NIR-Based Predictive Modelling for the Quantification of Sucrose, Glucose, and Fructose in Brown Sugar from Oil Palm Trunk Sap - Sharly Claudia Alghai Sani; Dase Hunaefi; Faleh Setia Budi; Yessie Widya Sari; Ilham Akbar Ibrahim; Noviani Rustanto (The University of Lampung, 2026)
Using oil palm trunk sap as a raw material for brown sugar is an innovative alternative for local product diversification. However, craftsmen's limited access to laboratory analysis methods is challenging to maintain product quality consistency. This study aims to evaluate the feasibility of using near-infrared spectroscopy (NIRS) combined with chemometric modelling for the estimation of sucrose, glucose, and fructose content in brown sugar derived from oil palm trunk sap. This method combines destructive analysis using high-performance liquid chromatography (HPLC) as a reference with non-destructive NIRS analysis and partial least squares regression (PLSR) modelling. The prediction model performed very well for glucose with an R² of 0.991, while for sucrose it was 0.850 and fructose 0.860. However, the relatively high values of SEC and SEP and the low prediction consistency (<20%) indicate that the current chemometric strategy is not yet fully adequate, suggesting the need for a larger and more process-representative sample set, more rigorous consideration of sample representativeness and laboratory reference uncertainty (SEL), and the inclusion of laboratory reference error (SEL) from HPLC data to enable more robust and reliable model development. These findings indicate that NIRS has potential as a fast and non-destructive method for brown sugar quality control, but further development is needed to make the model more reliable under various production conditions.