Development of a New Three-Dimensional Fluorescence Spectroscopy Method Coupling with Multilinear Pattern Recognition to Discriminate the Variety and Grade of Green Tea

Rapid identification of Radix Astragali origin by using fluorescence probe combined with chemometrics

Fluorescent probes for metal ion recognition can be divided into selective probes, weakly selective probes, and non-selective probes roughly. Weakly selective probes are not often used for quantitative analysis of metal ions due to their overlapping spectra resulting from simultaneous interactions with multiple metal ions. Conversely, the different metal ions contained in herbal medicine extracts from different geographical origins will produce corresponding fluorescence fingerprint profiles after interaction with weakly selective fluorescence probes. The performance can be used in the study of origin tracing of food or Chinese herbal medicine. Weakly selective fluorescent probes of benzimidazole derivatives have been synthesized and attempted to be used in the origin tracing of Radix Astragali in this work. Radix Astragali from different origins will produce different fluorescence fingerprint spectra due to the difference of metal ions and content in combination with the probe. Excitation-emission matrix (EEM) fluorescence spectroscopy in conjunction with N-way partial least squares discriminant analysis (N-PLS-DA), and unfolded partial least squares discriminant analysis (U-PLS-DA) were used to identify the origin of 150 Radix Astragali samples from five geographical origins. The prediction results showed that the correct recognition rates of the U-PLS-DA model and N-PLS-DA model are 95.92% and 93.88%, respectively. In comparison, the results of U-PLS-DA are slightly better than those of N-PLS-DA. These findings indicate that EEM fluorescence spectroscopy based on weakly selective fluorescent probes combined with multi-way chemometrics provides a good idea for the origin tracing of traditional Chinese medicine.

A sensor array based on a nanozyme with polyphenol oxidase activity for the identification of tea polyphenols and Chinese green tea

Green tea is popular among consumers because of its high nutritional value and unique flavor. There is often a strong correlation among the type of tea, its quality level and the price. Therefore, the rapid identification of tea types and the judgment of tea quality grades are particularly important. In this work, a novel sensor array based on nanozyme with polyphenol oxidase (PPO) activity is proposed for the identification of tea polyphenols (TPs) and Chinese green tea. The absorption spectra changes of the nanozyme and its substrate in the presence of different TPs were first investigated. The feature spectra were scientifically selected using genetic algorithm (GA), and then a sensor array with 15 sensing units (5 wavelengths × 3 time) was constructed. Combined with the support vector machine (SVM) discriminative model, the discriminative rate of this sensor array was 100% for different concentrations of typical TPs in Chinese green tea with a detection limit of 5 μM. In addition, the identification of different concentrations of the same tea polyphenols and mixed tea polyphenols have also been achieved. Based on the above study, we further developed a facile and efficient new method for the category differentiation and adulteration identification of green tea, and the accuracy of this array was 96.88% and 100% for eight types of green teas and different adulteration ratios of Biluochun, respectively. This work has significance for the rapid discrimination of green tea brands and adulteration.

Egeria densa organic extracts: an eco-friendly approach to suppress Microcystis aeruginosa growth through allelopathy

Macrophytes play a significant role in shaping plankton communities by shading, controlling water turbulence, and nutrient availability, while also producing allelochemicals causing varying effects on different organisms. Many researchers have shown that when live macrophytes are present, they inhibit cyanobacteria. However, their widespread use is often limited due to numerous concerns, including invasive characteristics. This study focused on the applicability of Egeria densa extracts to suppress Microcystis aeruginosa. We employed pure water and dimethyl sulfoxide, to obtain compounds from E. densa. The results revealed that E. densa aqueous extracts stimulated M. aeruginosa growth, whereas organic extracts exhibited suppression. Specifically, at low concentrations of organics extracts (0.5 and 1 g/L), after day 4, the growth inhibition was confirmed by significantly higher (p < 0.05) stress levels shown in cells treated with low concentrations. The highest inhibition rate of 32% was observed at 0.5 g/L. However, high concentrations of organic extracts (3 and 6 g/L), showed increased growth compared with control. These results suggest that high concentrations of organic extracts from E. densa potentially suppress allelochemical-induced M. aeruginosa inhibition due to high nutrient availability. In comparison with an aqueous solvent, the use of organic solvent seems to be more effective in efficiently extracting allelochemicals from E. densa.

