AIMC Topic: Food Storage

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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...

Deep learning combined Monte Carlo simulation reveal the fundamental light propagation in apple puree: Monitoring the quality changes from different cultivar, storage period and heating duration.

Food research international (Ottawa, Ont.)
This work explored the light propagation of purees from a large variability of apple cultivar, storage period and heating duration based on their optical absorption (μ) and reduced scattering (μ') properties at 900-1650 nm, in order to better monitor...

X-ray irradiation as a potential postharvest treatment for maintaining the quality of lily (Lilium davidii var. unicolor) bulbs and predicting shelf life using an artificial neural network.

Food research international (Ottawa, Ont.)
This study aimed to investigate the impact of X-ray irradiation pretreatment at varying doses (0.5, 1.0, 1.5, 2.0 kGy) on the preservation quality of lily bulbs and to elucidate its potential regulatory mechanisms. The findings revealed that X-ray ir...

Evaluation and prediction of the physical properties and quality of Jatobá-do-Cerrado seeds processed and stored in different conditions using machine learning models.

Scientific reports
The conservation of seed quality throughout storage depends on established conditions, monitoring, sampling and laboratory analysis, which are subject to errors and require technical and financial resources. Thus, machine learning techniques can help...

Machine learning-based optimal temperature management model for safety and quality control of perishable food supply chain.

Scientific reports
The management of a food supply chain is difficult and complex because of the product's short shelf-life, time-sensitivity, and perishable nature which must be carefully considered to minimize food waste. Temperature-controlled perishable food supply...

Lipidomics combined with random forest machine learning algorithms to reveal freshness markers for duck eggs during storage in different rearing systems.

Poultry science
The differences in lipids in duck eggs between the 2 rearing systems during storage have not been fully studied. Herein, we propose untargeted lipidomics combined with a random forest (RF) algorithm to identify potential marker lipids based on ultra-...

Deep learning models with optimized fluorescence spectroscopy to advance freshness of rainbow trout predicting under nonisothermal storage conditions.

Food chemistry
This study established long short-term memory (LSTM), convolution neural network long short-term memory (CNN_LSTM), and radial basis function neural network (RBFNN) based on optimized excitation-emission matrix (EEM) from fish eye fluid to predict fr...

Machine learning-enabled hyperspectral approaches for structural characterization of precooked noodles during refrigerated storage.

Food chemistry
The structural features of precooked noodles during refrigerated storage were non-destructively characterized using hyperspectral imaging (HSI) technology along with conventional analytical methods. The precooked noodles displayed a more rigid textur...

Development of machine learning-based shelf-life prediction models for multiple marine fish species and construction of a real-time prediction platform.

Food chemistry
At least 10 million tons of seafood products are spoiled or damaged during transportation or storage every year worldwide. Monitoring the freshness of seafood in real time has become especially important. In this study, four machine learning algorith...

Rapid and non-destructive microbial quality prediction of fresh pork stored under modified atmospheres by using selected-ion flow-tube mass spectrometry and machine learning.

Meat science
Volatile organic compounds (VOCs) indicative of pork microbial spoilage can be quantified rapidly at trace levels using selected-ion flow-tube mass spectrometry (SIFT-MS). Packaging atmosphere is one of the factors influencing VOC production patterns...