FOOD CHEMISTRY
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2023
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IF 8.8
Xiaoyu Yang,Zhichun Bi,Chenghui Yin,Shuyi Zhang,Donghui Song,Hui Huang,Yongxin Li
Tieguanyin (TGY) is one of top ten famous teas in China, but in the process of brand building there is the phenomenon of falsehood, thus harming the interests of consumers. To solve the adulterate problem of TGY, a colorimetric sensor array (CSA) based on peroxidase activity of nanozyme was constructed. Nanozymes can catalyze 3,3′,5,5′-tetramethylbenzidine (TMB) to 3,3′,5,5′-tetramethyl -[1,1′-bis(cyclohexyl)]-2,2′,5,5′-tetraene-4,4′-diimine (oxTMB), while the tea polyphenols (TPs) can inhibit this process, and the degree of inhibition varies significantly with the reaction time. We selected two nanozymes and three reaction time points to construct CSA. It can successfully distinguish TPs in TGY. The discriminative analysis can achieve: (1) distinction between TGY and adulterated tea, (2) discrimination of TGY in various seasons and seasonal adulteration in different degrees. The method constructed in this work is promising for both the class and quality differentiation of TGY and other teas with TPs as the main activity.
研究领域:
食品科学
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分析化学
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生物技术
✓ 已验证
产品关联
DOI
PubMed
高影响因子
MICROCHEMICAL JOURNAL
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2025
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IF 5.1
Tongcan Cui,Hong Chen,Jinyan Li,Jianpeng Zhou,Lifeng Han,Xiaoxuan Tian,Feng He,Xiaoliang Chen,Hong Wang
Fructus Gardeniae (FG) is a commonly used medicinal and edible herb, with its quality influenced by factors such as harvest batches, geographical origins, and adulteration. Traditional methods for quality control of FG are time-consuming and invasive, creating an urgent need for a more rapid and efficient alternative. This study aims to develop a rapid and cost-effective strategy for FG quality control by utilizing near-infrared (NIR) spectroscopy combined with machine learning algorithms. Partial least squares (PLS) regression models were optimized through spectral preprocessing and variable selection techniques to predict four key quality markers in FG. In addition, four classification models, including orthogonal partial least squares discriminant analysis (OPLS-DA), random forest (RF), gray wolf optimizer-support vector machine (GWO-SVM), and convolutional neural network (CNN), were employed to distinguish between FG, Fructus Gardeniae Grandiflorae (FGG), and adulterated mixtures (MIX). The optimized PLS models successfully predicted the contents of genipin-1- β -D-gentiobioside, geniposide, crocin I, and crocin II, with residual predictive deviation (RPD) values of 2.4870, 3.1134, 1.4775, and 1.5316, respectively. The classification accuracy for OPLS-DA, RF, GWO-SVM, and CNN models was 82.93%, 82.93%, 95.12%, and 92.68%, respectively, with GWO-SVM achieving the best performance. Only two of the 41 validation samples were misclassified by the GWO-SVM model. In conclusion, this study provides a comprehensive and rapid solution for detecting FG adulteration and evaluating quality, offering significant improvements in efficiency compared to traditional methods.
研究领域:
中药学
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分析化学
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人工智能
✓ 已验证
产品关联
DOI
高影响因子
SENSORS AND ACTUATORS B-CHEMICAL
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2022
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IF 9.221
Liangli Li,Maohua Yang,Mei Zhang,Mingyan Jia
As an important antitussive and expectorant herbal medicine, Fritillariae cirrhosae bulbus (FCB) is rare and expensive, leading to massive adulterations in the market. Herein, we report a solid colorimetric sensor array (CSA) method for the rapid identification of FCB and its adulterations. This CSA was fabricated by printing nine pH indicators on a mixed cellulose ester membrane via a commercially available inkjet printer filled with polyethylene glycol-containing ink. This CSA displayed color changes to various analytes, as their solution of varied pH values triggers chemical reactions with the nine pH indicators (and other potential interactions). The digital database of these colorimetric response patterns was collected automatically using a homemade program. The outstanding identification capability of the CSA was confirmed via the analysis of 1,2-diols and catechols. The principal component analysis result demonstrated that this CSA could distinguish pure FCB powder from its six common adulterations. The linear discriminant analysis models were established for the qualitative identification of adulterated FCB samples with 89.58 %− 100 % accuracy. Furthermore, partial least squares regression models were developed for the quantitation of adulterant percentages with correlation coefficients higher than 0.87. We expect that the inkjet-printed CSA provided an effective on-site screening method for identifying adulterated FCB.
研究领域:
中药学
·
分析化学
✓ 已验证
DOI
高影响因子
危险品化学品经营许可证（带存储）