What Can Fluorescence Tell Us About Wine?

Abstract

Rapid and cost-effective measurements of the autofluorescence of wine can provide valuable information on the brand, origin, age, and composition of wine and may be helpful for the authentication of wine and detection of forgery. The list of fluorescent components of wines includes flavonoids, phenolic acids, stilbenes, some vitamins, aromatic amino acids, NADH, and Maillard reaction products. Distinguishing between various fluorophores is not simple, and chemometrics are usually employed to analyze the fluorescence spectra of wines. Front-face fluorescence is especially useful in the analysis of wine, obviating the need for sample dilution. Front-face measurements are possible using most plate readers, so they are commonly available. Additionally, the use of fluorescent probes allows for the detection and quantification of specific wine components, such as resveratrol, oxygen, total iron, copper, hydrogen sulfite, and haze-forming proteins. Fluorescence measurements can thus be useful for at least a preliminary rapid evaluation of wine properties.

Full text

Academic Editor: Cosima
Damiana Calvano
Received: 8 March 2025
Revised: 30 March 2025
Accepted: 3 April 2025
Published: 4 April 2025
Citation: Sadowska-Bartosz, I.;
Bartosz, G. What Can Fluorescence
Tell Us About Wine? Int. J. Mol. Sci.
2025,26, 3384. https://doi.org/
10.3390/ijms26073384
Copyright: © 2025 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license
(https://creativecommons.org/
licenses/by/4.0/).
Review
What Can Fluorescence Tell Us About Wine?
Izabela Sadowska-Bartosz * and Grzegorz Bartosz
Laboratory of Analytical Biochemistry, Institute of Food Technology and Nutrition, Faculty of Technology and
Life Sciences, University of Rzeszow, 4 Zelwerowicza Street, 35-601 Rzeszow, Poland; gbartosz@ur.edu.pl
*Correspondence: isadowska@ur.edu.pl
Abstract: Rapid and cost-effective measurements of the autofluorescence of wine can
provide valuable information on the brand, origin, age, and composition of wine and
may be helpful for the authentication of wine and detection of forgery. The list of fluores-
cent components of wines includes flavonoids, phenolic acids, stilbenes, some vitamins,
aromatic amino acids, NADH, and Maillard reaction products. Distinguishing between
various fluorophores is not simple, and chemometrics are usually employed to analyze the
fluorescence spectra of wines. Front-face fluorescence is especially useful in the analysis of
wine, obviating the need for sample dilution. Front-face measurements are possible using
most plate readers, so they are commonly available. Additionally, the use of fluorescent
probes allows for the detection and quantification of specific wine components, such as
resveratrol, oxygen, total iron, copper, hydrogen sulfite, and haze-forming proteins. Fluo-
rescence measurements can thus be useful for at least a preliminary rapid evaluation of
wine properties.
Keywords: wine; fluorescence; front-face fluorescence; chemometrics; PARAFAC; PCA;
fluorescent probes
1. Introduction
Wine is one of the oldest and most commonly consumed beverages, consumed since
Neolithic times for social, personal, and religious reasons [
1
–
3
]. Wine is the product
of the alcoholic fermentation of grapes, although it is also produced from other fruits.
Natural wines contain 9–14% ethanol, while dessert and appetizer wines may contain
15–21% ethanol [
4
]. The cardioprotective effects of wine have been postulated and debated.
The low prevalence of ischemic heart disease, in spite of the high intake of saturated fat,
observed in France, was ascribed to the high consumption of red wine and referred to as
the French paradox [
5
–
7
]. Wine is a complex mixture of various compounds including
alcohols (mainly ethanol but also small amounts of other alcohols), sugars, esters, aldehydes,
ketones, organic acids, amino acids, biogenic amines, furanic compounds, lactones, acetals,
phenols, polyphenols, metal ions, terpenes, norisoprenoids, methoxypyrazines, and thiols.
All these compounds may affect the taste, aroma, and ither properties of wine and thus
consumer preferences [8].
Among the various techniques used to analyze wine, fluorescence spectroscopy is one
of less frequently employed. The term “fluorescence” has been introduced to describe the
unusual properties of the mineral fluorite, also called fluorspar (calcium fluoride) [
9
]. Fluo-
rescence denotes a phenomenon occurring in some molecules (fluorophores), consisting of
an immediate (in the nanosecond range) emission of light by a substance irradiated with
a light of a shorter wavelength or UV radiation. The phenomenon occurs in three stages.
In the first step, the fluorophore is excited to an electronic singlet state by absorption of
Int. J. Mol. Sci. 2025,26, 3384 https://doi.org/10.3390/ijms26073384

