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- A possibility for non-invasive diagnosis of superficial scald in 'Rocha' pear based on chlorophyll a fluorescence, colorimetry, and the relation between alpha-farnesene and conjugated trienolsPublication . Guerra, Rui Manuel Farinha das Neves; Garde, I. V.; Antunes, Maria Dulce; da Silva, J. M.; Antunes, Rosário; Cavaco, A. M.This study aimed to identify physiological markers in superficially scalded 'Rocha' pear (Pyrus communis L 'Rocha') that would relate to chlorophyll a fluorescence (CF), allowing a non-invasive diagnosis of the disorder. Conditions chosen before shelf life provided two fruit groups with different developing patterns and severity of superficial scald: T fruit fully developed the disorder in storage, while C fruit developed it progressively throughout shelf life. Principal component analysis (PCA) of all the measured variables, and simple linear correlations among several major parameters and scald index (SI)/shelf life showed that scald and ripening/aging were concurring processes, and that it was not possible to isolate a particular variable that could deliver a direct non-invasive diagnosis of the disorder. For both fruit groups the SI resulted from the balance between the reducing power (OD200) and the content of conjugated trienols (CTos) and alpha-farnesene (alpha-Farn) in the fruit peel. At OD200 > 150 there was a linear relationship between CTos and OD200, suggesting that the level of antioxidants was self-adjusted in order to compensate the CTos level. However, at OD200 < 150 this relationship disappeared. A consistent linear relationship between dos and alpha-Farn existed throughout shelf life in both fruit groups, contrarily to the early storage stage, when those compounds do not relate linearly. The CF variables F-0, F-v/F-m, and the colorimetric variables, L* and h degrees were used in multi-linear regressions with other physiological variables. The regressions were made on one of the fruit groups and validated through the other. Reliable regressions to alpha-Farn and CTos were obtained (R approximate to 0.6; rmsec approximate to rmsep). Our results suggest that a model based on CF and colorimetric parameters could be used to diagnose non-invasively both the contents and the relationship between alpha-Farn and CTos and hence the stage of scald development. (C) 2011 Elsevier By. All rights reserved.
- Chlorophyll a Fluorescence: a Fast and Low-Cost Tool to Detect Superficial Scald in 'Rocha' Pear (Pyrus communis L. 'Rocha')?Publication . Garde, I.; Antunes, Maria Dulce; da Silva, J. M.; Guerra, Rui Manuel Farinha das Neves; Cavaco, A. M.This study aimed to test whether the chlorophyll a (Chla) fluorescence determined by a low-cost non-modulated fluorometer could provide fast, reliable and non-invasive estimators of superficial scald in 'Rocha' pear (Pyrus communis L. 'Rocha'). Fruit were harvested before the optimal maturation stage and cold stored under normal atmosphere for 7 months (NA: 0 degrees C, 90-95% RH) and 2 in controlled atmosphere (CA: 0 degrees C, 90-95% RH, 1.5 kPaO(2) + 0.5 kPa CO2) (T), or harvested at the optimal maturation stage and cold stored for 9 months under CA (C). Then, they were transferred to shelf-life conditions (22+/-2 degrees C, 70% RH) and followed for 7 d. Chla fluorescence, scald index (SI), ripening attributes, alpha-farnesene, conjugated trienols, and photosynthetic pigments were determined for each pear in both groups. Conditions chosen before shelf-life did not prevent the subsequent ripening of any fruit, but changed dramatically the superficial scald development pattern: in C fruit, the disorder developed progressively during shelf-life, whereas in T fruit, it peaked during storage. C fruit exhibited a significant negative correlation (R=-0.65; p<0.05) between Fv/Fm and scald development, but not with ripening (R=-0.15; p<0.05). As expected, the opposite was observed in T fruit, in which only a low, positive, yet significant correlation was found between Fv/Fm and ripening (R=0.44; p<0.05). The multiple regression approach using Fv/Fm and other Chla fluorescence parameters produced an equation from which we calculated the 'predicted' scald index in C fruit. This correlated clearly (R=0.73; p<0.05) with the real values visually assessed. If color values a*, b* and Hue were included in this multiple regression, the correlation was significantly enhanced (0.91; p<0.05). Although preliminary, this study has shown that basic Chla fluorescence parameters are valuable estimators of superficial scald in 'Rocha' pear and might be used in the early detection of the disorder.
- 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).
- 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.
- On the application of spatially resolved reflectance and diffuse light backscattering goniometry to the prediction of firmness in apple ‘bravo de esmolfe’Publication . Guerra, Rui Manuel Farinha das Neves; Almeida, Sandro; Cavaco, A. M.; Antunes, Maria Dulce; Nunes, CarlaIn this study we have made exploratory tests on a set of 40 apples (Malus domestica Borkh.) ‘Bravo de Esmolfe’, using spatially resolved reflectance (SRR) and diffuse light backscattering goniometry (DLBG). The objective was to test the potential of DLBG for firmness prediction, as compared with SRR, whose potential has been already proved in the literature. SRR is performed with a red diode laser and a CMOS camera. DLBG uses the same laser shining on the apple and a photomultiplier tube collecting the light reemitted from a small area, at angles ranging from 90 deg (tangent to the surface) to 180 deg (normal to the surface). From the measurements several parameters have been calculated (e.g. decay exponent for SRR profiles, anisotropy factor for the DLBG angular distributions) and Partial Least squares (PLS) models for the prediction of firmness were build. The model based on DLBG variables (only) and on SRR variables (only) gave similar results. From here we conclude that, within the obvious statistical limitations of the test, DLBG seems to match the potential of SRR for firmness prediction. The possibility of combining both measures in one model is also discussed.