Identifying the Producer and Grade of Matcha Tea through Three-Dimensional Fluorescence Spectroscopy Analysis and Distance Discrimination

The three-dimensional fluorescence spectroscopy features the advantage of obtaining emission spectra at different excitation wavelengths and providing more detailed information. This study established a simple method to discriminate both the producer and grade of matcha tea by coupling three-dimensional fluorescence spectroscopy analysis and distance discrimination. The matcha tea was extracted three times and three-dimensional fluorescence spectroscopies of these tea infusions were scanned; then, the dimension of three-dimensional fluorescence spectroscopies was reduced by the integration at three specific areas showing local peaks of fluorescence intensity, and a series of vectors were constructed based on a combination of integrated vectors of the three tea infusions; finally, four distances were used to discriminate the producer and grade of matcha tea, and two discriminative patterns were compared. The results indicated that proper vector construction, appropriate discriminative distance, and correct steps are three key factors to ensure the high accuracy of the discrimination. The vector based on the three-dimensional fluorescence spectroscopy of all three tea infusions resulted in a higher accuracy than those only based on spectroscopy of one or two tea infusions, and the first tea infusion was more sensitive than the other tea infusion. The Mahalanobis distance had a higher accuracy that was up to 100% when the vector is appropriate, while the other three distances were about 60-90%. The two-step discriminative pattern, identifying the producer first and the grade second, showed a higher accuracy and a smaller uncertainty than the one-step pattern of identifying both directly. These key conclusions above help discriminate the producer and grade of matcha in a quick, accurate, and green method through three-dimensional fluorescence spectroscopy, as well as in quality inspections and identifying the critical parameters of the producing process.

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.

Tracing sources of oilfield wastewater based on excitation-emission matrix fluorescence spectroscopy coupled with chemical pattern recognition techniques

In order to prevent the illegal discharge of oilfield wastewater, this work proposed excitation-emission matrix fluorescence (EEMF) spectroscopy coupled with two kinds of chemical pattern recognition methods for tracing the sources of oilfield wastewater. The first pattern recognition method was built from the relative concentrations extracted by alternating trilinear decomposition (ATLD) based on partial least squares-discriminant analysis (PLS-DA) algorithm, and the other one was modeled based on strictly multi-way partial least squares-discriminant analysis (N-PLS-DA). Both methods showed good discrimination abilities for oilfield wastewater samples from three different sources. The total recognition rates of the training and prediction sets are 100%, the values of sensitivity and selectivity are 1. This study showed that EEMF spectroscopy combined with chemical pattern recognition techniques could be used as a potential tool for tracing the sources of oilfield wastewater.

A Machine Learning Method for the Fine-Grained Classification of Green Tea with Geographical Indication Using a MOS-Based Electronic Nose

Chinese green tea is known for its health-functional properties. There are many green tea categories, which have sub-categories with geographical indications (GTSGI). Several high-quality GTSGI planted in specific areas are labeled as famous GTSGI (FGTSGI) and are expensive. However, the subtle differences between the categories complicate the fine-grained classification of the GTSGI. This study proposes a novel framework consisting of a convolutional neural network backbone (CNN backbone) and a support vector machine classifier (SVM classifier), namely, CNN-SVM for the classification of Maofeng green tea categories (six sub-categories) and Maojian green tea categories (six sub-categories) using electronic nose data. A multi-channel input matrix was constructed for the CNN backbone to extract deep features from different sensor signals. An SVM classifier was employed to improve the classification performance due to its high discrimination ability for small sample sizes. The effectiveness of this framework was verified by comparing it with four other machine learning models (SVM, CNN-Shi, CNN-SVM-Shi, and CNN). The proposed framework had the best performance for classifying the GTSGI and identifying the FGTSGI. The high accuracy and strong robustness of the CNN-SVM show its potential for the fine-grained classification of multiple highly similar teas.