Int. J. Mol. Sci. 2025,26, 3384 2 of 16
an external photon (h
νex
). In the second step, the excited-state molecule interacts with
the molecular environment in several different ways, including vibrational relaxation,
quenching, and energy transfer. In the third and final step, a photon (h
νem
) of a longer
wavelength than that of the exciting light is emitted, while the fluorophore returns to its
ground state. The difference between the positions of the band maxima of the absorption
and emission spectra is called the Stokes shift [10,11].
Fluorescence spectroscopy offers several advantages over other analytical methods. It
is more sensitive than other spectroscopic techniques by 2–3 orders of magnitude [
12
–
16
]. In
many cases, this technique allows for the rapid assessment of samples without the need for
complex preparation processes. Such a measurement is non-destructive and cost-effective.
Another approach is based on the use of specific fluorescent probes that can be introduced
for the selective measurement of specific compounds or processes, for example, Ca
2+
[
17
]
or Mn
2+
concentration [
18
], the generation of reactive oxygen species [
19
], or changes in
membrane potential [20].
In conventional fluorescence spectroscopy, two types of spectra are usually measured.
Recording the emission intensity as a function of the emission wavelength
λem
at a fixed
excitation wavelength
λex
yields an emission spectrum. When
λex
is scanned at a fixed
λem
, an excitation spectrum is recorded [
10
,
11
,
21
]. Synchronous spectra are obtained when
excitation and emission are scanned simultaneously, with a fixed interval between the
excitation and emission wavelengths [22,23].
Like any technique, fluorescence spectroscopy has its limitations. Usually, right-angle
measurements of fluorescence are employed. In this arrangement, fluorescence emitted
from the whole sample in a plane perpendicular to that of the incident light is measured
(Figure 1). However, if the absorbance of the sample at the excitation wavelength is higher
than 0.05, the incident light does not reach the whole volume of the sample. In this situ-
ation, the intensity of the emitted light may not be accurately measured due to potential
scattering and absorption effects. In such cases, it is crucial to dilute the sample or ad-
just the experimental setup to ensure that the measurements reflect the true fluorescence
characteristics of the sample, proportional to fluorophore concentration. This is a common
problem when measuring food products, including wine, which are often turbid or opaque
or have high concentrations of the fluorophores. However, dilution can lead to the loss
of organization of the food matrix. To avoid these problems, the method of front-face
fluorescence spectroscopy is used. In this method, fluorescence is emitted from the surface
layer of the probe. Thus, excitation of the sample and measurement of its emitted radiation
are carried out in the same cell-face. The passage of radiation through the bulk solution
is avoided and the scattered light and depolarization phenomena are minimized. The
incidence angle of the excitation radiation is between 30◦and 60◦
mboxciteB10-ijms-3546198,B23-ijms-3546198,B24-ijms-3546198,B25-ijms-3546198. Most mi-
croplate readers have the option of front-face fluorescence measurement, which may be
an incentive for the broader use of this approach. Front-face fluorescence proved to be
especially useful in the analysis of wine [26–30].
Int. J. Mol. Sci. 2025, 26, x FOR PEER REVIEW 3 of 17
Figure 1. Right-angle and front-face fluorescence.
In principle, the fluorescence signal of a given sample is the sum of the fluorescence
contributions from each of the inherent fluorophores. However, fluorescence emission de-
pends on the surroundings of the fluorophore, and in complex systems and concentrated
solutions, the fluorescence may not be additive due to the phenomena of quenching, in-
teractions with the molecular environment of the fluorophores, and fluorescence reab-
sorption [10,11].
2. Chemometric Approach to Wine Analysis
In fluorometric studies of complex systems, including wine, a simple emission spec-
trum for one excitation wavelength is not sufficient to characterize the fluorescent prop-
erties of the sample. Instead, a set of emission spectra for a range of different excitation
wavelengths λ
ex
is often recorded to obtain a three-dimensional plot, the so-called fluo-
rescence excitation–emission matrix (EEM) (Figure 2).
The excitation–emission matrix can be obtained for each fluorophore. The overall flu-
orescence EEM for a sample can be described according to Equation (1):
𝐸𝐸𝑀 
∑
a



 𝑏

󰇛𝜆

󰇜  𝑐

󰇛𝜆

󰇜 (1)
where i is the number of a fluorophore, n is the total number of fluorescent species present
in the sample, a
i
is a concentration-dependent factor characteristic for each fluorophore, b
i
(λ
ex
) describes the excitation characteristics, and c
i
(λ
em
) describes the emission character-
istics of the i
th
fluorophore.
Figure 2. Emission–excitation matrix of a pre-barreled New Zealand wine. From [30], with the kind
permission of authors and Elsevier.
The simultaneous presence of multiple fluorophores of interlapping excitation and
emission characteristics makes the identification of contributions of individual fluoro-
phores difficult and requires the use of chemometrics, a chemical discipline using mathe-
matics, statistics, and formal logic to analyze the data. Chemometrics uses such tools as
Figure 1. Right-angle and front-face fluorescence.

Int. J. Mol. Sci. 2025,26, 3384 3 of 16
In principle, the fluorescence signal of a given sample is the sum of the fluorescence
contributions from each of the inherent fluorophores. However, fluorescence emission
depends on the surroundings of the fluorophore, and in complex systems and concen-
trated solutions, the fluorescence may not be additive due to the phenomena of quenching,
interactions with the molecular environment of the fluorophores, and fluorescence reab-
sorption [10,11].
2. Chemometric Approach to Wine Analysis
In fluorometric studies of complex systems, including wine, a simple emission spec-
trum for one excitation wavelength is not sufficient to characterize the fluorescent properties
of the sample. Instead, a set of emission spectra for a range of different excitation wave-
lengths
λex
is often recorded to obtain a three-dimensional plot, the so-called fluorescence
excitation–emission matrix (EEM) (Figure 2).
Int. J. Mol. Sci. 2025, 26, x FOR PEER REVIEW 3 of 17
Figure 1. Right-angle and front-face fluorescence.
In principle, the fluorescence signal of a given sample is the sum of the fluorescence
contributions from each of the inherent fluorophores. However, fluorescence emission de-
pends on the surroundings of the fluorophore, and in complex systems and concentrated
solutions, the fluorescence may not be additive due to the phenomena of quenching, in-
teractions with the molecular environment of the fluorophores, and fluorescence reab-
sorption [10,11].
2. Chemometric Approach to Wine Analysis
In fluorometric studies of complex systems, including wine, a simple emission spec-
trum for one excitation wavelength is not sufficient to characterize the fluorescent prop-
erties of the sample. Instead, a set of emission spectra for a range of different excitation
wavelengths λ
ex
is often recorded to obtain a three-dimensional plot, the so-called fluo-
rescence excitation–emission matrix (EEM) (Figure 2).
The excitation–emission matrix can be obtained for each fluorophore. The overall flu-
orescence EEM for a sample can be described according to Equation (1):
𝐸𝐸𝑀 
∑
a



