1
|
So JH, Joe SY, Hwang SH, Hong SJ, Lee SH. Current advances in detection of abnormal egg: a review. JOURNAL OF ANIMAL SCIENCE AND TECHNOLOGY 2022; 64:813-829. [PMID: 36287780 PMCID: PMC9574607 DOI: 10.5187/jast.2022.e56] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Revised: 06/21/2022] [Accepted: 06/22/2022] [Indexed: 11/06/2022]
Abstract
Internal and external defects of eggs should be detected to prevent
cross-contamination of intact eggs by abnormal eggs during storage. Emerging
detection technologies for abnormal eggs were introduced as an alternative to
human inspection. The advanced technologies could rapidly detect abnormal eggs.
Abnormal egg detection technologies using acoustic response, machine vision, and
spectroscopy have been commercialized in the poultry industry. Non-destructive
egg quality assessment methods meanwhile could preserve the value of eggs and
improve detection efficiency. In order to improve detection efficiency, it is
essential to select a proper algorithm for classifying the types of abnormal
eggs. This review deals with the performance of the detection technologies for
various types of abnormal eggs in recently published resources. In addition, the
discriminant methods and detection algorithms of abnormal eggs reported in the
published literature were investigated. Although the majority of the studies
were conducted on a laboratory scale, the developed detection technologies for
internal and external defects in eggs were technically feasible to obtain the
excellent detection accuracy. To apply the developed detection technologies to
the poultry industry, it is necessary to achieve the detection rates required
from the industry.
Collapse
Affiliation(s)
- Jun-Hwi So
- Department of Smart Agriculture Systems,
Chungnam National University, Daejeon 34134, Korea
| | - Sung Yong Joe
- Department of Biosystems Machinery
Engineering, Chungnam National University, Daejeon 34134,
Korea
| | - Seon Ho Hwang
- Department of Smart Agriculture Systems,
Chungnam National University, Daejeon 34134, Korea
| | - Soon Jung Hong
- Department of Liberal Arts, Korea National
University of Agriculture and Fisheries, Jeonju 54874,
Korea
| | - Seung Hyun Lee
- Department of Smart Agriculture Systems,
Chungnam National University, Daejeon 34134, Korea,Department of Biosystems Machinery
Engineering, Chungnam National University, Daejeon 34134,
Korea,Corresponding author: Seung Hyun Lee,
Department of Smart Agriculture Systems, Chungnam National University, Daejeon
34134, Korea. Tel: +82-42-821-6718, E-mail:
| |
Collapse
|
2
|
Song S, Zhu Y, Huang Z, Lin Y, Shi X, Guo H. Isolation, identification and thermal inactivation of dominant spoilage bacteria in egg curds. Lebensm Wiss Technol 2021. [DOI: 10.1016/j.lwt.2020.110357] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
|
3
|
Ye CW, Yousaf K, Qi C, Liu C, Chen KJ. Broiler stunned state detection based on an improved fast region-based convolutional neural network algorithm. Poult Sci 2020; 99:637-646. [PMID: 32416852 PMCID: PMC7587773 DOI: 10.3382/ps/pez564] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2019] [Accepted: 09/24/2019] [Indexed: 11/20/2022] Open
Abstract
An improved fast region-based convolutional neural network (RCNN) algorithm is proposed to improve the accuracy and efficiency of recognizing broilers in a stunned state. The algorithm recognizes 3 stunned state conditions: insufficiently stunned, moderately stunned, and excessively stunned. Image samples of stunned broilers were collected from a slaughter line using an image acquisition platform. According to the format of PASCAL VOC (pattern analysis, statistical modeling, and computational learning visual object classes) dataset, a dataset for each broiler stunned state condition was obtained using an annotation tool to mark the chicken head and wing area in the original image. A rotation and flip data augmentation method was used to enhance the effectiveness of the datasets. Based on the principle of a residual network, a multi-layer residual module (MRM) was constructed to facilitate more detailed feature extraction. A model was then developed (entitled here Faster-RCNN+MRMnet) and used to detect broiler stunned state conditions. When applied to a reinforcing dataset containing 27,828 images of chickens in a stunned state, the identification accuracy of the model was 98.06%. This was significantly higher than both the established back propagation neural network model (90.11%) and another Faster-RCNN model (96.86%). The proposed algorithm can complete the inspection of the stunned state of more than 40,000 broilers per hour. The approach can be used for online inspection applications to increase efficiency, reduce labor and cost, and yield significant benefits for poultry processing plants.
