1. To evaluate the performance of visible and near-infrared (Vis/NIR) spectroscopic models for discriminating true pale, soft and exudative (PSE), normal and dark, firm and dry (DFD) broiler breast meat in different conditions of preprocessing methods, spectral ranges, characteristic wavelength selection and water-holding capacity (WHC) indexes were assessed. 2. Quality attributes of 214 intact chicken fillets (pectoralis major), such as lightness (L*), pH and WHC indicators including drip loss (DL), water gain and expressible fluid were measured. Fillets were grouped into PSE, normal and DFD categories based on combination of L*, pH and WHC threshold criteria. Classification models were developed using support vector machine based methods on characteristic wavelengths selected from the unprocessed or 2nd-derivative spectra, respectively, in three spectral subsets of 400-2500, 400-1100 and 1100-2500 nm. 3. Better classification of three meat groups was obtained based on unprocessed spectra (72-94%) than 2nd-derivative spectra (55-72%). The classification based on 400-2500 nm (91% average) and 400-1100 nm (89% average) performed better than that on 1100-2500 nm (78% average). In terms of the three different WHC indicators, the combination of L*, pH and DL produced better results than the other two groups, with recognition accuracy of 94.4% using 400-2500-nm range. 4. These analytical results suggest that for a better classification of true PSE, normal and DFD broiler breast meat with Vis/NIR spectra, unprocessed spectra wavelengths should be used, ranges of 400-1000 nm should be included in the data collection, and DL as an indicator of WHC might provide a better prediction model.
Keywords: Broiler; L* and pH; quality defects; support vector machine; water-holding capacity.