 𝑏

󰇛𝜆

󰇜  𝑐

󰇛𝜆

󰇜 (1)
where i is the number of a fluorophore, n is the total number of fluorescent species present
in the sample, a
i
is a concentration-dependent factor characteristic for each fluorophore, b
i
(λ
ex
) describes the excitation characteristics, and c
i
(λ
em
) describes the emission character-
istics of the i
th
fluorophore.
Figure 2. Emission–excitation matrix of a pre-barreled New Zealand wine. From [30], with the kind
permission of authors and Elsevier.
The simultaneous presence of multiple fluorophores of interlapping excitation and
emission characteristics makes the identification of contributions of individual fluoro-
phores difficult and requires the use of chemometrics, a chemical discipline using mathe-
matics, statistics, and formal logic to analyze the data. Chemometrics uses such tools as
Figure 2. Emission–excitation matrix of a pre-barreled New Zealand wine. From [
30
], with the kind
permission of authors and Elsevier.
The excitation–emission matrix can be obtained for each fluorophore. The overall
fluorescence EEM for a sample can be described according to Equation (1):
EEM =∑n
i=1ai×bi(λex )×ci(λem )(1)
where iis the number of a fluorophore, nis the total number of fluorescent species present in
the sample, a
i
is a concentration-dependent factor characteristic for each fluorophore, b
i
(
λex
)
describes the excitation characteristics, and c
i
(
λem
) describes the emission characteristics
of the ith fluorophore.
The simultaneous presence of multiple fluorophores of interlapping excitation and
emission characteristics makes the identification of contributions of individual fluorophores
difficult and requires the use of chemometrics, a chemical discipline using mathematics,
statistics, and formal logic to analyze the data. Chemometrics uses such tools as parallel
factor analysis (PARAFAC), principal component analysis (PCA), or partial least squares
(PLS) regression [31–33].
The parallel factor analysis (PARAFAC) model is based on a decomposition of a
complex set of fluorescence data into several PARAFAC components corresponding to
individual fluorophores (or fluorophore groups) present in the samples. In the analysis of
data, the relative concentration of components in the mixture can be determined, and the
excitation and emission loadings can be used for the identification of fluorophores. The
three-way data array is thus decomposed into a set of sample scores, a
if
, loadings for the
emission mode, b
jf
, and loadings for the excitation mode, c
kf
. The principle of this approach

Int. J. Mol. Sci. 2025,26, 3384 4 of 16
is to minimize the sum of squares of the residual, e
ijk
in Equation (2), using the least-squares
algorithm
xijk =∑F
f=1ai f bj f ck f +eijk (2)
where x
ijk
represents the data for sample iin variables jand kof the two different variable
dimensions [12,31].
Principal component analysis (PCA) is another mathematical procedure that decom-
poses the data matrix with nsamples and pcolumns (variables, e.g., wavelengths) into
the product of a scores matrix, with nrows and d<pcolumns (principal components,
PCs). The scores are the positions of the samples in the space of the principal components,
and the loadings are the contributions of the original variables to the PCs. All PCs are
mutually orthogonal, and each successive PC contains less of the total variability of the
initial dataset. This procedure reduces the dimensionality of the data, which enables the
effective visualization, classification, and regression of multivariate data [
9
]. The PCA
components do not necessarily have a clear physical meaning, but they can be efficiently
used to understand and classify the wine data. After PCA, data modeling can be further
progressed using, e.g., Soft Independent Modeling of Class Analogy (SIMCA) or machine
learning as a data modeling alternative [34,35].
Multivariate classification methods or pattern-recognition methods are used for group-
ing samples with similar characteristics. They include supervised and non-supervised
methods.
Non-supervised or exploratory methods can group data into clusters. They are often
useful at an early stage of a study to compare subpopulations, such as different batches of a
product. Cluster analysis can be performed with simple means, such as hierarchical cluster
analysis (HCA) or PCA. HCA compares the similarity between the samples based on their
measured variables. The samples are grouped into clusters according to their closeness in a
multidimensional space and are usually presented in the form of dendrograms [
12
]. PCA
can also be used to find relationships between different parameters and the detection of
possible clusters within the samples [32,36,37].
Sometimes, non-negative matrix factorization (NMF) may be more suitable than PCA.
In this method, only positive solution values can be obtained, and thus, this method
provides a more realistic approximation to the original data than PCA, which allows for
both positive and negative values [38].
In the supervised or discriminant analysis methods, each fluorescence spectrum is
preliminarily assigned to a definite class, with comprehensive libraries of spectra represent-
ing various versions of each product being constructed in a calibration process. Principal
component or partial least squares (PLS) analyses are often applied to spectral datasets
to reduce the size of a dataset and co-linearity. Spectral data are analyzed using various
methods such as linear discriminant analysis (LDA) [
39
], factorial discriminate analysis
(FDA) [
39
,
40
], or k-nearest neighbors (kNN) [
41
]. The analysis aims at the formulation
of weighted linear combinations of the data to minimize the within-class variance and to
maximize the between-class variance. If the samples studied are numerous enough, they
can be separated into two sets: a training set to elaborate the method (calibration) and a
test set to validate it. The elaborated classification rules are later used for allocating new or
unknown samples to the most probable subclass [25].
The second stage of analysis is often the factorial discriminate analysis (FDA). This
method is useful when the data are preliminarily transformed into their PCs. In the first
stage, a stepwise discriminant analysis is performed to select the most relevant PCs for
the discrimination of variables when the qualitative classes are initially defined. FDA
allows for the construction of new synthetic variables (discriminant factors) from the linear
combinations of the selected PCs to achieve a better separation of the centers of gravity

