Fatty acid, cholesterol, vitamin, and mineral content of cooked beef cuts from a national study

The impact of dietary fat type on lipid profiles in lean mass hyper-responder phenotype

Key Clinical Message

Although the lean mass hyper-responder (LMHR) phenotype is well known, its diagnosis is impeded by the influence of fat type and intake on the lipid profile. Accordingly, a detailed assessment is warranted if LMHR is suspected.

Abstract

A 47-year-old man with suspected familial hypercholesterolemia presented with elevated triglyceride and low-density lipoprotein cholesterol levels. He had adhered to a ketogenic diet and was suspected of a lean mass hyper-responder phenotype; however, his lipid profile did not meet the definition. His lipid profile improved through dietary management without medication.

Protein and Lipid Digestibility of Pasture-Raised and Grain-Finished Beef: An In Vitro Comparison

This study compared the digestibility of protein and fat components of pasture-raised and grain-finished beef using an in vitro oral-gastro-small intestinal digestion model. Two commonly consumed beef cuts, tenderloin (Psoas major) and striploin (Longissimus dorsi) were selected for this study. There were no substantial differences between the pasture-raised and grain-finished cuts of meat in terms of protein digestibility, as shown by the protein and peptide breakdown (observed through SDS-PAGE) and the degree of hydrolysis as measured by free amino nitrogen. Tenderloin, however, showed significantly (p < 0.05) higher overall protein digestibility than striploin. Both striploin and tenderloin digests from pasture-raised beef released significantly (p < 0.05) higher total amounts of free long-chain n−3 PUFAs and lower amounts of many free saturated fatty acids, notably palmitic and myristic acids, than those from grain-finished animals. The results suggest greater health benefits from consuming pasture-raised beef, particularly tenderloin.

Machine learning models to predict micronutrient profile in food after processing

The information on nutritional profile of cooked foods is important to both food manufacturers and consumers, and a major challenge to obtaining precise information is the inherent variation in composition across biological samples of any given raw ingredient. The ideal solution would address precision and generability, but the current solutions are limited in their capabilities; analytical methods are too costly to scale, retention-factor based methods are scalable but approximate, and kinetic models are bespoke to a food and nutrient. We provide an alternate solution that predicts the micronutrient profile in cooked food from the raw food composition, and for multiple foods. The prediction model is trained on an existing food composition dataset and has a 31% lower error on average (across all foods, processes and nutrients) than predictions obtained using the baseline method of retention-factors. Our results argue that data scaling and transformation prior to training the models is important to mitigate any yield bias. This study demonstrates the potential of machine learning methods over current solutions, and additionally provides guidance for the future generation of food composition data, specifically for sampling approach, data quality checks, and data representation standards.

Evaluation and Application of Different Cholesterol-Lowering Lactic Acid Bacteria as Potential Meat Starters

ABSTRACT

A total of 115 isolates of lactic acid bacteria were screened from traditional fermented foods in Guizhou Province, People's Republic of China. The cholesterol removal rates of 86 isolates ranged from 7.29 to 25.66%, and 18 isolates showed a cholesterol removal rate of more than 15%. According to the results of physiological and biological tests, 13 isolates were selected to determine the fermentation performance; 9 isolates-MT-4, MT-2, PJ-15, SR2-2, SQ-4, SQ-7, ST2-2, ST2-6, and NR1-7-had high tolerance of bile salt and acid and had a survival rate of more than 96% under pH 3.0 and 0.3% bile salt. ST2-2, SR2-2, NR1-7, SQ-4, and MT-4 had high survival rate in different concentrations of NaCl and NaNO2 under different temperatures. According to BLAST comparison results of the 16S rRNA sequence in the GenBank database and the genetic distance of the 16S rRNA sequence with an ortho-connected algorithm, SR2-2, NR1-7, and ST2-2 were identified as Lactobacillus plantarum, MT-4 was identified as Lactobacillus pentosus, and SQ-4 was identified as Lactobacillus paraplantarum. Moreover, strains SQ-4 and MT-4 were added to fermented beef. Results showed that the fermented beef had delicious taste and was popular to consumers because of its proper pH, pleasant colors, high viable cell count, and suitable content of bound and immobilized water. These results provide a basis for the development of new starter formulation for the production of high-quality fermented meat products.

HIGHLIGHTS