Intelligent evaluation of color sensory quality of black tea by visible-near infrared spectroscopy technology: A comparison of spectra and color data information

Correlation analysis and modeling application from objective indicators to subjective evaluation of scented tea: A case study of rose tea

Different scented teas provide various choices for consumers from appearance, aroma, flavor and others. Aiming to define advantages and market positions of different scented teas and promote optimization of market structure, characteristics for scented tea favored by consumers and outstanding attributes of different scented teas should be clarified. Rose tea was taken as study object. Sensory evaluation and consumer acceptance were investigated. GC-MS and HPLC fingerprints were established. Physicochemical characteristics were determined. RGB integration analysis was inventively proposed for correlation analysis. The volatile compounds with spicy, green or herbal odor as camphene, β-phenethyl acetate, eugenol, and physicochemical parameters as antioxidant capacity, reducing sugar content, pH showed positive correlation with popular sensory properties. Six models for consumer preference by objective description were built through GA-SVR (accuracy = 1), and APP was developed. The research mode of scented tea has been successfully established to study multiple subjective characteristics with measurable objective parameters.

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

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

Characterization of phenolic components of black teas of different origins and the effect of brewing duration on quality properties

This research aims to identify the phytochemical constituents of 79 different samples of black tea, including varieties from India, Iran (IrT), Turkey (TT), and Sri Lanka. In addition, this study investigates the effect of varying brewing times on the quality characteristics of tea. Therefore, we analyzed the phytochemical content of tea using a novel LC-MS/MS method that we developed, which identifies 53 different phenolic compounds. Furthermore, objective evaluations were conducted on the total phenolic compound, total flavonoid compound, antioxidant activity, and color values at 15, 30, and 60-min brewing intervals. The prevailing phenolic compounds discovered in the corresponding tea classifications were quantitatively analyzed to be quinic acid, epicatechin gallate, epigallocatechin gallate, epicatechin, epigallocatechin, gallic acid, nicotiflorine, and isoquercitrin. The study found that the TT and IrT groups had the richest phytochemical content and the highest antioxidant activity. The Turkish tea group had the highest measurement for the desired red color, which is considered a sensory property. Infusion color, antioxidant activity, and total phenolic and flavonoid contents showed significant increases with prolonged brewing time. It was important to note that the chemical composition of tea varies according to its origin and brewing conditions. Extending the brewing time improved the quality of the tea. It should be noted, however, that longer brewing times result in a more intense release of flavonoids, and this increase may have a pro-oxidant effect.

Multi-level color classification of post-consumer plastic packaging flakes by hyperspectral imaging for optimizing the recycling process

In a circular economy perspective, the development of fast and efficient sensor-based recognition strategies of plastic waste, not only by polymer but also by color, plays a crucial role for the production of high quality secondary raw materials in recycling plants. In this work, mixed colored flakes of high-density polyethylene (HDPE) from packaging waste were simultaneously classified by hyperspectral imaging working in the visible range (400-750 nm), combined with machine learning. Two classification models were built and compared: (1) Partial Least Square-Discriminant Analysis (PLS-DA) for 6 HDPE macro-color classes identification (i.e., white, blue, green, red, orange and yellow) and (2) hierarchical PLS-DA for a more accurate discrimination of the different HDPE color tones, providing as output 14 color classes. The obtained classification results were excellent for both models, with values of Recall, Specificity, Accuracy, and F-score in prediction close to 1. The proposed methodological approach can be utilized as sensor-based sorting logic in plastic recycling plants, tuning the output based on the required needs of the recycling plant, allowing to obtain a high-quality recycled HDPE of different colors, optimizing the plastic recycling process, in agreement with the principles of circular economy.

Rapid identification of the variety of maize seeds based on near-infrared spectroscopy coupled with locally linear embedding

Maize is the main cereal crop in China. In the process of maize planting, the selection of suitable maize varieties is an important link to achieving a high yield. Because the appearance of maize seeds is very similar, it is difficult to accurately identify their species with the naked eye. In order to realize the rapid identification of different varieties of maize seeds, this paper proposes a rapid identification method of maize varieties based on near-infrared (NIR) spectroscopy coupled with locally linear embedding (LLE) and a support vector machine (SVM). The NIR data, preprocessed by multiple scattering correction (MSC), were dimensionally reduced by LLE, a principal component analysis (PCA), and isometric mapping (Isomap), and combined with SVM to establish a maize variety identification model. The results show that the LLE-SVM model has the best performance, whose classification accuracy and kappa coefficient of the test set can reach 100% and 1.00. The classification accuracy and kappa coefficient of the LLE-SVM model are better than the PCA-SVM model and Isomap-SVM model. Therefore, LLE can reduce the complexity of the model and improve the accuracy of the model. It can be used for the rapid identification of maize varieties and provide a new idea for the classification and identification of other agricultural products.

