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Citation: Li, X.; Wen, D.; He, Y.; Liu,
Y.; Han, F.; Su, J.; Lai, S.; Zhuang, M.;
Gao, F.; Li, Z. Progresses and
Prospects on Glucosinolate Detection
in Cruciferous Plants. Foods 2024,13,
4141. https://doi.org/10.3390/
foods13244141
Academic Editors: Francesca Buiarelli
and Simone Angeloni
Received: 31 October 2024
Revised: 14 December 2024
Accepted: 19 December 2024
Published: 20 December 2024
Copyright: © 2024 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
Progresses and Prospects on Glucosinolate Detection in
Cruciferous Plants
Xuaner Li
†
, Dongna Wen
†
, Yafei He, Yumei Liu, Fengqing Han, Jialin Su, Shangxiang Lai, Mu Zhuang, Fuxing Gao
and Zhansheng Li *
State Key Laboratory of Vegetable Biobreeding, Institute of Vegetables and Flowers, Chinese Academy of
Agricultural Sciences, Beijing 100081, China; lixuaner057@163.com (X.L.)
*Correspondence: lizhansheng@caas.cn
†These authors contributed equally to this work.
Abstract: This review provides a comprehensive summary of the latest international research on de-
tection methods for glucosinolates in cruciferous plants. This article examines various analytical tech-
niques, including high-performance liquid chromatography (HPLC), liquid chromatography–mass
spectrometry (LC-MS), enzyme-linked immunosorbent assay (ELISA), and capillary electrophoresis
(CE), while highlighting their respective advantages and limitations. Additionally, this review delves
into recent advancements in sample preparation, extraction, and quantification methods, offering
valuable insights into the accurate and efficient determination of glucosinolate content across diverse
plant materials. Furthermore, it underscores the critical importance of the standardization and
validation of these methodologies to ensure reliable glucosinolate analyses in both scientific research
and industrial applications.
Keywords: glucosinolate; cruciferous crops; HPLC; LC-MS/MS; determination
1. Introduction
Glucosinolates are a class of organic compounds found in cruciferous vegetables, such
as broccoli, cabbage, kale, and Brussels sprouts [
1
–
3
]. These compounds have generated
significant interest due to their potential health benefits and their role as bioactive com-
pounds in plant defense mechanisms. The detection and quantification of glucosinolates
are essential for both nutritional research and the advancement of plant breeding initia-
tives [
4
–
8
]. Glucosinolates are widely distributed across various plant parts, including
roots, stems, leaves, and seeds. This widespread presence underscores the importance
of their analysis. The significance of glucosinolates is not only attributed to their direct
consumption by humans and animals but also to the potential health benefits they may
confer [9,10]. The molecular structure of glucosinolates is illustrated in Figure 1[11].
Foods 2024, 13, 4141 https://doi.org/10.3390/foods13244141
Review
Progresses and Prospects on Glucosinolate Detection in
Cruciferous Plants
Xuaner Li
†
, Dongna Wen
†
, Yafei He, Yumei Liu, Fengqing Han, Jialin Su, Shangxiang Lai, Mu Zhuang,
Fuxing Gao and Zhansheng Li *
State Key Laboratory of Vegetable Biobreeding, Institute of Vegetables and Flowers, Chinese Academy of
Agricultural Sciences, Beijing 100081, China; lixuaner057@163.com (X.L.); wendongna0713@163.com (D.W.);
yafeiseed@163.com (Y.H.); liuyumei@caas.cn (Y.L.); hanfengqing@caas.cn (F.H.); sujialin007@163.com (J.S.);
shangxianglai@163.com (S.L.); zhuangmu@caas.cn (M.Z.); gaofuxin@caas.cn (F.G.)
* Correspondence: lizhansheng@caas.cn
†
These authors contributed equally to this work.
Abstract: This review provides a comprehensive summary of the latest international re-
search on detection methods for glucosinolates in cruciferous plants. This article examines
various analytical techniques, including high-performance liquid chromatography
(HPLC), liquid chromatography–mass spectrometry (LC-MS), enzyme-linked immuno-
sorbent assay (ELISA), and capillary electrophoresis (CE), while highlighting their respec-
tive advantages and limitations. Additionally, this review delves into recent advance-
ments in sample preparation, extraction, and quantification methods, offering valuable
insights into the accurate and efficient determination of glucosinolate content across di-
verse plant materials. Furthermore, it underscores the critical importance of the standard-
ization and validation of these methodologies to ensure reliable glucosinolate analyses in
both scientific research and industrial applications.
Keywords: glucosinolate; cruciferous crops; HPLC; LC-MS/MS; determination
1. Introduction
Glucosinolates are a class of organic compounds found in cruciferous vegetables,
such as broccoli, cabbage, kale, and Brussels sprouts [1–3]. These compounds have gener-
ated significant interest due to their potential health benefits and their role as bioactive
compounds in plant defense mechanisms. The detection and quantification of glucosin-
olates are essential for both nutritional research and the advancement of plant breeding
initiatives [4–8]. Glucosinolates are widely distributed across various plant parts, includ-
ing roots, stems, leaves, and seeds. This widespread presence underscores the importance
of their analysis. The significance of glucosinolates is not only aributed to their direct
consumption by humans and animals but also to the potential health benefits they may
confer [9,10]. The
molecular
structure of glucosinolates is illustrated in Figure 1 [11].
Academic Editors: Francesca
Buiarelli and Simone Angeloni
Received: 31 October 2024
Revised: 14 December 2024
Accepted: 19 December 2024
Published: 20 December 2024
Citation: Li, X.; Wen, D.; He, Y.;
Liu, Y.; Han, F.; Su, J.; Lai, S.;
Zhuang, M.; Gao, F.; Li, Z.
Progresses and Prospects on
Glucosinolate Detection in
Cruciferous Plants. Foods 2024, 13,
4141. hps://doi.org/10.3390/
foods13244141
Copyright: © 2024 by the authors.
Licensee MDPI, Basel, Swierland.
This article is an open access article
distributed under the terms and con-
ditions of the Creative Commons At-
tribution (CC BY) license (hps://cre-
ativecommons.org/licenses/by/4.0/).
Figure 1. Molecular of glucosinolate structure.
Foods 2024,13, 4141. https://doi.org/10.3390/foods13244141 https://www.mdpi.com/journal/foods
Foods 2024,13, 4141 2 of 22
Currently, a variety of analytical techniques have been utilized to quantify glu-
cosinolates in various plant tissues, including leaves, roots, buds, and seeds. The most
frequently employed methods encompass HPLC, GC, and CE [
12
–
14
]. As previously
mentioned, the content of glucosinolates can be analyzed either by directly measuring
intact glucosinolates or by indirectly assessing their degradation products. However,
certain analytical methods, such as gas chromatography (GC), may not be appropriate
for direct analysis due to the non-volatile or thermally unstable properties exhibited
by some glucosinolates. Consequently, it is often necessary to convert these analytes
into volatile derivatives to facilitate effective detection. HPLC has been extensively
utilized for the analysis of glucosinolates; however, the sample preparation process
often necessitates desulfation. This procedure may lead to incomplete desulfation, self-
dimerization, and self-degradation, which can potentially compromise the reliability of
the final data. Furthermore, the absence of standards for certain desulfated compounds
poses a significant challenge to this methodology [15].
