AI Medical Compendium Journal:
Food chemistry

Showing 11 to 20 of 202 articles

Exploring the impact of bioactive peptides from fermented Milk proteins: A review with emphasis on health implications and artificial intelligence integration.

Food chemistry
This review explores the health benefits of bioactive peptides (BAPs) from fermented milk proteins, emphasizing the transformative role of artificial intelligence (AI) and machine learning (ML) in advancing this field. BAPs exhibit diverse biological...

Robust DEEP heterogeneous ensemble and META-learning for honey authentication.

Food chemistry
Food fraud raises significant concerns to consumer health and economic integrity, with the adulteration of honey by sugary syrups representing one of the most prevalent forms of economically motivated adulteration. This study presents a novel framewo...

Simultaneous detection of citrus internal quality attributes using near-infrared spectroscopy and hyperspectral imaging with multi-task deep learning and instrumental transfer learning.

Food chemistry
Simultaneous determination of multiple quality attributes of citrus fruits using hyperspectral imaging (HSI) and near-infrared (NIR) spectroscopy and successfully transferring the models among different instruments are two main challenges. In this st...

Applying deep learning algorithms for non-invasive estimation of carotenoid content in the foot muscle of Pacific abalone with different colors.

Food chemistry
Carotenoids are vital pigments influencing both the coloration and health of aquatic organisms, particularly in species such as the Pacific abalone (Haliotis discus hannai). In this study, we identified the major carotenoids in abalone foot muscle us...

Data integrity of food and machine learning: Strategies, advances and prospective.

Food chemistry
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...

Application of artificial intelligence in the rapid determination of moisture content in medicine food homology substances.

Food chemistry
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...

Intelligent monitoring of fruit and vegetable freshness in supply chain based on 3D printing and lightweight deep convolutional neural networks (DCNN).

Food chemistry
In this study, an innovative intelligent system for supervising the quality of fresh produce was proposed, which combined 3D printing technology and deep convolutional neural networks (DCNN). Through 3D printing technology, sensitive, lightweight, an...

Identification of Fusarium sambucinum species complex by surface-enhanced Raman spectroscopy and XGBoost algorithm.

Food chemistry
Rapid and reliable identification of Fusarium fungi is crucial, due to their role in food spoilage and potential toxicity. Traditional identification methods are often time-consuming and resource-intensive. This study explores the use of surface-enha...

An efficient method for chili pepper variety classification and origin tracing based on an electronic nose and deep learning.

Food chemistry
The quality of chili peppers is closely related to their variety and geographical origin. The market often substitutes high-quality chili peppers with inferior ones, and cross-contamination occurs during processing. The existing methods cannot quickl...

Rapid detection of the viability of naturally aged maize seeds using multimodal data fusion and explainable deep learning techniques.

Food chemistry
Seed viability, a key indicator for quality assessment, directly impacts the emergence of field seedlings. The existing nondestructive testing model for maize seed vitality based on naturally aged seeds and predominantly relying on single-modal data ...