The qualitative and quantitative assessment of tea quality based on E-nose, E-tongue and E-eye combined with chemometrics

Rapid evaluation of Curcuma origin and quality based on E-eye, flash GC e-nose, and FT-NIR combined with machine learning technologies

Curcuma, a key ingredient in curry and a popular health supplement, has been subject to adulteration and fraudulent origin labeling. In this study, E-eye, Flash GC e-nose, and FT-NIR, combined with machine learning and multivariate algorithms, were employed for origin identification and quantitative prediction of curcuma constituents. The results indicated that E-eye performed poorly in origin classification, while Flash GC e-nose identified flavor markers distinguishing curcuma from different origins but lacked precise quantification. After processing the FT-NIR spectra with SNV, the accuracy of three machine learning models, including SVM, increased from 83.3 % to 100 %. Additionally, PLSR models for three constituents, including curcumin, achieved mean R2 values exceeding 0.99 in both training and prediction sets, demonstrating excellent linearity and predictive accuracy. Overall, the study demonstrated that FT-NIR combined with multivariate algorithms provides an effective and feasible method for rapid origin identification and quality assessment of curcuma.

Investigation of the classification criteria and flavor compounds in diversified commercially ripened Pu-erh teas

Commercially ripened Pu-erh teas (CRPTs) exhibit a complex flavor profile that poses challenges to standardized production. This study combines traditional sensory evaluation with electronic tongue technology to assess the overall flavor and taste attributes of CRPTs both qualitatively and quantitatively. Multivariate statistical techniques, including Principal Component Analysis (PCA), Hierarchical Cluster Analysis (HCA), and Partial Least Squares Discriminant Analysis (PLS-DA), were used to classify the samples into four categories: Heavy and Thick (HT_CRPT), Heavy and Mellow (HM_CRPT), Mellow and Thick (MT_CRPT), and Mellow and Light (ML_CRPT). HT_CRPT exhibits the highest richness, HM_CRPT demonstrates the greatest astringency and aftertaste-A with the least sweetness (p < 0.05), MT_CRPT presents the most umami and the least richness, while ML_CRPT has the lowest levels of astringency, umami, and bitterness (p < 0.05). CRPTs vary in liquid color from brownish-red to reddish-brown, with HM_CRPT exhibiting the darkest hue, characterized by L* and a* values of 56.15 ± 2.56 and 30.93 ± 1.85, respectively. The principal chemical components of CRPTs encompass theabrownins, phenylpropanoids, polyketides, lipids, and lipid-like molecules. A total of 282 metabolites, including (-)-Epigallocatechin 3,4'-di-gallate, flavonol 3-O-D-glucoside, and isorhamnetin 4'-O-glucuronide significantly influence the flavor of CRPTs (VIP > 1 and p < 0.05). 24 and 35 components underlie liquid color and taste, respectively, with taste compounds interacting with taste receptors primarily through van der Waals forces and hydrogen bonding. A flavor wheel was developed to illustrate in detail the classification criteria and flavor compounds of various CRPTs. The findings establish a scientific foundation for the development of a standardized evaluation system for CRPTs and the creation of a diverse range of products.

The establishment of ham grade, sensory scores and key flavor substances prediction models for Jinhua ham via E-nose combined with machine learning

Unique organoleptic and flavor attributes of Jinhua ham are associated with their qualities. However, methods for quickly predicting the grade of hams, sensory scores and key flavor substances have not been systematically established. This study used sensory evaluation and E-nose to analyze the sensory differences for different grades of Jinhua ham. GC-MS was combined with O2PLS and correlation analysis to identify the key flavor substances. Classification model based on E-nose response signals was established by logical regression to predict the ham grades, which displayed a high classification performance, with the accuracy of 0.87. Moreover, linear regression and random forest models were established to predict the sensory score of hams and the concentrations of key flavor substances, meanwhile the R2 were all above 0.7, demonstrating the model applicability. This work provides a theoretical basis for rapidly predicting qualitative and quantitative parameters of Jinhua ham by E-nose with machine learning.

Deep Learning-Assisted Multiplexed Electrochemical Fingerprinting for Chinese Tea Identification

Selectively differential identification of natural components with similar chemical structures in complex matrices is still a challenging task by conventional analytical strategies. Herein, we developed a landmark (DaXing airport)-inspired laser engraving sensor array that combined multiplex electrochemical fingerprinting technology with a one-dimensional convolutional neural network (1D-CNN) for rapidly precise detection of three tea polyphenols and the differentiation of 24 distinct types of Chinese teas. This sensing strategy employs a diverse array of three different working electrode configurations as a multivariate sensor (bare electrode, nanoenzyme electrode, and bioenzyme electrode), generating distinct electrochemical fingerprints in complex samples. By utilizing a self-designed 1D-CNN algorithm for feature extraction, the identification of electrochemical fingerprints is significantly improved, thereby enhancing the predictive accuracy for tea polyphenols and Chinese teas. This platform successfully achieves detection of three tea polyphenols, distinguishing six Chinese tea series and 24 tea varieties with accuracy rates of 98.84 and 97.68%, respectively. Notably, the deep learning-assisted multiplexed electrochemical fingerprinting technique achieves better accuracy for tea identification compared with other representative machine learning methods. This advancement offers a rapid and reliable approach to enhancing the development of identification and authentication processes for agricultural products.

Dynamic changes in chemical components, volatile profile and antioxidant properties of Xanthoceras sorbifolium leaf tea during manufacturing process

In order to elucidate the dynamic changes of quality characteristics in Xanthoceras sorbifolium leaf tea (XLT), this work investigated the chemical components, volatile profile, and antioxidant properties at key stages of fixation, shaping, and drying. The process of fixation and drying emerged as critical stages that affected the chemical components and taste of XLT. Total polyphenols, reducing sugars and chlorophyll were the differential compounds, which decreased by 29.1 %, 63.3 % and 69.5 % during processing, respectively. Threonine, glycine, arginine, reducing sugar, caffeine, catechin (C), and epicatechin gallate (ECG) were primary taste characteristic compounds. A total of 95 volatile organic components (VOCs) and 24 key odorants with relative odor activity value (ROAV) > 1.0 were identified in XLT, ethyl 2-methylbutanoate-D, dimethyl disulfide, and ethyl isobutyrate (ROAV = 100) were the central contributors to volatiles profile. Moreover, tea manufacturing enhanced the antioxidant potency composite (APC) by 1.1 %, primarily due to its high water extract content.

