Simultaneous determination of six main types of lipid-soluble pigments in green tea by visible and near-infrared spectroscopy

The chemical basis of aroma/taste and color formation in green tea infusion during cold brewing revealed by metabolomics analysis

In this study, metabolomics and chemometrics were utilized to comprehensively investigate chemical mechanisms of aroma, taste, and color formation in cold-brewed green tea (4 °C). The results showed that the typical flavor of cold-brewed green tea (tea-to-water ratio: 1:50 g/mL) developed gradually after 1 h. Compared with the hot-brewed (80 °C) condition, volatile alcohols accumulated more under cold-brewing conditions. The extraction rate of bitter compounds such as caffeine decreased by more than 40 %, while the umami compound L-theanine increased about 9.2 % compared to hot-brewed green tea. The low temperatures also reduced flavonoid extraction ratio and retained high level of chlorophyll, resulting in a greener infusion. These differences led to cold-brewed green tea exhibiting a floral aroma, umami, sweet taste, and green color. This study revealed the impact of extraction temperature on the extraction efficiency of compounds from green tea. These findings can provide analysis methods for controlling quality of cold-brewed green tea.

Lycopene detection in cherry tomatoes with feature enhancement and data fusion

Lycopene, a biologically active phytochemical with health benefits, is a key quality indicator for cherry tomatoes. While ultraviolet/visible/near-infrared (UV/Vis/NIR) spectroscopy holds promise for large-scale online lycopene detection, capturing its characteristic signals is challenging due to the low lycopene concentration in cherry tomatoes. This study improved the prediction accuracy of lycopene by supplementing spectral data with image information through spectral feature enhancement and spectra-image fusion. The feasibility of using UV/Vis/NIR spectra and image features to predict lycopene content was validated. By enhancing spectral bands corresponding to colors correlated with lycopene, the performance of the spectral model was improved. Additionally, direct spectra-image fusion further enhanced the prediction accuracy, achieving RP2, RMSEP, and RPD as 0.95, 8.96 mg/kg, and 4.25, respectively. Overall, this research offers valuable insights into supplementing spectral data with image information to improve the accuracy of non-destructive lycopene detection, providing practical implications for online fruit quality prediction.

Seasonal variation in non-volatile flavor substances of fresh tea leaves (Camellia sinensis) by integrated lipidomics and metabolomics using UHPLC-Q-Exactive mass spectrometry

Harvest season exerts great influence on tea quality. Herein, the variations in non-volatile flavor substances in spring and summer fresh tea leaves of four varieties were comprehensively investigated by integrating UHPLC-Q-Exactive based lipidomics and metabolomics. A total of 327 lipids and 99 metabolites were detected, among which, 221 and 58 molecules were significantly differential. The molecular species of phospholipids, glycolipids and acylglycerolipids showed most prominent and structure-dependent seasonal changes, relating to polar head, unsaturation and total acyl length. Particularly, spring tea contained higher amount in aroma precursors of highly unsaturated glycolipids and phosphatidic acids. The contents of umami-enhancing amino acids and phenolic acids, e.g., theanine, theogallin and gallotannins, were increased in spring. Besides, catechins, theaflavins, theasinensins and flavone/flavonol glycosides showed diverse changes. These phytochemical differences covered key aroma precursors, tastants and colorants, and may confer superior flavor of black tea processed using spring leaves, which was verified by sensory evaluation.

Effect of different drying temperature settings on the color characteristics of Tencha

Color is critical factor in the commercialization of Matcha. In this study, sensory evaluation, color difference analysis, as well as targeted and non-targeted analyses were employed to investigate the impact of different drying temperature settings on the color characteristics of Tencha. The findings revealed that compared to a single drying temperature setting, a two-stage or multi-stage drying process more effectively preserved the color quality of Tencha. Specifically, a setting involving an initial period of high-temperature drying followed by low-temperature drying (samples T_6, T_7, T_10, and T_13) resulted in superior tea color quality, characterized by higher chlorophyll content and lower levels of lutein and β-carotene. Chemometric analysis identified chlorophylls and their derivatives (chlorophyll a/b, pheophytin a/b, pyropheophytin a/b) as the key factors influencing Tencha's color. These results can provide valuable insights for optimizing tea processing methods to enhance quality.