Int. J. Mol. Sci. 2025,26, 3384 5 of 16
of the classes considered. Individual samples are assigned to classes where the distance
from the centers of gravity is the shortest. Similarity maps and patterns can be drawn, as in
PCA [25,39].
The most frequently used multivariate regression methods for quantitative fluores-
cence analysis are partial least-squares regression (PLSR) and principal component regres-
sion (PCR). Both methods can be used for whole spectra and selected spectral regions,
allowing for the inclusion of more information in the calibration model. PCR uses the
principal components provided by PCA to perform regression on the sample parameter
to be predicted. PLSR points the directions of greatest variability by comparison of the
information on both spectral and target properties with the new axes (PLSR components or
PLSR factors). The first principal component or factor in PCR represents the widest varia-
tions in the spectrum, while in PLSR, it represents the most relevant variations, showing
the best correlation with property values of a target [42–44].
3. Fluorescent Components of Wines
The main fluorescent components of wines are polyphenols. Phenolic compounds are
secondary metabolites found in grapes and wine that can be classified into two groups:
flavonoids and non-flavonoids (phenolic acids and stilbenes) [
45
,
46
]. The phenolic composi-
tion of wine is dependent on many factors, including conditions of grape berry development
and ripening, the grape cultivar and ripeness at harvest, and the technology of fermentation
and aging [47,48].
Within wine flavonoids, three subgroups are important: flavonols, flavan-3-ols, and
anthocyanins. Flavonols are found in grape skins as glycosides of myricetin, quercetin,
kaempferol, isorhamnetin, syringetin, rutin, and laricitrin [
49
,
50
]. Flavan-3-ols (monomeric
catechins and polymeric proanthocyanidins) are another large family of polyphenolic
compounds comprising mainly catechin, epicatechin, gallocatechin, epigallocatechin, and
their corresponding polymers, which are found in the skin and seed of the grape [
51
,
52
].
Proanthocyanidins (condensed tannins) are phenolic compounds of a polyflavan-3-ol struc-
ture [
53
,
54
]. Anthocyanins are extracted from the red grape skins during maceration and
fermentation [
55
,
56
]. They are highly reactive and easily enter into chemical reactions with
other red wine components, such as aldehydes or polyphenols (e.g., tannins), producing
new anthocyanin derivatives. Both anthocyanins and anthocyanin-derived pigments con-
tribute to the color of young red wines and play a crucial role in the evolution of wine
color during aging [
57
–
61
]. Anthocyanin
−
pyruvic acid adducts appear to be the major
anthocyanin derivatives detected by HPLC after only 1 or 2 years of aging in Port wine [
58
].
Flavonoids are excited in the region of 260–268 nm and emit in the range of 370–422 nm,
except for flavan-3-ols which are excited at 278–290 nm and emit in the wavelength range
of 310–360 nm [
62
], although fluorescence at
λex
/
λem
of 280/310 nm was also reported for
catechin [63].
Anthocyanins are weakly fluorescent in solution. A fluorescence quantum yield of
4.1 ×10−3
for malvidin 3,5-diglucoside was reported [
64
], probably due to the efficient
excited state proton transfer to water [
65
]. It may be the main reason why the fluorescence
of red wines has been poorly studied. Cyanidin-3-glucoside and malvidin 3,5-diglucoside
were reported to have absorption maxima at 220 nm and about 280 nm and fluorescence
maxima at 308 and 293 nm, respectively. However, aggregation or complexation to other
molecules can induce a significant fluorescence of the resulting anthocyanin-derived com-
pound [66].
Phenolic acids, comprising mainly of caftaric, coutaric, fertaric, and tartaric, are nor-
mally found as esters. Hydroxycinnamic esters are one of the most abundant groups of
phenolic compounds found in grapes. Stilbene-like compounds include resveratrol, its

