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Article

Effect of Blue Light Intensity During Spreading on the Aroma of Green Tea

by
Youyue He
1,2,
Yan Tang
1,
Shiyue Song
2,
Lailong Li
3,
Shaoshuai An
3,
Guoming Zhou
3,
Jing Zhu
3,
Song Li
4,
Yue Yin
4,
Anburaj Jeyaraj
2,
Chunju Peng
1,
Xinghui Li
2 and
Guanghui Zeng
1,*
1
Wenzhou Key Laboratory of Early Sprouting Tea Breeding, Wenzhou Vocational College of Science and Technology (Wenzhou Academy of Agricultural Sciences), Wenzhou 325006, China
2
College of Horticulture, Nanjing Agricultural University, Nanjing 210095, China
3
China Huaneng Group Co., Ltd., No. 6, FuXingMenNei St, Xicheng District, Beijing 100031, China
4
Nanjing Agro-Tech Extension and Service Center, Nanjing 210029, China
*
Author to whom correspondence should be addressed.
Foods 2025, 14(8), 1308; https://doi.org/10.3390/foods14081308
Submission received: 20 February 2025 / Revised: 26 March 2025 / Accepted: 3 April 2025 / Published: 9 April 2025
(This article belongs to the Section Drinks and Liquid Nutrition)
Browse Figures
Versions Notes

Abstract

:
Spreading is the key process for ensuring green tea quality. However, the effect of blue light intensity conditions on the formation of green tea aroma and the evolution of key volatile compounds has not been assessed to date. Four tea samples treated with different light conditions (blue light intensities) were used to investigate the effect of spreading treatment on changes in the composition and content of volatile compounds. Volatile compounds in green tea samples were detected using headspace-solid phase microextraction and gas chromatography-mass spectrometry under different light conditions. Orthogonal partial least squares discriminant analysis (OPLS-DA) and relative odor activity value (rOAV) analyses were then applied to clarify the best blue light condition for forming aroma and associated compounds. The 116 volatile compounds were detected in the green tea samples, of which alcohols were the most abundant. The findings demonstrated that MBL (middle-intensity blue light; 150 μmol/(m2∙s)) treatment was the most effective condition for developing an intense and persistent fruity and floral scent compared to HBL (high-intensity blue light; 300 μmol/(m2∙s)) and LBL (low-intensity blue light; 75 μmol/(m2∙s)). This study underscores how blue light intensity conditions shape green tea aromas and offers operational insights. It also provides a theoretical basis for controlling light conditions in the process of green tea spreading

Graphical Abstract

1. Introduction

Teas are typically categorized into three main groups according to their fermentation levels: non-fermented (green and white teas), semi-fermented (oolong), and fully fermented (black and pu-erh teas) [1]. In recent years, the health benefits of green tea have led to its rising popularity, especially in East Asian countries [2]. The production of green tea generally consists of four stages: spreading, pan firing, rolling, and drying [3]. During green tea production, spreading is intended to lower the moisture content of tea leaves and significantly affects their physical and chemical characteristics, such as tannins, polyphenols, theine, amino acids, vitamins, and minerals [4]. In general, spreading is a preliminary and indispensable craft closely related to the formation of the desired sensory quality of made tea [5].
The quality of tea’s flavor is determined by aroma and taste, which are dependent on volatile and non-volatile compounds [6]. Aroma plays a vital role in determining the quality of tea, which includes a variety of aroma compounds, such as esters, alcohols, acids, ketones, and terpenes [7]. Although green tea has 260 volatile compounds, not all of them have an impact on the tea’s aroma [8]. In Laoshan green tea, a total of 50 aroma compounds were detected, of which 24 were key contributors to OAV > 1, indicating that these compounds contributed significantly to the overall aroma of tea [9]. Furthermore, in Enshi Yulu green tea, 134 volatile compounds were found using HS-SPME and GC-MS; however, only 25 of these were considered significant because their rOAV was more than 1 [10]. Thus, the overall fragrance of green tea is determined by only a few significant compounds.
Light is a major environmental factor that influences the production of metabolites in plants. Stimulating with particular monochromatic or polychromatic light can boost the biosynthesis of various secondary metabolites [11]. During the withering/spreading process of tea in the postharvest period, light can improve the quality of tea. Using blue and red light during the withering process significantly decreased the grassy taste in tea leaves; blue light boosted the floral scent, while red light boosted the fruity scent [12]. Red-light withering, as opposed to natural withering, can greatly enhance the taste quality of summer–autumn black tea by lessening its bitterness and astringency. Exposure to red light during withering can greatly enhance glycosidase activity in the later stages, thereby boosting the aroma quality of black tea in later processing [13]. Exposure to red light primarily interferes with the biosynthesis of secondary metabolites and pathways related to amino acid synthesis and metabolism. It enhances the accumulation of amino acids and theaflavins, affecting the chemical foundation of black tea quality [14]. Under red and blue light treatments, the tea retained its natural form, dark green color, fresh aroma, and mellow taste; the tea exposed to green light had a pure aroma and a smooth taste, while the red light added a floral aroma [15]. Using warm-colored light during the spreading process greatly enhanced the overall quality of green tea, especially with yellow light [16]. Nonetheless, with our present knowledge, the impact of changes in blue light intensity on green tea volatiles remains unclear, necessitating additional research.
This study used DARK spreading as a control and designed blue light intensity treatments to uncover how different blue light intensities affects the aroma and volatile metabolites of green tea. Through the quantification and analysis of volatile compounds, the study elucidated the influence of various blue light intensities on green tea aroma and found the best conditions for spreading. Key differential volatile compounds resulting from varying blue light intensity treatments were identified using significant difference, variables important in the projection (VIP), and rOAV analysis. The findings are expected to offer theoretical backing for technological advancements and quality enhancement of green tea. The in-depth study of blue light intensity provided a theoretical basis and technical support for the regulation and quality control of green tea aroma under different blue light intensities during the spreading process.

2. Materials and Methods

2.1. Plant Materials and Treatments

The tea plant variety, “Fudingdabai” (Camellia sinensis), is suitable for the manufacture of green tea, which was selected for the experiments. Fresh tea leaves, consisting of one bud and two leaves, were picked from Jiangsu Tea Expo Park in Nanjing, China. First, the collected fresh leaves were evenly spread on the withering equipment with three different blue light intensity-emitting diodes (LED, 420–500 nm) [low-intensity blue light (LBL, 75 μmol/(m2∙s)), middle-intensity blue light (MBL, 150 μmol/(m2∙s)); high-intensity blue light (HBL, 300 μmol/(m2∙s))] (Figure 1) and the control group (DARK) was treated in the dark. All groups were subjected to spreading for 8 h under an indoor temperature of 22–26 °C. Next, they were fixed at 200 °C for 2 min. Ultimately, the tea leaves underwent drying at 80 °C for 120 min for further analysis.

2.2. Sensory Analysis of Green Tea Aroma

Sensory analysis referred to the method of Zhu et al. [17], with a slight adjustment. The tea infusion was prepared by brewing 3 g of green tea with 150 mL of boiling pure water. Following this, a sensory test was conducted on the tea infusion, and each tea sample was assigned a three-digit code. The samples were randomly given to 6 (3 men and 3 women) panelists who were trained specially for a quantitative descriptive analysis (QDA) of intensity values and aroma descriptors after brewing for 5 min. Six sensory attributes of tea samples were determined as follows: baked, green/grass, sweet, floral, fruity, and pungent. A scale of 0 to 10 was used to measure the intensities of the aroma attributes; the higher the scores, the stronger the intensities. Intensity levels were categorized as follows: 0 for none or not perceptible, 3 for weak, 5 for moderate, 7 for high, and 10 for extremely high. Each sample underwent three evaluations per panelist, with results presented as the mean.

