First derivative prediction of raw broiler shear force using visible short wave near infrared spectroscopy

Abstract

A non-destructive,fast, reliable and low cost technique which is Near-Infrared Spectroscopy (NIRS) is required to replace conventional destructive texture analyser in shear force measurement. The combination of visible and shortwave near infrared (VIS-SWNIR) spectrometer and principal component regression (PCR) to assess the quality attribute of raw broiler meat texture (shear force value (kg)) was investigated. Wavelength region of visible and shortwave 662-1005 nm was selected for prediction after pre-processing. Absorbance spectra was pre-processed using the optimal Savitzky-Golay smoothing mode with 1st order derivative, 2nd degree polynomial and 31 filter points to remove the baseline shift effect. Potential outliers were identified through externally studentised residual approach. The PCR model were trained with 90 samples in calibration and validated with 44 samples in prediction datasets. From the PCR analysis, correlation coefficient of calibration (RC), the root mean square calibration (RMSEC), correlation coefficient of prediction (RP) and the root mean square prediction (RMSEP) of visible and shortwave (662-1005 nm) with 4 principal components were 0.4645,0.0898, 0.4231 and 0.0945. The predicted results can be improved by applying the 2nd order derivative and the non-linear model.