Regenerative Flexible Upconversion-Luminescence Biosensor for Visual Detection of Diethylstilbestrol Based on Smartphone Imaging

Diethylstilbestrol (DES), an endocrine disrupting chemical, has been linked to serious health problems in humans. In this work, a regenerative flexible upconversion-fluorescence biosensor was designed for the detection of DES in foodstuffs and environmental samples. Herein, amino-functionalized upconversion nanoparticles (UCNPs) were synthesized and immobilized on the surface of a flexible polydimethylsiloxane substrate, which was further modified with complementary DNA and dabcyl-labeled DES aptamer. The fluorescence resonance energy transfer (FRET) system was established for DES detection between dabcyl and UCNPs as the acceptor and donor pairs, respectively, which resulted in the quenching of the upconversion luminescence intensity. In the presence of a target, the FRET system was destroyed and upconversion fluorescence was restored due to the stronger affinity of the aptamer toward DES. The designed biosensor was also implemented in a dual-mode signal readout based on images from a smartphone and spectra from a spectrometer. Under the optimized experimental conditions, good linear relationships were achieved based on imaging (y = 53.055x + 36.175, R² = 0.9851) and spectral data (y = 1.1582x + 1.9561, R² = 0.9897). The designed biosensor revealed great practicability with a spiked recovery rate of 77.91-97.95% for DES detection in real environment and foodstuff samples. Furthermore, the proposed biosensor was regenerated seven times with an accuracy threshold of 80% demonstrating its durability and reusability. Thus, this biosensor is expected to be applied to point-of-care and on-site detection based on the developed portable smartphone device and android application.

Physicochemical indicators coupled with multivariate analysis for comprehensive evaluation of matcha sensory quality

The sensory quality of matcha is a pivotal factor in determining consumer acceptance. However, the human sensory panel test is difficult to popularize by virtue of professional requirements and inability to evaluate large samples. The analysis showed that physicochemical indicators of matcha were significantly related to sensory quality. Hence, principal component analysis (PCA) based on selected key physicochemical indicators was proposed to evaluate the sensory quality of matcha in this research. The eight key indicators were selected from twenty-four physicochemical indicators based on least absolute shrinkage and selection operator (LASSO) for the establishment of the PCA comprehensive evaluation model. The results demonstrated that the PCA comprehensive evaluation model achieved superior performance, with -0.895 r_c (correlation coefficient in calibration set) and -0.883 r_p (correlation coefficient in prediction set) for overall sensory quality. This work demonstrated that LASSO-PCA comprehensive evaluation as an objective protocol has great potential in predicting matcha sensory quality.

Development of a novel green tea quality roadmap and the complex sensory-associated characteristics exploration using rapid near-infrared spectroscopy technology

Nondestructive instrumental identification of the green tea quality instead of professional human panel tests is highly desired for industrial application recently. The special flavor is a key quality-trait that influence consumer preference. However, flavonoids, as well as sensory-associated compounds, which play a critical role in the quality-traits profile of green tea samples have been poorly investigated. In this study, we were proposing an objective and accurate near infrared spectroscopy (NIRS) profile to support quality control within the entire green tea sensory evaluation chain, the complexity of green tea samples' sensory analysis was performed by two complementary methods: the standard calculation and the novel NIRS roadmap coupled with chemometrics. The green tea samples' physical quality, gustatory index, and nutritional index were measured respectively, which taking into consideration the gustatory evaluation of green tea for five commercially representative overall quality ("very bad", "bad", "regular", "good" and "excellent"). Our findings highlight the underexplored role of NIRS in chemical-to-sensory relationships and its widespread importance and utility in green tea quality improvement. Collectively, the comprehensive characterization of sensory-associated attribution allowed the identification of a wide array of spectrometric features, mostly related to moisture, soluble solids (SS), tea polyphenol (TPP), epigallocatechin gallate (EGCG), epicatechin (EC) and tea polysaccharide (TPS), which can be used as putative biomarkers to rapidly evaluate the green tea flavor variations related to rank differences. Otherwise, the NIRS' data were split into the calibration (n = 80) and prediction (n = 40) set independently, which showed high correlation coefficient with Rp-values of 0.9024, 0.9020 in physical and total cup quality, respectively. In this research, we demonstrated that NIRS was an easily-generated strategy and able to close the loop to feedback into the process for advanced process control. However, the established models should be improved by more green tea samples from different regions.

