Random Forest (RF) based identification of rice powder mixture using terahertz spectroscopy

Abstract

Adulteration is a severe problem in agriculture field. It may cause some dishonest traders in the marketing side. For their own wish, dishonest traders make adulteration products for higher profit. In consumer side, agro product adulterations provide some unhealthy situation. It may cause severe health problem and also some incurable disease. Its right time, need to find an adulteration various level of agro products. For detecting rice adulteration, to collect spectral data for mixed rice powder (low quality rice powder and high quality rice powder), and a Fast Independent Component Analysis (FICA) algorithm is used to extract valuable information. Then, apply Random Forest Classifier algorithm for classifying purpose, whether the mixture is low quality or high quality. It achieves high accuracy with a single model and also easy to implement.

Interval-based sparse ensemble multi-class classification algorithm for terahertz data

“…Due to its characteristics of penetration, fingerprint, and non-ionization, THz spectroscopy has demonstrated great potential in non-destructive detection, especially in the fields of food, medicine, environmental protection, and some industrial fields [2] , [3] , [4] , [5] . With THz spectra, Friska et al [6] utilized fast independent component analysis (ICA) and random forest (RF) algorithms to identify adulterated rice. Pan et al [7] applied the support vector machine (SVM) with an enhanced cuckoo search algorithm to classify ginseng of different growth ages.…”