In contrast to conventional methods for the determination of meat chemical composition and quality, near infrared spectroscopy (NIRS) enables rapid, simple and simultaneous assessment of numerous meat properties. The present article is a review of published studies that examined the ability of NIRS to predict different meat properties. According to the published results, NIRS shows a great potential to replace the expensive and time-consuming chemical analysis of meat composition. On the other hand, NIRS is less accurate for predicting different attributes of meat quality. In view of meat quality evaluation, the use of NIRS appears more promising when categorizing meat into quality classes on the basis of meat quality traits for example discriminating between feeding regimes, discriminating fresh from frozen-thawed meat, discriminating strains, etc. The performance of NIRS to predict meat properties seems limited by the reliability of the method to which it is calibrated. Moreover, the use of NIRS may also be limited by the fact that it needs a laborious calibration for every purpose. In spite of that, NIRS is considered to be a very promising method for rapid meat evaluation.    

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Prediction ability of near infrared (NIR) spectroscopy for intramuscular fat content (IMF) determination was studied. The material comprised 126 muscle samples; 46 pig longissimus dorsi and semitendinosus and 34 beef longissimus dorsi muscle samples. The IMF content was chemically determined in duplicate using two different chemical methods; fat extraction according to Folch et al. and Soxhlet extraction with hydrolysis according to SIST ISO 1443. Folch extraction underestimated IMF content compared to Soxhlet extraction with hydrolysis (-0.32%, P < 0.0001). Similar repeatability was obtained for Folch and Soxhlet extraction with hydrolysis (0.17% and 0.18%, respectively, P < 0.0001). Sample spectra were scanned from 400–2500 nm by the NIR Systems model 6500 spectrophotometer (Silver Spring, MD, USA) and analysed by WinISI II on minced and intact (pork only) samples. Modified partial least squares regression was used to develop models and to obtain calibration statistics: coefficient of determination in calibration( R2 C) and cross-validation ( R2 CV) and standard error in calibration ( SEC) and cross-validation ( SECV). We prepared different models (for a single muscle/common, by applying NIR spectrum or the whole spectrum, on intact and minced samples). Obtained models proved the remarkable prediction ability of NIR spectroscopy to determine IMF content ( R2 CV between 0.84 and 0.99; SECV between 0.14% and 0.53%) and confirms the potential of NIR spectroscopy to replace laborious chemical procedures. Regarding the factors studied, calibrations were less accurate for intact than for minced samples; the use of an NIR spectrum compared to the whole spectrum had no important effect on the prediction ability. According to calibration statistics, the prediction using a common equation for several muscles seems more reliable than the equations within the muscle, but the latter showed lower bias.

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