Potato flour content determination of potato–wheat flour mixture based on hyperspectral imaging
Summary Potato–wheat flour mixture (PWFM) has important significance for the development of potato staple food products. However, conventional chemical methods are unable to detect the content of potato flour. Hyperspectral imaging (HSI) technology combined with chemometrics was investigated to pred...
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Published in | International journal of food science & technology Vol. 58; no. 9; pp. 4780 - 4789 |
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Main Authors | , , , , , , , |
Format | Journal Article |
Language | English |
Published |
Oxford
Wiley Subscription Services, Inc
01.09.2023
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Subjects | |
Online Access | Get full text |
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Summary: | Summary
Potato–wheat flour mixture (PWFM) has important significance for the development of potato staple food products. However, conventional chemical methods are unable to detect the content of potato flour. Hyperspectral imaging (HSI) technology combined with chemometrics was investigated to predict the feasibility of potato flour content in PWFM. In this study, seven pretreatment algorithms were performed to process the raw spectral data. The results showed that the standard normalised variables (SNV) to establish a partial least square regression (PLSR) model had the most predictive accuracy with the determination coefficient of prediction (Rp2) of 0.9729. The characteristic wavelengths of raw spectral information were extracted using three algorithms to reduce the model complexity. Eventually, the extracted characteristic wavelengths of potato flour content combine with SNV to establish PLSR, principal component regression (PCR) and support vector regression (SVR) models. The results revealed that the optimal model was SNV‐competitive adaptive reweighted sampling (CARS)‐PLSR with the values of Rp2 of 0.9853. These results showed that HSI determination of potato flour content in PWFM was feasible and provided a non‐destructive method.
Hyperspectral image flow chart for prediction of potato flour content. |
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ISSN: | 0950-5423 1365-2621 |
DOI: | 10.1111/ijfs.16587 |