Evaluation of machine learning algorithms -k nearest neighbors and support vector machines- for strawberries classification during food drying process

GAMBOA SANTOS J.; CAMPAÑONE, L.A.

Congreso; MACI 2021; 2021

In the present work, supervised machine learning (ML) algorithms, k-NN and SVM, were applied to classifystrawberry samples during a microwave-assisted drying process. A dataset of 1150 strawberry records containinginformation about images of two pre-treatments types (fresh, FR and osmotically pre-treated, OD), three ranges of dryingtimes (short 70 min) and three physical characteristics previously selected(shrinkage, brightness and saturation) was used to perform the ML classifiers. The k-NN and SVM models led to goodaccuracy values, 0.94 for sample type and 0.90 for drying time categories. Since colour and morphological changes arerelated to changes in the product quality, these results are useful to evaluate the losses of nutritional and sensorialproperties taking place during the in-line processing of strawberries, by means of a non-invasive monitoring technique.