Near infrared hyperspectral imaging and spectral unmixing methods for evaluation of fiber distribution in enriched pasta
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...
| Autores principales: | , , , , , , |
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| Formato: | article |
| Lenguaje: | Inglés |
| Publicado: |
Elsevier
2021
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| Materias: | |
| Acceso en línea: | http://hdl.handle.net/20.500.11939/7076 https://www.sciencedirect.com/science/article/abs/pii/S0308814620323797 |
| _version_ | 1855032541290954752 |
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| author | Badaró, Amanda Teixeira Amigo, José M. Blasco, José Aleixos, Nuria Ríos-Ferreira, Amanda Clerici, Maria Teresa Barbin, Douglas Fernandes |
| author_browse | Aleixos, Nuria Amigo, José M. Badaró, Amanda Teixeira Barbin, Douglas Fernandes Blasco, José Clerici, Maria Teresa Ríos-Ferreira, Amanda |
| author_facet | Badaró, Amanda Teixeira Amigo, José M. Blasco, José Aleixos, Nuria Ríos-Ferreira, Amanda Clerici, Maria Teresa Barbin, Douglas Fernandes |
| author_sort | Badaró, Amanda Teixeira |
| collection | ReDivia |
| description | 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. |
| format | article |
| id | ReDivia7076 |
| institution | Instituto Valenciano de Investigaciones Agrarias (IVIA) |
| language | Inglés |
| publishDate | 2021 |
| publishDateRange | 2021 |
| publishDateSort | 2021 |
| publisher | Elsevier |
| publisherStr | Elsevier |
| record_format | dspace |
| spelling | ReDivia70762025-04-25T14:48:08Z Near infrared hyperspectral imaging and spectral unmixing methods for evaluation of fiber distribution in enriched pasta Badaró, Amanda Teixeira Amigo, José M. Blasco, José Aleixos, Nuria Ríos-Ferreira, Amanda Clerici, Maria Teresa Barbin, Douglas Fernandes Hyperspectral imaging NIR Spectral unmixing Multivariate curve resolution Q01 Food science and technology Q04 Food composition Pasta 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. 2021-02-08T09:00:58Z 2021-02-08T09:00:58Z 2021 article acceptedVersion Badaró, A T., Amigo, J. M., Blasco, J., Aleixos, N., Rios-Ferreira, A., Clerici, M. T. & Barbin, D. F. (2021). Near infrared hyperspectral imaging and spectral unmixing methods for evaluation of fiber distribution in enriched pasta. Food Chemistry, 343, 128517. 0308-8146 http://hdl.handle.net/20.500.11939/7076 10.1016/j.foodchem.2020.128517 https://www.sciencedirect.com/science/article/abs/pii/S0308814620323797 en Atribución-NoComercial-SinDerivadas 3.0 España Atribución-NoComercial-SinDerivadas 3.0 España http://creativecommons.org/licenses/by-nc-nd/3.0/es/ openAccess Elsevier electronico |
| spellingShingle | Hyperspectral imaging NIR Spectral unmixing Multivariate curve resolution Q01 Food science and technology Q04 Food composition Pasta Badaró, Amanda Teixeira Amigo, José M. Blasco, José Aleixos, Nuria Ríos-Ferreira, Amanda Clerici, Maria Teresa Barbin, Douglas Fernandes Near infrared hyperspectral imaging and spectral unmixing methods for evaluation of fiber distribution in enriched pasta |
| title | Near infrared hyperspectral imaging and spectral unmixing methods for evaluation of fiber distribution in enriched pasta |
| title_full | Near infrared hyperspectral imaging and spectral unmixing methods for evaluation of fiber distribution in enriched pasta |
| title_fullStr | Near infrared hyperspectral imaging and spectral unmixing methods for evaluation of fiber distribution in enriched pasta |
| title_full_unstemmed | Near infrared hyperspectral imaging and spectral unmixing methods for evaluation of fiber distribution in enriched pasta |
| title_short | Near infrared hyperspectral imaging and spectral unmixing methods for evaluation of fiber distribution in enriched pasta |
| title_sort | near infrared hyperspectral imaging and spectral unmixing methods for evaluation of fiber distribution in enriched pasta |
| topic | Hyperspectral imaging NIR Spectral unmixing Multivariate curve resolution Q01 Food science and technology Q04 Food composition Pasta |
| url | http://hdl.handle.net/20.500.11939/7076 https://www.sciencedirect.com/science/article/abs/pii/S0308814620323797 |
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