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Nondestructive Detection of Moisture Content in Palm Oil by Using Portable Vibrational Spectroscopy and Optimal Prediction Algorithms
Rapid and nondestructive measurement of moisture content in crude palm oil is essential for promoting the shelf-stability and quality. In this research, micro NIR spectrometer coupled with a multivariate calibration model was used to collect and analyse fingerprinted information from palm oil sample...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9904916/ https://www.ncbi.nlm.nih.gov/pubmed/36760654 http://dx.doi.org/10.1155/2023/3364720 |
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author | Teye, Ernest Amuah, Charles L. Y. Yeh, Tai-Sheng Nyorkeh, Regina |
author_facet | Teye, Ernest Amuah, Charles L. Y. Yeh, Tai-Sheng Nyorkeh, Regina |
author_sort | Teye, Ernest |
collection | PubMed |
description | Rapid and nondestructive measurement of moisture content in crude palm oil is essential for promoting the shelf-stability and quality. In this research, micro NIR spectrometer coupled with a multivariate calibration model was used to collect and analyse fingerprinted information from palm oil samples at different moisture contents. Several preprocessing methods such as standard normal variant (SNV), multiplicative scatter correction (MSC), Savitzky–Golay first derivative (SGD1), Savitzky–Golay second derivative (SGD2) together with partial least square (PLS) regression techniques, full PLS, interval PLS (iPLS), synergy interval PLS (SiPLS), genetic algorithm PLS (GAPLS), and successive projection algorithm PLS (SPA-PLS) were comparatively employed to construct an optimum quantitative prediction model for moisture content in crude palm oil. The models were evaluated according to the coefficient of determination and root mean square error in calibration (Rc and RMSEC) and prediction (Rp and RMSEC) set, respectively. The model SGD1 + SiPLS was the optimal novel algorithm obtained among the others with the performance of Rc = 0.968 and RMSEC = 0.468 in the calibration set and Rp = 0.956 and RMSEP = 0.361 in the prediction set. The results showed that rapid and nondestructive determination of moisture content in palm oil is feasible and this would go a long way to facilitating quality control of crude palm oil. |
format | Online Article Text |
id | pubmed-9904916 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-99049162023-02-08 Nondestructive Detection of Moisture Content in Palm Oil by Using Portable Vibrational Spectroscopy and Optimal Prediction Algorithms Teye, Ernest Amuah, Charles L. Y. Yeh, Tai-Sheng Nyorkeh, Regina J Anal Methods Chem Research Article Rapid and nondestructive measurement of moisture content in crude palm oil is essential for promoting the shelf-stability and quality. In this research, micro NIR spectrometer coupled with a multivariate calibration model was used to collect and analyse fingerprinted information from palm oil samples at different moisture contents. Several preprocessing methods such as standard normal variant (SNV), multiplicative scatter correction (MSC), Savitzky–Golay first derivative (SGD1), Savitzky–Golay second derivative (SGD2) together with partial least square (PLS) regression techniques, full PLS, interval PLS (iPLS), synergy interval PLS (SiPLS), genetic algorithm PLS (GAPLS), and successive projection algorithm PLS (SPA-PLS) were comparatively employed to construct an optimum quantitative prediction model for moisture content in crude palm oil. The models were evaluated according to the coefficient of determination and root mean square error in calibration (Rc and RMSEC) and prediction (Rp and RMSEC) set, respectively. The model SGD1 + SiPLS was the optimal novel algorithm obtained among the others with the performance of Rc = 0.968 and RMSEC = 0.468 in the calibration set and Rp = 0.956 and RMSEP = 0.361 in the prediction set. The results showed that rapid and nondestructive determination of moisture content in palm oil is feasible and this would go a long way to facilitating quality control of crude palm oil. Hindawi 2023-01-31 /pmc/articles/PMC9904916/ /pubmed/36760654 http://dx.doi.org/10.1155/2023/3364720 Text en Copyright © 2023 Ernest Teye et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Teye, Ernest Amuah, Charles L. Y. Yeh, Tai-Sheng Nyorkeh, Regina Nondestructive Detection of Moisture Content in Palm Oil by Using Portable Vibrational Spectroscopy and Optimal Prediction Algorithms |
title | Nondestructive Detection of Moisture Content in Palm Oil by Using Portable Vibrational Spectroscopy and Optimal Prediction Algorithms |
title_full | Nondestructive Detection of Moisture Content in Palm Oil by Using Portable Vibrational Spectroscopy and Optimal Prediction Algorithms |
title_fullStr | Nondestructive Detection of Moisture Content in Palm Oil by Using Portable Vibrational Spectroscopy and Optimal Prediction Algorithms |
title_full_unstemmed | Nondestructive Detection of Moisture Content in Palm Oil by Using Portable Vibrational Spectroscopy and Optimal Prediction Algorithms |
title_short | Nondestructive Detection of Moisture Content in Palm Oil by Using Portable Vibrational Spectroscopy and Optimal Prediction Algorithms |
title_sort | nondestructive detection of moisture content in palm oil by using portable vibrational spectroscopy and optimal prediction algorithms |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9904916/ https://www.ncbi.nlm.nih.gov/pubmed/36760654 http://dx.doi.org/10.1155/2023/3364720 |
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