Multivariate analysis for the improvement and development of novelty and green analytical techniques in the characterization of raw materials, products and coproducts of the sugar industry.
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Date
2025
Journal Title
Journal ISSN
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Publisher
Universidad de Concepción
Abstract
Multivariate analysis for the improvement and development of novelty and green analytical techniques in the characterization of raw materials, products and coproducts of the sugar industry.
The main objective of this Ph.D. thesis was to develop and improve analytical strategies based mainly on vibrational spectroscopy, combined with multivariate analysis, to address challenges related to molasses authentication, post-harvest quality monitoring, and the structural-chemical characterization of sugar beet roots.
The multi-isotopic stable isotope ratio mass spectrometry (IRMS) approach combined with multivariate analysis, successfully discriminated cane from beet molasses, outperforming single-isotope analysis. Similarly, infrared spectroscopy across different spectral ranges, using benchtop and portable instruments, combined with chemometric models, also enabled classification of molasses by botanical origin. Moreover, the environmental sustainability of each technique was quantified, an aspect poorly reported. Visible-near infrared (VIS-NIR) spectroscopy and hyperspectral imaging (HSI) supported with chemometric models accurately classified sugar beet roots by storage time, detecting early post-harvest changes, mainly associated with pigments, browning, and oxidation, beyond the discriminatory capacity of sugar high performance liquid chromatography (HPLC) analysis. Furthermore, the analysis of VIS-NIR and Fourier transform infrared (FT-IR) hyperspectral imaging combined with multivariate curve resolution alternating least squares (MCR-ALS) enabled a detailed assessment of the ultrastructural organization of sugar beet roots, linking anatomical zones with pigments, soluble sugars, and structural polysaccharides. Finally, VIS-NIR HSI combined with partial least squares (PLS) regression enabled the quantification of sucrose and fructose in sugar beet roots, with relative prediction errors of 11.98% and 14.14%, respectively. Although quantification accuracy was limited, the models generated spatial maps consistent with tissue-specific sugar distribution, providing a valuable insights into internal variability.
The spectroscopic-chemometric strategies demonstrated to be a robust, versatile, and sustainable analytical toolbox for the sugar industry. These approaches supported authentication and quality assessment for decision making in the agro-industrial sector, while providing effective alternatives to conventional destructive and resource-intensive methods, in line with the principles of green analytical chemistry.
Description
Tesis presentada para optar al grado de Doctor en Ciencias y Tecnología Analítica.
Keywords
Multivariate analysis, Raw materials, Sugar trade