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Flavor and Aroma Analysis as an Approach to Quality Control of Foods

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A special issue of Foods (ISSN 2304-8158). This special issue belongs to the section "Food Quality and Safety".

Deadline for manuscript submissions: 31 July 2026 | Viewed by 6735

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Editors

Dr. Ying Gao

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Guest Editor

Tea Research Institute, Chinese Academy of Agricultural Sciences, Hangzhou 310008, China
Interests: flavor and aroma compounds; food processing; quality assessment; novel analytical methods; AI-driven chemometrics
Special Issues, Collections and Topics in MDPI journals

Dr. Yuming Wei

E-Mail Website
Guest Editor

Tea Research Institute, Chinese Academy of Agricultural Sciences, Hangzhou 310008, China
Interests: food flavor; sensomics; food processing; quality evaluation; cross-modal interactive perception

Dr. Qing Meng

E-Mail Website
Guest Editor

College of Food Science, Southwest University, Beibei District, Chongqing 400715, China
Interests: flavor analysis; sensomics; metabonomics; tea processing; tea quality evaluation

Special Issue Information

Dear Colleagues,

Flavor and aroma are critical determinants of food quality, with a profound impact on consumer acceptance and preference. Revealing the association between sensory attributes and food quality is the foundation for controlling food quality through flavor and aroma analysis. The development of novel analytical techniques and the application of chemometric methods make it easier to screen key markers as reliable indicators of food quality. Due to the complexity of food matrices, sample pretreatment and method optimization may be required to more accurately and efficiently measure these target compounds. The rapid advancement of artificial intelligence has accelerated the establishment of precise models based on flavor and aroma analysis to detect adulteration, monitor freshness, and optimize the processing conditions of food. By integrating sensory science, analytical techniques, and data processing, this Special Issue aims to provide valuable insights into the complex chemistry behind food flavors and pave the way for smarter, data-driven quality control systems.

Dr. Ying Gao
Dr. Yuming Wei
Dr. Qing Meng
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

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Keywords

sensomics
analytical method optimization
quality assessment
food control
algorithm model

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Published Papers (6 papers)

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Research

23 pages, 10961 KB

Open AccessArticle

Multi-Granularity Domain Adversarial Learning for Cross-Domain Tea Classification Using Electronic Nose Signals

by Xiaoran Wang and Yu Gu

Foods 2026, 15(8), 1376; https://doi.org/10.3390/foods15081376 - 15 Apr 2026

Viewed by 538

Abstract

Rapid and reliable tea classification is valuable for routine product screening, yet conventional sensory or physicochemical methods are subjective or time-consuming. Electronic nose (E-nose) sensing provides a fast alternative, but performance often degrades under domain shifts caused by different tea types, commercial categories, or acquisition conditions. This study proposes MGDA-Net, a multi-granularity domain adversarial network for cross-domain tea classification using E-nose time-series signals. MGDA-Net learns local temporal dynamics via a CNN branch and global contextual dependencies via a self-attention branch, and fuses them through an adaptive gating module. A branch-level adversarial alignment strategy is introduced to reduce source–target discrepancy at both local and global feature levels. A three-stage training procedure, consisting of source pretraining, adversarial alignment, and target fine-tuning, enables knowledge transfer from a labeled green tea source-domain to two target tasks. Experiments on oolong tea commercial-category classification (6 classes) and jasmine tea retail price-level classification (8 classes) show that MGDA-Net achieves mean accuracies of 99.31 ± 0.69% and 99.38 ± 0.51% over 10 independent runs, substantially outperforming all compared baseline methods. Ablation studies, feature-space analyses, and label-efficiency experiments further confirm the contribution of each component and show that MGDA-Net maintains mean accuracies above 87% when only 40% of the target-domain labels are used for fine-tuning. These findings suggest that MGDA-Net is a promising approach for cross-domain tea classification using E-nose data. Full article

(This article belongs to the Special Issue Flavor and Aroma Analysis as an Approach to Quality Control of Foods)

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19 pages, 2645 KB

Open AccessArticle

Prediction of Quality Substance Content of Hakka Stir-Fried Green Tea Based on Multiple Features of Near-Infrared Spectroscopy

by Yanjiang Qiu, Ting Tang, Jiacheng Guo, Yunfang Zeng, Zihao Li, Qiaoyi Zhou, Dongxia Liang and Caijin Ling