Investigation of the quality of Lu'an Guapian tea during Grain Rain period by sensory evaluation, objective quantitative indexes and metabolomics

The harvest date is a crucial factor in determining tea quality. For Lu'an Guapian (LAGP) tea, Grain Rain period (GRP) represents a pivotal phase in the transformation of tea quality. The sensory evaluation, computer vision and E-tongue revealed that the liquor color score, B and G values of tea infusion were increased during GRP, while the astringency, bitterness intensities and the R value of the tea infusion were decreased. Consequently, the tea infusion exhibited a greener hue and the taste became appropriate during GRP. Non-targeted metabolomics revealed that the majority of amino acids and derivatives was reduced during GRP. Furthermore, flavonoids, in particular flavonol glycosides, exhibited considerable variation during GRP. Finally, nine metabolites were identified as markers for quality transformation during GRP by PLS and Random Forest. This study investigated the quality of LAGP teas during GRP and filled the gap in the variation of LAGP tea quality during GRP.

Hyperspectral Imaging Combined with Deep Transfer Learning to Evaluate Flavonoids Content in Ginkgo biloba Leaves

Ginkgo biloba is a famous economic tree. Ginkgo leaves have been utilized as raw materials for medicines and health products due to their rich active ingredient composition, especially flavonoids. Since the routine measurement of total flavones is time-consuming and destructive, rapid, non-destructive detection of total flavones in ginkgo leaves is of significant importance to producers and consumers. Hyperspectral imaging technology is a rapid and non-destructive technique for determining the total flavonoid content. In this study, we discuss five modeling methods, and three spectral preprocessing methods are discussed. Bayesian Ridge (BR) and multiplicative scatter correction (MCS) were selected as the best model and the best pretreatment method, respectively. The spectral prediction results based on the BR + MCS treatment were very accurate (RTest2 = 0.87; RMSETest = 1.03 mg/g), showing a high correlation with the analytical measurements. In addition, we also found that the more and deeper the leaf cracks, the higher the flavonoid content, which helps to evaluate leaf quality more quickly and easily. In short, hyperspectral imaging is an effective technique for rapid and accurate determination of total flavonoids in ginkgo leaves and has great potential for developing an online quality detection system for ginkgo leaves.

Comprehensive Analysis of the Yield and Leaf Quality of Fresh Tea (Camellia sinensis cv. Jin Xuan) under Different Nitrogen Fertilization Levels

Reasonable application of nitrogen fertilizer can improve the yield and quality of tea. This study used Jin Xuan as the tested variety and applied nitrogen fertilizer at rates of 0 kg/ha (N0), 150 kg/ha (N150), 300 kg/ha (N300), and 450 kg/ha (N450) in the summer and autumn seasons to analyze the effects of nitrogen application on the quality components and gene expression of tea leaves. The results showed that the N150 treatment significantly increased total polyphenols (TP), total catechins (TC), and caffeine contents, with the most significant increase observed in the content of six monomers of catechins (EGCG, ECG, EGC, GCG, GC, and EC) in the summer. The N300 treatment significantly increased TP and AA contents in the autumn while decreasing TC content. Additionally, the N300 treatment significantly increased caffeine and theanine contents in the autumn. Notably, the N300 treatment significantly increased both summer and autumn tea yields. Multivariate statistical analysis showed that TPs, AAs, TCs, EGC, and caffeine were key factors affecting the quality of Jin Xuan. Furthermore, the N150 treatment upregulated the expression of the phenylalanine ammonia-lyase (PAL) gene, which may increase the accumulation of catechins. In conclusion, it is recommended to apply 150 kg/ha of nitrogen fertilizer in the summer and 300 kg/ha of nitrogen fertilizer in the autumn. This recommendation provides a theoretical basis for improving the quality and yield of tea leaves in summer and autumn.

Synchronously Predicting Tea Polyphenol and Epigallocatechin Gallate in Tea Leaves Using Fourier Transform-Near-Infrared Spectroscopy and Machine Learning

Tea polyphenol and epigallocatechin gallate (EGCG) were considered as key components of tea. The rapid prediction of these two components can be beneficial for tea quality control and product development for tea producers, breeders and consumers. This study aimed to develop reliable models for tea polyphenols and EGCG content prediction during the breeding process using Fourier Transform-near infrared (FT-NIR) spectroscopy combined with machine learning algorithms. Various spectral preprocessing methods including Savitzky-Golay smoothing (SG), standard normal variate (SNV), vector normalization (VN), multiplicative scatter correction (MSC) and first derivative (FD) were applied to improve the quality of the collected spectra. Partial least squares regression (PLSR) and least squares support vector regression (LS-SVR) were introduced to establish models for tea polyphenol and EGCG content prediction based on different preprocessed spectral data. Variable selection algorithms, including competitive adaptive reweighted sampling (CARS) and random forest (RF), were further utilized to identify key spectral bands to improve the efficiency of the models. The results demonstrate that the optimal model for tea polyphenols calibration was the LS-SVR with R_p = 0.975 and RPD = 4.540 based on SG-smoothed full spectra. For EGCG detection, the best model was the LS-SVR with R_p = 0.936 and RPD = 2.841 using full original spectra as model inputs. The application of variable selection algorithms further improved the predictive performance of the models. The LS-SVR model for tea polyphenols prediction with R_p = 0.978 and RPD = 4.833 used 30 CARS-selected variables, while the LS-SVR model build on 27 RF-selected variables achieved the best predictive ability with R_p = 0.944 and RPD = 3.049, respectively, for EGCG prediction. The results demonstrate a potential of FT-NIR spectroscopy combined with machine learning for the rapid screening of genotypes with high tea polyphenol and EGCG content in tea leaves.

