LAUSR

Factor analysis (FA) method was tested to assess quality of chicken breast fillets with the visible/near-infrared (Vis/NIR) spectroscopy with wavelength range between 400 and 2500 nm. According to inherent correlation, three factors were extracted from the measured eight quality traits (L*, a*, b*, pH, moisture, drip loss, expressible fluid, and salt-induced water gain). The extracted “grade factor” (F1), “color factor” (F2), and “moisture factor” (F3) could respectively represent the characteristics and the variation tendency of the corresponding quality traits and were defined as three new quality assessment indexes. Furthermore, partial least squares regression (PLSR) models were established to quantitatively relate spectral information to eight individual quality traits and three factors. The results indicated that the models for predicting each factor performed better than those for individual quality traits. Key wavelengths of each quality trait were then selected, and the corresponding spectra were taken to build new PLSR prediction models. The selected key wavelengths showed obvious practical significance, and the new models had comparable predictive performance to those models developed based on the full spectra, among which the new models of F1 and F2 had acceptable and robust predictive abilities (R2p = 0.73, RPD = 1.91; R2p = 0.74, RPD = 1.97). Our results in the present study demonstrate the potential for FA and Vis/NIR spectroscopy as a useful method to assess the quality of chicken breast fillets.