Genetic Algorithm for Feature and Latent Variable Selection for Nutrient Assessment in Horticultural Products

Vibrational spectroscopy can be used for rapid determination of chemical quality markers in horticultural produce to improve quality control, optimize harvest times and maximize profits. Most commonly, spectral data are calibrated against chemical reference data (acquired using traditional, slower analytical methods) using partial least squares regression (PLSR). However, predictive performance of PLSR can be limited by the small number of instances, high dimensionality and collinearity of spectroscopic data. Here, a new genetic algorithm (GA) for PLSR feature and latent variable selection is proposed to predict concentrations of 18 important bioactive components across three New Zealand horticultural products from infrared, near-infrared and Raman spectral data sets. Models generated using the GA-enhanced PLSR method have notably better generalization and are less complex than the standard PLSR method. GA-enhanced PLSR models are produced from each spectroscopic data set individually, and from a data set that combines all three techniques.