Browsing by Author "Guerra, Rui Manuel Farinha das Neves"
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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.
- Detecting early mealybug infestation stages on tomato plants using optical spectroscopyPublication . Canário, David; Figueiredo, E.; Franco, J. C.; Guerra, Rui Manuel Farinha das NevesMealybugs (Hemiptera: Pseudococcidae) are important pests in agricultural and ornamental crops, including the tomato. Damage by mealybugs is characterized by a reduction in plant photosynthesis and growth due to sap feeding and also, as a result of honeydew excretion, from sooty mould development and virus transmission. The effectiveness of mealybug control strategies, including the application of insecticides and biological control, depends on the ability to detect the infestation at an early stage. Monitoring by visual observation is not very effective and is time-consuming. Optical spectroscopy represents a potential tool for detecting plant biotic stresses, including that caused by insect pests. In this study, we tested the feasibility of using optical spectroscopy for the early detection of mealybug infestation of tomato plants. An experiment was carried out using potted plants under field conditions, with 15 replicates per treatment and a randomised design. Two treatments were considered: 1) infested plants inoculated with three mealybug egg masses; and 2) control plants without mealybugs. The distance between pots was kept at 80 cm and the plants were frequently inspected to ensure control plants were not infested with mealybugs. The following parameters were recorded weekly over 5 weeks for each plant: 1) reflectance of marked leaves was measured with a USB4000 spectrometer across the wavelength 400-1,000 nm; 2) plant height; 3) leaf size; 4) mealybug density; and 5) presence and density of other pests. Results of principal component analysis (PCA) second derivative of the leaf reflectance showed a clear distinction between control and infested plants and a separation of components in the near infrared (NIR) region on the last day of the analysis (57 days). The reduction in absorption in the NIR region may be due to an increase in the quantity of air spaces within the leaf's mesophyll, changing the spatial distribution of the leaves' refractive index and, as a consequence, the light scattering contribution to the reflectance spectra. When tracking the evolution of the leaves' absorbance, infested leaves relative to control leaves had a tendency over time to have reduced absorbance in photosystem II and NIR plateau wavelengths. The evolution over time of the reflectance of analysed leaves at each wavelength fitted a quadratic curve, the coefficients of which discriminated between infested and control plants. This methodology has the potential to provide an objective measure of the degree of infestation by pests and the potential impact on the crop.
- Determination of the botanical origin of honey by sensor fusion of impedance e-tongue and optical spectroscopyPublication . Ulloa, P. A.; Guerra, Rui Manuel Farinha das Neves; Cavaco, A. M.; Costa, Ana M. Rosa da; Figueira, A.C.; Fernandes, A.The aim of this study was to discriminate four commercial brands of Portuguese honeys according to their botanical origin by sensor fusion of impedance electronic tongue (e-tongue) and optical spectroscopy (UV–Vis–NIR) assisted by Principal Component Analysis (PCA) and Cluster Analysis (CA). We have also introduced a new technique for variable selection through one-dimensional clustering which proved very useful for data fusion. The results were referenced against standard sample identification by classical melissopalynology analysis. Individual analysis of each technique showed that the e-tongue clearly outperformed the optical techniques. The electronic and optical spectra were fitted to analytical models and the model coefficients were used as new variables for PCA and CA. This approach has improved honey classification by the e-tongue but not by the optical methods. Data from the three techniques was then considered simultaneously. Simple concatenation of all matrices did not improve the classification results. Multi-way PCA (MPCA) proved to be a good option for data fusion yielding 100% classification success. Finally, a variable selection method based on one-dimensional clustering was used to define two new approaches to sensor fusion, and both yielded sample clusters even better defined than using MPCA. In this work we demonstrate for the first time the feasibility of sensor fusion of electronic and optical spectroscopy data and propose a new variable selection method that improved significantly the classification of the samples through multivariate statistical analysis.
