Evaluating the ability of rapid evaporative ionization mass spectrometry to differentiate beef palatability based on consumer preference.

Rapid Evaporative Ionization Mass Spectrometry (REIMS) is a type of ambient ionization mass spectrometry, which enables real-time evaluation of several complex traits from a single measurement. The objective of this study was to evaluate the capability of REIMS analysis of raw samples coupled with chemometrics to accurately identify and predict cooked beef palatability. REIMS analysis and consumer sensory evaluation were conducted for beef arm center roasts (n = 20), top loin steaks (n = 20), top sirloin steaks (n = 20), and 20% lipid ground beef (n = 20). These data were used to train predictive models for six classification sets representing different sensory traits. The maximum prediction accuracies achieved (from high to low): beefy flavor acceptance (86.25%), juiciness acceptance (83.75%), overall acceptance (81.25%), overall flavor acceptance (81.25%), grilled flavor acceptance (78.75%), and tenderness acceptance (75%). The current study demonstrates that REIMS analysis of raw meat has the potential to predict and classify cooked beef palatability. Supplementary Information: The online version contains supplementary material available at 10.1007/s13197-022-05562-6.

Dietary Meat Categories and Descriptions in Chronic Disease Research Are Substantively Different within and between Experimental and Observational Studies: A Systematic Review and Landscape Analysis.

This systematic review and landscape analysis describes patterns in dietary meat (skeletal muscle and associated tissues from mammalian, avian, and aquatic species; i.e., muscle foods) categories (CAT) and descriptions (DESCR) used throughout nutrition-related chronic disease literature, as there is anecdotally noted variation. A total of 1020 CAT and 776 DESCR were identified from 369 articles that assessed muscle food consumption and primary prevention of cardiovascular disease, obesity, type 2 diabetes, or cancer in adults ≥19 y from PubMed, Cochrane, and CINAHL up to March 2018. Specificity of CAT was analyzed on an empirical 1-7 ordinal scale as: 1) broad/undescriptive, "fish"; 2) muscle food type, "red meat"; 3) species, "poultry"; 4) broad + 1 descriptor, "processed meat"; 5) type/species + 1 descriptor, "fresh red meat"; 6) broad/type + 2 descriptors, "poached lean fish"; and 7) specific product, "luncheon meat." Median CAT specificity for randomized controlled trials (RCTs) and observational studies (OBSs) was 3 and 2 points out of 7, respectively, with no differences between chronic disease types. Specificity of OBS CAT was higher in recent articles but RCT CAT became less specific starting in the 2000s. RCT CAT were 400% more likely to include species, 500% more likely to include leanness, but 400% less likely to include processing degree compared with OBS CAT. A DESCR was included for 76% and 82% of OBS and RCT CAT, respectively. Researchers described processed meat, red meat, and total meat CAT more commonly than poultry or fish CAT. Among processed meat DESCR, 31% included a common term used in public regulatory definitions. In conclusion, muscle food categories and descriptions are substantively different within and between experimental and observational studies and do not match regulatory definitions. A practical muscle food classification system is warranted to improve interpretation of evidence regarding muscle food consumption and chronic disease.

Broad and Inconsistent Muscle Food Classification Is Problematic for Dietary Guidance in the U.S.

Dietary recommendations regarding consumption of muscle foods, such as red meat, processed meat, poultry or fish, largely rely on current dietary intake assessment methods. This narrative review summarizes how U.S. intake values for various types of muscle foods are grouped and estimated via methods that include: (1) food frequency questionnaires; (2) food disappearance data from the U.S. Department of Agriculture Economic Research Service; and (3) dietary recall information from the National Health and Nutrition Examination Survey data. These reported methods inconsistently classify muscle foods into groups, such as those previously listed, which creates discrepancies in estimated intakes. Researchers who classify muscle foods into these groups do not consistently considered nutrient content, in turn leading to implications of scientific conclusions and dietary recommendations. Consequentially, these factors demonstrate a need for a more universal muscle food classification system. Further specification to this system would improve accuracy and precision in which researchers can classify muscle foods in nutrition research. Future multidisciplinary collaboration is needed to develop a new classification system via systematic review protocol of current literature.