Low-Cost Multispectral Sensor for Detecting Adulteration of Onion Powder with Machine Learning

Abstract

Onion powder has been frequently adulterated with cheaper materials to boost revenues for scammers. The effective technique is necessary for the application of authentication of onion powder. The aim of this study is application of low-cost multispectral sensor for detecting adulteration of onion powder. Traditional and machine learning algorithms including multiple linear regression (MLR), partial least square regression (PLS-R), nu-support vector regression (nu-SVR), and black propagation neural network (BPNN) were used to train prediction models. Visible and near infrared (Vis-NIR) spectral data was collected using low-cost multispectral sensor at wavelength of $610,680,730$, 760,810 and $860 \mathrm{~nm}$. Adulterated onion samples were prepared by blending corn flour and onion powder. The coefficient of determination $\left(R_{\mathrm{P}}^{2}\right)$ value of all algorithms was between 0.888 and 0.959 while the range of root mean square error of prediction (RMSEP) was $4.214-6.964 \%$. This finding point indicated that combination of low-cost Vis-NIR multispectral sensor and machine learning could be used for detecting adulteration of onion powder.