Quality Evaluation of Green and Dark Tea Grade Using Electronic Nose and Multivariate Statistical Analysis

A Gas Sensors Detection System for Real-Time Monitoring of Changes in Volatile Organic Compounds during Oolong Tea Processing

The oxidation step in Oolong tea processing significantly influences its final flavor and aroma. In this study, a gas sensors detection system based on 13 metal oxide semiconductors with strong stability and sensitivity to the aroma during the Oolong tea oxidation production is proposed. The gas sensors detection system consists of a gas path, a signal acquisition module, and a signal processing module. The characteristic response signals of the sensor exhibit rapid release of volatile organic compounds (VOCs) such as aldehydes, alcohols, and olefins during oxidative production. Furthermore, principal component analysis (PCA) is used to extract the features of the collected signals. Then, three classical recognition models and two convolutional neural network (CNN) deep learning models were established, including linear discriminant analysis (LDA), k-nearest neighbors (KNN), back-propagation neural network (BP-ANN), LeNet5, and AlexNet. The results indicate that the BP-ANN model achieved optimal recognition performance with a 3-4-1 topology at pc = 3 with accuracy rates for the calibration and prediction of 94.16% and 94.11%, respectively. Therefore, the proposed gas sensors detection system can effectively differentiate between the distinct stages of the Oolong tea oxidation process. This work can improve the stability of Oolong tea products and facilitate the automation of the oxidation process. The detection system is capable of long-term online real-time monitoring of the processing process.

Olfactory EEG induced by odor: Used for food identification and pleasure analysis

As the need for food authenticity verification increases, sensory evaluation of food odors has become widely recognized. This study presents a theory based on electroencephalography (EEG) to create an Olfactory Perception Dimensional Space (EEG-OPDS), using feature engineering and ensemble learning to establish material and emotional spaces based on odor perception and pleasure. The study examines the intrinsic connection between these two spaces and explores the mechanisms of integration and differentiation in constructing the OPDS. This method effectively visualizes various types of food odors while identifying their perceptual intensity and pleasantness. The average classification accuracy for odor recognition in an eight-category experiment is 96.1%. Conversely, the average classification accuracy for sensory pleasantness recognition in a two-category experiment is 98.8%. The theoretical approach proposed in this study, based on olfactory EEG signals to construct an OPDS, captures the subtle perceptual differences and individualized pleasantness responses to food odors.

Effects of pile-fermentation on the aroma quality of dark tea from a single large-leaf tea variety by GC × GC-QTOFMS and electronic nose

Aroma is one of the significant quality factors of dark tea (DT). However, for a single large-leaf tea variety, there are few studies analyzing the effect of pile-fermentation on the aroma quality of DT. The GC × GC-QTOFMS, electronic nose (E-nose) and GC-olfactometry (GC-O) techniques were employed to analysis the difference of tea products before and after pile-fermentation. A total of 149 volatile metabolites (VMs) were identified, with 92 VMs exhibiting differential characteristics. Among these, 31 VMs with OAV > 1.0 were found to be correlated with E-nose results (|r| > 0.8). Additionally, GC-O analysis validated seven major differential metabolites. Notably, naphthalene, 2-methylnaphthalene, and dibenzofuran were found to enhance the woody aroma, while (Z)-4-heptenal, 2-nonenal and 1-hexanol were associated with an increase in mushroom, fatty and sweet odors, respectively. Moreover, 1-octen-3-ol was linked to reducing pungent fishy smell. These findings could provide a certain theoretical basis for understanding the influence of pile-fermentation on the aroma quality of dark tea.

