Abstract

   Understanding how leaf maturity affects the flavor attributes of green
   tea is crucial for optimizing harvest timing and processing strategies.
   This study comprehensively characterized the flavor profiles of
   Fudingdabai green teas at three distinct leaf maturity stages—single
   bud (FDQSG), one bud + one leaf (FDMJ1G), and one bud + two leaves
   (FDTC2G)—using a multimodal approach integrating electronic nose,
   electronic tongue, HS-GC-IMS, relative odor activity value (rOAV)
   evaluation, and machine learning algorithms. A total of 85 volatile
   compounds (VOCs) were identified, of which 41 had rOAV > 1. Notably,
   2-methylbutanal, 2-ethyl-3,5-dimethylpyrazine, and linalool exhibited
   extremely high rOAVs (>1000). FDQSG was enriched with LOX
   (lipoxygenase)-derived fresh, grassy volatiles such as (Z)-3-hexen-1-ol
   and nonanal. FDMJ1G showed a pronounced accumulation of floral and
   fruity compounds, especially linalool (rOAV: 7400), while FDTC2G
   featured Maillard- and phenylalanine-derived volatiles like benzene
   acetaldehyde and 2,5-dimethylfuran, contributing to roasted and
   cocoa-like aromas. KEGG (Kyoto Encyclopedia of Genes and Genomes)
   analysis revealed significant enrichment in butanoate metabolism and
   monoterpenoid biosynthesis. Random forest–SHAP analysis identified 20
   key flavor markers, mostly VOCs, that effectively discriminated samples
   by tenderness grade. ROC–AUC validation further confirmed their
   diagnostic performance (accuracy ≥ 0.8). These findings provide a
   scientific basis for flavor-driven harvest management and the
   quality-oriented grading of Fudingdaibai green tea.

   Keywords: green tea, leaf tenderness, volatile compounds, HS-GC-IMS,
   rOAV, machine learning, Fudingdabai, KEGG pathway, flavor markers, tea
   grading

1. Introduction

   Green tea, a globally consumed non-fermented tea product, is prized for
   its distinctive fresh, floral, and umami sensory attributes
   [[40]1,[41]2], as well as its significant health benefits, including
   antioxidant [[42]3,[43]4], anti-inflammatory [[44]5], neuroprotective
   [[45]6], anticancer [[46]7], cardioprotective [[47]8],
   antihyperglycemic, and anti-obesity effects [[48]4]. These nutritional
   and sensory qualities are closely linked to the cultivar and maturity
   stage of the tea leaves used for processing. Among the diverse tea
   cultivars in China, Camellia sinensis cv. Fudingdabai stands out as a
   high-quality variety due to its favorable bud and leaf morphology
   [[49]9,[50]10]. It is widely cultivated not only in its native Fujian
   Province but also in regions such as Guizhou, where distinct
   geographical conditions contribute to unique flavor signatures and
   nutritional profiles of processed teas derived from this cultivar
   [[51]11,[52]12]. The Guizhou region, characterized by its low latitude,
   high altitude, and limited sunshine, provides an ideal ecological niche
   for premium tea production. Under this terroir, Fudingdaibai green tea
   (FDG) develops distinctive sensory attributes shaped by the synergy
   between cultivar and processing techniques [[53]12,[54]13,[55]14].

   A critical agronomic and quality-related factor influencing green tea
   is leaf maturity stage, typically categorized as single bud, one bud
   with one leaf, and one bud with two leaves. The maturation stage of tea
   shoots directly affects the chemical composition of flavor precursors
   and secondary metabolites [[56]15,[57]16,[58]17]. Prior studies have
   demonstrated that teas made from more tender shoots generally exhibit
   enhanced sweetness and umami, along with lower bitterness, which are
   associated with the accumulation of metabolites such as maltitol and
   L-aspartic acid [[59]18]. Similarly, in yellow tea, tender buds have
   been shown to possess higher antioxidant and α-glucosidase inhibitory
   activity, while mature leaves exhibit greater levels of Maillard
   reaction-derived heterocyclic volatiles [[60]19]. Recent research also
   suggests that the distribution of chiral aroma compounds in green tea
   varies systematically with leaf maturity [[61]20]. Additionally, Xu et
   al. [[62]17] found that green teas made from tender shoots had higher
   amino acid and polyphenol content, yielding fresher and sweeter taste
   profiles, whereas mature leaf teas exhibited intensified bitterness and
   astringency. Similar maturity-dependent changes in flavor-active
   compounds and bioactivity have been documented in Lithocarpus
   litseifolius (sweet tea) [[63]21], mulberry leaf tea [[64]22], black
   tea [[65]23], and white tea [[66]24], suggesting that leaf maturity is
   a general determinant of tea flavor and functional properties.

   Despite growing interest in the relationship between leaf maturity and
   tea quality, a systematic flavoromic investigation of how varying leaf
   maturity stages affect the flavor characteristics, volatile profiles,
   sensory attributes, and related metabolic pathways in Fudingdabai green
   tea (FDG)—especially from the Guizhou region—remains lacking.
   Clarifying these relationships is critical for identifying quality
   markers and establishing rapid grading frameworks, with important
   implications for harvest scheduling and processing control.

   Recent advancements in flavoromics have enabled more comprehensive tea
   flavor evaluation. Headspace gas chromatography–ion mobility
   spectrometry (HS-GC-IMS) has emerged as a powerful analytical platform
   owing to its high sensitivity, fast analysis time, and minimal sample
   preparation [[67]2,[68]25,[69]26,[70]27,[71]28]. This technique excels
   in detecting key volatiles such as aldehydes and ketones, and has been
   successfully applied to characterize the aroma of green, white, oolong,
   black, and dark teas, as well as baijiu and meat products
   [[72]2,[73]25,[74]26,[75]27,[76]28,[77]29,[78]30,[79]31,[80]32]. In
   parallel, electronic nose (E-nose) and electronic tongue (E-tongue)
   systems enable rapid quantification of overall aroma and taste
   features, thereby overcoming the limitations of traditional sensory
   evaluation [[81]31,[82]32]. Moreover, relative odor activity value
   (rOAV) analysis provides a quantitative estimation of the contribution
   of each volatile to the overall aroma profile [[83]25,[84]33]. However,
   the standalone application of these techniques is often insufficient to
   fully capture the complexity of food flavor systems.

   The integration of machine learning (ML) algorithms presents an
   effective solution for decoding such multidimensional datasets.
   Algorithms such as random forest (RF), support vector machine (SVM),
   k-nearest neighbor (KNN), Gaussian naïve Bayes (GNB), logistic
   regression (LR), and decision tree (DT) have shown strong performance
   in modeling nonlinear and high-dimensional flavor data, while also
   enabling the selection of key discriminative features [[85]34]. These
   tools have been increasingly used to evaluate and predict flavor
   quality in baijiu, tea, and other food products
   [[86]25,[87]26,[88]34,[89]35,[90]36]. Notably, ensemble modeling
   strategies combining multiple algorithms can further enhance prediction
   accuracy and reduce model bias [[91]34].

   Against this backdrop, the present study aimed to investigate the
   flavor characteristics of FDG processed from leaves of three different
   maturity stages—single bud (FDQSG), one bud plus one leaf (FDMJ1G), and
   one bud plus two leaves (FDTC2G)—harvested from Guizhou Province. By
   integrating HS-GC-IMS, E-nose, E-tongue, rOAV calculation, and multiple
   machine learning models, we sought to (1) characterize how tenderness
   affects the overall flavor profile of FDG, (2) identify key flavor
   markers associated with leaf maturity, and (3) elucidate potential
   biosynthetic pathways of differential volatile compounds. The findings
   offer novel insights into the flavor basis of tea grading and provide a
   theoretical foundation and technical support for optimized harvest
   timing and tailored processing of FDG.

2. Materials and Methods

2.1. Tea Samples and Chemical Reagents

   Three green tea samples representing different leaf maturity stages
   were processed from the fresh leaves of Camellia sinensis
   ‘Fudingdabai’. The harvested fresh shoots included single bud (FDQSG,
   Fuding Que She), one bud with one leaf (FDMJG, Fuding Maojian), and one
   bud with two leaves (FDTC2G, Fuding Taicha). All raw tea materials were
   collected in March and April 2023 from nine-year-old tea plants grown
   at the Hongfengshanyun Tea Farm in Qingzhen, Guizhou Province, China
   (106°22′ E, 26°31′ N). To ensure consistency and representativeness,
   all samples were harvested from equivalent positions on the tea bushes
   and then thoroughly homogenized prior to analysis. The processed green
   tea samples were stored at –4 °C until further evaluation. For
   instrumental analysis, each group was tested in triplicate for the
   electronic nose (E-nose) and HS-GC-IMS, and in quintuplicate (n = 5)
   for the electronic tongue (E-tongue) to ensure measurement accuracy and
   repeatability.