- 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.
- Preliminary Results on the Non-Destructive Determination of Pear (Pyrus communis L.) cv. Rocha Ripeness by Visible/Near Infrared Reflectance SpectroscopyPublication . Cavaco, A. M.; Antunes, Maria Dulce; da Silva, J. M.; Guerra, Rui Manuel Farinha das NevesPear (Pyrus communis L.), cv. Rocha was rapidly adopted by consumers due to its inherent quality and currently has great acceptance in both national and international markets, being mainly produced in the west region of Portugal. We report here a first approach to the use of the non-intrusive method of Visible/Near Infrared Reflectance Spectroscopy (Vis/NIRS) to estimate the ripeness of pear cv. Rocha. Mature unripe pears obtained from Frutoeste (Mafra, Portugal) after a six-month cold-storage, were maintained in a dark room at circa 20 degrees C during three weeks. They were followed using the Vis/NIRS in the wavelength band between 400 and 950 nm with two different configurations for the spectra acquisition, namely the Integrating Sphere (IS) and the Partial Transmittance (PT). The diffuse reflectance spectra obtained by the two configurations were compared with the respective fruit ripening parameters (colour, firmness, soluble solids content and % dry matter), determined through the standard techniques. Concerning the rough estimation of ripening parameters, data suggested an increase in both the intensity in the green to red band and pulp %dry matter, but a decreasing firmness. All other parameters remained constant. Relatively to the optical results, we have observed that the PT spectra exhibited clearer features than the IS spectra, especially from 700 nm onwards. This is probably due to the fact that the PT configuration probes more deeply into the fruit pulp. Three peaks at 600 (circa 30%), 725 and 812 nm (both at circa 50%) and a minimum at 675 nm, were identified in both IS and PT spectra. The values of reflectance peaks were approximately constant during ripening, but they moved to slightly lower wavelengths in the second week. A significant increase (circa 3-fold) in the minimal diffuse reflectance was observed in the second week, most probably associated partially, to a decrease in the fruit peel chlorophyll content.
- A Preliminary Approach to the Prediction of 'Rocha' Pear Skin Pigments by Vis/NIR Reflectance SpectroscopyPublication . Cavaco, A. M.; Antunes, Maria Dulce; da Silva, J. M.; Guerra, Rui Manuel Farinha das Neves'Rocha' pear (Pyrus communis L.) is an exclusively Portuguese certified pear cultivar commercialized worldwide. Mature unripe 'Rocha' pears were obtained from COOPVAL (Cadaval, Portugal) after 8 months at -0.5 degrees C, 94-96% RH and CA (2% O-2+0.5% CO2). Then, they were maintained in a dark room at 20+/-2 degrees C and 70% RH to simulate shelf life. For three weeks these fruit were followed along using Vis/NIR reflectance spectroscopy in the wavelength range of 400 to 950 nm, and their colour and firmness were evaluated by standard techniques. 'Rocha' pear firmness decreased significantly during shelf life, paralleled by the yellowing of the fruit skin (increase in a(star) and Hue angle). Pigments were extracted from fruit skin and assayed spectrophotometrically. Both Chla and Chlb contents decreased along ripening in shelf life, while contents of carotenoids remained constant. Vis/NIR reflectance spectra were correlated with the respective fruit skin pigments content by PLS. Prediction models were obtained for Chl (a, b, a+b), but not for carotenoids. Models were reasonably significant in regression terms [r(Chla)=0.898; r(Chlb)=0.897; r(Chla+b)=0.918], but the respective SDR (standard deviation ratio = standard deviation of the validation set/RMSEP)(2.2 (Chla), 2.3 (Chlb), 2.2 (Chla+b)) suggest that only a coarse quantitative prediction is possible for all models. Although Chla model required a higher number of latent variables [Lv(Chla)= 6; Lv(Chlb or Chla+b)= 3], similarity between RMSEC and RMSEP was lower for the other parameters [Chla: 4.6 and 4.6 g m(-2) Chlb: 3.9 and 2.3 g m(-2), Chla+b: 7.3 and 6.7 g m(-2)]. A better performance for these models has been expected, because most of the differences found in the Vis/NIR spectra in shelf life were in the Chl absorption region. However, only a coarse prediction capability was found. Thus, the data obtained suggest that changes on the background around 670 nm decrease the prediction capability of the PLS model and should be further investigated.
- Making sense of light: the use of optical spectroscopy techniques in plant sciences and agriculturePublication . Cavaco, A. M.; Utkin, Andrei B.; Marques da Silva, Jorge; Guerra, RuiAs a result of the development of non-invasive optical spectroscopy, the number of prospective technologies of plant monitoring is growing. Being implemented in devices with different functions and hardware, these technologies are increasingly using the most advanced data processing algorithms, including machine learning and more available computing power each time. Optical spectroscopy is widely used to evaluate plant tissues, diagnose crops, and study the response of plants to biotic and abiotic stress. Spectral methods can also assist in remote and non-invasive assessment of the physiology of photosynthetic biofilms and the impact of plant species on biodiversity and ecosystem stability. The emergence of high-throughput technologies for plant phenotyping and the accompanying need for methods for rapid and non-contact assessment of plant productivity has generated renewed interest in the application of optical spectroscopy in fundamental plant sciences and agriculture. In this perspective paper, starting with a brief overview of the scientific and technological backgrounds of optical spectroscopy and current mainstream techniques and applications, we foresee the future development of this family of optical spectroscopic methodologies.
- 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.