Quantitative Prediction of Beef Quality Using Visible and NIR Spectroscopy with Large Data Samples Under Industry Conditions

It is well known that the eating quality of beef has a significant influence on the repurchase behavior of consumers. There are several key factors that affect the perception of quality, including color, tenderness, juiciness, and flavor. To support consumer repurchase choices, there is a need for an objective measurement of quality that could be applied to meat prior to its sale. Objective approaches such as offered by spectral technologies may be useful, but the analytical algorithms used remain to be optimized. For visible and near infrared (VISNIR) spectroscopy, Partial Least Squares Regression (PLSR) is a widely used technique for meat related quality modeling and prediction. In this paper, a Support Vector Machine (SVM) based machine learning approach is presented to predict beef eating quality traits. Although SVM has been successfully used in various disciplines, it has not been applied extensively to the analysis of meat quality parameters. To this end, the performance of PLSR and SVM as tools for the analysis of meat tenderness is evaluated, using a large dataset acquired under industrial conditions. The spectral dataset was collected using VISNIR spectroscopy with the wavelength ranging from 350 to 1800 nm on 234 beef M. longissimus thoracis steaks from heifers, steers, and young bulls. As the dimensionality with the VISNIR data is very high (over 1600 spectral bands), the Principal Component Analysis (PCA) technique was applied for feature extraction and data reduction. The extracted principal components (less than 100) were then used for data modeling and prediction. The prediction results showed that SVM has a greater potential to predict beef eating quality than PLSR, especially for the prediction of tenderness. The infl uence of animal gender on beef quality prediction was also investigated, and it was found that beef quality traits were predicted most accurately in beef from young bulls.