   All chemical reagents were of chromatographic or analytical grade.
   Ethyl decanoate (≥98%, GC grade, TCI, Shanghai, China) was used as the
   internal standard for volatile compound analysis. Standard n-ketones
   for retention index (RI) calibration—2-butanone (99.5%), 2-pentanone
   (99.5%), 2-hexanone (99.5%), 2-heptanone (98%), 2-octanone (99%), and
   2-nonanone (99%)—were purchased from Sinopharm Chemical Reagent Co.,
   Ltd. (Shanghai, China). Sodium chloride (NaCl) and absolute ethanol
   were obtained from Jinshan Chemical Co., Ltd. (Chengdu, China), and
   distilled water was provided by Wahaha Group Co., Ltd. (Hangzhou,
   China).

2.2. Electronic Tongue Sensory Analysis

   Taste attributes of green tea infusions were assessed using a taste
   sensing system (SA402B Electronic Tongue, INSENT Inc., Kanagawa,
   Japan), which is equipped with five test sensors (AAE, CTO, CAO, COO,
   AE1) and two reference electrodes. These sensors detect the intensity
   of umami, saltiness, sourness, bitterness, and astringency, as well as
   aftertaste characteristics such as aftertaste-bitterness
   (aftertaste-B), aftertaste-astringency (aftertaste-A), and richness.

   Each tea sample (1.5 g) was extracted with 75 mL of boiling distilled
   water for 5 min. The resulting infusion was filtered through gauze and
   cooled to room temperature prior to measurement. Five independent
   replicates were prepared and analyzed for each tenderness type. The
   operating conditions and measurement procedure of the electronic tongue
   are detailed in [92]Table S1.

2.3. Electronic Nose Sensory Analysis

   The aroma profiles of green tea samples with varying leaf maturity
   stages were analyzed using a PEN3 electronic nose system (Airsense
   Analytics GmbH, Schwerin, Germany), which is equipped with a sensor
   array of ten metal oxide gas sensors. These sensors are sensitive to
   different classes of volatile compounds, including aromatic benzenes,
   nitrogen oxides, amines, hydrogen, short/long-chain alkanes, sulfur
   organics, alcohols, aldehydes, and ketones ([93]Table S2).

   For sample preparation, 0.5 g of liquid nitrogen-ground tea powder was
   accurately weighed into a 20 mL headspace vial. After adding 5 mL of
   boiling distilled water, the vial was immediately sealed to allow
   headspace extraction. The sealed vials were equilibrated in a water
   bath at 65 °C for 50 min before analysis.

   The PEN3 system was operated under the following conditions: sampling
   interval of 1 s, pre-sampling time of 5 s, measurement time of 150 s,
   and flushing time of 60 s. Zero-point calibration was performed
   automatically with clean air for 10 s before each measurement. Both the
   chamber and injection flow rates were set at 400 mL/min. The
   measurement lasted 120 s. The chamber was purged with filtered air
   between runs to restore baseline sensor responses.

2.4. HS-GC-IMS Analytical Method

   The analysis of volatile organic compounds (VOCs) in green tea samples
   was conducted using a FlavourSpec^® GC-IMS instrument (G.A.S.,
   Dortmund, Germany), equipped with an MXT-5 capillary column (15 m ×
   0.53 mm × 1.0 μm, Restek, Bellefonte, PA, USA), a headspace autosampler
   (CTC-PAL3, CTC Analytics AG, Zwingen, Switzerland), and an ion mobility
   spectrometry detector. For sample preparation, 1.0 g of finely ground
   tea sample was accurately weighed into a 20 mL headspace vial, to which
   20 μL of ethyl decanoate (100 ppm) was added as the internal standard.
   The vial was incubated at 80 °C for 15 min with agitation at 500 rpm. A
   500 μL aliquot of the headspace was injected into the GC-IMS system
   using a heated syringe (85 °C) in splitless mode.

   The GC-IMS operating parameters were as follows: the column temperature
   was maintained at 60 °C; the IMS drift tube was held at 45 °C; the
   carrier gas (nitrogen, ≥99.999%) flow rate was programmed as 2 mL/min
   (0–2 min), increasing linearly to 100 mL/min over 18 min. The drift gas
   (nitrogen, ≥99.999%) was supplied at a constant flow rate of 150
   mL/min. The total analysis time was 20 min. Retention indices (RI) were
   calculated using a homologous series of n-ketones (C4–C9) under
   identical chromatographic conditions. All measurements were performed
   in triplicate to ensure accuracy and reproducibility.

2.5. Qualitative and Quantitative Analysis

   The identification of volatile compounds was performed using VOCal
   software (version 0.4.07, rev.# 346; G.A.S., Dortmund, Germany),
   integrated with both the National Institute of Standards and Technology
   (NIST 17) mass spectral database and the proprietary GC-IMS library.
   Retention indices (RI) were calculated by analyzing a homologous series
   of n-alkanes (C4–C9) under identical chromatographic conditions as the
   tea samples. VOCs were identified by comparing both their RI values and
   drift times (Dt) with those in the reference databases, ensuring
   accurate compound assignment.

   For semi-quantitative analysis, the internal standard method was
   applied, using ethyl decanoate (100 ppm) as the reference. The
   concentrations of individual VOCs were expressed as relative values
   based on the ratio of the peak area of each compound to that of the
   internal standard.

   Additionally, rOAVs were calculated to evaluate the sensory relevance
   of each volatile compound. The rOAV was defined as the ratio of the
   compound’s relative concentration to its reported odor threshold in
   water. By integrating both chemical concentration and sensory detection
   threshold, rOAV provides a more reliable indication of human olfactory
   perception than concentration alone. In food, fragrance, and
   environmental odor analysis, this metric enables the prioritization of
   high-rOAV odorants (even at low concentrations) over abundant but
   sensorially negligible compounds with low rOAVs. VOCs with rOAV values
   equal to or greater than 1 were considered to contribute significantly
   to the overall aroma profile of the green tea samples [[94]25]. To
   assess the contribution of individual VOCs to the aroma profiles of FDG
   with different maturity stages, those meeting this criterion were
   extracted and summarized in [95]Table 1 for further discussion.

Table 1.

   Key aroma-active compounds in three tenderness grades of green tea
   identified using HS-GC-IMS-OAV analysis (rOAV > 1).
   NO. Volatile Compounds Odor Type Chemical Classes OT
   (μg/L) rOAV
   FDQSG FDMJ1G FDTC2G
   1 Linalool floral Terpenes 0.22 3.8 × 10^3 7.4 × 10^3 6.3 × 10^3
   2 trans-linalool oxide floral Terpenes 60 4.2 3.5 4.0
   3 Nonanal-M aldehydic Aldehydes 1.1 9.1 × 10^2 8.1 × 10^2 8.6 × 10^2
   4 Nonanal-D aldehydic Aldehydes 1.1 2.1 × 10^2 1.7 × 10^2 2.0 × 10^2
   5 (E)-2-octenal fatty Aldehydes 3.0 35 16 21
   6 Octanal-M aldehydic Aldehydes 0.587 389 343 422
   7 Heptanal-M green Aldehydes 2.80 135 166 171
   8 Heptanal-D green Aldehydes 2.80 736 514 423
   9 (E)-hept-2-enal green Aldehydes 13 5.6 0.95 0.89
   10 Octanal-D aldehydic Aldehydes 0.587 52.7 28.6 44.8
   11 Pentanal fermented Aldehydes 12 66 45 25
   12 2-methylbutanal cocoa Aldehydes 1.0 1.1 × 10^3 1.3 × 10^3 1.4 × 10^3
   13 3-methylbutanal aldehydic Aldehydes 1.1 5.8 × 10^2 7.3 × 10^2 6.9 ×
   10^2
   14 Butanal chocolate Aldehydes 2.0 69 94 45
   15 Hexanal green Aldehydes 2.4 1.7 × 10^2 71 41
   16 (E)-2-hexenal green Aldehydes 88.7 5.96 3.43 2.62
   17 methyl-5-hepten-2-one citrus Ketones 68 8.5 8.8 7.6
   18 2-Octanone earthy Ketones 50.2 2.74 0.71 0.92
   19 3-hydroxybutan-2-one buttery Ketones 14 9.7 9.5 7.0
   20 2-Propanone solvent Ketones 832 4.28 5.33 4.53
   21 1-penten-3-one spicy Ketones 23 5.9 1.4 1.1
   22 3-Pentanone ethereal Ketones 40 3.0 1.9 1.5
   23 n-Hexanol-M herbal Alcohols 5.6 42.3 46.1 39.4
   24 (Z)-3-hexen-1-ol green Alcohols 3.9 67 59 62
   25 n-Hexanol-D herbal Alcohols 5.6 7.6 7.7 6.5
   26 1.8-Cineole herbal Alcohols 1.10 11.7 11.4 12.5
   27 oct-1-en-3-ol earthy Alcohols 1.5 33 22 17
   28 pentan-1-ol-M fermented Alcohols 150.2 2.30 2.40 2.41
   29 pentan-1-ol-D fermented Alcohols 150.2 1.56 1.66 1.66
   30 2-Methylbutanol acetate fruity Esters 5.00 25.9 2.58 1.83
   31 Propyl isovalerate fruity Esters 8.70 1.61 1.21 0.96
   32 Ethyl Acetate ethereal Esters 5.00 54.5 95.9 92.3
   33 methyl acetate ethereal Esters 1500 1.080 1.070 1.430
   34 2-pentyl furan fruity Furans 5.80 19.0 13.8 11.6
   35 2-n-Butylfuran spicy Furans 5.0 5.8 3.4 3.0
   36 Methyl Salicylate minty Aromatics 40 3.8 5.3 4.1
   37 benzene acetaldehyde green Aromatics 6.3 9.2 7.6 14
   38 3-methylthiopropanal vegetable Sulfur-containing Compounds 0.45 8.5
   9.3 5.9
   39 Ethylsulfide coffee, meat Sulfur-containing Compounds 4.8 3.6 3.7
   5.2
   40 2-Ethyl-3,5-dimethylpyrazine nutty Pyrazines 0.040 1.42 × 10^3 1.44
   × 10^3 1.35 × 10^3
   41 2-acetyl-1-pyrroline popcorn Other Heterocyclics 0.12 57 66 1.6 ×
   10^2
   [96]Open in a new tab

