Beef marbling measurement using spectral imaging: A multiple linear regression approach

• Aredo, Victor ^[3] ; Velásquez, Lía ^[1] ; Siche, Nikol ^[2]
  1. [1] Universidade de São Paulo

     Universidade de São Paulo

     Brasil

     
  2. [2] Universidad Nacional de Trujillo

     Universidad Nacional de Trujillo

     Provincia de Trujillo, Perú

     
  3. [3] Departamento de Operaciones Unitarias. Facultad de Química e Ingeniería Química, Universidad Nacional Mayor de San Marcos, Av. República de Venezuela s/n, Lima

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• Localización: Manglar: Revista de Investigación Científica, ISSN-e 2414-1046, ISSN 1816-7667, Vol. 20, Nº. 4, 2023 (Ejemplar dedicado a: Octubre-Diciembre), págs. 333-339
• Idioma: español
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• Resumen

  • This study aimed at measuring beef marbling scores in an objective and simple manner through spectral imaging and multiple linear regression (MLR). Beef marbling prediction by hyperspectral imaging and partial least squares regression (PLSR) was analyzed to calibrate and evaluate an MLR model with a few selected wavelengths. Data came from 44 beef samples and consisted of their spectral signatures (75 wavelengths) from hyperspectral reflectance images (400-1000 nm) and their marbling scores assigned by evaluators. The wavelengths that presented regression coefficients with the highest absolute values in the PLSR model, were used to calibrate the MLR model by a backward stepwise approach (p < 0.05). The coefficient of determination for prediction (R2p) and the standard error of prediction (SEP) were evaluated. The MLR model was suitable for practical use because it required only 12 wavelengths for reliable predictions (R2p = 0.824 > 0.8; SEP = 11.4% < 15%). A model is proposed for the objective and simple measurement of beef marbling score using multispectral imaging technology.