2.3. Green Tea Aroma Extraction by HS-SPME

Volatile matter was extracted using HS-SPME and analyzed via GC-MS (Trace 1310/TSQ 9000, Thermo Fisher Scientific, Waltham, MA, USA). Microextraction fiber PDMS/DVB (65 μm) was used to adsorb volatile substances. The 1.50 g tea sample was placed in a 20-mL headspace vial, with 200 ng of ethyl caprate (Merck company, Darmstadt, Germany) serving as the internal standard, and then maintained at 80 °C to adsorb the volatile components for 40 min. Subsequently, the fiber head was desorbed at 250 °C for 5 min.

2.4. GC-MS Analysis of Volatile Composition

On a DB-5MS capillary column (30 mm × 0.25 mm × 0.22 μm, Agilent, Santa Clara, CA, USA), separation was carried out at 50 °C and held for 3 min, ramped to 100 °C at 10 °C/min, then ramped to 200 °C at 4 °C/min and held for 1 min, and ramped to 280 °C at 16 °C/min and maintained for 7 min. High-purity helium (>99.999%) was used at a flow rate of 1 mL/min. The MS was operated in electron impact ionization mode with an electron energy of 70 eV, ion source temperature of 230 °C, and a full scan range of 29–400 m/z. Volatiles were identified using the National Institute of Standards and Technology (NIST) mass spectral database and retention indices calculated from N-alkanes (C3–C25). The relative quantification of these compounds was determined by the peak area ratios of the quantitation ions of the internal standard, after normalizing according to the total peak area. Three replicates for the HS-SPME-GC-MS analysis were performed for each treatment.

2.5. rOAV Value Analysis

To determine the influence of aroma compounds, rOAV is commonly utilized, and compounds with an rOAV > 1 are regarded as key contributors to the aroma [18]. The rOAV [19] represents the proportion of volatile compound concentration in a component compared to the odor threshold. The odor threshold (OT) has been reviewed by Leffingwell and Associates Threshold Value Database (https://www.leffingwell.com/, (accessed on 12 December 2024)) and other literature. The method of Xie et al. [20] was used to calculate the rOAV value of the compound.

2.6. Statistical Analysis

One-way ANOVA was used to determine the significance of differences with SPSS version 26.0, and Duncan’s test identified significant differences (p < 0.05). SIMCA 14.1 (Umetrics, Umeå, Sweden) was used to perform the OPLS-DA. Radar plot and heat map were obtained by Origin Pro (v2024, Originlab Corporation, Northampton, MA, USA).

3. Results

3.1. Aroma Profiles

Tea infusions commonly have baked, green, sweet, floral, fruity, and pungent smells but their intensity varies. The aroma of green tea infusions was analyzed through sensory evaluations (Figure 2). The evaluations of the four tea samples revealed that the HBL treatment exhibited strong fruity aromas, moderate floral and baked scents, and a faint pungent odor. In contrast, the MBL treatment was characterized by strong sweet, floral, and fruity aromas, along with a faint green/grass odor. LBL treatment exhibited a strong green/grass and fruity scent, along with moderate floral and sweet aromas. In the DARK treatment, tea infusion displayed a strong green/grass scent and faint floral, sweet, and fruity aromas.

3.2. Identification and Quantification of the Aroma Compounds in Green Tea Samples by HS-SPME/GC-MS

GC-MS analysis showed significant differences in the volatile components of samples under various light conditions. The concentration of aroma and the number of volatile compounds differed significantly across various treatments. The total volatile concentration in MBL (13,463.39 ± 70.28 μg/kg) treatment was notably higher than in the DARK treatment (11,223.39 ± 58.37 μg/kg). In contrast, HBL (10,921.94 ± 47.52 μg/kg) treatment had a reduced aroma concentration, while LBL (11,234.43 ± 51.91 μg/kg) treatment matched that of the DARK treatment (Figure 3A). All green tea samples treated with blue light [HBL (83), MBL (85), and LBL (87)] had a greater number of aroma compounds detected than those treated with DARK treatment (73). Although the total aroma compound concentration of LBL treatment did not have statistically significant differences with the Dark treatment, the number of aroma compounds was the highest (Figure 3A). A Venn diagram (Figure 3B) was used to illustrate the logical connections among the four treatments, focusing on the key aroma compound. There were 44 shared aroma compounds found across the 4 treatments. Only 15, 4, and 2 aroma compounds were identified in HBL, LBL, and DARK, respectively. In short, the Venn diagram showed significant differences in the constitution of aroma compounds among treatments.
All 4 green tea samples shared 116 volatile compounds, as detailed in Table 1. Additionally, these compounds were sorted into 7 different chemical classes according to their chemical structure, including alcohols, aldehydes, ketones, esters, acids, hydrocarbons, heterocyclic compounds, and others, as depicted in Figure 4A. An analysis of the distribution of volatile compounds in each category showed that in green teas under different light treatments, the largest groups were hydrocarbons (29) and alcohols (21), followed by aldehydes (17), esters (15), heterocyclic compounds (13), and ketones (12). Conversely, acids (6) and others (3) had the fewest subclasses. Alcohols and hydrocarbons were the primary groups of aroma compounds, making up nearly 43% of the volatile compounds in green tea.
Figure 4B shows the distribution of various volatile compound categories under four different light irradiation treatments. In particular, the MBL treatment had a notably higher alcohol content than the other treatments. Geraniol and phenylethyl alcohol levels were highest in the MBL treatment, attaining 1613.60 ± 3.57 μg/kg and 1003.42 ± 0.9 μg/kg, respectively. Linalool levels peaked at 415.93 ± 0.85 μg/kg in the MBL treatment and dropped to 175.16 ± 4.36 μg/kg in the DARK treatment. In the DARK treatment, esters and acids had the highest contents, measuring 2275.36 ± 21.95 μg/kg and 298.14 ± 2.72 μg/kg, respectively. Aldehydes, which play a crucial role in tea aroma, were found in concentrations ranging from 1217.62 ± 14.12 μg/kg in the MBL treatment to 148.88 ± 0.75 μg/kg in the DARK treatment. In the HBL treatment, the levels of ketones and other compounds were the highest, measuring 1301.51 ± 7.73 μg/kg and 444.96 ± 1.73 μg/kg, respectively. Heterocyclic compounds showed a marked difference in distribution, varying from 1465.24 ± 2.82 μg/kg under MBL treatment to 990.09 ± 4.26 μg/kg under DARK treatment. The highest levels of hydrocarbon levels were found in the DARK (3780.83 ± 28.06 μg/kg), followed by LBL (3039.02 ± 32.82 μg/kg), MBL (2917.03 ± 29.38 μg/kg), and HBL (1503.98 ± 23.40 μg/kg).