Monitoring chlorophyll changes during Tencha processing using portable near-infrared spectroscopy

Monitoring chlorophyll during Tencha (the raw ingredient for matcha) processing is critical for determining the matcha's color and quality. The purpose of this study is to explore the mechanism of chlorophyll changes during Tencha processing and evaluate the viability of predicting its content by a portable near-infrared (NIR) spectrometer. The Tencha samples' spectral data were first preprocessed using various preprocessing techniques. Subsequently, iteratively variable subset optimization (IVSO), bootstrapping soft shrinkage (BOSS), and competitive adaptive reweighted sampling (CARS) were used to optimize and build partial least square (PLS) models. The CARS-PLS models achieved the best predictive accuracy, with correlation coefficients of prediction (R_p) = 0.9204 for chlorophyll a, R_p = 0.9282 for chlorophyll b, and R_p = 0.9385 for total chlorophyll. These findings suggest that NIR spectroscopy could be used as a surrogate for immediate quantification and monitoring of chlorophyll during Tencha processing.

Monitoring the major taste components during black tea fermentation using multielement fusion information in decision level

Hitherto, the intelligent detection of black tea fermentation quality is still a thought-provoking problem because of one-side sample information and poor model performance. This study proposed a novel method for the prediction of major chemical components including total catechins, soluble sugar and caffeine using hyperspectral imaging technology and electrical properties. The multielement fusion information were used to establish quantitative prediction models. The performance of model using multielement fusion information was better than that of model using single information. Subsequently, the stacking combination model using fusion data combined with feature selection algorithms for evaluating the fermentation quality of black tea. Our proposed strategy achieved better performance than classical linear and nonlinear algorithms, with the correlation coefficient of the prediction set (R_p) for total catechins, soluble sugar and caffeine being 0.9978, 0.9973 and 0.9560, respectively. The results demonstrated that our proposed strategy could effectively evaluate the fermentation quality of black tea.

Volatilomics Analysis of Jasmine Tea during Multiple Rounds of Scenting Processes

Jasmine tea is reprocessed from finished tea by absorbing the floral aroma of jasmine (Jasminum sambac (L.) Aiton); this process is commonly known as "scenting". Making high-quality jasmine tea with a refreshing aroma requires repeated scenting. To date, the detailed volatile organic compounds (VOCs) and the formation of a refreshing aroma as the number of scenting processes increases are largely unknown and therefore need further study. To this end, integrated sensory evaluation, widely targeted volatilomics analysis, multivariate statistical analyses, and odor activity value (OAV) analysis were performed. The results showed that the aroma freshness, concentration, purity, and persistence of jasmine tea gradually intensifies as the number of scenting processes increases, and the last round of scenting process without drying plays a significant role in improving the refreshing aroma. A total of 887 VOCs was detected in jasmine tea samples, and their types and contents increased with the number of scenting processes. In addition, eight VOCs, including ethyl (methylthio)acetate, (Z)-3-hexen-1-ol acetate, (E)-2-hexenal, 2-nonenal, (Z)-3-hexen-1-ol, (6Z)-nonen-1-ol, β-ionone, and benzyl acetate, were identified as key odorants responsible for the refreshing aroma of jasmine tea. This detailed information can expand our understanding of the formation of a refreshing aroma of jasmine tea.

