEB317463.
3http://www.ebi.ac.uk/ena/data/view/PRJEB31746
Meta-Metabolomic Analysis
Sample Preparation
The fermentation and soaking water samples were first thawed
and then microcentrifuged (19,400 ×gfor 15 min at 10◦C)
to remove the remaining plant material. The clear supernatants
were collected and subjected to a metabolomic analysis. In
the case of the bean samples taken at the fermentation and
soaking steps and the GB, the parchment and silver skin were
removed manually. The resulting beans were then frozen in
liquid nitrogen and ground to fine powders for extraction.
Three different extraction procedures were performed on the
bean powders (in triplicate), namely aqueous [ultrapure water
(Milli-Q; Merck, Billerica, MA, United States)], acidic (0.01 N
HCl; Merck), and organic solvent (40%, v/v, methanol; Merck)
extraction, during which ethylenediaminetetraacetic acid (0.2%,
m/m, Merck) and ascorbic acid (0.2%, m/m, Merck) were
added to inhibit enzyme activity and oxidation, respectively.
The moisture content of the beans was determined in triplicate
by means of an oven method, during which the ground bean
powders were dried at 105◦C for 24 h. All concentrations of
bean metabolites are expressed on a dry mass basis, unless
stated otherwise.
Quantification of Simple Carbohydrates and Sugar
Alcohols
The concentrations of simple carbohydrates (fructose, galactose,
glucose, mannose, and sucrose) and sugar alcohols (arabitol,
erythritol, glycerol, mannitol, myo-inositol, sorbitol, and
xylitol) in the processing waters and aqueous bean extracts
were determined by high-performance anion exchange
chromatography with pulsed amperometric detection (HPAEC-
PAD), using an ICS 3000 chromatograph equipped with a
CarboPac PA-100 and CarboPac MA-1 column (Dionex,
Sunnyvale, CA, United States), respectively, as described
previously (Zhang et al., 2019). Quantification was performed
via internal standardization in triplicate. The internal
standard (IS) solution consisted of rhamnose (20 mg/L;
Merck, Darmstadt, Germany) in acetonitrile (Merck). All
samples were mixed with the IS solution, microcentrifuged
(19,400 ×gfor 15 min at 10◦C), and filtered [Chromafil
0.20 µm polytetrafluoroethylene (PTFE; in the case of simple
carbohydrates) filters or polyethersulfone filters (in the case
of sugar alcohols); Macherey-Nagel, Düren, Germany] before
injection (10 µL) into the column.
Quantification of Organic Acids
The concentrations of organic acids (i.e., citric acid, fumaric
acid, gluconic acid, isocitric acid, lactic acid, malic acid, oxalic
acid, quinic acid, and succinic acid) in the processing waters
and acidic bean extracts were determined by ultra-performance
liquid chromatography coupled to tandem mass spectrometry
(UPLC-MS/MS), using an Acquity UPLC system equipped with
an HSS T3 column and a TQ tandem mass spectrometer
(Waters; Milford, MA, United States), as described previously
(Zhang et al., 2019). Quantification was performed through
external calibration in triplicate. All samples were mixed with
methanol, microcentrifuged (19,400 ×gfor 15 min at 10◦C), and
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filtered (Chromafil 0.2 µm PTFE filters) before injection (10 µL)
into the column.
Quantification of Chlorogenic Acids
The concentrations of six chlorogenic acid (CGA) isomers,
namely 3-caffeoylquinic acid (CQA), 4-CQA, 5-CQA, 3,4-
diCQA, 3,5-diCQA, and 4,5-diCQA, in the processing waters
and methanol bean extracts were determined by UPLC-MS/MS,
as described previously (Zhang et al., 2019). Quantification was
performed through internal standardization in triplicate. The
IS solution consisted of rosmarinic acid (1.0 mg/L; Merck)
in acetonitrile (Merck). All samples were mixed with the IS
solution, microcentrifuged (19,400 ×gfor 15 min at 10◦C), and
filtered (Chromafil 0.2 µm PTFE filters) before injection (10 µL)
into the column.
Quantification of Alkaloids and Other Phenolic Acids
The concentrations of alkaloids (i.e., caffeine and trigonelline),
ferulic acid, and caffeic acid in the processing waters and
acidic bean extracts were determined by UPLC-MS/MS, as
described previously (Zhang et al., 2019). Quantification was
performed through internal standardization in triplicate. The
IS solution consisted of 1-ethyl-4-(methoxycarbonyl)pyridinium
iodide (0.15 mg/L; Merck) in acetonitrile (Merck). All samples
were mixed with the IS solution, microcentrifuged (19,400 ×g
for 15 min at 10◦C), and filtered (Chromafil 0.2 µm PTFE filters)
before injection (10 µL) into the column.
Quantification of Free Amino Acids
The concentrations of 20 proteinogenic amino acids and one
non-proteinogenic amino acid (γ-aminobutyric acid, GABA) in
the processing waters and acidic bean extracts were determined
by HPLC coupled to tandem MS (HPLC-MS/MS) by means of an
Alliance 2695 chromatograph and Micromass Quattro MicroTM
mass spectrometer (Waters), as described previously (Zhang
et al., 2019). Quantification was performed through internal
standardization in triplicate. L-2-amino butyric acid (1.2 ng/mL;
Merck) was used as IS. All samples were microcentrifuged
(19,400 ×gfor 15 min at 10◦C) and filtered (Chromafil 0.2 µm
PTFE filters) before injection (10 µL) into the column.
Quantification of Short-Chain Fatty Acids and
Low-Molecular-Mass Volatiles
The concentrations of short-chain fatty acids (SCFAs, i.e., acetic
acid, butanoic acid, hexanoic acid, 2-methylpropanoic acid, 3-
methylbutanoic acid, pentanoic acid, and propanoic acid) and
low-molecular-mass volatiles (i.e., acetaldehyde, ethanol, ethyl
acetate, ethyl lactate, and isopentyl acetate) in the processing
waters and aqueous bean extracts were determined by gas
chromatography with flame ionization detection (GC-FID), as
described previously (De Bruyn et al., 2017). Quantification was
performed through internal standardization in triplicate. Briefly,
the samples were prepared with an IS solution of 1-butanol
(0.20 g/L; Merck) and injected (1 µL; split 20) into a Stabilwax-
DA column (Restek, Bellefonte, PA, United States) of a Focus GC
equipped with a flame ionization detector FID-80 (Interscience,
Breda, Netherlands).
Volatile Fingerprinting of Selected Green Coffee
Beans
Semi-quantitative volatile fingerprinting of the GB samples was
conducted by headspace/solid-phase microextraction coupled
with GC and time-of-flight MS (HS/SPME-GC-TOF-MS) in
triplicate, as described previously (Zhang et al., 2019). A Trace
1300 gas chromatograph (Thermo Fisher, Waltham, MA,
United States) equipped with a Stabilwax R
-MS column (Restek)
and a BenchTOF-HD mass spectrometer (Markes International,
Llantrisant, Wales, United Kingdom) was used. GB powder
(1.0 g) was incubated in a 10 mL screw-top headspace vial at
50◦C for 10 min, followed by extraction using a SPME fiber
(DVB/CAR/PDMS, 50/30 µm; Supelco, Merck) for 45 min. The
raw data were deconvoluted with TOF-DS software (Markes),
followed by identification of each peak via the NIST library
(National Institute of Standards and Technology, Gaithersburg,
MD, United States) and the Kovats Index (Afeefy et al., 2017).
The peak area of each compound identified, normalized to the
peak area of the IS and adjusted for the moisture content, was a
measure of the aroma intensity (Zhang et al., 2019).
Roasting and Sensory Evaluation
Bean roasting and cup evaluation were performed according
to an in-house standardized protocol (NR). GB from all
processing variants were roasted until the color of the roasted
beans reached a color test Neuhaus (CTN) value of 90
(Neuhaus Neotec, Ganderkesee, Germany). Coffees were brewed
with a Moccamaster coffee machine (Technivorm, Amerongen,
Netherlands) at a ratio of 50 g of roasted beans per liter of water.
They were served at 70◦C in 80 mL plastic cups. A quantitative
descriptive analysis was applied to measure the intensity of 27
sensory attributes, covering eleven odor attributes (OD), 13 flavor
attributes (FL), and 3 texture attributes (TX). The samples were
evaluated by 12 trained panelists at NR.
Statistical Analysis
The statistical analyses were conducted and visualized by various
R packages in RStudio (version 1.1.423; R Core Team, 2018),
including corrplot (Wei and Simko, 2017), ggplot2 (Wickham,
2016), gplots (Wickham, 2019), Hmisc (Harrell, 2018), lme4
(Bates et al., 2015), and vegan 2.5-2 (Oksanen et al., 2018).