Changes in the volatile compounds and characteristic aroma during liquid-state fermentation of instant dark tea by Eurotium cristatum

Instant dark tea (IDT) was prepared by liquid-state fermentation inoculating Eurotium cristatum. The changes in the volatile compounds and characteristic aroma of IDT during fermentation were analyzed using gas chromatography-mass spectrometry by collecting fermented samples after 0, 1, 3, 5, 7, and 9 days of fermentation. Components with high odor activity (log₂^FD ≥ 5) were verified by gas chromatography-olfactometry. A total of 107 compounds showed dynamic changes during fermentation over 9 days, including 17 alcohols, 7 acids, 10 ketones, 11 esters, 8 aldehydes, 37 hydrocarbons, 4 phenols, and 13 other compounds. The variety of flavor compounds increased gradually with time within the early stage and achieved a maximum of 79 compounds on day 7 of fermentation. β-Damascenone showed the highest odor activity (log₂^FD = 9) in the day 7 sample, followed by linalool and geraniol. These results indicate that fungal fermentation is critical to the formation of these aromas of IDT.

NIR Spectrometric Approach for Geographical Origin Identification and Taste Related Compounds Content Prediction of Lushan Yunwu Tea

Lushan Yunwu Tea is one of a unique Chinese tea series, and total polyphenols (TP), free amino acids (FAA), and polyphenols-to-amino acids ratio models (TP/FAA) represent its most important taste-related indicators. In this work, a feasibility study was proposed to simultaneously predict the authenticity identification and taste-related indicators of Lushan Yunwu tea, using near-infrared spectroscopy combined with multivariate analysis. Different waveband selections and spectral pre-processing methods were compared during the discriminant analysis (DA) and partial least squares (PLS) model-building process. The DA model achieved optimal performance in distinguishing Lushan Yunwu tea from other non-Lushan Yunwu teas, with a correct classification rate of up to 100%. The synergy interval partial least squares (siPLS) and backward interval partial least squares (biPLS) algorithms showed considerable advantages in improving the prediction performance of TP, FAA, and TP/FAA. The siPLS algorithms achieved the best prediction results for TP (R_P = 0.9407, RPD = 3.00), FAA (R_P = 0.9110, RPD = 2.21) and TP/FAA (R_P = 0.9377, RPD = 2.90). These results indicated that NIR spectroscopy was a useful and low-cost tool by which to offer definitive quantitative and qualitative analysis for Lushan Yunwu tea.

Prediction of Tea Polyphenols, Free Amino Acids and Caffeine Content in Tea Leaves during Wilting and Fermentation Using Hyperspectral Imaging

The withering and fermentation degrees are the key parameters to measure the processing technology of black tea. The traditional methods to judge the degree of withering and fermentation are time-consuming and inefficient. Here, a monitoring model of the biochemical components of tea leaves based on hyperspectral imaging technology was established to quantitatively judge the withering and fermentation degrees of fresh tea leaves. Hyperspectral imaging technology was used to obtain the spectral data during the withering and fermentation of the raw materials. The successive projections algorithm (SPA), competitive adaptive reweighted sampling (CARS), and uninformative variable elimination (UVE) are used to select the characteristic bands. Combined with the support vector machine (SVM), random forest (RF), and partial least square (PLS) methods, the monitoring models of the tea polyphenols (TPs), free amino acids (FAA) and caffeine (CAF) contents were established. The results show that: (1) CARS performs the best among the three feature band selection methods, and PLS performs the best among the three machine learning models; (2) the optimal models for predicting the content of the TPs, FAA, and CAF are CARS-PLS, SPA-PLS, and CARS-PLS, respectively, and the coefficient of determination of the prediction set is 0.91, 0.88, and 0.81, respectively; and (3) the best models for quantitatively judging the withering and fermentation degrees are FAA-SPA-PLS and TPs-CARS-PLS, respectively. The model proposed in this study can improve the monitoring efficiency of the biochemical components of tea leaves and provide a basis for the intelligent judgment of the withering and fermentation degrees in the process of black tea processing.