2.6. Feature Selection and Extraction of Key Flavor Attributes in Green Teas
of Different Leaf Maturity Stages

   To identify key sensory and chemical markers responsible for the
   quality differentiation among green tea samples of varying leaf
   maturity stages, multivariate statistical analysis and machine learning
   algorithms were employed for feature selection and extraction from
   datasets obtained through electronic nose, electronic tongue, and
   HS-GC-MS analyses.

   In the domain of machine learning, feature engineering methods are
   generally classified into two categories: feature extraction and
   feature selection. Principal component analysis (PCA) and partial least
   squares discriminant analysis (PLS-DA) were applied as typical feature
   extraction techniques to reduce data dimensionality while preserving
   variance and class separation. For feature selection, three
   methodological frameworks were considered: filter methods, wrapper
   methods, and embedded methods. Among these, the random forest
   (RF)-based wrapper method was used to rank the importance of individual
   features according to their contribution to classification performance.

   The most informative features identified were further subjected to
   classification modeling using six commonly adopted machine learning
   algorithms: k-nearest neighbors (KNN), random forest (RF), support
   vector machine (SVM), Gaussian naïve Bayes (GNB), logistic regression
   (LR), and decision tree (DT). Model performance was evaluated based on
   receiver operating characteristic (ROC) curves, accuracy, sensitivity,
   and specificity [[97]34,[98]35,[99]36].

   Prior to classification modeling, three complementary feature selection
   strategies were employed to identify key variables. First, the entire
   dataset was utilized without prior selection to assess the predictive
   performance of all available features. Second, feature importance was
   evaluated using Shapley Additive Explanations (SHAP) derived from the
   random forest model, and the top 20 features with the highest SHAP
   values were selected to construct reduced models. Third, each of the
   top 20 SHAP-ranked features was individually used to train
   classification models, allowing for the assessment of the independent
   predictive power of each variable [[100]34,[101]36].

   To optimize model performance, hyperparameter tuning was conducted for
   all classification algorithms using K-fold cross-validation, with the
   area under the receiver operating characteristic curve (AUC) serving as
   the primary evaluation metric. Features associated with AUC values
   between 0.80 and 0.90 were considered moderately discriminative, while
   those with AUC values above 0.90 were deemed to exhibit strong
   predictive ability [[102]25].

2.7. Data Analysis

   Volatile compounds detected by the electronic nose and HS-GC-IMS were
   analyzed in triplicate, while taste attributes measured using the
   electronic tongue were conducted in five replicates. All results were
   expressed as mean ± standard deviation (SD). Statistical significance
   was assessed using one-way analysis of variance (ANOVA) via SPSS
   Statistics 26.0 (IBM, Armonk, NY, USA), with a threshold of p < 0.05
   considered significant.

   To visualize GC-IMS results, two-dimensional top-view plots,
   three-dimensional topographic plots, and differential spectral
   fingerprints were generated using Reporter and Gallery Plot plug-ins
   (G.A.S., Dortmund, Germany). These visualizations facilitated
   comparison of volatile profiles across green tea samples of different
   leaf maturity stages.

   Multivariate statistical analyses were performed using MetaboAnalyst
   6.0 [[103]37]. Principal component analysis (PCA) and partial least
   squares discriminant analysis (PLS-DA) were applied to examine overall
   clustering and sample discrimination. Additional data visualization
   methods, including hierarchical cluster analysis (HCA), heatmaps, and
   volcano plots, were also employed within the MetaboAnalyst platform to
   identify and interpret significantly different variables. Pie charts
   and chord diagrams were created using OriginPro 2021 (OriginLab
   Corporation, Northampton, MA, USA) for the graphical presentation of
   selected variables and compound associations. K-means clustering and
   supervised machine learning analyses were conducted using the Biodeep
   Cloud Platform provided by Panomix (Suzhou, China), enabling feature
   discrimination and predictive model construction across green tea
   samples.

3. Results and Discussion

3.1. Electronic Tongue Analysis

   Taste profiles across green tea samples of differing tenderness were
   characterized using an electronic tongue ([104]Figure 1). Principal
   component analysis (PCA) revealed that PC1 and PC2 explained 85.5% and
   13.9% of variance, respectively, cumulatively capturing 99.6% of the
   sensory data ([105]Figure 1A). This demonstrates that the first two
   principal components effectively capture the majority of taste-related
   variation among samples. Clear separation in the PCA score plot
   indicates that leaf tenderness significantly influences flavor
   attributes. Specifically, the single-bud sample (FDQSG) was strongly
   associated with “umami” characteristics ([106]Figure 1B), likely
   reflecting enriched amino acid content [[107]38]. The one-bud, one-leaf
   tea (FDMJ1G) was marked by heightened sourness, astringency,
   bitterness, and pronounced bitter aftertaste (aftertaste-B),
   potentially attributable to increased caffeine levels in moderately
   mature leaves ([108]Figure 1B,C). By contrast, the one-bud, two-leaf
   tea (FDTC2G) correlated with saltiness, richness, and aftertaste
   astringency (Aftertaste-A), which may correspond to increased levels of
   tea polyphenols and catechins in fully developed leaves [[109]39].
   These results imply that a comprehensive investigation into the
   distribution of non-volatile compounds is necessary to elucidate the
   correlation between taste profiles and chemical constituents in green
   teas of different tenderness grades.

Figure 1.

   [110]Figure 1
   [111]Open in a new tab

   Electronic tongue (e-tongue) analysis of green tea samples with
   different leaf maturity stages. (A) Principal component analysis (PCA)
   score plot demonstrating sample clustering. (B) Biplot integrating
   sample distribution and variable contributions. (C) Heatmap visualizing
   taste attribute intensities across samples.

3.2. Electronic Nose Analysis

   A supervised PLS-DA model was constructed to classify the aroma
   profiles of the different samples. The model’s first two latent
   variables accounted for 78.2% and 20.0% of variance (totaling 98.2%)
   and clearly separated tenderness groups in the score plot ([112]Figure
   2A), indicating strong discriminative capacity. Model robustness was
   confirmed by cross-validated Q^2 = 0.988 and R^2 = 0.992, with 100%
   classification accuracy; permutation testing (n = 2000) yielded p < 5 ×
   10^−4, confirming statistical significance ([113]Figure 2C,D).

Figure 2.

   [114]Figure 2
   [115]Open in a new tab

   E-nose analysis of taste characteristics in green tea with different
   leaf maturity stages. (A) PLS-DA score plot demonstrating sample
   clustering based on tenderness grade. (B) Biplot with 95% confidence
   ellipses showing variable contributions to sample discrimination. (C)
   Cross-validation (CV) results indicate model prediction accuracy. Q2
   values (predictive ability) with asterisks (*) denote statistical
   significance (p < 0.05), indicating the model outperforms random
   chance. (D) Permutation test statistics (n = 2000) validating model
   significance (p < 0.05). (E) Variable importance in projection (VIP)
   scores plot highlighting key discriminant compounds. (F) Heatmap
   visualization of normalized sensor response patterns across samples
   (red: high intensity; blue: low intensity).

