The performance of a newly developed pocket-sized near-infrared (NIR) spectrometer was investigated by analysing 46 cheese samples for their water and fat content, and comparing results with a benchtop NIR device. Additionally, the automated data analysis of the pocket-sized spectrometer and its cloud-based data analysis software, designed for laypeople, was put to the test by comparing performances to a highly sophisticated multivariate data analysis software. All developed partial least squares regression (PLS-R) models yield a coefficient of determination (R²) of over 0.9, indicating high correlation between spectra and reference data for both spectrometers and all data analysis routes taken. In general, the analysis of grated cheese yields better results than whole pieces of cheese. Additionally, the ratios of performance to deviation (RPDs) and standard errors of prediction (SEPs) suggest that the performance of the pocket-sized spectrometer is comparable to the benchtop device. Small improvements are observable, when using sophisticated data analysis software, instead of automated tools.
Keywords: NIR, SCiO, pocket-sized spectrometer, cheese, fat, moisture, multivariate data analysis.