This study (purchase required, $USD 31.50) evaluated Fourier transform near-infrared (FT-NIR) spectroscopy combined with chemometric analysis as a rapid screening tool for the discrimination of sugar products differing in botanical origin and technological background.
A model was trained using white brown and white cane sugars, sourced direct from manufacturers. Spectra were collected from sugar crystals in reflectance mode and from sugar solutions in transmission mode, and the obtained data were processed using distance match and discriminant analysis models. The authors report successful discrimination between beet and cane.
A further model was trained with brown-type cane sugars classified as cane gold sugars (CGS), cane brown sugars (CBS), and cane raw sugars (CRS).. The authors report that these brown-type cane sugars showed markedly higher heterogeneity, with CBS representing the least distinct class and displaying variable similarity to CRS or CGS. Transmission spectra improved CBS class definition, whereas reflectance spectra better reflected relationships associated with the production route.
The authors conclude that FT-NIR spectroscopy supported by chemometrics is a promising rapid approach to assess sugar authenticity and to assess the quality of the brown-type cane sugar classification.
Photo by Kairi Kaljo on Unsplash
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