To explore the isolate identification data, a (-1,1)-scaled and
rotated principal component analysis (PCA) was performed
on a covariance matrix with and without the (-1,1)-scaled
factor loadings overlaid. To identify discriminating microbial
communities between the fermentations of the DM and DP
processes and to quantify the effect of the fermentation duration
on the microbial communities within the fermentations of these
processes, as determined culture-independently, a permutational
analysis of variance (PERMANOVA) based on the Bray-Curtis
dissimilarity matrix (105permutations) and similarity percentage
analysis were performed with the package vegan.
For further processing of the outcome of the functional
analysis of the shotgun metagenomic data, a heatmap was
constructed using the gplots package, with the data scaled per
sub-subclass and represented by a Zscore.
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To analyze the metabolomic data, heatmaps of the
fermentation and soaking water sample data were plotted,
based on Z-scores, and the data were clustered based on the
average distance between the points. Linear mixed effect models
(LMEM) were applied on the quantitative metabolomic data
of the GB, the volatile fingerprinting data of the GB, and the
sensory outcomes of the brewed coffees with the package lme4.
The model included three fixed effects, namely processing type
(DM and DP), fermentation duration (no fermentation, standard
fermentation, and extended fermentation), and application
of a soaking step (with and without soaking). The biological
repetition (first and second trials) was included as random effect
in the model. The significant levels of each fixed effect on the
compounds targeted or sensory attributes were calculated by
comparing the full model with a reduced model through an
analysis of variance (ANOVA), for which the targeted fixed
effect was excluded. The relative size of the effect (t-value)
of the compound or sensory attribute was reported when the
p-value was <0.05. In the case of GB volatile fingerprinting,
the volatiles of significant difference were grouped together,
based on their corresponding odor descriptions (Deibler and
Delwiche, 2004;Mosciano, 2018). PCAs were performed on
both the bean and water metabolomic datasets. Various PCAs
were plotted based on the quantitative metabolomic results of
the FB and SB, the quantitative metabolomic results of the GB,
and the sensory outcomes of the brewed coffees. Correlation
matrices were used for the metabolite compositions of the
beans, whereas a covariance matrix was used for the sensory
outcomes. The number of PCs retained were derived from the
Scree plot and eigenvalues obtained. Correlation analyses were
applied to link the metabolomic data of the bean and water
samples during the fermentation and soaking steps with the
underwater submersion duration, with the packages Hmisc and
corrplot. Based on Spearman correlations, only the correlations
with a p-value <0.05 were reported. Boxplots were used to
plot the metabolite compositions of beans before (during the
fermentation and soaking steps) and after drying (GB).
RESULTS
Temperature and pH
The temperature at the start of all fermentation variants
was approximately 15◦C (Supplementary Figure S1). During
fermentation, the temperature followed the day-night cycle. The
final temperatures of the fermentations of the DM processes
were higher than those of the DP ones (approximately 13◦C
versus 11◦C, respectively). For all fermentation variants, the
initial pH was approximately 6.0–6.5 and decreased continuously
until approximately 4.0. For the fermentations of both the DM
and DP processes, the major pH drop happened after 36 h
of fermentation.
Microbial Load of the Harvested Coffee
Cherries
The microbial counts on the surfaces of the harvested coffee
cherries ranged from log 4.0–6.0 (CFU/g) (Figure 2). The
highest microbial counts were found on PCA, MRS-S agar, and
Rapid’Enterobacteriaceae agar [approximately log 6.0 (CFU/g)],
indicating a relatively high presence of aerobic microorganisms,
presumptive LAB, and presumptive enterobacteria, respectively.
Lower counts were found on mDMS agar and YG agar
[approximately log 4.0 (CFU/g)], indicating a relatively low
presence of presumptive AAB and presumptive yeasts.
Culture-Dependent Microbial Community
Dynamics and Species Diversity During
Fermentation
The growth patterns of the microbial groups targeted showed no
major differences between all fermentation variants of the DP and
DM processes. Presumptive LAB and aerobic microorganisms
were the most prevalent microbial groups in all fermentation
variants. These two microbial groups displayed very similar
growth patterns during fermentation. The presumptive LAB
reached counts that were several orders of magnitude larger
than any other microbial group, especially in the later stages
of fermentation [>log 7.0 (CFU/g) after 24 h of fermentation].
In all fermentation variants, the final LAB counts were log
8.0–9.0 (CFU/g). The counts of the total aerobic microbiota
followed the dynamics of the LAB counts closely. No filamentous
fungi were found in any process. Presumptive yeasts and
enterobacteria were present in low numbers in all fermentation
variants [approximately log 5.0 (CFU/g)]. Yeast counts stayed
relatively constant over the course of the fermentation variants,
but enterobacterial counts decreased after an initial rise during
the initial stages of the fermentation variants (0–36 h). This
decrease in enterobacterial counts was not as pronounced in
the DM2 process. AAB counts were low or below the detection
limit [log 2.0 (CFU/g) for this experimental set-up] during all
fermentation variants.
Leuconostoc was the most prevalent LAB genus found culture-
dependently (Figure 3). Leuconostoc pseudomesenteroides,
Leuconostoc mesenteroides, and Leuconostoc holzapfelii were the
most frequent species. Lactococcus (especially Lactococcus
lactis) was also frequently found during fermentation
(especially in the DM processes; Supplementary Figure S2).
Lactobacillus plantarum was highly prevalent in the DP1 process
(Supplementary Figure S2). Other Lactobacillus species and
Weissella soli were occasionally found during all fermentation
variants. The yeasts Candida humilis and Hanseniaspora uvarum
were widely found in all fermentation variants. Other yeast
species, notably Pichia kluyveri and Torulaspora delbrueckii, were
found only sporadically throughout all fermentation variants.
The AAB Acetobacter and Gluconobacter were sparingly detected
at various time points of the fermentations of the DP processes.
No obvious trends in the species diversity of any microbial group
were found over the course of the fermentation variants. Still,
there was a tendency to find more specific species in one of the
two fermentation variants. Lactococcus lactis (1.7 times more),
H. uvarum (1.5 times more), and Leuc. holzapfelii (1.5 times
more) were found more in the fermentations of the DM than
in those of the DP processes. Conversely, some species were
found more in the fermentations of the DP than in those of the
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FIGURE 2 | Microbial counts of fresh coffee fruits (DX1CB and DX2CB) and fermenting beans of the Arabica coffee wet processing experiments, representing
fermentation of depulped (DP) and demucilaged (DM) beans, as determined through cultivation on five selective agar media, namely modified
deoxycholate-mannitol-sorbitol (mDMS) agar medium for the enumeration of presumptive acetic acid bacteria, de Man-Rogosa-Sharpe agar medium supplemented
with sorbic acid (MRS-S) for the enumeration of presumptive lactic acid bacteria, plate count agar (PCA) medium for the enumeration of the total aerobic microbiota,
Rapid’Enterobacteriaceae agar medium for the enumeration of presumptive enterobacteria, and yeast-glucose (YG) agar medium for the enumeration of
presumptive yeasts and filamentous fungi. Every row represents a selective agar medium and every column represents a process followed. The first disconnected
time point represents the microbial load on the surfaces of the coffee fruits. The following connected time points represent the microbial counts during fermentation.
DM processes, namely Lb. plantarum (not found), C. humilis
(4.4 times more), Leuc. mesenteroides (1.3 times more), and
P. kluyveri (not found). Consequently, the culture-dependent
approach revealed that the microbial species diversity remained
largely unchanged, whereas the size of the microbial populations
evolved as fermentation proceeded.
Culture-Independent Microbial
Community Dynamics and Diversity
During Fermentation
Metagenetics
The major share of the bacterial sequences obtained from
the fermentation variants of the DM and DP processes was
allocated to LAB (Figure 4). Within this group, Leuconostoc
and Lactococcus were by far the most prevalent ASVs. These
ASVs had high relative abundances throughout all fermentation
variants. Lactobacillus was only moderately present at the
beginning of the fermentation of the DP1 process. Weissella
was present solely at the end of fermentation of the DM
processes. Except for the increasing relative abundance of
Lactococcus over the course of the fermentations of the DM
processes, no obvious trend was found for the LAB ASVs.
Compared to the LAB ASVs, other ASVs were of minor relative
abundance during the fermentation variants. These ASVs were
present only in the beginning of the fermentation variants
(notably Alsobacter,Pseudomonas, and Yersinia) or were only
transiently present (such as Citrobacter). The fungal ASVs
showed more diverse relative abundance patterns over the
course of the fermentation variants. The most relative abundant
ASVs over all fermentation variants were Pichia,Candida,
Kazachstania,Papiliotrema,Cutaneotrichosporon,Hannaella,
and Zygotorulaspora. Some of the fungal ASVs were more relative
abundant in the fermentations of the DM processes than in those
of the DP processes (in particular Candida,Cutaneotrichosporon,
Lachancea,Pyrenochaeta, and Zygotorulaspora). In contrast, only
Hannaella seemed to be slightly more relative abundant in the
fermentations of the DP processes than in those of the DM ones.
