Predicting the age and type of tuocha tea by fourier transform infrared spectroscopy and chemometric data analysis

"Age" of tea: The impact of long-term storage on the aroma of Tuo tea and age prediction

This study investigates the dynamic changes in the aroma profile of Tuo tea during long-term storage, a process not well understood yet critical to the formation of aged tea's unique characteristics. Aroma profiling of Tuo tea samples stored for 2 to 25 years was conducted using sensory evaluation and the HS-SPME/GC × GC-QTOFMS technique, revealing a progressive transition from fresh, fruity, and floral scents to more stale, woody, and herbal notes. Among 275 identified volatiles, 55 were correlated with storage duration (|r| > 0.8, p < 0.05), and 49 differential compounds (VIP > 1, FC > 1.2, FC < 0.833, p < 0.05) were identified across three storage stages (2-4, 5-10, and 13-25 years). Furthermore, theaspirane, eucalyptol, o-xylene, and 1-ethylidene-1H-indene were selected as potential markers of Tuo tea aging, incorporating the implementation of a Random Forest (RF) model. Additionally, our model exhibited high accuracy in predicting the age of Tuo tea within a prediction error range of -2.51 to 2.84 years. This research contributes to a comprehensive understanding of the impact of storage time on tea aroma and aids in the precise identification of tea age.

Discrimination of the harvesting season of green tea by alcohol/salt-based aqueous two-phase systems combined with chemometric analysis

The flavor and aroma quality of green tea are closely related to the harvest season. The aim of this study was to identify the harvesting season of green tea by alcohol/salt-based aqueous two-phase system (ATPS) combined with chemometric analysis. In this paper, the single factor experiments (SFM) and response surface methodology (RSM) optimization were designed to investigate and select the optimal ATPS. A total of 180 green tea samples were studied in this work, including 86 spring tea and 94 autumn tea. After the active components in green tea samples were extracted by the optimal ethanol/(NH₄)₂SO₄ ATPS, the qualitative and quantitative analysis was realized based on HPLC-DAD combined with alternating trilinear decomposition-assisted multivariate curve resolution (ATLD-MCR) algorithm, with satisfactory spiked recoveries (86.00 %-112.45 %). The quantitative results obtained from ATLD-MCR model were subjected to chemometric pattern recognition analysis. The constructed partial least squares-discriminant analysis (PLS-DA) and orthogonal partial least squares-discriminant analysis (OPLS-DA) models showed better results than the principal component analysis (PCA) model, and the R²X_cum values (>0.835) and R²Y_cum (>0.937) were close to 1, the Q²_cum values were greater than 0.75 (>0.933), and the differences between R²Y_cum and Q²_cum were not larger than 0.2, indicating excellent cross-validation prediction performance of the models. Furthermore, the classification results based on the hierarchical clustering analysis (HCA) were consistent with the PCA, PLS-DA and OPLS-DA results, establishing a good correlation between tea active components and the harvesting seasons of green tea. Overall, the combination of ATPS and chemometric methods is accurate, sensitive, fast and reliable for the qualitative and quantitative determination of tea active components, providing guidance for the quality control of green tea.

Is infrared spectroscopy combined with multivariate analysis a promising tool for heparin authentication?

The investigation of the possibility to determine various characteristics of powder heparin (n = 115) was carried out with infrared spectroscopy. The evaluation of heparin samples included several parameters such as purity grade, distributing company, animal source as well as heparin species (i.e. Na-heparin, Ca-heparin, and heparinoids). Multivariate analysis using principal component analysis (PCA), soft independent modelling of class analogy (SIMCA), and partial least squares - discriminant analysis (PLS-DA) were applied for the modelling of spectral data. Different pre-processing methods were applied to IR spectral data; multiplicative scatter correction (MSC) was chosen as the most relevant. Obtained results were confirmed by nuclear magnetic resonance (NMR) spectroscopy. Good predictive ability of this approach demonstrates the potential of IR spectroscopy and chemometrics for screening of heparin quality. This approach, however, is designed as a screening tool and is not considered as a replacement for either of the methods required by USP and FDA.

