Model Optimization for the On-line Inspection of Internal Apple Quality by Shortwave Near-infrared Spectroscopy
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Abstract:
The critical part in the application of near-infrared (NIR) spectroscopy for the on-line inspection of internal fruit quality is to build quantitative analysis models with good robustness and high accuracy. A system based on shortwave NIR spectroscopy for on-line inspection of apple quality was developed. The spectra were collected in diffusion reflectance mode within the wavelength range of 500~1100 nm, and the conveyor belt speed was fixed to five samples per second. After the band intensity was normalized, genetic, successive projection, and ant colony optimization (ACO) algorithms were employed to select characteristic variables, following which the respective corresponding partial least square (PLS) models were constructed, and the spectral variable search mechanisms of these three methods were analyzed. Compared with the full spectral model, the predictive models built on the variable selection methods all exhibited better predictive performance with fewer variables, improved computational speed, and enhanced robustness. The best predictive performance was found in the model built using ACO-PLS, where the correlation coefficient of the prediction set was 0.9358 and the root mean square error of prediction was 0.2619 oBx. The results of this study demonstrate that NIR combined with variable selection methods can build an efficient model for the on-line determination of the apple soluble solid content, and has great potential for industrial application.
