Near infrared hyperspectral imaging and spectral unmixing methods for evaluation of fiber distribution in enriched pasta. (1st May 2021)

• Record Type: Journal Article
• Authors: Badaró, Amanda Teixeira; Amigo, José Manuel; Blasco, Jose; Aleixos, Nuria; Ferreira, Amanda Rios; Clerici, Maria Teresa Pedrosa Silva; Barbin, Douglas Fernandes
• Is Part Of: Food chemistry. Volume 343(2021)
• Publication Date: 2021-05-01
• Subjects: Pasta -- Hyperspectral imaging -- NIR -- Spectral unmixing -- Multivariate curve resolution; Food -- Analysis -- Periodicals; Food -- Composition -- Periodicals; 664
• DOI: 10.1016/j.foodchem.2020.128517
• Languages: English
• ISSNs: 0308-8146

Abstract

Highlights: Fiber distribution was evaluated in enriched pasta. Fiber content was predicted in enriched pasta samples. Concentration maps were developed as a tool to monitor the homogeneity in samples. Pasta was authenticated by hyperspectral imaging and spectral unmixing method. 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 R 2 V 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.