As is the case for the bacterial ASVs, no obvious trend was found
for the fungal ASVs. Still, some fungal ASVs were present only in
the beginning (such as Cutaneotrichosporon) or at the end (such
as Zygotorulaspora) of the fermentations of the DM processes.
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FIGURE 3 | Culture-dependent microbial species diversity along the fermentation process of depulped (DP) and demucilaged (DM) beans of the Arabica coffee wet
processing experiments. Colonies were identified upon isolation through plating of samples on selective agar media, followed by appropriate incubation. Lactic acid
bacteria (A) and yeasts (B) were the most prevalent microbial groups in all fermentation variants of the Arabica coffee wet processing experiments. Every column
represents a time series of one microbial species identified within one processing type (DM1, DM2, DP1, and DP2). Every dot represents one identification at one
time point for one microbial species.
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FIGURE 4 | Bacterial (A) and fungal (B) amplicon sequencing variants (ASVs) detected during fermentation of depulped (DP) and demucilaged (DM) beans of the
Arabica coffee wet processing experiments. To simplify visualization, individual ASVs were grouped by genus and represented in a dedicated panel. The different
colors denote the different fermentation variants (blue for the DP trials and orange for the DM trials).
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PERMANOVA indicated that the processing type had a
significant effect on the microbial community composition. The
processing type accounted for 29.93% of the variation in the
culture-independent dataset (df = 1, pseudo-F = 33.83, and
p<10−5). A total of 75 from 110 ASVs were significantly
discriminant between the DP and DM processes (p <0.05). The
top-ten discriminant ASVs were Papiliotrema 2, Kazachstania
1, Lactococcus 1, Capnodium 1, Cladosporium 2, Citrobacter
1, Vishniacozyma 1, Zymoseptoria 1, Sirobasidium 3, and
Lactobacillus 1 (Supplementary Figure S3A). Among these
ASVs, Lactococcus 1 was the only one that had a higher average
relative abundance in the fermentations of the DM processes
compared to those of the DP ones.
PERMANOVA further indicated that the fermentation
duration had a significant effect on the microbial community
composition. The fermentation duration accounted for 37.96%
of the variation in the culture-independent dataset (df = 9,
pseudo-F= 4.77, and p<10−5). A total of 22 from
110 ASVs were significantly discriminant between a standard
and extended fermentation duration across all fermentation
variants (p<0.05) (Supplementary Figure S3B). Among these
ASVs, only Lactobacillus 2 was more prevalent after extended
fermentation compared to standard fermentation duration.
Shotgun Metagenomics
Taxonomic assignment
Shotgun metagenomic sequencing of the six samples selected
yielded between 4,519,539 (sample DP1_F36) and 7,074,660
(sample DP2_F84) metagenomic sequences per sample,
excluding C. canephora-related sequences. Aligning these
metagenomic sequences to a customized database, representing
561 genomes, allowed taxonomic allocation of 76–87% of all
reads (Figure 5).
Members of the genera Leuconostoc and Lactococcus were
highly abundant in all six samples, even more pronounced
in the samples obtained from the fermentations of the DM
processes, for which there were relatively more metagenomic
sequences related to Lactococcus than to Leuconostoc. The
samples obtained from the fermentations of the DP processes
had an inversed relative composition, with more sequences
related to Leuconostoc than to Lactococcus, and with a substantial
amount of reads related to Lactobacillus. The latter genus had
a higher relative abundance at the end of the fermentations
(DP1_F84 and DP2_F84), compared to the samples taken during
the fermentations (DP1_F36 and DP2_F48), of the DP processes.
All other genera with a relative abundance higher than the cut-off
value of 0.1% had in fact a marginal relative abundance compared
to Leuconostoc,Lactococcus, and Lactobacillus.
Functional analysis
Assembly of the metagenomic sequences using MEGAHIT,
performed per sample, yielded in total 220,387 metagenomic
contigs, with a length of at least 1000 bp (Table 2). Using Prokka,
459,880 protein-encoding genes were predicted and annotated,
of which 46.0% (211,577) encoded known proteins. Of these
proteins, 56.7% (120,003) were annotated as enzymes with an
FIGURE 5 | Relative abundance (%) of all microbial genera identified (>0.1%) through fragment recruitment plots on a selection of 561 bacterial and fungal genomes
in the shotgun metagenomic data of the Arabica coffee wet processing experiments.
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TABLE 2 | Overview of the number of contigs assembled with MEGAHIT, and data regarding gene prediction and annotation with Prokka for six metagenomic samples
of the Arabica coffee post-harvest processing experiments.
Sample Number of contigs
(>1000 bp)
Number of
predicted genes
Number of
hypothetical proteins
Number of
known proteins
Number of known proteins
with an EC number
Number of unique
EC numbers
DM1_F72 23,483 48,238 27,694 20,544 11,726 902
DM2_F72 23,231 50,953 29,672 21,281 12,285 880
DP1_F36 31,888 67,938 36,752 31,186 18,180 1063
DP1_F84 54,693 112,631 59,240 53,391 29,917 1306
DP2_F48 43,078 87,888 46,635 41,253 23,289 1242
DP2_F84 44,014 92,232 48,310 43,922 24,606 1189
Total 220,387 459,880 248,303 211,577 120,003 1488
DM, demucilaging process; DP, depulping process; F, fermentation time point (in h).
EC number, which were further used as an indication for the
functional properties of the coffee fermentation ecosystem. The
number of unique EC numbers per sample ranged from 880
(sample DM2_F72) till 1306 (sample DP1_F84). The number
of unique EC numbers over all six samples was 1488. For each
sample, a count table was composed, reflecting the number
of occasions an EC number was found per sample. Next, the
list of 1488 EC numbers was compacted to the sub-subclass
level, resulting in a list of 176 sub-subclasses. Consequently,
the counts obtained for each EC number were summed for
all EC numbers within a sub-subclass per sample, representing
109,818 proteins, as the remaining number were only annotated
at the subclass or class levels. The ten most occurring enzyme
sub-subclasses belonged to the oxidoreductases, hydrolases,
transferases, glycosidases, and ligases, and accounted for 41.7%
of the 109,818 proteins (Table 3). Further, the full table with
the 176 sub-subclasses (Supplementary Table S1) was used
to calculate frequencies, which were used to draw a heatmap,
visualizing under- and over-representation of the sub-subclasses
in the six samples, scaled per sub-subclass and represented by
aZ-score (Supplementary Figure S4). The heatmap showed
a clustering based on processing type, with the two samples
from the fermentations of the DM processes (DM1_F72 and
DM2_F72) clustering together, but separated from the four
samples of the fermentations of the DP processes. Within the
latter set of samples, the two samples from the later fermentation
time point of 84 h (DP1_F84 and DP2_F84) clustered together,
reflecting that the different enzyme sub-subclasses were related
to the type of processing.
Dynamics of the Metabolomic Profiles of
the Processing Waters
Fermentation Waters
The temporal metabolomic profiles of the fermentation waters
were similar among the processing types (DM and DP)
and biological duplicates (first and second trials). The main
differences were the absolute concentrations of the compounds
targeted. The cumulative concentrations of all the compounds
quantified, especially monosaccharides, organic acids, alkaloids
and amino acids, were ten times higher in the fermentation
waters of the DP processes than in those of the DM ones
(Figure 6A). The compounds targeted could be divided into two
groups based on the temporal change of their profiles during
fermentation, namely rise-fall and rise-rise patterns (Figure 6A
and Supplementary Figure S5A). The rise-fall pattern referred
to a brief increment of their concentrations at the beginning of
the fermentations, which was succeeded by a decrement toward
the end. The compounds following this pattern included the
simple carbohydrates sucrose, glucose and fructose, the organic
acids citric acid, gluconic acid and malic acid, and the amino
acids arginine, isoleucine, leucine and lysine. These compounds
differed slightly in their timing to reach peak concentrations:
first with sucrose (first 8 h of fermentation), followed by citric
acid, malic acid, arginine, isoleucine, and lysine (after around
24 h), and then glucose, fructose, gluconic acid, and leucine
(after 36 h). In addition, the concentrations of these compounds
differed greatly at the end of the standard and extended
fermentation durations. For example, at the end of the standard
fermentations, fructose (0.5, 0.2, 6.0, and 5.4 mg/mL in DM1,
DM2, DP1, and DP2, respectively) and glucose (0.3, 0.1, 4.0, and
2.6 mg/mL, respectively) had relatively high concentrations in
the fermentation waters, whereas the concentrations of sucrose,
TABLE 3 | Overview of the ten most encountered EC sub-subclasses and their
functions, as obtained through functional analysis of the shotgun metagenomic
data of the Arabica coffee post-harvest processing experiments.
