Quality Characterization of Fava Bean-Fortified Bread Using Hyperspectral Imaging

• Published in: Foods Vol. 13; no. 2; p. 231
• Main Authors: Olakanmi, Sunday J, Jayas, Digvir S, Paliwal, Jitendra, Chaudhry, Muhammad Mudassir Arif, Findlay, Catherine Rui Jin
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 01.01.2024
• Subjects: Amino acids; Baked goods; Baked products; Bakery products; Beans; Bread; Broad beans; Cereals; Classification; Color; Discriminant analysis; Ethylenediaminetetraacetic acid; Flour; Food products; fortified bread; Gluten; Humidity; Hyperspectral imaging; Infrared analysis; Least squares; Legumes; Manufacturing; Mimosaceae; Moisture content; Multivariate analysis; prediction; Principal components analysis; Protein composition; Protein sources; Proteins; quality inspection; Short wave radiation; Wheat; Canada; Finland; United States > US; Switzerland; prediction; quality inspection; fortified bread; classification; hyperspectral imaging
• ISSN: 2304-8158; 2304-8158
• DOI: 10.3390/foods13020231

As the demand for alternative protein sources and nutritional improvement in baked goods grows, integrating legume-based ingredients, such as fava beans, into wheat flour presents an innovative alternative. This study investigates the potential of hyperspectral imaging (HSI) to predict the protein content (short-wave infrared (SWIR) range)) of fava bean-fortified bread and classify them based on their color characteristics (visible-near-infrared (Vis-NIR) range). Different multivariate analysis tools, such as principal component analysis (PCA), partial least square discriminant analysis (PLS-DA), and partial least square regression (PLSR), were utilized to assess the protein distribution and color quality parameters of bread samples. The result of the PLS-DA in the SWIR range yielded a classification accuracy of ˃99%, successfully classifying the samples based on their protein contents (low protein and high protein). The PLSR model showed an RMSEC of 0.086% and an RMSECV of 0.094%. Also, the external validation resulted in an RMSEP of 0.064%. The PLSR model possessed the capability to efficiently predict the protein content of the bread samples. The results suggest that HSI can be successfully used to classify bread samples based on their protein content and for the prediction of protein composition. Hyperspectral imaging can therefore be reliably implemented for the quality monitoring of baked goods in commercial bakeries.