Comprehensive evaluation of Dendrobium officinale from different geographical origins using near-infrared spectroscopy and chemometrics

Dendrobium officinale, often used as a kind of tea for daily drinks, has drawn increasing attention for its beneficial effects. Quality evaluation of D. officinale is of great significance to ensure its health care value and safeguard consumers' interest. Given that traditional analytical methods for assessing D. officinale quality are generally time-consuming and laborious, this study developed a comprehensive strategy, with the advantages of being rapid and efficient, enabling the quality evaluation of D. officinale from different geographical origins using near-infrared (NIR) spectroscopy and chemometrics. As the quality indicators, polysaccharides, polyphenols, total flavonoids, and total alkaloids were quantified. Three types of wavelength selection methods were used for model optimization and these were synergy interval (SI), genetic algorithm (GA), and competitive adaptive reweighted sampling (CARS). From the qualitative perspective, the geographical origins of D. officinale were differentiated by NIR spectroscopy combined with partial least squares-discriminant analysis (PLS-DA) and support vector classification (SVC). The PLS models constructed based on the wavelengths selected by CARS yielded the best performance for prediction of the contents of quality indicators in D. officinale. The root mean square error (RMSEP) and coefficient of determination (R_p²) in the independent test sets were 12.7768 g kg^-1 and 0.9586, 1.1346 g kg^-1 and 0.9670, 0.3938 g kg^-1 and 0.8803, 0.0825 and 0.7031 and for polysaccharides, polyphenols, total flavonoids, and total alkaloids, respectively. As for the origin identification, the nonlinear SVC was superior to the linear PLS-DA, with the correct recognition rates in calibration and prediction sets up to 100% and 100%, respectively. The overall results demonstrated the potential of NIR spectroscopy and chemometrics in the rapid determination of quality parameters and geographical origin. This study could provide a valuable reference for quality evaluation of D. officinale in a more rapid and comprehensive manner.

Green Tea Extract Enrichment: Mechanical and Physicochemical Properties Improvement of Rice Starch-Pectin Composite Film

The effects of green tea extract (GTE) at varying concentrations (0.000, 0.125, 0.250, 0.500, and 1.000%, w/v) on the properties of rice-starch-pectin (RS-P) blend films were investigated. The results showed that GTE addition enhanced (p < 0.05) the antioxidation properties (i.e., total phenolic content, DPPH radical scavenging activity, and ferric reducing antioxidant power) and thickness of the RS-P composite film. The darker appearance of the RS-T-GTE blend films was obtained in correspondence to the lower L* values. However, the a* and b* values were higher toward red and yellow as GTE increased. Though GTE did not significantly alter the film solubility, the moisture content and the water vapor permeability (WVP) of the resulting films were reduced. In addition, the GTE enrichment diminished the light transmission in the UV-Visible region (200−800 nm) and the transparency of the developed films. The inclusion of GTE also significantly (p < 0.05) lowered the tensile strength (TS) and elongation at break (EAB) of the developed film. The FT-IR spectra revealed the interactions between RS-P films and GTE with no changes in functional groups. The antimicrobial activity against Staphylococcus aureus (TISTR 764) was observed in the RS-P biocomposite film with 1% (w/v) GTE. These results suggested that the RS-P-GTE composite film has considerable potential for application as active food packaging.

The Application of Visible and Near-Infrared Spectroscopy Combined with Chemometrics in Classification of Dried Herbs

The fast differentiation and classification of herb samples are complicated processes due to the presence of many various chemical compounds. Traditionally, separation techniques have been employed for the identification and quantification of compounds present in different plant matrices, but they are tedious, time-consuming and destructive. Thus, a non-targeted approach would be specifically advantageous for this purpose. In the present study, spectroscopy in the visible and near-infrared range and pattern recognition techniques, including the principal component analysis (PCA), linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), regularized discriminant analysis (RDA), super k-nearest neighbor (SKNN) and support vector machine (SVM) techniques, were applied to develop classification models that enabled the discrimination of various commercial dried herbs, including mint, linden, nettle, sage and chamomile. The classification error rates in the validation data were below 10% for all the classification methods, except for SKNN. The results obtained confirm that spectroscopy and pattern recognition methods constitute a good non-destructive tool for the rapid identification of herb species that can be used in routine quality control by the pharmaceutical industry, as well as herbal suppliers, to avoid mislabeling.

Fruit ripening: dynamics and integrated analysis of carotenoids and anthocyanins

BACKGROUND

Fruits are vital food resources as they are loaded with bioactive compounds varying with different stages of ripening. As the fruit ripens, a dynamic color change is observed from green to yellow to red due to the biosynthesis of pigments like chlorophyll, carotenoids, and anthocyanins. Apart from making the fruit attractive and being a visual indicator of the ripening status, pigments add value to a ripened fruit by making them a source of nutraceuticals and industrial products. As the fruit matures, it undergoes biochemical changes which alter the pigment composition of fruits.

