Browsing by Subject "Spectroscopy"

Now showing 1 - 4 of 4
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    Bibliometric insights into the spectroscopy research field: a food science and technology case study
    (Taylor and Francis, 2020) Aleixandre Tudó, José Luis; Castelló-Cogollos, Lourdes; Aleixandre, José Luis; Aleixandre Benavent, Rafael; Información y Documentación
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    Open Access
    Convolutional neural networks for estimating the ripening state of fuji apples using visible and near-infrared spectroscopy
    (Springer , 2022-07-18) Benmouna, Brahim; García Mateos, Ginés; Sabzi, Sajad; Fernández Beltrán, Rubén; Parras Burgos, Dolores; Molina Martínez, José Miguel; Informática y Sistemas; Facultades de la UMU::Facultad de Informática
    The quality of fresh apple fruits is a major concern for consumers and manufacturers. Classification of these fruits according to their ripening stage is one of the most decisive factors in determining their quality. In this regard, the aim of this work is to develop a new method for non-destructive classification of the ripening state of Fuji apples using hyperspectral information in the visible and near-infrared (Vis/NIR) regions. Spectra of 172 apple samples in the range from 450 to 1000 nm were studied, which were selected from four different ripening stages. A convolutional neural network (CNN) model was proposed to perform the classification of the samples. The proposed method was compared with three alternative methods based on artificial neural networks (ANN), support vector machines (SVM), and k-nearest neighbors (KNN). The results revealed that the CNN method outperformed the alternative methods, achieving a correct classification rate (CCR) of 96.5%, compared with an average of 89.5%, 95.93%, and 91.68% for ANN, SVM, and KNN, respectively. These results will help in the development of a new device for fast and accurate estimation of the quality of apples.
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    Origin, accumulation and fate of dissolved organic matter in an extreme hypersaline shallow lake
    (Elsevier, 2022) Butturini, Andrea; Herzsprung, Peter; Lechtenfeld, O.J.; Alcorlo, Paloma; Benaiges-Fernandez, Robert; Berlanga, Mercedes; Boadella, Judit; Freixinos Campillo, Zeus; Gómez, Rosa; Sánchez-Montoya, María del Mar; Urmeneta, Jordi; Romaní, Anna; Ecología e Hidrología
    Hypersaline endorheic aquatic systems (H-SEAS) are lakes/shallow playas in arid and semiarid regions that undergo extreme oscillations in salinity and severe drought episodes. Although their geochemical uniqueness and microbiome have been deeply studied, very little is known about the availability and quality of dissolved organic matter (DOM) in the water column.. A H-SEAS from the Monegros Desert (Zaragoza, NE Spain) was studied during a hydrological wetting-drying-rewetting cycle. DOM analysis included: (i) a dissolved organic carbon (DOC) mass balance; (ii) spectroscopy (absorbance and fluorescence) and (iii) a molecular characterization with Fourier-transform ion cyclotron resonance mass spectrometry (FT-ICR-MS). The studied system stored a large amount of DOC and under the highest salinity conditions, salt-saturated waters (i.e., brines with salinity > 30%) accumulated a disproportionate quantity of DOC, indicating a significant in-situ net DOM production. Simultaneously, during the hydrological transition from wet to dry, the DOM pool showed strong alterations of it molecular composition. Spectroscopic methods indicated that aromatic and degraded DOM was rapidly replaced by fresher, relatively small, microbial-derived moieties with a large C/N ratio. FT-ICR-MS highlighted the accumulation of small, saturated and oxidized molecules (molecular O/C > 0.5), with a remarkable increase in the relative contribution of highly oxygenated (molecular O/C>0.9) compounds and a decrease of aliphatic and carboxyl-rich alicyclic moleculesThese results indicated that H-SEAS are extremely active in accumulating and processing DOM, with the notable release of organic solutes probably originated from decaying microplankton under large osmotic stress at extremely high salinities.
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    Open Access
    Using metaheuristic algorithms to improve the estimation of acidity in Fuji apples using NIR spectroscopy
    (Elsevier , 2022-11-01) Pourdarbani, Razieh; Sabzi, Sajad; Rohban, Mohammad H.; García Mateos, Ginés; Paliwal, Jitendra; Molina‑Martínez, José Miguel; Informática y Sistemas; Facultades de la UMU::Facultad de Informática
    This study focuses on the spectrochemical estimation of pH and titratable acidity (TA) of apples of Fuji variety at different stages of ripening. A novel approach is proposed for near-infrared (NIR) spectral analysis using hybrid machine learning methods that combine artificial neural networks (ANN) and metaheuristic algorithms. One hundred twenty samples were collected at three ripening stages and spectral data within two bands of NIR were extracted from each sample to predict the acidity properties. Alternatively, the 4 most effective wavelengths were also selected using a hybrid of ANN and the cultural algorithm. The experimental results prove that the models using spectral bands and the 4 most effective wavelengths are comparable, with a correlation coefficient, R, of 0.926 for the prediction of pH and 0.925 for TA using spectral bands, while for the second approach the R obtained were 0.924 and 0.920 for pH and TA, respectively. The models could not accurately predict extremely high or low pH and TA values, due to the clusters that formed after regression. However, for a classification problem in low/high acidity, both approaches were able to achieve a high accuracy of 100% for pH and 99.2% for TA.