Application of Fourier Transform Near-Infrared Spectroscopy and Chemometrics for Quantitative Analysis of Milk of Lime (MOL) Used in the Sugar Industry

Abstract

Milk of lime (MOL), a suspension of calcium oxide and calcium hydroxide, is vital in the purification of sugar beet and cane juices. This study evaluates the application of Fourier Transform Near-Infrared (FT-NIR) spectroscopy combined with chemometric models—Partial Least Squares (PLS) and Principal Component Regression (PCR)—for rapid, non-destructive assessment of key MOL parameters: density, total lime content, calcium oxide availability, and sucrose content. Ninety samples were analyzed using both wet chemistry and FT-NIR. The predictive performance was assessed using the coefficient of determination (R2). High predictive accuracy was observed for density (PLS: R2 = 0.8274; PCR: R2 = 0.8795) and calcium oxide availability (PLS: R2 = 0.9035; PCR: R2 = 0.9115). Total lime content showed moderate accuracy (PLS: R2 = 0.7748; PCR: R2 = 0.7983), while sucrose content exhibited low predictive power (PLS: R2 = 0.2312; PCR: R2 = 0.3747). The weak performance was noted for %CaO (PLS: R2 = 0.4893; PCR: R2 = 0.2409), likely due to spectral overlap and matrix complexity. Despite these challenges, FT-NIR remains a viable, reagent-free method for monitoring MOL, with the potential to enhance process control in the sugar industry. Future work should focus on refining calibration strategies and addressing spectral interferences to improve predictive accuracy for complex matrices.