The potency of Vis/NIR spectroscopy for classification of soybean based of colour

Abstract

Abstract Soybean in various colour is easy to identify using human eyes. However, it is hard to perform manual method for on-line production. Therefore, detection of colour for sorting the soybean is important especially for industries which require a rapid and real-time task. This research was conducted to study the potency of a modular type of VIS/NIR spectroscopy at wavelength of 350-1000 nm to classify black, green, and yellow of soybean seed and flour. Principal component analysis (PCA) and PCA Linear discriminant analysis (PCA-LDA) were used based on various spectra pre-processing techniques. Results showed that PCA-LDA model was able to classify soybean seeds of 97% accuracy and soybean flour of 100% accuracy.