Similar challenges arise when utilizing High-Resolution Mass Spectrometry (HRMS)
for the detection of isomeric glucosinolates. This limitation can be mitigated by employ-
ing LC-MS/MS, which adds an additional dimension to mass spectrometric detection,
allowing for the simultaneous scanning of precursor and fragment ions, thereby en-
hancing selectivity and sensitivity. Furthermore, compared with LC-MS, LC-MS/MS
typically necessitates reduced analysis durations, facilitating the confirmation of known
glucosinolates and the identification of novel compounds based on their fragmentation
patterns [
16
,
17
]. Recently, using a novel computerized system, GLS Finder [
18
–
22
], the
analysis of glucosinolates in 49 common cruciferous vegetables has been conducted
using data generated from high-resolution HPLC-MS/MS techniques. However, some
reviewers have pointed out that the reported results may be ambiguous, as the number of
chromatographic peaks observed for glucosinolates or their isomers exceeds the known
quantity of glucosinolates present in each extract [
23
]. This review aims to summarize
the latest advancements in detection methods for glucosinolates in cruciferous plants,
providing valuable insights for future research.
2. Determination Methods of Glucosinolates
Several methods have been developed for the detection of glucosinolates, with HPLC,
LC-MS, GC-MS CE, ELISA, NIRS, and NMR being the most prominent (Figure 2). Each
technique offers distinct advantages and limitations in terms of sensitivity, specificity,
throughput, and cost. The choice of an appropriate method depends on specific analytical
requirements such as the number of glucosinolate types to be detected, the desired level
of quantification, and available resources. This section discusses these four detection
technologies in detail.
2.1. HPLC
HPLC is a widely used general separation technique and one of the most extensively
applied analytical technologies for glucosinolates. It is characterized by high sensitivity,
good selectivity, and excellent reproducibility. The basis for HPLC to separate each chemical
entity in a sample mixture relies on its unique affinity for the adsorbent material in the chro-
matographic column or mobile phase, resulting in various components moving at different
rates and being separated. Previously known as high-pressure liquid chromatography,
it relies on high-pressure pumps to accelerate the separation speed. HPLC separation
primarily depends on inherent adjustable parameters of the mobile phase, such as polarity,
flow rate, pH value, composition, and some intrinsic characteristics of the sample matrix;
the type and nature of the stationary phase; and environmental factors like temperature
and detector type and settings [24].
Foods 2024,13, 4141 3 of 22
Foods 2024, 13, 4141 3 of 25
Figure 2. The characterization methods of glucosinolates.
2.1. HPLC
HPLC is a widely used general separation technique and one of the most extensively
applied analytical technologies for glucosinolates. It is characterized by high sensitivity,
good selectivity, and excellent reproducibility. The basis for HPLC to separate each chem-
ical entity in a sample mixture relies on its unique affinity for the adsorbent material in
the chromatographic column or mobile phase, resulting in various components moving
at different rates and being separated. Previously known as high-pressure liquid chroma-
tography, it relies on high-pressure pumps to accelerate the separation speed. HPLC sep-
aration primarily depends on inherent adjustable parameters of the mobile phase, such as
polarity, flow rate, pH value, composition, and some intrinsic characteristics of the sample
matrix; the type and nature of the stationary phase; and environmental factors like tem-
perature and detector type and seings [24].
HPLC and reflectance spectroscopy each possess distinct advantages and applica-
tions in the analysis of glucosinolates. HPLC is recognized as the most reliable and precise
technique, suitable for quantitative analysis in laboratory seings, providing detailed in-
formation on glucosinolate content. In contrast, reflectance spectroscopy serves as a non-
destructive method that can rapidly assess the reflective characteristics of plant surfaces,
indirectly indicating variations in glucosinolate concentrations within plants. This makes
it particularly useful for rapid screening and large-scale monitoring. In practical applica-
tions, the combination of these two techniques can complement each other, enhancing
both the efficiency and accuracy of glucosinolate analysis [25–27]. Lee et al. investigated
the applicability of near-infrared reflectance spectroscopy (NIRS) for estimating
Figure 2. The characterization methods of glucosinolates.
HPLC and reflectance spectroscopy each possess distinct advantages and applications
in the analysis of glucosinolates. HPLC is recognized as the most reliable and precise
technique, suitable for quantitative analysis in laboratory settings, providing detailed
information on glucosinolate content. In contrast, reflectance spectroscopy serves as a non-
destructive method that can rapidly assess the reflective characteristics of plant surfaces,
indirectly indicating variations in glucosinolate concentrations within plants. This makes it
particularly useful for rapid screening and large-scale monitoring. In practical applications,
the combination of these two techniques can complement each other, enhancing both the
efficiency and accuracy of glucosinolate analysis [
25
–
27
]. Lee et al. investigated the applica-
bility of near-infrared reflectance spectroscopy (NIRS) for estimating glucosinolate content
in whole seeds from various cruciferous plants. Their findings demonstrated the potential
of reflectance spectroscopy to estimate glycosinolate concentrations in real-time without
causing damage to crops while comparing it with high-performance liquid chromatography
used for calibration [
28
]. Tian et al. employed liquid chromatography-electrospray ion-
ization tandem mass spectrometry combined with selective reaction monitoring to detect
glucosinolate levels effectively. This method successfully quantified ten different types of
glucosinolates present in broccoli, broccoli sprouts, Brussels sprouts, and cauliflower while
exhibiting higher selectivity and sensitivity towards these compounds [29].
In conclusion, HPLC stands as the conventional technique for glucosinolate analysis.
Numerous novel methodologies have emerged that enhance the qualitative assessment of
glucosinolates, thereby broadening their analytical applications.
Foods 2024,13, 4141 4 of 22
2.2. LC-MS
LC-MS is a powerful technique that integrates the separation capabilities of liquid
chromatography with mass spectrometric detection. It exhibits high sensitivity and speci-
ficity, enabling the simultaneous identification and quantification of multiple analytes.
However, LC-MS is more complex and costly than HPLC, which limits its widespread
application. The sample processing procedure closely resembles that of HPLC, involving
the extraction of thioglucosides using appropriate solvents followed by separation via
liquid chromatography upon completion. The separated components are then introduced
into the mass spectrometer, which analyzes ions based on their mass-to-charge ratio (m/z)
to provide molecular mass and structural information. Key advantages include enhanced
sensitivity and selectivity, suitability for complex samples, and the capacity to yield insights
into molecular structure [
30
–
33
]. Mass spectrometry (MS), owing to its high sensitivity, has
been employed for the identification and quantification of glucosinolate compounds. A
method has also been developed that integrates liquid chromatography with MS without
necessitating desulfation. However, the accuracy and precision of this method are con-
strained by insufficient mass resolution. For instance, the mass-to-charge ratios (m/z) of
glycosides and glycoproteins (m/z422.0255 and m/z422.0585, respectively) are remarkably
similar. Although they share the same nominal mass of 422, their isotopic masses differ
by only 0.033 Da, making it challenging to distinguish them through mass analysis alone.
While these compounds can be separated chromatographically, the application of LC-MS
remains limited if certain glucosinolate peaks significantly overlap or if there is substantial
background interference in the monitoring channel [34].
Recent studies have demonstrated the effectiveness of LC-MS in identifying various
glucosinolates. For instance, Sz˝ucs et al. reported the detection of several minor glu-
cosinolates, including glucoraphanin and glucoiberin, in horseradish roots using liquid
chromatography–electrospray ionization mass spectrometry (LC-ESI-MS) [
35
]. Similarly,
Klimek-Szczykutowicz et al. utilized LC-MS/MS to identify five glucosinolates in methanol
extracts of cress, further underscoring the versatility of LC-MS in analyzing complex plant
matrices [
36
]. Given the structural diversity of glucosinolates, this capability is partic-
ularly significant as substantial variations may exist even within a single plant species.