Influence of Different Shaping Techniques on the Aroma Quality and Volatile Metabolites of Green Tea Revealed by Gas Chromatography Electronic Nose and Gas Chromatography-Tandem Mass Spectrometry

The shaping process is recognized as a crucial step in the manufacturing of green tea. However, its influence on aroma quality remains unclear. In this study, the effects of four shaping techniques, including flat green tea (FGT), straight green tea (SGT), phoenix green tea (PGT), and curled green tea (CGT), on the aroma quality and volatile metabolites of green tea were investigated by gas chromatography electronic nose (GC-E-Nose) and gas chromatography-tandem mass spectrometry (GC-MS/MS). The findings indicated that distinct shaping processes significantly influenced the development of the aroma quality and aroma components of green tea. The PGT processing facilitated the attainment of superior aroma quality of green tea. In total, 60 volatile components were identified by GC-MS/MS, with 54 of these compounds being consistently detected across four different shaping techniques. In particular, the PGT processing method was effective in yielding elevated levels of alcohols, esters and ketones. Moreover, 20 key odorants were screened out, with (E,E)-2,4-decadienal, (E,E)-2,4-nonadienal, phenylethyl alcohol, and benzeneacetaldehyde proven to be substantial contributors to the overall aromas of green tea under diverse shaping procedures. These key odorants were primarily derived from lipid degradation and the Maillard reaction. GC-E-Nose served as a significant adjunct to sensory evaluation, enabling the swift differentiation of green tea samples that have undergone various shaping processes. These findings offer both theoretical and technical perspectives that may guide the creation of innovative green tea products distinguished by their unique shapes.

Establishment of a Daqu Grade Classification Model Based on Computer Vision and Machine Learning

The grade of Daqu significantly influences the quality of Baijiu. To address the issues of high subjectivity, substantial labor costs, and low detection efficiency in Daqu grade evaluation, this study focused on light-flavor Daqu and proposed a two-layer classification structure model based on computer vision and machine learning. Target images were extracted using three image segmentation methods: threshold segmentation, morphological fusion, and K-means clustering. Feature factors were selected through methods including mean decrease accuracy based on random forest (RF-MDA), recursive feature elimination (RFE), LASSO regression, and ridge regression. The Daqu grade evaluation model was constructed using support vector machine (SVM), logistic regression (LR), random forest (RF), k-nearest neighbor (KNN), and a stacking model. The results indicated the following: (1) In terms of image segmentation performance, the morphological fusion method achieved an accuracy, precision, recall, F1-score, and AUC of 96.67%, 95.00%, 95.00%, 0.95, and 0.96, respectively. (2) For the classification of Daqu-P, Daqu-F, and Daqu-S, RF models performed best, achieving an accuracy, precision, recall, F1-score, and AUC of 96.67%, 97.50%, 97.50%, 0.97, and 0.99, respectively. (3) In distinguishing Daqu-P from Daqu-F, the combination of the RF-MDA method and the stacking model demonstrated the best performance, with an accuracy, precision, recall, F1-score, and AUC of 90.00%, 94.44%, 85.00%, 0.89, and 0.95, respectively. This study provides theoretical and technical support for efficient and objective Daqu grade evaluation.

Effects of different adzuki bean flour additions on structural and functional characteristics of extruded buckwheat noodles

BACKGROUND

Understanding the effects of different additions of adzuki bean flour (ABF) on structural and functional characteristics of extruded buckwheat noodles is important in developing high-quality starchy foods with desirable glycemic indexes. This study explored how varying amounts of ABF in extruded buckwheat noodles influenced their structural and functional characteristics.

RESULTS

The findings indicated that adding ABF substantially boosted the levels of protein and flavonoids, while decreasing the content of fat and starch. Adding ABF to the noodles extended the optimum cooking time and led to a reduction in both the stickiness of the cooked noodles and the pore size of the starch gel structure, compared with pure buckwheat noodles. Fourier transform infrared spectroscopy indicated that R1047/1022 increased with the content of ABF increased, while R1022/995 decreased. X-ray diffraction showed that the relative crystallinity of buckwheat noodles was enhanced with increasing ABF amount. Adding ABF notably significantly decreased the estimated glycemic index. The buckwheat noodles extruded with 20% ABF addition demonstrated notably stronger α-glucosidase inhibitory effects than those extruded with no ABF addition.

CONCLUSION

The present study demonstrates that the additions of ABF improved the structure and hypoglycemic activity of extruded buckwheat noodles while decreasing starch digestibility, and the optimal value was reached at an ABF addition of 20%. The study might fill gaps in starch noodle research and provide a new strategy for the development of functional food in the food industry. © 2024 Society of Chemical Industry.

Insight into aroma dynamic changes during the whole manufacturing process of smoked Lapsang Souchong tea

Smoked Lapsang Souchong tea (SLST) is a distinctive black tea known for its smokey, fruity, and pine flavor. This study analyzed the aroma dynamic changes during the whole manufacturing process of SLST utilizing E-nose, HS-SPME-GC-MS, and HS-SPME-GC-O-MS. Fresh tea leaves primarily contain green and floral volatiles, such as (E)-2-hexenal (OAV 33.41) and linalool (OAV 313.88). Withering and drying processes introduce pinewood smoke-derived phenolic and terpenoid compounds, such as guaiacols (OAV 77.05) and α-terpineol (OAV 1.08), crucial for the tea's smoky and woody aroma. The study further highlights glycoside hydrolysis and lipid oxidation pathways in generating key tea-derived volatiles during fermentation, contributing to the fruity and floral notes in SLST. Pinewood smoke was identified as a major source of smoky volatiles, with longifolene and α-terpineol being unique to pinewood. These findings elucidate the formation pathways of the key volatile compounds and the impact of traditional processing on SLST's aroma profile.

Effects of stir-frying on chemical profile, sensory quality and antioxidant activity of Chrysanthemi Flos: A metabolomics and sensory study

Chrysanthemi Flos has been consumed as floral tea for centuries, but the effects of stir-frying on its chemical profile, sensory characteristics, and bioactivity remain unclear. This study used untargeted metabolomics, sensory assessment (E-eye, E-nose, E-tongue), and antioxidant activity evaluation to investigate compositional changes and their effects. In the metabolomics analysis, a total of 101 non-volatile and 306 volatile differential metabolites were identified. During stir-frying of Chrysanthemi Flos, glycosidic bond rupture in flavonoid glycosides, thermal decomposition of caffeoylquinic acid, Maillard, and caramelization reactions occurred. This led to the formation of 4 flavonoid glycosides (luteolin, diosmetin, apigenin, and quercetin) and 33 roasted aroma compounds like pyrazines, furans, 2-methylbutanal, and 2-furanmethanol. The Spearson's correlation analysis of metabolomics, E-eye, and E-tongue data showed that these compositional changes not only resulted in darkening and scorching of the color of Chrysanthemi Flos after stir-frying, but also improved the bitter and astringent taste of Chrysanthemi Flos tea broths, and enhanced the antioxidant activity of Chrysanthemi Flos. These findings will provide new perspectives on the selection of processing methods for Chrysanthemi Flos.

The influence of processing methods on polyphenol profiling of tea leaves from the same large-leaf cultivar (Camellia sinensis var. assamica cv. Yunkang-10): nontargeted/targeted polyphenomics and electronic sensory analysis

Tea polyphenols transform under processing methods, but a systematic study on their changes in the same large-leaf tea cultivar is lacking. Here, Camellia sinensis var. assamica cv. Yunkang-10 leaves underwent six processing methods and were assessed using optimized nontargeted (UHPLC-Q-Exactive Orbitrap-MS) and targeted (UHPLC-QqQ-MS) polyphenomics, along with molecular networking analysis. 903 and 52 polyphenolic compounds (catechins, flavones and flavonols, and phenolic acids) were respectively relatively and absolutely quantified for the first time. Dark and black teas, with the lowest polyphenol content, differed from the other four tea types, although variations existed among these four teas. However, some flavonol and flavone aglycones (e.g. kaempferol, apigenin), as well as some phenolic acids (e.g. ellagic acid, gallic acid), exhibited higher levels in dark and black teas. Correlations between polyphenolic composition and electronic sensory characteristics were observed using E-tongue and E-eye. This study enriches understanding of polyphenol profiles in Chinese teas post diverse processing.

