Use of Electronic Nose and Tongue to Track Freshness of Cherry Tomatoes Squeezed for Juice Consumption: Comparison of Different Sensor Fusion Approaches

By-product hazelnut seed skin characteristics and properties in terms of use in food processing and human nutrition

The hazelnut seed skins (HSS) are by-products from roasting or blanching hazelnuts without direct second utilization. The generation of HSS creates an economic and environmental problem. The object of the study was a comprehensive analysis of the properties for reuse of HSS. Water extraction of industrial HSS was applied (water with sonication of the HSS for 10 min at 90 ℃). The extracts obtained were freeze-dried to facilitate analysis and future application. The HSS and their extracts were analysed. Polyphenols, antioxidants, allergens, antimicrobial properties and instrumental sensory analysis were examined. The total polyphenol content in the samples was 37.8-44.0 mg gallic acid equivalent g-1. Gallic acid was the major phenolic compound. The antioxidant capacity of the samples was 198.9-250.6 mg VCEAC g-1 (vitamin C equivalent) according to the ABTS method and 98.4-106.8 mg VCEAC g-1 in the DPPH method. The extracts inhibited all tested strains of pathogenic bacteria. Allergen content was reduced in HSS and the extracts. Instrumental sensory analysis showed differences between taste parameters and odour profile samples. HSS can be reused in food production as a bacteriostatic, antioxidant additive and sensory-creating factor due to various chemical compounds corresponding with taste and odour.

A Study on Origin Traceability of White Tea (White Peony) Based on Near-Infrared Spectroscopy and Machine Learning Algorithms

Identifying the geographical origins of white tea is of significance because the quality and price of white tea from different production areas vary largely from different growing environment and climatic conditions. In this study, we used near-infrared spectroscopy (NIRS) with white tea (n = 579) to produce models to discriminate these origins under different conditions. Continuous wavelet transform (CWT), min-max normalization (Minmax), multiplicative scattering correction (MSC) and standard normal variables (SNV) were used to preprocess the original spectra (OS). The approaches of principal component analysis (PCA), linear discriminant analysis (LDA) and successive projection algorithm (SPA) were used for features extraction. Subsequently, identification models of white tea from different provinces of China (DPC), different districts of Fujian Province (DDFP) and authenticity of Fuding white tea (AFWT) were established by K-nearest neighbors (KNN), random forest (RF) and support vector machine (SVM) algorithms. Among the established models, DPC-CWT-LDA-KNN, DDFP-OS-LDA-KNN and AFWT-OS-LDA-KNN have the best performances, with recognition accuracies of 88.97%, 93.88% and 97.96%, respectively; the area under curve (AUC) values were 0.85, 0.93 and 0.98, respectively. The research revealed that NIRS with machine learning algorithms can be an effective tool for the geographical origin traceability of white tea.

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 gas chromatography-ion mobility spectrometry (GC-IMS) and ultrafast gas chromatography electronic-nose (uf-GC E-nose) to distinguish four Chinese freshwater fishes at both raw and cooked status

The volatile organic compounds (VOCs) in four Chinese freshwater fishes (i.e., Hypophthalmichthys molitrix (H), Aristichthys nobilis (A), Lateolabrax japonicus (L), Parabramis pekinensis (P)) were separated using gas chromatography-ion mobility spectrometry (GC-IMS) and ultrafast gas chromatography electronic-nose (uf-GC E-nose). Principal component analysis (PCA) was applied to distinguish the VOCs identified from the four freshwater fishes in both raw and cooked states. Twenty compounds were identified from the spectral database of GC-IMS, including five aldehydes, eight alcohols, six ketones, and three esters. In addition, using GC E-nose, 32 compounds were isolated by the first column MTX-5, and 24 compounds were isolated by the second column MXT-1701. PCA results showed that the four fishes could be well discriminated against. The odor profiles of raw and cooked fishes were clearly different. This study demonstrated that specific signals provided from GC-IMS could differentiate freshwater fishes. GC-IMS and uf-GC E-nose could be developed further to distinguish aquatic products based on VOCs. PRACTICAL APPLICATIONS: Two new methods, gas chromatography-ion mobility spectrometry (GC-IMS) and ultrafast gas chromatography electronic-nose (uf-GC E-nose), were used to analyze the volatile organic compounds (VOCs) in four Chinese freshwater fishes at raw and cooked status. GC-IMS has the characteristics of fast detection speed and high sensitivity. The accuracy of the qualitative analysis of the compounds is better with GC-IMS (larger data volume, leading to a better in-depth statistical analysis). Uf-GC E-nose could provide a nondestructive, fast, relatively low cost, and trustworthy way for flavor analysis. According to the techniques, the established fingerprints of VOCs provided an additional tool for food analysis.

Biodegradable Food Packaging Materials and Prospects of the Fourth Industrial Revolution for Tomato Fruit and Product Handling

The environment and food safety are major areas of concern influencing the development of biodegradable packaging for partial replacement of petrochemical-based polymers. This review is aimed at updating the recent advances in biodegradable packaging material and the role of virtual technology and nanotechnology in the tomato supply chain. Some of the common biodegradable materials are gelatin, starch, chitosan, cellulose, and polylactic acid. The tensile strength, tear resistance, permeability, degradability, and solubility are some of the properties defining the selection and utilization of food packaging materials. Biodegradable films can be degraded in soil by microbial enzymatic actions and bioassimilation. Nanoparticles are incorporated into blended films to improve the performance of packaging materials. The prospects of the fourth industrial revolution can be realized with the use of virtual platforms such as sensor systems in authentification and traceability of food and packaging products. There is a research gap on the development of a hybrid sensor system unit that can integrate sampling headspace (SHS), detection unit, and data processing of big data for heterogeneous tomato-derived volatiles. Principal component analysis (PCA), linear discriminant analysis (LDA), and artificial neutral network (ANN) are some of the common mathematical models for data interpretation of sensor systems.

