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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. 相似文献