The application of LC-MS extends beyond identification; it also facilitates the quantifica-
tion of glucosinolates. For example, Xu et al. developed a hydrophilic interaction liquid
chromatography–tandem mass spectrometry (HILIC-MS/MS) method capable of simul-
taneously quantifying 22 different glucosinolates from various cruciferous vegetables,
showcasing both efficiency and sensitivity [
37
]. Furthermore, the integration of tandem
mass spectrometry (MS/MS) allows for the elucidation of glucosinolate structures through
fragmentation patterns that can be compared with known standards [
38
]. This approach
not only aids in confirming the identity of glucosinolates but also enhances the reliability of
quantitative analyses. Moreover, advancements in LC-MS methodologies have improved
detection limits and enabled the analysis of glucosinolates within complex biological sam-
ples. For instance, Hauder et al. developed a sensitive LC-MS/MS method for quantifying
metabolites derived from glucosinolates in human plasma and urine, highlighting its ap-
plicability in nutritional research [
39
]. This is crucial for understanding the bioavailability
and metabolism of dietary-derived glucosinolates post-consumption—factors that signifi-
cantly impact human health and disease prevention. In summary, LC-MS has become an
indispensable tool for analyzing glucosinolates by providing comprehensive insights into
their identification, quantification, and metabolic pathways. Ongoing improvements to
LC-MS technology are expected to enhance our understanding of gluconate chemistry and
its implications for plant biology as well as human health.
2.3. GC-MS
Gas chromatography–mass spectrometry (GC-MS) is a commonly utilized analyti-
cal technique for the analysis of thioglucosides and their decomposition products (such
as isothiocyanates) [
40
–
42
]. GC-MS possesses high separation efficiency and sensitivity,
Foods 2024,13, 4141 5 of 22
enabling the detection and identification of various thioglucosides [
43
,
44
]. The GC-MS
analysis of complete glucosinolates typically encompasses sample preparation steps, such
as extraction and derivatization [
37
,
39
]. Glucosinolates are usually transformed into desul-
furized forms or volatile decomposition products, like isothiocyanates, prior to GC-MS
analysis [
23
,
45
]. This step is requisite because complete glucosinolates are insufficiently
volatile to undergo direct GC-MS analysis [
46
,
47
]. Numerous studies have reported the
identification and quantification of glucosinolates in various plant species (including cru-
ciferous vegetables, Lepidium species, and Moringa oleifera) using GC-MS [
41
,
42
,
48
,
49
].
GC-MS analysis provides detailed information on the glucosinolate profile, including the
identification of individual glucosinolate compounds and their relative contents.
2.4. ELISA
ELISA is a fast, cost-efficient, and simple technique for detecting glucosinolates. It
relies on the specific interaction between antibodies and glucosinolate molecules. ELISA is
particularly effective for high-throughput screening, although it may suffer from issues such
as cross-reactivity and limited specificity. The method uses antibodies that are specifically
designed to target the glucosinolate of interest, binding to the molecule and generating a
detectable signal via an enzymatic reaction, making it highly suitable for the quantitative
measurement of particular glucosinolates [
50
,
51
]. Before analysis, it is necessary to prepare
specific antibodies for the target glucosinolate and then process the sample. After the
antibodies are ready, the extracted sample is diluted and added to the ELISA plate, where
antigen–antibody binding occurs. A substrate is then introduced, and the enzyme reacts
with it, resulting in a color change that can be measured with a spectrophotometer. While
ELISA offers high sensitivity and specificity, making it ideal for large-scale screening, it
has certain drawbacks, including the need for prior antibody preparation, a more complex
procedure, and limited ability to analyze unknown compounds.
The exploration of glucosinolates quantification has been conducted using ELISA
in conjunction with other analytical methods such as HPLC and mass spectrometry. For
instance, studies have demonstrated that HPLC can effectively separate and quantify
various types of glucosinolates, including aliphatic and indole glucosinolates, which is
crucial for understanding their biological activities and potential health benefits [
33
,
52
].
However, ELISA offers a simpler and faster alternative for screening large numbers of
samples, which is particularly beneficial for breeding programs aimed at developing crops
with enhanced glucosinolate profiles [
53
]. In the context of Brassica species, the profile of
glucosinolates can vary significantly based on genetic and environmental factors. Research
indicates that different varieties of cabbage exhibit distinct glucosinolate compositions,
potentially linked to the expression of specific biosynthetic genes [
52
,
54
]. The integration
of genetic studies with ELISA can facilitate the identification of key genes involved in
glucosinolate biosynthesis, thereby aiding in the selection of varieties with optimal levels
of these compounds for agricultural and nutritional purposes [
55
,
56
]. Optimizing sample
extraction and preparation processes for enzyme-linked immunosorbent assays (ELISAs)
enhances the recovery rate of glucosinolates. Techniques such as desulfurization and
enzymatic hydrolysis are commonly employed to convert glucosinolates into detectable
forms suitable for ELISA analysis [
57
,
58
]. Developing reliable extraction protocols is critical
to ensuring accurate quantification since the content of glucosinolates can be influenced by
factors such as plant maturity, processing methods, and storage conditions [59].
In summary, incorporating ELISA into research on mustard oil glycosides provides a
valuable method for rapid and effective quantification of these compounds within plant
tissues. By leveraging both ELISA’s capabilities alongside traditional methods like HPLC,
researchers can gain deeper insights into the genetic and environmental factors affecting
glucosinolate profiles—ultimately contributing to the development of crops with enhanced
health benefits.
Foods 2024,13, 4141 6 of 22
2.5. CE
Thiocyanates are bioactive compounds widely found in various plants, particularly
within the Brassicaceae family. Capillary electrophoresis (CE) has emerged as a powerful
analytical technique for the separation and analysis of thiocyanates. This method offers
distinct advantages due to its high resolution, low sample volume requirements, and
compatibility with complex biological matrices. Gonda et al. highlighted the role of CE
in analyzing the thiocyanate–myrosinase–isothiocyanate system, emphasizing its ability
to tolerate proteins and other macromolecules—an essential feature for analyzing plant
extracts containing thiocyanates alongside various other components [60].
The use of CE for separating thiocyanates presents challenges, especially when deal-
ing with real plant matrices. However, the inherent capabilities of this technology allow
the effective separation of ionized compounds based on charge-to-size ratios, which is
beneficial for thiocyanates possessing different ionic characteristics [
60
]. The integration of
mass spectrometry (MS) with CE further enhances the sensitivity and specificity of thio-
cyanate analysis, enabling the identification and quantification of individual components
within complex mixtures [
61
]. The combination of CE and MS has been shown to provide
high-resolution profiles of polysaccharide structures; similarly intricate analyses can be
applied to thiocyanates [
62
]. Moreover, advancements in capillary coating technologies
and developments in micellar electrokinetic chromatography (MEKC) have improved the
separation efficiency of thioglycosides within CE systems. These innovations facilitate
better interactions with analytes while enhancing both resolution and sensitivity [
60
,
63
].
For instance, Zhang et al. discussed the application of polyvinyl alcohol-coated capillaries
that significantly enhance stability and sensitivity during CE analysis—making it suitable
for examining highly polar molecules such as thioglycosides [63].
In conclusion, capillary electrophoresis represents a robust and versatile method for
analyzing thioglucosides characterized by high sensitivity, low sample consumption, and
an ability to resolve complex mixtures. When combined with advanced detection methods
like mass spectrometry, its applicability to phytochemical analysis is further augmented—
rendering it a valuable tool for investigating the health benefits and biochemical roles
associated with thiosugars present in diverse plant species.
2.6. NIRS
Near-infrared spectroscopy (NIRS) is a well-established technique for the rapid, non-
destructive, and simultaneous analysis of various seed quality parameters in Brassica
species, including glucosinolates. Several studies have demonstrated the effectiveness of
NIRS for quantifying glucosinolates in Brassica seeds and leaves. For example, Ratajczak
et al. Ratajczak et al. used NIRS calibrations to analyze seed components such as total
glucosinolates, alkenyl glucosinolates, and individual glucosinolates like gluconapin, glu-
cobrassiconapin, progoitrin, napoleiferin, and glucobrassicin in oilseed rape [
64
]. Similarly,
Barthet et al. and Kumar et al. showed that the optimal spectral regions for predicting total
glucosinolates in canola and rapeseed–mustard seeds are in the near-infrared range [
65
,
66
].