Int. J. Mol. Sci. 2025,26, 3384 6 of 16
glucoside piceid, astringin, and viniferins [
67
,
68
]. The excitation wavelengths of phenolic
acids (both derivatives of cinnamic acid and derivatives of benzoic acid), phenolic aldehy-
des, and stilbene-like compounds extend between 260 nm and 330 nm, while the emission
wave range is 320–440 nm [62].
Other fluorescent molecules present in wine, apart from polyphenols, are vitamins
and amino acids. Vitamin A (retinol) is present in wine in very small amounts. The
excitation maximum corresponds to about 335 nm, and the emission maximum to about
470 nm [
69
]. On the other hand, B-complex vitamins are the most abundant. Riboflavin
is present mainly as a component of flavin-mononucleotide (FMN) and flavin-adenine-
dinucleotide (FAD). Free riboflavin is also present in raw and processed fruits and is present
in significant amounts in wine. Flavin absorption is centered at about 450 nm and emission
at about 525 nm [
70
]. The fluorescent amino acid tryptophan and its ethyl ester have been
reported to be present in wines [
71
–
73
]. Tryptophan is excited at wavelengths around
280 nm and emits fluorescence in the range of 300–400 nm [
27
,
71
]. NADH is formed in
the fermentation processes that take place during the production of wines [
74
]. NADH
is excited at
340–350 nm
and emits fluorescence centered at 460–470 nm [
75
]. Fluorescent
oxidation products and Maillard products may be produced in the browning processes
during the aging and storage of wines [
76
]. Due to their heterogeneity, the absorption
maxima of Millard reaction products may range from 320 to 450 nm, while emission maxima
are in a broad range of 380–530 nm [77].
4. Analysis of Wine Autofluorescence
An example of an EEM of wine constructed by the measurement of a series of flu-
orescence spectra for a range of excitation wavelengths is shown in Figure 2. The list of
components tentatively assigned to four PARAFAC components used to interpret EEM of
this type is shown in Table 1.
Table 1. Tentative assignment of fluorophores to fluorescence components identified by PARAFAC in
New Zealand Pinot Noir wines, following [30].
Component λmax (exc) λmax (em) Tentatively Assigned Fluorophores
1 278 315 Monomeric catechins
2 278 360 Tryptophan, vanillic acid, syringic
acid, gallic acid
3 260 (370)/
390 Caffeic acid
4(278)/
320 415
Caffeic acid, p-coumaric acid, tyrosol
Analysis of these EEMs by PARAFAC makes it possible to determine the relative
contributions of each component to the spectra matrices. Other studies found that red
wines have four main fluorescence components, with the excitation and emission maxima
at the wavelength pairs of 260/380 nm, 275/323 nm, 330/410 nm, and 280/364 nm, respec-
tively [
27
,
78
]. A tentative identification of fluorophores performed by matching PARAFAC
score values with the HPLC analysis of wine revealed that the third component correlated
with concentrations of catechin and epicatechin [
30
,
36
]. Such measurements allowed for
distinguishing between Rioja and Ribera del Guadiana wines, discrimination between Rioja
and non-Rioja samples, discrimination between Crianza or Reserva wines compared to
young wines [27], and discrimination of wines according to the country of origin [78].
Components tentatively assigned to four PARAFAC components used for the analysis
of Cava wines are shown in Table 2. The fluorescence analysis of sparkling cava wines
was found to be a fast alternative method for the quality control of sparkling wines.
Specifically, monitoring the fluorophores centered at excitation/emission of 465/530 nm

Int. J. Mol. Sci. 2025,26, 3384 7 of 16
and 280/380 nm can provide useful information about the chemical changes occurring
during browning [76].
Table 2. Fluorescence components tentatively identified by PARAFAC in cava wines, and tentative
assignment of fluorophores. After [76].
Component λmax (exc) λmax (em) Tentatively Assigned Fluorophores
1 395 485 Unknown
2 365 440 Oxidation products, Maillard
products, NADH
3 465 530 Vitamin B2or riboflavin
4280, shoulder
at 350 415 Stilbenes such as trans-piceid and
trans-resveratrol
Pre-barreled New Zealand Pinot Noir wines showed an EEM with a component char-
acterized by excitation and emission maxima at around 277 nm and 330 nm, respectively.
The maximum signal intensity of this component was increased about two times in com-
parison with the grape juice. This increase is contributed by multiple fluorophores in the
wine, such as the phenolic acids (syringic, vanillic, gallic, caftaric, p-hydroxybenzoic and
caffeic acid, catechin, epicatechin and tryptophan) and other components that were not
present or present at much lower levels in grape juice [30].
In the study of red wines by dos Santos et al., the total phenolics region corresponded
to 260–360 nm excitation and 370–400 nm emission, the total condensed tannins were
the main contributor to the fluorescence in the region of excitation between 285 and
340 nm and emission in the range of 290–350 nm, and the total anthocyanins region
contributed to the signals with excitation between 280 and 300 nm and emission between
330 and 380 nm [
79
]. The analysis of EEMs of 200-times diluted Cabernet Sauvignon wines
from three regions of Australia and Bordeaux using discriminant analysis and support
vector machine discriminant analysis (SVMDA) made it possible to differentiate wines
according to the location of origin [
80
]. The analysis of EEMs of 150-times diluted Shiraz,
Cabernet Sauvignon, and Merlot wines from 10 locations in Australia by PLS and extreme
gradient boosting (XGB) discriminant analysis (a machine learning protocol) allowed for
the differentiation of samples by their variety and geographical origin [81].
The analysis of wine phenolic content by front-face fluorescence spectroscopy com-
bined with chemometrics was suggested to be a potentially useful tool for authentication
and quality control by regulatory bodies [
33
]. The use of principal component analysis
(PCA) and classification by factorial discriminant analysis (FDA) allowed for distinguish-
ing between German and French wines, demonstrating the possibility of identification of
wines according to variety and typicality [
26
]. Discrimination between Shiraz, Cabernet
Sauvignon, and Pinot Noir based on fluorescence can be improved by measurements at
different pH levels [28].
Front-face fluorescence spectroscopy in combination with PARAFAC was also shown
to be a promising tool for the discrimination of grape-derived products from different
clonal and vineyard site origins within a small geographical region in New Zealand. The
discrimination between grape clones was found to be due to higher concentrations of the
component at an excitation maximum of 260 nm and emission maximum of 390 nm, with a
shoulder at 370 nm, possibly contributed by caffeic acid-related fluorophores. The effect of
discrimination based on the vineyard site was indicated to be due to the component at an
excitation maximum of 278 nm and emission maximum of 360 nm, probably contributed
mainly by tryptophan and hydroxylated benzoic acid derivatives [30].
The fluorescence of bulk Slovak Tokaj wines was characterized by an excitation range
of 390 to 500 nm, with a maximum at 460 nm and emission in the range of 450 to 590 nm,
with a maximum of about 530 nm. These wines, when diluted 500 times, had fluores-

