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

Targeted versus Nontargeted Green Strategies Based on Headspace-Gas Chromatography-Ion Mobility Spectrometry Combined with Chemometrics for Rapid Detection of Fungal Contamination on Wheat Kernels

Conventional methods for detecting fungal contamination are generally time-consuming and sample-destructive, making them impossible for large-scale nondestructive detection and real-time analysis. Therefore, the potential of headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS) was examined for the rapid determination of fungal infection on wheat samples in a rapid and nondestructive manner. In addition, the validation experiment of detecting the percent A. flavus infection presented in simulated field samples was carried out. Because the dual separation of HS-GC-IMS could generate massive amounts of three-dimensional data, proper chemometric processing was required. In this study, two chemometric strategies including: (i) nontargeted spectral fingerprinting and (ii) targeted specific markers were introduced to evaluate the performances of classification and prediction models. Results showed that satisfying results for the differentiation of fungal species were obtained based on both strategies (>80%) by the genetic algorithm optimized support vector machine (GA-SVM), and better values were obtained based on the first strategy (100%). Likewise, the GA-SVM model based on the first strategy achieved the best prediction performances (R² = 0.979-0.998) of colony counts in fungal infected samples. The results of validation experiment showed that GA-SVM models based on the first strategy could still provide satisfactory classification (86.67%) and prediction (R² = 0.889) performances for percent A. flavus infection presented in simulated field samples at day 4. This study indicated the feasibility of HS-GC-IMS-based approaches for the early detection of fungal contamination in wheat kernels.

Rapid Identification of Different Grades of Huangshan Maofeng Tea Using Ultraviolet Spectrum and Color Difference

Tea is an important beverage in humans’ daily lives. For a long time, tea grade identification relied on sensory evaluation, which requires professional knowledge, so is difficult and troublesome for laypersons. Tea chemical component detection usually involves a series of procedures and multiple steps to obtain the final results. As such, a simple, rapid, and reliable method to judge the quality of tea is needed. Here, we propose a quick method that combines ultraviolet (UV) spectra and color difference to classify tea. The operations are simple and do not involve complex pretreatment. Each method requires only a few seconds for sample detection. In this study, famous Chinese green tea, Huangshan Maofeng, was selected. The traditional detection results of tea chemical components could not be used to directly determine tea grade. Then, digital instrument methods, UV spectrometry and colorimetry, were applied. The principal component analysis (PCA) plots of the single and combined signals of these two instruments showed that samples could be arranged according to grade. The combined signal PCA plot performed better with the sample grade descending in clockwise order. For grade prediction, the random forest (RF) model produced a better effect than the support vector machine (SVM) and the SVM + RF model. In the RF model, the training and testing accuracies of the combined signal were all 1. The grades of all samples were correctly predicted. From the above, the UV spectrum combined with color difference can be used to quickly and accurately classify the grade of Huangshan Maofeng tea. This method considerably increases the convenience of tea grade identification.

Historical Evolution and Food Control Achievements of Near Infrared Spectroscopy, Electronic Nose, and Electronic Tongue-Critical Overview

Amid today's stringent regulations and rising consumer awareness, failing to meet quality standards often results in health and financial compromises. In the lookout for solutions, the food industry has seen a surge in high-performing systems all along the production chain. By virtue of their wide-range designs, speed, and real-time data processing, the electronic tongue (E-tongue), electronic nose (E-nose), and near infrared (NIR) spectroscopy have been at the forefront of quality control technologies. The instruments have been used to fingerprint food properties and to control food production from farm-to-fork. Coupled with advanced chemometric tools, these high-throughput yet cost-effective tools have shifted the focus away from lengthy and laborious conventional methods. This special issue paper focuses on the historical overview of the instruments and their role in food quality measurements based on defined food matrices from the Codex General Standards. The instruments have been used to detect, classify, and predict adulteration of dairy products, sweeteners, beverages, fruits and vegetables, meat, and fish products. Multiple physico-chemical and sensory parameters of these foods have also been predicted with the instruments in combination with chemometrics. Their inherent potential for speedy, affordable, and reliable measurements makes them a perfect choice for food control. The high sensitivity of the instruments can sometimes be generally challenging due to the influence of environmental conditions, but mathematical correction techniques exist to combat these challenges.

Emerging techniques for determining the quality and safety of tea products: A review

Spectroscopic techniques, electrochemical methods, nanozymes, computer vision, and modified chromatographic techniques are the emerging techniques for determining the quality and safety parameters (e.g., physical, chemical, microbiological, and classified parameters, as well as inorganic and organic contaminants) of tea products (such as fresh tea leaves, commercial tea, tea beverage, tea powder, and tea bakery products) effectively. By simplifying the sample preparation, speeding up the detection process, reducing the interference of other substances contained in the sample, and improving the sensitivity and accuracy of the current standard techniques, the abovementioned emerging techniques achieve rapid, cost-effective, and nondestructive or slightly destructive determination of tea products, with some of them providing real-time detection results. Applying these emerging techniques in the whole industry of tea product processing, right from the picking of fresh tea leaves, fermentation of tea leaves, to the sensory evaluation of commercial tea, as well as developing portable devices for real-time and on-site determination of classified and safety parameters (e.g., the geographical origin, grade, and content of contaminants) will not only eliminate the strong dependence on professionals but also help mechanize the production of tea products, which deserves further research. Conducting a review on the application of spectroscopic techniques, electrochemical methods, nanozymes, computer vision, and modifications of chromatographic techniques for quality and safety determination of tea products may serve as guide for other types of foods and beverages, offering potential techniques for their detection and evaluation, which would promote the development of the food industry.

