Simultaneous quantifying and visualizing moisture, ash and protein distribution in sweet potato [Ipomoea batatas (L.) Lam] by NIR hyperspectral imaging
This study aimed to achieve the rapid evaluation of moisture, ash and protein of sweet potato simultaneously by near-infrared (NIR) hyperspectral imaging (900-1700 nm). Hyperspectral images of 300 samples for each parameter were acquired and the spectra within images were extracted, averaged and pre...
| Main Authors: | , , , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/173739 |
| _version_ | 1826117115145355264 |
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| author | He, Hong-Ju Wang, Yuling Wang, Yangyang Liu, Hongjie Zhang, Mian Ou, Xingqi |
| author2 | School of Chemistry, Chemical Engineering and Biotechnology |
| author_facet | School of Chemistry, Chemical Engineering and Biotechnology He, Hong-Ju Wang, Yuling Wang, Yangyang Liu, Hongjie Zhang, Mian Ou, Xingqi |
| author_sort | He, Hong-Ju |
| collection | NTU |
| description | This study aimed to achieve the rapid evaluation of moisture, ash and protein of sweet potato simultaneously by near-infrared (NIR) hyperspectral imaging (900-1700 nm). Hyperspectral images of 300 samples for each parameter were acquired and the spectra within images were extracted, averaged and preprocessed to relate to the three measured parameters, using partial least squares (PLS) algorithm, respectively, resulting in good performances. Nine, eleven and eleven informative wavelengths were selected to accelerate the prediction of the three parameters, generating a correlation coefficient of prediction (r P) of 0.984, 0.905, 0.935 and root mean square error of prediction (RMSEP) of 0.907%, 0.138%, 0.0941% for moisture, ash and protein, respectively. By transferring the best optimized PLS models to generate color chemical maps, the distributions and variations of the three parameters were visualized. NIR hyperspectral imaging is promising and can be applied to simultaneously evaluate multiple quality parameters of sweet potato. |
| first_indexed | 2024-10-01T04:22:27Z |
| format | Journal Article |
| id | ntu-10356/173739 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T04:22:27Z |
| publishDate | 2024 |
| record_format | dspace |
| spelling | ntu-10356/1737392024-03-01T15:31:46Z Simultaneous quantifying and visualizing moisture, ash and protein distribution in sweet potato [Ipomoea batatas (L.) Lam] by NIR hyperspectral imaging He, Hong-Ju Wang, Yuling Wang, Yangyang Liu, Hongjie Zhang, Mian Ou, Xingqi School of Chemistry, Chemical Engineering and Biotechnology Chemistry Sweet potato Moisture This study aimed to achieve the rapid evaluation of moisture, ash and protein of sweet potato simultaneously by near-infrared (NIR) hyperspectral imaging (900-1700 nm). Hyperspectral images of 300 samples for each parameter were acquired and the spectra within images were extracted, averaged and preprocessed to relate to the three measured parameters, using partial least squares (PLS) algorithm, respectively, resulting in good performances. Nine, eleven and eleven informative wavelengths were selected to accelerate the prediction of the three parameters, generating a correlation coefficient of prediction (r P) of 0.984, 0.905, 0.935 and root mean square error of prediction (RMSEP) of 0.907%, 0.138%, 0.0941% for moisture, ash and protein, respectively. By transferring the best optimized PLS models to generate color chemical maps, the distributions and variations of the three parameters were visualized. NIR hyperspectral imaging is promising and can be applied to simultaneously evaluate multiple quality parameters of sweet potato. Published version The authors acknowledge that this work was financially supported by Key Science & Technology Project of Henan Province (No. 222102110113), Horizontal Scientific Research of Henan Institute of Science and Technology (No. 2021410707000060), High Talents Project of Henan Institute of Science and Technology (No. 2015015). 2024-02-26T05:12:07Z 2024-02-26T05:12:07Z 2023 Journal Article He, H., Wang, Y., Wang, Y., Liu, H., Zhang, M. & Ou, X. (2023). Simultaneous quantifying and visualizing moisture, ash and protein distribution in sweet potato [Ipomoea batatas (L.) Lam] by NIR hyperspectral imaging. Food Chemistry: X, 18, 100631-. https://dx.doi.org/10.1016/j.fochx.2023.100631 2590-1575 https://hdl.handle.net/10356/173739 10.1016/j.fochx.2023.100631 36926310 2-s2.0-85149898189 18 100631 en Food Chemistry: X © 2023 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/bync-nd/4.0/). application/pdf |
| spellingShingle | Chemistry Sweet potato Moisture He, Hong-Ju Wang, Yuling Wang, Yangyang Liu, Hongjie Zhang, Mian Ou, Xingqi Simultaneous quantifying and visualizing moisture, ash and protein distribution in sweet potato [Ipomoea batatas (L.) Lam] by NIR hyperspectral imaging |
| title | Simultaneous quantifying and visualizing moisture, ash and protein distribution in sweet potato [Ipomoea batatas (L.) Lam] by NIR hyperspectral imaging |
| title_full | Simultaneous quantifying and visualizing moisture, ash and protein distribution in sweet potato [Ipomoea batatas (L.) Lam] by NIR hyperspectral imaging |
| title_fullStr | Simultaneous quantifying and visualizing moisture, ash and protein distribution in sweet potato [Ipomoea batatas (L.) Lam] by NIR hyperspectral imaging |
| title_full_unstemmed | Simultaneous quantifying and visualizing moisture, ash and protein distribution in sweet potato [Ipomoea batatas (L.) Lam] by NIR hyperspectral imaging |
| title_short | Simultaneous quantifying and visualizing moisture, ash and protein distribution in sweet potato [Ipomoea batatas (L.) Lam] by NIR hyperspectral imaging |
| title_sort | simultaneous quantifying and visualizing moisture ash and protein distribution in sweet potato ipomoea batatas l lam by nir hyperspectral imaging |
| topic | Chemistry Sweet potato Moisture |
| url | https://hdl.handle.net/10356/173739 |
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