Potential of smartphone-coupled micro NIR spectroscopy for quality control of green tea

Green tea adulterated with sugar and glutinous rice flour has an increased sensitivity to water, which affects the safety of the tea. A total of 475 samples of pure tea, sugar-adulterated tea, and glutinousrice-flour-adulterated tea were prepared and scanned using micro near infrared spectroscopy (NIRS). The collected NIRS data were qualitatively and quantitatively detected by a multi-layer algorithm model. Principal component analysis indicated that the three sample groups had an obvious separation trend. The discriminate rate of the optimal qualitative model, namely support vector machine, was 97.47% for the prediction set. A total of three wavelength selection methods were used to improve the performances of partial least squares regression and support vector machine regression (SVR) models. The non linear SVR models based on characteristic wavelengths selected by iteratively retaining informative variables algorithm provided satisfactory results for the identification of sugar and glutinous rice flour adulteration. The correlation coefficients for prediction (Rp) were >0.94, and the residual prediction deviation were >3. The results indicated that smartphone-based micro NIRS can be effectively used to qualitatively and quantitatively analyze adulterants in green tea. (C) 2020 Published by Elsevier B.V.

Automated summary

This study used micro near infrared spectroscopy to detect sugar and glutinous rice flour adulteration in green tea, achieving rapid identification and analysis of adulterants through multi-layer algorithm and support vector machine models. The results indicated the potential application of smartphone-based micro NIRS technology in the detection of adulterants in green tea.