Rapid and intelligent discrimination of Notopterygium incisum and Notopterygium franchetii by infrared spectroscopic fingerprints and electronic olfactory fingerprints

This preliminary research evaluated mid-infrared (MIR) spectroscopy, near-infrared (NIR) spectroscopy and electronic nose (E-nose) for the rapid identification of Notopterygium incisum and Notopterygium franchetii, which were both approved sources of Notopterygii Rhizoma et Radix (Chinese Pharmacopoeia, 2015) but possessed different chemical compositions and pharmacological activities. At the level of single variables, MIR showed quite a few discriminating peaks in the regions of 3000-2800 cm^-1 (the stretching bands of CH), 1770-1670 cm^-1 (the stretching bands of CO), and 1400-1200 cm^-1 (the bending bands of CH and the stretching bands of CO). Meanwhile, NIR only showed an intuitive discriminating peak near 4736 cm^-1 (the combination band of OH and CO stretching modes). E-nose response signals of N. incisum and N. franchetii were significant different (p < 0.05) on four sensors, i.e., LY2/LG, LY2/GH, LY2/gCT and LY2/gCTI. Using the infrared spectra or E-nose sensor responses as fingerprints, support vector machine (SVM) models can provide good recognition accuracy (100% for MIR and NIR models, 92.9% for E-nose model). This research indicated the feasibility of MIR, NIR and E-nose for the accurate, rapid, cheap and green identification of N. incisum and N. franchetii, which was desirable to assure the authenticity, efficacy and safety of related herb materials and products.

Collaborative Analysis on the Marked Ages of Rice Wines by Electronic Tongue and Nose based on Different Feature Data Sets

Aroma and taste are the most important attributes of alcoholic beverages. In the study, the self-developed electronic tongue (e-tongue) and electronic nose (e-nose) were used for evaluating the marked ages of rice wines. Six types of feature data sets (e-tongue data set, e-nose data set, direct-fusion data set, weighted-fusion data set, optimized direct-fusion data set, and optimized weighted-fusion data set) were used for identifying rice wines with different wine ages. Pearson coefficient analysis and variance inflation factor (VIF) analysis were used to optimize the fusion matrixes by removing the multicollinear information. Two types of discrimination methods (principal component analysis (PCA) and locality preserving projections (LPP)) were used for classifying rice wines, and LPP performed better than PCA in the discrimination work. The best result was obtained by LPP based on the weighted-fusion data set, and all the samples could be classified clearly in the LPP plot. Therefore, the weighted-fusion data were used as independent variables of partial least squares regression, extreme learning machine, and support vector machines (LIBSVM) for evaluating wine ages, respectively. All the methods performed well with good prediction results, and LIBSVM presented the best correlation coefficient (R² ≥ 0.9998).

Fuzzy Evaluation Output of Taste Information for Liquor Using Electronic Tongue Based on Cloud Model

As a taste bionic system, electronic tongues can be used to derive taste information for different types of food. On this basis, we have carried forward the work by making it, in addition to the ability of accurately distinguish samples, be more expressive by speaking evaluative language like human beings. Thus, this paper demonstrates the correlation between the qualitative digital output of the taste bionic system and the fuzzy evaluation language that conform to the human perception mode. First, through principal component analysis (PCA), backward cloud generator and forward cloud generator, two-dimensional cloud droplet groups of different flavor information were established by using liquor taste data collected by electronic tongue. Second, the frequency and order of the evaluation words for different flavor of liquor were obtained by counting and analyzing the data appeared in the artificial sensory evaluation experiment. According to the frequency and order of words, the cloud droplet range corresponding to each word was calculated in the cloud drop group. Finally, the fuzzy evaluations that originated from the eight groups of liquor data with different flavor were compared with the artificial sense, and the results indicated that the model developed in this work is capable of outputting fuzzy evaluation that is consistent with human perception rather than digital output. To sum up, this method enabled the electronic tongue system to generate an output, which conforms to human's descriptive language, making food detection technology a step closer to human perception.

