Detection of specific sugars in dairy process samples using multivariate curve resolution

Dairy process monitoring by application of multivariate curve resolution using alternating least squares is presented. Alternating least squares was used for resolving Fourier transform infrared spectral data from a dairy batch process in which lactose is enzymatically hydrolyzed to glucose and galactose. It was possible to extract four compounds (fat, lactose, and two other sugar components) from the spectral data obtained from nine process runs. Subsequently, the pure spectra obtained in this way were used to monitor the content of these compounds in two new process runs. In this way, alternating least squares made it possible to follow the hydrolysis process by Fourier transform infrared spectroscopy without the need for reference analyses. When the results were correlated to reference results for lactose, the accuracy was similar to that obtained when a partial least squares regression was performed on the same data; lactose correlation was 0.980 when alternating least squares was used and was 0.987 when partial least squares was used.