Int. J. Mol. Sci. 2025,26, 3384 8 of 16
cence characterized by excitation in the range of 250–350 nm and emission in the range
of 320–450 nm. An intense band was observed with excitation in the range of 270–280 nm
and emission centered at 350 nm, as well as a weak band with excitation at 300–310 and
emission at about 430–440 nm. This fluorescence was similar to those of phenolic acids
characterized by excitation/emission wavelengths: (gallic acid, 280/360 nm; protocatechuic
acid, 270/350 nm; caffeic acid, 262 and 325/426 nm; caftaric acid, 290 and 325/440 nm,
p-coumaric acid 290 and 309/404 nm) and catechin, 280/310 nm. The PLS regression
allowed for the estimation of the content of gallic, protocatechuic, caffeic, and p-coumaric
acids, and (+)-catechin from the fluorescence spectra of wines [63].
Synchronous spectra in the range of 260–290 nm, especially with the wavelength
difference between excitation and emission (
∆λ
) of 60 to 100 nm, allow for prediction of
the antioxidant capacity of wines based on the estimation of the concentrations of phenolic
compounds in sweet Slovak Tokaj wines [
82
]. Emission spectra corresponding to excitation
at 320 nm or synchronous spectra in the range of 300–400 nm, especially with
∆λ
of 80 nm,
allow for the determination of the sum of concentrations of coumarins in Tokaj wines [
83
].
A Port red wine aged 3 years showed a fluorescence maximum at about 595 nm when
excited at 500 nm. The fluorescence maximum was shifted by about 30 nm towards shorter
wavelengths concerning the young wine (fluorescence maximum at about 625 nm). Aged
wines usually contain larger amounts of polymerized anthocyanin (pol-Anth) pigments and
lower amounts of monomeric anthocyanin pigments than young wines. The fluorescence
band of pol-Anth isolated from wine and dissolved in 12% ethanol brought to pH 3.3 was
similar to that of aged wines (peak at 597 nm), while the spectra of mon-Anth showed
a bathochromic shift of about 40 nm in comparison with pol-Anth. The intensity of
fluorescence was higher for pol-Anth than mon-Anth [84].
The fluorescence intensity ratio at 700 nm to that of 560 nm of wines excited at 500 nm
was found to decrease when plotted against the relative share of pol-Anth and to decrease
in old wines, and it was proposed to be a measure of the pol-Anth/min-Anth ratio, and
thus of red wine age.
The treatment of young red wine with sulfur dioxide caused a hypsochromic shift in
the fluorescence spectrum to match the spectrum of old Port wine. This effect is due to
the formation of colorless and nonfluorescent compounds in the reaction of monomeric
anthocyanins with sulfur dioxide, which binds to carbon 4 of the C ring. Polymerized
anthocyanins remain unbleached because the site of sulfite binding is the same as that
engaged in the anthocyanin polymerization [84,85].
Wine anthocyanins react with pyruvic acid, forming pyranocyanins such as vitisin
A [
86
,
87
]. It should be noted that the term “vitisin A” is ambiguous since the same name is
used for another compound, one of resveratrol tetramers [
88
,
89
]. Malvidin 3-O-glucoside
(Mv 3-O-glc) is the major anthocyanin detected in young red wine and vitisin A (a pyruvic
adduct of Mv 3-O-glc) [
58
]. The fluorescence spectrum of Mv 3-O-glc at pH 1.0 showed
a peak at 610 nm, which increased in intensity and shifted to 638 nm at pH 3.3. Vitisin A
showed a broad emission band with a maximum around 720 nm at pH 1.0, while at pH 3.3,
a significant increase in fluorescence was observed around 630 nm. The fluorescence
quantum yield of vitisin A relative to Mv 3-O-glc was 0.81 and 0.86 at pH 1 and 3.3,
respectively. The increase in the fluorescence intensity, for both Mv 3-O-glc and vitisin A,
from more to less acidic conditions can be explained by proton quenching, the extent of
which is pH-dependent. Based on differences in the excitation spectra, the fluorescence
excitation ratio (FER) between wavelengths at the maximal difference and the isosbestic
point (FER
550nm/425nm
and FER
350nm/425nm
) was proposed to estimate the relative amounts
of Mv 3-O-glc and vitisin A, although in whole wines, the strong contribution of pol-Anth
to fluorescence makes it difficult to distinguish the two classes of pigments [84].