EC number Sub-subclass function Total
1.1.1.- Oxidoreductases, acting in the CH-OH group of donors,
with NAD(+) or NADP(+) as acceptor
6074
3.6.3.- Hydrolases, acting on acid anhydrides catalyzing
transmembrane movement of substances
5783
2.7.1.- Phosphotransferases with an alcohol group as acceptor 5735
2.1.1.- Methyltransferases 5280
2.7.7.- Nucleotidyltransferases 4459
2.3.1.- Acyltransferases, transferring groups other than amino-acyl
groups
4390
2.4.1.- Hexosyltransferases 4223
3.2.1.- Glycosidases 3599
6.1.1.- Ligases forming aminoacyl-tRNA and related compounds 3169
3.1.3.- Phosphoric monoester hydrolases 3108
The total number mentioned reflects the number of proteins that were annotated
with an EC number belonging to these 10 sub-subclasses for all six metagenomic
samples together.
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FIGURE 6 | Hierarchical clustering analysis and heatmap visualization of the chemical compositional profiles of the fermentation waters (A) and soaking waters (B)
from the demucilaged (DM) and depulped (DP) processes of the Arabica coffee wet processing experiments. The absolute concentrations (in mg/mL) were summed
and displayed at the right side of each heatmap.
citric acid, and malic acid were minimal in the fermentation
waters of all four processes. Toward the end of the extended
fermentations, the concentrations of fructose (0.1, 0.0, 4.6, and
2.4 mg/mL in DM1, DM2, DP1, and DP2, respectively) and
glucose (0.0, 0.0, 2.0, and 1.0 mg/mL, respectively) reached
lower levels. Furthermore, a small decrease of the galactose
concentrations appeared at the end of the fermentations of
the DM processes, which was not the case in those of the
DP ones. Second, the rise-rise pattern referred to a continuous
increment of the concentrations of the compounds targeted
throughout the fermentation steps. This pattern covered the
rest of the compounds targeted. Most of the microbial-related
metabolites (e.g., acetic acid, ethanol, glycerol, mannitol, and
lactic acid) started to accumulate only after 24 h or even
at a later stage of fermentation. The accumulation of these
compounds also occurred later in the fermentations of the
DM processes than in those of the DP ones. At the end
of the fermentations of the DP processes, mannitol (3.0 and
3.2 mg/mL in the first and second trials, respectively), lactic
acid (1.7 and 1.8 mg/mL, respectively), and acetic acid (0.9 and
0.7 mg/mL, respectively) were the most abundant compounds in
the fermentation waters. In contrast, the DM fermentation waters
reached lower concentrations of mannitol (0.4 and 0.1 mg/mL,
respectively), lactic acid (0.8 and 0.8 mg/mL, respectively),
ethanol (0.2 and 0.1 mg/mL, respectively), and acetic acid (0.1
and 0.1 mg/mL, respectively). Plant-related compounds, such as
trigonelline, caffeine, and most amino acids showed increasing
concentrations along fermentation. For instance, trigonelline
(0.1, 0.1, 0.3, and 0.2 mg/mL in DM1, DM2, DP1, and DP2,
respectively) and asparagine (0.0, 0.0, 0.8, and 0.3 mg/mL,
respectively) were among the most abundant compounds when
fermentation finished. Apart from asparagine, other free amino
acids, such as serine, aspartic acid, glutamine and GABA were at
much higher concentrations in the fermentation waters of the DP
processes than in those of the DM ones.
Soaking Waters
The absolute concentrations of all the compounds targeted were
much lower in the soaking waters than in the fermentation
waters, regardless of the processing types (DM and DP).
However, the temporal metabolomic profiles of the soaking
waters exhibited some differences when comparing the processes
with standard and extended fermentation (Figures 6B and
Supplementary Figures S5B,C). In the processes with standard
fermentation duration, two dynamic patterns were found. The
rise-fall pattern occurred in the case of sucrose, glucose,
fructose, citric acid and malic acid, whereas the rise-rise pattern
occurred for all of the other compounds targeted. In the
processes with extended fermentation duration, only a rise-stable
pattern was found, namely a concentration increment at the
beginning of the soaking step that was followed by a relatively
stable concentration. Further, differences occurred between the
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soaking waters of the DM and DP processes. The cumulative
concentrations in the soaking waters were slightly higher for
the DP processes than in those for the DM ones, and were
contributed by the presence of microbial metabolites (lactic
acid, mannitol, and succinic acid), alkaloids (trigonelline and
caffeine), and some amino acids (aspartic acid, arginine, serine,
and GABA). In the DP soaking waters, the most abundant
compounds at the end of the soaking step were mannitol,
lactic acid, fructose, and ethanol in the processes with both
standard and extended fermentation durations (Supplementary
Figures S5B,C). The mannitol concentrations at the end of
the soaking step were lower in the processes with standard
fermentation duration (0.10 and 0.13 mg/mL for DP1 and DP2,
respectively) than in the processes with extended fermentation
duration (0.29 and 0.27 mg/mL, respectively). In contrast, the
concentrations of lactic acid (0.19–0.34 mg/mL for the DP
processes), fructose (0.13–0.38 mg/mL), and ethanol (0.03–
0.09 mg/mL) were higher in the processes with standard
fermentation duration than in those with extended fermentation
duration. In the DM soaking waters, the most prevalent
compounds at the end of the processing were lactic acid
(0.04–0.14 mg/mL for the DM processes), ethanol (0.008–
0.041 mg/mL), acetic acid (0.007–0.023 mg/mL), and mannitol
(0.002–0.017 mg/mL).
Dynamics of the Metabolomic Profiles of
the Coffee Beans
Fermenting Beans
The temporal metabolomic profiles of the fermenting beans
from the DM and DP processes were similar. The compounds
targeted could be divided into four groups, based on the temporal
change of their profiles, namely (i) an off-phase evolution (in
the case of sucrose versus the monosaccharides glucose and
fructose), (ii) a rising trend, (iii) a decreasing trend, and (iv) a
relatively stable concentration along the fermentations (Figure 7
and Supplementary Figure S6A).
Concerning the first group, the glucose concentrations in
the fermenting beans always evolved in the same phase as
those of fructose, but off-phase with those of sucrose. This
took place in the DM and DP processes. However, the
glucose (0.062, 0.068, 0.220, and 0.200 g/100 g for DM1,
DM2, DP1 and DP2, respectively) and fructose concentrations
(0.062, 0.063, 0.360, and 0.310 g/100 g, respectively) were
more than three times higher in the DP beans than in the
DM ones at the start of the fermentations (Supplementary
Figure S6A). Yet, such differences were reduced toward the
end of the extended fermentation durations, as the glucose and
fructose concentrations decreased in the DP beans along the
fermentations. Concerning the second group (rising trend), the
concentrations of mannitol, succinic acid, lactic acid, alanine,
and glycine were positively correlated with the fermentation
time (p<0.05). Among these compounds, the concentration
build-ups of mannitol (0.13 and 0.14 g/100 g after 84 h of
fermentation for DP1 and DP2, respectively) and lactic acid
(0.038 and 0.048 g/100 g, respectively) were higher with the
DP processes, resulting in averages that were three and six
times higher than those in the DM beans, respectively, when
fermentation finished. The rest of the compounds showed
comparable concentrations for the DM and DP processes.
For example, the succinic acid (0.021, 0.025, 0.021, and
0.022 g/100 g for DM1, DM2, DP1, and DP2, respectively) and
alanine concentrations (0.038, 0.052, 0.056, and 0.059 g/100 g,
respectively) were similar in the DM and DP beans at the
end of the extended fermentation durations. Concerning the
third group (decreasing trend), the concentrations of malic
acid, citric acid, gluconic acid, caffeine, aspartic acid, arginine,
and histidine were negatively correlated with the fermentation
time (p<0.05). Among these compounds, the decrease of
the caffeine and citric acid concentrations was more associated
with the DM processes. The concentrations of arginine (0.012,
0.010, 0.022, and 0.024 g/100 g for DM1, DM2, DP1, and
DP2, respectively, at the end of the extended fermentation
durations) were higher in the DP beans than those in the DM
ones, whereas all the other compounds showed comparable
concentrations for the two processing types. Concerning the
fourth group (stable concentrations), the concentrations of some
amino acids (e.g., proline, isoleucine, and tyrosine) were higher in
the DP beans than in the DM ones, whereas the concentrations
of the three diCQA isomers were higher in the DM beans
than in the DP ones.
Soaking Beans
After the washing step, the concentrations of certain compounds
decreased in the coffee beans (Figure 7 and Supplementary
Figures S6B,C). For example, the concentrations of glucose,
fructose, mannitol, and lactic acid decreased, especially in the DP
processes. The lactic acid and mannitol concentrations were even
reduced, down to 1/3 and 1/20 after washing, respectively.
