Low-cost IoT-based multichannel spectral acquisition systems for roasted coffee beans evaluation: Case study of roasting degree classification using machine learning

Abstract

Spectral analyses have become dependable and promising analytical methods for objective evaluation of coffee bean quality. However, although commercial instruments are available, financially accessible device solutions still need to be provided for this technique to become more widely available, particularly among emerging small and medium-scale coffee enterprises. This study developed an innovative multichannel spectral acquisition system integrated with an Internet of Things (IoT) platform for evaluating roasted coffee beans . The system was built using commercially available low-cost components with a total cost of approximately 114 USD. A study on the roasting degree classification combined with five machine learning algorithms was conducted based on 18 channels of spectral data (410–940 nm) acquired from the proposed device. Original spectral datasets were directly used for model development to minimize data processing and simplify the implementation of the best machine learning model on the device. The results revealed that the Random Forest (RF) model demonstrated a satisfactory performance (validation accuracy values reaching 0.988, precision 0.988, recall 0.988, and F1-score 0.987). Therefore, the proposed system can effectively classifiy roasted coffee beans and might have important applications in assisting roasteries during the roasting process in a real-time, non-subjective, and non-invasive manner.