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

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.

Authenticity and species identification of Fritillariae cirrhosae: a data fusion method combining electronic nose, electronic tongue, electronic eye and near infrared spectroscopy

This paper focuses on determining the authenticity and identifying the species of Fritillariae cirrhosae using electronic nose, electronic tongue, and electronic eye sensors, near infrared and mid-level data fusion. 80 batches of Fritillariae cirrhosae and its counterfeits (including several batches of Fritillaria unibracteata Hsiao et K.C. Hsia, Fritillaria przewalskii Maxim, Fritillaria delavayi Franch and Fritillaria ussuriensis Maxim) were initially identified by Chinese medicine specialists and by criteria in the 2020 edition of Chinese Pharmacopoeia. After obtaining the information from several sensors we constructed single-source PLS-DA models for authenticity identification and single-source PCA-DA models for species identification. We selected variables of interest by VIP value and Wilk's lambda value, and we subsequently constructed the three-source fusion model of intelligent senses and the four-source fusion model of intelligent senses and near-infrared spectroscopy. We then explained and analyzed the four-source fusion models based on the sensitive substances detected by key sensors. The accuracies of single-source authenticity PLS-DA identification models based on electronic nose, electronic eye, electronic tongue sensors and near-infrared were respectively 96.25%, 91.25%, 97.50% and 97.50%. The accuracies of single-source PCA-DA species identification models were respectively 85%, 71.25%, 97.50% and 97.50%. After three-source data fusion, the accuracy of the authenticity identification of the PLS-DA identification model was 97.50% and the accuracy of the species identification of the PCA-DA model was 95%. After four-source data fusion, the accuracy of the authenticity of the PLS-DA identification model was 98.75% and the accuracy of the species identification of the PCA-DA model was 97.50%. In terms of authenticity identification, four-source data fusion can improve the performance of the model, while for the identification of the species the four-source data fusion failed to optimize the performance of the model. We conclude that electronic nose, electronic tongue, electronic eye data and near-infrared spectroscopy combined with data fusion and chemometrics methods can identify the authenticity and determine the species of Fritillariae cirrhosae. Our model explanation and analysis can help other researchers identify key quality factors for sample identification. This study aims to provide a reference method for the quality evaluation of Chinese herbs.

Applications of machine learning techniques for enhancing nondestructive food quality and safety detection

In considering the need of people all over the world for high-quality food, there has been a recent increase in interest in the role of nondestructive and rapid detection technologies in the food industry. Moreover, the analysis of data acquired by most nondestructive technologies is complex, time-consuming, and requires highly skilled operators. Meanwhile, the general applicability of various chemometric or statistical methods is affected by noise, sample, variability, and data complexity that vary under various testing conditions. Nowadays, machine learning (ML) techniques have a wide range of applications in the food industry, especially in nondestructive technology and equipment intelligence, due to their powerful ability in handling irrelevant information, extracting feature variables, and building calibration models. The review provides an introduction and comparison of machine learning techniques, and summarizes these algorithms as traditional machine learning (TML), and deep learning (DL). Moreover, several novel nondestructive technologies, namely acoustic analysis, machine vision (MV), electronic nose (E-nose), and spectral imaging, combined with different advanced ML techniques and their applications in food quality assessment such as variety identification and classification, safety inspection and processing control, are presented. In addition to this, the existing challenges and prospects are discussed. The result of this review indicates that nondestructive testing technologies combined with state-of-the-art machine learning techniques show great potential for monitoring the quality and safety of food products and different machine learning algorithms have their characteristics and applicability scenarios. Due to the nature of feature learning, DL is one of the most promising and powerful techniques for real-time applications, which needs further research for full and wide applications in the food industry.

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.

Distinguishing Different Varieties of Oolong Tea by Fluorescence Hyperspectral Technology Combined with Chemometrics

Oolong tea is a semi-fermented tea that is popular among people. This study aims to establish a classification method for oolong tea based on fluorescence hyperspectral technology(FHSI) combined with chemometrics. First, the spectral data of Tieguanyin, Benshan, Maoxie and Huangjingui were obtained. Then, standard normal variation (SNV) and multiple scatter correction (MSC) were used for preprocessing. Principal component analysis (PCA) was used for data visualization, and with tolerance ellipses that were drawn according to Hotelling, outliers in the spectra were removed. Variable importance for the projection (VIP) > 1 in partial least squares discriminant analysis (PLS−DA) was used for feature selection. Finally, the processed spectral data was entered into the support vector machine (SVM) and PLS−DA. MSC_VIP_PLS−DA was the best model for the classification of oolong tea. The results showed that the use of FHSI could accurately distinguish these four types of oolong tea and was able to identify the key wavelengths affecting the tea classification, which were 650.11, 660.29, 665.39, 675.6, 701.17, 706.31, 742.34 and 747.5 nm. In these wavelengths, different kinds of tea have significant differences (p < 0.05). This study could provide a non-destructive and rapid method for future tea identification.

