Monitoring green tea fixation quality by intelligent sensors: comparison of image and spectral information.

BACKGROUND: Intelligent monitoring of fixation quality is a prerequisite for automated green tea processing. To meet the requirements of intelligent monitoring of fixation quality in large-scale production, fast and non-destructive detection means are urgently needed. Here, smartphone-coupled micro near-infrared spectroscopy and a self-built computer vision system were used to perform rapid detection of the fixation quality in green tea processing lines. RESULTS: Spectral and image information from green tea samples with different fixation degrees were collected at-line by two intelligent monitoring sensors. Competitive adaptive reweighted sampling and correlation analysis were employed to select feature variables from spectral and color information as the target data for modeling, respectively. The developed least squares support vector machine (LS-SVM) model by spectral information and the LS-SVM model by image information achieved the best discriminations of sample fixation degree, with both prediction set accuracies of 100%. Compared to the spectral information, the image information-based support vector regression model performed better in moisture prediction, with a correlation coefficient of prediction of 0.9884 and residual predictive deviation of 6.46. CONCLUSION: The present study provided a rapid and low-cost means of monitoring fixation quality, and also provided theoretical support and technical guidance for the automation of the green tea fixation process. © 2022 Society of Chemical Industry.

High-speed identification system for fresh tea leaves based on phenotypic characteristics utilizing an improved genetic algorithm.

BACKGROUND: High-quality tea requires leaves of similar size and tenderness. The grade of the fresh leaves determines the quality of the tea. The automated classification of fresh tea leaves improves resource utilization and reduces manual picking costs. The present study proposes a method based on an improved genetic algorithm for identifying fresh tea leaves in high-speed parabolic motion using the phenotypic characteristics of the leaves. During parabolic flight, light is transmitted through the tea leaves, and six types of fresh tea leaves can be quickly identified by a camera. RESULTS: The influence of combinations of morphology, color, and custom corner-point morphological features on the classification results were investigated, and the necessary dimensionality of the model was tested. After feature selection and combination, the classification performance of the Naive Bayes, k-nearest neighbor, and support vector machine algorithms were compared. The recognition time of Naive Bayes was the shortest, whereas the accuracy of support vector machine had the best classification accuracy at approximately 97%. The support vector machine algorithm with only three feature dimensions (equivalent diameter, circularity, and skeleton endpoints) can meet production requirements with an accuracy rate reaching 92.5%. The proposed algorithm was tested by using the Swedish leaf and Flavia data sets, on which it achieved accuracies of 99.57% and 99.44%, respectively, demonstrating the flexibility and efficiency of the recognition scheme detailed in the present study. CONCLUSION: This research provides an efficient tea leaves recognition system that can be applied to production lines to reduce manual picking costs. © 2022 Society of Chemical Industry.

Rapid identification of the geographic origin of Taiping Houkui green tea using near-infrared spectroscopy combined with a variable selection method.

BACKGROUND: Most studies focus on the geographically larger production areas in tea traceability. However, famous high-quality tea is often produced in a narrow range of origins, which makes traceability a challenge. In this study, Taiping Houkui (TPHK) green tea of narrow geographical origin was rapidly identified using Fourier-transform near-infrared (FT-NIR) spectroscopy. RESULTS: First, spectral information of 114 TPHK samples from four production areas was acquired. Second, the synthetic minority over-sampling technique (SMOTE) was used to balance the sample data set, and three different spectral pre-processing methods were compared. Third, three feature variable selection algorithms were used to obtain the pre-processed spectral features. Finally, extreme learning machine (ELM) models based on the variables obtained from the selected features were established to trace the TPHK origin. The optimized ELM model achieves 95.35% classification accuracy in the test set. CONCLUSION: The present study demonstrates that the optimized variable selection method in combination with NIR spectroscopy represents a suitable strategy for tea traceability in narrow regions. © 2022 Society of Chemical Industry.

Untargeted Metabolomics Combined with Bioassay Reveals the Change in Critical Bioactive Compounds during the Processing of Qingzhuan Tea.