Jasinski et al. and Niemann et al. further highlighted the utility of NIRS for high-throughput
phenotyping of various seed quality traits, including glucosinolates, in Arabidopsis and
Brassica species [
67
,
68
]. They demonstrated that NIRS is a powerful, non-destructive
method to assess the content of major seed components, such as oil, protein, and glucosi-
nolates, and can be used to analyze natural variation in these traits. Additionally, several
studies have explored the use of NIRS, in combination with chemometric techniques like
partial least squares regression (PLSR) and stepwise multiple linear regression (SMLR), for
the quantification of glucosinolates and other bioactive compounds in Brassica leaves and
other plant materials [64,69–72].
Overall, the reviewed references provide strong evidence that NIRS is a reliable and
efficient technique for the analysis of glucosinolates and other quality parameters in Brassica
species, offering advantages such as speed, non-destructive analysis, and the ability to
simultaneously quantify multiple components [65,73–79].
Foods 2024,13, 4141 7 of 22
2.7. NMR
Nuclear Magnetic Resonance Spectroscopy (NMR) has been widely used for the struc-
tural elucidation and characterization of complete thioglucosides. Some studies have
utilized 1H, 13C, and 2D NMR techniques to identify and differentiate various thioglu-
coside structures [
80
]. 1H and 13C NMR spectra provide characteristic signals for the
thioglucose moiety, sulfate imido group, and side chains, allowing for the unambiguous
identification of individual thioglucosides [
80
]. For example, Badenes-Perez et al. con-
firmed the identity of 3-methoxybenzyl sinigrin (glucoside) in Limnanthes douglasii based
on NMR analysis of the complete sinigrin. The data were similar to previously reported
NMR data for the same compound. Furthermore, they determined that 3-hydroxybenzyl
thioglucoside is another thioglucoside present in the plant. Jaafaru et al. used 1H, 13C,
HSQC, and COSY NMR analysis to differentiate between the rhamnose and glucose frac-
tions in the sulfoglucoside-rich extracts of moringa and to identify the presence of aromatic
rings, especially the characteristic phenyl group of sulfoglucosides [
81
]. Furthermore,
Cannavacciuolo et al. reported the clear characterization of a new hydroxycinnamic acid
derivative 1-O-feruloyl-2-O-sinapoyl-
β
-D-glucopyranoside in radish sprouts (Raphanus
sativus) using NMR analysis [
82
]. They also identified glucosinolate benzoyl glucosinolate
in the same plant material. The reference also emphasizes the combination of NMR spec-
troscopy with other analytical techniques such as HPLC and mass spectrometry to provide
a comprehensive profile of thioglucoside spectra in various plant species [83–85].
In summary, the provided references demonstrate the valuable role of NMR spec-
troscopy in structural elucidation and identification of complete thioglucosides. The combi-
nation of 1H, 13C, and 2D NMR techniques can clearly characterize the thioglucose moiety,
sulfate imido group, and variable side chains, enabling the identification of individual
thioglucoside structures in complex plant extracts.
3. Qualitative and Quantitative of Glucosinolates
3.1. Sample Preparation
The preparation of samples for glucosinolate analysis is a critical step that significantly
impacts the integrity and quantification of these compounds. Various methods and condi-
tions have been investigated to optimize glucosinolate extraction, each carrying distinct
implications for the final results. A key concern in the preparation of glucosinolate samples
is the potential degradation of these compounds during processing. For example, González-
Hidalgo emphasized that the time elapsed between harvesting and processing can lead
to cellular damage, facilitating contact between glucosinolates and myrosinase, which
results in rapid hydrolysis and a subsequent reduction in glucosinolate concentrations
compared with fresh samples [
86
]. This degradation highlights the necessity of minimizing
the interval between harvest and processing to preserve glucosinolate levels.
Furthermore, the application of lyophilization (freeze-drying) may inadvertently
diminish glucosinolate levels, as noted by Major et al., who observed that various drying
methods did not significantly affect glucosinolate content in certain instances, indicating
that careful consideration of drying techniques is crucial [
87
]. The extraction process
can also be affected by the physical state of the plant material. For instance, mechanical
processing of vegetables, such as shredding, may initially reduce glucosinolate levels but
could subsequently result in an accumulation of these compounds over time due to the
release of myrosinase [
88
]. This phenomenon suggests that while immediate processing
may result in losses, subsequent reactions can potentially enhance glucosinolate levels,
underscoring the necessity for a balanced approach in sample preparation. Furthermore,
the analytical methods utilized for glucosinolate quantification must be both robust and
reliable. Techniques such as HPLC are frequently employed; however, the extraction
and purification steps can be labor-intensive and time-consuming [
89
]. Grosser and Dam
propose a streamlined HPLC method that simplifies the extraction process while ensuring
accuracy [
90
]. This is critical as the complexity of glucosinolate profiles necessitates precise
analytical techniques to ensure accurate quantification.
Foods 2024,13, 4141 8 of 22
In conclusion, the preparation of samples for glucosinolate analysis necessitates metic-
ulous attention to detail, encompassing the timing of processing, selection of extraction
solvents, and employed methodologies. The interplay among these factors can signif-
icantly influence the final glucosinolate content and its bioactive potential. Therefore,
adopting optimized extraction protocols and analytical methods is imperative for accurate
glucosinolate profiling.
3.2. Extraction
Several extraction methods have been utilized to isolate glucosinolates from plant ma-
terials, including solvent extraction, ultrasound-assisted extraction (UAE), and microwave-
assisted extraction (MAE). The selection of the optimal extraction method is critical for
achieving high recovery rates and purity of glucosinolates. The effective extraction of
glucosinolates—bioactive compounds present in cruciferous vegetables—can be accom-
plished through various techniques, each possessing unique advantages and challenges that
are essential for optimizing extraction efficiency. Solvent extraction remains a conventional
method for isolating glucosinolates, where the choice of solvent significantly affects the
yield and purity of the extracted compounds. For instance, studies have demonstrated that
an 80% methanol solution can effectively inactivate myrosinase, an enzyme responsible for
degrading glucosinolates, thereby preserving their concentrations during extraction [
91
,
92
].
Furthermore, the selection of extraction solvent and method is crucial for optimizing glu-
cosinolate recovery efficiency. Cold methanol extraction has demonstrated effectiveness, as
it not only preserves glucosinolate content but also mitigates complications associated with
elevated temperatures that may lead to degradation [
91
]. Doheny-Adams et al. demon-
strate that cold methanol extraction surpasses other methods, particularly in preserving
glucosinolate concentrations, thereby establishing it as a preferred technique for sample
preparation [
91
]. Furthermore, the application of hydroalcoholic mixtures has been shown
to enhance glucosinolate extraction yields, with varying efficiencies observed depending
on solvent composition [
93
]. However, traditional solvent extraction often necessitates
substantial time and solvent volumes, which may result in increased operational costs and
environmental concerns [94].
