title: Predicting beef tenderness using near-infrared spectroscopy.
abstract: The objective of this multiple-phase study was to determine the accuracy of an on-line near-infrared (NIR) spectral reflectance system to predict 14-d-aged cooked beef tenderness. In phase I, 292 carcasses (140 US Select, 152 US Choice) were selected (d 2) from 2 commercial beef processing facilities. After carcass selection, longissimus lumborum (LL) muscle sections (ribs 9 to 12) were individually identified, vacuum-packaged, and transported to the Oklahoma State University Meats Laboratory, where a 2.54-cm-thick steak (n = 1) was fabricated and stored in refrigerated conditions (1 degrees C +/- 1). Following a 30-min oxygenation period, a NIR spectral scan was obtained on the 12th-rib LL steak. Steaks (d 3) were individually vacuum-packaged and aged at 4 degrees C for a total of 14 d before cooking slice shear force (SSF) analysis. In phases II and III, 476 carcasses (258 US Select, 218 US Choice) were immediately NIR scanned after carcass presentation to in-plant USDA grading personnel. In a similar fashion, all LL steaks were aged (1 degrees C +/- 1) for 14 d before cooking (70 degrees C) and conducting SSF. Of the phase I and II samples, 39 (6.77%) were categorized as being tough (i.e., >/= 25 kg of SSF after the 14-d postmortem aging period). Of these 39 tough samples, 20 (3.7% error rate) were correctly placed in the 90% certification level. Another 10 tough samples were placed in the 80% certification level (2.0% error rate). The overall NIR certified tender group was 1.67 kg more tender (P < 0.05) than LL samples from the noncertified samples. When the NIR predicted samples to be tough, 10% of the samples were eliminated from the phase I and II LL populations at 90% certification. The population SSF mean improved in excess of 6.5 kg. For phase III, SSF evaluation by an independent third party indicated the NIR system was able to successfully sort tough from tender LL samples to 70% certification levels. It was concluded that NIR scanning offers an in-plant opportunity to sort carcasses into tenderness outcome groups for guaranteed-tender branded beef programs.
authors.affiliation: Oklahoma State University, Stillwater 74075, USA.
authors.forename: S R
authors.initials: SR
authors.lastname: Rust
authors.forename: D M
authors.initials: DM
authors.lastname: Price
authors.forename: J
authors.initials: J
authors.lastname: Subbiah
authors.forename: G
authors.initials: G
authors.lastname: Kranzler
authors.forename: G G
authors.initials: GG
authors.lastname: Hilton
authors.forename: D L
authors.initials: DL
authors.lastname: Vanoverbeke
authors.forename: J B
authors.initials: JB
authors.lastname: Morgan
mesh_terms: D000818:Animals; D002417:Cattle; D003296:Cooking / Q000379:methods*; D008460:Meat / Q000032:analysis* / Q000592:standards*; D019265:Spectroscopy, Near-Infrared / Q000662:veterinary*