Qingzhuan tea (QZT) is a typical Chinese dark tea that has a long-time manufacturing process. In the present study, liquid chromatography coupled with tandem mass spectrometry was used to study the chemical changes of tea samples during QZT processing. Untargeted metabolomics analysis revealed that the pile-fermentation and turnover (post-fermentation, FT) was the crucial stage in transforming the main compounds of QZT, whose contents of flavan-3-ols and flavonoids glycosides were decreased significantly. The bioactivities, including the antioxidant capacities and inhibitory effects on α-amylase and α-glucosidase, were also reduced after the FT process. It was suggested that although the QZT sensory properties improved following pile-fermentation and aging, the bioactivities remained restrained. Correlation analysis indicated that the main galloylated catechins and flavonoid glycosides were highly related to their antioxidant capacity and inhibitory effects on α-amylase and α-glucosidase.

Evaluation of Dianhong black tea quality using near-infrared hyperspectral imaging technology.

BACKGROUND: Tea (Camellia sinensis L) is a highly nutritious beverage with commercial value globally. However, it is at risk of economic fraud. This study aims to develop a powerful evaluation method to distinguish Chinese official Dianhong tea from various other categories, employing hyperspectral imaging (HSI) technology and chemometric algorithms. RESULTS: Two matrix statistical algorithms encompassing a gray-level co-occurrence matrix (GLCM) and a gradient co-occurrence matrix (GLGCM) are used to extract HSI texture data. Three novel spectral variable screening methods are utilized to select wavenumbers of near-infrared (NIR) spectra: iteratively retaining informative variables (IRIV), interval random frog, and variable combination population analysis. Feature fusion of image texture characteristics and spectra data are the eigenvectors for model building. Authentic classification models are constructed using the extreme learning machine approach and the least squares support vector machine (LSSVM) approach, coupling them with features from wavelength extraction techniques for assessing the quality of Dianhong black tea. The results demonstrate that the LSSVM model using fused data (IRIV + GLGCM) provides the best results and achieves a predictive precision of 99.57%. CONCLUSION: This study confirms that HSI coupled with LSSVM is effective in differentiating authentic Dianhong black tea samples. © 2020 Society of Chemical Industry.

Cognitive spectroscopy for evaluating Chinese black tea grades (Camellia sinensis): near-infrared spectroscopy and evolutionary algorithms.

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.

Rapid detection of quality index of postharvest fresh tea leaves using hyperspectral imaging.

BACKGROUND: The quality of fresh tea leaves after harvest determines, to some extent, the quality and price of commercial tea. A fast and accurate method to evaluate the quality of fresh tea leaves is required. RESULTS: In this study, the potential of hyperspectral imaging in the range of 328-1115 nm for the rapid prediction of moisture, total nitrogen, crude fiber contents, and quality index value was investigated. Ninety samples of eight tea-leaf varieties and two picking standards were tested. Quantitative partial least squares regression (PLSR) models were established using a full spectrum, whereas multiple linear regression (MLR) models were developed using characteristic wavelengths selected by a successive projections algorithm (SPA) and competitive adaptive reweighted sampling. The results showed that the optimal SPA-MLR models for moisture, total nitrogen, crude fiber contents, and quality index value yielded optimal performance with coefficients of determination for prediction (R2 p) of 0.9357, 0.8543, 0.8188, 0.9168; root mean square error of 0.3437, 0.1097, 0.3795, 1.0358; and residual prediction deviation of 4.00, 2.56, 2.31, and 3.51, respectively. CONCLUSION: The results suggested that the hyperspectral imaging technique coupled with chemometrics was a promising tool for the rapid and nondestructive measurement of tea-leaf quality, and had the potential to develop multispectral imaging systems for future online detection of tea-leaf quality. © 2020 Society of Chemical Industry.

Qualitative and quantitative diagnosis of nitrogen nutrition of tea plants under field condition using hyperspectral imaging coupled with chemometrics.

