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

A colorimetric sensor for rapid discrimination of tea polyphenols and tea authentication based on Rh-decorated Pd nanocubes with high peroxidase-like activity

The rapid development of nanozymes has offered substantial opportunities for the fields of biomedicine, chemical sensing, and food safety. Among these applications, multichannel sensors, with the capability of simultaneously detecting multiple target analytes, hold promise for the practical application of nanozymes in chemical sensing with high detection efficiency. In this study, Rh-decorated Pd nanocubes (Pd-Rh nanocubes) with significantly enhanced peroxidase-like activity are synthesized through the mediation of underpotential deposition (UPD) and subsequently employed to develop a multichannel colorimetric sensor for discriminating tea polyphenols (TPs) and tea authentication. Based on a single reactive unit of efficient catalytic oxidation of 3,3',5,5'-tetramethylbenzidine dihydrochloride (TMB), the nanozyme-based multichannel colorimetric sensor responds to each analyte in as short as 1 min. With the aid of principal component analysis (PCA) and hierarchical cluster analysis (HCA), various TPs and types of tea can be accurately identified. This work not only provides a new type of simply structured and highly active nanozymes but also develops a concise and rapid multichannel sensor for practical application in tea authentication and quality inspection.

Continuous production machine for separating and shaping Taiping Houkui tea

In response to the challenges of low automation and a lack of a continuous processing system for Taiping Houkui tea, this study proposed a design scheme for a continuous processing line and built a continuous processing prototype for testing by combining the production requirements of Taiping Houkui tea, the characteristics of withered leaves, and the existing relevant production equipment. First, the physical properties of Taiping Houkui tea were determined. A simulation was performed using the Hertz-Mindlin model, and the motion states of the tea leaves were obtained under different conditions to define the parameter design range of the experimental platform and verify its structural rationality. Then, the response surface methodology was used to optimize the working parameter ranges and obtain the best working parameters for the feeding and kneading mechanisms. Finally, a continuous production prototype was constructed for further production verification. The experimental results show that the success rate of continuous production on this platform was 70.68%, with an average output of approximately 0.4 kg/h for Taiping Houkui dry tea on a single slide track, and the produced tea was similar to manually made tea. This demonstrates that the continuous production technique has high feasibility and provides a reference for continuous production of Taiping Houkui tea.

Garlic Origin Traceability and Identification Based on Fusion of Multi-Source Heterogeneous Spectral Information

The chemical composition and nutritional content of garlic are greatly impacted by its production location, leading to distinct flavor profiles and functional properties among garlic varieties from diverse origins. Consequently, these variations determine the preference and acceptance among diverse consumer groups. In this study, purple-skinned garlic samples were collected from five regions in China: Yunnan, Shandong, Henan, Anhui, and Jiangsu Provinces. Mid-infrared spectroscopy and ultraviolet spectroscopy were utilized to analyze the components of garlic cells. Three preprocessing methods, including Multiple Scattering Correction (MSC), Savitzky-Golay Smoothing (SG Smoothing), and Standard Normalized Variate (SNV), were applied to reduce the background noise of spectroscopy data. Following variable feature extraction by Genetic Algorithm (GA), a variety of machine learning algorithms, including XGboost, Support Vector Classification (SVC), Random Forest (RF), and Artificial Neural Network (ANN), were used according to the fusion of spectral data to obtain the best processing results. The results showed that the best-performing model for ultraviolet spectroscopy data was SNV-GA-ANN, with an accuracy of 99.73%. The best-performing model for mid-infrared spectroscopy data was SNV-GA-RF, with an accuracy of 97.34%. After the fusion of ultraviolet and mid-infrared spectroscopy data, the SNV-GA-SVC, SNV-GA-RF, SNV-GA-ANN, and SNV-GA-XGboost models achieved 100% accuracy in both training and test sets. Although there were some differences in the accuracy of the four models under different preprocessing methods, the fusion of ultraviolet and mid-infrared spectroscopy data yielded the best outcomes, with an accuracy of 100%. Overall, the combination of ultraviolet and mid-infrared spectroscopy data fusion and chemometrics established in this study provides a theoretical foundation for identifying the origin of garlic, as well as that of other agricultural products.

A Study on Origin Traceability of White Tea (White Peony) Based on Near-Infrared Spectroscopy and Machine Learning Algorithms

Identifying the geographical origins of white tea is of significance because the quality and price of white tea from different production areas vary largely from different growing environment and climatic conditions. In this study, we used near-infrared spectroscopy (NIRS) with white tea (n = 579) to produce models to discriminate these origins under different conditions. Continuous wavelet transform (CWT), min-max normalization (Minmax), multiplicative scattering correction (MSC) and standard normal variables (SNV) were used to preprocess the original spectra (OS). The approaches of principal component analysis (PCA), linear discriminant analysis (LDA) and successive projection algorithm (SPA) were used for features extraction. Subsequently, identification models of white tea from different provinces of China (DPC), different districts of Fujian Province (DDFP) and authenticity of Fuding white tea (AFWT) were established by K-nearest neighbors (KNN), random forest (RF) and support vector machine (SVM) algorithms. Among the established models, DPC-CWT-LDA-KNN, DDFP-OS-LDA-KNN and AFWT-OS-LDA-KNN have the best performances, with recognition accuracies of 88.97%, 93.88% and 97.96%, respectively; the area under curve (AUC) values were 0.85, 0.93 and 0.98, respectively. The research revealed that NIRS with machine learning algorithms can be an effective tool for the geographical origin traceability of white tea.