A machine learning algorithm combining reinforcement learning and supervised learning is demonstrated for training of near infrared spectroscopy data for non-destructive measurement of fruit quality. The model optimizes the combination of pretreatment methods, discriminant methods and calibration methods and also the parameters used in the methods to achieve highest prediction correlations. The model achieves better results than manual combinations of the previously demonstrated models.
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