3.3. Multivariate Statistical Analysis of Volatile Compounds

Principal component analysis (PCA) is one of the most widely used data dimensionality reduction algorithms. The PCA score plot (Figure 5A) of the four green tea samples reveals unique characteristics of the samples with good reproducibility. The first two principal components, which together account for 84% of the variation, explaining 51.3% and 32.7% of the total variance, respectively. The use of hierarchical clustering analysis (HCA) revealed the similarities and differences between the groups. According to the results, MBL and LBL were in the same cluster, while HBL and DARK were in a different cluster (Figure 5B). The clustering revealed sensory similarities and differences in volatile composition related to sensory characteristics between four green tea samples exposed to different lighting, closely matching previous PCA study results.
OPLS-DA is a multivariate statistical analysis method, which is mainly used for classification and feature selection. The purpose of OPLS-DA is to distinguish samples from different groups and identify key variables that affect group classification. To explore the differences in primary compounds of samples under various lighting conditions, OPLS-DA models were constructed based on PCA and HCA analysis. According to the findings of the PCA and HCA analysis, in this study, the score plot showed a distinct separation between various sample groups, with the MBL and LBL samples clustering closer together (Figure 5C).
R2X and R2Y denote the interpretation rates for the X and Y matrices in the constructed model, while Q2 signifies the model’s predictive capability. All three metrics are nearly 1, suggesting the model’s stability and reliability. During the analysis, the fit index for the independent variable (R2X) was found to be 0.992, and for the dependent variable (R2Y), it was 0.999. Furthermore, the model prediction index (Q2) was determined to be 0.993, suggesting a strong fit with excellent predictive accuracy. To examine the model’s robustness more thoroughly, 200 permutation tests were conducted. The results showed that the OPLS-DA model has no overfitting, and model validation was effective (Figure 5D). The differences in green tea compounds according to the light condition were more clearly shown in the OPLS-DA loading plot (Figure 5E). Specifically, the levels of compounds, like maltol, 4-vinylphenol, limonene, dibutyl phthalate, 2,6,11-trimethyl-dodecane, and 2,3-octanedione, were notably greater in the MBL and LBL samples compared to the other treatments. Conversely, the levels of compounds, like hexanal, 4-methyl- tridecane, indole, hexanoic acid ethyl ester, and 3-methyl-butanoic acid, were notably elevated in the HBL samples. Compounds, such as 6-methyl-tridecane, 4-methyl-tridecane, 3-methyl-tridecane, and 2-ethyl-1-hexanol, were present in much higher concentrations in the DARK samples.
VIP was employed to pinpoint the key volatile components that distinguished the two groups. Components with VIP scores greater than 1 were typically deemed crucial for aroma quality. To explore key aroma compounds, VIP > 1 was used as the benchmark for screening key differentiating substances (Figure 5F).
A total of 53 volatile compounds ultimately fulfilled the VIP > 1 and p ≤ 0.05 conditions, indicating notable differences. The above 53 volatile compounds (Table S2) were divided into 4 categories according to the source: fatty acid-derived volatiles (FADVs, 32), amino acid-derived volatiles (AADVs, 5), volatile terpenoids (VTs, 7), carotenoid-derived volatiles (CDVs, 1), and others (8). Heatmaps were created to visually display how the content of these key volatile components changed during spreading under different light conditions, with red indicating upregulation and blue indicating downregulation [20]. In Figure 6, the first category, marked in blue, consisted of volatile compounds more connected to LBL treatment. The second category, marked in yellow (Figure 6), comprised volatile compounds were more linked to MBL treatment, such as geraniol and linalool. Volatile compounds more related to DARK treatment, such as 3-methyl-butanoic acid and dodecanoic acid, were included in the third category, shown in red in Figure 6. Nerol, which was more associated with HBL treatment, was part of the last category, marked in green in Figure 6.

3.4. Validation of Important Differential Volatile Compounds

A prior study indicated that the impact of aroma compounds in the system is influenced by the ratio of their concentration to the threshold [9]. Thus, we used the 53 compounds identified through OPLS-DA analysis in rOAV analysis to differentiate green tea samples exposed to various light conditions. Table 2 shows that 16 compounds were identified using GC-MS, each with rOAVs > 1. The rOAV of linalool exceeded 800, indicating a significant impact on the aroma, while hexanoic acid ethyl ester, geraniol, (Z)-2-octen-1-ol, naphthalene, and E-citral (geranial) (rOAV > 10), were identified as a key aroma component. The 16 volatile compounds that contribute significantly to the aroma of green tea under different light conditions during spreading were shown in Figure 7. MBL samples had the highest concentrations of coumarin and hotrienol from fatty acid-derived volatiles. The content of 5-ethyldihydro-2(3H)-furanone and (Z)-2-octen-1-ol were significantly increased under LBL treatment. Hexanoic acid ethyl ester significantly accumulated under HBL and LBL treatments, while delta-decalactone significantly accumulated under DARK treatment (Figure 7A). After spreading, the concentrations of geraniol, cis-linalool oxide (furanoid), trans-linalool oxide (furanoid), linalool, and E-citral (geranial) from volatile terpenoids were significantly higher in MBL samples, whereas levels of nerol were higher in HBL samples (Figure 7B). Indole and methyl salicylate were two amino acid-derived volatiles; indole significantly accumulated under LBL treatment, while methyl salicylate accumulated under MBL treatment (Figure 7C). In addition, the content of 2,6,6-trimethyl-1,3-cyclohexadiene-1-carboxaldehyde from carotenoid-derived volatiles was significantly higher in MBL than other treatments (Figure 7D). Naphthalene from other sources was accumulated only under increased blue light treatment after spreading (Figure 7E).

3.5. Relationship Network Analysis of Key Volatile Compounds

Pearson correlation analysis was performed to calculate correlation coefficients between variables. A network diagram was constructed to visualize intra-group correlations with absolute values exceeding the threshold of 0.8 and statistical significance (p < 0.05), revealing the internal structure of metabolite interactions. Nodes represent significantly differential metabolites, colored according to their regulation patterns: red for upregulated, blue for downregulated, and gray for non-differential metabolites. Edge colors denote interaction types, with red and blue lines representing positive and negative correlations, respectively. The edge thickness corresponds to the absolute magnitude of correlation coefficients, where thicker lines indicate stronger associations. This visualization strategy enables systematic identification of critical relationships between variables through characterization of both regulatory patterns and interaction strengths among significantly correlated metabolic features [22].
The analysis of the relationship network diagram for key compounds in green tea under HBL treatment revealed that the main volatile compounds could be categorized into two groups (Figure 8A). (Z)-2-Octen-1-ol, hotrienol and delta-decalactone in the first group of compounds showed a negative correlation with other compounds. Nerol, cis-linalool oxide (furanoid), trans-linalool oxide (furanoid), geraniol, linalool, E-citral (geranial), indole, methyl salicylate, 2,6,6-trimethyl-1,3-cyclohexadiene-1-carboxaldehyde, and naphthalene in the second group of compounds showed a positive correlation with other compounds. Hexanoic acid ethyl ester, (Z)-2-octen-1-ol, and delta-decalactone were negatively correlated with other substances in MBL treatment. Hotrienol, coumarin, nerol, cis-linalool oxide (furanoid), trans-linalool oxide (furanoid), geraniol, linalool, E-citral (geranial), methyl salicylate, 2,6,6-trimethyl-1,3-cyclohexadiene-1-carboxaldehyde, and naphthalene were positively correlated with other substances in MBL treatment. Nerol and delta-decalactone were negatively correlated with other substances in LBL treatment. Hexanoic acid ethyl ester, hotrienol, 5-ethyldihydro-2(3H)-furanone, coumarin, cis-linalool oxide (furanoid), trans-linalool oxide (furanoid), geraniol, linalool, E-citral (geranial), indole, methyl salicylate, 2,6,6-trimethyl-1,3-cyclohexadiene-1-carboxaldehyde, and naphthalene were positively correlated with other substances in LBL treatment.