Challenges and Opportunities of Implementing Data Fusion in Process Analytical Technology-A Review

The release of the FDA's guidance on Process Analytical Technology has motivated and supported the pharmaceutical industry to deliver consistent quality medicine by acquiring a deeper understanding of the product performance and process interplay. The technical opportunities to reach this high-level control have considerably evolved since 2004 due to the development of advanced analytical sensors and chemometric tools. However, their transfer to the highly regulated pharmaceutical sector has been limited. To this respect, data fusion strategies have been extensively applied in different sectors, such as food or chemical, to provide a more robust performance of the analytical platforms. This survey evaluates the challenges and opportunities of implementing data fusion within the PAT concept by identifying transfer opportunities from other sectors. Special attention is given to the data types available from pharmaceutical manufacturing and their compatibility with data fusion strategies. Furthermore, the integration into Pharma 4.0 is discussed.

Rapid detection of neutralising acid adulterants in raw milk using a milk component analyser and chemometrics

This study focused on the development of a method for the rapid detection of acid-neutralising adulterants in raw milk using a milk composition analyser. Qualitative analysis for the discrimination of different acid-neutralising acid adulterants in raw milk and quantification of NaSCN in adulterated raw milk were conducted, combined with chemometrics. The results showed that the milk component analyser combined with principal component analysis (PCA) could judge whether raw milk samples were adulterated but cannot identify the types of adulterated substances. Although partial least squares discrimination analysis (PLS-DA) can distinguish some adulterated raw milk samples, the accuracy rate was only 56.3%; the random forest (RF) model could recognise most adulterated raw milk samples with an accuracy rate of 97.5% and the F1-score was 0.9638. In the prediction model of NaSCN adulteration concentration in raw milk constructed by RF, the coefficient of determination (R²) was 0.9889, and the root means square error (RMSE) was 3.28 × 10^-4, suggesting a high prediction performance of the model. The effectiveness of the method for the detection of real samples in practical production was also proved. Based on the above results, it could conclude that the milk component analyser, combined with chemometrics, effectively distinguished acid-neutralising adulterants in raw milk. These findings provide a reference for the rapid detection of adulterants and the quality control of raw milk.

E-Senses, Panel Tests and Wearable Sensors: A Teamwork for Food Quality Assessment and Prediction of Consumer’s Choices

At present, food quality is of utmost importance, not only to comply with commercial regulations, but also to meet the expectations of consumers; this aspect includes sensory features capable of triggering emotions through the citizen’s perception. To date, key parameters for food quality assessment have been sought through analytical methods alone or in combination with a panel test, but the evaluation of panelists’ reactions via psychophysiological markers is now becoming increasingly popular. As such, the present review investigates recent applications of traditional and novel methods to the specific field. These include electronic senses (e-nose, e-tongue, and e-eye), sensory analysis, and wearables for emotion recognition. Given the advantages and limitations highlighted throughout the review for each approach (both traditional and innovative ones), it was possible to conclude that a synergy between traditional and innovative approaches could be the best way to optimally manage the trade-off between the accuracy of the information and feasibility of the investigation. This evidence could help in better planning future investigations in the field of food sciences, providing more reliable, objective, and unbiased results, but it also has important implications in the field of neuromarketing related to edible compounds.

Application of Multiple-Source Data Fusion for the Discrimination of Two Botanical Origins of Magnolia Officinalis Cortex Based on E-Nose Measurements, E-Tongue Measurements, and Chemical Analysis

Magnolia officinalis Rehd. et Wils. and Magnolia officinalis Rehd. et Wils. var. biloba Rehd. et Wils, as the legal botanical origins of Magnoliae Officinalis Cortex, are almost impossible to distinguish according to their appearance traits with respect to medicinal bark. The application of AFLP molecular markers for differentiating the two origins has not yet been successful. In this study, a combination of e-nose measurements, e-tongue measurements, and chemical analyses coupled with multiple-source data fusion was used to differentiate the two origins. Linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA) were applied to compare the discrimination results. It was shown that the e-nose system presented a good discriminant ability with a low classification error for both LDA and QDA compared with e-tongue measurements and chemical analyses. In addition, the discriminating capacity of LDA for low-level fusion with original data, similar to a combined system, was superior or equal to that acquired individually with the three approaches. For mid-level fusion, the combination of different principals extracted by PCA and variables obtained on the basis of PLS-VIP exhibited an analogous discrimination ability for LDA (classification error 0.0%) and was significantly superior to QDA (classification error 1.67-3.33%). As a result, the combined e-nose, e-tongue, and chemical analysis approach proved to be a powerful tool for differentiating the two origins of Magnoliae Officinalis Cortex.

