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BACKGROUND Grading represents an essential criterion for the quality assurance of black tea. The main objectives of the study were to develop a highly robust model for Chinese black tea of seven grades based on cognitive spectroscopy. RESULTS: Cognitive spectroscopy was proposed to combine near‐infrared spectroscopy (NIRS) with machine learning and evolutionary algorithms, selected feature information from complex spectral data and show the best results without human intervention. The NIRS measuring system was used to obtain the spectra of Chinese black tea samples of seven grades. The spectra acquired were preprocessed by standard normal variate transformation (SNV), multiplicative scatter correction (MSC) and minimum/maximum normalization (MIN/MAX), and the optimal pretreating method was implemented using principal component analysis combined with linear discriminant analysis algorithm. Three feature selection evolutionary algorithms, which were a genetic algorithm (GA), simulated annealing (SA) and particle swarm optimization (PSO), were compared to search the best preprocessed characteristic wavelengths. Cognitive models of Chinese black tea ranks were constructed using extreme learning machine (ELM), K‐nearest neighbor (KNN) and support vector machine (SVM) methods based on the selected characteristic variables. Experimental results revealed that the PSO–SVM model showed the best predictive performance with the correlation coefficients of prediction set (Rp) of 0.9838, the root mean square error of prediction (RMSEP) of 0.0246, and the correct discriminant rate (CDR) of 98.70%. The extracted feature wavelengths were only occupying 0.18% of the origin. CONCLUSION: The overall results demonstrated that cognitive spectroscopy could be utilized as a rapid strategy to identify Chinese black tea grades. © 2020 Society of Chemical Industry [ABSTRACT FROM AUTHOR]

The main objectives of the study are to understand and explore critical feature wavelengths of the obtained near-infrared (NIR) data relating to dianhong black tea quality categories, we propose a multi-variable selection strategy based on the variable space optimization from big to small which is the kernel idea of a variable combination of the improved genetic algorithm (IGA) and particle swarm optimization (PSO) in this study. A rapid description based on the NIR technology is implemented to assess black tea tenderness and rankings. First, 700 standard samples from dianhong black tea of seven quality classes are scanned using a NIR system. The raw spectra acquired are preprocessed by Savitzky-Golay (SG) filtering coupled with standard normal variate transformation (SNV). Then, the multi-variable selection algorithm (IGA-PSO) is applied to compare with the single method (the IGA and PSO) and search the optimal characteristic wavelengths. Finally, the identification models are developed using a decision tree (DT), partial least-squares discriminant analysis (PLS-DA), and support vector machine (SVM) based on different kernel functions combined with the effective features from the above variables screening paths for the discrimination of black tea quality. The results show that the IGA-PSO-SVM model with a radial basis function achieves the best predictive results with the correct discriminant rate (CDR) of 95.28% based on selected four characteristic variables in the prediction process. The overall results demonstrate that NIR combined with a multi-variable selection method can constitute a potential tool to understand the most important features involved in the evaluation of dianhong black tea quality helping the instrument manufacturers to achieve the development of low-cost and handheld NIR sensors. Unlabelled Image • Rapid description of black tea quality based on multi-variable selection strategy • Feature information was selected by the GA, IGA and PSO. • Classification models for black tea categories developed using DT, PLS-DA and SVM • The SVM discriminator is implemented with four different kernel functions. • The IGA-PSO-SVM model obtained better predictive results based on four feature wavelengths. [ABSTRACT FROM AUTHOR]

