Bootstrap-integrated machine learning techniques for the calibration of near-infrared (NIR) spectra.

AbstractNear-infrared (NIR) spectroscopy system is frequently used in food production because of its superior characteristics. Establishing models on spectral data for prediction has always been of interest. However, no satisfactory model has been developed on different data types for accuracy and reliability. In this study, an approach strategy is reported for the calibration of NIR spectra by utilizing a combination of the bootstrap technique and an ensemble method. The approach comprises three steps. First, data are resampled to create bootstrap samples. Second, four calibration models are applied to the grouped data: partial least squares regression (PLSR), support vector regression (SVR), backpropagation neural network (BPNN), and principal component analysis-backpropagation neural network (PCA-BPNN). Finally, predictions obtained after the calibration are combined using the ensemble method. The data studied include 215, 32, and 540 samples, which were characterized as hyperspectral, small sample, and categorical data, respectively. Root mean square error of prediction (RMSEP), R squared (R2), and residual prediction deviation (RPD) were used to describe the accuracy. Coverage probability of the prediction interval (PICP) and normalized average prediction interval width (NMPIW) were utilized to evaluate the reliability. The accuracies of the methods exhibited the following order: the proposed method > PLSR > SVR > BPNN ≈ PCA-BPNN. The results confirm that the reported model achieved satisfactory accuracy and was more reliable than the single calibration models. [ABSTRACT FROM AUTHOR]