A new approach for identifying the origin of brown sugar: Multiple chemical profiles combined with machine learning forms an accurate and reliable product traceability strategy

Abstract

The traceability of brown sugar for origin remains a challenge for market regulation. To establish a reliable system for origin identification, this study integrated multi-dimensional chemical profiling with machine learning techniques to preliminarily classify brown sugar produced in five regions of China. Mineral and phenolic compound profiles were obtained by using inductively coupled plasma optical emission spectrometry and liquid chromatography–tandem mass spectrometry, respectively. Chemometric analysis identified potassium as the most effective element for distinguishing among brown sugar production regions. Principal component analysis determined six principal components for initial explanation of origin differences. Machine learning models were used to further enhance reliability of origin identification. Upon validation, the Random Forest and Support Vector Machine models achieved classification accuracies of 96 % and 98 %, respectively. These results provide a preliminary scientific basis and methodological foundation for the origin traceability and authentication of brown sugar.