The Use of Fluorescence Spectroscopic Data and Machine-Learning Algorithms to Discriminate Red Onion Cultivar and Breeding Line

Abstract

The objective of this study was to evaluate differences between the red onion cultivar and breeding line using models based on selected fluorescence spectroscopic data built using machine-learning algorithms from different groups of Trees, Functions, Bayes, Meta, Rules, and Lazy. The combination of fluorescence spectroscopy and machine learning is an original approach to the non-destructive and objective discrimination of red onion samples. The selected fluorescence spectroscopic data were used to build models using algorithms from the groups of Trees, Functions, Bayes, Meta, Rules, and Lazy. The most satisfactory results were obtained using J48 and LMT (Logistic Model Tree) from the group of Trees, Multilayer Perceptron, and QDA (Quadratic Discriminant Analysis) from Functions, Naive Bayes from Bayes, Logit Boost from Meta, JRip from Rules, and LWL (Locally Weighted Learning) from Lazy. The average accuracy of discrimination of onion bulbs belonging to ‘Asenovgradska kaba’ and a red breeding line equal to 100% was found in the case of models developed using the LMT, Multilayer Perceptron, Naive Bayes, Logit Boost, and LWL algorithms. The TPR (True Positive Rate), Precision, and F-Measure of 1.000 and FPR (False Positive Rate) of 0.000, as well as the Kappa statistic of 1.0, were determined. The results revealed the usefulness of the approach combining fluorescence spectroscopy and machine learning to distinguish red onion cultivars and breeding lines.

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