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The main objective of this study was to evaluate the potential of near infrared (NIR) spectroscopy and machine learning in detecting microplastics (MPs) in chicken feed. The application of machine learning techniques in building optimal classification models for MPs-contaminated chicken feeds was explored. 80 chicken feed samples with non-contaminated and 240 MPs-contaminated chicken feed samples including polypropylene (PP), polyvinyl chloride (PVC), and polyethylene terephthalate (PET) were prepared, and the NIR diffuse reflectance spectra of all the samples were collected.

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In late gestation, the increased energy demand to support the rapid fetal growth can induce an acute negative energy balance associated with a high risk of pregnancy toxemia, especially for prolific ewes (carrying two or more fetuses). The current study was conducted to evaluate the effects of dietary energy during the last 6 weeks prepartum on the energy metabolism dynamic responses and the newborn lamb metabolic profile in prolific ewes. Forty-five crossbred (Dorset × Romanov) ewes were randomly assigned to 1 of 3 dietary energy densities: E: 8.

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The unique fatty acid composition of BSF larvae oil makes it suitable for various applications, including use in animal feed, aquaculture, biodiesel production, biomaterials, and the food industry. Determination of BSF larvae composition usually requires analytical methods with chemicals, thus needing emerging techniques for fast characterization of its composition. In this study, Near Infrared Hyperspectral Imaging (NIR-HSI) (928 - 2524 nm) coupled with chemometrics was applied to predict the lipid content and fatty acid composition in intact black soldier fly (BSF) larvae.

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Cardiac myxoma with high standardized uptake value of FDG-PET-CT in the right ventricular outflow tract.

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