Foods 2026, 15(3), 531; https://doi.org/10.3390/foods15030531 - 3 Feb 2026

Cited by 1 | Viewed by 607

Abstract

The contents of biochemical components, such as theanine, tea polyphenols, water extract, and soluble sugar in Hakka stir-fried green tea (HSGT), serve as important indicators reflecting the intrinsic quality of tea leaves. In this study, 171 HSGT samples are collected, and their near-infrared spectroscopy (NIRS), together with the contents of the four indicators, are determined. The aim is to establish prediction models for these four indicators by extracting multiple features from the NIRS data. First, the NIRS data is preprocessed. Then, multiple features are extracted using competitive adaptive reweighted sampling (CARS), adaptive Fourier decomposition (AFD), fast Fourier transform (FFT), continuous wavelet transform (CWT), and band combination (BC). Finally, ridge regression (RR) and partial least squares regression (PLSR) models are constructed based on the NIRS features to predict the four indicators. Experimental results show that the model combining multiple features, namely CARS + AFD + BC, delivers the best overall performance. Specifically, the RR model based on multiple features provides the most accurate predictions for theanine, tea polyphenols, and soluble sugar, while the PLSR model performs better for water extract. This study provides a rapid and accurate method for detecting the substance content in HSGT. Full article

(This article belongs to the Special Issue Flavor and Aroma Analysis as an Approach to Quality Control of Foods)

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19 pages, 48652 KB

Open AccessArticle

Chemical Drivers of Flavor Variation Across Cultivars and Grades of Fujian White Tea Revealed by Integrated Volatile and Non-Volatile Metabolomics

by Fuli Zong, Zi Yang, Linping Xiao, Yan Tong, Lan Shen, Zhijie Dong, Jianwei Zhou, Huan Cheng, Wenjun Wang and Donghong Liu

Foods 2026, 15(3), 458; https://doi.org/10.3390/foods15030458 - 28 Jan 2026

Cited by 1 | Viewed by 876

Abstract

Grade and cultivar are the important factors influencing white tea quality, but their relative metabolic contributions are not fully understood. Twelve white tea samples representing four major Fujian cultivars across three grades were analyzed using UHPLC–MS-based non-volatile metabolomics, HS-SPME–GC–MS volatile profiling, and sensory correlation analysis. In total, 47 non-volatile and 21 volatile markers were associated with grade differences, while 44 non-volatile and 26 volatile markers were linked to cultivar differences. Catechins and amino acids declined as grade decreased, whereas flavonol glycosides and gallic acid increased, accompanied by stronger astringency and reduced umami and sweetness. Aroma profiles showed a similar trend, with higher-grade teas dominated by floral notes and lower-grade teas exhibiting more herbal characteristics. Dimeric catechins, oxylipins, and aroma glycosides varied among cultivars. Volatile profiles separated the cultivars into two aroma groups: Fuding Dabai and Fuding Dahao showed more floral–fruity aromas, whereas Fuan Dabai and Zhenghe Dabai exhibited stronger herbal and aged aromas. Odor activity value analysis showed that linalool, geraniol, and (E,Z)-3,6-nonadien-1-ol were among the most abundant aroma-active compounds across white tea samples. These results provide chemical evidence for distinguishing white tea by grade and cultivar, with potential relevance to quality evaluation. Full article

(This article belongs to the Special Issue Flavor and Aroma Analysis as an Approach to Quality Control of Foods)

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20 pages, 2513 KB

Open AccessArticle

Influences of Fermentation Temperature on Volatile and Non-Volatile Compound Formation in Dark Tea: Mechanistic Insights Using Aspergillus niger as a Model Organism

by Rida Niaz, Mingjin Li, Qian Pu, Anlan Qu, Tianci Shen, Minghui Qi, Chengtao Wang, Lixia Chen, Shuang Wu and Youyi Huang

Foods 2026, 15(3), 441; https://doi.org/10.3390/foods15030441 - 26 Jan 2026

Cited by 2 | Viewed by 1050

Abstract

The mechanism of the quality formation of dark tea is not fully clear, particularly under variable fermentation temperatures. In this study, the tea fermented with Aspergillus niger (AN) at 25 (AN25) and 37 °C (AN37) exhibited the highest quality. Different fermentation temperatures primarily influenced the degradation of fatty acids and the hydrolysis of glycosides in the tea, with 37 °C being the most favorable for the release and accumulation of volatile compounds. Eighteen key volatiles were identified. Among these, benzaldehyde (a 120.9% increase compared to CK), α-ionone (957.8%), linalool (172.2%), and nonanal (22.8%) were present at high levels in AN37, and these compounds served as the main aroma contributors. Inoculation with AN and fermentation temperature primarily influences the levels of total polyphenols, organic acids and their derivatives, as well as amino acids and their metabolites in dark tea. Total polyphenols, flavonoids, and nucleotide and its metabolites were more rapidly consumed at 25–37 °C, contributing to the improved taste of the tea infusion. Additionally, EGC, GC, melezitose, and sucrose showed significant negative correlations with the taste quality of the tea infusion (p < 0.05). These results are conducive to further understanding of the quality formation of dark tea. Full article

