Near infrared hyperspectral imaging and spectral unmixing methods for evaluation of fiber distribution in enriched pasta

Author(s)

Teixeira Badaro, Amanda ^{[1, 2, 3]} ; Manuel Amigo, Jose ^{[4, 5]} ; Blasco, Jose ^[3] ; Aleixos, Nuria ^[6] ; Ferreira, Amanda Rios ^[6] ; Pedrosa Silva Clerici, Maria Teresa ^[7] ; Barbin, Douglas Fernandes ^[2]

Affiliation

^[1] Univ Politecn Valencia, Dept Tecnol Alimentos, Camino Vera S-N, Valencia 46022 - Spain

^[2] Univ Estadual Campinas, Dept Food Engn, Rua Monteiro Lobato 80, BR-13083862 Campinas, SP - Brazil

^[3] Inst Valenciano Invest Agr IVIA, Ctr Agroingn, Ctra CV-315, Km 10, 7, Valencia 46113 - Spain

^[4] IKERBASQUE, Basque Fdn Sci, Bilbao 48011 - Spain

^[5] Univ Basque Country UPV EHU, Dept Analyt Chem, POB 644, Bilbao 48080, Basque Country - Spain

^[6] Univ Politecn Valencia, Dept Ingn Graf, Camino Vera S-N, Valencia 46022 - Spain

^[7] Univ Estadual Campinas, Dept Food Technol, Rua Monteiro Lobato 80, BR-13083862 Campinas, SP - Brazil

• Document type: Journal article
• Source: Food Chemistry; v. 343, MAY 1 2021.
• Web of Science Citations: 0

Abstract

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. (AU)

• FAPESP's process: 14/50951-4 - INCT 2014: Advanced Analytical Technologies
• Grantee: Celio Pasquini
• Support Opportunities: Research Projects - Thematic Grants
• FAPESP's process: 17/17628-3 - Applications of image analyses and NIR spectroscopy for quality assessment and authentication of food products
• Grantee: Amanda Teixeira Badaró
• Support Opportunities: Scholarships in Brazil - Doctorate (Direct)
• FAPESP's process: 08/57808-1 - National Institute of Advanced Analytical Science and Technology
• Grantee: Celio Pasquini
• Support Opportunities: Research Projects - Thematic Grants
• FAPESP's process: 19/06842-0 - Imaging techniques for determination of pasta composition during cooking
• Grantee: Amanda Teixeira Badaró
• Support Opportunities: Scholarships abroad - Research Internship - Doctorate (Direct)
• FAPESP's process: 15/24351-2 - Applications of image analyses and NIR spectroscopy for quality assessment and authentication of food products
• Grantee: Douglas Fernandes Barbin
• Support Opportunities: Research Grants - Young Investigators Grants