Are there any differences in the quality of high-mountain green tea before and after the first new leaves unfold? A comprehensive study based on E-sensors, whole metabolomics and sensory evaluation

High-mountain green tea, where the first new leaf hasn't yet unfurled, is prized for perceived superior quality, but this hasn't yet been verified by experimentation. Electronic sensors, whole metabolomics and sensory evaluation were employed to assess the quality of yymj (tea buds with a newly unfurled leaf) and qymj (tea buds without new leaves). The qymj proved to have significant advantages in aroma, color and shape, but still had some shortcomings in umami, bitterness and sourness. Differences in the content of volatile organic compounds (including alcohols, hydrocarbons and lipids) and nonvolatile organic compounds (flavonoids, amino acids, sugars, and phenolic acids) quality of high-mountain green teas with different maturity levels and provides well explained these quality differences. This study establishes a systematic approach to study the quality of high-mountain green tea at different maturity levels, and provides important reference information for consumers, governments and tea farmers.

Evaluation of the quality grade of Congou black tea by the fusion of GC-E-Nose, E-tongue, and E-eye

In the present study, the comprehensive quality of Congou black tea (CBT) including aroma, taste, and liquid color was investigated by a combination of gas chromatography electronic nose (GC-E-Nose), electronic tongue (E-tongue), and electronic eye (E-eye). An excellent discrimination of different quality grades of CBT was accomplished through the fusion of GC-E-Nose, E-tongue, and E-eye combined with orthogonal partial least squares discriminant analysis, with parameters of R2Y = 0.803 and Q2 = 0.740. Moreover, the quantitative evaluation of CBT quality was successfully achieved by partial least squares regression analysis, with the absolute error within 1.39 point, and the relative error within 1.62%. Additionally, 12 key variables were screened out by stepwise multiple linear regression analysis, which significantly contributed to the comprehensive quality score of CBT. Our results suggest that the fusion of multiple intelligent sensory technologies offers great potential and practicability in the quality evaluation of black tea.

Exploration of the effects of geographical regions on the volatile and non-volatile metabolites of black tea utilizing multiple intelligent sensory technologies and untargeted metabolomics analysis

Geographical regions profoundly influence the flavor characteristics of Congou black tea (CBT). In this study, 35 CBT samples from 7 geographical regions were comprehensively characterized by integrated multiple intelligent sensory technologies and untargeted metabolomics analysis. A satisfactory discrimination was achieved through the fusion of multiple intelligent sensory technologies (R2Y = 0.918, Q2 = 0.859). A total of 104 non-volatile and 169 volatile metabolites were identified by UHPLC-HRMS and GC-MS, respectively. Of these, 45 critical differential non-volatile metabolites and 76 pivotal differential volatile metabolites were pinpointed based on variable importance in projection >1 and p < 0.05. Moreover, 52 key odorants with OAV ≥ 1 were identified, with hexanal, phenylacetaldehyde, linalool, β-cyclocitral, methyl salicylate, geraniol, α-ethylidene phenylacetaldehyde, and trans-β-ionone being recognized as the common odorants across 7 geographical regions. The results provide theoretical support for a comprehensive understanding of the effect of geographical regions on the flavor of black tea.

Comparative Study on Morphological Features and Chemical Components of Wild and Cultivated Angelica sinensis Based on Bionic Technologies and Chemometrics

As a traditional Chinese medicine, Angelica sinensis is primarily sourced from cultivated plants due to the significant decline in wild resources. This shift raises concerns about potential differences in efficacy resulting from variations in morphological features and chemical composition between wild (WA) and cultivated (CA) A. sinensis. In this study, a suite of advanced analytical techniques including electronic nose, electronic tongue, and electronic eye, alongside headspace solid-phase microextraction coupled with gas chromatography-mass spectrometry and high-performance liquid chromatography, was applied to compare the morphological features and chemical components of WA and CA. Furthermore, principal component analysis and partial least-squares discriminant were employed for data analysis. The morphological features and chemical components of WA and CA were compared and analyzed. The results showed that three bionic technologies can distinguish WA from CA well and that fusion signals can distinguish better. There were differences between WA and CA in odor, taste, color, and content of the indicator components. There were correlations between the morphological features and the content of indicator components.

1H NMR Spectroscopy Combined with Machine-Learning Algorithm for Origin Recognition of Chinese Famous Green Tea Longjing Tea

Premium green tea is a high-value agricultural product significantly influenced by its geographical origin, making it susceptible to food fraud. This study utilized nuclear magnetic resonance (NMR) spectroscopy to perform chemical fingerprint analysis on 78 Longjing tea (LJT) samples from both protected designation of origin (PDO) regions (Zhejiang) and non-PDO regions (Sichuan, Guangxi, and Guizhou) in China. Unsupervised algorithms and heatmaps were employed for the visual analysis of the data from PDO and non-PDO teas while exploring the feasibility of linear and nonlinear machine-learning algorithms in discriminating the origin of LJT. The findings revealed that the nonlinear model random forest (92.2%), exhibited superior performance compared to the linear model linear discriminant analysis (85.6%). The random forest model identified 15 key marker metabolites for the geographical origin of LJT, such as kaempferol glycoside, glutamine, and ECG. The results support the conclusion that the integration of NMR with machine-learning classification serves as an effective tool for the quality assessment and origin identification of LJT.

Electronic eye and electronic tongue data fusion combined with a GETNet model for the traceability and detection of Astragalus

BACKGROUND

Astragalus is a widely used traditional Chinese medicine material that is easily confused due to its quality, price and other factors derived from different origins. This article describes a novel method for the rapid tracing and detection of Astragalus via the joint application of an electronic tongue (ET) and an electronic eye (EE) combined with a lightweight convoluted neural network (CNN)-transformer model. First, ET and EE systems were employed to measure the taste fingerprints and appearance images, respectively, of different Astragalus samples. Three spectral transform methods - the Markov transition field, short-time Fourier transform and recurrence plot - were utilized to convert the ET signals into 2D spectrograms. Then, the obtained ET spectrograms were fused with the EE image to obtain multimodal information. A lightweight hybrid model, termed GETNet, was designed to achieve pattern recognition for the Astragalus fusion information. The proposed model employed an improved transformer module and an improved Ghost bottleneck as its backbone network, complementarily utilizing the benefits of CNN and transformer architectures for local and global feature representation. Furthermore, the Ghost bottleneck was further optimized using a channel attention technique, which boosted the model's feature extraction effectiveness.

RESULTS

The experiments indicate that the proposed data fusion strategy based on ET and EE devices has better recognition accuracy than that attained with independent sensing devices.

CONCLUSION

The proposed method achieved high precision (99.1%) and recall (99.1%) values, providing a novel approach for rapidly identifying the origin of Astragalus, and it holds great promise for applications involving other types of Chinese herbal medicines. © 2024 Society of Chemical Industry.

