{"title":"A novel ZIF-8 mediated nanocomposite colorimetric sensor array for rapid identification of matcha grades, validated by density functional theory","authors":"","doi":"10.1016/j.jfca.2024.106864","DOIUrl":null,"url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":15867,"journal":{"name":"Journal of Food Composition and Analysis","volume":null,"pages":null},"PeriodicalIF":4.0000,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Food Composition and Analysis","FirstCategoryId":"97","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0889157524008986","RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"CHEMISTRY, APPLIED","Score":null,"Total":0}
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.
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.