title: Predicting pork quality using Vis/NIR spectroscopy.
abstract: Visible and near-infrared reflectance spectroscopy (Vis/NIRS) was used to predict the ultimate pH (pHu), color, intramuscular fat (IMF) and shear force (WBSF) of pork samples and to build classifiers capable of categorizing the samples by tenderness (tender or tough) and juiciness (juicy and dry). Spectra were collected from 400 to 1495nm, and 200 data points were generated for every sample (n=134). Sixty-seven percent of the sample set was used for calibration, and 33% was used for validation. Partial least squares (PLS) calibration models were developed for each characteristic measured. A coefficient of determination (R(2)) and residual prediction deviation (RPD) were used to evaluate the accuracy of the calibration models. The pHu and color prediction models developed in this study fit this classification, indicating that these predictive models can be used to predict quality traits of intact pork samples. The Vis/NIRS offered great potential for correctly classifying pork Longissimus into two tenderness and two juiciness classes.
authors.affiliation: Department of Animal Science, College of Animal Science and Food Engineering, University of São Paulo, Avenida Duque de Caxias Norte, 225, Caixa Postal 23, 13635-900, Pirassununga, SP, Brazil; Superior Institute of Engineering, Universidade do Algarve, Campus da Penha, Estrada da Penha, 8005-139 Faro, Portugal. Electronic address: julianabalage@usp.br.
authors.forename: Juliana Monteiro
authors.initials: JM
authors.lastname: Balage
authors.affiliation: Department of Animal Science, College of Animal Science and Food Engineering, University of São Paulo, Avenida Duque de Caxias Norte, 225, Caixa Postal 23, 13635-900, Pirassununga, SP, Brazil.
authors.forename: Saulo
authors.initials: S
authors.lastname: da Luz E Silva
authors.affiliation: Department of Animal Science, College of Animal Science and Food Engineering, University of São Paulo, Avenida Duque de Caxias Norte, 225, Caixa Postal 23, 13635-900, Pirassununga, SP, Brazil.
authors.forename: Catarina Abdalla
authors.initials: CA
authors.lastname: Gomide
authors.affiliation: Department of Veterinary Medicine, College of Animal Science and Food Engineering, University of São Paulo, Avenida Duque de Caxias Norte, 225 -,Caixa Postal 23, 13635-900, Pirassununga, SP, Brazil.
authors.forename: Marina de Nadai
authors.initials: Mde N
authors.lastname: Bonin
authors.affiliation: CIEO, Research Centre on Space and Organizations and Department of Food Engineering, Universidade do Algarve-Campus da Penha, Estrada da Penha, 8005-139 Faro, Portugal.
authors.forename: Ana Cristina
authors.initials: AC
authors.lastname: Figueira
mesh_terms: D000818:Animals; D002138:Calibration; D003116:Color; D063427:Food Quality; D006863:Hydrogen-Ion Concentration; D016018:Least-Squares Analysis; D008962:Models, Theoretical; D010641:Phenotype; D000069466:Red Meat / Q000032:analysis*; D019265:Spectroscopy, Near-Infrared; D013552:Swine
keywords: NIR spectroscopy; Pork; Quality classification; Quality prediction