RESULTS

The synthesis, degradation and retention pathways of fruit pigments are mediated by hormonal, genetic, and environmental factors. Manipulation of the underlying regulatory mechanisms during fruit ripening suggests ways to enhance the desired pigments in fruits by biotechnological interventions. Here we report, in-depth insight into the dynamics of a pigment change in ripening and the regulatory mechanisms in action.

CONCLUSIONS

This review emphasizes the role of pigments as an asset to a ripened fruit as they augment the nutritive value, antioxidant levels and the net carbon gain of fruits; pigments are a source for fruit biofortification have tremendous industrial value along with being a tool to predict the harvest. This report will be of great utility to the harvesters, traders, consumers, and natural product divisions to extract the leading nutraceutical and industrial potential of preferred pigments biosynthesized at different fruit ripening stages.

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.

Prediction of tea theanine content using near-infrared spectroscopy and flower pollination algorithm

In this study, near-infrared (NIR) spectroscopy was exploited for non-destructive determination of theanine content of oolong tea. The NIR spectral data (400-2500 nm) were correlated with the theanine level of 161 tea samples using partial least squares regression (PLSR) with different wavelengths selection methods, including the regression coefficient-based selection, uninformative variable elimination, variable importance in projection, selectivity ratio and flower pollination algorithm (FPA). The potential of using the FPA to select the discriminative wavelengths for PLSR was examined for the first time. The analysis showed that the PLSR with FPA method achieved better predictive results than the PLSR with full spectrum (PLSR-full). The developed simplified model using on FPA based on 12 latent variables and 89 selected wavelengths produced R-squared (R²) value and root mean squared error (RMSE) of 0.9542, 0.8794 and 0.2045, 0.3219 for calibration and prediction, respectively. For PLSR-full, the R² values of 0.9068, 0.8412 and RMSEs of 0.2916, 0.3693, were achieved for calibration and prediction. Also, the optimized model using FPA outperformed other wavelengths selection methods considered in this study. The obtained results indicated the feasibility of FPA to improve the predictability of the PLSR and reduce the model complexity. The nonlinear regression models of support vector machine regression and Gaussian process regression (GPR) were further utilized to evaluate the superiority of using the FPA in the wavelength selection. The results demonstrated that utilizing the wavelength selection method of FPA and nonlinear regression model of GPR could improve the predictive performance.

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.

Effects of Long-Term Nitrogen Fertilization on the Formation of Metabolites Related to Tea Quality in Subtropical China

As a main agronomic intervention in tea cultivation, nitrogen (N) application is useful to improve tea yield and quality. However, the effects of N application on the formation of tea quality-related metabolites have not been fully studied, especially in long-term field trials. In this study, a 10-year field experiment was conducted to investigate the effect of long-term N application treatments on tea quality-related metabolites, their precursors, and related gene expression. Long-term N application up-regulated the expression of key genes for chlorophyll synthesis and promoted its synthesis, thus increasing tea yield. It also significantly increased the contents of total free amino acids, especially l-theanine, in fresh tea leaves, while decreasing the catechin content, which is conducive to enhancing tea liquor freshness. However, long-term N application significantly reduced the contents of benzyl alcohol and 2-phenylethanol in fresh tea leaves, and also reduced (E)-nerolidol and indole in withered leaves, which were not conducive to the formation of floral and fruity aroma compounds. In general, an appropriate amount of N fertilizer (225 kg/hm²) balanced tea yield and quality. These results not only provide essential information on how N application affects tea quality, but also provide detailed experimental data for field fertilization.

Evaluation of black tea by using smartphone imaging coupled with micro-near-infrared spectrometer

Tea quality is generally assessed through panel sensory evaluation, which requires elaborate sample preparation steps. Here, a novel and low-cost evaluation method of using smartphone imaging coupled with micro-near-infrared (NIR) spectrometer based on digital light processing is proposed to classify the quality grades of Keemun black tea. RGB color information was obtained by Image J software, eight texture characteristics, including scheme, contrast, dissimilarity, entropy, correlation, second moment and variance, and homogeneity were obtained by ENVI software based on co - occurrence method from smartphone images, and spectral data were preprocessed with standard normal variate. A principal component analysis (PCA)-support vector machine (SVM) model was established to analyze the color, texture, and spectral data. Low-level and middle-level fusion strategies were introduced for analyzing the fusion data. The results indicated that the accuracy of the SVM model on mid-level data fusion (100.00%, 94.29% for calibration set and prediction set, respectively) was higher than that obtained for separate color (97.14%, 88.57%), texture (84.29%, 60%), spectrum (74.29%, 68.57%) evaluation, or low-level data fusion (88.57%, 82.86%). The best SVM model yielded satisfactory performance with 94.29% accuracy for the prediction sets. These results suggested that smartphone imaging coupled with micro-NIR spectroscopy is an effective and low-cost tool for evaluating tea quality.