4. Discussion

Tea is the second most widely consumed drink, surpassed only by water, in many societies [23]. Around the globe, its special flavor, fragrance, and health advantages are highly prized [24]. Typically, green tea quality is assessed by examining its color, aroma, taste, shape, and the appearance of its steeped leaves. The type of aroma is a crucial aspect of green tea quality, significantly influencing its market price and consumer preference [25]. Green tea’s scent [19] can be categorized into different types, such as floral, sweet, chestnut-like, and fresh aromas. Spreading, fixation, rolling, and drying all impact flavor; spreading receives more focus. As spreading occurs, volatile compounds, amino acids, and glycosides experience notable changes, which may lead to an increase in amino acids, suppression of catechin and flavonoid production, and a decrease in the phenol-to-amine ratio [26].
Part of the aroma components of tea are inherent in fresh leaves, and the other part is mainly formed during processing [27]. The initial step in processing green tea, spreading, has a direct impact on the tea quality. After spreading, the leaves start to wilt, their texture shifts from firm to soft, their color transitions from bright green to dark green, and ultimately, the leaves lose their greenness entirely. The aroma is also released, and the moisture level of the spread leaves decreases to 70 ± 2%, which is a moderate level, setting the stage for the next step in the fixation process [28]. In traditional processing, solar withering is widely used in the manufacturing process of tea to improve the overall quality. Indoor trough withering compensates for the environmental drawbacks in natural withering and enhances both efficiency and quality [5].
At present, the positive effects of different light conditions on tea quality have been confirmed, so the installation of LED lamps in traditional indoor withering chambers has been applied. The content and composition of volatile compounds are easily affected by the light conditions during different withering conditions. Red light and UV-B withering could enhance the activity and up-regulate the expression of β-primeverosidase and β-GLU during withering, leading to the release of free GBVs (glycosides), such as linalool and its oxides [13,29]. During the green tea spreading process, the red light was beneficial to accumulate components that produce a chestnut-like aroma [30]. Linalool synthase (CsLIS2, CsLIS3, CsLIS4) and farnesene synthase (CsFS5, CsFS10) were significantly up-regulated in spreading leaves after LTY (low temperature + yellow light) treatment and significantly improved the aroma quality of green tea products [31].
The pleasant scents of green tea are frequently characterized as tender and fresh, with notes like chestnuts and orchids. The unpleasant scents of green tea are frequently characterized as grassy, dull, and over-fired. An experienced sensory panel can subjectively differentiate these various aroma types, or they can be objectively distinguished using analytical instruments and chemometric techniques [32]. In the MBL treatment, the floral aroma was significantly more intense than in the DARK treatment, which might be attributed to higher linalool concentrations. Furthermore, the greater amounts of nerol might contribute to the stronger fruity aroma in HBL treatment. This discovery aligned with related studies, indicating that blue light treatment was the most effective in enhancing the floral aroma [12].
The qualitative and quantitative analysis showed notable variations in the aroma components of the four green tea samples when exposed to different light conditions during spreading. In this study, MBL treatment increased both the total content and the variety of aroma compounds, suggesting that it enhanced the aroma intensity of green tea. This could primarily be due to variations in the quantity of aroma precursors and enzyme activity influenced by light conditions during spreading [9]. In addition, light treatment also impacted the volatile compounds’ composition and content during spreading. MBL treatment led to a significant rise in alcohols and aldehydes, while HBL treatment caused a significant increase in ketones. In a similar manner, earlier studies indicated that blue light treatment, as opposed to DARK treatment, notably raised the levels of aldehydes, ketones and alcohols in black tea [33].
In plants, typical aromatic precursors are lipids, amino acids, geranyl/farnesyl pyrophosphate, and carotenoids, which can be converted into volatile compounds via the metabolic processes of lipid oxidation, amino acid breakdown, terpenoid synthesis, and carotenoid breakdown, respectively [34]. Therefore, the volatile compounds in tea can be categorized as FADVs, AADVs, VTs, and CDVs [35]. During withering, fresh leaves in vitro continue to exhibit strong synthesis, transformation, and metabolism of FADVs, AADVs, VTs, and CDVs [8,36]. In this study, 16 important differential compounds were identified, including 6 FADVs, 6 VTs, 2 AADVs, 1 CDV, and 1 other. This indicated that FADVs and VTs had major contribution to the formation of green tea aroma under different blue light intensity treatments.
Hexanoic acid ethyl ester, 5-ethyldihydro-2(3H)-furanone, hotrienol, delta-decalactone, coumarin, and (Z)-2-octen-1-ol were recognized as primary FADV aroma compounds due to its high rOAVs. Hotrienol, (Z)-2-octen-1-ol, delta-decalactone, and hexanoic acid ethyl ester were volatile fatty acid derivatives with green or grass odor. Under LBL and DARK treatment, the above three compounds accumulated in green tea, which might lead to the green and grass-like odor in green tea. Coumarin is a natural substance with a sweet, herb-like, and cherry blossom scent. It has been identified as a key compound contributing to the sweet aroma of Japanese and Chinese green tea [37]. Green tea treated with MBL had a notably higher coumarin content compared to other treatment groups, enhancing its sweet aroma of green tea. Furaneol, which imparts ’sweet, fruity, and caramel’ notes, is extensively found in berry fruits, like strawberry [6]. In Laoshan green tea, furaneol was identified as a key aroma compound [9]. 5-Ethyldihydro-2(3H)-furanone was only accumulated under LBL treatment, which may be the source of the sweet aroma of green tea under LBL treatment.
Geraniol, cis-linalool oxide (furanoid), trans-linalool oxide (furanoid), nerol, linalool, and E-citral (geranial) were identified as the key VTs of aroma. The “fruit sweet fragrance” of green tea was mainly composed of linalool, linalool oxide, geraniol, and nerol [31]. Linalool has been extensively researched and found to be the compound that imparts a chestnut-like aroma [19]. Geraniol is a colorless oily liquid with a rose-like scent, and it is often detected in green tea. Geraniol has been reported to exhibit positive correlations with the Huangshan Maofeng grade [38]. Linalool, linalool oxide, and geraniol were found to accumulate under MBL treatment, possibly leading to the fruity and sweet aroma in green tea. In sunlight withering, the increased activity of geranyl pyrophosphate synthase and alcohol dehydrogenase was associated with an enhanced floral aroma due to the enrichment of terpene-catalyzed products and benzeneacetaldehyde concentration [39]. MBL treatment with green tea may reduce isopentenyl pyrophosphate and dimethylallyl pyrophosphate concentrations, resulting in elevated levels of linalool and geraniol. Geraniol can be further converted to nerol, neral, and orcitral during green tea spreading [40]. Similarly, nerol accumulated under HBL conditions during spreading, suggesting that most geraniol was converted into nerol under HBL conditions.
According to previous research, indole (floral, animal-like) was believed to give a unique aroma to both Japanese green tea (Sen-cha) [41] and oolong tea [42]. In addition, indole [25] contributed to the chestnut-like aroma in green tea; it accumulates during LBL treatment, possibly serving as the floral and chestnut-like source. Methyl salicylate has a minty scent and has been found in green, oolong, and black tea [40]. Methyl salicylate accumulated under MBL treatment during spreading, which may be the sweet flavor source of green tea under MBL treatment. Characterized by its unique and sharp naphthyl odors, naphthalene is a polycyclic aromatic hydrocarbon formed from long-chain hydrocarbons when exposed to high temperatures [18]. In addition, CDV 2,6,6-trimethyl-1,3-cyclohexadiene-1-carboxaldehyde (woody, spicy, medicinal, powdery, herbal) was detected. The effect of 2,6,6-trimethyl-1,3-cyclohexadiene-1-carboxaldehyde on MBL treatment increases the refreshing aroma of green tea.