Raw Bowl Tea (Tuocha) Polyphenol Prevention of Nonalcoholic Fatty Liver Disease by Regulating Intestinal Function in Mice

A high-fat diet-induced C57BL/6N mouse model of non-alcoholic fatty liver disease (NAFLD) was established. The effect and mechanism of Raw Bowl Tea polyphenols (RBTP) on preventing NAFLD via regulating intestinal function were observed. The serum, liver, epididymis, small intestine tissues, and feces of mice were examined by biochemical and molecular biological methods, and the composition of RBTP was analyzed by HPLC assay. The results showed that RBTP could effectively reduce the body weight, liver weight, and liver index of NAFLD mice. The serum effects of RBTP were: (1) decreases in alanine aminotransferase (ALT), aspartate aminotransferase (AST), alkaline phosphatase (AKP), total cholesterol (TC), triglyceride (TG), low density lipoprotein cholesterol (LDL-C), D-lactate (D-LA), diamine oxidase (DAO), lipopolysaccharide (LPS), and an increase of high density lipoprotein cholesterol (HDL-C) levels; (2) a decrease of inflammatory cytokines such as interleukin 1 beta (IL-1β), interleukin 4 (IL-4), interleukin 6 (IL-6), interleukin 10 (IL-10), tumor necrosis factor alpha (TNF-α), and interferon gamma (INF-γ); (3) a decrease the reactive oxygen species (ROS) level in liver tissue; and (4) alleviation of pathological injuries of liver, epididymis, and small intestinal tissues caused by NAFLD and protection of body tissues. qPCR and Western blot results showed that RBTP could up-regulate the mRNA and protein expressions of LPL, PPAR-α, CYP7A1, and CPT1, and down-regulate PPAR-γ and C/EBP-α in the liver of NAFLD mice. In addition, RBTP up-regulated the expression of occludin and ZO-1, and down-regulated the expression of CD36 and TNF-α in the small intestines of NAFLD mice. Studies on mice feces showed that RBTP reduced the level of Firmicutes and increased the minimum levels of Bacteroides and Akkermansia, as well as reduced the proportion of Firmicutes/Bacteroides in the feces of NAFLD mice, which play a role in regulating intestinal microecology. Component analysis showed that RBTP contained seven polyphenolic compounds: Gallic acid, (-)-epigallocatechin, catechin, L-epicatechin, (-)-epigallocatechin gallate, (-)-gallocatechin gallate, and (-)-epicatechin gallate (ECG), and high levels of caffeine, (-)-epigallocatechin (EGC), and ECG. RBTP improved the intestinal environment of NAFLD mice with the contained active ingredients, thus playing a role in preventing NAFLD. The effect was positively correlated with the dose of 100 mg/kg, which was even better than that of the clinical drug bezafibrate.

A Phytochemical-Sensing Strategy Based on Mass Spectrometry Imaging and Metabolic Profiling for Understanding the Functionality of the Medicinal Herb Green Tea

Low-molecular-weight phytochemicals have health benefits and reduce the risk of diseases, but the mechanisms underlying their activities have remained elusive because of the lack of a methodology that can easily visualize the exact behavior of such small molecules. Recently, we developed an in situ label-free imaging technique, called mass spectrometry imaging, for visualizing spatially-resolved biotransformations based on simultaneous mapping of the major bioactive green tea polyphenol and its phase II metabolites. In addition, we established a mass spectrometry-based metabolic profiling technique capable of evaluating the bioactivities of diverse green tea extracts, which contain multiple phytochemicals, by focusing on their compositional balances. This methodology allowed us to simultaneously evaluate the relative contributions of the multiple compounds present in a multicomponent system to its bioactivity. This review highlights small molecule-sensing techniques for visualizing the complex behaviors of herbal components and linking such information to an enhanced understanding of the functionalities of multicomponent medicinal herbs.

