AIMC Topic: Nutritive Value

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Comparative metabolomics profiling reveals the aroma and nutritional diversity of eight wild edible plants.

Food chemistry
Wild edible plants are important yet undervalued vegetables, due to limited knowledge of their bioactive components and nutritional/functional potential. In this study, we used full-spectrum metabolomics and machine learning to analyze the aroma-rela...

Impact of dairy intake on circulating fatty acids and associations with blood pressure: A randomized crossover trial.

Nutrition, metabolism, and cardiovascular diseases : NMCD
BACKGROUND AND AIMS: This study aimed to investigate the effects of high and adequate dairy intake (>4, 2-3 serving/day, respectively) on circulating fatty acids (FAs) and their associations with blood pressure (BP).

Tracking nutritional and quality changes in frozen pork: A 12-month study using 7 categories of meat parameters and VIS/NIR spectroscopy.

Food chemistry
Frozen pork stocks are critical for stabilizing food security and prices, but assessing nutritional and physicochemical changes during freezing remains challenging. This study conducted a 12-month frozen storage experiment at -20 °C on 50 pigs' longi...

Rapid and accurate identification and quantification of Lycium barbarum L. components: Integrating deep learning and NMR for nutritional assessment.

Food research international (Ottawa, Ont.)
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...

Using visible and NIR hyperspectral imaging and machine learning for nondestructive detection of nutrient contents in sorghum.

Scientific reports
Nondestructive, rapid, and accurate detection of nutritional compositions in sorghum is crucial for agricultural and food industries. In our study, the crude protein, tannin, and crude fat contents of sorghum variety samples were taken as the researc...

Unleashing the nutritional potential of Brassica microgreens: A case study on seed priming with Vermicompost.

Food chemistry
Microgreens constitute ready-to-eat functional foods, being rich sources of phytonutrients and phytochemicals. Because of their short life cycle, seed priming is a promising strategy to fortify their functional outcome. Vermicompost was applied as se...

An Evaluation of ChatGPT for Nutrient Content Estimation from Meal Photographs.

Nutrients
: Advances in artificial intelligence now allow combined use of large language and vision models; however, there has been limited evaluation of their potential in dietary assessment. This study aimed to evaluate the accuracy of ChatGPT-4 in estimatin...

Nutrient based classification of Phyllospora comosa biomasses using machine learning algorithms: Towards sustainable valorisation.

Food research international (Ottawa, Ont.)
Sustainable seaweed value chains necessitate accurate biomass biochemical characterisation that leads to product development, geographical authentications and quality and sustainability assurances. Underutilised yet abundantly available seaweed speci...

Enhancing dietary analysis: Using machine learning for food caloric and health risk assessment.

Journal of food science
In the wake of growing concerns regarding diet-related health issues, this study investigates the application of machine learning methods to estimate the energy content and classify the health risks of foods based on the USDA National Nutrient Databa...

ProTformer: Transformer-based model for superior prediction of protein content in lablab bean (Lablab purpureus L.) using Near-Infrared Reflectance spectroscopy.

Food research international (Ottawa, Ont.)
Lablab bean (Lablab purpureus L.), known for its higher protein content provides a promising alternative to reduce reliance on animal-based proteins and support sustainable agriculture. Nowadays, traditional methods for nutritional profiling have bee...