Electronic Sensor Technologies in Monitoring Quality of Tea: A Review

Tea, after water, is the most frequently consumed beverage in the world. The fermentation of tea leaves has a pivotal role in its quality and is usually monitored using the laboratory analytical instruments and olfactory perception of tea tasters. Developing electronic sensing platforms (ESPs), in terms of an electronic nose (e-nose), electronic tongue (e-tongue), and electronic eye (e-eye) equipped with progressive data processing algorithms, not only can accurately accelerate the consumer-based sensory quality assessment of tea, but also can define new standards for this bioactive product, to meet worldwide market demand. Using the complex data sets from electronic signals integrated with multivariate statistics can, thus, contribute to quality prediction and discrimination. The latest achievements and available solutions, to solve future problems and for easy and accurate real-time analysis of the sensory-chemical properties of tea and its products, are reviewed using bio-mimicking ESPs. These advanced sensing technologies, which measure the aroma, taste, and color profiles and input the data into mathematical classification algorithms, can discriminate different teas based on their price, geographical origins, harvest, fermentation, storage times, quality grades, and adulteration ratio. Although voltammetric and fluorescent sensor arrays are emerging for designing e-tongue systems, potentiometric electrodes are more often employed to monitor the taste profiles of tea. The use of a feature-level fusion strategy can significantly improve the efficiency and accuracy of prediction models, accompanied by the pattern recognition associations between the sensory properties and biochemical profiles of tea.

Identification and characterization of volatile compounds in Lentinula edodes during vacuum freeze-drying

In this study, modified headspace solid-phase microextraction (HS-SPME) coupled with gas chromatography-mass spectrometry (GC-MS) and electronic nose (E-nose) were utilized to investigate the dynamic aroma changes of Lentinula edodes (L. edodes) at different stages of vacuum freeze drying (VFD). The extraction efficiency of volatile compounds from vacuum freeze-dried L. edodes was improved by optimizing five parameters of the HS-SPME. A total of 50 volatiles were identified in L. edodes from different VFD stages by GC-MS. Alcohols, aldehydes, and volatile sulfur-containing compounds (VSCs) were the main flavor constituents of fresh L. edodes, frozen L. edodes, and secondary dried L. edodes. Aldehydes, ketones, and VSCs were the main aroma groups in L. edodes after primary drying. There were 20 volatiles as key odorants with the odor activity values greater than 1, in which esters appeared only before secondary drying of L. edodes. These findings could contribute to a comprehensive insight into the formation mechanism of flavor in the VFD process of L. edodes. PRACTICAL APPLICATIONS: Lentinula edodes is the second most widely cultivated edible fungus worldwide. It is considered a valuable health food not just because of its abundance of nutrients but also because of its delicious taste. This study investigated the regularity regarding the changes of volatile compounds in L. edodes during vacuum freeze drying. The results of the present study offer valuable knowledge for the formation mechanism of volatile substances in the drying process of L. edodes, which can be beneficial to promote the development and utilization of flavor substances in L. edodes.

Rapid characterization of the volatile profiles in Pu-erh tea by gas phase electronic nose and microchamber/thermal extractor combined with TD-GC-MS

Aroma plays an important role in the quality of Pu-erh tea. However, the quality evaluation of Pu-erh tea aroma is heavily relied on the experience of sensory evaluation, and the theoretical research is relatively scarce. In the present work, the volatile compounds in Pu-erh tea were characterized by using gas phase electronic nose (e-nose) and microchamber/thermal extractor (µ-CTE) combined with thermal desorption coupled to gas chromatography-mass spectrometry (TD-GC-MS). A satisfactory discrimination model (R² Y = 0.95, Q²  = 0.807) was obtained by using orthogonal partial least squares discriminant analysis (OPLS-DA) based on the odor fingerprint of different brands of Pu-erh tea. In addition, based on the double criterion of multivariate analysis with VIP >1.0 and univariate analysis with p ≤ 0.001, 39 volatile components were identified to contribute greatly to the discrimination of five brands of Pu-erh tea. The results suggested that gas phase e-nose and µ-CTE combined with TD-GC/MS were simple, rapid techniques to characterize the volatile compounds in Pu-erh tea and were allowed to effectively distinguish different brands of Pu-erh tea, which would provide an important reference on the quality assessment of Pu-erh tea. PRACTICAL APPLICATION: This work demonstrates that the volatile compounds in Pu-erh tea are simply and rapidly characterized by using µ-CTE/TD-GC/MS and gas phase e-nose, allowing to effectively distinguish different brands of Pu-erh tea, which can provide an important reference for the quality assessment and authentication of Pu-erh tea.

