Application of texture analysis of b-mode ultrasound images for the quantification and prediction of intramuscular fat in living beef cattle: A methodological study. (August 2020)

• Record Type: Journal Article
• Authors: Fiore, Enrico; Fabbri, Giorgia; Gallo, Luigi; Morgante, Massimo; Muraro, Michele; Boso, Matteo; Gianesella, Matteo
• Is Part Of: Research in veterinary science. Volume 131(2020)
• Page Start: 254
• Page End: 258
• Publication Date: 2020-08
• Subjects: Beef cattle -- Fat prediction -- Intramuscular fat -- Texture analysis -- Ultrasonography; AUC Area Under the Curve -- DMI Dry Matter Intake -- IMF Intramuscular fat -- LD Longissimus Dorsi -- IMFpred Predicted IMF -- IMFqa Quantified IMF -- RTU Real-Time Ultrasound -- ROC Receiver Operating Characteristic -- ROI Region of Interest -- TMR Total Mixed Ration -- VIF Variance Inflation Factor; Veterinary medicine -- Periodicals; Veterinary Medicine -- Periodicals; Médecine vétérinaire -- Périodiques; Médecine vétérinaire -- Recherche -- Périodiques; Diergeneeskunde; 636.089
• DOI: 10.1016/j.rvsc.2020.04.020
• Languages: English
• ISSNs: 0034-5288

Abstract

Abstract: Intramuscular fat (IMF) contributes significantly to the aroma and tenderness of the meat, therefore playing a key role in quality determination. Yet, IMF determination methods rely on visual inspection or on fat extraction from meat samples after animals' slaughter. The aim of this methodological study was the elaboration of a process capable of predicting IMF% using real-time ultrasound (RTU) images in live beef cattle. The longissimus dorsi (LD) muscle of 26 Charolaise heifers was investigated. In vivo ultrasound images were taken and texture analysis was performed. One week after the animals' slaughter, the whole twelfth rib cut was collected, and IMF% was determined by extraction with petrol ether (Randall) method. Animals were divided in 3 groups depending on their mean lipid content percentage in 100 g meat (Group 1: IMF ≤ 4.24%; Group 2: 4.25% ≤ IMF ≤ 5.75%; Group 3: IMF ≥ 5.76%). Texture parameters were selected by a stepwise linear discriminant analysis using IMF% measured by chemical extraction (IMFqa) as the dependent variable, and the results of the texture analysis as explanatory variables. 6 variables were found predictive and molded into a multiple regression equation, this equation was then validated using IMFqa as ground truth. A high linear correlation between IMFqa and IMFpred was evident (r 2 = 0.8504), ROC analysis perfomed on IMFpred comparing it to IMFqa showed a sensitivity of 80% and a specificity of 93.7%, while results from the Bland-Altman plot were ± 1.96 (±1.11SD). Highlights: Texture parameters were molded in a regression equation to predict lipid content. A high correlation accuracy was found between effective and predicted lipid content. Between quantified and predicted lipid sensitivity was 80% and specificity 93.7%.