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- Estimation of soluble solids content and fruit temperature in 'rocha' pear using Vis-NIR spectroscopy and the spectraNet–32 deep learning architecturePublication . Martins, J. A; Rodrigues, Daniela; Cavaco, A. M.; Antunes, Maria Dulce; Guerra, Rui Manuel Farinha das NevesSpectra-based methods are becoming increasingly important in Precision Agriculture as they offer non-destructive, quick tools for measuring the quality of produce. This study introduces a novel approach for esti-mating the soluble solids content (SSC) of 'Rocha' pears using the SpectraNet-32 deep learning architecture, which operates on 1D fruit spectra in the visible to near-infrared region (Vis-NIRS). This method was also able to estimate fruit temperatures, which improved the SSC prediction performance. The dataset consisted of 3300 spectra from 1650 'Rocha' pears collected from local markets over several weeks during the 2010 and 2011 seasons, which had varying edaphoclimatic conditions. Two types of partial least squares (PLS) feature selection methods, under various configurations, were applied to the input spectra to identify the most significant wavelengths for training SpectraNet-32. The model's robustness was also compared to a similar state-of-the-art deep learning architecture, DeepSpectra, as well as four other classical machine learning algorithms: PLS, multiple linear regression (MLR), support vector machine (SVM), and multi-layer perceptron (MLP). In total, 23 different experimental method configurations were assessed, with 150 neural networks each. SpectraNet-32 consistently outperformed other methods in several metrics. On average, it was 6.1% better than PLS in terms of the root mean square error of prediction (RMSEP, 1.08 vs. 1.15%), 7.7% better in prediction gain (PG, 1.67 vs. 1.55), 3.6% better in the coefficient of determination (R2, 0.58 vs. 0.56) and 5.8% better in the coefficient of variation (CV%, 8.35 vs. 8.86).
- Avaliação das propriedades anti-radicalares de frutos e legumesPublication . Rodrigues, Daniela; Antunes, Maria Dulce
- 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.
- 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.