   The bi-plot ([116]Figure 2B,F) revealed clear sensor–odor
   relationships: FDQSG correlated with sensors W2W, W5S, and W1W; FDTC2G
   with W5C, W1C, W1S, and W2S; and FDMJ1G overlapped with both but
   displayed stronger sensitivity to W3C. VIP analysis (VIP > 1)
   identified W3C (aromatic amines), W3S (long-chain alkanes), W1S
   (short-chain alkanes), and W6S (hydrogen-related compounds) as key
   discriminators ([117]Figure 2E, [118]Table S3).

3.3. HS-GC-IMS Analysis

3.3.1. Qualitative and Quantitative VOC Profiling

   The overall volatile compound profiles of green teas with different
   leaf maturity stages were systematically analyzed using HS-GC-IMS.
   Compound identification was based on retention time, drift time, and
   retention index values derived from IMS data. Using the Reporter
   plug-in, both three-dimensional ([119]Figure 3A) and two-dimensional
   ([120]Figure 3B) topographic visualizations were generated to
   comprehensively characterize the volatile fingerprints of the tea
   samples. In the 3D representation, the X-axis corresponds to ion drift
   time, the Y-axis to gas chromatographic retention time, and the Z-axis
   to the signal intensity of each volatile compound. The 2D topographic
   plot offers a more intuitive overview of VOC distribution across the
   samples, effectively illustrating characteristic fingerprints for green
   teas of varying leaf tenderness. As shown in [121]Figure 3B, the
   spectra were normalized and aligned using the reactive ion peak (RIP),
   which appears at a drift time of 1.0 (marked by a vertical reference
   line). Each feature point to the right of the RIP represents an
   individual volatile component detected in the sample. The color
   gradient from light blue to deep red indicates increasing compound
   concentration, with deeper red denoting higher relative abundance.

Figure 3.

   [122]Figure 3
   [123]Open in a new tab

   Volatile organic compound profiling of three tenderness-grade green
   teas using HS-GC-IMS. (A) Three-dimensional topographic plots showing
   ion mobility drift time (x-axis), GC retention time (y-axis), and
   normalized signal intensity (z-axis, a.u.). (B) Two-dimensional
   topographic plots displaying VOC fingerprint patterns, with RIP
   (Reaction Ion Peak) normalized to 1.0 (vertical line) and compound
   signals color-coded by concentration (red: high; blue: low). (C)
   Differential comparison plots generated by spectral subtraction
   (reference: FDQSG), where red indicates upregulated and blue indicates
   downregulated VOCs relative to the reference.

   [124]Figure 3 demonstrates the presence of multiple characteristic
   peaks, indicating that green tea samples of different maturity levels
   contain a diverse array of volatile organic compounds (VOCs). To
   further elucidate differences in volatile profiles among the teas, a
   differential comparison method was employed ([125]Figure 3C), with the
   single-bud sample (FDQSG) set as the reference. Relative differences in
   VOC intensity between FDQSG and the other two samples (FDMJ1G and
   FDTC2G) were computed to generate visual differential plots. In these
   maps, white regions indicate no significant difference; red regions
   reflect relative enrichment of specific compounds in the compared
   sample, whereas blue regions indicate reduced abundance relative to the
   reference. The differential plots revealed a substantially greater
   number of blue regions in both FDMJ1G and FDTC2G compared with FDQSG,
   suggesting that increased leaf maturity is associated with a general
   decline in the concentration of many volatile aroma compounds. Further
   identification and visualization of these decreasing compounds require
   detailed examination of the VOC fingerprint maps.

   In total, 85 VOCs were detected ([126]Table S4), comprising 8 terpenes,
   19 aldehydes, 13 ketones, 9 alcohols, 10 esters, 6 furans, 3 aromatics,
   3 sulfur compounds, 3 pyrazines, 2 acids, 1 heterocyclic
   (2-acetyl-1-pyrroline), and 8 unidentified peaks. Notably, five
   VOCs—nonanal-D, heptanal-D, octanal-D, n-hexanol-D, and
   pentan-1-ol-D—were identified as forming dimers, a phenomenon
   attributed to proton-bound dimerization (2M+H)^+ at elevated
   concentrations [[127]27]. The dominant compound classes were aldehydes
   (22.4%), ketones (15.3%), alcohols (11.8%), and esters (11.8%), with
   terpenes at 8.2% ([128]Figure 4A).

Figure 4.

   [129]Figure 4
   [130]Open in a new tab

   Qualitative and quantitative analysis of volatile organic compounds
   (VOCs) in green tea samples. (A) Proportional distribution of VOC
   chemical classes (pie chart with percentage annotations). (B) Chord
   diagram illustrating compound-specific correlations between tenderness
   grades and VOC abundances (band width corresponds to relative
   concentration).

   Quantitative analysis revealed the highest total VOC content in FDMJ1G
   (22.03 mg/L), followed by FDQSG and FDTC2G. FDQSG was notable for its
   aldehyde, sulfur compound, pyrazine, and acid content. FDMJ1G was
   enriched in terpenes, ketones, furans, aromatics, and unknown
   compounds, whereas FDTC2G had higher levels of alcohols, esters,
   heterocyclics (e.g., 2-acetyl-1-pyrroline), and sulfides—including
   compounds associated with “popcorn”, “meaty”, and “coffee” aromas
   ([131]Table S5, [132]Figure 4B). These compositional differences
   suggest the involvement of distinct metabolic pathways across different
   leaf maturity levels, which may have important implications for
   optimizing processing strategies and guiding product classification and
   grading in green tea production.

   To further identify characteristic volatile compounds and distinguish
   aroma-active substances among green tea samples with different leaf
   maturity stages, fingerprint profiles were constructed to visualize the
   concentration changes of individual VOCs ([133]Figure 5). Specifically,
   regions I, III, and IV correspond to the potential characteristic VOCs
   of FDQSG, FDMJ11G, and FDTC2G, respectively. Notably, region I,
   representing FDQSG (single bud), featured the highest number of
   discriminative compounds (21 in total), predominantly composed of C5–C9
   short-chain aldehydes and ketones that impart fresh and green notes.
   Additionally, short-chain esters (e.g., butyl propanoate) and
   unsaturated aldehydes (e.g., (E)-2-octenal) contributed fruity and
   ester-like aromas, while unsaturated alcohols such as oct-1-en-3-ol
   provided mushroom, earthy, and green sensory characteristics. These
   compounds are recognized as key odor-active constituents in Nen Xiang
   (NX), Li Xiang (LX), and Qing Xiang (QX) style green teas [[134]40].
   Furthermore, pyrazines (e.g., 2-ethyl-6-methylpyrazine) and furans
   (e.g., 2-n-butylfuran), common products of Maillard reactions,
   contributed roasty and nutty aromas [[135]41]. Collectively, these
   volatiles define the fresh and green aroma characteristics typical of
   single-bud green teas.

Figure 5.

   [136]Figure 5
   [137]Open in a new tab

   Characteristic VOC fingerprints of FDG across different tenderness
   grades. Roman numerals indicate clusters of VOCs: I, predominantly
   enriched in FDQSG; III, predominantly enriched in FDMJ1G; VI,
   predominantly enriched in FDTC2G; II and V, co-enriched across all FDG
   grades; IV, predominantly co-enriched in FDMJ1G and FDTC2G.

   In contrast, Region III, associated with FDMJ11G (one bud with one
   leaf), contained 12 representative VOCs, including methyl salicylate,
   linalool, 2-butoxyethanol, n-propyl acetate, 2-methyl-2-pentenal,
   beta-pinene, 2-acetylfuran, furfural, 3-methylthiopropanal, and three
   unknown compounds. As an intermediate maturity stage, FDMJ11G exhibited
   a volatile profile suggestive of a metabolic transition between young
   and mature leaves. This was characterized by terpenoid dominance (e.g.,
   linalool, beta-pinene) and a balance between esters and aldehydes,
   reflecting a dynamic conversion from alcohols to aldehydes and
   subsequently to esters. Linalool and methyl salicylate contributed
   complex floral attributes (citrus, floral, rose, wintergreen, minty),
   which distinguished this profile from the fresh-green aroma of FDQSG.
   Additionally, furfural and 2-acetylfuran provided roasted undertones,
   mitigating excessive grassy notes. Intriguingly, Chen-Yang Shao’s study
   [[138]20] on baked green teas with varying tenderness reported a
   significant increase in methyl salicylate with leaf maturity, which
   differs from our findings—likely due to varietal differences.