5. Conclusions

The intensity of blue light significantly influenced the sensory quality and aroma development in green tea. This research systematically examined the volatile components of green tea subjected to various light treatments during the spreading stage using HS-SPME-GC-MS. MBL was identified as the optimal light condition for creating high-quality green tea with a robust and enduring fruity and flower aroma. OPLS-DA and rOAV analysis identified the 16 main volatile compounds. Blue light intensity significantly impacted different sources of volatile compounds; MBL treatment being the most effective in forming and transforming volatile terpenes, thereby enhancing the fruity and floral aroma in green tea. Enhancing the nerol content through HBL treatment improved the flower aroma in green tea. To provide technical guidance to produce high-quality green tea, the influence of different light conditions on the formation of aroma during tea spreading was systematically studied. The research results provide a theoretical basis and technical support for the targeted processing of high-quality green tea.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/foods14081308/s1, Table S1: Volatile compounds were detected by HS-SPME-GC-MS in green tea under different light conditions; Table S2: Volatile components screened according to VIP > 1 and p < 0.05 in green tea under different light conditions.

Author Contributions

Y.H.: Conceptualization, Investigation, Methodology, Writing original draft. Y.T.: Data curation, Formal analysis. S.S.: Formal analysis, Investigation. L.L.: Conceptualization, Investigation, Funding acquisition. S.A.: Investigation, Funding acquisition. G.Z. (Guoming Zhou): Investigation, Funding acquisition. J.Z.: Formal analysis, Investigation, Funding acquisition. S.L.: Data curation. Y.Y.: Writing—review & editing. A.J.: Investigation. C.P.: Validation, X.L.: Conceptualization, Writing—review & editing, Funding acquisition. G.Z. (Guanghui Zeng): Conceptualization, Writing—review & editing, Funding acquisition. All authors have read and agreed to the published version of the manuscript.

Funding

This work was funded by the Research and demonstration project of key technologies of tea garden photovoltaic power generation (HNKJ22-H135), the Modern Agriculture and Health Industry Research Institute Project of Wencheng County (2022NKY03), the Yongjia Modern Agricultural Industry Research Institute Industrial upgrading project (2023YJ002), the Industrial Enhancement Project of Cangnan Modern Agricultural Industry Research Institute (2024CNYJY07), the Wenxian Science and Technology Plan Project (2024CX001), the Key Research and Development Program of Sichuan Province (2023YFN0010), the Expert Workstation of Yunnan Province (202305AF150198), and the Nanjing Agricultural Major Technology Collaborative Promotion Plan Project (2024NJXTTG 10).

Institutional Review Board Statement

In this study, tea sensory evaluation adhered to the method outlined in the Chinese National Standard Methodology (GB/T 23776-2018 [43]). The applied tea samples were safe for consumption without any additional reagent, so ethics approval was not required for this research. Furthermore, the national laws do not require ethical approval for sensory evaluation, and there are no human ethics cmmittees or formal documentation procedures available for sensory evaluation. However, appropriate protocols for protecting the rights and privacy of all participants were utilized during the study, e.g., no coercion to participate, full disclosure of the study requirements and risks, verbal consent of participants, and the ability to withdraw from the study at any time.

Informed Consent Statement

Verbal informed consent was obtained from the participants.

Data Availability Statement

The original contributions presented in the study are included in the article; further inquiries can be directed to the corresponding author.

Conflicts of Interest

The author Lailong Li was employed by the company Huaneng Group Co., Ltd. He participated in Conceptualization, Investigation, and Funding acquisition in the study. Author Shaoshuai An was employed by the company Huaneng Group Co., Ltd. He participated in Investigation and Funding acquisition in the study. Author Guoming Zhou was employed by the company Huaneng Group Co., Ltd. He participated in Investigation and Funding acquisition in the study. Author Jing Zhu was employed by the company Huaneng Group Co., Ltd. She participated in Formal analysis, Investigation, and Funding acquisition in the study. The involvement of authors from the company had no effect on the objectivity or authenticity of the study. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The authors declare that this study received funding from “Research and Demonstration of Key Technologies for Intelligent Coupling of Flexible Mounting Photovoltaic Systems and Tea Plantations in the Short-Sunlight Jiangnan Ecological-Climate Zone” (Project Number: HNKJ22-H135). The funder was not involved in the study design, collection, analysis, interpretation of data, the writing of this article, or the decision to submit it for publication. The involvement of the funder had no impact on the objectivity or authenticity of the study.

Abbreviations

The following abbreviations are used in this manuscript:
AADVsAmino acid-derived volatiles
CDVsCarotenoid-derived volatiles.
DARKDark treatment
FADVsFatty acid-derived volatiles
GC-MSGas chromatography-mass spectrometry
HBLHigh-intensity blue light treatment
HCAHierarchical clustering analysis
HS-SPMEHeadspace solid-phase microextraction
LBLLow-intensity blue light treatment
LEDLight emitting diode
MBLMiddle-intensity blue light treatment
NISTNational Institute of Standards and Technology
OPLS-DAOrthogonal partial least squares discriminant analysis
PCAPrincipal component analysis
rOAVRelative odor activity value
VIPVariables important in the projection
VTsVolatile terpenoids

