Evaluation of Mutton Process Flavor Quality by Flavor Fingerprint and Partial Least Squares-discriminant Analysis
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Abstract:
The volatile components in twenty-five mutton samples from different batches were analyzed by headspace– solid phase microextraction gas chromatography mass spectroscopy (SPME-GC-MS) and gas chromatography-olfactometry (GC-O). Thirty-two common volatiles were identified as the key compounds for the evaluation of the characteristic mutton flavor. Based on these 32 compounds, the variables were classified, and the partial least squares discriminant analysis (PLS-DA) model was developed for the identification of natural mutton flavor relative to the types and content of the corresponding compounds in five common meat samples. The correlation coefficient of the PLS-DA model was > 0.90, and the root mean square error of calibration (RMSEC) and root mean square error of prediction (RMSEP) were < 0.3, indicating that this model achieved a good fit and could be used for the identification of natural mutton flavor. Subsequently, PLS-DA model was used to evaluate the degree of natural flavor simulation for a series of mutton process flavor (MPF) samples that were prepared in our lab. The results showed that there were significant differences among MPF samples that were prepared by different Maillard reaction systems. The order of the flavor quality for the MPF samples studied was as follows: MPF5 > MPF6-8 > MPF2 > MPF4 > MPF1 > MPF3. In order to validate the accuracy and reliability of the model for the evaluation of mutton flavor, descriptive sensory analysis (DSA), previously established by our group, was used to examine the results obtained from PLS-DA model. The results were consistent, indicating that PLS-DA model can replace the traditional DSA method in the evaluation of the flavor quality of MPFs.
