Routine Monitoring of Instrument Stability in a Milk Testing Laboratory With ASCA: A Pilot Study

Abstract

Mid-infrared spectroscopy has been developed as a reliable and rapid tool for routine analysis of fat, protein, lactose and other components in liquid milk. However, variations within and between FTIR instruments, even within the same milk testing laboratory, present a challenge to the accuracy of measurement of particularly minor components in the milk, such as individual fatty acids or proteins. In this study we have used Analysis of variance–Simultaneous Component Analysis (ASCA), to monitor the spectral variation between and within each of four different FOSS FTIR spectrometers over each week in an independent milk testing laboratory over 4 years, between August 2017 and March 2021 (223 weeks). On everyday of each week, spectra of the same pilot milk sample were recorded approximately every hour on each of the four instruments. Overall, variations between instruments had the largest effect on spectral variation over each week, making a significant contribution every week. Within each instrument, day-to-day variations over the week were also significant for all but two of the weeks measured, however it contributed less to the variance overall. At certain times other factors not explained by weekday variation or inter-instrument variation dominated the variance in the spectra. Examination of the scores and loadings of the weekly ASCA analysis allowed identification of changes in the spectral regions affected by drifts in each instrument over time. This was found to particularly affect some of the fatty acid predictions.

Miniaturized NIR Spectrometers in a Nutshell: Shining Light over Sources of Variance

“…ANOVA-Simultaneous Component Analysis (ASCA) [30,31] was used to evaluate the significance of the main considered factors and their interactions. ASCA was proposed for the analysis of multivariate datasets obtained through designed experiments [32]. The model consists in the partitioning of the original data matrix into a set of matrices corresponding to design factors (e.g., a and b) and their interactions (ab) and decomposing them through simultaneous component analysis (SCA):…”