In contrast, UAE has emerged as a more efficient alternative. This method employs ul-
trasonic waves to generate cavitation bubbles that disrupt plant cell walls and facilitate the
release of glucosinolates into the solvent [
95
,
96
]. For instance, studies have demonstrated
that UAE achieves higher extraction efficiencies for glucosinolates from Camelina sativa
by optimizing parameters such as solvent type and extraction duration [
96
]. Additionally,
the application of UAE has been shown to improve the extraction of phenolic compounds,
which are often co-extracted with glucosinolates, thereby further increasing the nutritional
value of the extracts [
97
]. MAE is a technique that has garnered attention for its capacity
to enhance extraction efficiency. MAE utilizes microwave energy to heat both the solvent
and sample, resulting in the rapid extraction of glucosinolates due to the elevated tem-
perature and pressure within the plant matrix [
98
,
99
]. Studies have demonstrated that
MAE can yield results comparable to or superior to those obtained through UAE and
traditional solvent extraction methods, particularly regarding extraction speed and solvent
efficiency [
94
,
98
]. The integration of MAE with optimization techniques such as response
surface methodology has been shown to maximize glucosinolate yields while minimizing
solvent consumption [
99
]. In summary, while traditional solvent extraction methods are ef-
fective for glucosinolate extraction, UAE and MAE present significant advantages in terms
of efficiency, yield, and environmental impact. The selection of the appropriate extraction
method should be informed by the specific requirements of the target glucosinolates and
the desired purity of the final extract.
3.3. Purification
An effective method for extracting thiocyanates is HPLC, which can isolate and
quantify these compounds from plant tissues. Grosser and Dam described a straightforward
Foods 2024,13, 4141 9 of 22
approach using HPLC for the extraction and analysis of thiocyanates, emphasizing that
their technique can be applied to samples with low concentrations of thiocyanates, such
as soil [
90
]. This method does not require freeze-drying; due to the moisture content
in fresh plant materials, freeze-drying complicates the quantification process. Similarly,
Doheny-Adams demonstrated that cold methanol extraction is highly effective for isolating
thiocyanates, outperforming other methods except for specific compounds like rapeseed oil
derivatives [
91
]. Their findings underscore the importance of utilizing appropriate solvents
and conditions to maximize thiocyanate yield. The purification process typically involves
chromatographic techniques. For instance, Saha et al. reported on obtaining glucosinolate
through glucose mustard oxidation followed by purification using DEAE-Sephadex and
Sephadex G10 [
100
]. This highlights the practicality of ion-exchange chromatography
in separating specific thiocyanates. Additionally, Hebert et al. investigated the use of
macroporous anion exchange resins for separating and purifying mustard oil glucosinolates
while optimizing processes to enhance recovery rates [
101
]. Their work emphasizes the
versatility of chromatographic methods in purifying thiocyanates.
Furthermore, ultrasonic-assisted extraction has been studied as a means to increase
yields of thiocyanates. Martínez-Zamora et al. combined ultrasound with post-harvest
processing to improve synthesis efficiency for sulforaphane—a notable type of thiocyanate—
demonstrating that this method not only aids in extraction but also potentially enhances
the biological activity of extracted compounds [102].
In addition to these methodologies, desulfurization’s role in purifying thiocyanates has
been highlighted by Chengzhi et al. and Xie et al. They noted that desulfurization is a critical
step in preparing analyzable forms of thiocyantes since it converts them into more suitable
formats for detection and quantification via HPLC. This step is essential for accurately
assessing various plant extracts’ profiles concerning their thiosulfinate content [
103
,
104
]. In
conclusion, purifying thiosulfinate represents a multifaceted process integrating diverse
extraction and chromatographic techniques. The choice of methodology significantly
impacts both the yield and purity of final products; thus, selecting an appropriate strategy
based on target-specific thiosulfinate types and source materials is crucial (Table 1).
Table 1. Common analytical techniques and extraction and purification procedures of glucosinolates.
Analytical Techniques Extraction Purification
HPLC Solvent Extraction Ion-Exchange Chromatography
LC-MS UAE Gel Permeation Chromatography (GPC)
GC-MS MAE DEAE-Sephadex and Sephadex G-10
NMR Enzymatic Extraction Method
NIRS Cold Methanol Extraction
ELASA
Note: This table recapitulates the analytical techniques and extraction and purification procedures for glucosino-
lates in the recent decade, as referred to above.
3.4. Quantification
The quantification of glucosinolates can be accomplished by constructing calibration
curves with standard compounds or by comparing the peak areas of samples against
those of internal standards. The choice of a quantification method is dependent on the
analytical technique utilized and the availability of suitable standards. The quantification
of glucosinolates, a class of sulfur-containing compounds predominantly present in the
Brassicaceae family, is essential for elucidating their biological activities and nutritional
significance. Various methodologies have been developed to accurately assess glucosinolate
content in plant tissues, each possessing distinct advantages and limitations.
HPLC is one of the most widely employed techniques for quantifying glucosinolates.
This method facilitates the separation and identification of individual glucosinolates based
on their distinct chemical properties (Table 2). For instance, research has demonstrated
that HPLC can effectively differentiate among various glucosinolates present in differ-
Foods 2024,13, 4141 10 of 22
ent cruciferous crops and Brassica species, thereby providing comprehensive profiles of
their content [
40
,
105
,
106
]. The quantification process generally entails the extraction of
glucosinolates from plant tissues, followed by hydrolysis to liberate glucose, which is sub-
sequently measured spectrophotometrically [
65
,
107
]. This methodology has been validated
in numerous studies, affirming its reliability for both total and individual glucosinolate
measurements (Table 3) [99,108–110].
Table 2. The individual compounds of glucosinolates found in cruciferous plants.
Family Species Tissues and Organs GSLs Compounds
Brassicaceae
Brassica napus
Seed, leaves, stems, roots
6-15
Brassica juncea 7-17
Capsella bursa-pastoris Medic. 3-7
Brassica oleracea. var. botrytis 4-9
Brassica rapa 2-11
Brassica carinata A Braun
Seed, leaves, stems
3-8
Camelina sativa
3-12
Camelina rumelica subsp. rumelica
Camelina macrocarpa
Brassica oleracea var capitata Seeds, leaves 3-12
Brassica oleracea convar capitata var alba Florets, seedlings 3-14
Brassica oleracea var italica
Seed, leaves, stems, roots, seedlings
7-16
Raphannus sativus Roots, seeds 6-14
Arabidopsis thaliana Leaf, florets, flowers, seedlings 3-23
Note: This table references some previous reports [2,111–113].
Table 3. The profiles of glucosinolates detected in cruciferous plants.
Glucosinolate Types Chemical Names Common Names Characterization Methods
Aliphatic GSLs
Methyl GSL Glucocapparin M, N
1-Methylethyl GSL Glucputranjivin U, I, M, N
3-Methoxycarbonyl-propyl GSL Glucoerypestrin N
Ethyl GSL Glucolepidiin Thiourea-type
4-Oxoheptyl GSL Glucocapanglin Deducted from I and 5-oxooctanoic acid
5-Oxoheptyl GSL Gluconorcappasalin Thiourea-type, I compared with GSL
5-Oxooctyl GSL Glucocappasalin U, I of GSL Mn
2-Hydroxy-2-methylpropyl GSL Glucoconringiin M, N
(2S)-2-Hydroxy-2-methylbutyl GSL Glucocleomin N of desGSL
(1R)-1-(Hydroxymethyl)-propyl GSL Glucosisaustricin M, N of desGSL
(2S)-2-Methylbutyl GSL Glucojiaputin U, I, M, N of GSL and desGSL
(1S)-1-Methylpropyl GSL Glucocochlearin M, N of GSL Mn
3-(Methylsulfanyl)propyl GSL Glucoibervirin M, N of GSL
4-Oxoheptyl GSL Glucocapangulin Deduction from I,5-oxooctanoic acid
4-(Methylsulfanyl)butyl GSL Glucoerucin U, I, M, N of GSL
5-(Methylsulfanyl)pentyl GSL Glucoberteroin U, I, M, N of GSL; U, M, N of desGSL
6-(Methylsulfanyl)hexyl GSL Glucolesquerellin U, I, M, N of GSL
(R)-11-(Methylsulfinyl)-propyl glucosinolate
Glucoiberin M, N, X-ray of GSL; U
(R/S)-4-(Methylsulfinyl)-butyl glucosinolate
Glucoraphanin M, N of GSL, U
(R/S)-5-(Methylsulfinyl)pentyl GSL Glucoalyssin M, N of GSL
(R/S)-6-(Methylsulfinyl)-hexyl GSL Glucohesperin U, I, M, N of GSL
(R/S)-8-(Methylsulfinyl)-octyl GSL Glucohirsutin U, I, M, N of GSL
(R/S)-9-(Methylsulfinyl)-nonyl GSL Glucoarabin U, I, M, N of GSL
Foods 2024,13, 4141 11 of 22
Table 3. Cont.
