Shortwave Infrared Hyperspectral Imaging for the Determination and Visualization of Chemical Contents of Wheat and Tuber Flour
Understanding the physicochemical properties of flour in food preparation is important to determine the appropriate food utilization and processing. Some crops are processed into flour to improve their shelf life and extend their applications in food preparation. This study employed shortwave infrar...
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| Main Authors: | , , , , , |
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| Format: | Article PeerReviewed |
| Language: | English |
| Published: |
Insight Society
2022
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| Subjects: | |
| Online Access: | https://repository.ugm.ac.id/284567/1/14266-41550-1-PB.pdf https://repository.ugm.ac.id/284567/ https://www.scopus.com/inward/record.uri?eid=2-s2.0-85136106438&doi=10.18517%2fijaseit.12.4.14266&partnerID=40&md5=20f7fe7bc480623fd304d2328964f9a1 |
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| Institution: | Universitas Gadjah Mada |
| Language: | English |
| Summary: | Understanding the physicochemical properties of flour in food preparation is important to determine the appropriate food utilization and processing. Some crops are processed into flour to improve their shelf life and extend their applications in food preparation. This study employed shortwave infrared hyperspectral imaging (SWIR HSI) coupled with multivariate analyses to determine wheat's protein, starch, amylose, glucose, and moisture compositions and several tuber flours. Tubers used were arrowroot, Canna edulis, modified cassava flour, taro, and sweet potato (purple, yellow, and white color). Hyperspectral images of all flour samples were captured using the SWIR HSI system at the wavelength range of 895–2504 nm in reflectance mode. The extracted spectral data were then processed and analyzed using partial least square regression (PLSR). Normalization (mean, max, and range), multiple scatter correction, standard normal variate, first and second Savitzky–Golay derivatives, and smoothing spectral pre-processing were applied to reduce scattering noise resulting from the HSI system. The PLSR models predicted the chemical concentrations of all samples with coefficients of determination of 0.85-0.97, 0.83-0.96, and 0.85-0.96 for calibration, validation, and prediction, respectively. Moreover, the models resulted in standard errors of 0.61-27.26, 0.63-27.71, and 0.63-29.14 for calibration, validation, and prediction. The concentration and distribution of protein, starch, amylose, glucose, and moisture in the flour samples were visualized by chemical imaging. This paper confirmed the potential of HSI for the rapid interpretation of chemical contents in different flour samples |
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