Rapid and accurate quality evaluation of Angelicae Sinensis Radix based on near-infrared spectroscopy and Bayesian optimized LSTM network

The authentic traditional Chinese medicines (TCMs) including Angelicae Sinensis Radix (ASR) are the representative of high-quality herbals in China. However, ASR from authentic region being adulterated or counterfeited is frequently occurring, and there is still a lack of rapid quality evaluation methods for identifying the authentic ASR. In this study, the color features of ASR were firstly characterized. The results showed that the authentic ASR cannot be fully identified by color characteristics. Then near-infrared (NIR) spectroscopy combined with Bayesian optimized long short-term memory (BO-LSTM) was used to evaluate the quality of ASR, and the performance of BO-LSTM with common classification and regression algorithms was compared. The results revealed that following the pretreatment of NIR spectra, the optimal NIR spectra combined with BO-LSTM not only successfully distinguished authentic, non-authentic, and adulterated ASR with 100 % accuracy, but also accurately predicted the adulteration concentration of authentic ASR (R2 > 0.99). Moreover, BO-LSTM demonstrated excellent performance in classification and regression compared with common algorithms (ANN, SVM, PLSR, etc.). Overall, the proposed strategy could quickly and accurately evaluate the quality of ASR, which provided a reference for other TCMs.

Rapid and Online Microvolume Flow-Through Dialysis Probe for Sample Preparation in Veterinary Drug Residue Analysis

A rapid and online microvolume flow-through dialysis probe designed for sample preparation in the analysis of veterinary drug residues is introduced. This study addresses the need for efficient and green sample preparation methods that reduce chemical waste and reagent use. The dialysis probe integrates with liquid chromatography and mass spectrometry (LC-MS) systems, facilitating automated, high-throughput analysis. The dialysis method utilizes minimal reagent volumes per sample, significantly reducing the generation of solvent waste compared to traditional sample preparation techniques. Several veterinary drugs were spiked into tissue homogenates and analyzed to validate the probe's efficacy. A diagnostic sensitivity of >97% and specificity of >95% were obtained for this performance evaluation. The results demonstrated the effective removal of cellular debris and particulates, ensuring sample integrity and preventing instrument clogging. The automated dialysis probe yielded recovery rates between 27 and 77% for multiple analytes, confirming its potential to streamline veterinary drug residue analysis, while adhering to green chemistry principles. The approach highlights substantial improvements in both environmental impact and operational efficiency, presenting a viable alternative to conventional sample preparation methods in regulatory and research applications.

Traditional method of rhubarb processing optimized by combining flavor analysis with anthraquinone content determination

Introduction

Rhubarb is a popular food that relieves constipation and aids with weight loss. The traditional method of preparation, includes steaming and sun-drying rhubarb nine times (SDR-9) to reduce its toxicity and increase efficacy.

Methods

Flavor analysis includes odor analysis by gas chromatography-ion mobility spectrometry and taste characterization using an electronic tongue.

Results

Odor analysis of the samples prepared through SDR-9 identified 61 volatile compounds, including aldehydes, esters, alcohols, ketones, acids, alkenes, and furans. Of these, 13 volatile components were the key substances associated with odor. This enabled the process to be divided into two stages: 1-5 times of steaming and sun-drying and 6-9 times. In the second stage, SDR-6 and SDR-9 were grouped together in terms of odor. Analysis using electronic tongue revealed that the most prominent taste was bitterness. A radar map indicated that the bitterness response was the highest for raw rhubarb, whereas that for processed (steamed and sun-dried) rhubarb decreased. Orthogonal partial least squares discriminant analysis (OPLS-DA) clustering results for SDR-6 and SDR-9 samples indicated that their tastes were similar. Anthraquinones were analyzed via high-performance liquid chromatography; moreover, analysis of the taste and components of the SDR samples revealed a significant correlation.

Discussion

These results indicate that there are similarities between SDR-6 and SDR-9 in terms of smell, taste, and composition, indicating that the steaming and sun-drying cycles can be conducted six times instead of nine.

Fungal fermentation of Fuzhuan brick tea: A comprehensive evaluation of sensory properties using chemometrics, visible near-infrared spectroscopy, and electronic nose

Fuzhuan brick tea (FBT) fungal fermentation is a key factor in achieving its unique dark color, aroma, and taste. Therefore, it is essential to develop a rapid and reliable method that could assess its quality during FBT fermentation process. This study focused on using electronic nose (e-nose) and spectroscopy combination with sensory evaluations and physicochemical measurements for building machine learning (ML) models of FBT. The results showed that the fused data achieved 100 % accuracy in classifying the FBT fermentation process. The SPA-MLR method was the best prediction model for FBT quality (R2 = 0.95, RMSEP = 0.07, RPD = 4.23), and the fermentation process was visualized. Where, it was effectively detecting the degree of fermentation relationship with the quality characteristics. In conclusion, the current study's novelty comes from the established real-time method that could sensitively detect the unique post-fermentation quality components based on the integration of spectral, and e-nose and ML approaches.

An efficient artificial intelligence algorithm for predicting the sensory quality of green and black teas based on the key chemical indices

The key components dominating the quality of green tea and black tea are still unclear. Here, we respectively produced green and black teas in March and June, and investigated the correlations between sensory quality and chemical compositions of dry teas by multivariate statistics, bioinformatics and artificial intelligence algorithm. The key chemical indices were screened out to establish tea sensory quality-prediction models based on the result of OPLS-DA and random forest, namely 4 flavonol glycosides of green tea and 8 indices of black tea (4 pigments, epigallocatechin, kaempferol-3-O-rhamnosyl-glucoside, ratios of caffeine/total catechins and epi/non-epi catechins). Compared with OPLS-DA and random forest, the support vector machine model had good sensory quality-prediction performance for both green tea and black tea (F1-score > 0.92), even based on the indices of fresh tea leaves. Our study explores the potential of artificial intelligence algorithm in classification and prediction of tea products with different sensory quality.

Insight into the chemical compositions of Anhua dark teas derived from identical tea materials: A multi-omics, electronic sensory, and microbial sequencing analysis

Anhua dark teas (DTs), including Tianjian tea, Qianliang tea, Hei brick tea, and Fu brick tea, are unique fermented teas from China's Anhua County; yet their chemical composition differences remain unclear. Herein, metabolomics, volatolomics, and electronic sensory assessments were employed to analyze and compare chemical compositions and sensory characteristics of five types of Anhua DTs. All of these teas were derived from identical tea materials. Chemical compositions differed significantly among Anhua DTs, with Tianjian tea remarkable. Long-lasting fermentation and complex processing methods led to transformation of multiple compounds, particularly catechins. Eighteen volatile compounds with OVA > 1 were key aroma contributors in Anhua DTs. Internal transcribed spacer and 16S ribosomal DNA sequencing showed that Eurotium, Pseudomonas, and Bacillus are dominant microorganisms in Anhua DTs. Furthermore, this study unveiled notable differences in chemical compositions between Anhua DTs and five other traditional types of tea. This research enhances our understanding of Anhua DTs processing.