Along the 24-h soaking step, the metabolomic profiles of the
soaking beans remained relatively stable, despite that certain
compounds experienced concentration changes depending on
the processing type (DM and DP) and the fermentation duration
(standard and extended; Supplementary Figures S6B,C). In the
processes with standard fermentation duration, the succinic acid
concentrations were positively correlated with the soaking time
in both the DM and DP processes, whereas the aspartic acid and
arginine concentrations were negatively correlated (p<0.05).
In the processes with extended fermentation duration, lactic
acid and mannitol had higher initial concentrations compared
to the processes with standard fermentation duration, but their
concentrations diverged to similar levels toward the end of
soaking in both the DM and DP processes. When comparing
the bean compositions between the start and the end of soaking,
the concentrations of mannitol, glycerol, citric acid, malic acid,
quinic acid, lactic acid, asparagine, and aspartic acid were higher
at the start of soaking, whereas the concentrations of succinic acid
and glycine were higher at the end of soaking.
A PCA based on a correlation matrix included the
metabolomic data of the FB and SB from the DM and DP
processes (Supplementary Figure S7A). Two PCs were obtained,
explaining 41% of the total variance. PC1 was characterized by
positive loadings of certain amino acids (isoleucine, tyrosine,
proline, leucine, phenylalanine, etc.), glycerol, glucose, fructose,
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FIGURE 7 | Comparison of the chemical compounds in the coffee bean samples of different processing steps of the Arabica coffee wet processing experiments.
The selected compounds were present at either lower concentrations in the green coffee beans (GB) than in the corresponding soaking beans (SB) (A) or higher
concentrations in the GB than in the corresponding SB (B). The selected bean samples represent the end of the fermentations (FB; ), before and after soaking (•),
and their corresponding GB (⊗) across the different processing variants. The freshly demucilaged beans and the GB produced in the control processes (C, black) are
also included. Only the chemical compounds with consistent trends are shown.
mannitol, acetic acid, and lactic acid, as well as negative loadings
of 4,5-diCQA, 3-CQA, 3,4-diCQA, caffeine, and citric acid. PC2
was characterized by positive loadings of succinic acid, acetic
acid, alanine, and isocitric acid, as well as negative loadings of
aspartic acid, serine, fumaric acid, asparagine, malic acid, sucrose,
and fructose. Based on these two PCs, the DP beans were more
associated with the positive values of PC1, and the DM beans
were more associated with the negative values of PC1. The FB
were more associated with the negative values of PC2, and the SB
were more associated with the positive values of PC2.
Green Coffee Beans Produced by
Different Processing Variants
Quantitative Analyses
Compared to the start of drying, most metabolites tended to
degrade during drying, with several exceptions (Figure 7 and
Supplementary Figure S8). The corresponding GB contained
lower concentrations of glycerol, fumaric acid, lactic acid,
succinic acid, trigonelline, alanine, and tryptophan (Figure 7A),
as well as higher concentrations of compounds such as isocitric
acid, 4,5-diCQA, aspartic acid, GABA, glutamic acid, proline,
and serine (p<0.05) (Figure 7B). In the control processes, the
concentrations of glucose, fructose, succinic acid, mannitol, lactic
acid, and alanine did not show significant differences before and
after the drying step.
The GB produced from the control processes contained
higher concentrations of glucose, fructose, citric acid, malic acid,
asparagine, and aspartic acid than all the other GB processed
from the DM and DP processes (Figure 7). In contrast, the
concentrations of mannitol, succinic acid, lactic acid, alanine,
tyrosine, proline, glutamic acid, and glutamine were higher
in the DM and DP beans than in the control GB, whereas
beans from the DP1 process had the highest concentrations
of mannitol and lactic acid. Some compounds, including citric
acid, malic acid, gluconic acid, isocitric acid, alanine, isoleucine
and aspartic acid, had decreasing concentrations with long
underwater times. Other compounds, including quinic acid,
caffeine, 4-CQA, 5-CQA, and glutamic acid had decreasing
concentrations with long underwater times in the DM processes
but not in the DP ones.
LMEM were established to test the effect of different
processing variants on the metabolite concentrations in the GB,
namely the processing type, fermentation duration, and soaking
(Figure 8A). The linear model indicated a strong impact of the
fermentation duration (21 compounds with p<0.05), followed
by the processing type (18 compounds with p<0.05), and a
soaking step (13 compounds with p<0.05). A long fermentation
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FIGURE 8 | Impacts of the processing parameters of the Arabica coffee wet processing experiments on the chemical compositions of the green coffee beans (A)
and the sensory profiles of the brewed coffees (B), based on the effective size (t-value) generated from linear mixed effect models. Only the chemical compounds
and sensory attributes with a probability level less than 0.05 are listed. The chemical compounds from different chemical groups (A), as well as the flavor and odor
attributes from the sensory analyses (B), are indicated in different colors. OD, odor; FL, flavor.
duration had a significant negative impact on the concentrations
of fructose, glucose, gluconic acid, isocitric acid, citric acid,
fumaric acid, succinic acid, 3,4-diCQA, and certain amino acids
(including methionine, phenylalanine, alanine, aspartic acid,
arginine, asparagine, serine, and isoleucine), and positive impacts
on the concentrations of glutamine, histidine, and lysine. The DP
processes, compared to the DM ones, had significant negative
impacts on the concentrations of glucose and certain amino
acids (including aspartic acid, methionine, phenylalanine, and
glycine), but positive impacts on the concentrations of lactic
acid, mannitol, isocitric acid, 3,4-diCQA, 3,5-diCQA, 4-CQA,
and certain amino acids (including tyrosine, valine, glutamine,
threonine, proline, alanine, and GABA). The application of
an extra soaking step had significant negative impacts on the
concentrations of isocitric acid, citric acid, 4-CQA, 5-CQA,
3,5-diCQA, 4,5-diCQA, and certain amino acids (including
arginine, glycine, methionine, isoleucine, aspartic acid, and
phenylalanine), and a significant positive impact on the fumaric
acid concentration.
A PCA based on a correlation matrix of the same dataset
resulted in two PCs, explaining 42% of the total variance
(Supplementary Figure S7B). PC1 was characterized by positive
loadings of mannitol, lactic acid, quinic acid, glutamic acid,
isoleucine, and proline, and negative loadings of galactose,
myo-inositol, serine, aspartic acid, and asparagine. PC2 was
characterized by negative loadings of gluconic acid, arginine,
3,4-diCQA, and 4,5-diCQA. The GB from the control processes
were positioned at the negative values of PC1 and PC2. The
GB produced from the DP1 process were more associated with
the negative values of PC1 and the positive values of PC2.
The extended-processed GB of both the DM and DP processes
were more associated with the positive values of PC2, whereas
the standard-processed ones were more associated with the
negative values of PC2.
Semi-Quantitative Volatile Profiling
Around 200 compounds were found in the GB samples. The most
abundant groups were aliphatic alcohols, aliphatic alkanes, and
benzene derivatives, followed by aliphatic aldehydes, heterocyclic
compounds, and aliphatic ketones. Terpenes, sulfur-containing
compounds, and lactones were present in lower abundances. The
total aroma intensities of the standard-processed GB of the DM
processes were lower compared to the ones from other processing
variants. The application of soaking decreased the total aroma
intensities, except for the extended-processed GB from the DP2
process (Figure 9).
LMEM were established to test the effect of different
processing variants on the compounds targeted in the volatile
profiles of the GB, namely the processing type, fermentation
duration, and soaking (Figure 9). The linear model indicated
a strong impact of the processing type (43 volatile compounds
with p<0.05) and fermentation duration (40 volatile compounds
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FIGURE 9 | Impacts of the processing parameters of the Arabica coffee wet processing experiments on the volatile profiles of the green coffee beans, based on the
effective size (t-value) generated from linear mixed effect models. Only the volatiles with a significance value less than 0.05 are listed. The odor type of each of the
volatiles is indicated with a different color.
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Zhang et al. Post-harvest Processing Affects Coffee Quality
with p <0.05), followed by the application of a soaking
step (13 compounds with p<0.05). The DP processes had
significant positive impacts on volatile compounds such as
3,5-octadien-2-one (fruity), 2-methyl-1-butanol (fruity), 5-ethyl-
2(5H)-furanone, 3-methylphenol (woody), and trans-linalool
oxide (floral) (with p<0.001), and negative impacts on volatile
compounds such as benzeneacetaldehyde (floral), 3,7-dimethyl-
1,6-octadien-3-ol (linalool, floral), and 6-methyl-3,5-heptadiene-
2-one (green) (with p<0.001). The longer fermentation
durations had significant positive impacts on trans-linalool
oxide (floral), 3,5-octadien-2-one (fruity), 1-nonanol (floral),
ethanol (alcohol), 3-methylphenol (woody), and linalool (floral),
and negative impacts on 1-methyl-1H-pyrrole (woody) and 2-
methylpropanoic acid (buttery) (p<0.001). The application of
soaking gave positive impacts on linalool (floral) and negative
impacts on 6-methyl-3,5-heptadien-2-one (green), 6-methyl-5-
hepten-2-one (fruity), and propanoic acid (cheesy).
