Comparative study of several machine learning algorithms for classification of unifloral honeys
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Comparative study of several machine learning algorithms for classification of unifloral honeys

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Comparative study of several machine learning algorithms for classification of unifloral honeys

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dc.contributor.author Mateo Jiménez, Fernando
dc.contributor.author Tarazona, Andrea
dc.contributor.author Mateo Jiménez, Eva María
dc.date.accessioned 2022-04-05T14:16:11Z
dc.date.available 2022-04-05T14:16:11Z
dc.date.issued 2021
dc.identifier.uri https://hdl.handle.net/10550/82179
dc.description.abstract Unifloral honeys are highly demanded by honey consumers, especially in Europe. To ensure that a honey belongs to a very appreciated botanical class, the classical methodology is palynological analysis to identify and count pollen grains. Highly trained personnel are needed to perform this task, which complicates the characterization of honey botanical origins. Organoleptic assessment of honey by expert personnel helps to confirm such classification. In this study, the ability of different machine learning (ML) algorithms to correctly classify seven types of Spanish honeys of single botanical origins (rosemary, citrus, lavender, sunflower, eucalyptus, heather and forest honeydew) was investigated comparatively. The botanical origin of the samples was ascertained by pollen analysis complemented with organoleptic assessment. Physicochemical parameters such as electrical conductivity, pH, water content, carbohydrates and color of unifloral honeys were used to build the dataset. The following ML algorithms were tested: penalized discriminant analysis (PDA), shrinkage discriminant analysis (SDA), high-dimensional discriminant analysis (HDDA), nearest shrunken centroids (PAM), partial least squares (PLS), C5.0 tree, extremely randomized trees (ET), weighted k-nearest neighbors (KKNN), artificial neural networks (ANN), random forest (RF), support vector machine (SVM) with linear and radial kernels and extreme gradient boosting trees (XGBoost). The ML models were optimized by repeated 10-fold cross-validation primarily on the basis of log loss or accuracy metrics, and their performance was compared on a test set in order to select the best predicting model. Built models using PDA produced the best results in terms of overall accuracy on the test set. ANN, ET, RF and XGBoost models also provided good results, while SVM proved to be the worst.
dc.language.iso eng
dc.relation.ispartof Foods, 2021, vol. 10, num. 1543, p. 1-20
dc.rights.uri info:eu-repo/semantics/openAccess
dc.source Mateo Jiménez, Fernando Tarazona, Andrea Mateo Jiménez, Eva María 2021 Comparative study of several machine learning algorithms for classification of unifloral honeys Foods 10 1543 1 20
dc.subject Apicultura
dc.subject Intel·ligència artificial
dc.subject Aliments Consum
dc.title Comparative study of several machine learning algorithms for classification of unifloral honeys
dc.type info:eu-repo/semantics/article
dc.date.updated 2022-04-05T14:16:11Z
dc.identifier.doi https://doi.org/10.3390/foods10071543
dc.identifier.idgrec 151195

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