title: Development of a system for classification of pork loins for tenderness using visible and near-infrared reflectance spectroscopy.
abstract: Boneless pork loins (n = 901) were evaluated either on the loin boning and trimming line of large-scale commercial plants (n = 465) or at the US Meat Animal Research Center abattoir (n = 436). Exposed LM on the ventral side of boneless loins was evaluated with visible and near-infrared spectroscopy (VISNIR; 450 to 1,000 nm) using a commercial system that was developed for on-line evaluation of beef tenderness. Boneless loin sections were aged (2°C) until 14 d postmortem, and two 2.54-cm-thick chops were obtained from the 11th-rib region. Fresh (never frozen) chops were cooked (71°C) and LM slice shear force (SSF) was measured on each of the 2 chops. Those 2 values were averaged, and that value was used for all analyses. Loins were blocked by plant (n = 3), production day (n = 24), and observed SSF (mean = 13.9 kg; SD = 3.7 kg; CV = 26.8%; range 6.4 to 32.4 kg). One-half of the loins were assigned to a calibration data set, which was used to develop regression equations, and one-half of the loins were assigned to a prediction data set, which was used to validate the regression equations. A partial least-squares regression model was developed, and loins were classified as predicted tender or not predicted tender if their VISNIR-predicted SSF was <14.0 kg or ≥14.0 kg, respectively. Analysis of variance was used to determine the effect of VISNIR classification on SSF. The calibration data set and prediction data set had 61.9 and 60.9% of the loins classified as predicted tender, respectively. For both the calibration data set and the prediction data set, mean SSF was less for loins predicted tender than loins not predicted tender (P < 0.001). Relative to loins that were not predicted tender, the percentage of loins with SSF ≥20 kg was less for loins predicted tender in the calibration data set (3.6 vs. 8.1%) and prediction data set (1.8 vs. 13.6%). These results clearly indicate that the VISNIR technology could be used to noninvasively classify pork loins on-line for tenderness.
authors.affiliation: USDA-ARS, Roman L. Hruska US Meat Animal Research Center, Clay Center, NE 68933-0166, USA. steven.shackelford@ars.usda.gov
authors.forename: S D
authors.initials: SD
authors.lastname: Shackelford
authors.forename: D A
authors.initials: DA
authors.lastname: King
authors.forename: T L
authors.initials: TL
authors.lastname: Wheeler
mesh_terms: D000818:Animals; D005524:Food Technology / Q000379:methods*; D008460:Meat / Q000145:classification / Q000592:standards*; D018482:Muscle, Skeletal / Q000737:chemistry*; D011237:Predictive Value of Tests; D012044:Regression Analysis; D013055:Spectrophotometry, Infrared / Q000379:methods / Q000662:veterinary*; D019265:Spectroscopy, Near-Infrared / Q000379:methods / Q000662:veterinary*; D013552:Swine