Sensory Analysis
The scores for the sensory notes were close to each other
across the samples. LMEM were established to evaluate the
effect of different processing variants on the sensory profile,
namely the processing type, fermentation duration, and soaking
(Figure 8B). The linear model indicated a strong impact of the
fermentation duration (five parameters with p<0.05), followed
by the processing type (two parameters with p<0.05), and a
soaking step (two parameters with p<0.05). A long fermentation
duration had a positive impact on the fruity (OD and FL) and
acidity (FL) notes and a negative impact on the cereal (OD)
and floral (OD) notes. Compared to the DM processes, the DP
processes had a positive impact on the fruity (OD) notes and a
negative impact on the humus/earthy (FL) notes. The application
of soaking had a positive impact on the overall flavor intensity
and a negative impact on the spicy (OD) notes.
According to the PCA analysis, two PCs covered 48% of
the data variability (Supplementary Figure S7C). Smoothness
(TX) was more associated with the positive values of PC1,
whereas bitter and roasty (FL) notes were more associated
with the negative values of PC1. Fruity and acidic (FL) notes,
as well as fruity, burnt, and cereal (OD) notes were more
associated with the negative values of PC2. The samples from the
processes with extended fermentation duration were associated
with the negative values of PC1, whereas the samples from
the processes with extended fermentation duration and the
control processes were associated with the positive values of
PC1. No major separations were found between the processing
types (DM and DP).
Metabolite Diffusion From Different
Coffee Substrates
The beans and pulps from the fresh coffee cherries displayed
different metabolomic profiles, whereby the pulps contained
more glucose, fructose, mannitol, some organic acids (malic
acid, quinic acid, succinic acid, isocitric acid, gluconic acid,
and 5-ketogluconic acid), and certain amino acids (arginine,
asparagine, serine, glutamic acid, and glutamine). The soluble
portions of both beans and pulps decreased after 72 h of
submersion, whereby the decrease was more extensive in the
pulps (Figure 10). The concentrations of glucose, fructose,
trigonelline, malic acid, 3-CQA, 3,5-diCQA, 4,5-diCQA, aspartic
acid, and asparagine were lower in the beans after submersion,
whereas the concentrations of ethanol, mannitol, lactic acid,
succinic acid, alanine, glutamic acid, and GABA increased
in the beans after submersion. In the pulps, most of the
compounds targeted had lower concentrations after submersion,
except for ethyl acetate, lactic acid, 4-CQA, 5-CQA, asparagine,
glycine, and GABA.
Furthermore, the loss of the soluble portions of the beans
and pulps corresponded to the increments of metabolites in their
surrounding waters. In the case of the pulp waters, glucose,
fructose, myo-inositol, malic acid, quinic acid, fumaric acid,
alanine, asparagine, aspartic acid, glutamic acid, GABA, and
serine reached higher concentrations compared to those in
the bean waters. Compounds such as ethanol and lactic acid
reached similar concentrations after 72 h in both the bean
and pulp waters.
DISCUSSION
In the past, the fermentation step of wet coffee processing
has been studied, mainly from a microbiological point of
view, by means of both cultivation-based methods and culture-
independent approaches (Silva et al., 2008;Vaughan et al., 2015;
Feng et al., 2016;Hamdouche et al., 2016;De Bruyn et al., 2017;
Pereira et al., 2017;Waters et al., 2017;Zhang et al., 2019). Indeed,
most of these studies focused on the species diversity and in
some cases the activities of the coffee bean microbiota to try
to explain green coffee bean production, yield, and quality. The
present study tackled the dynamics of the microbial communities
and metabolomic profiles of the coffee beans, processing waters
(fermentation and soaking waters) and green coffee beans, and
the sensory quality of the coffee brews, thereby evaluating the
impacts of multiple processing parameters (demucilaging and
depulping, fermentation duration, and soaking).
Both demucilaged and depulped processing methods are
commonly applied by coffee producers, depending on the water
resources and infrastructure available (Murthy and Naidu, 2012).
The main technical difference between these two processes,
i.e., the amount of mucilage attached to the beans at the start
of the fermentation step, was translated into differences in
the microbial community compositions and in the metabolite
concentrations present in the fermentation waters. As a result,
the DP processes contained a richer nutrient reservoir for
the microorganisms and plant enzymes present, resulting in
stronger fermentation effects for the DP processes than for the
DM ones. Nevertheless, the comparable temporal profiles of
the metabolites in the fermentation waters of both processing
types implied similar microbial activities, plant material diffusion
dynamics, and enzymatic reactions. The mucilage of matured
coffee cherries is copious in carbohydrates, organic acids,
alkaloids, and free amino acids (Neu et al., 2016;Zhang et al.,
2019). As the main plant material present in the fermentation
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FIGURE 10 | Compositional changes in the demucilaged beans and pulps, and in their corresponding water samples, before and after 72 h of soaking in separate
buckets. D0B and D72B refer to the water and bean samples from the diffusion tests of the demucilaged beans at 0 and 72 h, respectively, whereas D0P and D72P
refer to the water and pulp samples from the diffusion tests of the pulps at 0 and 72 h, respectively.
waters, these compounds gradually diffused into the surrounding
water, due to steep concentration gradients (Cheng et al., 2016).
Along with the increasing concentrations of nutrients in the
fermentation waters, some chemical compounds were used by the
microorganisms as substrates to be fermented or converted into
diverse microbial metabolites.
LAB, in particular mesophilic species, were predominant from
the beginning of the fermentations, albeit in lower counts than
what was found in previous similar experiments carried out in
Ecuador (De Bruyn et al., 2017;Zhang et al., 2019). In contrast to
these previous experiments, AAB appeared sporadically, whereas
yeasts showed no major changes in counts. This low prevalence
of microorganisms might be due to environmental factors,
such as the temperature, which was rather low compared to
other coffee fermentation experiments performed before (De
Bruyn et al., 2017;Zhang et al., 2019), or due to intrinsic
factors, such as the coffee variety used (Brando and Brando,
2014;Silva, 2014;Waters et al., 2017;Pereira et al., 2019).
Furthermore, the microbial community compositions were
affected by both the processing type and fermentation duration.
Among the LAB, species of Leuconostoc seemed to be prevalent
during all fermentations, whereas other communities appeared
to be more associated with fermentations of either the DM
or DP processes. These tendencies were confirmed through
amplicon sequencing, elucidating that microbial communities
were distributed differently, although largely shared between
fermentations of the DM and DP processes. Based on shotgun
metagenomic sequencing, a more detailed insight was obtained,
as Leuconostoc had a much higher relative abundance in the
fermentations of the DP processes compared to those of the
DM ones. In the latter fermentations, Lactococcus was present in
higher relative abundances.
Microbial communities preferentially associated with one
processing type could be more adapted to the concomitant
conditions (i.e., substrate concentrations), which inferred a
competitive advantage for these communities and thus increased
the relative abundances of their respective ASVs. Corresponding
to the metabolite composition differences in the DM and
DP fermentation waters, genera performing well during the
fermentations of the DM processes could be less fastidious or
more efficient in nutrient uptake, because part of the mucilage
was stripped away mechanically during demucilaging, which
translated into faster-growing LAB genera that outcompeted
slower-growing ones. To that effect, a specific Lactococcus ASV
seemed to be well-adapted to the lower substrate concentrations
in the fermentation waters of the DM processes. Conversely, a
specific Lactobacillus ASV was present only in the comparatively
high-nutrient conditions of the fermentation waters of the DP
processes. This was partly reflected in the isolate identifications
as well, as specific DM samples had particularly high factor
loadings in the PCA for Lc. lactis and, conversely, specific
DP samples had particularly high factor loadings in the PCA
for Lb. plantarum. The fermentation duration also affected
the microbial community compositions significantly. Notably,
a specific Lactobacillus ASV increased in relative abundance
when the fermentation duration was extended. Other ASVs that
were significantly discriminant between standard and extended
fermentation durations decreased as the fermentation duration
increased. As mentioned above and compared to other coffee
fermentations in which LAB occurred in relatively high counts,
the present study encountered a higher prevalence of Lactococcus
and a lower prevalence of Lactobacillus (De Bruyn et al., 2017;
Zhang et al., 2019). This could be due to the relative low
environmental temperature during the current experiments.
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Indeed, lactococci can grow at temperatures as low as 10◦C,
whereas lactobacilli have a broad temperature range in which
they can grow, their optimal growth temperatures ranging from
30–40◦C (Kim, 2014;Pot et al., 2014).
