Prediction of adipose tissue composition using raman spectroscopy: Average properties and individual fatty acids
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Raman spectroscopy has been used for the first time to predict the FA composition of unextracted adipose tissue of pork, beef, lamb, and chicken. It was found that the bulk unsaturation parameters could be predicted successfully [R2=0.97, root mean square error of prediction (RMSEP)=4.6% of 4 δ], with cis unsaturation, which accounted for the majority of the unsaturation, giving similar correlations. The combined abundance of all measured PUFA (≥2 double bonds per chain) was also well predicted with R2=0.97 and RMSEP=4.0% of 4 δ. Trans unsaturation was not as well modeled (R2=0.52, RMSEP=18% of 4 δ); this reduced prediction ability can be attributed to the low levels of trans FA found in adipose tissue (0.035 times the cis unsaturation level). For the individual FA, the average partial least squares (PLS) regression coefficient of the 18 most abundant FA (relative abundances ranging from 0.1 to 38.6% of the total FA content) was R2=0.73; the average RMSEP=11.9% of 4 δ. Regression coefficients and prediction errors for the five most abundant FA were all better than the average value (in some cases as low as RMSEP=4.7% of 4 δ). Cross-correlation between the abundances of the minor FA and more abundant acids could be determined by principal component analysis methods, and the resulting groups of correlated compounds were also well predicted using PLS. The accuracy of the prediction of individual FA was at least as good as other spectroscopic methods, and the extremely straightforward sampling method meant that very rapid analysis of samples at ambient temperature was easily achieved. This work shows that Raman profiling of hundreds of samples per day is easily achievable with an automated sampling system.
principal component analysis
partial least squares
root mean square error of prediction
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