(This article belongs to the Special Issue Flavor and Aroma Analysis as an Approach to Quality Control of Foods)

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17 pages, 4042 KB

Open AccessArticle

Spectral Multi-Scale Attention Fusion Network for Rapid Detection of Black Tea Adulteration Using a Handheld Spectrometer

by Jiawei Tang, Yongyan Chen, Qing Meng, Bo Zhao, Dongling Qiao, Guohua Zhao and Jia Chen

Foods 2025, 14(24), 4261; https://doi.org/10.3390/foods14244261 - 10 Dec 2025

Cited by 1 | Viewed by 745

Abstract

Black tea is a widely consumed beverage whose high economic value has led some producers to illegally add artificial colorants such as Sunset Yellow, Tartrazine, and Ponceau 4R, posing health risks. Although near-infrared (NIR) spectroscopy offers a rapid, non-destructive detection method, its use in trace-level colorant detection is limited due to low adulterant concentrations and interference from natural tea pigments. Hence, we developed a rapid, non-destructive method for detecting trace adulteration (from 0.1 to 0.5 g·kg⁻¹) in black tea with artificial colorants using a handheld near-infrared spectrometer. To enhance sensitivity to low-level adulteration, we proposed a novel Spectral Multi-scale Attention Fusion Network (SMAFNet), designed to dynamically integrate multiscale features. SMAFNet consists of spectral preprocessing, multi-scale feature extraction, and cross-scale attention fusion modules. Comparative experiments with traditional machine-learning models demonstrated that SMAFNet achieved superior performance even at low adulteration levels. Sample sets (each including 36 samples) adulterated with Sunset Yellow, Tartrazine, and Ponceau 4R, SMAFNet achieved accuracies of 97.22–100%, F1-scores of 0.9879–1.00, and 100% recall. These findings confirm the feasibility and robustness of combining NIR with SMAFNet for the rapid and discriminative detection of trace colorants in black tea, offering a practical framework for on-site food safety monitoring and quality control. Full article

(This article belongs to the Special Issue Flavor and Aroma Analysis as an Approach to Quality Control of Foods)

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17 pages, 891 KB

Open AccessArticle

Volatile Profiling of Tongcheng Xiaohua Tea from Different Geographical Origins: A Multimethod Investigation Using Sensory Analysis, E-Nose, HS-SPME-GC-MS, and Chemometrics

by Ge Jin, Chenyue Bi, Anqi Ji, Jieyi Hu, Yuanrong Zhang, Lumin Yang, Sunhao Wu, Zhaoyang Shen, Zhou Zhou, Xiao Li, Huaguang Qin, Dan Mu, Ruyan Hou and Yan Wu

Foods 2025, 14(11), 1996; https://doi.org/10.3390/foods14111996 - 5 Jun 2025

Cited by 7 | Viewed by 2126

Abstract

The evaluation of region-specific aroma characteristics in green tea remains critical for quality control. This study systematically analyzed eight Tongcheng Xiaohua tea samples (standard and premium batches) originating from four distinct regions using sensory analysis, electronic nose (E-nose), headspace solid-phase microextraction coupled with gas chromatography–mass spectrometry (HS-SPME-GC-MS), and chemometrics. The E-nose results demonstrated that the volatile characteristics of Tongcheng Xiaohua tea exhibit distinct geographical signatures, confirming the regional specificity of its aroma. HS-SPME-GC-MS identified 66 volatile metabolites across samples, with 18 key odorants (OAV > 1) including linalool, geraniol, (Z)-jasmone, and β-ionone driving aroma profiles. The partial least squares–discriminant analysis (PLS-DA) model, combined with variable importance in projection (VIP) scores and OAV, identified seven compounds that effectively differentiate the origins, among which α-pinene and β-cyclocitral emerged as novel markers imparting unique regional characteristics. Further comparative analysis between standard and premium grades revealed 2-methyl butanal, 3-methyl butanal, and dimethyl sulfide as main differential metabolites. Notably, the influence of geographical origin on metabolite profiles was found to be more significant than batch effects. These findings establish a robust analytical framework for origin traceability, quality standardization, and flavor optimization in tea production, providing valuable insights for the tea industry. Full article

(This article belongs to the Special Issue Flavor and Aroma Analysis as an Approach to Quality Control of Foods)

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