Rapid discrimination of quality grade of black tea based on near-infrared spectroscopy (NIRS), electronic nose (E-nose) and data fusion

For the manufacturing and sale of tea, rapid discrimination of overall quality grade is of great importance. However, present evaluation methods are time-consuming and labor-intensive. This study investigated the feasibility of combining advantages of near-infrared spectroscopy (NIRS) and electronic nose (E-nose) to assess the tea quality. We found that NIRS and E-nose models effectively identify taste and aroma quality grades, with the highest accuracies of 99.63% and 97.00%, respectively, by comparing different principal component numbers and classification algorithms. Additionally, the quantitative models based on NIRS predicted the contents of key substances. Based on this, NIRS and E-nose data were fused in the feature-level to build the overall quality evaluation model, achieving accuracies of 98.13%, 96.63% and 97.75% by support vector machine, K-nearest neighbors, and artificial neural network, respectively. This study reveals that the integration of NIRS and E-nose presents a novel and effective approach for rapidly identifying tea quality.

E-eye and FT-NIR combined with multivariate algorithms to rapidly evaluate the dynamic changes in the quality of Gastrodia elata during steaming process

Gastrodia elata (GE) is traditionally subjected to steaming, and steaming duration plays a crucially important role in determining GE quality. This study examined the variations in bioactive components during the steaming process and proposed the utilization of electronic eye and Fourier Transform near-infrared (FT-NIR) spectroscopy for quality assessment. The findings revealed that the levels of parishin E parishin B, parishin A, and gastrodin initially rose and subsequently declined, while 4-Hydroxybenzyl alcohol exhibited a rapid decrease followed by stabilization. With prolonged steaming, the brightness of GE decreased, while the red and yellow tones became more pronounced and the color saturation increased. FT-NIR divided the steaming process into three stages: 0 min (raw GE), 0-9 min (partially steamed GE), and 9-30 min (fully steamed GE), and the partial least squares regression models effectively predicted the levels of five components. Overall, this study provided valuable insights into quality control in food processing.

Physicochemical, antibacterial and aromatic qualities of herbaceous peony (Paeonia lactiflora pall) tea with different varieties

The aim of this study was to evaluate the effect of five varieties on the quality of herbaceous peony tea by physicochemical analysis, sensory evaluation, antimicrobial capacity analysis and a combination of gas chromatography with quadruple time of flight mass spectrometry (GC-QTOF). Antibacterial and antioxidant analyses revealed that the ABTS free radical scavenging rate of HPT was high, ranging from 82.20% to 87.40% overall. 'Madame Claude Tain' had the strongest inhibitory ability against Staphylococcus aureus with an inhibitory effect of 12.65 mm. The sensory evaluation showed that 'Angel cheeks' had the highest overall sensory score. GC-QTOF combined with orthogonal projections to latent structures discriminant analysis showed that 22 volatile components were the key aroma components of herbaceous peony tea. Different varieties of herbaceous peony tea had a unique characteristic aroma. 'Angel cheeks' imparted lily-like and chestnut fragrances, which were attributed to linalool and 3,5-octadien-2-one. 'Sea Shell', 'Mother's Choice' and 'Angel Cheek' had a medicinal aroma, which may be due to the presence of o-cymene. Overall, 'Angel cheeks' was the most suitable for developing high-quality herbaceous peony tea in five varieties. This study provided a theoretical basis and technical guidance for the development of herbaceous peony.

Integration of non-targeted/targeted metabolomics and electronic sensor technology reveals the chemical and sensor variation in 12 representative yellow teas

Yellow tea is a lightly fermented tea with unique sensory qualities and health benefits. However, chemical composition and sensory quality of yellow tea products have rarely been studied. 12 representative yellow teas, which were basically covered the main products of yellow tea, were chosen in this study. Combined analysis of non-targeted/targeted metabolomics and electronic sensor technologies (E-eye, E-nose, E-tongue) revealed the chemical and sensor variation. The results showed that yellow big tea differed greatly from yellow bud teas and yellow little teas, but yellow bud teas could not be effectively distinguished from yellow little teas based on chemical constituents and electronic sensory characteristics. Sensor variation of yellow teas might be attributed to some compounds related to bitterness and aftertaste-bitterness (4'-dehydroxylated gallocatechin-3-O-gallate, dehydrotheasinensin C, myricitin 3-O-galactoside, phloroglucinol), aftertaste-astringency (methyl gallate, 1,5-digalloylglucose, 2,6-digalloylglucose), and sweetness (maltotriose). This study provided a comprehensive understanding of yellow tea on chemical composition and sensory quality.

An advance in novel intelligent sensory technologies: From an implicit-tracking perspective of food perception

Food sensory evaluation mainly includes explicit and implicit measurement methods. Implicit measures of consumer perception are gaining significant attention in food sensory and consumer science as they provide effective, subconscious, objective analysis. A wide range of advanced technologies are now available for analyzing physiological and psychological responses, including facial analysis technology, neuroimaging technology, autonomic nervous system technology, and behavioral pattern measurement. However, researchers in the food field often lack systematic knowledge of these multidisciplinary technologies and struggle with interpreting their results. In order to bridge this gap, this review systematically describes the principles and highlights the applications in food sensory and consumer science of facial analysis technologies such as eye tracking, facial electromyography, and automatic facial expression analysis, as well as neuroimaging technologies like electroencephalography, magnetoencephalography, functional magnetic resonance imaging, and functional near-infrared spectroscopy. Furthermore, we critically compare and discuss these advanced implicit techniques in the context of food sensory research and then accordingly propose prospects. Ultimately, we conclude that implicit measures should be complemented by traditional explicit measures to capture responses beyond preference. Facial analysis technologies offer a more objective reflection of sensory perception and attitudes toward food, whereas neuroimaging techniques provide valuable insight into the implicit physiological responses during food consumption. To enhance the interpretability and generalizability of implicit measurement results, further sensory studies are needed. Looking ahead, the combination of different methodological techniques in real-life situations holds promise for consumer sensory science in the field of food research.

Elucidating the effect of different processing methods on the sensory quality of chestnuts based on multi-scale molecular sensory science

Chestnuts are known for their unique flavor and nutritional value. However, the flavor changes in chestnuts after processing remain unclear. Multi-intelligent sensory technologies and headspace solid-phase microextraction-arrow gas chromatography-mass spectrometry (HS-SPME-Arrow-GC-MS) combined with multivariate statistical analysis were applied to evaluate the effect of packaging and heat sterilization procedures on the sensory quality of chestnuts. The results showed that the significant variations (p < 0.05) between the different chestnut processing methods were revealed via the electronic eye (E-eye), electronic nose (E-nose), and electronic tongue (E-tongue). The packaging had a more significant influence on the sensory quality of the chestnuts than heat sterilization procedures. HS-SPME-Arrow-GC-MS identified 83 volatile compounds. The processed chestnuts exhibited higher aldehyde, ester, and alkene concentrations, while N2 packaging was more favorable to flavor elicitation and retention. Therefore, combining intelligent sensory techniques with GC-MS can rapidly determine the chestnut quality and guide industrial production.