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Figure 1. Spectral photon distribution (SPD) of blue light equipment.
Figure 1. Spectral photon distribution (SPD) of blue light equipment.
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Figure 2. The radar of sensory aroma attributes profiles in green teas under different light conditions.
Figure 2. The radar of sensory aroma attributes profiles in green teas under different light conditions.
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Figure 3. Total amount and quantity of volatile substances. (A) Overall aroma concentration (upper, μg/kg tea leaves relatives to internal standard (ethyl caprate) and the number of compounds (lower) conducted in the tea leaves spread in different light conditions; (B) Venn diagram of aroma compounds of green tea under different light conditions. Different letters in the same column indicate a significant difference between light conditions (p < 0.05).
Figure 3. Total amount and quantity of volatile substances. (A) Overall aroma concentration (upper, μg/kg tea leaves relatives to internal standard (ethyl caprate) and the number of compounds (lower) conducted in the tea leaves spread in different light conditions; (B) Venn diagram of aroma compounds of green tea under different light conditions. Different letters in the same column indicate a significant difference between light conditions (p < 0.05).
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Figure 4. Classification and content of volatile substances. (A) Category and contents of compounds in green tea samples under different light conditions. (B) Content comparison of different categories of volatile compounds. Different letters indicated significance difference (p < 0.05).
Figure 4. Classification and content of volatile substances. (A) Category and contents of compounds in green tea samples under different light conditions. (B) Content comparison of different categories of volatile compounds. Different letters indicated significance difference (p < 0.05).
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Figure 5. Multivariate statistical analysis of volatile compounds identified under different light conditions. (A) Principal component analysis; (B) Hierarchical cluster analysis; (C) Orthogonal partial least squares discriminant analysis. R2X = 0.995, R2Y = 0.999, Q2 = 0.999. (D) Hypothesis testing of the OPLS-DA model. (E) Orthogonal partial least squares discriminant analysis loading plot for volatile compounds. X represents 116 volatile compounds, and Y represents 4 light treatments. The numbers in the figure correspond to the serial number of substances in Table S1. (F) Orthogonal partial least squares discriminant analysis VIP plot with volatile compounds in green tea treated with different light conditions. Yellow bars represent volatile compounds with VIP > 1.
Figure 5. Multivariate statistical analysis of volatile compounds identified under different light conditions. (A) Principal component analysis; (B) Hierarchical cluster analysis; (C) Orthogonal partial least squares discriminant analysis. R2X = 0.995, R2Y = 0.999, Q2 = 0.999. (D) Hypothesis testing of the OPLS-DA model. (E) Orthogonal partial least squares discriminant analysis loading plot for volatile compounds. X represents 116 volatile compounds, and Y represents 4 light treatments. The numbers in the figure correspond to the serial number of substances in Table S1. (F) Orthogonal partial least squares discriminant analysis VIP plot with volatile compounds in green tea treated with different light conditions. Yellow bars represent volatile compounds with VIP > 1.
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Figure 6. Analysis of 53 differential volatile compounds with VIP > 1 in green tea samples with different spreading light treatments.
Figure 6. Analysis of 53 differential volatile compounds with VIP > 1 in green tea samples with different spreading light treatments.
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Figure 7. Effect of different light conditions on the contents of 16 important differential volatile compounds contributing to the aroma of green tea. (A) FADVs. (B) VTs. (C) AADVs. (D) CDVs. (E) Other sources of volatiles. Red indicates upregulation, and blue indicates downregulation.
Figure 7. Effect of different light conditions on the contents of 16 important differential volatile compounds contributing to the aroma of green tea. (A) FADVs. (B) VTs. (C) AADVs. (D) CDVs. (E) Other sources of volatiles. Red indicates upregulation, and blue indicates downregulation.
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Figure 8. Interaction analysis of key volatile compounds based on correlation coefficients. (A) Analysis of interactions between key volatile compounds under HBL treatment during spreading. (B) Analysis of interactions between key volatile compounds under MBL treatment during spreading. (C) Analysis of interactions between key volatile compounds under LBL treatment during spreading. The numbers in the figure correspond to the serial numbers of the substances in Table 2.
Figure 8. Interaction analysis of key volatile compounds based on correlation coefficients. (A) Analysis of interactions between key volatile compounds under HBL treatment during spreading. (B) Analysis of interactions between key volatile compounds under MBL treatment during spreading. (C) Analysis of interactions between key volatile compounds under LBL treatment during spreading. The numbers in the figure correspond to the serial numbers of the substances in Table 2.
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Table 1. The details of volatile compounds identified by GC-MS.
Table 1. The details of volatile compounds identified by GC-MS.
No.NameRtRIContent (μg/kg, DW)
HBLMBLLBLDARK
Alcohols
11-Penten-3-ol2.33684256.62 ± 0.25 a186.79 ± 0.39 b141.71 ± 0.99 c134.71 ± 2.41 d
21-Pentanol3.4276570.62 ± 0.23 a38.86 ± 0.55 b20.71 ± 0.11 d25.61 ± 0.07 c
32-Hexanol3.99803N.dN.d17.62 ± 1.05 aN.d
4(Z)-3-Hexen-1-ol5.05857108.04 ± 1.74 a77.22 ± 0.49 b51.54 ± 0.92 c46.21 ± 1.16 d
51-Hexanol5.2586827.6 ± 0.42 a15.5 ± 0.19 c16.49 ± 0.43 b8.51 ± 0.22 d
61-Heptanol7.179706.88 ± 0.04 bN.dN.d9.42 ± 0.31 a
71-Octen-3-ol7.38980354.33 ± 2.96 b162.18 ± 1.34 d237.21 ± 1.35 c393.34 ± 2.37 a
82-Ethyl-1-hexanol8.281030N.dN.d10.41 ± 0.33 b13.32 ± 0.51 a
9Benzyl alcohol8.401036309.14 ± 0.43 b327.76 ± 0.32 a190.82 ± 1.19 c113.32 ± 2.26 d
10(Z)-2-Octen-1-ol9.061067N.dN.d36.12 ± 22.15 a17.82 ± 0.64 ab
11(E)-2-Octen-1-ol9.10106886.11 ± 1.07 aN.dN.dN.d
12cis-Linalool oxide (furanoid)9.201074140.99 ± 1.58 c225.52 ± 0.37 a156.12 ± 2.88 b104.9 ± 1.78 d
13trans-Linalool oxide (furanoid)9.531086462.35 ± 2.12 c668.91 ± 0.6 a477.59 ± 1.34 b313.93 ± 4.15 d
14Linalool9.761099188.1 ± 1.87 c415.93 ± 0.85 a285.27 ± 2.26 b175.16 ± 4.36 d
15Hotrienol9.87110757.05 ± 1.16 d114.65 ± 0.96 a87.26 ± 1.91 b62.2 ± 1.34 c
16Phenylethyl alcohol10.101116912.04 ± 1.7 b1003.42 ± 0.97 a648.07 ± 8.75 c465.13 ± 4.37 d
172,6-Dimethyl-3,7-octadiene-2,6-diol11.82119073.01 ± 0.17 b114.74 ± 0.57 a75.75 ± 2.03 b46.18 ± 0.95 c
184-Vinylphenol12.79121936.54 ± 0.76 c66.58 ± 2.14 b82.98 ± 4.71 aN.d
19Nerol12.97122858.21 ± 0.89 a42.19 ± 1.49 bN.d24.77 ± 0.03 c
20Geraniol13.6312551152.5 ± 0.06 b1613.6 ± 3.57 a1107.96 ± 7.29 c1016.93 ± 1.58 d
21Phytol36.062114N.d56.95 ± 0.99 c155.1 ± 0.57 a146.9 ± 4.37 b
Aldehydes
223-Methyl-butanal2.1265253.01 ± 2.41 a38.4 ± 0.24 b27.23 ± 0.05 c30.31 ± 0.84 c
232-Methyl-butanal2.1966262.69 ± 0.24 b66.55 ± 0.61 a56.6 ± 0.9 c52.04 ± 1.38 d
24Hexanal3.91800138.63 ± 0.27 aN.dN.d19.48 ± 0.32 b
25(E)-2-hexenal4.67854N.dN.d7.89 ± 1.84 aN.d
26Benzaldehyde7.0996276.1 ± 0.11 a35.66 ± 0.49 b16.75 ± 0.19 c9.53 ± 0.19 d
27(E,E)-2,4-Heptadienal7.911011N.d8.44 ± 2.2 a8.68 ± 2.13 aN.d
28Benzeneacetaldehyde8.62104577.95 ± 2.02 a30.1 ± 0.98 b19.53 ± 0.43 c13.34 ± 1.09 d
29(E)-2-octenal8.831060115.52 ± 2.78 aN.dN.dN.d
30(E,Z)-2,6-Nonadienal11.0011553.44 ± 0.18 c34.77 ± 0.92 a17.72 ± 0.34 bN.d
312,6,6-Trimethyl-1,3-cyclohexadiene-1-carboxaldehyde12.18120118.59 ± 0.25 c23.33 ± 0.33 a20.52 ± 0.35 b14.61 ± 0.07 d
32Decanal12.24120569.86 ± 0.5 a44.91 ± 1.78 b35.31 ± 1.55 cN.d
33β-Cyclocitral12.81122022.98 ± 0.7 b27.13 ± 1.37 a17.54 ± 0.42 c9.58 ± 0.16 d
34Neral13.29124064.65 ± 14.74 a62.12 ± 10.81 a42.64 ± 1.09 aN.d
35(E)-2-Decenal13.8312632.83 ± 0.3 c246.31 ± 3.32 a127.05 ± 1.27 bN.d