Glucosinolate Types Chemical Names Common Names Characterization Methods
Aliphatic GSLs
(R/S)-10-(Methylsulfinyl)decyl GSL Glucocamelinin M, N of GSL
3-(Methylsulfonyl)-propyl GSL Glucocheirolin M of GSL; N of desGSL
4-(Methylsulfonyl)butyl GSL Glucoerysolin M of GSL; M, N of desGSL
(R/S, 3E)-4-(Methylsulfiny1)-but-3-enyl GSL
Glucoraphenin M, N of GSL; U, N of desGSL
(R)-4-(Cystein-S-yl) butyl GSL Glucorucolamine M, N of desGSL
4-(β-D-Glucopyranosyl-disulfanyl)-butyl
GSL Diglucothiobeinin M of GSL; M, N of desGSL
6-Benzoyl-4 (methylsulfanyl)butyl GSL 6′-Benzoyl-glucoerucin U, M, N of desGSL
6′-Benzoyl-4(methylsulfinyl)butyl GSL 6′-Benzoyl-
glucopharanin U, M, N of desGSL
(R/S,3E)-6-Sinapoyl-4
(methylsulfinyl)but-3-enyl GSL 6′-Sinapoyl-
glucoraphenin U, I, M, N of desGSL
Allyl glucosinolate Sinigrin M, N, X-ray of GSL; U
But-3-enyl GSL Gluconapin M, N of GSL; U,
Pent-4-enyl GSL Glucobrassicanapin M of GSL
(2S)-2-Hydroxypent 4-enylGSL Gluconapoleiferin M of GSL
(2R)-2-Hydroxybut 3-enylGSL Progoitrin M, N of GSL; U, M, N of desGSL
(2S)-2-Hydroxybut 3-enylGSL Epiprogoitrin M, N of GSL; U, M, N of desGSL
Chemical Names Common Names Characterization Methods
Glucosinolate types (1R)-2-Bezoyloxt-1-methylethyl GSL Glucobenzosisymbrin U, I of ITC
Aromatic GSLs
(1R)-1-(Benzoyloxymethyl) propyl GSL Glucobenzsisaustricin Thiourease-type, I compared with GSL
Benzyl GSL Glucotropaeolin M, N of GSL; U, M, N of desGSL
3-Hydroxybenzyl GSL Glucolepigramin M of GSL; M, N of desGSL
3-Methoxybenzyl GSL Glucolimnanthin M, N of GSL; U, M, N of desGSL
4-Hydroxybenzyl GSL Glucosinalbin U, M, N of GSL Mn
4-Methoxybenzyl GSL Glucoaubrietin M, N of desGSL
3,4-Dihydroxybenzyl GSL Glucomatronalin M of GSL
4-Hydorxy3-methoxybenzyl GSL 3-Methoxysinalbin U, M, N of desGSL
3-Hydroxy-4-methoxybenzyl GSL Glucobretschneiderin U, I, M, N of GSL
4-Hydorxy-3,5-dimethoxybenzyl GSL 3,5-Dimethoxy-sinalbin U, M, N of desGSL
2-Phenylethyl GSL Gluconasturtiin N of GSL; U, M, N of desGSL
2-hydroxy-2-phenylethyl GSL Glucobarbarin M, N of GSL Mn
(2R)-2-Hydroxy-2-phenylethyl GSL Epiglucobarbarin M, N of GSL Mn
2-(4-Methoxy-phenyl) ethyl GSL Glucoarmoracin N of GSL, M, N of desGSL
(2R)-2-Hydroxy-2-(4-hydroxyphenyl) ethyl
GSL p-Hydroxy-
epiglucobarbarin M, N of GSL; U, M, N of desGSL
(2S)-2-Hydroxy-2(4-hydroxyphenyl) ethyl
GSL p-Hydroxy-
glucobarbarin U, M, N of desGSL
4-(4′-O-acetyl-α-c-4-rhamnopyranosyloxy)-
benzyl GSL 4-Mcetyl-
glucomoringin M of GSL and ITC
6′-Isoferuloyl-2 phenylethyl GSL 6′-Isoferuloyl
gluconasturtiin M of GSL, U
6′-Isoferuloyl-(2R) 2-hydroxy-2phenylethyl
GSL 6′-Isoferuloyl
epiglucobarbarin M, N of GSL; U
6′-Isoferuloyl-(2S) 2-hydroxy-2phenylethyl
GSL 6′-Isoferuloyl
glucobarbarin M, N of GSL; U, M, N of desGSL
4-(α-L-Rhamnopyranosyloxy) benzyl GSL Glucomorinigin M, N of GSL Mn
4-Methoxyindol-3-yl GSL Glucorapassicin M U, I, M, N of synthesized GSL
Foods 2024,13, 4141 12 of 22
Table 3. Cont.
Glucosinolate Types Chemical Names Common Names Characterization Methods
Indolic GSLs
Indol-3-ymethyl GSL Glucobrassicin U, I, M, N of GSL Mn
4-Hydroxyindol-3-ylmethyl GSL 4-Hydroxy-
glucobrassicin M of GSL; U, M, N of desGSL
4-Methoxyindol-3-ylmethyl GSL 4-Methoxy-
glucobrassicin U, M, M, N of GSL Mn
1-Methoxyindol-3-ylmethyl GSL Neoglucobrassicin U, I M, N of GSL; M, N of desGSL
1,4-Dimethoxyindol-3-ymethyl GSL 1,4-Dimethoxy-
glucobrassicin U, M, N of desGSL
1-Acetylindol-3-ymethyl GSL N-Acetyl-
glucobrassicin M of desGSL
1-Sulfoindol-3-ylmethyl GSL N-Sulfo-glucobrassicin U, I, M, N of GSL
6′-Isoferuloylindol-3-ylmethyl GSL 6′-Isoferuloyl-
glucobrassicin M of GSL; U, M, N of desGSL
Note: This table references some previous reports [
2
,
114
,
115
]. MS stands for M, NMR stands for N, UV stands for
U, IR stands for I, and desGSL stands for Mn.
Concurrently, spectrophotometric methods have been developed for the rapid and
cost-effective quantification of glucosinolates. For instance, a straightforward spectrophoto-
metric approach was proposed to estimate total glucosinolates in mustard de-oiled cake,
illustrating the potential for less resource-intensive techniques [
116
]. Furthermore, near-
infrared spectroscopy (NIRS) has emerged as a promising alternative for non-destructive
analysis of glucosinolates in intact seeds and plant tissues [
53
,
117
]. This method capitalizes
on the unique spectral signatures of glucosinolates, enabling swift assessments without
extensive sample preparation.
The quantification of glucosinolates is influenced by a multitude of factors, including
plant species, developmental stages, and environmental conditions. For example, the
content of glucosinolates can vary significantly among different cultivars of Brassica and in
response to abiotic stresses such as salinity and temperature [75,99,118,119]. Furthermore,
genetic factors play a crucial role in determining the glucosinolate profiles of different
plant varieties. This knowledge can be utilized in breeding programs designed to enhance
nutritional quality [
55
,
120
,
121
]. Additionally, the biological activity of glucosinolates is
closely associated with their hydrolysis products—such as isothiocyanates—which are
well-known for their health-promoting properties [
99
,
122
,
123
]. Therefore, understanding
quantification methods not only facilitates the assessment of the nutritional value of Brassica
vegetables but also provides insights into their potential health benefits. The quantification
of glucosinolates is a complex process that can be accomplished using various analytical
techniques, primarily HPLC and spectrophotometry. The selection of a method may be
influenced by particular research objectives, the resources at hand, and the nature of the
samples being analyzed. Further research into the factors affecting glucosinolate content
will enhance our understanding of these important phytochemicals.