E-sensing and nanoscale-sensing devices associated with data processing algorithms applied to food quality control: a systematic review

Devices of human-based senses such as e-noses, e-tongues and e-eyes can be used to analyze different compounds in several food matrices. These sensors allow the detection of one or more compounds present in complex food samples, and the responses obtained can be used for several goals when different chemometric tools are applied. In this systematic review, we used Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines, to address issues such as e-sensing with chemometric methods for food quality control (FQC). A total of 109 eligible articles were selected from PubMed, Scopus and Web of Science. Thus, we predicted that the association between e-sensing and chemometric tools is essential for FQC. Most studies have applied preliminary approaches like exploratory analysis, while the classification/regression methods have been less investigated. It is worth mentioning that non-linear methods based on artificial intelligence/machine learning, in most cases, had classification/regression performances superior to non-liner, although their applications were seen less often. Another approach that has generated promising results is the data fusion between e-sensing devices or in conjunction with other analytical techniques. Furthermore, some future trends in the application of miniaturized devices and nanoscale sensors are also discussed.

Aroma dynamic characteristics during the process of variable-temperature final firing of Congou black tea by electronic nose and comprehensive two-dimensional gas chromatography coupled to time-of-flight mass spectrometry

The drying technology is crucial to the quality of Congou black tea. In this study, the aroma dynamic characteristics during the variable-temperature final firing of Congou black tea was investigated by electronic nose (e-nose) and comprehensive two-dimensional gas chromatography coupled to time-of-flight mass spectrometry (GC × GC-TOFMS). Varying drying temperatures and time obtained distinctly different types of aroma characteristics such as faint scent, floral aroma, and sweet fragrance. GC × GC-TOFMS identified a total of 243 volatile compounds. Clear discrimination among different variable-temperature final firing samples was achieved by using partial least squares discriminant analysis (R2Y = 0.95, Q2 = 0.727). Based on a dual criterion of variable importance in the projection value (VIP > 1.0) and one-way ANOVA (p < 0.05), ninety-one specific volatile biomarkers were identified, including 2,6-dimethyl-2,6-octadiene and 2,5-diethylpyrazine with VIP > 1.5. In addition, for the overall odor perception, e-nose was able to distinguish the subtle difference during the variable-temperature final firing process.

An Online Tea Fixation State Monitoring Algorithm Based on Image Energy Attention Mechanism and Supervised Clustering (IEAMSC)

This study aimed at the shortcomings of existing fixation algorithms that are image-based only, and an effective tea fixation state monitoring algorithm was proposed. An adaptive filtering algorithm was used to automatically filter the ineffective information. Using the energy extractor, the complete energy information of each fixation image was extracted. The image energy attention mechanism was used to identify the prominent features, and based on these, the energy data was mapped to generate the data points as the training data. The cluster idea was adopted, and the training data feed the features trainer. The trend center data of the tea processing energy clustering was generated from different color channels. The corresponding decision function was designed which is based on the distance of the cluster center. The fixation degree of each monitoring image set was measured by the decision function. The Euclidean distance of the energy clustering center of the three channels with the same fixation time progressively approached. The triangle formed by these three points had a trend of gradually shrinking, which was first discovered by us. The detection results showed high accuracy compared with the common classification algorithms. It indicates that the algorithm proposed has positive guiding and reference significance.