Sustainable authentication of molasses' botanical origin using infrared spectroscopy: Accuracy and greenness evaluation of spectral techniques

Molasses, a byproduct of sugarcane and sugar beet processing, is widely utilized in the food, fermentation, and animal feed industries. However, authenticating its botanical origin remains challenging, often relying on costly, time-consuming, chemically intensive, and environmentally unsustainable methods. In response to increasing demands for sustainable analytical alternatives, this study aimed to develop and compare infrared spectroscopic methods to classify cane and beet molasses, focusing on sustainability of techniques while maintaining analytical performance. Data of portable and benchtop Fourier Transform Near-Infrared (FT-NIR) and Fourier Transform Mid-Infrared (FT-IR) spectrometers were evaluated using chemometric approaches, such as Principal Component Analysis (PCA) and classification models like Partial Least Squares Discriminant Analysis (PLS-DA) and k-Nearest Neighbors (k-NN). The Analytical GREEnness (AGREE) metric was employed to assess the sustainability of each technique, while analytical accuracy was evaluated using figures of merit derived from confusion matrices. FT-IR spectroscopy achieved the highest classification accuracy (0 % error) and revealed that beet molasses exhibiting higher protein content, whereas cane molasses contained more fructose. However, FT-IR scored the lowest in terms of greenness due to higher energy demands and sample handling in comparison with the other techniques. In contrast, portable FT-NIR was the most sustainable technique (AGREE score = 0.86, scale from 0 to 1), albeit with a slightly higher classification error (8.3 %). These findings demonstrate the potential of infrared spectroscopy as a reliable and sustainable solution for molasses authentication and show that sustainability–accuracy trade-offs can be quantitatively assessed to support informed decision-making in the analytical process of sugar industry.