Extensive evaluation of prediction performance for 15 pork quality traits using large scale VIS/NIRS data

Visible and near-infrared spectroscopy (VIS/NIRS) has been extensively used in the livestock and food industries to quantify meat quality. Here, we collected VIS/NIRS data of 1206 pigs longissimus muscle, measured the corresponding 15 meat quality traits, and used seven models to predict these meat quality traits. The prediction performances of 7 models varied among predicted traits, with the Rcv2 of most traits above 0.9 in the best model. We have also established a new method, spectral-wide association analysis (SWAS), to select the feature wavelengths of measured traits. Results showed that the prediction performance is proportionate to the number of identified significant association wavelengths. We used the selected wavelengths to perform prediction again, and the prediction accuracy was similar to results with full wavelength using the best model, indicating effectiveness of feature wavelengths selection methods.

Automated summary

This article introduces the application of visible and near-infrared spectroscopy (VIS/NIRS) in quantifying meat quality in the livestock and food industries. By collecting spectral data of pig longissimus muscle and using models to predict meat quality traits, it is found that the performance of different models varies for different traits. The authors also propose a new method to select feature wavelengths, and the results show that the prediction performance is proportional to the number of identified significant association wavelengths.