Fluorescence of Raw Cane Sugars Evaluated by Chemometrics

• Published in: Journal of agricultural and food chemistry Vol. 48; no. 10; pp. 4955 - 4962
• Main Authors: Baunsgaard, Dorrit, Nørgaard, Lars, Godshall, Mary An
• Format: Journal Article
• Language: English
• Published: Washington, DC American Chemical Society 01-10-2000
• Subjects: Biological and medical sciences; Carbohydrates - chemistry; Colorimetry; Fluorescent Dyes; Food industries; Fundamental and applied biological sciences. Psychology; Spectrometry, Fluorescence; Sucrose - chemistry; Sugar industries; Storage; Fluorophore; Raw sugar; Fluorescence; Cane sugar; Chemical composition; Sugar industry; Measurement method; Precursor; Chemometrics; Comparative study; Coloration
• ISSN: 0021-8561; 1520-5118
• DOI: 10.1021/jf000131j

In a fluorescence study of raw cane sugar samples, two-way and three-way chemometric methods have been used to extract information about the individual fluorophores in the sugar from fluorescence excitation−emission landscapes. A sample set of 47 raw sugar samples representing a varied selection was analyzed, and three individual fluorophores with (275, 350) nm, (340, 420) nm, and (390, 460) nm as their approximate excitation and emission maxima were found. The spectral profiles of the fluorophores were estimated with the three-way decomposition model PARAFAC. Two-way principal component analysis (PCA) of unfolded fluorescence landscapes confirmed the PARAFAC results and showed patterns of samples related to time of storage. Partial least squares (PLS) calibration models of color at 420 nm had a high model error due to the very high color range of the raw sugars, but variable selection performed on the fluorescence data revealed that all three fluorophores were correlated to color. The (275, 350) nm fluorophore is considered as a color precursor to the color developed on storage and the (340, 420) nm and (390, 460) nm fluorophores show colorant polymer characteristics. Keywords: Raw cane sugar; color; fluorescence; principal component analysis; partial least squares; multiway decomposition