BACKGROUND: Rapid and accurate diagnosis of nitrogen (N) status in field crops is of great significance for site-specific N fertilizer management. This study aimed to evaluate the potential of hyperspectral imaging coupled with chemometrics for the qualitative and quantitative diagnosis of N status in tea plants under field conditions. RESULTS: Hyperspectral data from mature leaves of tea plants with different N application rates were preprocessed by standard normal variate (SNV). Partial least squares discriminative analysis (PLS-DA) and least squares-support vector machines (LS-SVM) were used for the classification of different N status. Furthermore, partial least squares regression (PLSR) was used for the prediction of N content. The results showed that the LS-SVM model yielded better performance with correct classification rates of 82% and 92% in prediction sets for the diagnosis of different N application rates and N status, respectively. The PLSR model for leaf N content (LNC) showed excellent performance, with correlation coefficients of 0.924, root mean square error of 0.209, and residual predictive deviation of 2.686 in the prediction set. In addition, the important wavebands of the PLSR model were interpreted based on regression coefficients. CONCLUSION: Overall, our results suggest that the hyperspectral imaging technique can be an effective and accurate tool for qualitative and quantitative diagnosis of N status in tea plants. © 2019 Society of Chemical Industry.

Cross-Category Tea Polyphenols Evaluation Model Based on Feature Fusion of Electronic Nose and Hyperspectral Imagery.

Tea polyphenols are important ingredients for evaluating tea quality. The rapid development of sensors provides an efficient method for nondestructive detection of tea polyphenols. Previous studies have shown that features obtained from single or multiple sensors yield better results in detecting interior tea quality. However, due to their lack of external features, it is difficult to meet the general evaluation model for the quality of the interior and exterior of tea. In addition, some features do not fully reflect the sensor signals of tea for several categories. Therefore, a feature fusion method based on time and frequency domains from electronic nose (E-nose) and hyperspectral imagery (HSI) is proposed to estimate the polyphenol content of tea for cross-category evaluation. The random forest and the gradient boosting decision tree (GBDT) are used to evaluate the feature importance to obtain the optimized features. Three models based on different features for cross-category tea (black tea, green tea, and yellow tea) were compared, including grid support vector regression (Grid-SVR), random forest (RF), and extreme gradient boosting (XGBoost). The results show that the accuracy of fusion features based on the time and frequency domain from the electronic nose and hyperspectral image system is higher than that of the features from single sensor. Whether based on all original features or optimized features, the performance of XGBoost is the best among the three regression algorithms (R2 = 0.998, RMSE = 0.434). Results indicate that the proposed method in this study can improve the estimation accuracy of tea polyphenol content for cross-category evaluation, which provides a technical basis for predicting other components of tea.

Evaluating green tea quality based on multisensor data fusion combining hyperspectral imaging and olfactory visualization systems.

BACKGROUND: The instrumental evaluation of tea quality using digital sensors instead of human panel tests has attracted much attention globally. However, individual sensors do not meet the requirements of discriminant accuracy as a result of incomprehensive sensor information. Considering the major factors in the sensory evaluation of tea, the study integrated multisensor information, including spectral, image and olfaction feature information. RESULTS: To investigate spectral and image information obtained from hyperspectral spectrometers of different bands, principal components analysis was used for dimension reduction and different types of supervised learning algorithms (linear discriminant analysis, K-nearest neighbour and support vector machine) were selected for comparison. Spectral feature information in the near infrared region and image feature information in the visible-near infrared/near infrared region achieved greater accuracy for classification. The results indicated that a support vector machine outperformed other methods with respect to multisensor data fusion, which improved the accuracy of evaluating green tea quality compared to using individual sensor data. The overall accuracy of the calibration set increased from 75% using optimal single sensor information to 92% using multisensor information, and the overall accuracy of the prediction set increased from 78% to 92%. CONCLUSION: Overall, it can be concluded that multisensory data accurately identify six grades of tea. © 2018 Society of Chemical Industry.

Identification of geographical origin of Keemun black tea based on its volatile composition coupled with multivariate statistical analyses.

BACKGROUND: Keemun black tea (KBT) is one of the most popular tea beverages in China as a result of its unique flavor and potential health benefits. The geographical origin of KBT influences its quality and price. The present study aimed to apply a head-space solid phase microextraction approach and gas chromatography-mass spectrometry combined with chemometric analysis to profile the volatile compounds of KBT collected from five production areas. RESULTS: Thirty-one peaks were detected in 61 KBT samples. Hierarchical cluster analysis, principal component analysis (PCA), k-nearest neighbor (k-NN) and stepwise linear discriminant analysis (SLDA) were employed to visualize the volatile fractions. The results of unsupervised statistical tools were compared using a test for similarities and distinctions, which showed that different sources may be associated. A satisfying combination of average recognition (91.7%) and cross-validation prediction abilities (84.6%) was obtained for the PCA-k-NN. Among all of the statistical tools, SLDA provided promising results, with 100% recognition and 96.4% prediction ability. CONCLUSION: The results obtained in the present study indicate that the volatile compounds can be used as indicators to identify the geographical origin of KBT. © 2019 Society of Chemical Industry.