• Assessing tea rank by Vis-NIR spectra and a hybrid variable selection strategy. • Feature data are selected by the GA, IRIV, VCPA, VCPA-GA, and VCPA-IRIV. • Authentication models for tea ranks are created using LSSVM, RF, and BPNN. • The VCPA-IRIV-LSSVM model obtains superior performance. Tea is one of the most consumed and popular beverages around the world. Authenticating tea quality is essential to resist counterfeit goods and prevent fraud. This study innovatively aims to use a visible-near infrared (Vis-NIR) sensor combined with a variable combination population analysis (VCPA)-based hybrid variable selection algorithm to realize the authentication of tea quality. First, the spectra of 700 tea samples from seven different grades are acquired using a Vis-NIR sensor. Second, the raw spectra acquired are preprocessed by Savitzky-Golay convolution smoothing combined with first derivative (SG1), standard normal variate transformation (SNV), and multiplicative scatter correction (MSC), and the best pretreating technique is determined by support vector machine (SVM) algorithm. Third, the VCPA-based hybrid method optimizes the variable space of the optimal pretreated spectra data in the first step, and a genetic algorithm (GA) and iteratively retaining informative variables (IRIV) are utilized to execute further optimization and shrink the variable in the second step. Finally, authentication models of tea quality are constructed employing least squares support vector machine (LSSVM), random forest (RF), and back propagation neural network (BPNN) approaches based on the selected variables. The results show that the VCPA-IRIV-LSSVM model obtains the best predictive performance with an accuracy of 97.40% in the predictive process. Our findings demonstrate that it is feasible to apply the inexpensive vis-NIR sensor to assess quality attributes in tea. Also, the VCPA-based hybrid method can search the combination of the optimal wavelength and improve the identification accuracy and robustness of the model and can assist sensor manufacturers in developing a cheap and user-friendly smartphone-coupled micro spectroscopic sensor for authentication of tea quality. [ABSTRACT FROM AUTHOR]

Instrumental test of black tea samples instead of human panel test is attracting massive attention recently. This study focused on an investigation of the feasibility for estimation of the color sensory quality of black tea samples using the VIS-NIR spectroscopy technique, comparing the performances of models based on the spectra and color information. In model calibration, the variables were first selected by genetic algorithm (GA); then the nonlinear back propagation-artificial neural network (BPANN) models were established based on the optimal variables. In comparison with the other models, GA-BPANN models from spectra data information showed the best performance, with the correlation coefficient of 0.8935, and the root mean square error of 0.392 in the prediction set. In addition, models based on the spectra information provided better performance than that based on the color parameters. Therefore, the VIS-NIR spectroscopy technique is a promising tool for rapid and accurate evaluation of the sensory quality of black tea samples. [ABSTRACT FROM AUTHOR]

• A novel solution phase CSA was used for monitoring black tea fermentation. • UV-visible spectroscopy was employed to obtain CSA features in real time. • The accuracy of CARS-SVM model for discriminating fermentation degree was 100%. • Accurate prediction models for catechins and theaflavins were obtained. Rapid monitoring of fermentation quality has been the key to realizing the intelligent processing of black tea. In our study, mixing ratios, sensing array components and reaction times were optimized before an optimal solution phase colorimetric sensor array was constructed. The characteristic spectral information of the array was obtained by UV–visible spectroscopy and subsequently combined with machine learning algorithms to construct a black tea fermentation quality evaluation model. The competitive adaptive reweighting algorithms (CARS)-support vector machine model discriminated the black tea fermentation degree with 100% accuracy. For quantification of catechins and four theaflavins (TF, TFDG, TF-3-G, and TF-3′-G), the correlation coefficients of the CARS least square support vector machine model prediction set were 0.91, 0.86, 0.76, 0.72 and 0.79, respectively. The results obtained within 2 min enabled accurate monitoring of the fermentation quality of black tea, which provides a new method and idea for intelligent black tea processing. [ABSTRACT FROM AUTHOR]

[Display omitted] • Six from fifteen pH indicators sensitive to the gas of withered leaves were selected. • An effective gas sensor array including six selected pH indicators was prepared. • Sensor array combining with hyperspectral imaging were used for withering evaluation. • PCA effectively reduced dimensionality of HSI data and improved model's accuracy. • Developed LS-SVM model can accurately determine withering degree of black tea. Withering is one of the most critical steps in the processing of black tea. The degree of withering affects the aroma quality of the finished tea. In this study, we used a pH indicator-based colorimetric sensor array in combination with hyperspectral imaging to intelligently evaluate the withering degree. After analyzing the difference between images taken before and after the reaction of pH indicators with withered leaves, six pH indicators were selected to build a sensor array. Then, the hyperspectral image of each pH indicator was obtained at wavelengths between 400 and 1000 nm. Nonlinear support vector machine (SVM) and least-squares (LS) SVM models were established to determine the degree of withering. Results revealed that the spectral information from single pH indicator failed to accurately evaluate the withering degree. The LS-SVM model achieved satisfactory discriminant results with the low-level data fusion of six pH indicators followed by principal component analysis for dimensionality reduction. The optimal model yielded accuracies of 93.75% and 90.00% for the calibration and prediction sets, respectively. The results indicated that colorimetric sensor array in combination with hyperspectral imaging can effectively determine the withering degree, thus providing a novel method for the intelligent processing of food and tea. [ABSTRACT FROM AUTHOR]

