Beef marbling measurement using spectral imaging: A multiple linear regression approach
DOI:
https://doi.org/10.57188/manglar.2023.038Resumen
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.
Citas
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Derechos de autor 2023 Victor Aredo, Lía Velásquez, Nikol Siche
Esta obra está bajo una licencia internacional Creative Commons Atribución 4.0.
Manglar is an open access journal distributed under the terms and conditions of Creative Commons Attribution 4.0 International license
