Proposal of a novel near-infrared spectral analysis method for constructing robust and high-precision models

Abstract

Non-destructive testing of food quality with Near-Infrared Spectroscopy (NIR) is becoming common. The prediction models are constructed between NIR spectra and quality parameters. Many investigations have been done for the construction of high predictive models. Although some models indeed have suitably predictive accuracy, those models work well in only limited data domains and the accuracy decreases with time. Hence the models should be reconstructed with new data by wasting samples of objective foods and measuring the quality. To perform both the reduction of the loss of the food and the high performance of the models, overlapped peaks of NIR spectra should be considered because the overlapped peaks make relationships between NIR spectra and quality parameters unclear. Derivation of spectra is generally used to solve this problem. An adequate order of derivative changes depending on how peaks are overlapping, but the dependence of an adequate derivative order on the number of training samples remains to be clarified. Therefore we propose a method using some kinds of derivative order of spectra according to the number of samples for the construction of regression models. The effectiveness of the proposed method was confirmed thorough the analyses of simulation data and real data.