Deciphering superior quality of Pu-erh tea from thousands of years' old trees based on the chemical profile

Pu-erh teas from thousands of years' old trees (TPT) equip with both superior flavors and powerful antioxidative capacities. With UHPLC-Q-TOF-MS approach, TPTs' chemical profiles were characterized by comparing with Pu-erh teas from ecological trees (EPT). TPTs are discovered to possess higher contents of amino acids, fatty acids, phenolic acids, nucleosides and nucleobases but lower contents of flavonoids and caffeine congeners based on 117 discriminative constituents from 305 identified ones. Particularly, a series of caffeic acid congeners including ten new hydroxycinnamic acid depsides with higher contents in TPTs are discovered, and caffeic acid with a fold change of 638 is the foremost discriminative component. Furthermore, distinguishing constituent proportion including caffeic acid congeners in TPTs are found to take great responsibilities for their more powerful antioxidative abilities and superior flavors especially more aroma and pleasant bitterness. This research provides information for deciphering formation of TPTs' superior qualities based on chemical profile.

Intelligent evaluation of taste constituents and polyphenols-to-amino acids ratio in matcha tea powder using near infrared spectroscopy

Matcha tea is rich in taste and bioactive constituents, quality evaluation of matcha tea is important to ensure flavor and efficacy. Near-infrared spectroscopy (NIR) in combination with variable selection algorithms was proposed as a fast and non-destructive method for the quality evaluation of matcha tea. Total polyphenols (TP), free amino acids (FAA), and polyphenols-to-amino acids ratio (TP/FAA) were assessed as the taste quality indicators. Successive projections algorithm (SPA), genetic algorithm (GA), and simulated annealing (SA) were subsequently developed from the synergy interval partial least squares (SiPLS). The overall results revealed that SiPLS-SPA and SiPLS-SA models combined with NIR exhibited higher predictive capabilities for the effective determination of TP, FAA and TP/FAA with correlation coefficient in the prediction set (R_p) of R_p > 0.97, R_p > 0.98 and R_p > 0.98 respectively. Therefore, this simple and efficient technique could be practically exploited for tea quality control assessment.

Simultaneous quantification of chemical constituents in matcha with visible-near infrared hyperspectral imaging technology

This study aimed to assess the feasibility of identifying multiple chemical constituents in matcha using visible-near infrared hyperspectral imaging (VNIR-HSI) technology. Regions of interest (ROIs) were first defined in order to calculate the representative mean spectrum of each sample. Subsequently, the standard normal variate (SNV) method was applied to correct the characteristic spectra. Competitive adaptive reweighted sampling (CARS) and bootstrapping soft shrinkage (BOSS) were used to optimize the models. They were built based on partial least squares (PLS), creating two models referred to as CARS-PLS and BOSS-PLS. The BOSS-PLS models achieved best predictive accuracy, with coefficients of determination predicted to be 0.8077 for caffeine, 0.7098 for tea polyphenols (TPs), 0.7942 for free amino acids (FAAs), 0.8314 for the ratio of TPs to FAAs, and 0.8473 for chlorophyll. These findings highlight the potential of VNIR-HSI technology as a rapid and nondestructive alternative for simultaneous quantification of chemical constituents in matcha.

Identification of tea based on CARS-SWR variable optimization of visible/near-infrared spectrum

BACKGROUND

The identification of tea varieties is essential to obtain high-quality tea that can command a high price. To identify tea varieties quickly and non-destructively, and to fight against counterfeit and inferior products in the tea market, a new method of visible / near-infrared spectrum processing based on competitive adaptive reweighting algorithms-stepwise regression analysis (CARS-SWR) variable optimization is proposed in this paper.

RESULTS

The spectral data of five different tea varieties were obtained by visible / near-infrared spectrometry. The spectral data were preprocessed by the multivariate scattering correction (MSC) algorithm. First, 20 wavelength variables were selected by CARS, and then six optimal wavelength variables were selected using the SWR method, based on the CARS optimal variables. The generalized regression neural network (GRNN) classification model and probabilistic neural network (PNN) classification model were established, based on spectral information from the full wavelength, the CARS preferred wavelength variable, the SWR preferred wavelength variable, and the CARS-SWR preferred wavelength variable.