Rapid Identification of Different Grades of Huangshan Maofeng Tea Using Ultraviolet Spectrum and Color Difference

Tea is an important beverage in humans’ daily lives. For a long time, tea grade identification relied on sensory evaluation, which requires professional knowledge, so is difficult and troublesome for laypersons. Tea chemical component detection usually involves a series of procedures and multiple steps to obtain the final results. As such, a simple, rapid, and reliable method to judge the quality of tea is needed. Here, we propose a quick method that combines ultraviolet (UV) spectra and color difference to classify tea. The operations are simple and do not involve complex pretreatment. Each method requires only a few seconds for sample detection. In this study, famous Chinese green tea, Huangshan Maofeng, was selected. The traditional detection results of tea chemical components could not be used to directly determine tea grade. Then, digital instrument methods, UV spectrometry and colorimetry, were applied. The principal component analysis (PCA) plots of the single and combined signals of these two instruments showed that samples could be arranged according to grade. The combined signal PCA plot performed better with the sample grade descending in clockwise order. For grade prediction, the random forest (RF) model produced a better effect than the support vector machine (SVM) and the SVM + RF model. In the RF model, the training and testing accuracies of the combined signal were all 1. The grades of all samples were correctly predicted. From the above, the UV spectrum combined with color difference can be used to quickly and accurately classify the grade of Huangshan Maofeng tea. This method considerably increases the convenience of tea grade identification.

Effect of aquatic macrophyte growth on landscape water quality improvement

The water of urban landscape park is often confronted with microalgal blooms due to its stagnancy. Bioremediation using the combined emergent and submerged plants to control the microalgae growth was investigated in the present study. Two water bodies (Bei and Xin) of Yuyuantan Park in Beijing were selected for the field experiments, and the other lakes with different vegetation of macrophytes were selected as the comparison. The concentrations of chlorophyll a (chl a), chemical oxygen demand (COD), total nitrogen (TN), and total phosphorus (TP), and water temperature and transparency were monitored before and after bioremediation from 2015 to 2017. Results showed that the effects of microalgal inhibition were more significant 2 years after bioremediation. Specifically, the chl a of Dong Lake without any vegetation of macrophytes was up to 65.1 μg/L in summer of 2017, while the Bei and Xin Lakes was only 6.2 and 11.3 μg/L, respectively. In addition, the water quality and transparency also improved, with water bodies being crystal clear. Submerged plants played major roles in microalgal control and water quality improvement, compared to the lakes with only emergent plants. The intensity of humic acid-like substances in three-dimensional fluorescent spectra was stronger for the lakes with submerged plants.

Tea types classification with data fusion of UV-Vis, synchronous fluorescence and NIR spectroscopies and chemometric analysis

The potential of selected spectroscopic methods - UV-Vis, synchronous fluorescence and NIR as well a data fusion of the measurements by these methods - for the classification of tea samples with respect to the production process was examined. Four classification methods - Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), Regularized Discriminant Analysis (RDA) and Support Vector Machine (SVM) - were used to analyze spectroscopic data. PCA analysis was applied prior to classification methods to reduce multidimensionality of the data. Classification error rates were used to evaluate the performance of these methods in the classification of tea samples. The results indicate that black, green, white, yellow, dark, and oolong teas, which are produced by different methods, are characterized by different UV-Vis, fluorescence, and NIR spectra. The lowest error rates in the calibration and validation data sets for individual spectroscopies and data fusion models were obtained with the use of the QDA and SVM methods, and did not exceed 3.3% and 0.0%, respectively. The lowest classification error rates in the validation data sets for individual spectroscopies were obtained with the use of RDA (12,8%), SVM (6,7%), and QDA (2,7%), for the UV-Vis, SF, and NIR spectroscopies, respectively. NIR spectroscopy combined with QDA outperformed other individual spectroscopic methods. Very low classification errors in the validation data sets - below 3% - were obtained for all the data fusion data sets (SF + UV-Vis, SF + NIR, NIR + UV-Vis combined with the SVM method). The results show that UV-Vis, fluorescence and near infrared spectroscopies may complement each other, giving lower errors for the classification of tea types.