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

Since 5-hydroxymethylfurfural (5-HMF) is carcinogenic to humans, its detection in foods is essential. This study performed near-infrared (NIR) spectroscopy (11998–4000 cm−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 (11667 cm−1, 11666 cm−1, 10905 cm−1, 7096 cm−1, 7095 cm−1, 7094 cm−1, 7093 cm−1, 7092 cm−1, 5054 cm−1, 5026 cm−1, 5025 cm−1, and 5024 cm−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.