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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.
- Nutritional quality changes of fresh-cut tomato during shelf lifePublication . Antunes, Maria Dulce; Rodrigues, Daniela; Pantazis, V.; Cavaco, A. M.; Siomos, A.; Miguel, Maria GraçaEffects of dip treatments on nutritional quality preservation during the shelf life of fresh-cut tomato (Licopersicum esculentum Mill.) cv. Eufrates were investigated. Fresh-cut tomatoes were dipped in solutions of 2% ascorbic acid, citric acid, and calcium lactate for 2 min, then stored at 4°C for 20 days. Color (L*, a*, and b*), firmness, °Brix, phenolics, ascorbic acid content, antioxidant activity (DPPH), and sugars were measured during storage. Pathogen development was monitored, and a sensory evaluation was performed. Ascorbic acid was better in maintaining firmness. No treatments significantly affected °Brix, color, or sugars. Ascorbic acid maintained a higher antioxidant capacity, phenolics, and ascorbic acid content, and was better at reducing bacterial growth, while citric acid treatment was better at prevention of yeast and molds proliferation. Fresh-cut tomatoes showed good quality after 10 days of shelf life, except for flavor with the calcium lactate treatment. Ascorbic acid treatment better preserved the general and nutritional quality parameters. © 2013 The Korean Society of Food Science and Technology and Springer Science+Business Media Dordrecht.
- Non-destructive soluble solids content determination for ‘Rocha’ Pear Based on VIS-SWNIR spectroscopy under ‘Real World’ sorting facility conditionsPublication . Passos, Dário; Rodrigues, Daniela; Cavaco, Ana M.; Antunes, Maria Dulce; Guerra, Rui Manuel Farinha das NevesIn this paper we report a method to determine the soluble solids content (SSC) of 'Rocha' pear (Pyrus communis L. cv. Rocha) based on their short-wave NIR reflectance spectra (500-1100 nm) measured in conditions similar to those found in packinghouse fruit sorting facilities. We obtained 3300 reflectance spectra from pears acquired from different lots, producers and with diverse storage times and ripening stages. The macroscopic properties of the pears, such as size, temperature and SSC were measured under controlled laboratory conditions. For the spectral analysis, we implemented a computational pipeline that incorporates multiple pre-processing techniques including a feature selection procedure, various multivariate regression models and three different validation strategies. This benchmark allowed us to find the best model/preproccesing procedure for SSC prediction from our data. From the several calibration models tested, we have found that Support Vector Machines provides the best predictions metrics with an RMSEP of around 0.82 ∘ Brix and 1.09 ∘ Brix for internal and external validation strategies respectively. The latter validation was implemented to assess the prediction accuracy of this calibration method under more 'real world-like' conditions. We also show that incorporating information about the fruit temperature and size to the calibration models improves SSC predictability. Our results indicate that the methodology presented here could be implemented in existing packinghouse facilities for single fruit SSC characterization.