Rapid prediction of chlorophylls and carotenoids content in tea leaves under different levels of nitrogen application based on hyperspectral imaging.

BACKGROUND: Photosynthetic pigments perform critical physiological functions in tea plants. Their content is an essential indicator of photosynthetic efficiency and nutritional status. The present study aimed to predict chlorophyll a (Chl a), chlorophyll b (Chl b), total chlorophyll (total Chl), and carotenoid (Car) content in tea leaves under different levels of nitrogen treatment using hyperspectral imaging (HSI) in combination with variable selection algorithms. RESULTS: A total of 150 samples were collected and scanned using the HSI system. The mean spectrum in the region of interest (ROI) was extracted, and the pigment content was measured by traditional chemical methods. Five and seven optimal wavelengths (OWs) were selected using the regression coefficients (RCs) of partial least squares regression (PLSR) and the second-derivative (2-Der), respectively. The optimal 2-Der-PLSR models for Chl a, Chl b, total Chl, and Car performed remarkably well based on seven OWs with correlation coefficients of prediction (RP ) of 0.9337, 0.9322, 0.9333 and 0.9036, root mean square errors in prediction (RMSEP) of 0.1100, 0.0511, 0.1620, and 0.0300 mg g-1 , respectively. CONCLUSION: The results of this study revealed that HSI combined with variable selection method can be employed as a rapid and accurate method for predicting the content of pigments in tea plants. © 2018 Society of Chemical Industry.

Geographical origin traceability of Keemun black tea based on its non-volatile composition combined with chemometrics.

BACKGROUND: Non-volatile compounds play a key role in the quality and price of Keemun black tea (KBT). The non-volatile compounds in KBT samples from different producing areas normally vary greatly. The development of rapid methods for tracing the geographical origin of KBT is useful. In this study, we develop models for the discrimination of KBT's geographical origin based on non-volatile compounds. RESULTS: Seventy-two KBT samples were collected from five towns in Anhui province to determine 13 KBT compounds by high-performance liquid chromatography (HPLC). Analysis of variance showed that the content of 13 compounds in KBT indicated significant differences (P < 0.05) among five towns. Three multivariate statistical models including principal component analysis (PCA), soft independent modeling of class analogy (SIMCA), and linear discriminant analysis (LDA) were built to discriminate origin. Principal component analysis effectively extracted three principal components, namely theaflavins, galloylated catechins, and simple catechins. The high sensitivity (64.5%-99.2%) was achieved of SIMCA model. To establish the discriminant functions, six variables (gallic acid, (+)-catechin, (-)-epigallocatechin gallate, theaflavin-3-gallate, theaflavin-3,3'-di-gallate, and total theaflavins) were chosen from 13 variables, and LDA was applied. This gave a satisfactory overall correct classification rate (94.4%) and cross-validation rate (88.9%) for KBT samples. CONCLUSION: The results showed that HPLC analysis together with chemometrics is a reliable approach for tracing KBT and guaranteeing its authenticity. © 2019 Society of Chemical Industry.

The Core Role of Bacillus subtilis and Aspergillus fumigatus in Pile-Fermentation Processing of Qingzhuan Brick Tea.

To identify the microorganisms responsible for the formation of the main quality components of Qingzhuan brick tea (QZBT) during solid-state fermentation (SSF), predominant thermoduric strains were isolated from the tea leaves collected during SSF. According to their capability of releasing cellulase, pectase, protease, and polyphenol oxidase, four strains were selected as starter cultures to ferment sun-dried tea leaves during artificially inoculated SSF. According to the major enzymatic activities and quality components content (tea polyphenols, catechins, amino acids, soluble sugar, and theabrownin), it was found that Aspergillus fumigatus M1 had a significant effect on the transformation of polyphenols and Bacillus subtilis X4 could enhance the ability of bioconversion of strain M1. Strain X4 and M1 may be the core microbes responsible for developing these biochemical components of QZBT, as the values of quality components of tea leaves fermented by these two strains for 6 days was very close to that of the sample naturally fermented for 35 days in the tea factory. The results could be significant in developing industrial starters for the manufacture of QZBT and stabilizing the product quality of different batches.