Detecting the Bitterness of Milk-Protein-Derived Peptides Using an Electronic Tongue

Bitterness is a considerable limiting factor for the application of bioactive peptides in the food industry. The objective of this study was to compare the level of bitterness of milk-protein-derived peptides using an electronic tongue (E-tongue). Liquid milk protein concentrate (LMPC) was prepared from ultra-heat-treated skimmed cow’s milk. It was initially hydrolyzed with different concentrations of trypsin, namely, 0.008 g·L−1, 0.016 g·L−1 and 0.032 g·L−1. In a later exercise, tryptic-hydrolyzed LMPC (LMPC-T) was further hydrolyzed using Lactobacillus bulgaricus and Streptococcus thermophilus. The effect of glucose in microbial hydrolysis was studied. The bitterness of peptides was evaluated with respect to quinine, a standard bittering agent. The level of bitterness of the peptides after microbial hydrolysis of LMPC-T (LMPC-T-F and LMPC-T-FG) was evaluated using a potentiometric E-tongue equipped with a sensor array that had seven chemically modified field-effect transistor sensors. The results of the measurements were evaluated using principal component analysis (PCA), and subsequently, a classification of the models was built using the linear discriminant analysis (LDA) method. The bitterness of peptides in LMPC-T-F and LMPC-T-FG was increased with the increase in the concentration of trypsin. The bitterness of peptides was reduced in LMPC-T-FG compared with LMPC-T-F. The potential application of the E-tongue using a standard model solution with quinine was shown to follow the bitterness of peptides.

Application of MOS Gas Sensors Coupled with Chemometrics Methods to Predict the Amount of Sugar and Carbohydrates in Potatoes

Five potato varieties were studied using an electronic nose with nine MOS sensors. Parameters measured included carbohydrate content, sugar level, and the toughness of the potatoes. Routine tests were carried out while the signals for each potato were measured, simultaneously, using an electronic nose. The signals obtained indicated the concentration of various chemical components. In addition to support vector machines (SVMs that were used for the classification of the samples, chemometric methods, such as the partial least squares regression (PLSR) method, the principal component regression (PCR) method, and the multiple linear regression (MLR) method, were used to create separate regression models for sugar and carbohydrates. The predictive power of the regression models was characterized by a coefficient of determination (R²), a root-mean-square error of prediction (RMSEP), and offsets. PLSR was able to accurately model the relationship between the smells of different types of potatoes, sugar, and carbohydrates. The highest and lowest accuracy of models for predicting sugar and carbohydrates was related to Marfona potatoes and Sprite cultivar potatoes. In general, in all cultivars, the accuracy in predicting the amount of carbohydrates was somewhat better than the accuracy in predicting the amount of sugar. Moreover, the linear function had 100% accuracy for training and validation in the C-SVM method for classification of five potato groups. The electronic nose could be used as a fast and non-destructive method for detecting different potato varieties. Researchers in the food industry will find this method extremely useful in selecting the desired product and samples.

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.

Progress of Research on the Application of Nanoelectronic Smelling in the Field of Food

In the past 20 years, the development of an artificial olfactory system has made great progress and improvements. In recent years, as a new type of sensor, nanoelectronic smelling has been widely used in the food and drug industry because of its advantages of accurate sensitivity and good selectivity. This paper reviews the latest applications and progress of nanoelectronic smelling in animal-, plant-, and microbial-based foods. This includes an analysis of the status of nanoelectronic smelling in animal-based foods, an analysis of its harmful composition in plant-based foods, and an analysis of the microorganism quantity in microbial-based foods. We also conduct a flavor component analysis and an assessment of the advantages of nanoelectronic smelling. On this basis, the principles and structures of nanoelectronic smelling are also analyzed. Finally, the limitations and challenges of nanoelectronic smelling are summarized, and the future development of nanoelectronic smelling is proposed.

