Identification of fiber added to semolina by near infrared (NIR) spectral techniques

•We identify different fibers using spectroscopy techniques.•Hyperspectral imaging was useful for classification of semolina with fiber.•Prediction of fiber content in semolina was performed by regression models of raw spectra.•Chemical maps provided visual distribution of fiber in semolina. Ingredi...

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Bibliographic Details
Published in:Food chemistry 2019-08, Vol.289, p.195-203
Main Authors: Badaró, Amanda Teixeira, Morimitsu, Fernanda Lie, Ferreira, Amanda Rios, Clerici, Maria Teresa Pedrosa Silva, Fernandes Barbin, Douglas
Format: Article
Language:English
Subjects:
Fiber
Hyperspectral imaging
Near infrared spectroscopy
Semolina
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Summary:•We identify different fibers using spectroscopy techniques.•Hyperspectral imaging was useful for classification of semolina with fiber.•Prediction of fiber content in semolina was performed by regression models of raw spectra.•Chemical maps provided visual distribution of fiber in semolina. Ingredients added in food products can increase the nutritional value, but also affect their functional properties. After processing, determination of added ingredients is difficult, thus it is important to develop rapid techniques for quantification of food ingredients. In the current work, near infrared spectroscopy (NIRS) and hyperspectral imaging (NIR-HSI) were investigated to quantify the amount of fiber added to semolina and its distribution. NIR spectra were acquired to compare the accuracy in the classification, quantification and distribution of fibers added to semolina. Principal Component Analyses (PCA) and Soft Independent Modeling of Class Analogy (SIMCA) were used for classification. Partial Least Squares Regression (PLSR) models applied to NIR-HSI spectra showed R2P between 0.85 and 0.98, and RMSEP between 0.5 and 1%, and were used for prediction map of the samples. These results showed that NIR-HSI technique can be used for the identification and quantification of fiber added to semolina.
ISSN:0308-8146
1873-7072
DOI:10.1016/j.foodchem.2019.03.057