Discrimination of nitrogen fertilizer levels of tea plant (Camellia sinensis) based on hyperspectral imaging.

BACKGROUND: Nitrogen (N) fertilizer plays an important role in tea plantation management, with significant impacts on the photosynthetic capacity, productivity and nutrition status of tea plants. The present study aimed to establish a method for the discrimination of N fertilizer levels using hyperspectral imaging technique. RESULTS: Spectral data were extracted from the region of interest, followed by the first derivative to reduce background noise. Five optimal wavelengths were selected by principal component analysis. Texture features were extracted from the images at optimal wavelengths by gray-level gradient co-occurrence matrix. Support vector machine (SVM) and extreme learning machine were used to build classification models based on spectral data, optimal wavelengths, texture features and data fusion, respectively. The SVM model using fused data gave the best performance with highest correct classification rate of 100% for prediction set. CONCLUSION: The overall results indicated that visible and near-infrared hyperspectral imaging combined with SVM were effective in discriminating N fertilizer levels of tea plants. © 2018 Society of Chemical Industry.

Determination of free amino acids in tea by a novel method of reversed-phase high performance liquid chromatography applying 6-Aminoquinolyl-N-Hydroxysuccinimidyl carbamate reagent.

We describe a novel analytical method for quantification of free amino acids in tea using variable mobile phase pH, elution gradient and column temperature of reversed-phase high-performance liquid chromatography (RP-HPLC). The study of mobile phase pH 5.7 was chosen to simultaneous quantification of 19 free amino acids in tea, while it improved maximum resolution of glutamine, histidine and theanine. Elution gradient was adapted for enhancing the solution of free amino acids, mainly because of adjustment of mobile phase A and B. The column temperature of 40 °C was conducive to separate free amino acids in tea. The limit of detection (LOD) and limit of quantitation (LOQ) of this method were in the range of 0.097-0.228 nmol/mL and 0.323-0.761 nmol/mL, respectively. The relative standard deviation of intraday and interday ranged in 0.099-1.909% and 3.231-7.025%, respectively, indicating that the method was reproducible and precise, while recovery ranged between 81.06-112.78%, showing that the method had an acceptable accuracy. This method was applied for the quantification of free amino acids in six types of tea. Multivariate analysis identified serine, glutamine, theanine and leucine as the most influencing factor for classify among analyzed sample.

Highly selective defluoridation of brick tea infusion by tea waste supported aluminum oxides.

BACKGROUND: Brick tea usually contains very high fluoride, which may affect human health. Biosorbents have received much attention for selective removal of fluoride because of low cost, environmental friendliness, and relative safeness. RESULTS: In the present study, a highly selective fluoride tea waste based biosorbent, namely, aluminum (Al) oxide decorated tea waste (Tea-Al), was successfully prepared. The Tea-Al biosorbent was characterized by energy-dispersive spectrometry, Fourier transform infrared spectroscopy, powder X-ray diffraction and X-ray photoelectron spectroscopic analysis. The Tea-Al sample exhibited remarkably selective adsorption for fluoride (52.90%), but a weaker adsorption for other major constituents of brick tea infusion, such as catechins, polyphenols and caffeine, under the same conditions. Fluoride adsorption by Tea-Al for different times obeyed the surface reaction and adsorption isotherms fit the Freundlich model. In addition, the fluoride adsorption mechanism appeared to be an ion exchange between hydroxyl and fluoride ions. CONCLUSION: Results from this study demonstrated that Tea-Al is a promising biosorbent useful for the removal of fluoride in brick tea infusion. © 2016 Society of Chemical Industry.

[Rapid and Dynamic Determination Models of Amino Acids and Catechins Concentrations during the Processing Procedures of Keemun Black Tea].

