Broiler meats tenderness prediction using near infrared spectroscopy against non-linear predictive modelling

Abstract

Near infrared (NIR) spectroscopy is a non-invasive analytical technique known for its ability to assess the quality attributes of meat products. However, the linear models utilized, partial least square (PLS) and principal component regression (PCR) achieved unsatisfactory performances of meat physical attributes prediction. Hence, in this research, for its inherent advantages in modelling nonlinear system, artificial neural network (ANN) is augmented to the components of PCR and PLS. Through the augmentation, the principal component neural network (PCNN) and latent variable neural network (LVNN) models are developed. From the results obtained, it shows that PCNN and LVNN successfully surpassed their respective linear versions of PCR and PLS by 70% higher shear force prediction performances. The LVNN proved to achieve the best prediction in breast meat with root mean square error of prediction (RMSEP) of 0.0769 kg and coefficient of determination (RP2) of 0.8201 whilst for drumsticks, RMSEP=0.1494 kg and RP2=0.8606. NIR spectroscopy technology integrated with machine learning yields a promising non-invasive technique in predicting the shear force of intact raw broiler meat.