Detection and Quantification of Tomato Paste Adulteration Using Conventional and Rapid Analytical Methods

Tomato, and its concentrate are important food ingredients with outstanding gastronomic and industrial importance due to their unique organoleptic, dietary, and compositional properties. Various forms of food adulteration are often suspected in the different tomato-based products causing major economic and sometimes even health problems for the farmers, food industry and consumers. Near infrared (NIR) spectroscopy and electronic tongue (e-tongue) have been lauded as advanced, high sensitivity techniques for quality control. The aim of the present research was to detect and predict relatively low concentration of adulterants, such as paprika seed and corn starch (0.5, 1, 2, 5, 10%), sucrose and salt (0.5, 1, 2, 5%), in tomato paste using conventional (soluble solid content, consistency) and advanced analytical techniques (NIR spectroscopy, e-tongue). The results obtained with the conventional methods were analyzed with univariate statistics (ANOVA), while the data obtained with advanced analytical methods were analyzed with multivariate methods (Principal component analysis (PCA), linear discriminant analysis (LDA), partial least squares regression (PLSR). The conventional methods were only able to detect adulteration at higher concentrations (5–10%). For NIRS and e-tongue, good accuracies were obtained, even in identifying minimal adulterant concentrations (0.5%). Comparatively, NIR spectroscopy proved to be easier to implement and more accurate during our evaluations, when the adulterant contents were estimated with R² above 0.96 and root mean square error (RMSE) below 1%.

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.

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).

Tracing Geographical Origins of Teas Based on FT-NIR Spectroscopy: Introduction of Model Updating and Imbalanced Data Handling Approaches

This work presents a reliable approach to trace teas' geographical origins despite changes in teas caused by different harvest years. A total of 1447 tea samples collected from various areas in 2014 (660 samples) and 2015 (787 samples) were detected by FT-NIR. Seven classifiers trained on the 2014 dataset all succeeded to trace origins of samples collected in 2014; however, they all failed to predict origins for the 2015 samples due to different data distributions and imbalanced dataset. Three outlier detection based undersampling approaches-one-class SVM (OC-SVM), isolation forest and elliptic envelope-were then proposed; as a result, the highest macro average recall (MAR) for the 2015 dataset was improved from 56.86% to 73.95% (by SVM). A model updating approach was also applied, and the prediction MAR was significantly improved with increase in the updating rate. The best MAR (90.31%) was first achieved by the OC-SVM combined SVM classifier at a 50% rate.

Smart Animal Agriculture: Application of Real-Time Sensors to Improve Animal Well-Being and Production

Consumption of animal products such as meat, milk, and eggs in first-world countries has leveled off, but it is rising precipitously in developing countries. Agriculture will have to increase its output to meet demand, opening the door to increased automation and technological innovation; intensified, sustainable farming; and precision livestock farming (PLF) applications. Early indicators of medical problems, which use sensors to alert cattle farmers early concerning individual animals that need special care, are proliferating. Wearable technologies dominate the market. In less-value-per-animal systems like sheep, goat, pig, poultry, and fish, one sensor, like a camera or robot per herd/flock/school, rather than one sensor per animal, will become common. PLF sensors generate huge amounts of data, and many actors benefit from PLF data. No standards currently exist for sharing sensor-generated data, limiting the use of commercial sensors. Technologies providing accurate data can enhance a well-managed farm. Development of methods to turn the data into actionable solutions is critical.

Effects of 3 Feeding Modes on the Volatile and Nonvolatile Compounds in the Edible Tissues of Female Chinese Mitten Crab (Eriocheir sinensis)

To reveal the impact of different feeding modes on the flavor quality of female Chinese mitten crab (Eriocheir sinensis) this study was conducted to compare the sensory evaluation scores, flavor compounds in meat and hepatopancreas of female E. sinensis fed with 3 feeding modes, that is, natural diets (NDs), traditional diets (TDs), and formulated diets (FDs). The result showed that crabs fed with ND had significantly lower sensory scores than the other 2 feeding modes in both edible tissues. The odor and taste profiles were evaluated by Electronic nose (E-nose) and tongue (E-tongue) techniques, respectively; results of perchloric acid showed each edible tissue had significant differences among the 3 modes. Contents of volatile compounds were measured by Headspace-solid phase micro extraction combined with gas chromatography-mass spectrometer. A total of 35 and 44 volatile compounds were identified in meat and hepatopancreas, respectively. ND mode of meat had the highest relative odor activity value (ROAV) summation among the 3 diet modes. TD mode of hepatopancreas had significantly higher ROAV summations. Based on the analysis of free amino acids and 5'-nucleotides, nonvolatile compounds were evaluated by equivalent umami concentration (EUC) and taste active values (TAVs) methods. For both meat and hepatopancreas, TD had the highest contents of umami amino acid, as for the 5'-nucleotide, FD had the highest 5'-inosin monophosphate concentrations. Overall, the EUC and TAVs of TD were higher than that of FD, whereas ND mode had the lowest values in the 2 edible tissues. In conclusion, TD mode had the best performance in terms of sensory evaluation, ROAVs of aroma-active compounds, and nonvolatile active compounds.