All the LAB species identified fermented the mucilage
carbohydrates to lactic acid, ethanol, acetic acid, and/or
mannitol through the Embden-Meyerhof-Parnas pathway
(homofermentative LAB) or phosphoketolase pathway
(heterofermentative LAB) (Zaunmüller et al., 2006;Gänzle,
2015). Malic acid could be converted into lactic acid through
malolactic fermentation, whereas citric acid could be converted
into lactic acid and acetic acid, following pyruvate metabolism,
or succinic acid through the reductive branch of the tricarboxylic
acid cycle (Gänzle, 2015). Branched-chain amino acids
(isoleucine and leucine), arginine, and lysine could be converted
into the corresponding α-keto acids through transamination, or
further to the corresponding aldehydes and alcohols through
consecutive decarboxylation and dehydrogenation (Fernández
and Zúñiga, 2006;Gänzle, 2015). Finally, due to prolonged
fermentation, a successive pattern of substrate consumption
occurred, i.e., first sucrose, followed by monosaccharides, organic
acids, and branched-chain amino acids. Consequently, the higher
substrate concentrations in the fermentation waters of the DP
processes yielded a higher accumulation of microbial metabolites
(especially lactic acid and mannitol) in these fermentation
waters compared to those of the DM ones, which gave a greater
fermentation effect on the fermenting beans.
Further, the metabolite compositions of the fermenting
beans were modified through the combination of microbial
activities and the endogenous bean metabolism. Indeed, multiple
microbial metabolites accumulated onto the fermenting beans
upon fermentation, especially lactic acid and mannitol. This
was more pronounced in the fermentations of the DP
processes, demonstrating the positive effects of microbial
activities during coffee fermentation, especially with abundantly
present substrates and long fermentation durations. Further,
many compounds in the beans were affected by their endogenous
metabolism, due to hypoxia. Under hypoxia, the anaerobic
ethanol and lactate fermentations and the GABA shunt are
activated in the beans, due to oxygen-limiting conditions
(Bytof et al., 2005). Aspartic acid can be converted into
oxaloacetate via aspartate aminotransferase, and glutamic acid
into α-ketoglutarate via glutamate dehydrogenase or GABA via
glutamate decarboxylase (Fait et al., 2007;Hildebrandt et al.,
2015). Stimulated by prolonged oxygen deprivation, GABA
can be further converted into succinic acid, to participate in
the tricarboxylic acid cycle, simultaneously producing alanine
from pyruvate (Haäusler et al., 2014;Snowden et al., 2015).
This corroborated with decreasing aspartic acid and increasing
succinic acid and alanine concentrations in the beans along
the fermentations, as reported before in other dicotyledons
(Limami et al., 2008;Sato et al., 2016). In addition, the off-
phase pattern of sucrose and its constituting monosaccharides
glucose and fructose confirmed the diverse equilibrium and
remobilization of the carbohydrate resources in the beans (De
Bruyn et al., 2017;Zhang et al., 2019). Although present at lower
abundances than LAB, the contribution of yeasts to changes in the
fermentation waters, for instance, consumption of carbohydrates
and branched-chain amino acids as well as fusel alcohol and ester
production through Ehrlich pathway, cannot be excluded.
After the fermentation step, the washing and soaking
steps removed the liquefied mucilage from the beans and
also facilitated drying (Brando and Brando, 2014). However,
compounds of microbial origin diffused from the fermenting
beans into the clean surrounding waters and this was more
pronounced in the DP processes and/or processes with extended
fermentation duration. The distinct speeds of metabolite
accumulations between the processes with standard and
extended fermentation durations were the consequences of the
concentration gradients present. Larger concentration gradients
in the processes with extended fermentation duration accelerated
the diffusion of metabolites into the soaking waters, whereas
processes with standard fermentation duration showed a more
gradual accumulation. In contrast, the starting concentrations
of the microbial substrates (especially sucrose, glucose, citric
acid, and malic acid) were higher in the soaking waters of
the processes with standard fermentation duration, allowing
the microorganisms to metabolize them, and resulting in a
concomitant increment of the lactic acid concentrations. The
fermentation effects on the coffee beans were lessened after
washing and soaking. Only a small fraction of the microbial
metabolites (in the DM and DP processes) or glucose and
fructose (in the DP processes) were retained on the beans after
soaking. Consequently, the concentration differences of the
microbial metabolites between the different processing variants
were more obvious on the coffee beans without soaking than
on the ones after soaking. Furthermore, an active endogenous
bean metabolism during soaking continued to modify the
bean compositions, especially concerning the concentrations of
alanine, aspartic acid, and succinic acid.
The diffusion tests of the coffee substrates suggested that
both the beans and pulps could lose their soluble portions into
the surrounding water after prolonged underwater submersion.
Compared to the beans, the soluble portions diffused more easily
from the pulps, probably due to the more porous and fleshy
structure of the pulps (Borém et al., 2014). This might explain
some local practices that add pulp to the fermentation tank to
create greater fermentation effects, as the pulp can be used as an
additional substrate by the microorganisms. In the case of pulp-
free demucilaged beans, the loss of the soluble portions of the
beans and the accumulation of metabolites in the surrounding
water implied that a certain degree of diffusion also took place
during prolonged underwater submersion. In combination with
the endogenous bean metabolism and microbial activities, all of
these factors could modify the metabolite compositions of the
beans during water submersion.
The drying stage is the stage generating the final green coffee
beans, during which the beans gradually lose their metabolic
activities, with a concomitant moisture drop and microbial
activities of less significance (Waters et al., 2017;Zhang et al.,
2019). By comparing coffee beans before and after drying,
differences in their metabolite compositions occurred, due to
both chemical degradation and seed responses to drought
stress (Bytof et al., 2005;Rendón et al., 2013). In response to
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the dehydration stress, amino acids such as GABA, proline,
aspartic acid, and glutamic acid accumulated in the drying
beans as a result of an increased biosynthesis to maintain cell
membrane stability (Bytof et al., 2005;Farooq et al., 2009). The
decreasing concentrations of fumarate, malate, and succinate
and the increasing concentrations of isocitrate implied responses
of tricarboxylic acid cycle intermediates under dehydration and
seed respiration (Selmar et al., 2006;Bai et al., 2012). The
reduction of monosaccharides, amino acids, and other organic
acids could also be caused by a persistent metabolism in the
beans during prolonged drying. Furthermore, interconversions
and degradation of CGAs were reflected in changes of the quinic
acid and CQA concentrations and ratios between the CGA
isomers in the beans. This also corresponded to a loss of total
CQAs that occurred during drying, confirming previous results
(De Bruyn et al., 2017;Zhang et al., 2019). Such continuous
changes in the bean compositions underlined the importance
of the drying practices and durations on-farm, where either
good or poor practices would have an impact on the final green
coffee bean quality.
As many changes took place along the processing, the
metabolite compositions of the green coffee beans were inevitably
affected by the processing parameters, primarily the fermentation
duration and processing type. The green coffee beans from
the control processes, which were actually equivalent to semi-
dry processing, retained higher levels of volatiles and non-
volatiles compared to the wet-processed ones. The remnants of
the mucilage and absence of underwater submersion effectively
reduced the potential loss of substances and dry materials, caused
by diffusion and abiotic stress (Borém et al., 2014). In contrast,
the wet-processed green coffee beans were subjected to the
effects of both underwater fermentation and endogenous bean
metabolism. As addressed during previous studies, the influence
of the fermentation duration on the green coffee beans was
associated with the presence of substrates from the mucilage
and microbial activities (De Bruyn et al., 2017;Zhang et al.,
2019). A high mucilage amount, as seen in the fermentation
waters of the DP processes, reinforced the fermentation effect
and could be retained on the green coffee beans, especially in
the absence of soaking. The application of soaking facilitated the
removal of the fermentation effects, as reflected in a significant
loss of both volatile and non-volatile compounds. Furthermore,
these processing parameters also affected the volatile profiles
of the green coffee beans and could be linked to the cup
quality (Gonzalez-Rios et al., 2007). The volatiles related to
floral and fruity notes of the green coffee beans seemed to be
enhanced by a long fermentation duration. The volatiles related
to floral notes also appeared at higher relative abundances in
the DM-processed green coffee beans, whereas the fruity-related
volatiles appeared more in the DP-processed ones. Most of
these compounds might survive the roasting process and, hence,
modify the sensory profiles of the brewed coffees. Moreover, the
loss of these substances tended to be more obvious for the DM-
processed green coffee beans than for the DP-processed ones.