   Region IV represented the characteristic VOCs of FDTC2G (one bud with
   two leaves), which included 3-pentanol, 2-acetyl-1-pyrroline,
   2,5-dimethylfuran, benzene acetaldehyde, 2-butanone, and ethylsulfide.
   The VOC profile of this group, derived from more mature leaves,
   reflected pronounced Maillard reaction activity during processing,
   facilitating the formation of compounds such as 2-acetyl-1-pyrroline
   (popcorn-like) and 2,5-dimethylfuran meaty, gravy, roasted beef juice).
   The accumulation of 3-pentanol and 2-butanone suggested elevated lipid
   oxidation in FDTC2G compared to the less mature counterparts.
   3-Pentanol (herbal) and benzene acetaldehyde (green, floral, sweet)
   co-occurred with 2-acetyl-1-pyrroline (meaty, roasted) to create a
   layered and mellow flavor profile for two-leaf green tea. Previous
   research has identified dimethyl sulfide (cooked corn-like) as a key
   contributor to the fresh and delicate aroma of green tea
   [[139]28,[140]42], while ethylsulfide—another key VOC in
   FDTC2G—conveyed coffee- and meat-like notes. The conversion pathway
   between dimethyl sulfide and ethylsulfide may be critical for
   modulating the aroma type and quality of FDG, warranting further
   investigation.

   Furthermore, Regions II and V reflected common VOCs shared by all three
   green tea types. Based on their concentrations and aroma
   characteristics, six major shared compounds were identified with levels
   exceeding 1000 µg/L: linalool (citrus, floral, rose), heptanal-D
   (green), 2-methylbutanal (cocoa), 2-propanone (ethereal, apple, pear),
   mesityl oxide (vegetal), and methyl acetate (ethereal sweet, fruity).
   Among these, linalool and heptanal are well-established as key
   aroma-active compounds in green tea [[141]2,[142]43,[143]44].
   Additionally, 2-methylbutanal (VIP = 2.70) and 3-methylbutanal (VIP =
   1.30) have been reported as differential markers between yellow teas of
   different tenderness (LYT vs. BYT/SYT) [[144]19], further suggesting
   that leaf maturity strongly influences the accumulation of
   2-methylbutanal.

   Additionally, region IV contained compounds commonly expressed in both
   FDMJ11G and FDTC2G, notably benzaldehyde (sharp sweet, almond, cherry)
   and beta-ocimene (floral). Benzaldehyde has been cited as a marker for
   chestnut-like aroma in green tea [[145]45], while beta-ocimene, with
   OAV > 1, is a key floral contributor in flower-scented green teas
   [[146]40].

   Overall, fingerprint-based VOC profiling provided valuable insights
   into the compositional and sensory differentiation among green teas
   with varying tenderness. However, it should be noted that aroma
   perception is influenced not only by compound concentration but also by
   the odor threshold in a given matrix. Thus, high-concentration
   compounds may not necessarily dominate aroma perception, and
   low-concentration compounds could still play key roles. Quantitative
   verification of these contributions requires further rOAV (relative
   Odor Activity Value) analysis.

3.3.2. Analysis of VOCs Variation Among FDG of Different Tenderness

   Hierarchical cluster analysis (HCA) of all detected VOCs effectively
   distinguished the green tea samples by tenderness level ([147]Figure
   6A). Notably, the two more mature samples, FDMJ1G and FDTC2G, clustered
   together on the same branch, while the most tender sample, FDQSG,
   formed a separate clade, indicating clear differentiation based on VOC
   composition. Subsequent K-means clustering divided the complete set of
   VOCs into four distinct clusters ([148]Figure 6B), each exhibiting
   unique trends associated with leaf tenderness. Specifically, Cluster 2
   contained VOCs whose concentrations increased as leaf tenderness
   decreased; Cluster 4 showed the opposite trend, with a decline in
   compound abundance as tenderness decreased. Meanwhile, Cluster 1
   exhibited a U-shaped pattern (initial decline followed by an increase),
   and Cluster 3 demonstrated an inverted U-shape (initial increase
   followed by a decrease).

Figure 6.

   [149]Figure 6
   [150]Open in a new tab

   Cluster analysis of VOCs in three tenderness grades of green tea. (A)
   Hierarchical clustering analysis (HCA) dendrogram showing similarity
   relationships. (B) K-means clustering plot demonstrating VOC grouping
   patterns.

   Focusing on Cluster 2, which included 12 compounds that increased with
   decreasing tenderness (see [151]Table S4), this group exhibited a wide
   spectrum of sensory attributes such as citrus, cocoa, fruity, roasted,
   coffee, and popcorn-like aromas. Several compounds also possessed
   functional properties beyond aroma: for example, Octanal-M is known for
   its antioxidant and antimicrobial activity [[152]46], while 3-Pentanol
   has been reported to stimulate plant immune responses [[153]47]. In
   contrast, Cluster 4, comprising 37 VOCs that decreased in abundance
   with increasing leaf maturity, was characterized predominantly by
   fresh, green, fatty, fruity, and earthy notes. This suggests a
   substantial loss of freshness and green-earthy qualities as tenderness
   diminishes.

   Specifically, Cluster 1, with 11 VOCs—primarily aldehydes, alcohols,
   and ketones—displayed a pattern of decreasing and then increasing
   concentrations across the tenderness gradient. These compounds were
   associated with floral, green, and citrusy aromas. Whereas, Cluster 3,
   encompassing 25 VOCs, exhibited greater structural diversity and
   included terpenes, short-chain aldehydes/ketones, and aromatic
   compounds. This cluster was dominated by floral-terpenic, woody, and
   bready notes, with key compounds such as linalool (citrus, floral,
   woody), cis-linalool oxide (earthy, floral, woody), β-ocimene (citrus,
   tropical, green), and β-pinene (pine, woody, resinous). Preliminary
   analysis suggested that the FDMJ1G sample exhibited notably higher
   expression levels of terpene-related compounds. These dynamic
   clustering patterns offer valuable insights into the transformation of
   volatile profiles across different maturity levels of green tea,
   highlighting both flavor transitions and potential bio-functional
   implications.

   To further elucidate the intergroup differences in VOCs among green
   teas of varying leaf tenderness, volcano plots were constructed based
   on fold change (FC) and statistical significance (adjusted p < 0.05,
   |log[2]FC| ≥ 1). A total of 17 VOCs showed significant differences
   between FDMJ1G and FDQSG, of which 8 were upregulated and 9 were
   downregulated ([154]Figure 7A). The upregulated compounds primarily
   included terpenes such as linalool and β-pinene, which are associated
   with enhanced plant defense and antimicrobial activity
   [[155]48,[156]49], contributing floral and dry woody notes. Aromatic
   aldehydes (e.g., benzaldehyde) and furans (e.g., furfural) impart
   bitter almond, bready, and baked aromas, while esters (e.g., ethyl
   acetate, n-propyl acetate) and ketones (e.g., 2-butanone) enrich fruity
   characteristics. In contrast, the downregulated VOCs were predominantly
   green leaf volatiles responsible for fresh-green notes, including
   (E)-2-pentenal, (E)-hept-2-enal, 4,5-dihydro-3(2H)-thiophenone, and
   several fruity, earthy, herbal, and spicy compounds such as
   2-methylbutanol acetate, butyl propanoate, 2-octanone, and
   1-penten-3-one.

Figure 7.

   [157]Figure 7
   [158]Open in a new tab

   Comparative analysis of VOCs among three tenderness-grade green teas
   using volcano plots. (A) FDMJ1G vs. FDQSG (thresholds: |log2FC| > 1.0,
   p < 0.05). (B) FDTC2G vs. FDQSG (thresholds: |log2FC| > 1.0, p < 0.05).
   (C) FDTC2G vs. FDMJ1G (thresholds: |log2FC| > 1.0, p < 0.05).

   Similarly, in the FDTC2G vs. FDQSG comparison ([159]Figure 7B), 10 VOCs
   were significantly upregulated and 10 were downregulated. The
   upregulated compounds—linalool, methyl isobutyl ketone, benzaldehyde,
   2,5-dimethylfuran, 2-butanone, ethyl acetate, 3-pentanol,
   2-acetyl-1-pyrroline, ethylsulfide, and benzene
   acetaldehyde—collectively contributed floral, almond, bready, sweet
   fruity, toasted grain, coffee, meaty, and honey-like aromas. On the
   other hand, downregulated compounds included 2-methylbutanol acetate,
   1-penten-3-one, 2-octanone, (E)-2-pentenal, hexanal,
   4,5-dihydro-3(2H)-thiophenone, butyl propanoate,
   2-ethyl-6-methylpyrazine, and (E)-hept-2-enal, which are associated
   with green, herbal, fatty, and roasted potato–like aromas. The
   reduction of these compounds indicates a decline in the fresh, fruity,
   and green sensory qualities.

