The potency of Vis/NIR spectroscopy for classification of soybean based of colour M Fahri Reza Pahlawan, Betty Mei Ari Murti, Rudiati Evi Masithoh
Department of Agricultural and Biosystems Engineering, Faculty of Agricultural Technology, Universitas Gadjah Mada, Jl. Flora No. 1 Bulaksumur, Yogyakarta 55281, Indonesia
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.