Characterization of selected Chinese soybean paste based on flavor profiles using HS-SPME-GC/MS, E-nose and E-tongue combined with chemometrics

Headspace solid-phase microextraction gas chromatography-mass spectrometry (HS-SPME-GC/MS) with electronic nose (E-nose) and electronic tongue (E-tongue) was applied for flavor characterization of traditional Chinese fermented soybean paste. Considering geographical distribution and market representation, twelve kinds of samples were selected to investigate the feasibility. A total of 57 volatile organic compounds (VOCs) were identified, of which 8 volatiles were found in all samples. Linear discrimination analysis (LDA) of fusion data exhibited a high discriminant accuracy of 97.22%. Compared with partial least squares regression (PLSR), support vector machine regression (SVR) analysis exhibited a more satisfying performance on predicting the content of esters, total acids, reducing sugar, salinity and amino acid nitrogen, of which correlation coefficients for prediction (Rp) were about 0.803, 0.949, 0.960, 0.896, 0.923 respectively. This study suggests that intelligent sensing technologies combined with chemometrics can be a promising tool for flavor characterization of fermented soybean paste or other food matrixes.

Effect of coating on flavor metabolism of fish under different storage temperatures

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Coating could reduce the accumulation of aldehydes and the accumulation of alcohols during fish storage process.

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Coating could slow down the accumulation of off-taste FFAs during fish storage process.

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GEO-gelatin coating (GGC) worked better than gelatin coating (GC) in maintaining fish flavor.

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Metabolic mechanisms of fish flavor at different storage temperatures were different.

Two edible coatings (gelatin coating and ginger essential oil-gelatin coating) were prepared to maintain the flavor quality of fish fillets at two storage temperatures (4 °C and 25 °C). The effects of coating on fish fillets were evaluated by detecting the physical properties, microstructure, microbial properties, volatile flavor and taste flavor of fish. In the same coating method, fish fillets stored at 4 °C showed better effect than that at 25 °C on maintain water content, color and texture, however, fish fillets stored at 25 °C were closer to fresh fish in volatile flavor and taste flavor than that at 4 °C; whatever the storage temperature, coating could slow down the growth of fish microorganisms, maintain water content, color, texture, volatile flavor and taste flavor of fish fillets; GGC exhibited better effect on maintain flavor quality than GC.

A novel strategy for discriminating different cultivation and screening odor and taste flavor compounds in Xinhui tangerine peel using E-nose, E-tongue, and chemometrics

A rapid strategy for discriminating Quanzhi (QZ) and Bozhi (BZ) of different cultivation of Xinhui tangerine peel was established by combining electronic nose, electronic tongue and chemometrics, which can be used as tool in the market to identify food fraud. 30 volatiles and 34 low molecular weight compounds of characteristic fingerprints of Xinhui tangerine peel of 108 samples were identified using GC-MS and UHPLC-Q-TOF-MS. Key compounds of BZ and QZ were screened and further compared by chemometrics. We discriminated odor and taste of BZ and QZ using electronic nose and electronic tongue, respectively. Our studies showed that β-myrcene, limonene, β-trans-Ocimene, γ-terpinene and terpinolene, etc, were screened the chief volatile flavor compounds by Spearman's rank correlation. Hydroxymethyl furfural, hesperitin, nobiletin and tangeretin, etc, were screened the key taste flavor compounds based gray relational analysis and partial least squares regression. Our study provides further insight for quality evaluation of Xinhui tangerine peel.

Electrochemical Sensors and Biosensors for the Analysis of Tea Components: A Bibliometric Review

Tea is a popular beverage all around the world. Tea composition, quality monitoring, and tea identification have all been the subject of extensive research due to concerns about the nutritional value and safety of tea intake. In the last 2 decades, research into tea employing electrochemical biosensing technologies has received a lot of interest. Despite the fact that electrochemical biosensing is not yet the most widely utilized approach for tea analysis, it has emerged as a promising technology due to its high sensitivity, speed, and low cost. Through bibliometric analysis, we give a systematic survey of the literature on electrochemical analysis of tea from 1994 to 2021 in this study. Electrochemical analysis in the study of tea can be split into three distinct stages, according to the bibliometric analysis. After chromatographic separation of materials, electrochemical techniques were initially used only as a detection tool. Many key components of tea, including as tea polyphenols, gallic acid, caffeic acid, and others, have electrochemical activity, and their electrochemical behavior is being investigated. High-performance electrochemical sensors have steadily become a hot research issue as materials science, particularly nanomaterials, and has progressed. This review not only highlights these processes, but also analyzes and contrasts the relevant literature. This evaluation also provides future views in this area based on the bibliometric findings.