   By contrast, relatively fewer differential compounds were observed
   between the two more mature samples (FDTC2G vs. FDMJ1G). Only four VOCs
   showed significant upregulation in FDTC2G (2,5-dimethylfuran,
   3-pentanol, 2-acetyl-1-pyrroline, and benzene acetaldehyde), while five
   VOCs (including (E)-2-pentenal, furfural, n-propyl acetate, and
   2-methyl-2-pentenal) were significantly downregulated ([160]Figure 7C).
   The upregulated compounds contributed roasted, nutty, and fermented
   notes, indicating a metabolomic shift characteristic of more mature tea
   leaves. The downregulated compounds were associated with fresh and
   fruity aromas, echoing the trend observed between FDQSG and the more
   mature samples.

   In summary, comprehensive analysis revealed distinct volatile compound
   signatures across the three tenderness grades of green tea,
   demonstrating clear metabolic stratification associated with leaf
   maturity. In greater detail, the volatile profile of FDQSG (single bud)
   is predominantly characterized by high levels of C6 aldehydes and
   alcohols (e.g., hexanal, (Z)-3-hexenol) and monoterpenes (e.g., nerol),
   which jointly contribute to the fresh, green, fruity, and floral notes
   typically associated with cut grass and citrus aromas. By contrast,
   FDMJ1G (one bud plus one leaf) represents an intermediate stage of leaf
   maturity, where the aroma profile reflects a transition in metabolic
   activity. The relative balance between terpenes and esters, along with
   the emergence of Maillard reaction–derived compounds such as
   benzaldehyde and furfural, results in a more complex and enriched aroma
   bouquet. Notably, compounds like linalool (floral), β-pinene (woody),
   and benzaldehyde (almond-like) are markedly elevated, while the content
   of C6 aldehydes shows a progressive decline. In the case of FDTC2G (one
   bud plus two leaves), the volatile composition is dominated by Maillard
   reaction products (e.g., 2,5-dimethylfuran, 2-acetyl-1-pyrroline),
   fermentation-related esters (e.g., ethyl acetate), and microbial
   metabolites (e.g., 3-pentanol), collectively imparting roasted, nutty,
   and savory characteristics. Interestingly, while
   2-ethyl-6-methylpyrazine—a key compound contributing to roasted
   potato–like aromas—is known to enhance roasted notes, its concentration
   decreases with increasing leaf maturity, implying the involvement of a
   more intricate regulatory mechanism that warrants further
   investigation.

   Overall, the findings highlight that FDQSG is particularly rich in
   fresh, green, and fruity aromatic attributes, making it highly suitable
   for low-temperature fixation processes that aim to retain delicate
   floral volatiles. This observation is consistent with previous reports
   on large-leaf black teas with varying degrees of tenderness [[161]18],
   although the specific compounds contributing to freshness and
   fruitiness may vary depending on cultivar and processing techniques.
   FDMJ1G exhibits a more balanced and multidimensional aroma profile,
   integrating both floral and fruity characteristics, indicative of an
   intermediate metabolic state. In contrast, FDTC2G is enriched in nutty,
   roasted, and fermented sweet–meaty notes, reflecting greater metabolic
   transformation during processing. These findings are also in agreement
   with previous research on yellow teas processed from leaves of
   different maturity levels [[162]19], where teas made from more mature
   leaves were associated with enhanced roasted aromas, while bud-only
   teas demonstrated higher intensities of fresh and floral sensory
   attributes.

   These results suggest that leaf maturity markedly influences volatile
   metabolite composition and aroma profile. Notably, some VOCs with lower
   concentrations may still significantly impact scent if their odor
   activity (rOAV) is high, warranting further sensory threshold
   validation.

3.3.3. Multivariate Statistical Analysis

   To further investigate the differential VOCs and their evolution across
   green teas of varying tenderness, a partial least squares discriminant
   analysis (PLS-DA) model based on VOCs was employed ([163]Figure 8). The
   PLS-DA model yielded high explanatory and predictive power, with R^2X =
   0.909, R^2Y = 0.999, and Q^2 = 0.998. Additionally, a 200-time
   permutation test was conducted to assess model robustness, showing a
   Q^2 regression intercept of −0.57 on the Y-axis, which is below zero
   ([164]Figure 8B), indicating that the model is reliable and not
   overfitted. As shown in the score plot ([165]Figure 8A), PC1 accounted
   for 61.6% of the variance, while PC2 contributed 29.3%, together
   explaining 90.9% of the total variance. This demonstrates that the
   first two principal components sufficiently represent the original
   dataset. The three green tea samples—FDQSG, FDMJ1G, and FDTC2G—were
   well-separated in the model without any overlap, indicating that the
   HS-GC-IMS method for VOC acquisition and detection provided strong
   classification ability for tea samples of different leaf maturity
   stages. Moreover, the strong clustering of VOC profiles within each
   group reflects high repeatability and stability in VOC detection for
   samples of the same tenderness level.

Figure 8.

   [166]Figure 8
   [167]Open in a new tab

   Multivariate analysis of green tea tenderness grades using partial
   least squares-discriminant analysis (PLS-DA). (A) PLS-DA score plot
   demonstrating group separation. (B) Permutation test results (n = 200)
   validating model robustness. (C) Loading plot highlighting highly
   correlated compounds contributing to group discrimination. Both grey
   and black circles represent VOCs, with the black circles marking the
   top 20 VOCs with the highest explanatory contribution to the original
   variables.

   The loading plot ([168]Figure 8C) illustrates the contribution of
   highly correlated VOCs to the principal components, further clarifying
   the differences in volatile profiles among the three green tea samples
   of varying tenderness. The plot highlights the VOCs most strongly
   associated with each sample group—FDQSG, FDMJ1G, and FDTC2G—offering
   additional insight into their respective aroma characteristics. As
   shown in [169]Figure 8C, FDQSG exhibits a larger number of highly
   correlated VOCs compared to the other two groups. These findings are
   consistent with the characteristic compounds identified in [170]Figure
   5. Specifically, FDQSG is predominantly associated with C5–C9
   short-chain aldehydes and ketones, short-chain esters, which contribute
   to its fresh and green aroma. FDMJ1G features a balanced profile of
   esters and aldehydes, reflecting an intermediate metabolic state. In
   contrast, FDTC2G is characterized by Maillard reaction products and
   3-pentanol, indicating a shift toward roasted, nutty, and fermented
   flavor attributes as leaf maturity increases.

   To further identify key VOCs responsible for differentiating among
   green tea samples, variable importance in projection (VIP) scores were
   calculated. VIP scores reflect the contribution and explanatory power
   of each variable to the classification and discrimination of the sample
   groups. Compounds with VIP scores > 1 were considered significant
   discriminators. As shown in [171]Table S6, 31 VOCs with VIP > 1 were
   identified as key differential volatile markers in the PLS-DA model
   based on HS-GC-IMS. These VOCs serve as candidate markers for
   distinguishing green teas of different leaf maturity stages. These VOCs
   serve not only as markers for distinguishing green teas of different
   leaf maturity stages but also as foundational features for building
   subsequent machine learning models aimed at identifying key
   characteristic parameters.

3.3.4. rOAV Analysis

   To assess the contribution of individual volatile compounds (VOCs) to
   the overall aroma profile of FDG with different tenderness grades, the
   relative odor activity value (rOAV) of each compound was calculated
   ([172]Table S5). Compounds with rOAVs greater than 1 are generally
   considered to have a perceptible impact on aroma [[173]33], and thus
   VOCs meeting this criterion were extracted and summarized in [174]Table
   1 for further discussion.

   In total, 41 volatile compounds exhibited rOAV values exceeding 1.
   Among them, FDQSG contained 33 such compounds, FDMJ1G had 32, and
   FDTC2G had 31. Notably, 30 VOCs with OAV > 1 were common across all
   three groups, indicating that the majority of the identified volatiles
   contribute meaningfully to the aroma profile of FDG. Among these,
   compounds with extremely high rOAV (rOAV > 1000) were primarily
   aldehydes (e.g., 2-methylbutanal), terpenes (linalool), and pyrazines
   (2-ethyl-3,5-dimethylpyrazine), which serve as key odorants.
   Specifically, 2-methylbutanal (chocolate/cocoa-like) and
   3-methylbutanal (chocolate, peach) demonstrated remarkably high rOAVs
   (>1000 and 580–730, respectively), confirming their potent sensory
   effects. Linalool, a floral terpene, exhibited the highest rOAV across
   all samples (ranging from 3800 to 7400), despite its relatively low
   concentration due to its ultra-low odor threshold (OT = 0.22 μg/L),
   making it a major aroma-contributing compound. Likewise,
   2-ethyl-3,5-dimethylpyrazine, with its nutty character and extremely
   low OT (0.040 μg/L), also showed high rOAVs (1350–1440), indicating its
   persistent and potent contribution.

