Near infrared hyperspectral imaging and spectral unmixing methods for evaluation of fiber distribution in enriched pasta
| Autor: | José Blasco, Maria Teresa Pedrosa Silva Clerici, Nuria Aleixos, Douglas Fernandes Barbin, Amanda Teixeira Badaró, José Manuel Amigo, Amanda Rios Ferreira |
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| Rok vydání: | 2021 |
| Předmět: |
Dietary Fiber
Materials science EXPRESION GRAFICA EN LA INGENIERIA Hyperspectral imaging Pasta Flour Wheat flour 01 natural sciences Least squares Analytical Chemistry Q04 Food composition 0404 agricultural biotechnology Fiber Least-Squares Analysis Near infrared hyperspectral imaging Triticum Multivariate curve resolution Spectroscopy Near-Infrared 010401 analytical chemistry Water 04 agricultural and veterinary sciences General Medicine Hyperspectral Imaging NIR Q01 Food science and technology 040401 food science 0104 chemical sciences Biological system Food Analysis Spectral unmixing Food Science |
| Zdroj: | electronico ReDivia. Repositorio Digital del Instituto Valenciano de Investigaciones Agrarias instname ReDivia: Repositorio Digital del Instituto Valenciano de Investigaciones Agrarias Instituto Valenciano de Investigaciones Agrarias (IVIA) RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia |
| Popis: | [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. 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. |
| Databáze: | OpenAIRE |
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