AI Article Synopsis
- Food safety concerns necessitate precise and efficient analytical techniques to ensure quality, moving away from traditional, costly methods.
- Various methods, including spectroscopy, chromatography, DNA barcoding, and IRMS, are employed to detect food fraud, with NI spectroscopy and hyperspectral imaging being particularly favored for their speed, cost-effectiveness, and ability to analyze different food types without causing damage.
- The advancement in chemometric techniques and the integration of multivariate analysis, such as AI and principal component analysis, enhance the detection of food fraud, making these technologies increasingly relevant in the food industry.
Article Abstract
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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| http://dx.doi.org/10.1016/j.foodchem.2024.139817 | DOI Listing |
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