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.

Quantitative visualization of photosynthetic pigments in tea leaves based on Raman spectroscopy and calibration model transfer

BACKGROUND

Photosynthetic pigments participating in the absorption, transformation and transfer of light energy play a very important role in plant growth. While, the spatial distribution of foliar pigments is an important indicator of environmental stress, such as pests, diseases and heavy metal stress.

RESULTS

In this paper, in situ quantitative visualization of chlorophyll and carotenoid was realized by combining the Raman spectroscopy with calibration model transfer, and a laboratory Raman spectral model was successfully extended to a portable field spectral measurement. Firstly, a nondestructive and fast model for determination of chlorophyll and carotenoid in tea leaf was established based on confocal micro-Raman spectrometer in the laboratory. Then the spectral model was extended to a real-time foliar map scanning spectra of a field portable Raman spectrometer through calibration model transfer, and the spectral variation between the confocal micro-Raman spectrometer in the laboratory and the portable Raman spectrometer were effectively corrected by the direct standardization (DS) algorithm. The portable map scanning Raman spectra of the tea leaves after the model transfer were got into the established quantitative determination model to predict the concentration of photosynthetic pigments at each pixel of the tea leaves. The predicted photosynthetic pigments concentration of each pixel was imaged to illustrate the distribution map of foliar pigments. Statistical analysis showed that the predicted pigment contents were highly correlated with the real contents.

CONCLUSIONS

It can be concluded that the Raman spectroscopy was applicable for in situ, non-destructive and rapid quantitative detecting and imaging of photosynthetic pigment concentration in tea leaves, and the spectral detection model established based on the laboratory Raman spectrometer can be applied to a portable field spectrometer for quantitatively imaging of the foliar pigments.

Nondestructive monitoring of polyphenols and caffeine during green tea processing using Vis-NIR spectroscopy

Increasing consumption of green tea is attributed to the beneficial effects of its constituents, especially polyphenols, on human health, which can be varied during leaf processing. Processing technology has the most important effect on green tea quality. This study investigated the system dynamics of eight catechins, gallic acid, and caffeine in the processing of two varieties of tea, from fresh leaves to finished tea. It was found that complex biochemical changes can occur through hydrolysis under different humidity and heating conditions during the tea processing. This process had a significant effect on catechin composition in the finished tea. The potential application of visible and near-infrared (Vis-NIR) spectroscopy for fast monitoring polyphenol and caffeine contents in tea leaves during the processing procedure has been investigated. It was found that a combination of PCA (principal component analysis) and Vis-NIR spectroscopy can successfully classify the two varieties of tea samples and the five tea processing procedures, while quantitative determination of the constituents was realized by combined regression analysis and Vis-NIR spectra. Furthermore, successive projections algorithm (SPA) was proposed to extract and optimize spectral variables that reflected the molecular characteristics of the constituents for the development of determination models. Modeling results showed that the models had good predictability and robustness based on the extracted spectral characteristics. The coefficients of determination for all calibration sets and prediction sets were higher than 0.862 and 0.834, respectively, which indicated high capability of Vis-NIR spectroscopy for the determination of the constituents during the leaf processing. Meanwhile, this analytical method could quickly monitor quality characteristics and provide feedback for real-time controlling of tea processing machines. Furthermore, the study on complex biochemical changes that occurred during the tea processing would provide a theoretical basis for improving the content of quality components and effective controlling processes.

Micro-NIR spectrometer for quality assessment of tea: Comparison of local and global models

Near-infrared (NIR) spectroscopy is an effective tool for analyzing components relevant to tea quality, especially catechins and caffeine. In this study, we predicted catechins and caffeine content in green and black tea, the main consumed tea types worldwide, by using a micro-NIR spectrometer connected to a smartphone. Local models were established separately for green and black tea samples, and these samples were combined to create global models. Different spectral preprocessing methods were combined with linear partial-least squares regression and nonlinear support vector machine regression (SVR) to obtain accurate models. Standard normal variate (SNV)-based SNV-SVR models exhibited accurate predictive performance for both catechins and caffeine. For the prediction of quality components of tea, the global models obtained results comparable to those of the local models. The optimal global models for catechins and caffeine were SNV-SVR and particle swarm optimization (PSO)-simplified SNV-PSO-SVR, which achieved the best predictive performance with correlation coefficients in prediction (Rp) of 0.98 and 0.93, root mean square errors in prediction of 9.83 and 2.71, and residual predictive deviations of 4.44 and 2.60, respectively. Therefore, the proposed low-price, compact, and portable micro-NIR spectrometer connected to smartphones is an effective tool for analyzing tea quality.