Toward the Development of Combined Artificial Sensing Systems for Food Quality Evaluation: A Review on the Application of Data Fusion of Electronic Noses, Electronic Tongues and Electronic Eyes

Devices known as electronic noses (ENs), electronic tongues (ETs), and electronic eyes (EEs) have been developed in recent years in the in situ study of real matrices with little or no manipulation of the sample at all. The final goal could be the evaluation of overall quality parameters such as sensory features, indicated by the "smell", "taste", and "color" of the sample under investigation or in the quantitative detection of analytes. The output of these sensing systems can be analyzed using multivariate data analysis strategies to relate specific patterns in the signals with the required information. In addition, using suitable data-fusion techniques, the combination of data collected from ETs, ENs, and EEs can provide more accurate information about the sample than any of the individual sensing devices. This review's purpose is to collect recent advances in the development of combined ET, EN, and EE systems for assessing food quality, paying particular attention to the different data-fusion strategies applied.

Recent techniques for the authentication of the geographical origin of tea leaves from camellia sinensis: A review

Tea is one of the most important beverages worldwide, is produced in several distinct geographical regions, and is traded on the global market. The ability to determine the geographical origin of tea products helps to ensure authenticity and traceability. This paper reviews the recent research on authentication of tea using a combination of instrumental and chemometric methods. To determine the production region of a tea sample, instrumental methods based on analyzing isotope and mineral element contents are suitable because they are less affected by tea variety and processing methods. Chemometric analysis has proven to be a valuable method to identify tea. Principal component analysis (PCA) and linear discriminant analysis (LDA) are the most preferred methods for processing large amounts of data obtained through instrumental component analysis.

Analysis of volatility characteristics of five jujube varieties in Xinjiang Province, China, by HS-SPME-GC/MS and E-nose

In this study, headspace solid-phase microextraction coupled with gas chromatography-mass spectrometry (HS-SPME-GC/MS) was used to identify individual volatile compounds in five jujube varieties, and E-nose was used to identify their flavor. The results showed that a total of 45 volatile compounds were detected by GC-MS in the five varieties, and the proportion of acids was the highest (38.29%-54.95%), followed by that of aldehydes (22.94%-47.93%) and esters (6.33%-26.61%). Moreover, different varieties had obviously different volatile components. E-nose analysis showed that the R7 and R9 sensors were more sensitive to the aroma of jujube than other sensors. The strong response of R7 sensor was attributed to terpenes (or structurally similar substances) in jujube fruit, such as 1-penten-3-one, 2-octenal, (E)-2-heptanaldehyde, and (E)-2-hexenal and that of R9 sensor was attributed to the cyclic volatile components such as benzaldehyde, benzoic acid, and methyl benzoate. The multivariate data analysis (PCA, OPLS-DA, and HCA) of the results of GC/MS and E-nose showed that the five varieties could be divided into three groups: (1) Ziziphus jujuba Mill. cv. Huizao (HZ) and Z. jujuba cv. Junzao (JZ). Acids were the main volatile components for this group (accounting for 47.44% and 54.95%, respectively); (2) Z. jujuba cv. Hamidazao (HMDZ). This group had the most abundant volatile components (41), and the concentrations were also the highest (1285.43 µg/kg); (3) Winter jujube 1 (Z. jujuba cv. Dongzao, WJ1) and Winter jujube 2 (Z. jujuba cv. Dongzao, WJ2). The proportion of acids (38.38% and 38.29%) and aldehydes (40.35% and 38.19%) were similar in the two varieties. Therefore, the combination of headspace solid-phase microextraction coupled with gas chromatography-mass spectrometry and E-nose could quickly and accurately identify the volatile components in jujube varieties from macro- and microperspectives. This study can provide guidance for the evaluation and distinguishing of jujube varieties and jujube cultivation and processing.

Recent Progress in Smart Electronic Nose Technologies Enabled with Machine Learning Methods

Machine learning methods enable the electronic nose (E-Nose) for precise odor identification with both qualitative and quantitative analysis. Advanced machine learning methods are crucial for the E-Nose to gain high performance and strengthen its capability in many applications, including robotics, food engineering, environment monitoring, and medical diagnosis. Recently, many machine learning techniques have been studied, developed, and integrated into feature extraction, modeling, and gas sensor drift compensation. The purpose of feature extraction is to keep robust pattern information in raw signals while removing redundancy and noise. With the extracted feature, a proper modeling method can effectively use the information for prediction. In addition, drift compensation is adopted to relieve the model accuracy degradation due to the gas sensor drifting. These recent advances have significantly promoted the prediction accuracy and stability of the E-Nose. This review is engaged to provide a summary of recent progress in advanced machine learning methods in E-Nose technologies and give an insight into new research directions in feature extraction, modeling, and sensor drift compensation.

