Prediction of raw broiler shear force using visible and short wave near infrared spectroscopy

• Main Authors: Ghazali, R., Rahim, H. A.
• Format: Conference or Workshop Item
• Published: Institute of Electrical and Electronics Engineers Inc. 2016
• Subjects: TK Electrical engineering. Electronics Nuclear engineering
• Online Access: http://eprints.utm.my/id/eprint/73132/; https://www.scopus.com/inward/record.uri?eid=2-s2.0-84983540153&doi=10.1109%2fCSPA.2016.7515852&partnerID=40&md5=eeecf88b75832ced6234c5c8999efaf9

Tenderness is one of the quality that will affect consumer perception in meat. Traditionally, meat quality grading was done destructively by the human graders destructive measurements. Destructive measurement caused less accurate results, time-consuming and costly. Hence, a low cost, fast, reliable and non-destructive technique which is Near-Infrared Spectroscopy (NIRS) is required in order to gain accurate results in tenderness prediction. 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. Two wavelength regions: visible and shortwave 662- 1005 nm and shortwave 700-1005 nm. Absorbance spectra was pre-processed using the optimal Savitzky-Golay smoothing mode which was the 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.