A comparative study regarding the adulteration detection of honey: Physicochemical parameters vs. impedimetric data

Honey adulteration is a major issue for European Union and its members because of an unfair practice of different producers and beekeepers, many adulterations involve the addition of sweet, concentrated syrups which may appear like honey. In our study we analysed the influence of adulteration of tilia honey with different syrups (such as corn, rice, inverted sugar, agave, maple syrups) in different percentages (5%, 10%, and 20% respectively) on physicochemical parameters (moisture content, L*, hab,cab, pH, free acidity, electrical conductivity (EC), 5-hydroxymetilfurfural (HMF), fructose, glucose, sucrose, turanose, trehalose, melesitose and raffinose) and impedimetric properties using electrochemical impedance spectroscopy. The impedimetric sensing was made using an electrochemical cell composed of two gold electrodes, and the frequency ranged between 0.1 kHz and 100 kHz. The impedimetric parameters (Z', Z″ and phase) and Randal circuit parameters can distinguish the authentic honeys from the adulterated ones (based on the adulteration agent and adulteration percentage, respectively). The partial least squares - discriminant analysis (PLS-DA) and support vector machines (SVM) were used in a binary mode to separate the authentic honeys from the adulterated ones, and the SVM proved to separate much better than PLS-DA.

Review of technology advances to assess rice quality traits and consumer perception

The increase in rice consumption and demand for high-quality rice is impacted by the growth of socioeconomic status in developing countries and consumer awareness of the health benefits of rice consumption. The latter aspects drive the need for rapid, low-cost, and reliable quality assessment methods to produce high-quality rice according to consumer preference. This is important to ensure the sustainability of the rice value chain and, therefore, accelerate the rice industry toward digital agriculture. This review article focuses on the measurements of the physicochemical and sensory quality of rice, including new and emerging technology advances, particularly in the development of low-cost, non-destructive, and rapid digital sensing techniques to assess rice quality traits and consumer perceptions. In addition, the prospects for potential applications of emerging technologies (i.e., sensors, computer vision, machine learning, and artificial intelligence) to assess rice quality and consumer preferences are discussed. The integration of these technologies shows promising potential in the forthcoming to be adopted by the rice industry to assess rice quality traits and consumer preferences at a lower cost, shorter time, and more objectively compared to the traditional approaches.

Online Monitoring of Sourdough Fermentation Using a Gas Sensor Array with Multivariate Data Analysis

Sourdough can improve bakery products' shelf life, sensory properties, and nutrient composition. To ensure high-quality sourdough, the fermentation has to be monitored. The characteristic process variables for sourdough fermentation are pH and the degree of acidity measured as total titratable acidity (TTA). The time- and cost-intensive offline measurement of process variables can be improved by utilizing online gas measurements in prediction models. Therefore, a gas sensor array (GSA) system was used to monitor the fermentation process of sourdough online by correlation of exhaust gas data with offline measurement values of the process variables. Three methods were tested to utilize the extracted features from GSA to create the models. The most robust prediction models were achieved using a PCA (Principal Component Analysis) on all features and combined two fermentations. The calibrations with the extracted features had a percentage root mean square error (RMSE) from 1.4% to 12% for the pH and from 2.7% to 9.3% for the TTA. The coefficient of determination (R2) for these calibrations was 0.94 to 0.998 for the pH and 0.947 to 0.994 for the TTA. The obtained results indicate that the online measurement of exhaust gas from sourdough fermentations with gas sensor arrays can be a cheap and efficient application to predict pH and TTA.

A machine learning method for juice human sensory hedonic prediction using electronic sensory features

This study proposed a method that combines fused electronic sensory analysis technology with artificial neural network to predict the human sensory hedonic of fruit juice. Quantitative descriptive analysis (QDA) and the scoring test method were utilized for human sensory evaluation. The first step involved modeling the fused e-sensory features with human sensory attributes, followed by establishing a fitting model of human sensory attributes and acceptance. The R2 and RMSE values obtained were 0.77 and 0.42 (QDA method), and 0.63 and 0.63 (scoring test method). Finally, the relationship between the fusion e-sensory features and the human sensory hedonic was established. Model-1 achieved an R2 of 0.95 and an RMSE of 0.04, while model-2 achieved an R2 value of 0.88 and an RMSE value of 0.21. This study demonstrates the potential of fusing e-sensory technologies to replace human senses, which may lead to the development of devices with simultaneous multiple senses.

Quantitative Analysis of Bioactive Compounds in Commercial Teas: Profiling Catechin Alkaloids, Phenolic Acids, and Flavonols Using Targeted Statistical Approaches

Tea, an extensively consumed and globally popular beverage, has diverse chemical compositions that ascertain its quality and categorization. In this investigation, we formulated an analytical and quantification approach employing reversed-phase ultra-high-performance liquid chromatography (UHPLC) methodology coupled with diode-array detection (DAD) to precisely quantify 20 principal constituents within 121 tea samples spanning 6 distinct variants. The constituents include alkaloids, catechins, flavonols, and phenolic acids. Our findings delineate that the variances in chemical constitution across dissimilar tea types predominantly hinge upon the intricacies of their processing protocols. Notably, green and yellow teas evinced elevated concentrations of total chemical moieties vis à vis other tea classifications. Remarkably divergent levels of alkaloids, catechins, flavonols, and phenolic acids were ascertained among the disparate tea classifications. By leveraging random forest analysis, we ascertained gallocatechin, epigallocatechin gallate, and epicatechin gallate as pivotal biomarkers for effective tea classification within the principal cadre of tea catechins. Our outcomes distinctly underscore substantial dissimilarities in the specific compounds inherent to varying tea categories, as ascertained via the devised and duly validated approach. The implications of this compositional elucidation serve as a pertinent benchmark for the comprehensive assessment and classification of tea specimens.

The types of brewing water affect tea infusion flavor by changing the tea mineral dissolution

The effects of different brewing water samples, including natural drinking water (NDW), pure water (PW), mineral water (MW), distilled water (DW), and tap water (TW) on flavor and quality of green tea infusion were investigated. The results showed the dissolution rate of mineral substances varied greatly depend on the type of water used. Notably, the tea infusion brewed with MW showed the highest taste response and darker but higher brightness in color. Furthermore, the content of volatile compounds was highest in tea infusion brewed with NDW and lowest in tea infusion brewed with MW. The mineral substances content and pH were the main factors affecting volatile compounds in green tea infusion. Thereinto, Ca²⁺ and Fe³⁺ remarkably affected the content of alcohols and aldehydes in volatile compounds. These results suggested that water with a neutral pH value and lower mineral substance content is more conducive for brewing green tea.

Review of recent advances on health benefits, microbial transformations, and authenticity identification of Citri reticulatae Pericarpium bioactive compounds

The extensive health-promoting effects of Citri Reticulatae Pericarpium (CRP) have attracted researchers' interest. The difference in storage time, varieties and origin of CRP are closely related to the content of bioactive compounds they contain. The consitituent transformation mediated by environmental microorganisms (bacteria and fungi) and the production of new bioactive components during the storage process may be the main reason for 'the older, the better' of CRP. In addition, the gap in price between different varieties can be as large as 8 times, while the difference due to age can even reach 20 times, making the 'marketing young-CRP as old-CRP and counterfeiting origin' flood the entire market, seriously harming consumers' interests. However, so far, the research on CRP is relatively decentralized. In particular, a summary of the microbial transformation and authenticity identification of CRP has not been reported. Therefore, this review systematically summarized the recent advances on the main bioactive compounds, the major biological activities, the microbial transformation process, the structure, and content changes of the active substances during the transformation process, and authenticity identification of CRP. Furthermore, challenges and perspectives concerning the future research on CRP were proposed.

