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Abstract
Near infrared spectral analysis is a widely used tool for non-destructive determination of moisture content in food. Moisture in potato crisps is often measured using near infrared analysis, however shape contours and curvatures in samples causes scattering of light, which reduces the accuracy of the measurement. The piecewise multiple scatter correction algorithm is commonly used to reduce the effect of light scattering from the sample. Implementation of the piecewise multiple scatter correction algorithm requires careful selection of scatter correction window size and combinations of spectral wavelength predictors. This paper considers different search heuristics for the purpose of piecewise multiple scatter correction window size selection and optimal spectral wavelength predictor selection. The search heuristics under consideration are a genetic algorithm, hill climbing, feature selection and full spectrum modeling. Calibration models using partial least square regression are formed from the scatter corrected data. The standard error of a cross-validated calibration data set was used to compare the performance of the different techniques. It has been found that a genetic algorithm produced the lowest cross validated standard error from the techniques considered. This suggests that the use of a genetic algorithm resulted in more judicious selection of near infrared model parameters.