AIMC Topic: Food Analysis

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A FPGA based recurrent neural networks-based impedance spectroscopy system for detection of YAKE in tuna.

Scientific reports Jul 1, 2025
This paper evaluates the use of impedance spectroscopy combined with artificial intelligence. Both technologies have been widely used in food classification and it is proposed a way to improve classifications using recurrent neural networks that trea...
Neural Networks, Computer Animals Dielectric Spectroscopy Tuna Recurrent Neural Networks Food Analysis Seafood
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Characterize and explore the dynamic changes in the volatility profiles of sauce-flavor baijiu during different rounds by GC-IMS, GC-MS and GC×GC-MS combined with machine learning.

Food research international (Ottawa, Ont.) May 5, 2025
The production process of sauce-flavor baijiu (SFB) involves seven distillations, yielding base baijiu of 7 rounds (RSFB), which are then blended to form the final product. Therefore, the quality of the base baijiu is closely related to the quality o...
Volatile Organic Compounds Food Handling Gas Chromatography-Mass Spectrometry Taste Flavoring Agents Volatilization Machine Learning Food Analysis
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Spectroscopic techniques combined with chemometrics for rapid detection of food adulteration: Applications, perspectives, and challenges.

Food research international (Ottawa, Ont.) Apr 20, 2025
Food adulteration is an important threat to food safety and can be difficult to detect. Some analytical methods are complex and difficult to meet the needs of large numbers of samples. In this study, we introduced the application of six spectroscopic...
Spectroscopy, Near-Infrared Spectrum Analysis, Raman Spectroscopy, Fourier Transform Infrared Spectrum Analysis Food Contamination Food Analysis Chemometrics
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Integrating mass defect filtering and targeted molecular networking for foodomics research: A case study of Magnolia officinalis cortex.

Food research international (Ottawa, Ont.) Apr 18, 2025
Mass spectrometry (MS)-based foodomics is widely used to tackle complex challenges in food science, although its effectiveness is often hampered by extensive data redundancy. To address this limitation, a novel MS-based foodomics strategy, integratin...
Lignans Magnolia Support Vector Machine Mass Spectrometry Alkaloids Glycosides Food Analysis
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Data integrity of food and machine learning: Strategies, advances and prospective.

Food chemistry Mar 17, 2025
Data integrity is an emerging concept aimed at recording real food properties in the form of data throughout the food lifecycle. However, due to the one-sided nature of current food control data, the comprehensive implementation of data integrity has...
Algorithms Food Analysis Machine Learning
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Application of artificial intelligence in the rapid determination of moisture content in medicine food homology substances.

Food chemistry Mar 16, 2025
Moisture content is crucial in quality testing of medicine food homology substances. This study aimed to present a new modeling method for moisture content based on near-infrared spectroscopy. When comparing three methods of partial least squares reg...
Food Analysis Least-Squares Analysis Spectroscopy, Near-Infrared Water Neural Networks, Computer Artificial Intelligence
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Deep-learning-assisted chemo-responsive alizarin red S-based hydrogel sensor for the rapid freshness sensing of aquatic product.

Food research international (Ottawa, Ont.) Mar 15, 2025
Rapid detection of freshness particularly in aquatic products demands efficient sensing strategies. Here, a novel deep-learning-assisted chemo-responsive alizarin red S-based hydrogel sensing platform was established for rapid freshness assay of aqua...
Fishes Hydrogels Food Analysis Animals Hydrogen-Ion Concentration Deep Learning Alginates Anthraquinones Seafood
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Rapid and accurate identification and quantification of Lycium barbarum L. components: Integrating deep learning and NMR for nutritional assessment.

Food research international (Ottawa, Ont.) Mar 15, 2025
Lycium barbarum L. (L. barbarum), revered for its nutritional and commercial value, exhibits variable nutritional contents depending on the consumption method. This study introduces an innovative approach, the Identification and Quantification of L.b...
Neural Networks, Computer Magnetic Resonance Spectroscopy Nutrition Assessment Fruit Lycium Deep Learning Nutritive Value Food Analysis
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Foodomics approaches: New insights in phenolic compounds analysis.

Food research international (Ottawa, Ont.) Mar 13, 2025
Fruits, vegetables, and plant-based foods contain several bioactive substances such as phenolic compounds (PCs), that are plant secondary metabolites with attributed health properties. The study of the metabolic pathways of PCs, including those relat...
Fruit Metabolomics Proteomics Vegetables Phenols Food Analysis
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Origin traceability of agricultural products: A lightweight collaborative neural network for spectral information processing.

Food research international (Ottawa, Ont.) Mar 6, 2025
The natural conditions of various regions, including climate, soil, and water quality, significantly influence the nutrient composition and quality of agricultural products. Identifying the origin of agricultural products can prevent adulteration, im...
Arachis Food Quality Oryza Crops, Agricultural Neural Networks, Computer Food Contamination Agriculture Food Analysis
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