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139 results on '"Amigo, Jose Manuel"'

4. HYPERCam v1: Hyperspectral Camera Based on a Low-Cost Hardware and Software Architecture for Food Analysis Through Image Processing

Author

Hernandez-Jayo, Unai, Martin, Haizea, Amigo, Jose Manuel, Garcia-Zubia, Javier, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Auer, Michael E., editor, El-Seoud, Samir A., editor, and Karam, Omar H., editor

Published
2023
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19. Radial Textures: A New Approach to Analyze Meat Quality by Using MRI

Author

Caballero, Daniel, Caro, Andrés, Amigo, José Manuel, Ávila, Mar, Antequera, Teresa, Pérez-Palacios, Trinidad, Hutchison, David, Editorial Board Member, Kanade, Takeo, Editorial Board Member, Kittler, Josef, Editorial Board Member, Kleinberg, Jon M., Editorial Board Member, Mattern, Friedemann, Editorial Board Member, Mitchell, John C., Editorial Board Member, Naor, Moni, Editorial Board Member, Pandu Rangan, C., Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Terzopoulos, Demetri, Editorial Board Member, Tygar, Doug, Editorial Board Member, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Vera-Rodriguez, Ruben, editor, Fierrez, Julian, editor, and Morales, Aythami, editor

Published
2019
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23. Unlocking Preclinical Alzheimer's: A Multi-Year Label-Free In Vitro Raman Spectroscopy Study Empowered by Chemometrics.

Author

Lopez, Eneko, Etxebarria-Elezgarai, Jaione, García-Sebastián, Maite, Altuna, Miren, Ecay-Torres, Mirian, Estanga, Ainara, Tainta, Mikel, López, Carolina, Martínez-Lage, Pablo, Amigo, Jose Manuel, and Seifert, Andreas

Subjects
ALZHEIMER'S disease, AUTOPSY, CHEMOMETRICS, NEURODEGENERATION, CEREBROSPINAL fluid, RAMAN spectroscopy
Abstract

Alzheimer's disease is a progressive neurodegenerative disorder, the early detection of which is crucial for timely intervention and enrollment in clinical trials. However, the preclinical diagnosis of Alzheimer's encounters difficulties with gold-standard methods. The current definitive diagnosis of Alzheimer's still relies on expensive instrumentation and post-mortem histological examinations. Here, we explore label-free Raman spectroscopy with machine learning as an alternative to preclinical Alzheimer's diagnosis. A special feature of this study is the inclusion of patient samples from different cohorts, sampled and measured in different years. To develop reliable classification models, partial least squares discriminant analysis in combination with variable selection methods identified discriminative molecules, including nucleic acids, amino acids, proteins, and carbohydrates such as taurine/hypotaurine and guanine, when applied to Raman spectra taken from dried samples of cerebrospinal fluid. The robustness of the model is remarkable, as the discriminative molecules could be identified in different cohorts and years. A unified model notably classifies preclinical Alzheimer's, which is particularly surprising because of Raman spectroscopy's high sensitivity regarding different measurement conditions. The presented results demonstrate the capability of Raman spectroscopy to detect preclinical Alzheimer's disease for the first time and offer invaluable opportunities for future clinical applications and diagnostic methods. [ABSTRACT FROM AUTHOR]

Published
2024
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24. Development of a New Fractal Algorithm to Predict Quality Traits of MRI Loins

Author

Caballero, Daniel, Caro, Andrés, Amigo, José Manuel, Dahl, Anders B., Ersbøll, Bjarne K., Pérez-Palacios, Trinidad, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Felsberg, Michael, editor, Heyden, Anders, editor, and Krüger, Norbert, editor

Published
2017
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39. Characterization of pyrite weathering products by Raman hyperspectral imaging and chemometrics techniques

Author

Muñoz, E, Gosetti, F, Ballabio, D, Ando, S, Gómez-Laserna, O, Amigo, J, Garzanti, E, Muñoz, Enmanuel Cruz, Gosetti, Fabio, Ballabio, Davide, Ando, Sergio, Gómez-Laserna, Olivia, Amigo, Jose Manuel, Garzanti, Eduardo, Muñoz, E, Gosetti, F, Ballabio, D, Ando, S, Gómez-Laserna, O, Amigo, J, Garzanti, E, Muñoz, Enmanuel Cruz, Gosetti, Fabio, Ballabio, Davide, Ando, Sergio, Gómez-Laserna, Olivia, Amigo, Jose Manuel, and Garzanti, Eduardo

Abstract

In this study, we propose a new approach based on Raman hyperspectral imaging and chemometrics for the analysis of chemically heterogeneous surfaces of weathered minerals. A pyrite sample showing a heterogeneous surface with different alteration products, is used to test the validity of the technique. Principal Component Analysis (PCA) was initially used to evaluate data structure and identify Raman phases and vibrational modes related to minor and major weathering features. Afterwards, Multivariate Curve Resolution-alternating least squares (MCR-ALS) was carried out to identify the specific chemical components of the major weathering phases. Finally, for the analysis of the minor weathering phases, K-means clustering was carried out to identify groups of similar pixels of low percentage of variance in the image. Three major components are found in the analyzed sample, corresponding to pyrite and hematite vibrational modes, as well as features of an amorphous alteration product patina. Four additional spectral signatures revealing the presence of sulfates were found after the analysis of minor components. The proposed method enables a semi-quantitative threshold-based characterization of chemical features and provides a visual representation of the phase distribution on the surface of the sample.

Published
2023

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