- Effect of light quality supplied by light emitting diodes (LEDs) on growth and biochemical profiles of Nannochloropsis oculata and Tetraselmis chuiiPublication . Schulze, Peter S.C.; Pereira, Hugo; Schueler, Lisa; Guerra, Rui Manuel Farinha das Neves; Barreira, Luísa; Perales, Jose A.; Varela, João; Santos, TamaraBiochemical components obtained by microalgal biomass can be induced by specific wavelengths and processed to high value food/feed supplements or pharma- and nutraceuticals. Two biotechnologically relevant microalgae, Nannochloropsis oculata and Tetraselmis chuii, were exposed to non-tailored LEDs light sources emitting either mono- or multichromatic light with low red but significant blue (<450 nm) photon content, or tailored light sources with high blue or high red photon emissions: fluorescent light (FL), di- or multichromatic LED mixes. Growth of N. oculata and T. chuii under tailored light resulted in a approximate to 24% increase of the average biomass productivity as compared to cultures lit by non-tailored light sources. FL induced the highest C:N ratios in both algae (N. oculata: 7.91 +/- 0.09 and T. chuii: 11.29 +/- 0.03), highest total lipid (48.37 +/- 1.07%) in N. oculata and carbohydrate (55.31 +/- 1.02%) in T. chuii biomass. Among non-tailored light sources, monochromatic LEDs with emission peaks 465, 630 and 660 nm induced a approximate to 29% increase of carbohydrates and a approximate to 20% decrease of protein levels as compared to LEDs peaking at 405 nm and cool-and warm white LEDs. In conclusion, as FL have low photon conversion efficiencies (PCE), particularly within the red wavelength range, LEDs emitting at the 390-450 and 630-690 nm wavebands should be combined for optimal carbon fixation, nitrogen and phosphate uptake. (C) 2016 Elsevier B.V. All rights reserved.
- 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).
- 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.
- Flashing LEDs for microalgal productionPublication . Schulze, Peter S.C.; Guerra, Rui Manuel Farinha das Neves; Pereira, Hugo; Lisa Schueler, Lisa M. Schueler; J. C. or Varela J. or Varela J.C.S., VarelaFlashing lights are next-generation tools to mitigate light attenuation and increase the photosynthetic efficiency of microalgal cultivation systems illuminated by light-emitting diodes (LEDs). Optimal flashing light conditions depend on the reaction kinetics and properties of the linear electron transfer chain, energy dissipation, and storage mechanisms of a phototroph. In particular, extremely short and intense light flashes potentially mitigate light attenuation in photobioreactors without impairing photosynthesis. Intelligently controlling flashing light units and selecting electronic components can maximize light emission and energy efficiency. We discuss the biological, physical, and technical properties of flashing lights for algal production. We combine recent findings about photosynthetic pathways, self-shading in photobioreactors, and developments in solid-state technology towards the biotechnological application of LEDs to microalgal production.
- Gene transcripts responsive to drought stress identified in Citrus macrophylla bark tissue transcriptome have a modified response in plants infected by Citrus tristeza virusPublication . da Silva, Melina; Pinto, Patricia IS; Guerra, Rui Manuel Farinha das Neves; Duarte, Amilcar; Power, Deborah; Marques, N T.Citrus macrophylla Wester (CM) has valuable agronomic characteristics such as the ability to grow in saline soils, although with low tolerance to prolonged drought stress (DS). To understand the mechanisms that characterize CM response to water scarcity, this study compared transcriptome profile changes in CM stem tissue when exposed to DS and identified a total of 2745 differentially expressed transcripts (DETs, fold change > 2), of which 631 were up-regulated and 2114 were down-regulated. DETs up-regulated by DS were enriched in pathways such as the redox and osmotic system or soluble carbohydrates and in transcripts for low molecular weight proteins such as late embryogenesis abundant protein (LEA). Down-regulated transcripts were mainly assigned to photosynthesis, transport, phenylpropanoids, calcium dependent kinases, brassinosteroids and other hormones including salicylic acid and abscisic acid. To assess the interplay between DS and Citrus tristeza virus (CTV) infection, twelve genes were profiled by quantitative Real-Time PCR (qPCR) analysis in control and CTV-infected CM plants, with or without DS. The twelve analyzed transcripts were significantly correlated (r = 0.82, p < 0.001) with the RNA-Seq results and gave insight into the responses of CM to drought and/or to infection with CTV. Transcriptome results unveiled highly responsive genes to DS in stem tissue, which may be candidates for genetic selection of high drought tolerant plants of CM.