4. Standardization and Validation of Glucosinolate Detection Methods
4.1. Standardization
Standardizing glucosinolate detection methods is crucial to ensure accurate and reli-
able results. This involves establishing standardized protocols for sample preparation, ex-
traction, and quantification, as well as using certified reference materials and participating
in inter-laboratory comparison studies. The standardization of these methodologies is cru-
cial for ensuring consistency and reliability in the analysis of bioactive compounds present
in cruciferous vegetables, which provide significant health benefits. Various methodologies
have been developed and validated, each with its own advantages and limitations.
HPLC is a widely employed technique for the analysis of glucosinolates. Vastenhout
et al. have developed a method to evaluate the kinetics of glucosinolate hydrolysis using
Foods 2024,13, 4141 13 of 22
HPLC, demonstrating that absorbance exhibits a linear relationship with concentration,
as confirmed by UV-Vis spectroscopy. This relationship is crucial for ensuring accurate
quantification [
124
]. Similarly, Gallaher et al. developed and validated a spectrophotometric
method for quantifying total glucosinolates in cruciferous vegetables, highlighting the
reliability and speed of their approach, which has broad applicability [
54
]. The use of HPLC
in combination with MS has also been emphasized by Frank et al., who employed LC-TOF-
MS to screen mustard seeds for glucosinolates, demonstrating the method’s effectiveness
in the identification and quantification of these compounds [
42
]. Additionally, Nuclear
Magnetic Resonance (NMR) spectroscopy has been proposed as a promising method for the
quantification of glucosinolates. Yuan et al. observed that although NMR is not commonly
utilized for this purpose, it offers advantages such as eliminating the need for calibration
standards, which can streamline the analysis process [
40
]. This is particularly pertinent
when considering the complexity of plant matrices, where conventional methods may
encounter difficulties due to interference from other compounds.
The extraction protocols employed prior to analysis are equally critical and can sig-
nificantly influence the results. Neal et al. presented a methodology for the extraction
of glucosinolates from leaves of Arabidopsis thaliana, wherein the identities of the peaks
were validated using standards, thereby ensuring the reliability and accuracy of the find-
ings [
125
]. Furthermore, Major et al. examined the influence of myrosinase activity during
the extraction process, highlighting that enzymatic activity can result in a decrease in total
glucosinolate concentration. This underscores the necessity for meticulous control over
extraction conditions [
87
]. Recent advancements have also concentrated on optimizing
extraction methods to enhance glucosinolate yield. For instance, Meza et al. developed a
UPLC-DAD method that refined sample preparation procedures to achieve high specificity
and accuracy in glucosinolate quantification [
92
]. Furthermore, ultrasound-assisted extrac-
tion has been investigated as a more environmentally friendly alternative, demonstrating
the potential to improve extraction efficiency while minimizing solvent usage [
96
]. Thus,
the standardization of glucosinolate detection methods necessitates a combination of re-
liable analytical techniques, optimized extraction protocols, and careful consideration of
enzymatic activity. The integration of HPLC with MS, along with emerging methodologies
such as NMR and ultrasound-assisted extraction, constitutes a comprehensive approach to
glucosinolate analysis that can enhance the accuracy and reproducibility of results across
various studies.
4.2. Validation
The validation of glucosinolate detection methods involves evaluating key parameters
such as linearity, precision, accuracy, limit of detection (LOD), and limit of quantification
(LOQ). This rigorous process ensures that the method is suitable for its intended application
and generates reliable results. The validation of these methods is crucial to affirm their
dependability and appropriateness for specific uses. Essential parameters for validation
include linearity, precision, accuracy, LOD, and LOQ; each parameter plays a vital role
in confirming that the analytical methods utilized can consistently deliver valid results.
Linearity refers to the method’s capacity to yield results that are directly proportional to
the analyte concentration within a specified range. For instance, studies have shown that
HPLC methods can achieve exceptional linearity for glucosinolate quantification, with
correlation coefficients (R
2
) frequently exceeding 0.99, thereby indicating a robust linear
relationship between concentration and response [
45
,
90
]. This is crucial for ensuring that
the method can accurately quantify glucosinolates across a spectrum of concentrations
typically encountered in plant samples.
Precision, which evaluates the reproducibility of the method, is another critical param-
eter for validation. It is typically assessed through repeatability and intermediate precision
studies. For instance, the precision of HPLC methods for glucosinolate analysis has been
reported with relative standard deviations (RSDs) generally below 5%, indicating high
reproducibility in measurements [
90
,
91
]. Similarly, methodologies such as UPLC-DAD
Foods 2024,13, 4141 14 of 22
have demonstrated consistent results across various laboratories, further reinforcing the
reliability of these techniques [
92
,
99
]. Accuracy, which quantifies the proximity of mea-
sured values to true values, is a fundamental aspect of method validation. Various studies
have utilized recovery experiments to evaluate accuracy, wherein known quantities of
glucosinolates are added to samples, and the recovery rates are subsequently calculated.
Reports indicate that recovery rates for glucosinolates typically range from 90% to 110%,
thereby confirming the reliability of the methods employed [
45
,
91
]. Such accuracy is essen-
tial for ensuring that reported glucosinolate levels accurately reflect true concentrations in
the samples.
LOD and LOQ are essential parameters for evaluating the sensitivity of analytical
methods. LOD, or limit of detection, refers to the lowest concentration of an analyte that
can be reliably identified, whereas LOQ, or limit of quantification, signifies the minimum
concentration that can be quantified with acceptable precision and accuracy. Studies have
demonstrated that HPLC methods can achieve LODs in the low micromolar range for vari-
ous glucosinolates, rendering them suitable for detecting trace levels of these compounds in
complex matrices [
45
,
90
]. This sensitivity is particularly vital in food safety and nutritional
studies, where low glucosinolate levels may carry significant biological implications. The
validation of glucosinolate detection methods through an assessment of linearity, precision,
accuracy, LOD, and LOQ is essential to ensure result reliability. A rigorous evaluation of
these parameters confirms that the employed methods are appropriate for their intended
purposes, thereby bolstering research and applications across fields such as nutrition, food
science, and plant biology.
5. Future Prospects
5.1. Development of New Analytical Techniques
The development of novel analytical techniques, such as ion mobility spectrometry
(IMS) and NMR, has the potential to significantly enhance the sensitivity, selectivity, and
throughput of glucosinolate analysis. Glucosinolates, which are sulfur-containing com-
pounds present in cruciferous vegetables, have garnered significant attention due to their
health benefits and roles in plant defense mechanisms. Traditional methods for glucosi-
nolate analysis—primarily HPLC—while effective, may be limited in terms of sensitivity
and specificity. The integration of advanced analytical techniques like IMS and NMR can
effectively address these limitations.
