Towards combinatorial spectroscopy: the case of minor milk fatty acids determination

Chemometrical models for determination of milk fatty acids (FA) are typically developed using spectral data from a single spectroscopy technique, e.g., mid-infrared spectroscopy in milk control. Such models perform poorly in determining minor components and are highly dependent on the spectral data source and on the type of matrix. In milk fat, the unsuccessful determination of minor (fatty acids lower than 1.0 g/100g in total fat) FA is often the result of: (1) the molecular structure similarity between the minor and the major FA within the milk fat matrix (thus the chemical signature specific to individual fatty acids has restricted specificity), and (2) the low signal intensity (detection limit) for specific vibrational modes. To overcome these limitations, data from different types of spectroscopy techniques, which brings additional chemical information in relation to the variation of the FA, could be included in the regression models to improve quantification. Here, Fourier transform (FT) Raman spectra were concatenated with attenuated total reflectance FT infrared (ATR/FTIR) spectra. The new combinatorial models showed up to 25% decrease in the root mean squared error of cross-validation (RMSECV) values, accompanied with a higher Rcv(2) for most individual FA or sums of FA groups, as compared to regression models based on Raman only or ATR/FTIR only spectra. In addition, improved models included less PLS components indicating an increased robustness. Interpretation of the most contributing regression coefficients indicated the value of newly combined spectral regions as carriers of specific chemical information. Although requiring additional spectroscopy instrumentation and prolonged acquisition time, this new combinatorial approach can be automated and is sufficient for semi-routine determination of the milk FA profile.