Determination of acetone in cow milk by Fourier transform infrared spectroscopy for the detection of subclinical ketosis.

Fourier transform infrared analysis (FTIR) was used in combination with partial least squares regression (PLS) to predict the concentration of acetone in milk. FTIR spectra were compared with results of a gas-chromatographic head space method. Principal component analysis of whole spectra (3000 to 1000 cm(-1)) suggested to reduce the spectrum of analysis for acetone to 1450 to 1200 cm(-1). A second derivative was applied to the spectra to remove baseline effects and further enhance the spectral features. Full cross-validation was used to compare the reference with predicted acetone concentrations of samples not included in model development. PLS applied to the full spectral range resulted in a complex 19-factor model with a cross-validation error of 0.22 mM. After reducing the spectrum and taking the second derivative, we obtained a model with seven factors that yielded a cross-validation error of 0.21 mM. This compares favorably with a previously reported model with 20 factors and an error of 0.25 mM. Using PLS predictions to identify cows with subclinical ketosis resulted in 95 to 100% sensitivity and 96 to 100% specificity when the threshold for subclinical ketosis was 0.4 to 1.0 mM. The corresponding positive predictive values were > or = 76% and the negative predictive values > 98% throughout an assumed range of subclinical ketosis prevalence of 10 to 30%.