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Hydration of hydrogels studied by near‐infrared hyperspectral imaging
Author(s) -
Caponigro Vicky,
Marini Federico,
Gowen Aoife
Publication year - 2018
Publication title -
journal of chemometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.47
H-Index - 92
eISSN - 1099-128X
pISSN - 0886-9383
DOI - 10.1002/cem.2972
Subject(s) - hyperspectral imaging , partial least squares regression , self healing hydrogels , absorbance , near infrared spectroscopy , water content , chemical imaging , biological system , materials science , moisture , chemometrics , dehydration , analytical chemistry (journal) , chemistry , chromatography , optics , computer science , artificial intelligence , composite material , machine learning , biochemistry , physics , geotechnical engineering , polymer chemistry , biology , engineering
Hydrogels are an important class of biomaterials that can absorb large quantities of water. In this study, changes in hydration of natural hydrogels (agar, chitosan, gelatin, starch, and blends of each with chitosan) during storage and rehydration were studied by using near‐infrared hyperspectral imaging (NIR‐HSI). Moisture content was calculated based on changes in sample weight during hydration. The NIR‐HSI data were acquired by using a push‐broom system operating in diffuse reflectance in the wavelength range 943 to 1650 nm. A novel synthesis method was developed to enable common preparation of each hydrogel. Mean spectra obtained from the hyperspectral images were analyzed, and predictive models for moisture content were developed by using partial least squares regression. Models were compared in predictive performance by using an independent validation set of data. The optimal model in predictive performance was a 1 latent variable partial least squares regression model developed on second derivative and mean centered pseudo‐absorbance data in the wavelength range 943 to 1272 nm. This model was applied to pixel spectra from samples in the validation set to inspect spatial variations during dehydration and rehydration. Challenges associated with NIR‐HSI of hydrogels with a large variation in moisture content are discussed.