Prediction of chicken quality attributes by near infrared spectroscopy

Abstract

In the present study, near-infrared (NIR) reflectance was tested as a potential technique to predict quality attributes of chicken breast (Pectoralis major). Spectra in the wavelengths between 400 and 2500nm were analysed using principal component analysis (PCA) and quality attributes were predicted using partial least-squares regression (PLSR). PCA performed on NIR dataset revealed the influence of muscle reflectance (L(∗)) influencing the spectra. PCA was not successful to completely discriminate between pale, soft and exudative (PSE) and pale-only muscles. High-quality PLSR were obtained for L(∗) and pH models predicted individually (R(2)CV of 0.91 and 0.81, and SECV of 1.99 and 0.07, respectively). Water-holding capacity was the most challenging attribute to determine (R(2)CV of 0.70 and SECV of 2.40%). Sample mincing and different spectra pre-treatments were not necessary to maximise the predictive performance of models. Results suggest that NIR spectroscopy can become useful tool for quality assessment of chicken meat.

Keywords: Classification; NIR; PSE; Pale poultry muscle; Partial least squares regression.