Beef Tenderness Prediction by a Combination of Statistical Methods: Chemometrics and Supervised Learning to Manage Integrative Farm-To-Meat Continuum Data

Abstract

This trial aimed to integrate metadata that spread over farm-to-fork continuum of 110 Protected Designation of Origin (PDO)Maine-Anjou cows and combine two statistical approaches that are chemometrics and supervised learning; to identify the potential predictors of beef tenderness analyzed using the instrumental Warner-Bratzler Shear force (WBSF). Accordingly, 60 variables including WBSF and belonging to 4 levels of the continuum that are farm-slaughterhouse-muscle-meat were analyzed by Partial Least Squares (PLS) and three decision tree methods (C&RT: classification and regression tree; QUEST: quick, unbiased, efficient regression tree and CHAID: Chi-squared Automatic Interaction Detection) to select the driving factors of beef tenderness and propose predictive decision tools. The former method retained 24 variables from 59 to explain 75% of WBSF. Among the 24 variables, six were from farm level, four from slaughterhouse level, 11 were from muscle level which are mostly protein biomarkers, and three were from meat level. The decision trees applied on the variables retained by the PLS model, allowed identifying three WBSF classes (Tender (WBSF ≤ 40 N/cm2), Medium (40 N/cm2 < WBSF < 45 N/cm2), and Tough (WBSF ≥ 45 N/cm2)) using CHAID as the best decision tree method. The resultant model yielded an overall predictive accuracy of 69.4% by five splitting variables (total collagen, µ-calpain, fiber area, age of weaning and ultimate pH). Therefore, two decision model rules allow achieving tender meat on PDO Maine-Anjou cows: (i) IF (total collagen < 3.6 μg OH-proline/mg) AND (µ-calpain ≥ 169 arbitrary units (AU)) AND (ultimate pH < 5.55) THEN meat was very tender (mean WBSF values = 36.2 N/cm2, n = 12); or (ii) IF (total collagen < 3.6 μg OH-proline/mg) AND (µ-calpain < 169 AU) AND (age of weaning < 7.75 months) AND (fiber area < 3100 µm2) THEN meat was tender (mean WBSF values = 39.4 N/cm2, n = 30).

What are the drivers of beef sensory quality using metadata of intramuscular connective tissue, fatty acids and muscle fiber characteristics?

“…The relationships for each sensory quality (or PLS models) were built first per muscle of each study and second per muscle across studys (St1 + St2 = LT muscle or St1 + St3 = RA muscle). The filter method with the variable importance in the projection (VIP) set at the level of VIP > 0.8 was used for variable selection as described by Gagaoua et al (2019). For the selection of the variables, the jack-knife method was included in the PLS regression as a selective parameter.…”

Understanding the Determination of Meat Quality Using Biochemical Characteristics of the Muscle: Stress at Slaughter and Other Missing Keys

“…In the latter case, results are not necessarily relevant for normal commercial slaughter conditions, where stress levels are generally high. Stress status at slaughter is not the only factor to take into account; muscle, animal gender, age, breed, feeding and rearing/production system, among others, influence meat quality and should be standardized or introduced into the model [ 2 , 9 , 52 , 112 , 113 , 114 , 115 , 116 , 117 ]. It is essential to take into account all of these factors if we are to succeed in producing pre-hoc statistical models predicting meat quality based on biochemical characteristics of the muscle or other phenotypical features.…”

Protein Array-Based Approach to Evaluate Biomarkers of Beef Tenderness and Marbling in Cows: Understanding of the Underlying Mechanisms and Prediction

“…Raw data were scrutinized for data entry errors and outliers using Smirnov–Grubb’s outlier test at a significance level of 5%. Then, all the data were normalized for replicates (experiment) and the factor related to the rearing practices of the animals [ 19 , 44 ]. This step was based on Z-scores, which represent the number of standard deviations for each observation relative to the mean of the corresponding replicate/condition.…”