A novel ZIF-8 mediated nanocomposite colorimetric sensor array for rapid identification of matcha grades, validated by density functional theory

IF 4 2区 农林科学 Q2 CHEMISTRY, APPLIED
Yu Wang , Muhammad Shoaib , Junyong Wang , Hao Lin , Quansheng Chen , Qin Ouyang
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引用次数: 0

Abstract

The rapid and intelligent assessment of matcha grades remains crucial in quality control. This study proposed a colorimetric sensor array (CSA) mediated with zeolitic imidazolate framework-8 (ZIF-8) to identify matcha grades. The ZIF-8 mediated CSA was innovatively developed by encapsulating the pH indicator and metal porphyrin within the ZIF-8 structure, improving their stability and functionality. ZIF-8 mediated CSA exhibited significantly increased sensitivity towards matcha samples driven by the preconcentration of volatile organic compounds (VOCs) facilitated by ZIF-8. As a result, the response signal values of the CSA increased by 1.13–4.75 times after ZIF-8 mediation. Subsequently, qualitative models for identifying matcha grades were established using K-Nearest Neighbor and Artificial Neural Network (ANN). The ANN model showed the higher discrimination accuracy. Compared with common CSA, the ANN model based on ZIF-8 mediated CSA achieved a better identification rate of 95 %, and recognition accuracy was improved by 7.5 % in the model. These results indicated that the ZIF-8 mediated CSA with a porous structure demonstrated enhanced specificity in capturing VOCs. Ultimately, the density functional theory has confirmed that the ZIF-8-mediated CSA exhibits high selectivity towards matcha's characteristic VOCs. These results highlight the potential of this novel CSA for the rapid identification and grading of matcha.
一种新型 ZIF-8 介导的纳米复合比色传感器阵列,用于快速识别抹茶等级,并通过密度泛函理论进行了验证
快速、智能地评估抹茶等级对质量控制至关重要。本研究提出了一种以沸石咪唑酸框架-8(ZIF-8)为介导的比色传感器阵列(CSA),用于识别抹茶等级。ZIF-8 介导的 CSA 是通过在 ZIF-8 结构中封装 pH 指示剂和金属卟啉,提高其稳定性和功能性而创新开发的。由于 ZIF-8 促进了挥发性有机化合物 (VOC) 的预浓缩,ZIF-8 介导 CSA 对抹茶样品的灵敏度明显提高。因此,经 ZIF-8 调解后,CSA 的响应信号值增加了 1.13-4.75 倍。随后,利用 K-近邻和人工神经网络(ANN)建立了鉴定抹茶等级的定性模型。ANN 模型显示出更高的辨别精度。与普通 CSA 相比,基于 ZIF-8 介导 CSA 的 ANN 模型的识别率更高,达到 95%,识别准确率提高了 7.5%。这些结果表明,ZIF-8 介导的多孔结构 CSA 在捕获挥发性有机化合物方面具有更强的特异性。最终,密度泛函理论证实,ZIF-8 介导的 CSA 对抹茶特有的挥发性有机化合物具有很高的选择性。这些结果凸显了这种新型 CSA 在快速鉴定和分级抹茶方面的潜力。
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来源期刊
Journal of Food Composition and Analysis
Journal of Food Composition and Analysis 工程技术-食品科技
CiteScore
6.20
自引率
11.60%
发文量
601
审稿时长
53 days
期刊介绍: The Journal of Food Composition and Analysis publishes manuscripts on scientific aspects of data on the chemical composition of human foods, with particular emphasis on actual data on composition of foods; analytical methods; studies on the manipulation, storage, distribution and use of food composition data; and studies on the statistics, use and distribution of such data and data systems. The Journal''s basis is nutrient composition, with increasing emphasis on bioactive non-nutrient and anti-nutrient components. Papers must provide sufficient description of the food samples, analytical methods, quality control procedures and statistical treatments of the data to permit the end users of the food composition data to evaluate the appropriateness of such data in their projects. The Journal does not publish papers on: microbiological compounds; sensory quality; aromatics/volatiles in food and wine; essential oils; organoleptic characteristics of food; physical properties; or clinical papers and pharmacology-related papers.
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