Development of a variety and quality evaluation method for Amomi fructus using GC, electronic tongue, and electronic nose

Amomi fructus is rich in volatile components and valuable as a medicine and edible spice. However, the quality of commercially available A. fructus varies, and issues with mixed sources and adulteration by similar products are common. In addition, due to incomplete identification methods, rapid detection of the purchased A. fructus quality is still an issue. In this study, we developed qualitative and quantitative evaluation models to assess the variety and quality of A. fructus using GC, electronic tongue, and electronic nose to provide a rapid and accurate variety and quality evaluation method of A. fructus. The models performed well; the qualitative authenticity model had an accuracy of 1.00 (n = 64), the accuracy of the qualitative origin model was 0.86 (n = 44), and the quantitative model was optimal on the sensory fusion data from the electronic tongue and electronic nose combined with borneol acetate content, with R ² = 0.7944, RMSEF = 0.1050, and RMSEP = 0.1349. The electronic tongue and electronic nose combined with GC quickly and accurately evaluated the variety and quality of A. fructus, and the introduction of multi-source information fusion technology improved the model prediction accuracy. This study provides a useful tool for quality evaluation of medicine and food.

Spectroscopic studies on a natural biomarker for the identification of origin and quality of tea extracts for the development of a portable and field deployable prototype

Even in the era of smart technologies and IoT enabled devices, tea testing technique continues to be a person specific subjective task. In this study, we have employed optical spectroscopy-based detection technique for the quantitative validation of tea quality. In this regard, we have employed the external quantum yield of quercetin at 450 nm (λ_ex = 360 nm), which is an enzymatic product generated by the activity of β-glucosidase on rutin, a naturally occurring metabolite responsible for tea-flavour (quality). We have found that a specific point in a graph representing Optical Density and external Quantum Yield as independent and dependent variables respectively of an aqueous tea extract objectively indicates a specific variety of the tea. A variety of tea samples from various geographical origin have been analysed with the developed technique and found to be useful for the tea quality assessment. The principal component analysis distinctly showed the tea samples originated from Nepal and Darjeeling having similar external quantum yield, while the tea samples from Assam region had a lower external quantum yield. Furthermore, we have employed experimental and computational biology techniques for the detection of adulteration and health benefit of the tea extracts. In order to assure the portability/field use, we have also developed a prototype which confirms the results obtained in the laboratory. We are of the opinion that the simple user interface and almost zero maintenance cost of the device will make it useful and attractive with minimally trained manpower at low resource setting.

A rapid aroma quantification method: Colorimetric sensor-coupled multidimensional spectroscopy applied to black tea aroma

Aroma affects the quality of black tea, and the rapid evaluation of aroma quality is the key to realize the intelligent processing of black tea. A simple colorimetric sensor array coupled with a hyperspectral system was proposed for the rapid quantitative detection of key volatile organic compounds (VOCs) in black tea. Feature variables were screened based on competitive adaptive reweighted sampling (CARS). Furthermore, the performance of the models for VOCs quantitative prediction was compared. For the quantitative prediction of linalool, benzeneacetaldehyde, hexanal, methyl salicylate, and geraniol, the CARS-least-squares support vector machine model's correlation coefficients were 0.89, 0.95, 0.88, 0.80, and 0.78, respectively. The interaction mechanism of array dyes with VOCs was based on density flooding theory. The optimized highest occupied molecular orbital levels, lowest unoccupied molecular orbital energy levels, dipole moments, and intermolecular distances were determined to be strongly correlated with interactions between array dyes and VOCs.

Rapid detection of sunset yellow adulteration in tea powder with variable selection coupled to machine learning tools using spectral data

In the present study sunset yellow (SY), a synthetic colour, which is a common adulterant in tea powders has been analysed using FT-IR spectral data coupled to machine learning tools for efficient classification and quantification of the SY adulteration. Earlier established real coded genetic algorithm (RCGA) was used as variable selection method to predict the key fingerprints of SY in the FT-IR spectra. Here, RCGA was used to select 20, 30, 40, 50 and 60 characteristic wavenumbers for SY. Classification was carried using support vector machine (SVM), random forest (RF) and extreme gradient boosting (XGB) classifiers. SVM classifier using 50 variables could give an accuracy of 0.90 amongst the three. Quantification of SY based on PLS (partial least squares), LS-SVM (least squares-SVM), RF and XGBoost were built on characteristic wavenumbers. Both RF and LS-SVM models were observed to be superior to PLS when coupled to RCGA obtained 20 fingerprint variables. Overall, RCGA-LS-SVM model resulted in lowest RMSECV (0.1956) with regression co-efficient values RC 2 = 0.9989 and RP 2 = 0.9979, when 50 fingerprint variables were used. These results demonstrated that FT-IR combined with RCGA-LS-SVM procedure could be a robust technique for rapid detection of SY in tea powder.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13197-023-05694-3.

E-eye, flash GC E-nose and HS-GC-MS combined with chemometrics to identify the adulterants and geographical origins of Ziziphi Spinosae Semen

Ziziphi Spinosae Semen (ZSS), a valuable seed food, has faced increasing authenticity issues. In this study, the adulterants and geographical origins of ZSS were successfully identified by electronic eye, flash gas chromatography electronic nose (Flash GC e-nose) and headspace gas chromatography-mass spectrometry (HS-GC-MS). As a result, there were color differences between ZSS and adulterants, mainly represented by the a* value of ZSS was less than adulterants. In ZSS, 29 and 32 compounds were detected by Flash GC e-nose and HS-GC-MS. Spicy, sweety, fruity and herbal were the main flavor of ZSS. Five compounds were determined to be responsible for flavor differences between different geographical origins. In the HS-GC-MS analysis, the relative content of Hexanoic acid was the highest in ZSS from Hebei and Shandong, while 2,4-Decadien-1-ol was the highest in Shaanxi. Overall, this study provided a meaningful strategy for addressing authenticity problems of ZSS and other seed foods.

Characterization of the Pure Black Tea Wine Fermentation Process by Electronic Nose and Tongue-Based Techniques with Nutritional Characteristics

Wine is an alcoholic beverage, consisting of several compounds in various ranges of concentrations. Wine quality is usually assessed by a sensory panel of trained personnel. Electronic tongues (e-tongues) and electronic noses (e-noses) have been established in recent years to assess the quality of beverages and foods. Response surface and electronic analysis tools were used to examine the quality of black tea wine. The results indicated the optimum initial sugar level (25 °Brix), yeast addition (0.5%), and fermentation temperature (25 °C) for Golden Peony black tea wine. The black tea wine produced under these conditions with 14.0% vol alcohol has as an orange-red color, full wine and tea flavor, and mild and mellow taste. The sourness of the wine was most affected by fermentation factors-yeast addition, fermentation temperature, and initial sugar level. Alcohols, aldehydes, ketones, and alkanes contributed to most of the volatile components under the influence of yeast addition and fermentation temperature. In contrast, nitrogen oxides, aromatics, and organic sulfides contributed under the influence of the initial sugar level. This study provided a facilitated strategy for obtaining the optimum black tea wine fermentation process through electronic nose and tongue-based techniques. The analysis of wines requires new technologies able to detect various different compounds simultaneously, providing worldwide information about the sample instead of information about specific compounds.

