Teixeira Badaró, A.; Amigo, JM.; Blasco, J.; Aleixos Borrás, MN.; Rios Ferreira, A.; Pedrosa Silva Clerici, MT.; Fernandes Barbin, D. (2021). Near infrared hyperspectral imaging and spectral unmixing methods for evaluation of fiber distribution in enriched pasta. Food Chemistry. 343:1-9. https://doi.org/10.1016/j.foodchem.2020.128517
Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/176260
Título:
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Near infrared hyperspectral imaging and spectral unmixing methods for evaluation of fiber distribution in enriched pasta
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Autor:
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Teixeira Badaró, Amanda
Amigo, José Manuel
Blasco, Jose
Aleixos Borrás, María Nuria
Rios Ferreira, Amanda
Pedrosa Silva Clerici, Maria Teresa
Fernandes Barbin, Douglas
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Entidad UPV:
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Universitat Politècnica de València. Departamento de Mecanización y Tecnología Agraria - Departament de Mecanització i Tecnologia Agrària
Universitat Politècnica de València. Departamento de Ingeniería Gráfica - Departament d'Enginyeria Gràfica
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Fecha difusión:
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Resumen:
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[EN] Pasta is mostly composed by wheat flour and water. Nevertheless, flour can be partially replaced by fibers to provide extra nutrients in the diet. However, fiber can affect the technological quality of pasta if not ...[+]
[EN] Pasta is mostly composed by wheat flour and water. Nevertheless, flour can be partially replaced by fibers to provide extra nutrients in the diet. However, fiber can affect the technological quality of pasta if not properly distributed. Usually, determinations of parameters in pasta are destructive and time-consuming. The use of Near Infrared-Hyperspectral Imaging (NIR-HSI), together with machine learning methods, is valuable to improve the efficiency in the assessment of pasta quality. This work aimed to investigate the ability of NIR-HSI and augmented Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) for the evaluation, resolution and quantification of fiber distribution in enriched pasta. Results showed R2V between 0.28 and 0.89, %LOF < 6%, variance explained over 99%, and similarity between pure and recovered spectra over 96% and 98% in models using pure flour and control as initial estimates, respectively, demonstrating the applicability of NIR-HSI and MCR-ALS in the identification of fiber in pasta.
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Palabras clave:
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Pasta
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Hyperspectral imaging
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NIR
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Spectral unmixing
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Multivariate curve resolution
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Derechos de uso:
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Reconocimiento - No comercial - Sin obra derivada (by-nc-nd)
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Fuente:
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Food Chemistry. (issn:
0308-8146
)
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DOI:
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10.1016/j.foodchem.2020.128517
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Editorial:
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Elsevier
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Versión del editor:
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https://doi.org/10.1016/j.foodchem.2020.128517
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Código del Proyecto:
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info:eu-repo/grantAgreement/FAPESP//2008%2F57808-1/
...[+]
info:eu-repo/grantAgreement/FAPESP//2008%2F57808-1/
info:eu-repo/grantAgreement/FAPESP//2014%2F50951-4/
info:eu-repo/grantAgreement/FAPESP//2015%2F24351-2/
info:eu-repo/grantAgreement/FAPESP//2017%2F17628-3/
info:eu-repo/grantAgreement/FAPESP//2019%2F06842-0/
info:eu-repo/grantAgreement/IVIA//51918/
info:eu-repo/grantAgreement/CAPES//001/
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Agradecimientos:
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This work was supported by the Coordenaçao de Aperfeicoamento de Pessoal de Nivel Superior -Brasil (CAPES) [Finance Code 001]; Sao Paulo Research Foundation (FAPESP) [grant numbers 2008/57808-1, 2014/50951-4, 2015/24351-2, ...[+]
This work was supported by the Coordenaçao de Aperfeicoamento de Pessoal de Nivel Superior -Brasil (CAPES) [Finance Code 001]; Sao Paulo Research Foundation (FAPESP) [grant numbers 2008/57808-1, 2014/50951-4, 2015/24351-2, 2017/17628-3, 2019/06842-0]; and by GVA-IVIA and FEDER funds through project IVIA-51918. The authors would like to thank Nutrassim Food Ingredients company for the donation of the fiber samples, the support provided by Mrs. Cristiane Vidal during NIR-HSI system operation and data processing and Dr. Celio Pasquini for promptly receiving us in the laboratory that he coordinates (Grupo de instrumentaçao e automaçao em quimica analitica, Instituto de quimica, Universidade Estadual de Campinas, Campinas-SP, Brazil) to data acquisition.
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Tipo:
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Artículo
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