Rapid Determination of Aflatoxin B1 Contamination in Peanut Oil by Fourier Transform Near-Infrared Spectroscopy

IF 16.4 1区化学 Q1 CHEMISTRY, MULTIDISCIPLINARY

Accounts of Chemical Research Pub Date : 2022-07-01 DOI:10.1155/2022/9223424

Wanqing Yao, Ruanshan Liu, Zhaocheng Xu, Yuling Zhang, Yingming Deng, Hongwei Guo

{"title":"Rapid Determination of Aflatoxin B1 Contamination in Peanut Oil by Fourier Transform Near-Infrared Spectroscopy","authors":"Wanqing Yao, Ruanshan Liu, Zhaocheng Xu, Yuling Zhang, Yingming Deng, Hongwei Guo","doi":"10.1155/2022/9223424","DOIUrl":null,"url":null,"abstract":"Aflatoxin B1 (AFB1) contamination in peanut oil brings about a significant threat to human health. A method based on Fourier transform near-infrared (FT-NIR) spectroscopy was developed for qualitative and quantitative analysis of AFB1 contamination in peanut oil. A total of 94 samples were collected in the transmission mode and processed by a derivative and smoothing filter. Principal component analysis (PCA), discriminant analysis (DA), and partial least squares regression (PLS) were applied to establish the qualitative and quantitative analysis models. It was demonstrated that the qualitative model could distinguish effectively between the positive and negative samples with identification accuracy up to 100%. The correlation coefficient (R2), the root mean square error of calibration (RMSCE), and the relative percent deviation (RPD) for the quantitative model were 0.951, 3.87%, and 4.52, respectively. There was a good linear relationship between the predicted and reference concentrations of the samples with a significant correlation coefficient of 0.981. The qualitative and quantitative analysis models developed in this work may provide reference for researchers engaged in nondestructive testing of food and agricultural products.","PeriodicalId":1,"journal":{"name":"Accounts of Chemical Research","volume":null,"pages":null},"PeriodicalIF":16.4000,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Accounts of Chemical Research","FirstCategoryId":"92","ListUrlMain":"https://doi.org/10.1155/2022/9223424","RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CHEMISTRY, MULTIDISCIPLINARY","Score":null,"Total":0}

引用次数: 3

Abstract

Aflatoxin B1 (AFB1) contamination in peanut oil brings about a significant threat to human health. A method based on Fourier transform near-infrared (FT-NIR) spectroscopy was developed for qualitative and quantitative analysis of AFB1 contamination in peanut oil. A total of 94 samples were collected in the transmission mode and processed by a derivative and smoothing filter. Principal component analysis (PCA), discriminant analysis (DA), and partial least squares regression (PLS) were applied to establish the qualitative and quantitative analysis models. It was demonstrated that the qualitative model could distinguish effectively between the positive and negative samples with identification accuracy up to 100%. The correlation coefficient (R2), the root mean square error of calibration (RMSCE), and the relative percent deviation (RPD) for the quantitative model were 0.951, 3.87%, and 4.52, respectively. There was a good linear relationship between the predicted and reference concentrations of the samples with a significant correlation coefficient of 0.981. The qualitative and quantitative analysis models developed in this work may provide reference for researchers engaged in nondestructive testing of food and agricultural products.

查看原文本刊更多论文

傅里叶变换近红外光谱法快速测定花生油中黄曲霉毒素B1污染

花生油中黄曲霉毒素B1 (AFB1)污染对人体健康造成重大威胁。建立了一种基于傅里叶变换近红外(FT-NIR)光谱的花生油中AFB1污染的定性和定量分析方法。在传输模式下共采集了94个样本，并进行了导数滤波和平滑滤波处理。采用主成分分析(PCA)、判别分析(DA)和偏最小二乘回归(PLS)建立定性和定量分析模型。结果表明，该定性模型能有效区分正负样品，识别准确率达100%。定量模型的相关系数(R2)、校准均方根误差(RMSCE)和相对百分比偏差(RPD)分别为0.951、3.87%和4.52。样品的预测浓度与参比浓度呈良好的线性关系，相关系数为0.981。本文建立的定性和定量分析模型可为从事食品和农产品无损检测的研究人员提供参考。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Accounts of Chemical Research 化学-化学综合

CiteScore

31.40

自引率

1.10%

发文量

312

审稿时长

2 months

期刊介绍： Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance. Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.