Int. J. Mol. Sci. 2025,26, 3384 9 of 16
One technique of measuring the fluorescence of dye molecules concentrated on the
surface of wine as a difference between fluorescence was proposed based on the estimation
of the difference between the fluorescence from the surface and from inside the wine [90].
Simple measurements of wine fluorescence may not require a spectrofluorimeter or
plate reader. The fluorescence spectrum of a red wine from the Lazio region obtained by
a computer screen photo-assisted technique (a combination of computer monitors and
webcams) was characterized by an excitation maximum at 450 nm, a shoulder at 520 nm,
and an emission maximum at about 610 nm [91].
5. Application of Fluorescent Probes for the Analysis of Wine
The use of fluorescent probes allows for the selective estimation of the content of
chosen parameters of components of wine using a spectrofluorimeter, plate reader, or
fluorescence microscope. The applications of flow cytometry for the analysis of wine are
not included in this review, as they require specialized equipment.
A fluorescence-based sensor based on the measurement of the luminescent lifetime
of a reference metal/porphyrin complex was proposed for the real-time monitoring of
oxygen concentration during wine fermentation. The sensor allows for the determination
of the concentrations of O
2
in wine below 10–40
µ
g/L at 20
◦
C, i.e., between 0.1 and 0.5%
O
2
[
92
]. The precise determination of oxygen content during wine production is important,
as oxygen induces changes in the chemical and sensory profile of wines, including the final
alcohol content. Moderate red wine exposure to oxygen has a positive impact on the color,
aroma, and taste of red wine’s properties. However, oxygen negatively affects the quality
and sensory properties of white wines [93,94].
A fluorescence assay for resveratrol determination based on Förster Resonance En-
ergy Transfer (FRET), via competitive supramolecular recognition, between p-sulfonated
calix [
6
] arene-(CX6)-modified reduced graphene oxide (CX6@RGO) and Rhodamine B- or
rhodamine 123–resveratrol complex was explored. Resveratrol present in wine competes
with the complex, releasing rhodamine and causing an increase in the fluorescence intensity.
This assay, not requiring any wine pretreatment, allows for the determination of resveratrol
with a detection limit of 0.5 µM [95].
The off-odors of wine are of considerable concern in the wine industry. They can
develop after the wine has been bottled, and no corrective action is possible. Hydro-
gen disulfide is the main component of wine off-odors, which are also contributed by
mercaptans and volatile sulfur compounds [
96
,
97
]. A fluorescent probe, 4-methyl-2-oxo-
2H-chromen-7-yl-thiophene-2-carboxylate, was used to estimate the level of hydrogen
sulfide in wine. Three red wines bought in a Beijing supermarket were found to contain
0.48, 0.45, and 0.55 µM H2S when estimated using this probe [98].
A fluorescent probe (E)-2-((4-(benzo[d]thiazol-2-yl)benzylidene)amino)phenol (BT-
PAP) was applied for the determination of total iron (Fe
2+
/Fe
3+
) in wine. The proposed
assay based on the probe allows for the estimation of iron with a limit of detection of
1.16
µ
M and a linearity of response up to 200
µ
M. In three samples of Chinese red wines,
the iron concentration was found to be 24–37 µM with this probe [99].
At least three fluorescent probes have been applied for estimation of the copper
level in wines. The application of a fluorescent coumarin-based probe for the de-
tection of copper(II) in wine revealed Cu
2+
concentrations of 0.22–0.46 in three red
wines. The detection limit of the method was 62 nm, and the range of linearity was
0–16
µ
M [
100
]. A pH-sensitive chemosensor based on Rhodamine B coupled to a tetraaza-
macrocyclic ring, 3 [N-(9-(2-((1,4,7,10-tetraazacyclotridecan-5-yl)methyl)-3-oxoisoindolin-1-
yl)-6-(diethylamino)-3H-xanthen-3-ylidene)-N-ethylethanamine], was used to determine
the Cu
2+
concentration in white wine. The detection limit of the sensor is 43.8 nM. The

Int. J. Mol. Sci. 2025,26, 3384 10 of 16
Cu
2+
concentration in six samples of a 2016 Burgundy Chardonnay was found to be
0.03–0.76 mg/L
(0.47–11.96
µ
M) [
101
]. A fluorometric assay based on the simultaneous use
of two fluorescent probes and the measurement of the ratio of fluorescence intensities at
two wavelengths allows for the determination of the copper concentration in wine, with
a limit of detection of 46.5 nM and a linear range up to 4
µ
M. The Cu
2+
concentrations in
three samples of red wine were found to be 22–36 µg/L (0.35–0.57 µM) [102].
Ochratoxin A is a hepatotoxic, genotoxic, cytotoxic, and teratogenic mycotoxin pro-
duced by several fungal species, mainly of the genera Aspergillus and Penicillium, that can
contaminate wine. A fluorescence polarization immunoassay for ochratoxin A possesses a
detection limit of 0.11±0.05 ng/mL [103].
Protein haze is an esthetic problem in white wines caused by the persistence of
grape pathogenesis-related proteins that are highly stable during winemaking. Some of
these proteins precipitate over time, especially at elevated temperatures, forming a turbid
haze [
104
,
105
]. A rapid fluorescence-based technology to detect haze-forming proteins in
white wines was developed based on the use of a new fluorescent probe binding selectively
to haze-forming proteins. The method has a detection limit of 2 mg/L and a linear range of
4 to 400 mg/L. It is sensitive enough, since the minimal concentration of proteins to form
haze is 12 mg/L [106].
Staining lactic acid bacteria with the Live/Dead BacLight
TM
staining kit and fluo-
rescent microscopy assessment allows for control of the viability of lactic acid bacteria
and thus the process of malolactic fermentation used for the biological deacidification of
wines [107].
Examples of the application of fluorescence measurements to wine are listed in Table 3.
Table 3. Results of fluorescence analysis of wine.
Wine Analysis Results Reference
French and German wines Front phase, PCA
Differentiation between Gamay and
Dornfelder wines, discrimination
between typical and non-typical
Beaujolais wines
[26]
Red wines PARAFAC Discrimination according to the
country of origin and grape variety [78]
Red wines Front phase, PARAFAC
Separation between Rioja and Ribera
del Guadiana wines, discrimination
between Rioja and non-Rioja samples
for Crianza and Reserva wines
compared to young wines
[27]
White Argentinian wines PCA, PARAFAC, other
algorithms; best results with
U-PLS-DA
Discrimination between the type of
grape used for wine production [108]
New Zealand Pinot Noir Front phase, PARAFAC Detection of differences in vineyard
site, grape clone, winemaking process,
and barrel properties [30]
South African red wines Front phase, PARAFAC, PCA,
Bayesian optimization
Classification of South African red
wine cultivars based on unique
fluorescent fingerprints [33]
White wines PCA-LDA Discrimination between Furmint,
Lipovina, and Muscat Blanc wines [109]
Pinot Gris and Riesling wines
(Romania), Riesling (Romania)
and Sauvignon (France)
Classical right-angle fluorimetry,
PARAFAC, SIMCA Classification based on the site
of origin [110]
Cabernet Sauvignon wines from 3
regions of Australia and Bordeaux
EEM of 200 times diluted wines
analyzed by DA and SVMDA Discrimination of wines according
to location [80]