Renal Graft Fibrosis and Inflammation Quantification by an Automated Fourier-Transform Infrared Imaging Technique

Renal interstitial fibrosis and interstitial active inflammation are the main histologic features of renal allograft biopsy specimens. Fibrosis is currently assessed by semiquantitative subjective analysis, and color image analysis has been developed to improve the reliability and repeatability of this evaluation. However, these techniques fail to distinguish fibrosis from constitutive collagen or active inflammation. We developed an automatic, reproducible Fourier-transform infrared (FTIR) imaging-based technique for simultaneous quantification of fibrosis and inflammation in renal allograft biopsy specimens. We generated and validated a classification model using 49 renal biopsy specimens and subsequently tested the robustness of this classification algorithm on 166 renal grafts. Finally, we explored the clinical relevance of fibrosis quantification using FTIR imaging by comparing results with renal function at 3 months after transplantation (M3) and the variation of renal function between M3 and M12. We showed excellent robustness for fibrosis and inflammation classification, with >90% of renal biopsy specimens adequately classified by FTIR imaging. Finally, fibrosis quantification by FTIR imaging correlated with renal function at M3, and the variation in fibrosis between M3 and M12 correlated well with the variation in renal function over the same period. This study shows that FTIR-based analysis of renal graft biopsy specimens is a reproducible and reliable label-free technique for quantifying fibrosis and active inflammation. This technique seems to be more relevant than digital image analysis and promising for both research studies and routine clinical practice.

Using FTIR spectra and pattern recognition for discrimination of tea varieties

In order to classify typical Chinese tea varieties, Fourier transform infrared spectroscopy (FTIR) of tea polysaccharides (TPS) was used as an accurate and economical method. Partial least squares (PLS) modeling method along with a self-organizing map (SOM) neural network method was utilized due to the diversity and heterozygosis between teas. FTIR spectra results of tea extracts after spectra preprocessing were used as input data for PLS and SOM multivariate statistical analyses respectively. The predicted correlation coefficient of optimization PLS model was 0.9994, and root mean square error of calibration and cross-validation (RMSECV) was 0.03285. The features of PLS can be visualized in principal component (PC) space, contributing to discover correlation between different classes of spectra samples. After that, a data matrix consisted of the scores on the selected 3PCs computed by principle component analysis (PCA) and the characteristic spectrum data was used as inputs for training of SOM neural network. Compared with the PLS linear technique's recognition rate of 67% only, the correct recognition rate of the PLS-SOM as a non-linear classification algorithm to differentiate types of tea reaches up to 100%. And the models become reliable and provide a reasonable clustering of tea varieties.

Application of gas chromatography-mass spectrometry and chemometrics methods for assessing volatile profiles of Pu-erh tea with different processing methods and ageing years

A method was developed based on head-space solid phase microextraction/gas chromatography-mass spectrometry (HS-SPME/GC-MS) combined with multivariate statistical methods to assess volatile profiles in different types of Pu-erh teas.

Combining electronic tongue array and chemometrics for discriminating the specific geographical origins of green tea

The feasibility of electronic tongue and multivariate analysis was investigated for discriminating the specific geographical origins of a Chinese green tea with Protected Designation of Origin (PDO). 155 Longjing tea samples from three subareas were collected and analyzed by an electronic tongue array of 7 sensors. To remove the influence of abnormal measurements and samples, robust principal component analysis (ROBPCA) was used to detect outliers in each class. Partial least squares discriminant analysis (PLSDA) was then used to develop a classification model. The prediction sensitivity/specificity of PLSDA was 1.000/1.000, 1.000/0.967, and 0.950/1.000 for longjing from Xihu, Qiantang, and Yuezhou, respectively. Electronic tongue and chemometrics can provide a rapid and reliable tool for discriminating the specific producing areas of Longjing.

A near-infrared reflectance spectroscopy method for direct analysis of several chemical components and properties of fruit, for example, Chinese hawthorn

Near-infrared spectroscopy (NIRS) calibrations were developed for the discrimination of Chinese hawthorn (Crataegus pinnatifida Bge. var. major) fruit from three geographical regions as well as for the estimation of the total sugar, total acid, total phenolic content, and total antioxidant activity. Principal component analysis (PCA) was used for the discrimination of the fruit on the basis of their geographical origin. Three pattern recognition methods, linear discriminant analysis, partial least-squares-discriminant analysis, and back-propagation artificial neural networks, were applied to classify and compare these samples. Furthermore, three multivariate calibration models based on the first derivative NIR spectroscopy, partial least-squares regression, back-propagation artificial neural networks, and least-squares-support vector machines, were constructed for quantitative analysis of the four analytes, total sugar, total acid, total phenolic content, and total antioxidant activity, and validated by prediction data sets.