[Display omitted] • A novel spectral-based gas monitoring method was proposed. • Image and spectral feature information of CSA were extracted and compared. • An N-TPP-based CSA was firstly applied in black tea fermentation monitoring. The rapid and scientific method for monitoring the quality of black tea fermentation is of great significance to the quality control of black tea production. This study proposed a novel method for evaluating the fermentation quality of black tea by using hyperspectral imaging technology with self-assembled nanoporphyrin (N-TPP) dyes, which were used as aroma capture probes in the black tea fermentation process. Scanning electron microscopy and ultraviolet–visible spectroscopy were performed to characterize the N-TPP. Then, the results of the colorimetric sensor array (conventional camera color method) and the proposed hyperspectral methods were compared. Finally, the hyperspectral information of N-TPP with higher sensitivity was collected, and the qualitative models of evaluating black tea fermentation quality were established using support vector machine (SVM), extreme learning machine, and linear discriminant analysis. Among these models, the SVM model exhibited the highest discriminant accuracy. The accuracy of the SVM model based on the hyperspectral information of the self-assembled N-TPP array was 98.85 %, which was considerably higher than that (68.97 %) of the SVM model based on the color information of the porphyrin array. The results revealed that the proposed method can effectively improve the monitoring accuracy of black tea fermentation quality. [ABSTRACT FROM AUTHOR]

Withering of leaves is an important step in the processing of black tea and determines the taste and aroma of the tea. Currently, the withering degree of the leaves is mainly determined via sensory evaluation by the tea maker, and this is time-consuming and laborious. In this study, near-infrared spectroscopy, electronic eye, and colorimetric sensing array technologies were combined to evaluate the extent of withering. A low-cost micro-spectrometer, a self-built machine vision system, and a homemade colorimetric array were used to capture relevant information regarding the withered tea leaf samples in situ. Additionally, low- and middle-level data fusion strategies were modeled and compared using a support vector machine (SVM). The SVM model, which combined the information obtained using the three technologies, performed substantially better than a single technology. Low-level fusion achieved acceptable discriminant performance, with an accuracy of 90.00% for both the SVM and principal component analysis–SVM models for the prediction set. The middle-level fusion strategy achieved better performance than the low-level fusion strategy, with an optimal discriminant accuracy of 97.50% for SVM model. Hence, this study demonstrated that in situ and low-cost quality assessment and intelligent control of the withering process of black tea leaves are possible. [Display omitted] • E-nose, E-eye and NIRS were jointly used to monitor withering degree of black tea. • Low- and middle-level data fusion were critically used and compared. • Performance of data fusion in middle-level outperformed that of independent data. • It provides a low-cost, in situ method for quality monitoring of tea processing. [ABSTRACT FROM AUTHOR]

• Estimation of Congou black tea quality by an artificial lipid membrane taste sensor. • Optimizing taste features using ant colony optimization. • Multivariate analyses of quality categories and taste characteristics are performed. • The ACO-LS-SVM model achieves the desired predictive performance. The taste attribute is an important indicator to estimate the quality and rank of black tea. It is crucial to prevent fraud and avoid economic losses. This study creatively proposed the assessment of Congou black tea quality based on an electronic tongue system combined with the ant colony optimization (ACO) algorithm and stoichiometry. Firstly, the electronic tongue could effectively present the taste quality of black tea, capturing characteristic potential signals from 700 samples of seven different qualities and converting the signals into nine relative characteristic taste values. Then, the taste indicators from different artificial lipid membrane sensors were optimized using the ACO method. Finally, the discriminant models based on the extreme learning machine, support vector machine, partial least-squares discriminant analysis, and least squares-support vector machine (LS-SVM) were developed using the optimized taste characteristics for the assessment of black tea quality. Results indicated that the LS-SVM model, which was created using the five taste features, could obtain better predictive outcomes based on the generalization performance of the model. In the prediction set, the correct discriminant rate was 99.14%. The overall results demonstrate that the electronic tongue sensor array has potential application prospects for the assessment of Congou black tea products in the actual production process. [ABSTRACT FROM AUTHOR]