Mónica Sánchez-Parra , Juan Antonio Fernández Pierna , Vincent Baeten , José Manuel Muñoz-Redondo , José Luis Ordóñez-Díaz , José Manuel Moreno-Rojas
{"title":"利用傅立叶变换中红外(FT-MIR)和化学计量学快速筛查金枪鱼样品中组胺含量的食品安全问题","authors":"Mónica Sánchez-Parra , Juan Antonio Fernández Pierna , Vincent Baeten , José Manuel Muñoz-Redondo , José Luis Ordóñez-Díaz , José Manuel Moreno-Rojas","doi":"10.1016/j.jfoodeng.2024.112129","DOIUrl":null,"url":null,"abstract":"<div><p>Biogenic amines (BAs) generally result from the decarboxylation reaction of free amino acids as a result of the activity of different microorganisms. A build-up of these compounds can result in food being spoilt. Therefore, the rapid and precise detection of BAs like histamine is an important task for food safety. This research aimed to explore the potential of Fourier-Transform Mid-Infrared (FT-MIR) spectroscopy combined with chemometric methods to assess histamine in fresh tuna quantitatively. Based on the FT-MIR data, partial least squares regression models for the prediction of histamine were successfully constructed with R<sup>2</sup> > 0.90. Machine learning algorithms (partial least squares-discrimination analysis, k-nearest neighbors, and support vector machine) were applied, and excellent discrimination results were achieved based on the limits specified in two different legislations (EU and FDA). The results support the use of a rapid, economic and reliable approach for the discrimination of samples that could pose a health risk to consumers.</p></div>","PeriodicalId":359,"journal":{"name":"Journal of Food Engineering","volume":null,"pages":null},"PeriodicalIF":5.3000,"publicationDate":"2024-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Rapid screening of tuna samples for food safety issues related to histamine content using fourier-transform mid-infrared (FT-MIR) and chemometrics\",\"authors\":\"Mónica Sánchez-Parra , Juan Antonio Fernández Pierna , Vincent Baeten , José Manuel Muñoz-Redondo , José Luis Ordóñez-Díaz , José Manuel Moreno-Rojas\",\"doi\":\"10.1016/j.jfoodeng.2024.112129\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Biogenic amines (BAs) generally result from the decarboxylation reaction of free amino acids as a result of the activity of different microorganisms. A build-up of these compounds can result in food being spoilt. Therefore, the rapid and precise detection of BAs like histamine is an important task for food safety. This research aimed to explore the potential of Fourier-Transform Mid-Infrared (FT-MIR) spectroscopy combined with chemometric methods to assess histamine in fresh tuna quantitatively. Based on the FT-MIR data, partial least squares regression models for the prediction of histamine were successfully constructed with R<sup>2</sup> > 0.90. Machine learning algorithms (partial least squares-discrimination analysis, k-nearest neighbors, and support vector machine) were applied, and excellent discrimination results were achieved based on the limits specified in two different legislations (EU and FDA). The results support the use of a rapid, economic and reliable approach for the discrimination of samples that could pose a health risk to consumers.</p></div>\",\"PeriodicalId\":359,\"journal\":{\"name\":\"Journal of Food Engineering\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":5.3000,\"publicationDate\":\"2024-05-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Food Engineering\",\"FirstCategoryId\":\"97\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S026087742400195X\",\"RegionNum\":2,\"RegionCategory\":\"农林科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, CHEMICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Food Engineering","FirstCategoryId":"97","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S026087742400195X","RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, CHEMICAL","Score":null,"Total":0}
Rapid screening of tuna samples for food safety issues related to histamine content using fourier-transform mid-infrared (FT-MIR) and chemometrics
Biogenic amines (BAs) generally result from the decarboxylation reaction of free amino acids as a result of the activity of different microorganisms. A build-up of these compounds can result in food being spoilt. Therefore, the rapid and precise detection of BAs like histamine is an important task for food safety. This research aimed to explore the potential of Fourier-Transform Mid-Infrared (FT-MIR) spectroscopy combined with chemometric methods to assess histamine in fresh tuna quantitatively. Based on the FT-MIR data, partial least squares regression models for the prediction of histamine were successfully constructed with R2 > 0.90. Machine learning algorithms (partial least squares-discrimination analysis, k-nearest neighbors, and support vector machine) were applied, and excellent discrimination results were achieved based on the limits specified in two different legislations (EU and FDA). The results support the use of a rapid, economic and reliable approach for the discrimination of samples that could pose a health risk to consumers.
期刊介绍:
The journal publishes original research and review papers on any subject at the interface between food and engineering, particularly those of relevance to industry, including:
Engineering properties of foods, food physics and physical chemistry; processing, measurement, control, packaging, storage and distribution; engineering aspects of the design and production of novel foods and of food service and catering; design and operation of food processes, plant and equipment; economics of food engineering, including the economics of alternative processes.
Accounts of food engineering achievements are of particular value.