Application of nonlinear correction algorithm in near infrared spectra analysis during on-site quick detection

Abstract

Performance of portable near-infrared spectrometers is easily affected by various factors such as on-site environment, which results in a certain deviation in the on-site predicted results. Partially least square regression (PLSR), as a multiple linear regression method, has been widely used for the analysis of near-infrared (NIR) spectroscopy. However, due to the nonlinear characteristics of the relationship between spectral data and dependent variables, PLSR can easily lead to model errors. Stability and predictability decreased when PLSR is applied in on-site quick detection. How to reduce the errors caused by various environmental factors in the use of portable near-infrared spectrometers is a key issue in promoting the wide application of rapid detection technology based on near-infrared spectral analysis. In this study, the absorption spectra data of glucose solutions of different concentrations are collected by a portable near-infrared spectrometer. Several nonlinear correction algorithms are applied to study the effect of environmental interference during the measurement process. Firstly, the collected spectra data is preprocessed. Secondly, the data is modeled by nonlinear correction algorithms such as optimized artificial neural network (ANN), support vector regression (SVR), and random forest (RF). The impact of different models is compared with to the results using PLSR. It is found that compared with the PLSR linear method, the ANN, SVR and RF nonlinear correction algorithm can eliminate the interference of environmental factors in different degrees. Therefore, ANN, SVR and RF algorithm can improve the prediction accuracy of the model. This study shows that the use of nonlinear correction algorithm for data modeling of portable near-infrared spectrometers can effectively improve the predictive performance of the model.