Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Tsai, Sheng-Yu, Li, Cheng-Han, Jeng, Chien-Chung, Cheng, Ching-Wei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
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
_version_ 1783600650612899840
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
work_keys_str_mv AT tsaishengyu qualityassessmentduringincubationusingimageprocessing
AT lichenghan qualityassessmentduringincubationusingimageprocessing
AT jengchienchung qualityassessmentduringincubationusingimageprocessing
AT chengchingwei qualityassessmentduringincubationusingimageprocessing