NMR spectroscopy is particularly valuable due to its non-destructive and highly
informative characteristics. Recent advancements in NMR technology, including the de-
velopment of hyperpolarization techniques, have significantly enhanced the sensitivity
of NMR measurements. For instance, hyperpolarized water has been employed to im-
prove the detection of various compounds, including glucosinolates, by increasing the
signal-to-noise ratio in NMR experiments [
126
,
127
]. Furthermore, the application of dy-
namic nuclear polarization (DNP) has demonstrated promise in further enhancing NMR
sensitivity, facilitating the detection of low-abundance metabolites—crucial for analyzing
glucosinolates within complex biological matrices [
128
]. Furthermore, the incorporation
of magnetic nanoparticles in conjunction with NMR has emerged as a potent strategy
for enhancing detection capabilities. Magnetic nanoparticles can alter the local magnetic
environment, thereby influencing the relaxation rates of adjacent nuclei and improving the
overall sensitivity of NMR measurements [
129
,
130
]. This approach is particularly advanta-
geous for glucosinolate analysis, as it facilitates the detection of these compounds at lower
concentrations and within more complex samples.
IMS is another promising technique that can complement NMR in the analysis of
glucosinolates. IMS facilitates rapid separation and identification of ions based on their
mobility in a gas phase, thereby providing high-throughput analytical capabilities. The
integration of IMS with MS further enhances the specificity of glucosinolate detection
by enabling the identification of specific molecular ions associated with glucosinolates
and their degradation products [
131
,
132
]. This dual approach significantly improves the
Foods 2024,13, 4141 15 of 22
analytical workflow, allowing researchers to obtain comprehensive profiles of glucosino-
lates across various samples. The incorporation of sophisticated analytical methodologies,
including NMR and IMS, offers a significant opportunity to enhance the analysis of glu-
cosinolates. These methods not only improve sensitivity and selectivity but also facilitate
high-throughput analysis, rendering them invaluable tools in the investigation of these
important phytochemicals.
5.2. Miniaturization and Automation
The adoption of advanced analytical techniques, including NMR and IMS, provides a
considerable opportunity to enhance the analysis of glucosinolates. These methodologies
not only improve sensitivity and selectivity but also facilitate high-throughput analysis,
thereby establishing them as essential tools in the investigation of these important phy-
tochemicals. HPLC and Ultra-Performance Liquid Chromatography (UPLC) represent
the cutting edge of glucosinolate analysis. Notably, UPLC is recognized for its superior
separation efficiency, reduced solvent consumption, and shorter run times compared with
conventional HPLC methods. For example, Meza et al. demonstrated that UPLC provides
a more environmentally sustainable quantification of glucosinolates in Camelina seeds,
thereby minimizing ecological impact while enhancing throughput [
92
]. Similarly, Shi
et al. underscored the efficacy of HPLC coupled with diode array detection for quanti-
fying glucosinolates, highlighting its significance in high-throughput applications [
133
].
These methodologies enable the simultaneous detection of multiple glucosinolates, thus
streamlining the analytical process.
Furthermore, the integration of MS with chromatographic techniques has significantly
enhanced glucosinolate detection. Wu et al. utilized LC-MS/MS in multiple reaction moni-
toring mode to analyze glucosinolate profiles in red cabbage, demonstrating the method’s
sensitivity and its capacity to rapidly provide detailed compositional data [
33
]. This combi-
nation improves detection limits and facilitates the identification of various glucosinolates
within complex matrices, which is crucial for comprehensive profiling in high-throughput
applications. Automation plays a pivotal role in these advancements, facilitating the rapid
processing of samples with minimal human intervention. Techniques such as automated
sample preparation and robotic liquid handling systems can significantly reduce analysis
time and variability in results. However, the reference by Li et al. does not specifically
address glucosinolate analysis; rather, it focuses on genetically modified organisms [
134
].
Consequently, it is inappropriate to support claims regarding automation in glucosinolate
detection based on this source. Furthermore, the proposed application of biosensors and
microfluidic devices for real-time monitoring and analysis of glucosinolates lacks direct
corroboration from the cited references [
135
,
136
]. In conclusion, the miniaturization and
automation of glucosinolate detection methods are revolutionizing analytical practices by
facilitating high-throughput analysis, minimizing resource consumption, and expediting
analysis times. The integration of advanced chromatographic techniques with MS, along-
side automation, is paving the way for more efficient and effective glucosinolate profiling.
5.3. Application of Machine Learning (ML) and Artificial Intelligence
ML and artificial intelligence algorithms can be employed to optimize detection meth-
ods, predict glucosinolate content, and classify plant materials based on their glucosinolate
profiles. The application of ML and artificial intelligence (AI) in refining detection tech-
niques, forecasting glucosinolate levels, and categorizing plant materials according to their
glucosinolate profiles represents an emerging area of research that holds significant promise
for agricultural and food sciences. Glucosinolates, a group of sulfur-containing compounds
primarily located in Brassica species, are acknowledged for their health-promoting proper-
ties, which include cancer prevention and antioxidant effects [
99
,
127
,
137
]. The integration
of ML and AI has the potential to enhance the efficiency and accuracy of glucosinolate
analysis, which is critical for both breeding programs and food quality assessment.
Foods 2024,13, 4141 16 of 22
One of the key applications of machine learning in this context is the enhancement of
detection methods. Traditional techniques for glucosinolate quantification, such as HPLC,
while effective, can be time-consuming and necessitate extensive sample preparation [
92
].
Recent advancements in ML algorithms have demonstrated their ability to analyze complex
datasets generated from chromatographic techniques, enabling rapid identification and
quantification of glucosinolates [
23
]. For instance, the GLS-Finder platform employs UPLC
coupled with HRMS to conduct qualitative and semi-quantitative analyses of glucosinolates,
significantly reducing analysis time [
23
]. Furthermore, ML can be trained to predict glu-
cosinolate profiles based on various growth conditions and environmental factors, thereby
facilitating more targeted breeding strategies [
127
,
138
]. In addition to optimizing detection
methods, ML and artificial intelligence (AI) can be utilized to predict glucosinolate content
in various plant materials. Factors such as soil composition, climatic conditions, and plant
genetics significantly influence glucosinolate levels [
138
]. Statistical modeling approaches,
including regression analysis and neural networks, have been effectively employed to
forecast glucosinolate concentrations in crops such as Chinese cabbage and kale based
on these variables [
127
,
138
]. For instance, studies have demonstrated that environmental
factors like temperature and humidity are correlated with glucosinolate accumulation,
facilitating predictive models that assist in selecting optimal growing conditions for desired
glucosinolate profiles [131,138].
Furthermore, artificial intelligence (AI) can facilitate the classification of plant mate-
rials based on their glucosinolate content. By employing supervised learning techniques,
researchers can categorize different cultivars of Brassica species according to their glucosi-
nolate profiles, which is essential for both breeding and consumer preferences [
137
]. ML
algorithms are capable of analyzing spectral data from methods such as hyperspectral
imaging to classify plant materials rapidly and non-destructively, providing a valuable
tool for quality control in agricultural practices [
139
]. This classification capability can also
extend to identifying plant varieties with enhanced health-promoting properties, thereby
guiding breeding programs aimed at improving nutritional quality [
137
]. Thus, the in-
tegration of ML and AI into glucosinolate research offers significant advancements in
detection methods, predictive modeling, and the classification of plant materials. These
technologies not only enhance the efficiency of glucosinolate analysis but also contribute
to the development of crops with optimized health benefits, aligning with the increasing
demand for functional foods in the market.
Author Contributions: Conceptualization, Z.L.; writing—original draft preparation, X.L. and D.W.;
writing—review and editing, F.H., J.S., S.L., Y.L., Y.H., F.G. and M.Z.; supervision, Z.L.; project
administration, Z.L. All authors have read and agreed to the published version of the manuscript.
Funding: This work was supported by the National Nature Science Foundation (32172580), the
China Agriculture Research System (CARS-23-A05), and the Agricultural Science and Technology
Innovation Program (ASTIP).
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: No new data were created or analyzed in this study. Data sharing is
not applicable to this article.
Acknowledgments: Figure 1was created with BioRender.com, and we thank the support of Yafei
Seed Co., Ltd.
Conflicts of Interest: The authors declare no conflicts of interest.
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