Rapid and Nondestructive Determination of Egg Freshness Category and Marked Date of Lay using Spectral Fingerprint
The potential of nondestructive prediction of egg freshness based on near-infrared (NIR) spectra fingerprints would be beneficial to quality control officers and consumers alike. In this study, handheld NIR spectrometer in the range of 740 nm to 1070 nm and chemometrics were used to simultaneously d...
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Journal of Spectroscopy |
| Online Access: | http://dx.doi.org/10.1155/2020/8838542 |
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| _version_ | 1832566362674823168 |
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| author | Thomas O. S. Akowuah Ernest Teye Julius Hagan Kwasi Nyandey |
| author_facet | Thomas O. S. Akowuah Ernest Teye Julius Hagan Kwasi Nyandey |
| author_sort | Thomas O. S. Akowuah |
| collection | DOAJ |
| description | The potential of nondestructive prediction of egg freshness based on near-infrared (NIR) spectra fingerprints would be beneficial to quality control officers and consumers alike. In this study, handheld NIR spectrometer in the range of 740 nm to 1070 nm and chemometrics were used to simultaneously determine egg freshness based on marked date of lay for eggs stored under cold and ambient conditions. The spectra acquired from the eggs were preprocessed using multiplicative scatter correction and principal component analysis (MSC-PCA). Linear discriminant analysis (LDA) was used to build identification model to predict the category of freshness, while partial least square regression (PLS-R) was used to determine the marked date of lay. The performance of LDA model was above 95% identification rate in both calibration and prediction set for the eggs stored under ambient and cold storage. For eggs stored in ambient storage, LDA had 95.54% identification rate at 5 principal components, while at cold storage LDA has 100% identification rate at 5 principal components for determining the marked date of lay, and partial least square regression (PLS-R) gave R = 0.87 and RMSEI = 2.57 for ambient storage and R = 0.88 and RMSEI = 2.66 for cold storage in independent set, respectively. The results show that handheld spectrometer and multivariate analysis could be used for rapid and nondestructive measurement of egg freshness. This provides a novel solution for egg integrity prediction along the value chain. |
| format | Article |
| id | doaj-art-b6148381e6fb46d4bcc448ffa54c6942 |
| institution | Kabale University |
| issn | 2314-4920 2314-4939 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Spectroscopy |
| spelling | doaj-art-b6148381e6fb46d4bcc448ffa54c69422025-02-03T01:04:25ZengWileyJournal of Spectroscopy2314-49202314-49392020-01-01202010.1155/2020/88385428838542Rapid and Nondestructive Determination of Egg Freshness Category and Marked Date of Lay using Spectral FingerprintThomas O. S. Akowuah0Ernest Teye1Julius Hagan2Kwasi Nyandey3Department of Animal Science, University of Cape Coast, College of Agriculture and Natural Sciences, School of Agriculture, Cape Coast, GhanaDepartment of Agricultural Engineering, University of Cape Coast, College of Agriculture and Natural Sciences, School of Agriculture, Cape Coast, GhanaDepartment of Animal Science, University of Cape Coast, College of Agriculture and Natural Sciences, School of Agriculture, Cape Coast, GhanaDepartment of Physics, University of Cape Coast, College of Agriculture and Natural Sciences, School of Agriculture, Cape Coast, GhanaThe potential of nondestructive prediction of egg freshness based on near-infrared (NIR) spectra fingerprints would be beneficial to quality control officers and consumers alike. In this study, handheld NIR spectrometer in the range of 740 nm to 1070 nm and chemometrics were used to simultaneously determine egg freshness based on marked date of lay for eggs stored under cold and ambient conditions. The spectra acquired from the eggs were preprocessed using multiplicative scatter correction and principal component analysis (MSC-PCA). Linear discriminant analysis (LDA) was used to build identification model to predict the category of freshness, while partial least square regression (PLS-R) was used to determine the marked date of lay. The performance of LDA model was above 95% identification rate in both calibration and prediction set for the eggs stored under ambient and cold storage. For eggs stored in ambient storage, LDA had 95.54% identification rate at 5 principal components, while at cold storage LDA has 100% identification rate at 5 principal components for determining the marked date of lay, and partial least square regression (PLS-R) gave R = 0.87 and RMSEI = 2.57 for ambient storage and R = 0.88 and RMSEI = 2.66 for cold storage in independent set, respectively. The results show that handheld spectrometer and multivariate analysis could be used for rapid and nondestructive measurement of egg freshness. This provides a novel solution for egg integrity prediction along the value chain.http://dx.doi.org/10.1155/2020/8838542 |
| spellingShingle | Thomas O. S. Akowuah Ernest Teye Julius Hagan Kwasi Nyandey Rapid and Nondestructive Determination of Egg Freshness Category and Marked Date of Lay using Spectral Fingerprint Journal of Spectroscopy |
| title | Rapid and Nondestructive Determination of Egg Freshness Category and Marked Date of Lay using Spectral Fingerprint |
| title_full | Rapid and Nondestructive Determination of Egg Freshness Category and Marked Date of Lay using Spectral Fingerprint |
| title_fullStr | Rapid and Nondestructive Determination of Egg Freshness Category and Marked Date of Lay using Spectral Fingerprint |
| title_full_unstemmed | Rapid and Nondestructive Determination of Egg Freshness Category and Marked Date of Lay using Spectral Fingerprint |
| title_short | Rapid and Nondestructive Determination of Egg Freshness Category and Marked Date of Lay using Spectral Fingerprint |
| title_sort | rapid and nondestructive determination of egg freshness category and marked date of lay using spectral fingerprint |
| url | http://dx.doi.org/10.1155/2020/8838542 |
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