生化工程
质量(理念)
主成分分析
食品质量
化学计量学
计算机科学
多元统计
吞吐量
风险分析(工程)
食品工业
偏最小二乘回归
数据挖掘
工艺工程
机器学习
人工智能
业务
化学
工程类
食品科学
电信
哲学
认识论
无线
作者
Ramesh Sharma,Pinku Chandra Nath,Bibhab Kumar Lodh,Jayanti Mukherjee,Nibedita Mahata,K. Gopikrishna,Onkar Nath Tiwari,Biswanath Bhunia
出处
期刊:Food Chemistry
[Elsevier BV]
日期:2024-05-24
卷期号:454: 139817-139817
被引量:13
标识
DOI:10.1016/j.foodchem.2024.139817
摘要
Precise and reliable analytical techniques are required to guarantee food quality in light of the expanding concerns regarding food safety and quality. Because traditional procedures are expensive and time-consuming, quick food control techniques are required to ensure product quality. Various analytical techniques are used to identify and detect food fraud, including spectroscopy, chromatography, DNA barcoding, and inotrope ratio mass spectrometry (IRMS). Due to its quick findings, simplicity of use, high throughput, affordability, and non-destructive evaluations of numerous food matrices, NI spectroscopy and hyperspectral imaging are financially preferred in the food business. The applicability of this technology has increased with the development of chemometric techniques and near-infrared spectroscopy-based instruments. The current research also discusses the use of several multivariate analytical techniques in identifying food fraud, such as principal component analysis, partial least squares, cluster analysis, multivariate curve resolutions, and artificial intelligence.
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