An Online Tea Fixation State Monitoring Algorithm Based on Image Energy Attention Mechanism and Supervised Clustering (IEAMSC)

This study aimed at the shortcomings of existing fixation algorithms that are image-based only, and an effective tea fixation state monitoring algorithm was proposed. An adaptive filtering algorithm was used to automatically filter the ineffective information. Using the energy extractor, the complete energy information of each fixation image was extracted. The image energy attention mechanism was used to identify the prominent features, and based on these, the energy data was mapped to generate the data points as the training data. The cluster idea was adopted, and the training data feed the features trainer. The trend center data of the tea processing energy clustering was generated from different color channels. The corresponding decision function was designed which is based on the distance of the cluster center. The fixation degree of each monitoring image set was measured by the decision function. The Euclidean distance of the energy clustering center of the three channels with the same fixation time progressively approached. The triangle formed by these three points had a trend of gradually shrinking, which was first discovered by us. The detection results showed high accuracy compared with the common classification algorithms. It indicates that the algorithm proposed has positive guiding and reference significance.

Development of a comprehensive quality control method for the quantitative analysis of volatiles and lignans in Magnolia biondii Pamp. by near infrared spectroscopy

The quality of drug is vital to its curative effect, thus it is important to develop a comprehensive quality control method for commonly used drugs. In this study, we developed a Gas chromatography-mass spectrometry separation method for the qualitative and quantitative analysis of volatiles, together with a High-performance liquid chromatography-mass spectrometry separation method for lignans in Magnolia biondii Pamp.. 79 volatiles and 11 lignans were identified via comparing their chromatographic behavior and mass spectra data with those in the literature. The methods were then used to determine the contents of volatiles (1, 8-cineole, d-Limonene, α-terpineol, linalool, L-camphor brain and bornyl acetate) and lignans (epieudesmin, magnolin, epi-magnolin A and fargesin) in Magnolia biondii Pamp.. Subsequently, 13 qualitative models including volatiles (1, 8-cineole, d-Limonene, α-terpineol, linalool, L-camphor brain and bornyl acetate), water-soluble extractive, lignans (pinoresinol dimethyl ether, magnolin, epi-magnolin A and fargesin) and moisture were developed by Near-Infrared Spectroscopy based on partial least square regression herein. The reference values were obtained by High-performance liquid chromatography, Gas chromatography and etc., while the predicted values were attained from the NIR spectrum. Compared with the traditional detection methods, NIR technique methodology significantly improved the ability to evaluate the quality of Magnolia biondii Pamp., which had the advantages of convenience, celerity, highly efficiency, low cost, no harm to samples, no reagent consumption, and no pollution to the environment. Moreover, the systematic analysis method combined pharmaceutical analysis with pharmacochemistry was proposed to prepare volatiles, water-soluble extractive and lignans parts from the same sample. This way could extract more index components to be beneficial in the quality control of Magnolia biondii Pamp. roundly.

Rapid identification of Lilium species and polysaccharide contents based on near infrared spectroscopy and weighted partial least square method

Polysaccharide is the main active compound of Lilium, and showed many activities, such as hypoglycemic, antioxidant, immune-modulatory. There are three types' Lilium in China market, i.e. Lilium lancifolium Thunb (JD), Lilium davidiivar. Unicolor Salisb (L. davidii var)(LZBH), and Lilium brownii F.E. Brown var. viridulum Baker (BH). Near infrared spectroscopy (NIR) technique has become popular in the fields of quality control, due to its advantages, such as fast, non-destructive, and can detect several ingredients, simultaneously. In this study, a classification model was established based on NIR technique and random forest method to accurately distinguish three types' Lilium species, and the classification accuracy reached 94.37%. Furthermore, taking the effects of neighbor wavelength into account, a new weighted partial least square algorithm was proposed to establish an accurate and quantitative model for predicting the polysaccharide contents of these samples. In the model establishing process, some signal pre-treatment methods were optimized, and the validation results with highest determination coefficient (R²) and low root mean square errors of prediction (RMSEP) were, 0.9455 and 0.9098, respectively. The obtained results showed that combined NIR technique with chemometrics was an effective and green method for quality control.