Collapse
Affiliation(s)
- Chang-Wen Ye
- College of Engineering, Nanjing Agricultural University, Nanjing, 210031, China
| | - Khurram Yousaf
- College of Engineering, Nanjing Agricultural University, Nanjing, 210031, China
| | - Chao Qi
- College of Engineering, Nanjing Agricultural University, Nanjing, 210031, China
| | - Chao Liu
- College of Engineering, Nanjing Agricultural University, Nanjing, 210031, China
| | - Kun-Jie Chen
- College of Engineering, Nanjing Agricultural University, Nanjing, 210031, China.
| |
Collapse
|
4
|
Microbiota of eggs revealed by 16S rRNA-based sequencing: From raw materials produced by different suppliers to chilled pasteurized liquid products. Food Control 2019. [DOI: 10.1016/j.foodcont.2018.09.016] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
|
5
|
Montenegro AT, Garcia EA, Molino AB, Cruvinel JM, Ouros CC, Alves KS. METHODS TO EVALUATE THE EGGSHELL QUALITY OF TABLE EGGS. BRAZILIAN JOURNAL OF POULTRY SCIENCE 2019. [DOI: 10.1590/1806-9061-2019-1046] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Affiliation(s)
| | | | | | | | - CC Ouros
- São Paulo State University, Brazil
| | - KS Alves
- São Paulo State University, Brazil
| |
Collapse
|
6
|
Sun K, Zhang W, Pan L, Tu K. Recognition of a Cracked Hen Egg Image Using a Sequenced Wave Signal Extraction and Identification Algorithm. FOOD ANAL METHOD 2017. [DOI: 10.1007/s12161-017-1105-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
|
7
|
Geveke DJ, Gurtler JB, Jones DR, Bigley ABW. Inactivation of
Salmonella
in Shell Eggs by Hot Water Immersion and Its Effect on Quality. J Food Sci 2016; 81:M709-14. [PMID: 26878421 DOI: 10.1111/1750-3841.13233] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2015] [Accepted: 12/30/2015] [Indexed: 11/27/2022]
Affiliation(s)
- David J. Geveke
- Food Safety and Intervention Technologies Research Unit, Eastern Regional Research Center, Agricultural Research ServiceU.S. Dept. of Agriculture 600 East Mermaid Lane Wyndmoor Pa 19038 U.S.A
| | - Joshua B. Gurtler
- Food Safety and Intervention Technologies Research Unit, Eastern Regional Research Center, Agricultural Research ServiceU.S. Dept. of Agriculture 600 East Mermaid Lane Wyndmoor Pa 19038 U.S.A
| | - Deana R. Jones
- Egg Safety and Quality Research Unit, U.S. National Poultry Research Center, Agricultural Research ServiceU.S. Dept. of Agriculture 950 College Station Road Athens Ga. 30605 U.S.A
| | - Andrew B. W. Bigley
- Food Safety and Intervention Technologies Research Unit, Eastern Regional Research Center, Agricultural Research ServiceU.S. Dept. of Agriculture 600 East Mermaid Lane Wyndmoor Pa 19038 U.S.A
| |
Collapse
|
8
|
Wang H, Mao J, Zhang J, Jiang H, Wang J. Acoustic feature extraction and optimization of crack detection for eggshell. J FOOD ENG 2016. [DOI: 10.1016/j.jfoodeng.2015.10.030] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
|
9
|
Sungur C, Özkan H. A real time quality control application for animal production by image processing. JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 2015; 95:2850-2857. [PMID: 25428617 DOI: 10.1002/jsfa.7025] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/24/2014] [Revised: 11/05/2014] [Accepted: 11/23/2014] [Indexed: 06/04/2023]
Abstract
BACKGROUND Standards of hygiene and health are of major importance in food production, and quality control has become obligatory in this field. Thanks to rapidly developing technologies, it is now possible for automatic and safe quality control of food production. For this purpose, image-processing-based quality control systems used in industrial applications are being employed to analyze the quality of food products. In this study, quality control of chicken (Gallus domesticus) eggs was achieved using a real time image-processing technique. RESULTS In order to execute the quality control processes, a conveying mechanism was used. Eggs passing on a conveyor belt were continuously photographed in real time by cameras located above the belt. The images obtained were processed by various methods and techniques. Using digital instrumentation, the volume of the eggs was measured, broken/cracked eggs were separated and dirty eggs were determined. In accordance with international standards for classifying the quality of eggs, the class of separated eggs was determined through a fuzzy implication model. CONCLUSION According to tests carried out on thousands of eggs, a quality control process with an accuracy of 98% was possible.
