Rapid identification of coffee bean variety by near infrared hyperspectral imaging technology

Four different Chinese domestic coffee beans were identified rapidly by combining near infrared hyperspectral imaging technique and five kinds of discriminant models. A near-infrared hyperspectral imaging system covering the spectral range of 874-1734 nm was set up to capture hyperspectral images of coffee bean samples. The head and end of the spectra with obvious noises were removed, and the spectral data in the range of 925-1680 nm were extracted to establish discriminant models in the experiment. The sensitive wavelengths were selected from the full spectra by Successive Projections Algorithm (SPA). Five discriminant methods, including Partial Least Square-discriminant Analysis (PLS-DA), Random Forest (RF), K-nearest Neighbor algorithm (KNN), Support Vector Machine (SVM) and Extreme Learning Machine (ELM) were applied to the establishment of discriminant models based on the full spectra and the selected sensitive wavelength variables. The properties of the models were compared and valuated by three parameters, sensitivity, precision and specificity. Among all discriminant models, the ELM models based on the full spectra and the selected sensitive wavelength variables show the best identification results, respectively. For each coffee bean cultivar, the sensitivity, precision and specificity of ELM models based on full spectra and the sensitive wavelengths are all over 93.5% in both the calibration set and the prediction set. It concludes that Chinese domestic coffee beans could be identified by near-infrared hyperspectral imaging combined with discriminant models rapidly. Selecting the sensitive wavelengths reduces variables, but the identification effect is the same as that of the full spectra. ©, 2015, Chinese Academy of Sciences. All right reserved.