Discriminating geographical origins of green tea based on amino acid, polyphenol, and caffeine content through high-performance liquid chromatography: Taking Lu'an guapian tea as an example

Seventy-three Lu'an guapian tea (LAGP) samples were collected from 11 growing locations in the city of Lu'an, Anhui Province, China. Through high-performance liquid chromatography, 18 amino acids, along with gallic acid, caffeine, and five catechins, were quantitatively detected. Hierarchical cluster, correlation and principal component analysis, and a support vector machine were used for geographical discrimination. The findings suggested that the differences in tea quality between the inner and outer mountain regions are related to isoleucine, leucine, phenylalanine, and valine contents, with a correlation coefficient of more than 0.85. Principal component analysis combining with support vector machine was a feasible method. The identification rates for the inner and outer mountains were 97.96% in the training set and 95.83% in the prediction set. Furthermore, the identification rates for the three counties were 91.84% and 95.83% in the training and prediction sets, respectively.

Extraction of Green Tea Phenolics Using Water Bubbled with Gases

Water was bubbled with gases including nitrogen (N₂ ), oxygen (O₂ ), hydrogen (H₂ ), carbon dioxide (CO₂ ), and air for 10 min and phenolics from green tea leaves were extracted using the prepared gas-bubbled water. To retain the gases in water, the extraction conditions were maintained in an air-tight container at room temperature under magnetic stirring. Radical scavenging ability, total phenolic content, and phenolic profiles of the extracts were analyzed, and gas-bubbled water was examined to explain the differences in phenolic contents. Overall, green tea infusion prepared from H₂ -bubbled water contained significantly high levels of total phenolic compounds and antioxidant activity compared to other gas-bubbled waters including N₂ , O₂ , CO₂ , and air (P < 0.05).Control samples and those bubbled with CO₂ showed the lowest antioxidant activities in green tea infusion. However, green tea extracts with O₂ bubbling showed the lowest catechin content. Green tea leaves treated with hydrogen gas-bubbled water had much greater damage to their surface morphological properties compared to the other groups, which may explain the higher yield of phenolic compounds. Overall, hydrogen gas-bubbled water showed better extraction yield of phenolics from green tea leaves than other gas-bubbled water. PRACTICAL APPLICATION: Green tea or green tea infusion has diverse health beneficial functionality due to the presence of phenolic compounds. In this study, different gases including nitrogen, oxygen, hydrogen, and carbon dioxide were treated in water and these gas-bubbled water were used to extract phenolics from green tea leaves. Among them, hydrogen-bubbled water extracted the highest phenolic contents from tea leaves and showed the highest in vitro antioxidant ability in green tea infusion compared to other gas-bubbled water. This new knowledge could help to produce green teas with higher antioxidant activity in beverage industry.

Nondestructive Determination of Diastase Activity of Honey Based on Visible and Near-Infrared Spectroscopy

The activities of enzymes are the basis of evaluating the quality of honey. Beekeepers usually use concentrators to process natural honey into concentrated honey by concentrating it under high temperatures. Active enzymes are very sensitive to high temperatures and will lose their activity when they exceed a certain temperature. The objective of this work is to study the kinetic mechanism of the temperature effect on diastase activity and to develop a nondestructive approach for quick determination of the diastase activity of honey through a heating process based on visible and near-infrared (Vis/NIR) spectroscopy. A total of 110 samples, including three species of botanical origin, were used for this study. To explore the kinetic mechanism of diastase activity under high temperatures, the honey of three kinds of botanical origins were processed with thermal treatment to obtain a variety of diastase activity. Diastase activity represented with diastase number (DN) was measured according to the national standard method. The results showed that the diastase activity decreased with the increase of temperature and heating time, and the sensitivity of acacia and longan to temperature was higher than linen. The optimum temperature for production and processing is 60 °C. Unsupervised clustering analysis was adopted to detect spectral characteristics of these honeys, indicating that different botanical origins of honeys can be distinguished in principal component spaces. Partial least squares (PLS) and least squares-support vector machine (LS-SVM) algorithms were applied to develop quantitative relationships between Vis/NIR spectroscopy and diastase activity. The best result was obtained through Gaussian filter smoothing-standard normal variate (GF-SNV) pretreatment and the LS-SVM model, known as GF-SNV-LS-SVM, with a determination coefficient (R²) of prediction of 0.8872, and root mean square error (RMSE) of prediction of 0.2129. The overall results of this paper showed that the diastase activity of honey can be determined quickly and non-destructively with Vis/NIR spectral methods, which can be used to detect DN in the process of honey production and processing, and to maximize the nutrient content of honey.