Parameter Optimization of Rapeseed Oil Content Model Using a Miniature Near-infrared Spectrometer
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Abstract:
To select characteristic NIR wavelengths for rapid and nondestructive detection of rapeseed oil content, a miniature near-infrared (NIR) spectrometer was used in combination with methods including competitive adaptive reweighted sampling (CARS), genetic algorithm (GA), successive projections algorithm (SPA), uninformative variable elimination (UVE), backward interval partial least squares (BIPLS), and synergy interval partial least squares (SIPLS). Subsequently, partial least squares regression (PLSR) and least squares-supported vector machine (LS-SVM) regression model were established and the parameters of LS-SVM model were optimized. The results showed that for PLSR model, 26 characteristic wavelengths selected by BIPLS + GA produced the optimum model, where the correlation coefficient (Rp) and root mean square error of prediction (RMSEP) for prediction sets were 0.9330 and 0.0075, respectively. For LSS-VM model, 13 characteristic wavelengths selected by SIPLS+GA produced the optimum model and the correlation coefficient (Rp) and root mean square error of prediction (RMSEP) for prediction sets were 0.9192 and 0.0055, respectively. The above results demonstrate that parameter optimization and wavelength selection can not only effectively simplify the quantitative analysis model for rapeseed oil content based on miniature NIR spectrometry, but also enhance prediction accuracy and prediction stability. These data provide useful technical references for further applications of miniature NIR spectrometry.