CONCLUSION

It was found that the CARS-SWR-PNN model had the best classification effect by comparing different modeling results. The classification accuracy of its training set and test set reached 100%. This shows that the CARS-SWR preferred variable method combined with the visible / near-infrared spectrum is feasible for the rapid and non-destructive identification of tea varieties. © 2019 Society of Chemical Industry.

Evaluating aroma quality of black tea by an olfactory visualization system: Selection of feature sensor using particle swarm optimization

Aroma is an important index to evaluate the quality and grade of black tea. This work innovatively proposed the sensory evaluation of black tea aroma quality based on an olfactory visual sensor system. Firstly, the olfactory visualization system, which can visually represent the aroma quality of black tea, was assembled using a lab-made color sensitive sensor array including eleven porphyrins and one pH indicator for data acquisition and color components extraction. Then, the color components from different color sensitive spots were optimized using the particle swarm optimization (PSO) algorithm. Finally, the back propagation neural network (BPNN) model was developed using the optimized characteristic color components for the sensory evaluation of black tea aroma quality. Results demonstrated that the BPNN models, which were developed using three color components from FTPPFeCl (component G), MTPPTE (component B) and BTB (component B), can get better results based on comprehensive consideration of the generalization performance of the model and the fabrication cost of the sensor. In the validation set, the average of correlation coefficient (R_P) value was 0.8843 and the variance was 0.0362. The average of root mean square error of prediction (RMSEP) was 0.3811 and the variance was 0.0525. The overall results sufficiently reveal that the optimized sensor array has promising applications for the sensory evaluation of black tea products in the process of practical production.

Detection of Type, Blended Ratio, and Mixed Ratio of Pu'er Tea by Using Electronic Nose and Visible/Near Infrared Spectrometer

The objective of this study was to find an intelligent and fast method to detect the type, blended ratio, and mixed ratio of ancient Pu’er tea, which is significant in maintaining order in the Pu’er tea industry. An electronic nose (E-nose) and a visible near infrared spectrometer (VIS/NIR spectrometer) were applied for tea sampling. Feature extraction was conducted using both the traditional method and a convolutional neural network (CNN) technique. Linear discriminant analysis (LDA) and partial least square regression (PLSR) were applied for pattern recognition. After sampling while using the traditional method, the analysis of variance (ANOVA) results showed that the mean differential value of each sensor should be selected as the optimal feature extraction method for E-nose data, and raw data comparison results showed that 19 peak/valley values and two slope values were extracted. While the format of E-nose data was in accord with the input format for CNN, the VIS/NIR spectrometer data required matrixing to meet the format requirements. The LDA and PLSR analysis results showed that CNN has superior detection ability, being able to acquire more local features than the traditional method, but it has the risk of mixing in redundant information, which can act to reduce the detection ability. Multi-source information fusion (E-nose and VIS/NIR spectrometer fusion) can collect more features from different angles to improve the detection ability, but it also contains the risk of adding redundant information, which reduces the detection ability. For practical detection, the type of Pu’er tea should be recognizable using a VIS/NIR spectrometer and the traditional feature extraction method. The blended ratio of Pu’er tea should also be identifiable by using a VIS/NIR spectrometer with traditional feature extraction. Multi-source information fusion with traditional feature extraction should be used if the accuracy requirement is extremely high; otherwise, a VIS/NIR spectrometer with traditional feature extraction is preferred.

Fast sensing of imidacloprid residue in tea using surface-enhanced Raman scattering by comparative multivariate calibration

This study focused on the fabrication of a rapid, highly sensitive and inexpensive technique for the quantification of imidacloprid residue in green tea, based on surface-enhanced Raman scattering (SERS) using highly roughned surface flower shaped silver nanostructure (as SERS substrate) coupled with the chemometrics algorithm. The basic principle of this method is imidacloprid yielded SERS signal after adsorption on Ag-NF under laser excitation by the electromagnetic enhancement and the intensity of the peak is proportional to the concentration ranging from 1.0 × 10³ to 1.0 × 10^-4 μg/mL. Among the models used, the GA-PLS (Genetic algorithm-partial least square) exhibited superiority to quantify imidacloprid residue in green tea. The model achieved Rp (correlation coefficient) of 0.9702 with RPD of 4.95% in the test set and RSD for precision recorded up to 4.50%. Therefore, the proposed sensor could be employed to quantify imidacloprid residue in green tea for the safeguarding of quality and human health.

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.