The qualitative and quantitative assessment of xiaochaihu granules based on e-eye, e-nose, e-tongue and chemometrics

Xiaochaihu granules (XCHG), a famous Chinese patent medicine with high sales, have more than 100 approved number by China Food and Drug Administration (CFDA). Therefore, it is important to evaluate the quality of XCHG from different pharmaceutical companies. The data fusion of electronic eye (e-eye), electronic nose (e-nose) and electronic tongue (e-tongue) combined with chemometrics were applied for qualitative identification and quantitative prediction of XCHG quality. Firstly, main chemical constituents, such as saikosaponin b2, baicalin and glycyrrhizin were quantified with ultra-high-performance liquid chromatography (UHPLC). Secondly, the characteristic features of odor, color, and taste of XCHG were measured by e-nose, e-eye and e-tongue, and the Pearson correlation between constituents and e-signals was analyzed. Thirdly, partial least squares discrimination analysis (PLS-DA) of e-eye, e-nose and e-tongue were classified by the hierarchical clustering analysis (HCA) results of the main constituents of XCHG separately. Finally, partial least-squares regression (PLSR) was used to build the prediction model between components and data fusion of e-eye, e-nose and e-tongue. The results showed that saikosaponin b2, baicalin and glycyrrhizin were the three main components in XCHG samples. in which saikosaponin b2 ranged from 0.280 to 2.186 mg (relative standard deviation (RSD), 62.10 %), baicalin range from 25.883 mg to 49.108 mg (RSD, 16.64 %), and glycyrrhizin ranged from 0.897 mg to 6.052 mg (RSD, 40.32 %) of 31 batches of XCHG in each bag. Pearson correlation results showed that the main constituents were related to the core e-signals of XCHG, such as Eab, bitterness and R2 (odor sensitive to nitrogen oxide). Data fusion of e-eye, e-nose and e-tongue with main constitutes of XCHG using the PLSR model showed that the root mean square error (RMSE) values were 0.320 and 0.090 for saikosaponin b2 and licoricesaponin G2 (P < 0.000). The saikosaponin b2 and licoricesaponin G2 contents in XCHG could be predicted with integrated data of e-nose, e-eye, and e-tongue using the PLSR model.

Insight on Current Advances in Food Science and Technology for Feeding the World Population

While the world population is steadily increasing, the capacity of Earth to renew its resources is continuously declining. Consequently, the bioresources required for food production are diminishing and new approaches are needed to feed the current and future global population. In the last decades, scientists have developed novel strategies to reduce food loss and waste, improve food production, and find new ingredients, design and build new food structures, and introduce digitalization in the food system. In this work, we provide a general overview on circular economy, alternative technologies for food production such as cellular agriculture, and new sources of ingredients like microalgae, insects, and wood-derived fibers. We present a summary of the whole process of food design using creative problem-solving that fosters food innovation, and digitalization in the food sector such as artificial intelligence, augmented and virtual reality, and blockchain technology. Finally, we briefly discuss the effect of COVID-19 on the food system. This review has been written for a broad audience, covering a wide spectrum and giving insights on the most recent advances in the food science and technology area, presenting examples from both academic and industrial sides, in terms of concepts, technologies, and tools which will possibly help the world to achieve food security in the next 30 years.

Quality chemical analysis of crush-tear-curl (CTC) black tea from different geographical regions based on UHPLC-Orbitrap-MS

Crush-tear-curl (CTC) black tea is a popular beverage, owing to its unique taste characteristics and health benefits. However, differences in the taste quality and chemical profiles of CTC black tea from different geographical regions remain unclear. In this study, 28 CTC black tea samples were collected from six geographical regions and analyzed using electronic tongue and ultrahigh performance liquid chromatography-Orbitrap-mass spectroscopy. The e-tongue analysis indicated that each region's CTC black tea has its own relatively prominent taste characteristics: Sri Lanka (more umami and astringent), North India (more umami), China (more sweetness and astringent), South India (moderate umami and sweetness), and Kenya (moderate umami and astringent). Based on multivariate statistical analysis, 78 metabolites were tentatively identified and used as potential markers for CTC black tea of different origins, mainly including amino acids, flavone/flavonol glycosides, and pigments. Different metabolites, which contributed to the taste characteristics of CTC black tea, were clarified by partial least squares regression correlation analysis. Our findings may serve as useful references for future studies on origin traceability and quality characteristic determination of CTC black teas. PRACTICAL APPLICATION: This study provides useful references for future studies on the origin traceability and taste characteristic determination of CTC black teas from different geographical regions.