36E-Citral (geranial)14.0912712.97 ± 1.07 c484.22 ± 0.79 a262.97 ± 7.74 bN.d
37(E,E)-2,4-Decadienal15.021317N.d90.79 ± 23.27 a47.01 ± 2.38 bN.d
38Vanillin17.82140570.41 ± 2.51 a11.55 ± 1.38 bN.dN.d
Acids
393-Methyl-butanoic acid4.7086329.45 ± 0.54 b24.55 ± 0.15 c18.64 ± 0.18 d31.14 ± 0.24 a
402-Methyl-butanoic acid4.89861N.d8.54 ± 0.33 c14.78 ± 0.2 b20.29 ± 0.09 a
41Pentanoic acid5.4990436.06 ± 6.57 aN.dN.dN.d
42n-Decanoic acid16.84137354.5 ± 9.54 a43.49 ± 5.97 aN.dN.d
43Dodecanoic acid22.461568112.08 ± 2.99 bN.dN.d246.7 ± 3.32 a
44Tetradecanoic acid27.641769N.d12.31 ± 1.33 b77.86 ± 4.08 aN.d
Esters
45Ethyl Acetate1.8761218.88 ± 0.57 c78.78 ± 0.29 a68.33 ± 0.6 b67.52 ± 0.93 b
46Hexanoic acid ethyl ester7.76100012.62 ± 0.02 aN.d12.24 ± 0.44 a6.9 ± 0.1 b
47(E)-3-Hexenoic acid ethyl ester7.8610063.27 ± 0.59 aN.dN.dN.d
48Methyl salicylate12.041192142.13 ± 2.45 b174.14 ± 1.5 a118.26 ± 0.83 c95.51 ± 2.05 d
49(Z)-Hexanoic acid-3-hexenyl ester17.211380N.d287.95 ± 0.89 b434.35 ± 1.11 a181.14 ± 7.63 c
50(Z,Z)-3-Hexenoic acid-3-hexenyl ester17.341389N.dN.d80.74 ± 0.43 aN.d
51Diethyl phthalate23.161594173.09 ± 2.28 a26.16 ± 0.32 b23.53 ± 0.35 bN.d
52Methyl jasmonate23.63164832.53 ± 0.49 aN.dN.dN.d
532-Ethylhexyl-benzoic acid ester26.371735N.d127.54 ± 2.1 a86.27 ± 0.77 b24.84 ± 0.55 c
54Diisobutyl phthalate30.25187064.64 ± 1.62 c66.14 ± 0.28 c120.69 ± 0.6 a95.54 ± 2.69 b
55Methyl hexadecanoate31.741926N.d12.16 ± 1 b50.61 ± 2.12 aN.d
56Dibutyl phthalate32.62196553.48 ± 0.98 c90.26 ± 0.07 b213.87 ± 1.48 a95.65 ± 3.36 b
57Ethyl hexadecanoate33.38199331.37 ± 0.29 c71.89 ± 1.56 b74 ± 1.72 b92.84 ± 1.69 a
58(E)-2-Hexenyl-hexanoic acid ester17.431391N.d46.23 ± 1.31 c95.82 ± 4.6 a59.72 ± 0.63 b
59Delta-decalactone20.481495488.87 ± 7.27 b449.06 ± 0.77 b253.4 ± 5.09 c1555.69 ± 36.95 a
Ketons
602-Pentanone1.95655N.d49.92 ± 1.08 b61.15 ± 1.36 aN.d
612-Heptanone5.7289123.37 ± 4.65 aN.dN.dN.d
622,3-Octanedione7.42985N.d13.04 ± 2.33 b34.42 ± 3.29 aN.d
636-Methyl-5-hepten-2-one7.54986N.d25.94 ± 0.28 c35.68 ± 0.17 a30.02 ± 0.59 b
64(E)-3-Octen-2-one8.42103377.09 ± 0.58 aN.dN.dN.d
652,2,6-Trimethyl-cyclohexanone8.421036N.d12.7 ± 0.13 b16.93 ± 0.37 a12.45 ± 0.2 b
66(R,S)-5-Ethyl-6-methyl-3 E-hepten-2-one10.78114458.67 ± 0.39 a18.48 ± 0.44 bN.dN.d
67α-Ionone18.47142676.29 ± 2.63 aN.dN.dN.d
68Geranylacetone19.141456295.4 ± 6.23 aN.dN.dN.d
69trans-β-Ionone20.241486762.8 ± 4.48 a656.47 ± 1.52 b336.95 ± 3.21 c225.39 ± 4.8 d
70cis-Jasmone17.761394N.d105.61 ± 1.04 b144.78 ± 1.72 a83.82 ± 1.29 c
712,4-Nonanedione, 3-methyl13.45125211.23 ± 0.42 c64.17 ± 1.04 a33.91 ± 0.37 bN.d
Hydrocarbons
722-Methyl-nonane7.119644.62 ± 0.18 bN.d8.28 ± 0.09 aN.d
733,5,5-Trimethyl-2-hexene7.30985N.dN.dN.d137.75 ± 3.24 a
742,6-Dimethyl-nonane8.11101851.67 ± 1.28 a42.02 ± 1.06 c47.08 ± 2.13 b34.2 ± 0.67 d
75Limonene8.301030N.d8.43 ± 0.87 b13.6 ± 1.28 aN.d
76β-Ocimene8.671037N.d22.82 ± 0.9 a15.88 ± 0.89 bN.d
772,9-Dimethyl-decane10.3211269.01 ± 0.25 aN.dN.dN.d
78Naphthalene11.94118116.14 ± 0.61 a15.54 ± 2.3 a18.38 ± 0.58 aN.d
79Dodecane12.10120072.23 ± 0.87 b89.51 ± 0.05 a65.68 ± 0.33 c36.18 ± 3.07 d
802,4-Dimethyl-undecane12.38120828.71 ± 0.49 a26.16 ± 0.73 b15.81 ± 0.26 c15.51 ± 0.47 c
812,6-Dimethyl-undecane12.561210141.53 ± 1.95 b166.62 ± 2.12 a91.79 ± 0.62 c91.17 ± 1.11 c
824-Methyl-dodecane13.66125955.24 ± 15.05 aN.dN.dN.d
832,3-Dimethyl-undecane13.771251261.58 ± 7.56 aN.dN.dN.d
842,6,11-Trimethyl-dodecane14.491275N.d103.41 ± 20.41 a60.42 ± 0.68 b61.31 ± 1.02 b
85Tridecane14.8013008.25 ± 0.29 c86.36 ± 1.42 a58.8 ± 0.6 b55.54 ± 3.06 b
864,6-Dimethyl-dodecane15.34132562.48 ± 14.8 b199.48 ± 42.43 a117.46 ± 0.93 b117.59 ± 0.97 b
876-Methyl-tridecane16.321344N.d28.46 ± 5.12 b28.17 ± 9.44 b93.88 ± 5.15 a
885-Methyl-tridecane16.40134824.41 ± 1.08 bN.dN.d73.49 ± 1.12 a
894-Methyl-tridecane16.451359N.d28.08 ± 1.17 c32.69 ± 0.87 b78.63 ± 1.43 a
903-Methyl-tridecane16.79137133.54 ± 1.97 b41.28 ± 7.66 b45.18 ± 20.79 b154.67 ± 4.73 a
915-Methyl-tetradecane19.241453N.d224.51 ± 9.47 b144.59 ± 2.53 c322.47 ± 3.48 a
924-Methyl-tetradecane19.391459511.39 ± 6.69 a160.73 ± 0.92 c129.95 ± 5.4 d253.77 ± 0.8 b
932,3,7-Trimethyl-decane19.51146688.81 ± 0.75 d665.63 ± 6.45 b504.4 ± 5.8 c967.42 ± 8.51 a
943-Methyl-tetradecane19.64147083.38 ± 2.24 bN.dN.d440.51 ± 30.12 a
95Pentadecane20.561500N.d605.15 ± 2.9 b794.43 ± 14.97 aN.d
967-Methyl-pentadecane21.901541N.dN.dN.d192.88 ± 5 a
973-Methyl-pentadecane22.54157016.44 ± 1.08 c163.85 ± 12.13 b308.29 ± 22.13 aN.d
98Hexadecane23.261600N.d191.45 ± 1.56 c502.66 ± 2.23 b628.56 ± 6.06 a
994-Methyl-hexadecane23.84165918.56 ± 0.32 aN.dN.dN.d
100Neophytadiene29.651837N.d15.22 ± 0.27 c35.48 ± 0.43 a25.33 ± 0.53 b
Heterocyclic
1012-Ethyl-furan2.5270328.02 ± 0.69 a14.79 ± 0.24 b7.14 ± 0.21 c8.45 ± 0.54 c
1021-Ethyl-1H-pyrrole4.28821N.d12.58 ± 0.21 a10.27 ± 0.14 b12.72 ± 0.97 a
103Butyrolactone6.1191523.88 ± 0.07 a12.36 ± 0.5 bN.dN.d
1042-Pentylfuran7.63993163.98 ± 0.62 a93.09 ± 1.44 b53.49 ± 1.21 c27.51 ± 1.12 d
1052,3,5-Trimethylpyrazine7.86100826.75 ± 7.28 aN.dN.dN.d
1065-ethyldihydro-2(3H)-Furanone8.731057N.dN.d23.4 ± 0.34 aN.d
1072-Acetyl pyrrole8.86106429.41 ± 1.12 c176.02 ± 0.93 a177.28 ± 0.66 a147.3 ± 4.19 b
108Maltol10.081110N.d56.07 ± 0.92 aN.d5.51 ± 0.05 b
109Linalool oxide (pyranoid)11.451173325.7 ± 0.4 c558.02 ± 0.43 a424.04 ± 2.91 b289.31 ± 2.25 d
110Indole14.78129567.68 ± 1.43 aN.d70.67 ± 0.28 a58.04 ± 1.76 b
111Coumarin18.87144134.7 ± 1.9 c53.87 ± 0.7 a43.14 ± 0.81 b34.9 ± 0.28 c
112Dihydroactinolide21.461538605.45 ± 3.91 a462.74 ± 2.27 b264.46 ± 4.66 d373.34 ± 2.61 c
1137,9-Di-tert-butyl-1-oxaspiro(4,5)deca-6,9-diene-2,8-dione31.60192320.25 ± 0.67 c25.68 ± 0.15 b21.35 ± 0.01 c33.01 ± 1.35 a
Others
1141,1-Diethoxy-ethane2.81726222.43 ± 0.52 a10.62 ± 0.28 bN.d9.04 ± 0.99 b
115p-Vinylguaiacol14.861317114.84 ± 1.92 aN.dN.dN.d
1162,4-Di-tert-butylphenol20.941519107.69 ± 0.83 c256.55 ± 2.91 a176.8 ± 0.88 b251.01 ± 5.36 a
DW, dry weight. RT, Retention time. RI, Retention index calculated from a series of n-alkanes (C7-C40). N.d indicates lower than the instrument detection limit. Different letters in the same column indicate a significant difference between light conditions (p < 0.05).
Table 2. Aroma compounds identified in green teas under different light conditions.
Table 2. Aroma compounds identified in green teas under different light conditions.
No.Compound NameOdor CharacteristicOTs (ug/kg)rOAV
HBLMBLLBLDARK
1Hexanoic acid ethyl estercitrus, green0.718.03 ± 0.03 aN.d17.49 ± 0.63 a9.86 ± 0.14 b
2(Z)-2-Octen-1-olSweet, green, citrus, fatty, herbal, cucumber- like3N.dN.d12.04 ± 7.38 a5.94 ± 0.21 ab
3HotrienolFresh, floral, fruity1100.52 ± 0.01 d1.04 ± 0.01 a0.79 ± 0.02 b0.57 ± 0.01 c
45-Ethyldihydro-2(3 H)-furanoneCaramel, nutty, roasted, sweet, creamy9.7N.dN.d2.41 ± 0.03 aN.d
5CoumarinFloral113.15 ± 0.17 c4.9 ± 0.06 a3.92 ± 0.07 b3.17 ± 0.03 c
6delta-DecalactoneGreen, herbal667.41 ± 0.11 b6.8 ± 0.01 b3.84 ± 0.08 c23.57 ± 0.56 a
7NerolFresh, citrus, floral, green, sweet, lemon-like491.19 ± 0.02 a0.86 ± 0.03 bN.d0.51 ± 0.00 c
8cis-Linalool oxide (furanoid)Sweet, floral, creamy1900.74 ± 0.01 c1.19 ± 0.00 a0.82 ± 0.02 b0.55 ± 0.01 d
9trans-Linalool oxide (furanoid)Sweet, floral, creamy1902.43 ± 0.01 c3.52 ± 0.00 a2.51 ± 0.01 b1.65 ± 0.02 d
10GeraniolRose-like, sweet, honey-like7.5153.67 ± 0.01 b215.15 ± 0.48 a147.73 ± 0.97 c135.59 ± 0.21 d
11LinaloolFloral, sweet, grape-like, woody0.22854.99 ± 8.48 c1890.6 ± 3.86 a1296.68 ± 10.27 b796.2 ± 19.8 d
12E-Citral (geranial)Citrus, lemon-like12.97 ± 1.07 c484.22 ± 0.79 a262.97 ± 7.74 bN.d
13IndoleFloral, animal-like401.69 ± 0.04 aN.d1.77 ± 0.01 a1.45 ± 0.04 b
14Methyl salicylateMinty, wintergreen-like403.55 ± 0.06 b4.35 ± 0.04 a2.96 ± 0.02 c2.39 ± 0.05 d
152,6,6-Trimethyl-1,3-cyclohexadiene-1-carboxaldehydeWoody, spicy, medicinal, powdery, herbal36.20 ± 0.08 c7.78 ± 0.11 a6.84 ± 0.12 b4.87 ± 0.02 d
16NaphthalenePungent, tar0.4436.68 ± 1.39 a35.32 ± 5.22 a41.77 ± 1.32 aN.d
OTs: odor threshold in water based on the literature [21]. Relative odor activity value (rOAV), calculated as the ratio of odorant relative concentration in the green tea to odor thresholds in water. N.d indicates lower than the instrument detection limit. Different letters in the same column indicate a significant difference between light conditions (p < 0.05).
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MDPI and ACS Style