This could be credited to a lack of extra barriers on the beans and
a larger concentration gradient in the fermentation step of the
DM processes compared to the DP ones. Also, the reproducibility
of the experiments was better with the DM processes than with
the DP ones regarding both the processing water and bean
compositions, implying that the former processing type was less
dependent on the ambient temperature, water to bean ratio, or
other technical aspects. Lastly, the extra soaking step mainly
functioned as a cleaning system, whereby both volatile and
non-volatile compounds were lost into the surrounding water,
generating cleaner green coffee beans. This might explain the
empirical findings that soaking of fermented washed beans could
deliver a cleaner note to the cup compared to non-soaked ones
(Velmourougane, 2011;Murthy and Naidu, 2012).
The sensory profiles of the final coffees were the combined
result of roasting and brewing. Roasting changes the appearance,
physical structure, and chemical composition of green coffee
beans, while brewing extracts soluble compounds from the
flavorful roasted coffee beans into water (Poisson et al., 2017).
The low-molecular-mass compounds in the green coffee beans,
such as simple carbohydrates, free amino acids, trigonelline
and CGAs, are the key precursors during roasting and, hence,
undergo chemical reactions, whereas the high-molecular-mass
compounds are hardly affected by roasting but contribute to
the texture and mouthfeel of the brewed coffee (de Rosa et al.,
2016;Pereira et al., 2019). Although the aroma variation of the
brewed samples was constrained, certain attributes still exhibited
differences among the processing variants. Similar to the case
of the green coffee beans, the fermentation duration had the
greatest impact on the sensory quality of the brews, followed
by the processing type and the application of a soaking step.
The higher acidity and fruity notes of the brewed coffees, which
were generally desired, were enhanced by prolongation of the
fermentation step and the application of a depulping process.
However, the floral notes were more prominent in the control
processes, which could be related to the higher concentrations of
amino acids and organic acids present in the green coffee beans.
The application of a more ecological demucilaging process did
not result in striking differences in the cup quality, compared to
depulped processing, which was in accordance with the findings
of previous studies carried out at different locations (Brando and
Brando, 2014). However, there is still potential for a depulped
process in combination with soaking to lower the earthy and
spicy notes, which are normally related to Robusta beans or
low-quality Arabica beans (Borém et al., 2014). In the present
study, the Catimor variety used, a hybrid of the Caturra and
Timor varieties, inevitably carried a certain degree of flavor
inheritance from its Robusta ancestor (Sakiyama and Ferrão,
2014). Therefore, the use of depulping and soaking could provide
a margin for quality improvement of such varieties from a
processing perspective.
Lastly, the effect of terroir, the coffee variety available, and the
geographical location should also be considered when evaluating
the degree of influence of the processing parameters on the
coffee quality. Compared to a previous study with C. arabica
var. Typica on an Ecuadorian plantation (Zhang et al., 2019),
the initial microbial load, the microbial community dynamics
of the fermentation step, and the metabolite compositions of
the coffee beans and processing waters differed. The microbial
load of the coffee cherries was lower with C. arabica var.
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Zhang et al. Post-harvest Processing Affects Coffee Quality
Catimor in the present study, compared to the Typica variety
used in a previous study (Zhang et al., 2019). In the latter
case, the microbial load at the beginning of the fermentation
consisted mainly of LAB and was high, because the coffee
cherries exuded carbohydrate-rich juices, due to mechanical
pressure experienced in their storage bags during the harvesting-
depulping interval. The Catimor variety used in the present study
appeared to have a sturdier skin and might thus be less prone to
exuding juices than the Typica variety. Hence, the unavailability
of carbohydrate-rich substrates could render the epiphytic
microbiota (especially LAB) unable to grow significantly before
the onset of the actual fermentation. However, even when this
release of carbohydrates did not happen, the epiphytic LAB were
able to outcompete other microbial groups during fermentation
in both field experiments, likely due to the concomitant pH
decrease. This means that both the duration of the harvesting-
depulping interval and the coffee variety should be taken into
account, when considering a possible pre-processing growth
spurt of the coffee cherry epiphytic microbiota. In addition, the
extent of fermentation, as reflected in the fermentation water
compositions, was much lower in the present study. This was
probably attributed to the colder processing environment of the
location, the lower nutrient density of the fermentation water,
and the relatively smaller mucilage proportion of the cherries,
which were inherent to the variety used. The colder processing
environment especially set limiting factors on the growth of the
microbial communities. The lower counts of LAB, enterobacteria,
and yeasts, as well as the absence of AAB implied a difficulty of
these microorganisms to accommodate to the lower temperatures
under the current experimental conditions, since they have been
found in fermentations carried out in warmer regions before.
Nevertheless, the fermentation duration and the processing type
still played a great role in the metabolite compositions of the
green coffee beans and the sensory outcomes of the coffee cups,
whereas washing and soaking could lessen the fermentation
effects. In addition, the presence of the endogenous bean
metabolism was of influence and could modify the green coffee
bean compositions through diverse reactions. Still, different
processing parameters resulted in differences on the green coffee
bean compositional level.
CONCLUSION
The present wet coffee processing study carried out in
Yunnan, China, compared the effect of demucilaging and
depulping, the fermentation duration, and a soaking step
on the microbial community compositions, the metabolomic
profiles of the (green) coffee beans and processing waters,
and the cup quality. From the microbiological perspective, the
microbial composition was affected by both the processing
type and fermentation duration, particularly based on the
culture-independent microbial community composition. With
the exception of Lactococcus, even represented by one ASV,
all top discriminant microbial communities had a higher
prevalence in fermentations of DP processes than in those
of DM ones. The fermentation duration also affected the
microbial community composition significantly. An extended
fermentation duration increased the numerical prevalence of
LAB. Of the top discriminant species between standard and
extended fermentation durations, only Lactobacillus, represented
by one ASV, was more prevalent in the fermentations of
the DP processes than in those of the DM ones, even more
pronounced toward the end of the extended fermentation
durations, whereas all other ASVs decreased. From the
metabolomic and concomitant sensory perspectives, among
all the processing variants applied, the fermentation duration
(impacting the microbial community composition) had the
greatest impact on the green coffee bean compositions and
sensory quality of the brewed coffees, followed by the
processing type and the application of a soaking step. The
combination of depulping and long fermentation could enhance
the cup quality, especially the fruity notes of the brewed
coffees. The application of soaking tempered the positive
fermentation effects and standardized the green coffee bean
quality, regardless of the processing practices applied. As
an alternative for depulping, demucilaging could produce
comparable coffee quality. However, coffees resulting from
demucilaged beans were significantly less fruity in odor and
had higher humus flavor. Otherwise, demucilaging tended to be
more reproducible than depulping. Lastly, the impact strength
of each processing parameter examined also depended on
the coffee variety used and the local geographical conditions,
providing a great margin of opportunities for future research.
Summarizing, the present study showed that certain processing
parameters need to be carefully thought through, since they
will affect the microbial ecology and sensory characteristics of
the brewed coffee. Complementarily, it showed that certain
attributes of brewed coffee can be tweaked by changing the
processing parameters.
DATA AVAILABILITY STATEMENT
The datasets generated for this study can be found
in the European Nucleotide Archive of the European
Bioinformatics Institute.
AUTHOR CONTRIBUTIONS
SZ, FD, CM, GC, and ZC conducted the experiments on the
coffee plantation. FD performed the microbiological analyses. SZ
performed the metabolomic analyses. VP and SW performed the
bioinformatics analyses. CM coordinated the sensory analyses.
SZ, FD, VP, SW, and LD wrote the manuscript. All authors read,
revised, and approved the final version of the manuscript.
FUNDING
This work was supported by the Research Council
of the Vrije Universiteit Brussel (SRP7 and IOF342
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fmicb-10-02621 November 11, 2019 Time: 14:8 # 23
Zhang et al. Post-harvest Processing Affects Coffee Quality
projects), the Hercules Foundation (Grants UABR09004
and UAB13002), and Nestec S.A., a subsidiary
of Nestlé S.A.
ACKNOWLEDGMENTS
The authors would like to thank Mr. Feng Fan, Mr. Jiazhi Hou,
Ms. Yixiang Liu, and Ms. Yuping Shi for their help during
the field experiments and Ms. Marie Auda for her help in the
statistical analysis.
SUPPLEMENTARY MATERIAL
The Supplementary Material for this article can be found
online at: https://www.frontiersin.org/articles/10.3389/fmicb.
2019.02621/full#supplementary-material
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Conflict of Interest: GC and CM were employed by the company Nestlé.
The remaining authors declare that the research was conducted in the absence of
any commercial or financial relationships that could be construed as a potential
conflict of interest.
Copyright © 2019 Zhang, De Bruyn, Pothakos, Contreras, Cai, Moccand, Weckx and
De Vuyst. This is an open-access article distributed under the terms of the Creative
Commons Attribution License (CC BY). The use, distribution or reproduction in
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are credited and that the original publication in this journal is cited, in accordance
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Frontiers in Microbiology | www.frontiersin.org 24 November 2019 | Volume 10 | Article 2621