   Interestingly, linalool peaked in the intermediate maturity sample
   FDMJ1G, suggesting a metabolic surge at this developmental stage,
   whereas 2-methylbutanal increased progressively with maturity and
   reached its maximum in the most mature sample FDTC2G. Pyrazines, in
   contrast, remained relatively stable across all tenderness stages,
   indicating their sustained contribution to nutty and roasted
   attributes.

   Furthermore, a clear maturity-dependent shift in dominant aroma-active
   compounds was observed across the three leaf maturity stages of green
   tea. In early maturity (FDQSG, single bud), the aroma profile was
   primarily characterized by fresh, green, floral, and citrus-like
   attributes. This was largely driven by high rOAVs of terpenes such as
   linalool (3800) and trans-linalool oxide (4.2), along with C6 aldehydes
   and alcohols, including hexanal (170) and (Z)-3-hexen-1-ol (67), which
   contributed grassy and leafy freshness. In addition, the presence of
   2-methylbutanal (1100), a cocoa-like aldehyde, imparted a subtle sweet
   and nutty nuance. These volatiles collectively defined the
   characteristic fresh and green sensory quality typical of bud-only
   green teas.

   At the intermediate maturity stage (FDMJ1G, one bud plus one leaf), the
   aroma complexity increased significantly, reflecting a balance between
   floral, fruity, and nutty notes. Notably, linalool reached its highest
   rOAV (7400), indicating a floral surge likely linked to transient
   metabolic activation. Meanwhile, 2-ethyl-3,5-dimethylpyrazine (1400)
   provided a stable nutty backbone. A substantial decline in hexanal
   (from 170 to 71; −58% vs. FDQSG) marked the attenuation of green
   freshness. Concurrently, elevated levels of Maillard- and
   fermentation-associated compounds such as 2-acetyl-1-pyrroline and
   ester volatiles (e.g., 2-methylbutanol acetate) contributed toasted,
   sweet, and fruity nuances, indicating active secondary metabolic
   transitions.

   In contrast, late maturity (FDTC2G, one bud plus two leaves) was marked
   by a pronounced shift toward roasted, nutty, and savory sensory
   attributes. The sharp increase in Maillard-derived 2-acetyl-1-pyrroline
   (from 66 to 160; +142% vs. FDMJ1G) contributed intense popcorn-like
   notes. The persistent high levels of 2-ethyl-3,5-dimethylpyrazine
   (~1350) ensured continuity in nutty perception. These changes
   collectively reflect a maturity-dependent transformation in aroma
   composition, likely governed by both enzymatic and non-enzymatic
   reactions during leaf development and subsequent processing.

   To further validate the contributions of aroma-active compounds (rOAV >
   1) listed in [175]Table 1 to the overall flavor perception of green tea
   infusions, Pearson correlation analysis was performed between these
   volatiles and the responses of 10 E-nose sensors (W1C, W3C, W5C, W1S,
   W2S, W3S, W5S, W6S, W1W, W2W), as well as 8 E-tongue taste attributes
   (sourness, bitterness, astringency, aftertaste-A, aftertaste-B, umami,
   richness, saltiness) ([176]Table S7). Correlation pairs meeting the
   criteria of |r| ≥ 0.90 and p < 0.05 were considered statistically
   significant.

   Notably, 2-acetyl-1-pyrroline showed extremely strong positive
   correlations with W1S, W2S, and W5C sensors, establishing it as a core
   marker for “sweet/popcorn-like” aroma recognition by the electronic
   nose. Simultaneously, it significantly suppressed bitterness,
   aftertaste-B, and sourness responses, suggesting its synergistic role
   in reducing bitterness and sourness. Likewise, methyl acetate exhibited
   positive correlations with richness and saltiness, and a negative
   correlation with aftertaste-B, indicating its role in reinforcing umami
   and mellow taste characteristics. In addition, C6–C9 aldehydes (e.g.,
   hexanal, (E)-2-hexenal, pentanal) consistently showed strong positive
   correlations with the W5S channel, enabling rapid discrimination of
   tenderness grades. Moreover, ethyl sulfide demonstrated robust positive
   correlations with multiple sensors (W1S/W2S/W5C/W1C), confirming its
   distinct “coffee-like” characteristics in E-nose detection. In the
   taste dimension, ethyl sulfide exhibited strong negative correlations
   with aftertaste-B and bitterness, indicating that beyond contributing
   to characteristic coffee-like notes, this compound may also play a
   modulatory role in suppressing bitterness and lingering astringency,
   thereby enhancing the overall smoothness and roundness of the tea
   infusion.

   In summary, aroma-active VOCs such as 2-acetyl-1-pyrroline, methyl
   acetate, C6–C9 aldehydes, and acetate esters exhibited highly
   significant correlations with key E-nose channels (e.g., W5S, W2W, W1S)
   and E-tongue taste indices (e.g., richness and aftertaste-B). These
   strong chemical–sensor couplings highlight their potential as core
   diagnostic markers for the rapid classification of green tea leaf
   maturity levels. Previous studies on food flavor have indicated that
   volatile aroma compounds can modulate taste perception through
   cross-modal interactions [[177]50,[178]51]. The specific mechanisms by
   which these volatiles influence taste attributes—such as bitterness,
   umami, and aftertaste—merit further in-depth investigation in future
   research.

3.3.5. KEGG Functional Annotation and Enrichment of VOCs

   To further investigate the potential biosynthetic pathways associated
   with differential VOCs in FDG of varying tenderness, the identified
   VOCs were cross-referenced with the KEGG database to retrieve detailed
   information on their involved metabolic pathways. Enrichment analysis
   was subsequently performed to identify pathways, and the annotated
   results are presented in [179]Figure 9 and [180]Table S8. A total of
   eight key VOCs were mapped to four metabolic pathways: butanoate
   metabolism, monoterpenoid biosynthesis, zeatin biosynthesis, and
   phenylalanine metabolism. Among them, butanoate metabolism and
   monoterpenoid biosynthesis were significantly enriched ([181]Figure
   9A), indicating their critical roles in the metabolic differentiation
   of aroma profiles among green teas of different maturity stages. As
   illustrated in [182]Figure 9B–D and detailed in [183]Table S8, the
   butanoate metabolism pathway was associated with 2-propanone, butanal,
   and methyl acetate; monoterpenoid biosynthesis was linked to nerol,
   β-pinene, and 1,8-cineole. In contrast, zeatin biosynthesis involved
   3-methyl-2-butenal, and phenylalanine metabolism was associated with
   benzene acetaldehyde.

Figure 9.

   [184]Figure 9
   [185]Open in a new tab

   KEGG pathway enrichment analysis of green tea metabolites across leaf
   maturity stages. (A) Scatter plot of enriched pathways. (B–D)
   Pathway-metabolite networks (blue: pathways; color gradient: log2FC).

   Pairwise comparison of VOC accumulation patterns revealed that
   2-propanone (apple, pear aroma), butanal (chocolate), β-pinene (dry
   woody), and 3-methyl-2-butenal (sweet, fruity, nutty, almond) were
   predominantly enriched in FDMJ1G (one bud + one leaf), suggesting
   enhanced activity of butanoate metabolism and monoterpene biosynthesis
   at the intermediate maturity stage. In contrast, methyl acetate and
   benzene acetaldehyde, which contribute sweet, fruity, floral, honey,
   and cocoa-like aromas, were more highly expressed in FDTC2G (one bud +
   two leaves). Meanwhile, nerol, a floral monoterpene, was specifically
   enriched in FDQSG (single bud), supporting its strong floral character.

   The KEGG-based enrichment results, when integrated with previous aroma
   and rOAV analyses, provide a coherent metabolic explanation for the VOC
   variations observed among the three tenderness grades. Specifically,
   FDMJ1G was characterized by a notable burst in monoterpenoid
   biosynthesis and active butanoate metabolism, consistent with its
   complex and balanced aroma profile. FDQSG was dominated by C6
   aldehydes/alcohols (e.g., hexanal, (Z)-3-hexenol), mainly derived from
   the lipoxygenase (LOX) pathway, contributing to its fresh and grassy
   aroma [[186]52]. In contrast, FDTC2G showed enrichment in Maillard
   reaction products, esterification-derived volatiles, and
   phenylalanine-derived sweet-aromatic compounds such as benzene
   acetaldehyde, reflecting a maturity-dependent shift toward roasted and
   sweet-fruity notes.

