ROBUST PREDICTION OF MEAT QUALITY ATTRIBUTES USING NEAR INFRARED SPECTROSCOPY

Abstract

The main purpose of this present study is to evaluate the ability of near infrared technology as an alternative method in determining and assessing quality parameters of meat product where in this case is frozen beef. At first, beef samples from chest and legs parts were sliced and taken at the amount of 100g per sample to be frozen. Then spectra data of beef samples were obtained using near infrared spectrophotometer (PSD IR i16) in wavelength range from 1000 to 2500 nm with optical gain 4x. Actual protein contents were obtained by Kjeldahl method and measured in triplicate. The near infrared spectra data were enhanced and improved by means of mean centering (MC) and baseline shift correction (BSC) methods. The results showed that protein content of frozen beef samples can be predicted rapidly with maximum correlation coefficient is 0.91. Heat properties of beef samples changes exponentially during freezing and thus, optimum freezing temperature and time can be predicted as well. Based on those obtained results, it may conclude that near infrared technology can assess frozen beef qualities rapidly and effectively.