用于定量检测各种植物油掺假的便携式 LED 诱导荧光系统

IF 4 2区农林科学 Q2 CHEMISTRY, APPLIED

Journal of Food Composition and Analysis Pub Date : 2024-11-08 DOI:10.1016/j.jfca.2024.106934

QingHua Liu , Yifan Zhang , Meifeng Wu , Minmin Li , Litao Tong , Huihui Yang , Bei Fan , Jun Liu , Fengzhong Wang , Long Li

{"title":"用于定量检测各种植物油掺假的便携式 LED 诱导荧光系统","authors":"QingHua Liu , Yifan Zhang , Meifeng Wu , Minmin Li , Litao Tong , Huihui Yang , Bei Fan , Jun Liu , Fengzhong Wang , Long Li","doi":"10.1016/j.jfca.2024.106934","DOIUrl":null,"url":null,"abstract":"<div><div>This study developed a portable LED-induced fluorescence detection system for quantitative detection of various vegetable oil adulteration. Eight common vegetable oils and 14 different adulterated samples with adulteration concentrations ranging from 0 % to 50 % were prepared. Before quantitative analysis, different classification models were established for the determination of types of adulteration oil. The overall recognition accuracy was greater than 98 %. Furthermore, with a simple calculation, the proposed normalized spectral ratio (NSR) preprocessing method was used to eliminate the light scattering effects in the raw fluorescence spectra. In addition, the competitive adaptive reweighted sampling (CARS) method was used to select characteristic wavelengths. The final oil adulteration quantitative analysis model was NSR_CARS+PLS. The range of correlation coefficient (<em>R</em>p) and root-mean-square-error (<em>RMSEP</em>) for the prediction datasets were 0.9548–0.9974 and 1.0265 %-5.0236 %, respectively.</div></div>","PeriodicalId":15867,"journal":{"name":"Journal of Food Composition and Analysis","volume":"137 ","pages":"Article 106934"},"PeriodicalIF":4.0000,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A portable LED-induced fluorescence system for quantitative detection of different kinds of vegetable oil adulteration\",\"authors\":\"QingHua Liu , Yifan Zhang , Meifeng Wu , Minmin Li , Litao Tong , Huihui Yang , Bei Fan , Jun Liu , Fengzhong Wang , Long Li\",\"doi\":\"10.1016/j.jfca.2024.106934\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>This study developed a portable LED-induced fluorescence detection system for quantitative detection of various vegetable oil adulteration. Eight common vegetable oils and 14 different adulterated samples with adulteration concentrations ranging from 0 % to 50 % were prepared. Before quantitative analysis, different classification models were established for the determination of types of adulteration oil. The overall recognition accuracy was greater than 98 %. Furthermore, with a simple calculation, the proposed normalized spectral ratio (NSR) preprocessing method was used to eliminate the light scattering effects in the raw fluorescence spectra. In addition, the competitive adaptive reweighted sampling (CARS) method was used to select characteristic wavelengths. The final oil adulteration quantitative analysis model was NSR_CARS+PLS. The range of correlation coefficient (<em>R</em>p) and root-mean-square-error (<em>RMSEP</em>) for the prediction datasets were 0.9548–0.9974 and 1.0265 %-5.0236 %, respectively.</div></div>\",\"PeriodicalId\":15867,\"journal\":{\"name\":\"Journal of Food Composition and Analysis\",\"volume\":\"137 \",\"pages\":\"Article 106934\"},\"PeriodicalIF\":4.0000,\"publicationDate\":\"2024-11-08\",\"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/S0889157524009682\",\"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}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Food Composition and Analysis","FirstCategoryId":"97","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0889157524009682","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

摘要

本研究开发了一种用于定量检测各种植物油掺假的便携式 LED 诱导荧光检测系统。研究人员制备了 8 种常见植物油和 14 种不同的掺假样品，掺假浓度从 0 % 到 50 % 不等。在定量分析之前，建立了不同的分类模型来确定掺假油的类型。总体识别准确率超过 98%。此外，通过简单的计算，利用提出的归一化光谱比（NSR）预处理方法消除了原始荧光光谱中的光散射效应。此外，还使用了竞争性自适应加权采样（CARS）方法来选择特征波长。最终的油品掺假定量分析模型为 NSR_CARS+PLS。预测数据集的相关系数（Rp）和均方根误差（RMSEP）范围分别为 0.9548-0.9974 和 1.0265 %-5.0236 %。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

A portable LED-induced fluorescence system for quantitative detection of different kinds of vegetable oil adulteration

This study developed a portable LED-induced fluorescence detection system for quantitative detection of various vegetable oil adulteration. Eight common vegetable oils and 14 different adulterated samples with adulteration concentrations ranging from 0 % to 50 % were prepared. Before quantitative analysis, different classification models were established for the determination of types of adulteration oil. The overall recognition accuracy was greater than 98 %. Furthermore, with a simple calculation, the proposed normalized spectral ratio (NSR) preprocessing method was used to eliminate the light scattering effects in the raw fluorescence spectra. In addition, the competitive adaptive reweighted sampling (CARS) method was used to select characteristic wavelengths. The final oil adulteration quantitative analysis model was NSR_CARS+PLS. The range of correlation coefficient (Rp) and root-mean-square-error (RMSEP) for the prediction datasets were 0.9548–0.9974 and 1.0265 %-5.0236 %, respectively.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

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.