Using Genetic Algorithms to Select Wavelengths in Near-Infrared Spectra: Application to Sugar Content Prediction in Cherries

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Abstract
The best parameter for evaluating fruit taste quality is sugar content. This parameter can be nondestructively measured by nearinfrared spectrometry. Models that give the best performances are based on classical partial least-squares regression (PLSR) applied over the whole spectrum. Selecting the best wavelengths improves the precision and the robustness of these models. Genetic algorithms (GAs) applied to the results of a cross-validation make this selection in a stochastic way. The influence of GA tuning and of the cross-validation parameters is discussed. An application of this technique on cherry samples decreases the cross-validation error by 20% and divides the prediction error by more than 3.