Uncovering the effect of Moringa oleifera Lam. leaf addition to Fuzhuan Brick Tea on sensory properties, volatile profiles and anti-obesity activity

As a nutritious plant with valuable potential, the Moringa oleifera Lam. leaf addition to Fuzhuan Brick Tea (FBT) for co-fermentation is an industrial innovation and a new route to make full use of Moringa oleifera Lam. leaves. However, the sensory properties, volatile profiles and anti-obesity activity of Fuzhuan Brick (Moringa oleifera Lam.) tea (MFBT) are still unknown. The results demonstrated that MFBT has richer and more complex smell and taste, better color and higher overall acceptance scores. In total, 57 volatile flavor compounds, consisting of 3 acids, 16 hydrocarbons, 5 esters, 8 ketones, 13 aldehydes, 6 alcohols and others, were identified using HS-SPME-GC-MS. The characteristic odor components in MFBT were 3-buten-2-one, 4-(2,6,6-trimethyl-1-cyclohexen-1-yl)- and 1-cyclohexene-1-carboxaldehyde, 2,6,6-trimethyl-, which gave it a floral, woody, sweet, herbal and fruity aroma. 2-Octenal, (E) contributed significantly to the aroma of FBT, which could impart fresh, fatty and green aromas. In addition, MFBT could better regulate lipid accumulation, glucose tolerance, insulin tolerance and inflammation response more effectively than FBT. The mechanism is that MFBT could better regulate the dysbiosis of gut microbiota induced by HFFD, mainly increasing the abundance of beneficial bacteria such as SCFA-producing bacteria (Bacteroidetes, Lactobacillaceae, Bacteroidales_S24-7_group and Clostridiaceae_1) and decreasing the abundance of harmful bacteria such as pro-inflammatory/obesity and metabolic syndrome-related bacteria (Proteobacteria, Deferribacteres, Desulfovibrio, Catenibacterium and Helicobacter), which in turn increased feces short-chain fatty acids and lowered circulating lipopolysaccharides. These results suggested that co-fermentation with Moringa oleifera Lam. leaf could significantly improve the quality and enhance the anti-obesity effect of FBT.

Optimization of the Mixed Gas Detection Method Based on Neural Network Algorithm

Real-time mixed gas detection has attracted significant interest for being a key factor for applications of the electronic nose (E-nose). However, mixed gas detection still faces the challenge of long detection time and a large amount of training data. Therefore, in this work, we propose a feasible way to realize low-cost fast detection of mixed gases, which uses only the part response data of the adsorption process as the training set. Our results indicated that the proposed method significantly reduced the number of training sets and the prediction time of mixed gas. Moreover, it can achieve new concentration prediction of mixed gas using only the response data of the first 10 s, and the training set proportion can reduce to 60%. In addition, the convolutional neural network model can realize both the smaller training set but also the higher accuracy of mixed gas. Our findings provide an effective way to improve the detection efficiency and accuracy of E-noses for the experimental measurement.

Identification of Volatile Organic Liquids by Combining an Array of Fiber-Optic Sensors and Machine Learning

In this paper, we report an array of fiber-optic sensors based on the Fabry-Perot interference principle and machine learning-based analyses for identifying volatile organic liquids (VOLs). Three optical fiber tip sensors with different surfaces were included in the array of sensors to improve the accuracy for identifying liquids: an intrinsic (unmodified) flat cleaved endface, a hydrophobic-coated endface, and a hydrophilic-coated endface. The time-transient responses of evaporating droplets from the optical fiber tip sensors were monitored and collected following the controlled immersion tests of 11 different organic liquids. A continuous wavelet transform was used to convert the time-transient response signal into images. These images were then utilized to train convolution neural networks for classification (identification of VOLs). We show that diversity in the information collected using the array of three sensors helps machine learning-based methods perform significantly better. We explore different pipelines for combining the information from the array of sensors within a machine learning framework and their effect on the robustness of models. The results showed that the machine learning-based methods achieved high accuracy in their classification of different liquids based on their droplet evaporation time-transient events.

The Impact of Wet Fermentation on Coffee Quality Traits and Volatile Compounds Using Digital Technologies

Fermentation is critical for developing coffee’s physicochemical properties. This study aimed to assess the differences in quality traits between fermented and unfermented coffee with four grinding sizes of coffee powder using multiple digital technologies. A total of N = 2 coffee treatments—(i) dry processing and (ii) wet fermentation—with grinding levels (250, 350, 550, and 750 µm) were analysed using near-infrared spectrometry (NIR), electronic nose (e-nose), and headspace/gas chromatography–mass spectrometry (HS-SPME-GC-MS) coupled with machine learning (ML) modelling. Most overtones detected by NIR were within the ranges of 1700–2000 nm and 2200–2396 nm, while the enhanced peak responses of fermented coffee were lower. The overall voltage of nine e-nose sensors obtained from fermented coffee (250 µm) was significantly higher. There were two ML classification models to classify processing and brewing methods using NIR (Model 1) and e-nose (Model 2) values as inputs that were highly accurate (93.9% and 91.2%, respectively). Highly precise ML regression Model 3 and Model 4 based on the same inputs for NIR (R = 0.96) and e-nose (R = 0.99) were developed, respectively, to assess 14 volatile aromatic compounds obtained by GC-MS. Fermented coffee showed higher 2-methylpyrazine (2.20 ng/mL) and furfuryl acetate (2.36 ng/mL) content, which induces a stronger fruity aroma. This proposed rapid, reliable, and low-cost method was shown to be effective in distinguishing coffee postharvest processing methods and evaluating their volatile compounds, which has the potential to be applied for coffee differentiation and quality assurance and control.

Combining e-nose and e-tongue for improved recognition of instant starch noodles seasonings

Seasonings play a key role in determining sensory attributes of instant starch noodles. Controlling and improving the quality of seasoning is becoming important. In this study, five different brands along with fifteen instant starch noodles seasonings (seasoning powder, seasoning mixture sauce and the mixture of powder and sauce) were characterized by electronic nose (e-nose) and electronic tongue (e-tongue). Feature-level fusion for the integration of the signals was introduced to integrate the e-nose and e-tongue signals, aiming at improving the performances of identification and prediction models. Principal component analysis (PCA) explained over 85.00% of the total variance in e-nose data and e-tongue data, discriminated all samples. Multilayer perceptron neural networks analysis (MLPN) modeling demonstrated that the identification rate of the combined data was basically 100%. PCA, cluster analysis (CA), and MLPN proved that the classification results acquired from the combined e-nose and e-tongue data were better than individual e-nose and e-tongue result. This work demonstrated that in combination e-nose and e-tongue provided more comprehensive information about the seasonings compared to each individual e-nose and e-tongue. E-nose and e-tongue technologies hold great potential in the production, quality control, and flavor detection of instant starch noodles seasonings.