He, Y.; Tang, Y.; Song, S.; Li, L.; An, S.; Zhou, G.; Zhu, J.; Li, S.; Yin, Y.; Jeyaraj, A.; et al. Effect of Blue Light Intensity During Spreading on the Aroma of Green Tea. Foods 2025, 14, 1308. https://doi.org/10.3390/foods14081308

AMA Style

He Y, Tang Y, Song S, Li L, An S, Zhou G, Zhu J, Li S, Yin Y, Jeyaraj A, et al. Effect of Blue Light Intensity During Spreading on the Aroma of Green Tea. Foods. 2025; 14(8):1308. https://doi.org/10.3390/foods14081308

Chicago/Turabian Style

He, Youyue, Yan Tang, Shiyue Song, Lailong Li, Shaoshuai An, Guoming Zhou, Jing Zhu, Song Li, Yue Yin, Anburaj Jeyaraj, and et al. 2025. "Effect of Blue Light Intensity During Spreading on the Aroma of Green Tea" Foods 14, no. 8: 1308. https://doi.org/10.3390/foods14081308

APA Style

He, Y., Tang, Y., Song, S., Li, L., An, S., Zhou, G., Zhu, J., Li, S., Yin, Y., Jeyaraj, A., Peng, C., Li, X., & Zeng, G. (2025). Effect of Blue Light Intensity During Spreading on the Aroma of Green Tea. Foods, 14(8), 1308. https://doi.org/10.3390/foods14081308

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