   Taken together, these findings suggest that the differential
   accumulation of aroma-related VOCs across leaf maturity stages is
   closely tied to the dynamic modulation of key biosynthetic pathways,
   particularly butanoate metabolism and monoterpenoid biosynthesis. These
   pathways may serve as biochemical markers for evaluating and optimizing
   green tea quality in relation to raw material tenderness.

3.4. Identification of Key Flavor Markers of Green Teas of Different
Tenderness Based on Six Machine Learning Models

   To identify key flavor markers differentiating FDG at distinct maturity
   stages, data from three complementary analytical platforms—electronic
   tongue (E-tongue), electronic nose (E-nose), and HS-GC-MS—were
   comprehensively integrated. The feature set comprised 8 E-tongue
   sensors, 4 E-nose gas sensors, 31 VOCs with VIP > 1 identified through
   the PLS-DA model, and 41 aroma-active compounds with rOAV > 1.

   These selected features were subjected to classification analysis using
   six supervised machine learning algorithms: KNN, RF, SVM, GNB, LR, and
   DT. To enhance model robustness and mitigate overfitting, a 10-fold
   cross-validation strategy was employed. Classification performance was
   evaluated using receiver operating characteristic–area under the curve
   (ROC–AUC), where AUC values between 0.8–0.9 and 0.9–1.0 indicate good
   and excellent discrimination, respectively.

   As illustrated in [187]Figure 10A, five models—KNN, RF, GNB, LR, and
   DT—achieved perfect classification accuracy (AUC = 1.0), underscoring
   their high reliability for this task. SVM showed comparatively lower
   performance, suggesting model-specific limitations for this dataset.
   Among all models, the RF classifier was prioritized for subsequent
   analysis due to its well-documented resistance to overfitting and high
   prediction accuracy in high-dimensional biological datasets [[188]53].

Figure 10.

   [189]Figure 10
   [190]Open in a new tab

   Machine learning-driven identification of flavor markers in FDG of
   different tenderness grades. (A) Receiver operating characteristic
   (ROC) curves of six algorithms (KNN, RF, SVM, GNB, LR, DT) using all
   features. (B) Bee swarm plot of top 20 discriminant features ranked by
   SHAP importance. (C) Box plots of selected markers’ relative abundance.

   Feature contribution was assessed using Shapley Additive Explanations
   (SHAP) values derived from the RF model. The top 20 ranked features
   ([191]Figure 10B) were identified as the most discriminative indicators
   of tea maturity. Notably, 19 of these features were VOCs detected via
   HS-GC-IMS, while one corresponded to an E-tongue taste sensor
   (bitterness). In contrast, no E-nose variables ranked among the top
   contributors, likely reflecting the broader classification granularity
   of gas sensors, which may be less sensitive to subtle within-cultivar
   aroma differences than compound-specific VOCs or taste-related
   attributes.

   Boxplot visualization of the top 20 features ([192]Figure 10C)
   elucidated their abundance patterns across tea maturity groups. The
   FDQSG group (single bud) exhibited the greatest number of elevated
   features (n = 10), including (Z)-3-hexen-1-ol, (E)-2-octenal, propyl
   isovalerate, n-hexanol-D, octanal-D, 2-ethyl-6-methylpyrazine,
   n-hexanol-M, nonanal-D, butyl propanoate, and two unidentified VOCs.
   These compounds conferred a characteristic flavor profile rich in
   fresh, green, citrusy, herbal, and subtly roasted-potato-like notes,
   emphasizing the sensory freshness typical of bud-only green tea. In the
   FDMJ1G group (one bud + one leaf), seven features were enriched,
   including bitterness (E-tongue), methyl salicylate,
   2-methyl-2-pentenal, 2-propanone, n-hexanol-M, 3-methylthiopropanal,
   and linalool. Collectively, these contributed to a complex and layered
   aroma composed of floral, fruity, minty, herbal, and vegetable-like
   elements, reflecting an intermediate degree of leaf maturity and
   associated biochemical transitions. Conversely, the FDTC2G group (one
   bud + two leaves) demonstrated elevated levels of only three
   VOCs—benzene acetaldehyde, 2,5-dimethylfuran, and octanal-M—resulting
   in a sensory profile characterized by sweet-floral, roasted, cocoa, and
   mildly herbal notes. These compounds are consistent with enhanced
   Maillard reaction products and phenylalanine-derived volatiles
   typically associated with more mature tea leaves.

   To assess the discriminative power of individual features,
   single-variable classification analyses were conducted across all six
   models, and corresponding ROC curves were generated ([193]Table S9).
   Among them, RF and LR models exhibited superior classification
   performance, indicating their robustness and suitability for feature
   prioritization. All markers, except for (Z)-3-hexen-1-ol and
   n-Hexanol-D, achieved individual prediction accuracies above 0.8,
   confirming their reliability as discriminative features. The slightly
   lower performance of these two compounds may be attributed to their
   relatively broad distribution across samples or potential co-elution
   effects.

   In summary, the machine learning-based integration of multimodal sensor
   data effectively identified robust flavor biomarkers associated with
   different leaf maturities in FDG. VOCs were the most influential
   feature group, and the RF and LR models proved most reliable for
   classification and marker selection. These findings provide a
   methodological foundation for flavor-targeted quality control and
   harvest timing optimization in green tea processing.

4. Conclusions

   This study provides a comprehensive multi-platform analysis of the
   flavor differences among FDG made from raw materials of three distinct
   tenderness grades—single bud (FDQSG), one bud plus one leaf (FDMJ1G),
   and one bud plus two leaves (FDTC2G). Multimodal sensory evaluation
   using E-tongue, E-nose, and HS-GC-IMS revealed clear differences in
   taste and volatile compound profiles among the three sample types.
   FDQSG exhibited higher umami and stronger fresh, green, and herbal
   notes, with key volatiles such as (Z)-3-hexen-1-ol, (E)-2-octenal, and
   nonanal reflecting high LOX pathway activity. FDMJ1G presented a
   complex transitional profile, marked by a peak in linalool (rOAV:
   7400), along with floral, fruity, and minty volatiles like methyl
   salicylate and 2-propanone. FDTC2G, in contrast, was enriched in
   Maillard-derived and phenylalanine metabolism–related volatiles such as
   2-acetyl-1-pyrroline, 2,5-dimethylfuran, and benzene acetaldehyde,
   contributing to roasted, nutty, and cocoa-like notes. Through KEGG
   pathway enrichment, four metabolic routes were associated with key
   volatile formation, notably butanoate metabolism and monoterpenoid
   biosynthesis, both significantly enriched. Specific markers were also
   identified for each tenderness grade: Nerol in FDQSG, beta-pinene and
   3-methyl-2-butenal in FDMJ1G, and methyl acetate and benzene
   acetaldehyde in FDTC2G. Machine learning modeling, particularly random
   forest combined with SHAP analysis, identified 20 top-ranking
   discriminant markers, with HS-GC-IMS-derived VOCs playing a dominant
   role. Among them, FDQSG was associated with the highest number of
   enriched markers (n = 10), reinforcing its sensory profile dominated by
   freshness and vibrancy. Single-feature ROC–AUC validation confirmed
   high diagnostic accuracy (≥0.8) for most key indicators, supporting
   their robustness as maturity-dependent flavor biomarkers.

   While this study offers a comprehensive framework for understanding
   maturity-related flavor shifts in FDG, several limitations remain.
   Interannual variability in agronomic and climatic factors—such as
   rainfall patterns, sunlight exposure, and temperature regimes—may
   influence the biosynthesis and accumulation of both volatile and
   non-volatile metabolites, thereby affecting flavor expression. These
   environmental effects should be systematically evaluated in future
   multi-year studies to consolidate the generalizability of the findings.
   Moreover, potential sensor cross-sensitivity and matrix effects
   inherent in E-nose and E-tongue systems may influence the precision of
   sensor responses and should be carefully considered in future
   validation efforts. In addition, while the identified volatile
   compounds with high rOAV values and significant sensor correlations
   offer valuable insights into the aroma landscape, their direct
   contributions to perceived flavor require further verification through
   aroma recombination and omission experiments, as well as human sensory
   evaluation. To further elucidate the mechanistic basis of taste
   differences (e.g., umami, bitterness), integration of non-volatile
   metabolite profiling—such as amino acids and polyphenols—will be
   essential. Finally, the identity and aroma contribution of unknown VOCs
   warrant future elucidation via GC–O–MS sniffing techniques.
   Collectively, these findings provide a molecular basis for
   understanding the impact of leaf maturity on flavor in FDG. The results
   offer practical guidance for harvest timing, raw material selection,
   and grade-specific processing, facilitating more refined quality
   control and targeted flavor design in the green tea industry.

Acknowledgments