The State of Lifelong Learning in Service Robots:

Service robots are appearing more and more in our daily life. The development of service robots combines multiple fields of research, from object perception to object manipulation. The state-of-the-art continues to improve to make a proper coupling between object perception and manipulation. This coupling is necessary for service robots not only to perform various tasks in a reasonable amount of time but also to continually adapt to new environments and safely interact with non-expert human users. Nowadays, robots are able to recognize various objects, and quickly plan a collision-free trajectory to grasp a target object in predefined settings. Besides, in most of the cases, there is a reliance on large amounts of training data. Therefore, the knowledge of such robots is fixed after the training phase, and any changes in the environment require complicated, time-consuming, and expensive robot re-programming by human experts. Therefore, these approaches are still too rigid for real-life applications in unstructured environments, where a significant portion of the environment is unknown and cannot be directly sensed or controlled. In such environments, no matter how extensive the training data used for batch learning, a robot will always face new objects. Therefore, apart from batch learning, the robot should be able to continually learn about new object categories and grasp affordances from very few training examples on-site. Moreover, apart from robot self-learning, non-expert users could interactively guide the process of experience acquisition by teaching new concepts, or by correcting insufficient or erroneous concepts. In this way, the robot will constantly learn how to help humans in everyday tasks by gaining more and more experiences without the need for re-programming. In this paper, we review a set of previously published works and discuss advances in service robots from object perception to complex object manipulation and shed light on the current challenges and bottlenecks.

Color-reflected chemical regulations of the scorched rhubarb (Rhei Radix et Rhizoma) revealed by the integration analysis of visible spectrophotometry, Fourier transform infrared spectroscopy and high performance liquid chromatography

Rhubarb has been used as herbal purgative with a worldwide long history. In traditional Chinese medicine, rhubarb can be stir-baked to scorch to eliminate the purgative function when it is a side effect. Under-scorched rhubarb still has the side effect of purgative, while over-scorched rhubarb can lose all bioactivities. Empirically, the degree of scorching is determined by manual observation of the rhubarb color. In order to find the reasonable and objective scorching endpoint criteria, visible spectrophotometry, FTIR spectroscopy and HPLC were used to reveal the color-reflected chemical changes. It was found that the blackening of rhubarb corresponded to the elimination of combined anthraquinones and the rise-fall inflection of free anthraquinones. The scorching endpoint criteria should include the upper limit for combined anthraquinones to avoid under-scorch and the lower limit for free anthraquinones to avoid over-scorch. Visible and FTIR spectroscopy can be process analytical techniques for the rhubarb scorching.

Emerging nondestructive approaches for meat quality and safety evaluation-A review

Meat is one of the most consumed agro-products because it contains proteins, minerals, and essential vitamins, all of which play critical roles in the human diet and health. Meat is a perishable food product because of its high moisture content, and as such there are concerns about its quality, stability, and safety. There are two widely used methods for monitoring meat quality attributes: subjective sensory evaluation and chemical/instrumentation tests. However, these methods are labor-intensive, time-consuming, and destructive. To overcome the shortfalls of these conventional approaches, several researchers have developed fast and nondestructive techniques. Recently, electronic nose (e-nose), computer vision (CV), spectroscopy, hyperspectral imaging (HSI), and multispectral imaging (MSI) technologies have been explored as nondestructive methods in meat quality and safety evaluation. However, most of the studies on the application of these novel technologies are still in the preliminary stages and are carried out in isolation, often without comprehensive information on the most suitable approach. This lack of cohesive information on the strength and shortcomings of each technique could impact their application and commercialization for the detection of important meat attributes such as pH, marbling, or microbial spoilage. Here, we provide a comprehensive review of recent nondestructive technologies (e-nose, CV, spectroscopy, HSI, and MSI), as well as their applications and limitations in the detection and evaluation of meat quality and safety issues, such as contamination, adulteration, and quality classification. A discussion is also included on the challenges and future outlooks of the respective technologies and their various applications.

The effect of Eurotium cristatum (MF800948) fermentation on the quality of autumn green tea

Autumn green tea (AT) has poor taste quality for its strong astringency. This study aims to improve the taste quality as well as the aroma of AT by Eurotium cristatum (MF800948) fermentation and to produce a fermented autumn green tea (FT). Results showed that the aroma quality of AT was improved, and the content of terpene alcohols that impart characteristic flowery aroma to FT significantly increased. The umami intensity of FT was comparable to that of AT while the astringency tasted much weaker mainly due to the oxidation of the catechins. The results also confirmed that theabrownins exhibited strong umami taste, not astringent taste. Finally, a metabolic map was analyzed to show the effect of E. cristatum (MF800948) on the quality of AT, and to visualize the changes of differential compounds in AT and FT. The work provides insights into the quality improvement of autumn green tea.