Tea is one of the most popular beverages in the world. For the contribution to the taste and healthy functions of tea, amino acids and catechins are important components. Among different kinds of black teas in the world, Keemun black tea has the famous and specific fragrance, "Keemun aroma". During the processing procedure of Keemun black tea, the contents of amino acids and catechins changed greatly, and the differences of these concentrations during processing varied significantly. However, a rapid and dynamic determination method during the processing procedure was not existed up to now. In order to find out a rapid determination method for the contents of amino acids and catechins during the processing procedure of Keemun black tea, the materials of fresh leaves, withered leaves, twisted leaves, fermented leaves, and crude tea (after drying) were selected to acquire their corresponding near infrared spectroscopy and obtain their contents of amino acids and catechins by chemical analysis method. The original spectra data were preprocessed by the Standard Normal Variate Transformation (SNVT) method. And the model of Near Infrared (NIR) spectroscopy with the contents of amino acids and catechins combined with Synergy Interval Partial Least squares (Si-PLS) was established in this study. The correlation coefficients and the cross validation root mean square error are treated as the efficient indexes for evaluating models. The results showed that the optimal prediction model of amino acids by Si-PLS contained 20 spectral intervals combined with 4 subintervals and 9 principal component factors. The correlation coefficient and the root mean square error of the calibration set were 0. 955 8 and 1. 768, respectively; the correlation coefficient and the root mean square error of the prediction set were 0. 949 5 and 2. 16, respectively. And the optimal prediction model of catechins by Si-PLS contained 20 spectral intervals combined with 3 subintervals and 10 principal component factors. The correlation coefficient and the root mean square error of the calibration set were 0. 940 1 and 1. 22, respectively; the correlation coefficient and the root mean square error of the prediction set were 0. 938 5 and 1. 17, respectively. The results showed that the established models had good accuracy which could provide a theoretical foundation for the online determination of tea chemical components during processing.

The shaking and standing processing improve the aroma quality of summer black tea.

Black teas harvested during the summer season usually have the defect of low aroma intensity, resulting in unacceptability from consumers. The shaking and standing (SS) process is key to the production of oolong tea and is believed to significantly improve the aroma quality. However, the specific effects of the shaking process on the aroma quality of black tea have not been elucidated. SSBT has a higher aroma intensity than BT, especially floral and sweet odors. By Aroma Extract Dilution Analysis (AEDA), 27 volatiles with flavor dilution factor (FD) above 8 were selected, of which 20 had odor activity values (OAV) values above 1; among them, 9 floral and sweet volatiles with high OAV were linalool (485 for BT, 918 for SSBT), (E)-ß-ionone (389, 699), geraniol (315, 493), ß-myrcene (25, 62), (E)-2-hexenal (2, 7), phenylacetaldehyde (44, 75), (Z)-3-hexenyl hexanoate (19, 41), 1-hexanol (9, 26), and 2-phenylethanol (2,3). Aroma reconstitution of these 20 volatiles showed reliable results of floral, sweet, fruity, and roasted odors, further validating the aroma and intensity profiles of the key odorants. Overall, our results reveal that the metabolite mechanism of the SS process improves the aroma quality of black tea, providing a theoretical basis and guiding measures for the production of high-aroma black tea.

Rapid detection of ash content in black tea using a homemade miniature near-infrared spectroscopy.

Ash is a testing index with both health inspection value and quality decision value, and it is an essential detection item in the import and export trade of tea. To realize the rapid and effective quantitative analysis of ash content in tea, this study proposed the use of a homemade miniature near-infrared (NIR) spectroscopy combined with multivariate analysis for the rapid detection of ash content in black tea. First, NIR data of black tea samples from different countries were acquired and optimized by the spectral preprocessing method. Then, the optimized pre-processed spectral data were used as features, and four feature wavelength selection algorithms, such as competitive adaptive reweighted sampling, iteratively retaining informative variables (IRIV), variable combination population analysis (VCPA)-IRIV, and interval variable iterative space shrinkage approach (IVISSA), were utilized to optimize the feature spectra. Finally, the support vector machine regression (SVR) algorithm was employed to construct the quantitative models of ash content in black tea by combining the optimal wavelengths obtained from the four feature selection methods mentioned above. The experimental results showed that the IVISSA-SVR model had the best performance, with correlation coefficient (Rp), root mean square errors of prediction (RMSEP), and relative prediction deviation (RPD) of 0.9546, 0.0192, and 5.59 for the prediction set, respectively. The results demonstrate that a miniature NIR sensing system combined with chemometrics as an effective analytical tool can realize the rapid detection of ash content in black tea.