Preprotection of Tea Polysaccharides with Different Molecular Weights Can Reduce the Adhesion between Renal Epithelial Cells and Nano-Calcium Oxalate Crystals

Crystal adhesion is an important link in the formation of kidney stones. This study investigated and compared the adhesion differences between nano-calcium oxalate monohydrate (COM) and human renal proximal tubule epithelial (HK-2) cells before and after treatment with tea polysaccharides (TPSs) TPS0, TPS1, TPS2, and TPS3 with molecular weights of 10.88, 8.16, 4.82, and 2.31 kDa, respectively. TPS treatment effectively reduced the damage of COM to HK-2 cells, thereby resulting in increased cell activity, decreased release of lactate dehydrogenase, cell morphology recovery, decreased level of reactive oxygen species, increased mitochondrial membrane potential, increased lysosomal integrity, decreased expression of adhesion molecule osteopontin and eversion of phosphatidylserine, and decreased crystal adhesion. Among the TPSs, TPS2 with moderate molecular weight had the best protective effect on cells and the strongest effect on the inhibition of crystal adhesion. Thus, TPS2 may be a potential anticalculus drug.

Classification and Identification of Industrial Gases Based on Electronic Nose Technology

Rapid detection and identification of industrial gases is a challenging problem. They have a complex composition and different specifications. This paper presents a method based on the kernel discriminant analysis (KDA) algorithm to identify industrial gases. The smell prints of four typical industrial gases were collected by an electronic nose. The extracted features of the collected gases were employed for gas identification using different classification algorithms, including principal component analysis (PCA), linear discriminant analysis (LDA), PCA + LDA, and KDA. In order to obtain better classification results, we reduced the dimensions of the original high-dimensional data, and chose a good classifier. The KDA algorithm provided a high classification accuracy of 100% by selecting the offset of the kernel function c = 10 and the degree of freedom d = 5. It was found that this accuracy was 4.17% higher than the one obtained using PCA. In the case of standard deviation, the KDA algorithm has the highest recognition rate and the least time consumption.

Physicochemical Aspects and Sensory Profiles as Various Potential Factors for Comprehensive Quality Assessment of Nü-Er-Cha Produced from Rhamnus heterophylla Oliv

Nü-Er-Cha, produced from the leaves of Rhamnus heterophylla Oliv., is known as an herbal tea and used in the treatment of bleeding, irregular menstruation and dysentery. A method was developed for the quality assessment of herbal tea, Nü-Er-Cha, adopting physical parameters, chemical constituents and sensory profiles as various potential factors. Their inner relationship was mined by multivariate statistical analysis tools, and the three factors were integrated by a technique for order preference by a similarity to ideal solution (TOPSIS) approach to comprehensively analyze the characters of Nü-Er-Cha. Viscosity was also introduced to the physical parameter determination besides conductivity, pH and color. Seven common peaks of eight batches of Nü-Er-Cha were marked by a high performance liquid chromatography (HPLC) fingerprint. They were further identified by HPLC mass spectrometry/mass spectrometry (HPLC-MS/MS) as hydroxybenzoic acids and flavanol glycosides. Fifty trained members participated in the sensory evaluation. Significant correlations between total sensory scores and conductivity, viscosity as well as pH were observed, a relatively innovative result for the quality assessment of herbal teas. The common peaks, belonging to hydroxybenzoic acids and flavanol glycosides, were mainly related to the color of infusions and leaves. The result of the TOPSIS analysis showed that S3 and S4 ranked as the top two in the comprehensive quality assessment. This may be related to rhamnetin triglycoside with a galactose/glucose and two rhamnoses, which had a higher peak response in S3 and S4 than that in the other samples. The present study may contribute to a better understanding of the relationship regarding physical properties, chemical composition and sensory profiles, and it may supply ideas for the comprehensive quality assessment of the herbal tea Nü-Er-Cha.

Comprehensive evaluation of the composition of Mingshan Laochuancha green tea and demonstration of hypolipidemic activity in a zebrafish obesity model

Laochuancha is an ancient tea plant originating from the Mingshan district of Ya'an city, Sichuan province, China, which is used to produce tea products with excellent quality. Mingshan Laochuancha green tea (MLGT) is a type of green tea manufactured from Laochuancha tea leaves. Currently, not much is known regarding the chemical compositions of MLGT and its bioactivity. Herein, the present study explores, for the first time, the chemical compositions and hypolipidemic activity of MLGT. It was observed that K was the most abundant element of 26.58 mg g⁻¹, and contents of toxic As, Cd, Cr and Pb elements were all below concentration limits. Alcohols (55.65%) were the main volatiles, and numerous volatiles with chestnut-like aroma were detected. Total content of 21 amino acids was 28.61 mg g⁻¹, and amino acids with velvety-like and umami taste totally accounted for 65.39%. The high content of amino acids and low ratio of polyphenols to total amino acids were attributed to strong umami and mellow taste of MLGT. Moreover, catechins and alkaloids were abundant in MLGT, where EGCG (85.82 mg g⁻¹) and caffeine (33.78 mg g⁻¹) were at highest content. Analyses of chemical compositions revealed excellent quality of MLGT. Correspondingly, MLGT showed potent hypolipidemic activity, and water extract of MLGT at 200 μg mL⁻¹ significantly reduced lipid level to 43.06% of high-fat zebrafish. Results firstly revealed the quality characteristics of MLGT and provided further insights into bioactivity of Laochuancha.

MLGT was investigated for the first time, and results revealed excellent quality and potent hypolipidemic activity of MLGT.