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Quality Assessment during Incubation Using Image Processing
The fertilized egg is an indispensable production platform for making egg-based vaccines. This study was divided into two parts. In the first part, image processing was employed to analyze the absorption spectrum of fertilized eggs; the results show that the 580-nm band had the most significant chan...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7589749/ https://www.ncbi.nlm.nih.gov/pubmed/33096735 http://dx.doi.org/10.3390/s20205951 |
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author | Tsai, Sheng-Yu Li, Cheng-Han Jeng, Chien-Chung Cheng, Ching-Wei |
author_facet | Tsai, Sheng-Yu Li, Cheng-Han Jeng, Chien-Chung Cheng, Ching-Wei |
author_sort | Tsai, Sheng-Yu |
collection | PubMed |
description | The fertilized egg is an indispensable production platform for making egg-based vaccines. This study was divided into two parts. In the first part, image processing was employed to analyze the absorption spectrum of fertilized eggs; the results show that the 580-nm band had the most significant change. In the second part, a 590-nm-wavelength LED was selected as the light source for the developed detection device. Using this device, sample images (in RGB color space) of the eggs were obtained every day during the experiment. After calculating the grayscale value of the red layer, the receiver operating characteristic curve was used to analyze the daily data to obtain the area under the curve. Subsequently, the best daily grayscale value for classifying unfertilized eggs and dead-in-shell eggs was obtained. Finally, an industrial prototype of the device designed and fabricated in this study was operated and verified. The results show that the accuracy for detecting unfertilized eggs was up to 98% on the seventh day, with the sensitivity and Youden’s index being 82% and 0.813, respectively. On the ninth day, both accuracy and sensitivity reached 100%, and Youden’s index reached a value of 1, showing good classification ability. Considering the industrial operating conditions, this method was demonstrated to be commercially applicable because, when used to detect unfertilized eggs and dead-in-shell eggs on the ninth day, it could achieve accuracy and sensitivity of 100% at the speed of five eggs per second. |
format | Online Article Text |
id | pubmed-7589749 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-75897492020-10-29 Quality Assessment during Incubation Using Image Processing Tsai, Sheng-Yu Li, Cheng-Han Jeng, Chien-Chung Cheng, Ching-Wei Sensors (Basel) Letter The fertilized egg is an indispensable production platform for making egg-based vaccines. This study was divided into two parts. In the first part, image processing was employed to analyze the absorption spectrum of fertilized eggs; the results show that the 580-nm band had the most significant change. In the second part, a 590-nm-wavelength LED was selected as the light source for the developed detection device. Using this device, sample images (in RGB color space) of the eggs were obtained every day during the experiment. After calculating the grayscale value of the red layer, the receiver operating characteristic curve was used to analyze the daily data to obtain the area under the curve. Subsequently, the best daily grayscale value for classifying unfertilized eggs and dead-in-shell eggs was obtained. Finally, an industrial prototype of the device designed and fabricated in this study was operated and verified. The results show that the accuracy for detecting unfertilized eggs was up to 98% on the seventh day, with the sensitivity and Youden’s index being 82% and 0.813, respectively. On the ninth day, both accuracy and sensitivity reached 100%, and Youden’s index reached a value of 1, showing good classification ability. Considering the industrial operating conditions, this method was demonstrated to be commercially applicable because, when used to detect unfertilized eggs and dead-in-shell eggs on the ninth day, it could achieve accuracy and sensitivity of 100% at the speed of five eggs per second. MDPI 2020-10-21 /pmc/articles/PMC7589749/ /pubmed/33096735 http://dx.doi.org/10.3390/s20205951 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Letter Tsai, Sheng-Yu Li, Cheng-Han Jeng, Chien-Chung Cheng, Ching-Wei Quality Assessment during Incubation Using Image Processing |
title | Quality Assessment during Incubation Using Image Processing |
title_full | Quality Assessment during Incubation Using Image Processing |
title_fullStr | Quality Assessment during Incubation Using Image Processing |
title_full_unstemmed | Quality Assessment during Incubation Using Image Processing |
title_short | Quality Assessment during Incubation Using Image Processing |
title_sort | quality assessment during incubation using image processing |
topic | Letter |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7589749/ https://www.ncbi.nlm.nih.gov/pubmed/33096735 http://dx.doi.org/10.3390/s20205951 |
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