Near-infrared spectroscopy (NIR) is widely used for non-destructive food quality check. The prediction models are constructed between NIR spectra and quality parameters. However, because of the noise included in spectra and the duplication between the peaks of the target components and those of the other components, the prediction accuracy of the models decreases. To avoid this problem, derivative spectra are used in modeling. Derivation of spectra has an effect to emphasize the small and narrow peaks so that the affection of peak duplication decreases. On the other hand, derivation of spectra also has an effect to enlarge the noise. The impacts of these effects change as the derivative changes, hence it is necessary to select the adequate derivative for each data. Besides, if there are several peaks of the target components, the adequate derivative is different for each peak. In this paper, we therefore construct regression models using the spectra, the first, second and third derivative spectra, and the combinations of them. The accuracy of the models which are constructed with different derivative spectra or the combinations of them changes when the number of the training data changes. Thus, we proposed a method to select the proper model according to the number of the training data. The selection is performed based on the prediction accuracy of each model. A simulation data set that mimics the spectra where three different peaks duplicate was analyzed using the proposed method. Then, the proposed method was applied to sugar content prediction of oranges. The results showed that the most accurate model changed as the number of the training data changed, and that the effectiveness of the proposed method was proved.

Gas chromatography (GC) and two-dimensional GC (GC-GC) are widely used for separation, structure elucidation and quantitative analysis. In GC and GC-GC, the chemical structure is elucidated by comparing the measured retention time of each compound with the database. But, structure elucidation is infeasible if the retention time is not available from the database. Thus, quantitative structure retention relationship (QSRR) is proposed to predict the retention time from the structure. Some researchers constructed the QSRR models specialized for the limited types of compounds. In this study, we aim to construct the QSRR models that can predict the retention time of various compounds in GC-GC with high accuracy. In addition, we propose a structure elucidation method based on the inverse analysis with the models. First, the objective value of the retention time is set. Then, structure elucidation is accomplished by comparing the objective value with the predicted retention time of new structures. The prediction errors can be a problem in comparison between the predicted and objective values. We deal with this problem by setting an acceptable error for each compound based on the reliability of the predicted value. The analysis with the measurement data proved the effectiveness of the proposed method.

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.