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- A TSS classification study of 'Rocha' pear (Pyrus communis L.) based on non-invasive visible/near infra-red reflectance spectraPublication . Bexiga, Florentino; Rodrigues, Daniela; Guerra, Rui Manuel Farinha das Neves; Brazio, António; Balegas, Tiago; Cavaco, A. M.; Antunes, Maria Dulce; Valente de Oliveira, JOSÉThe study focuses on the application of machine learning techniques for classifying the internal quality of 'Rocha' Pear (Pyrus communis L.), i.e., the total soluble solids (TSS), using the non-invasive technique of visible/near infra-red reflectance spectroscopy. Six representative classifiers were evaluated under realistic experimental conditions. The classifiers include representatives of classic parametric (logistic and multiple linear regression), non-parametric distance based methods (K-nearest neighbors), correlation-based (partial least squares), ensemble methods (random forests) and maximum margin classifiers (support vector machines). The classifiers were assessed against metrics such as accuracy, Cohen's Kappa, F-Measure, and the area under the precision recall curve (AUC) in a 10 x 10-fold cross-validation plan. For result analysis non-parametric statistical test of hypotheses were employed. A total of 4880 fruit samples from different origins, maturation states, and harvest years were considered. The main conclusion is that the maximum margin classifier outperforms all the others studied ones, including the commonly used partial least squares. The conclusion holds for both a reflectance spectrum with 1024 features and for a 128 subsample of these. An estimate of the out-of-sample performance for the best classifier is also provided.
- Feature discovery in NIR spectroscopy based Rocha pear classificationPublication . Daniel, Mariana; Guerra, Rui Manuel Farinha das Neves; Brazio, António; Rodrigues, Daniela; M. Cavaco, A.; Antunes, Maria Dulce; Valente de Oliveira, JOSÉNon-invasive techniques for automatic fruit classification are gaining importance in the global agro-industry as they allow for optimizing harvesting, storage, management, and distribution decisions. Visible, near infra-red (NIR) diffuse reflectance spectroscopy is one of the most employed techniques in such fruit classification. Typically, after the acquisition of a fruit reflectance spectrum the wavelength domain signal is preprocessed and a classifier is designed. Up to now, little or no work considered the problem of feature generation and selection of the reflectance spectrum. This work aims at filling this gap, by exploiting a feature engineering phase before the classifier. The usual approach where the classifier is fed directly with the reflectances measured at each wavelength is contrasted with the proposed division of the spectra into bands and their characterization in wavelength, frequency, and wavelength-frequency domains. Feature selection is also applied for optimizing efficiency, predictive accuracy, and for mitigating over-training. A total of 3050 Rocha pear samples from different origins and harvest years are considered. Statistical tests of hypotheses on classification results of soluble solids content - a predictor of both fruit sweetness and ripeness - show that the proposed preliminary phase of feature engineering outperforms the usual direct approach both in terms of accuracy and in the number of necessary features. Moreover, the method allows for the identification of features that are physical chemistry meaningful.