Detection of the 5-hydroxymethylfurfural content in roasted coffee using machine learning based on near-infrared spectroscopy.

Abstract

Since 5-hydroxymethylfurfural (5-HMF) is carcinogenic to humans, its detection in foods is essential. This study performed near-infrared (NIR) spectroscopy (11998-4000cm-1) to determine the 5-HMF content in roasted coffee. The random forest (RF) was used to extract important wavenumbers, after which three machine learning models (ordinary least square (OLS), support vector machine (SVM), and RF) were established for the prediction. RF obtained the best prediction results (Rc2=0.98 and Rp2=0.92) compared with OLS and SVM and effectively extracted the important wavenumbers (11667cm-1, 11666cm-1, 10905cm-1, 7096cm-1, 7095cm-1, 7094cm-1, 7093cm-1, 7092cm-1, 5054cm-1, 5026cm-1, 5025cm-1, and 5024cm-1). The results demonstrated that machine learning models based on NIR spectroscopy could provide a non-destructive approach for determining 5-HMF content in roasted coffee.