Rapid and real-time detection of black tea fermentation quality by using an inexpensive data fusion system

Intelligent identification of black tea fermentation quality is becoming a bottleneck to industrial automation. This study presents at-line rapid detection of black tea fermentation quality at industrial scale based on low-cost micro-near-infrared spectroscopy (NIRS) and laboratory-made computer vision system (CVS). High-performance liquid chromatography and a spectrophotometer were used for determining the content of catechins and theaflavins, and the color of tea samples, respectively. Hierarchical cluster analysis combined with sensory evaluation was used to group samples through different fermentation degrees. A principal component analysis-support vector machine (SVM) model was developed to discriminate the black tea fermentation degree using color, spectral, and data fusion information; high accuracy (calibration = 95.89%, prediction = 89.19%) was achieved using mid-level data fusion. In addition, SVM model for theaflavins content prediction was established. The results indicated that the micro-NIRS combined with CVS proved a portable and low-cost tool for evaluating the black tea fermentation quality.

Evaluation of black tea by using smartphone imaging coupled with micro-near-infrared spectrometer

Tea quality is generally assessed through panel sensory evaluation, which requires elaborate sample preparation steps. Here, a novel and low-cost evaluation method of using smartphone imaging coupled with micro-near-infrared (NIR) spectrometer based on digital light processing is proposed to classify the quality grades of Keemun black tea. RGB color information was obtained by Image J software, eight texture characteristics, including scheme, contrast, dissimilarity, entropy, correlation, second moment and variance, and homogeneity were obtained by ENVI software based on co - occurrence method from smartphone images, and spectral data were preprocessed with standard normal variate. A principal component analysis (PCA)-support vector machine (SVM) model was established to analyze the color, texture, and spectral data. Low-level and middle-level fusion strategies were introduced for analyzing the fusion data. The results indicated that the accuracy of the SVM model on mid-level data fusion (100.00%, 94.29% for calibration set and prediction set, respectively) was higher than that obtained for separate color (97.14%, 88.57%), texture (84.29%, 60%), spectrum (74.29%, 68.57%) evaluation, or low-level data fusion (88.57%, 82.86%). The best SVM model yielded satisfactory performance with 94.29% accuracy for the prediction sets. These results suggested that smartphone imaging coupled with micro-NIR spectroscopy is an effective and low-cost tool for evaluating tea quality.

Stability assessment of extracts obtained from Arbutus unedo L. fruits in powder and solution systems using machine-learning methodologies

Arbutus unedo L. (strawberry tree) has showed considerable content in phenolic compounds, especially flavan-3-ols (catechin, gallocatechin, among others). The interest of flavan-3-ols has increased due their bioactive actions, namely antioxidant and antimicrobial activities, and by association of their consumption to diverse health benefits including the prevention of obesity, cardiovascular diseases or cancer. These compounds, mainly catechin, have been showed potential for use as natural preservative in foodstuffs; however, their degradation is increased by pH and temperature of processing and storage, which can limit their use by food industry. To model the degradation kinetics of these compounds under different conditions of storage, three kinds of machine learning models were developed: i) random forest, ii) support vector machine and iii) artificial neural network. The selected models can be used to track the kinetics of the different compounds and properties under study without the prior knowledge requirement of the reaction system.

Classification of Bee Pollen and Prediction of Sensory and Colorimetric Attributes-A Sensometric Fusion Approach by e-Nose, e-Tongue and NIR

The chemical composition of bee pollens differs greatly and depends primarily on the botanical origin of the product. Therefore, it is a crucially important task to discriminate pollens of different plant species. In our work, we aim to determine the applicability of microscopic pollen analysis, spectral colour measurement, sensory, NIR spectroscopy, e-nose and e-tongue methods for the classification of bee pollen of five different botanical origins. Chemometric methods (PCA, LDA) were used to classify bee pollen loads by analysing the statistical pattern of the samples and to determine the independent and combined effects of the above-mentioned methods. The results of the microscopic analysis identified 100% of sunflower, red clover, rapeseed and two polyfloral pollens mainly containing lakeshore bulrush and spiny plumeless thistle. The colour profiles of the samples were different for the five different samples. E-nose and NIR provided 100% classification accuracy, while e-tongue > 94% classification accuracy for the botanical origin identification using LDA. Partial least square regression (PLS) results built to regress on the sensory and spectral colour attributes using the fused data of NIR spectroscopy, e-nose and e-tongue showed higher than 0.8 R² during the validation except for one attribute, which was much higher compared to the independent models built for instruments.