Collapse
Affiliation(s)
- Cemil Sungur
- Vocational School of Technical Sciences, Electrical and Energy, Selçuk University, Konya, Turkey
| | - Halil Özkan
- Natural and Applied Sciences, Selçuk University, Konya, Turkey
| |
Collapse
|
10
|
Jones D, Karcher D, Abdo Z. Effect of a commercial housing system on egg quality during extended storage. Poult Sci 2014; 93:1282-8. [DOI: 10.3382/ps.2013-03631] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
|
11
|
Prediction modelling of storage time and quality measurements using visible-near infrared spectra of pasteurized shell eggs. JOURNAL OF FOOD MEASUREMENT AND CHARACTERIZATION 2013. [DOI: 10.1007/s11694-013-9144-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
|
12
|
Jones D, Broussard V, Lawrence K, Yoon S, Heitschmidt G. Dynamic and static shell properties of white and brown shell eggs exposed to modified pressure microcrack detection technology. Poult Sci 2012; 91:2658-61. [DOI: 10.3382/ps.2011-01908] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
|
13
|
Jones DR, Lawrence KC, Yoon SC, Heitschmidt GW. Salmonella contamination in shell eggs exposed to modified-pressure imaging for microcrack detection. Poult Sci 2011; 90:1616-9. [PMID: 21673180 DOI: 10.3382/ps.2010-01155] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
Microcracks in egg shells are a food safety risk and are difficult for professional human graders to detect. Modified-pressure imaging technology with 99.6% accuracy has been developed to detect microcracks. This study was conducted to determine whether the microcrack detection system would increase penetration of Salmonella into egg contents or lead to cross-contamination within the system. Thirty dozen grade A large white retail eggs were used for each of 3 replicates. Cracked eggs were removed and 72 eggs/replicate were dip inoculated in buffered peptone water containing 10(5) cfu/mL of nalidixic acid-resistant Salmonella Typhimurium (ST), whereas 144 eggs were dipped in sterile buffered peptone water. All eggs were incubated overnight at 25°C before imaging. Forty-five eggs of each treatment were imaged in the following order: control, inoculated, control. Imaged and nonimaged eggs from each treatment were used for cultural analysis of a shell rinse, shell emulsion, and contents sample for each egg. The ST levels were monitored on brilliant green sulfa agar with 200 mg/L of nalidixic acid. Egg contents were also enriched to determine the prevalence of ST in low levels. Salmonella Typhimurium was not detected on or in any of the control eggs, including the eggs imaged after the inoculated eggs. The highest level of ST was detected in inoculated shell emulsions (4.79 log cfu/mL). No differences in ST levels were found for any sample location between imaged and nonimaged inoculated eggs. Therefore, the modified-pressure imaging system for microcrack detection did not result in microbial cross-contamination or increase the level of microbial penetration in inoculated eggs. The imaging system can be used to assess eggs for cracks without negative food safety implications.
Collapse
Affiliation(s)
- D R Jones
- USDA Agricultural Research Service, Athens, GA, USA.
| | | | | | | |
Collapse
|