Int. J. Mol. Sci. 2025,26, 3384 11 of 16
Table 3. Cont.
Wine Analysis Results Reference
Shiraz, Cabernet Sauvignon, and
Merlot wines from 10 locations in
Australia
EEM of 150-times diluted wines
analyzed by XGB discriminant
analysis and PLS
Discrimination of wine brand and
geographical location [81]
Four- to six-butt Tokaj wines PCA followed by LDA
Distinguishing between botrytized
wines of different quality (4-, 5- and 6-
butt wines) and between
unadulterated and adulterated wines
[111]
Cava sparkling wines PARAFAC Monitoring of browning in
sparkling wines [76]
Ribera del Guadiana and Rioja
wines Front phase, U-PLS/RBL
Good results for the quantification of
caffeic and vanillic acids and
resveratrol; acceptable results
for epicatechin
[112]
Red wines (Cabernet Sauvignon) Front-phase fluorescence; PCA,
RMSE and MAE
Estimation of the content of total
phenolics, total condensed tannins,
and total anthocyanins following the
course of fermentation
[79]
White Chardonnay wines PARAFAC Detection of the effect of SO2
treatment and/or vintage, even after
several years of bottle aging [113]
Porto wines and table red wines,
Portugal Diluted wines, standard
fluorescence spectra
Fluorescence F700nm/F560nm ratio as a
measure of monomeric/polymeric
anthocyanins; excitation ratio
Fex350nm/Fex 550 ratio as a measure of
vitisin
A/malvidin-3-O-glucoside ratio
[84]
Sweet Tokay wines Synchronous emission spectra
(260–290 nm), ∆λof 60 to 100 nm
Prediction of antioxidant capacity of
wines based on estimation of the
concentrations of
phenolic compounds
[82]
Tokaj wines Spectra at λex = 320 nm or
synchronous fluorescence spectra Determination of sum of
concentrations of coumarins [83]
Tokaj wines Bulk and diluted (500 times), PLS Estimation of concentrations of gallic,
protocatechuic, caffeic, and
p-coumaric acids and (+) catechin [63]
White and red wines Fluorescence sensor Estimation of oxygen level in wine [92]
Red wines FRET-based fluorescence assay Estimation of resveratrol
concentration in wine [95]
Red wines Fluorescent probe
4-methyl-2-oxo-2H-chromen-7-yl-
thiophene-2-carboxylate Estimation of the level of H2S in wine [98]
Red wine BTPAP fluorescent probe
Estimation of Fe
2+
/Fe
3+
concentration
in wine [99]
Red wine Coumarin-based fluorescent
probe Estimation of Cu2+ concentration in
wine [100]
White wine Macrocyclic Rhodamine B-based
fluorescent probe Estimation of Cu2+ concentration
in wine [101]
Red wines Simultaneous use of two
fluorescent probes, fluorescence
intensity ratio at two wavelengths
Estimation of Cu2+ concentration
in wine [102]
White wines New fluorescent probe Estimation of concentration of
haze-forming proteins [106]
Red wines Fluorescence polarization Immunoassay for ochratoxin [103]
DA, discriminant analysis; MAE, mean absolute; PLS, partial least squares regression; RMSE, root mean square
error; SIMCA, Soft Independent Modeling Classification Analogy; SVMDA, support vector machine discriminant
analysis; U-PLS/RBL, unfolded-partial least squares coupled to residual bilinearization; XGB, extreme gradient
boosting.

Int. J. Mol. Sci. 2025,26, 3384 12 of 16
Wine has been one of the most popular drinks worldwide for thousands of years.
Monitoring wine quality and controlling forgeries concerning the false reporting of wine
origin and quality is important. Fluorescence spectroscopy, allowing for a rapid estimation
of a range of wine properties, requiring only a fluorimeter or plate reader, and available
in most laboratories, may be very useful in this respect, especially for preliminary wine
screening.
Author Contributions: Conceptualization, I.S.-B. and G.B.; methodology, I.S.-B. and G.B.; investi-
gation, I.S.-B. and G.B.; writing—original draft preparation, I.S.-B. and G.B.; writing—review and
editing, I.S.-B. and G.B.; supervision, I.S.-B.; project administration, I.S.-B.; funding acquisition, I.S.-B.
All authors have read and agreed to the published version of the manuscript.
Funding: This study was performed within the project “Modification of anthocyanins/anthocyanidins
as new markers of food oxidation” (number of the application 2023/51/B/NZ9/02490), financed by
the National Science Centre (NCN), Poland, in the program “Opus 26”.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: Data will be available from the corresponding author upon reasonable
request.
Conflicts of Interest: The authors declare no conflicts of interest.
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