Prediction of beef marbling using Hyperspectral Imaging (HSI) and Partial Least Squares Regression (PLSR)


  • Victor Aredo Universidad Nacional de Trujillo
  • Lía Velásquez Programa de pós-graduação em Engenharia e Ciência de Materiais. Faculdade de Zootecnia e Engenharia de Alimentos. Universidade de São Paulo, Brazil.
  • Raúl Siche Facultad de Ciencias Agropecuarias, Universidad Nacional de Trujillo, Peru.


Palabras clave:

hyperspectral image, marbling, partial least squares, prediction.


The aim of this study was to build a model to predict the beef marbling using HSI and Partial Least Squares Regression (PLSR). Totally 58 samples of longissmus dorsi muscle were scanned by a HSI system (400 - 1000 nm) in reflectance mode, using 44 samples to build the PLSR model and 14 samples to model validation. The Japanese Beef Marbling Standard (BMS) was used as reference by 15 middle-trained judges for the samples evaluation. The scores were assigned as continuous values and varied from 1.2 to 5.3 BMS. The PLSR model showed a high correlation coefficient in the prediction (r = 0.95), a low Standard Error of Calibration (SEC) of 0.2 BMS score, and a low Standard Error of Prediction (SEP) of 0.3 BMS score.


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Received December 07, 2016.

Accepted May 08, 2017.

Corresponding author: (R. Siche).




Cómo citar

Aredo, V., Velásquez, L., & Siche, R. (2017). Prediction of beef marbling using Hyperspectral Imaging (HSI) and Partial Least Squares Regression (PLSR). Scientia Agropecuaria, 8(2), 169-174.



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