- Identification of asymptomatic plants infected with Citrus tristeza virus from a time series of leaf spectral characteristicsPublication . Afonso, Andreia; Guerra, Rui Manuel Farinha das Neves; Cavaco, A. M.; Pinto, Patricia IS; Andrade, André; Duarte, Amílcar; Power, Deborah; Marques, N T.Citrus tristeza virus (CTV) affects citrus crops with differing severity, depending on the viral strain, the citrus cultivar and the scion/rootstock combinations. In this study we address the problem of identifying asymptomatic infected plants using reflectance spectra of the leaves in the visible/near infrared region. Sixteen young citrus plants (8 Citrus x clementina hort. ex Tanaka ‘Fina’ and 8 Citrus sinensis (L.) Osbeck ‘Valencia Late’) were split into control and T318A isolate infected groups. Measurements of reflectance in the 400-1100 nm range, in two leaves per plant, were performed monthly over 6 months and the presence of the virus was confirmed by IC/RT-PCR and real-time PCR. The spectra acquired in a single day of measurements was inconsistent for inoculated and control plants. However, by monitoring the same leaves over 6 months it was possible to identify infected plants on the basis of the spectra time evolution. In order to achieve this a simple unfolding implementation of 3-way PCA was applied such that group separation in the scores plot was spontaneous and not forced by any a priori assumption. The model was tested through leave-one-out cross validation with a good rate of correct classification for the left out sample. A real situation was simulated by applying the NPCA algorithm to healthy plants only and checking if the infected ones would be projected on the model scores plot as outliers. Again, a good rate of classification was obtained. Finally, we discuss the spectral features that may be associated with the clustering obtained through NPCA and their physiological significance. Reflectance measurements between infected and healthy samples of two citrus cultivars and their correlation with real-time PCR results for the presence of CTV suggest reflectance spectra of the leaves in the visible/near infrared region is a promising tool for plant stress monitoring linked to the presence of CTV infection prior to symptom expression.
- Non-destructive follow-up of ‘Jintao’ kiwifruit ripening through VIS-NIR spectroscopy – individual vs. average calibration model’s predictionsPublication . Afonso, Andreia M.; Antunes, Maria Dulce; Cruz, Sandra; Cavaco, A. M.; Guerra, Rui Manuel Farinha das NevesVisible/near infrared spectroscopy (Vis-NIRS) was used to monitor the yellow-fleshed kiwifruit (Actinidia chinensis Planch 'Jintao') ripening on two selected orchards along 13 weeks, from pre-harvest to the late harvest. Calibration models for several Internal Quality Attibutes (IQA) were built from the spectral data of 375 individual kiwifruit. The analyzed IQA were L*, a* and b* from the CIELAB color space, hue angle, chroma, firmness, dry matter (DM), soluble solids content (SSC), juice pH and titratable acidity (TA). Different pre-processing methods were tested for the construction of PLS calibration models. SSC and Hue were the best performing models with a correlation coefficient of 0.81 and 0.88, and root mean square error of prediction (RMSEP) of 1.27% and 1.95 degrees, respectively. The interpretation of the models in terms of the known absorption bands and the impact of signal to noise ratio (SNR) in them is discussed. The calibration models were used to perform average predictions of the IQA on orchard subareas, for each day of the experiment. These average predictions were compared with the IQA's average reference values on the same subareas and days. The model's metrics improved significantly through the averaging procedure, with RMSEP = 0.26-0.36% and R-2 = 0.99 for SSC; and RMSEP = 0.42 degrees - 0.56 degrees and R-2 = 1 for Hue. Since orchard management is done essentially through averages and not individual values, this result reinforces the applicability of the NIR technology for follow-up of fruit ripening in the tree.