Intelligent Prediction of Abalone Boiling Time and Temperature Using Apparent Characteristics

Xiaoyan Fang; Jiaxu Dong; Huihui Wang; Xu Zhang; Xueheng Tao

doi:doi:10.11648/j.ijnfs.20170606.12

| Peer-Reviewed

Intelligent Prediction of Abalone Boiling Time and Temperature Using Apparent Characteristics

Xiaoyan Fang, Jiaxu Dong, Huihui Wang, Xu Zhang, Xueheng Tao

Published in International Journal of Nutrition and Food Sciences (Volume 6, Issue 6)

Received: 2 August 2017 Accepted: 26 August 2017 Published: 26 September 2017

Views: Downloads:

Download PDF

Share This Article

Twitter
Linked In
Facebook

Abstract

In this study, changes in the apparent characteristics of abalone under a range of boiling temperatures and times were assessed. The trends of shape, color, and texture were statistically analyzed, while the Back Propagation (BP) neural network model was established by monitoring these 3 characteristics under different times and temperatures. This achieved a model of the characteristic parameters to predict the optimum boiling time and temperature, which can be used as a reference for abalone-processing technology. The results show that, although the model is acceptable, the BP neural network model with color feature offered the best predictor rate at 81.74%.

Published in	International Journal of Nutrition and Food Sciences (Volume 6, Issue 6)
DOI	10.11648/j.ijnfs.20170606.12
Page(s)	221-227
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2017. Published by Science Publishing Group

Keywords

Abalone, BP Neural Network, Image Feature, RGB Color Model, Gray-Level, Co-occurrence Matrix

References

[1]	Bei-wei Zhu, Xiu-ping Dong, Li-mei Sun, et al. Effect of thermal treatment on the texture and microstructure of abalonemuscle (Haliotis discus) [J]. Food Science and Biotechnology, 2011, 20(6): 1467－1473.
[2]	Xiu-ping Dong, Qi-xin Yuan, Hang Qi et al. Isolation and Characterization of Pepsin-Soluble Collagen from Abalone (Haliotis discus hannai) Gastropod Muscle Part Ⅱ[J]. Food science and technology research, 2012, 18(2): 271-278.
[3]	Jin Zhou; Zhong-Hua Cai and Ke-Zhi Xing et al. Potential mechanisms of phthalate ester embryotoxicity in the abalone Haliotis diversicolor supertexta [J]. Environmental Pollution, 2011, 159(5): 1114-1122.
[4]	Gui-hua Xiao, Bei-wei Zhu, Xiu-ping Dong, et al. Effects of Hot Processing Conditions on Partial Processing Properties of Abalone [J]. Journal of Dalian Institute of Light Industry, 2012, 31(1): 1-7. DOI: 10.3969/j.issn.1674-1404.2012.01.001.
[5]	Jie Ouyang, Jia-yu TAN, Jian Shen, Effect of Freezing Method and Temperature on the Quality of Abalone [J]. Modern food science and technology, 2014, 06: 214-218+139.
[6]	Xin Gao; Hiroo Ogawa*; Yuri Tashiro; Naomichi Iso, Rheological properties and structural changes in raw and cooked abalone meat. Fisheries Sci. 67: 314-320 (2001).
[7]	Xin Gao, Zhao-hui Zhang, Zhi-xu Tanag, et al. The Relationships between Rheological Properties and Structural Changes of Chilled Abalone Meat [J]. Journal of Qingdao Ocean University (English Edition), 2003, 2(2): 171-176.
[8]	Li Deng, Yan Li, Xiu-ping Dong, et al. Chemical interactions and textural characteristics of abalone [9] Zhang YQ, Study on the processing methods of abalone and its rheological characteristic [D]. China Ocean University, 2008. DOI: 10.7666/d.y1337933.
[9]	Yaqi Zhang, Study on the processing methods of abalone and its rheological characteristic [D]. China Ocean University, 2008. DOI: 10.7666/d.y1337933.
[10]	Xin GAO, Zhi-xu Tang, Zhao-hui Zhang, et al. Rheological Properties and Structural Changes in Different Sections of Boiled Abalone Meat [J]. Journal of Ocean University of China, 2003, 2(1): 44-48.
[11]	Xiu-li Ma. Research on Friut Surface Quality Detection Based on Digital Image Processing [D]. Northeastern University, 2011.
[12]	Hao YU, Yan Sun. Image Search based on Color Moment and Shape Invariant Moment [J]. Computer knowledge and technology. 2015, 11(19): 174-175.
[13]	Ye-qin Wang, Zhi –guo Zhao. Expression on the Basis of the Timber Surface Color Characteristic of the Histogram and Color Moment Method [J]. Forestry Science, 2006, 31(5): 56-58. DOI: 10.3969/j.issn.1001-9499.2006.05.020.
[14]	Shao-bo Zhang, Shu-hai Quan, Ying Shi, et al. Study on Image Retrieval Algorithm Based on Color Moment [J]. Computer Engineering, 2014, 40(6): 252-255. DOI: 10.3969/j.issn.1000-3428.2014.06.054.
[15]	Ding Han, Pei Wu, Qiang Zhang, et al. Feature extraction and image recognition of typical grassland forage based on color moment [J]. Journal of Agricultural Engineering, 2016, 32(23): 168-175. DOI: 10.11975/j.issn.1002-6819.2016.23.023.
[16]	Chuan-hua Zeng, Hong Chen, Yun Gao, et al. Bamboo Color Grading Method Based on SVM and Color Moment [J]. Hubei Agricultural Sciences, 2010, 49(2): 455-457. DOI: 10.3969/j.issn.0439-8114.2010.02.065.
[17]	Peng-peng Jiao, Yi-zheng Guo, Li-juan Liu, et al. Implementation of Gray Level Co－occurrence Matrix Texture Feature Extraction Using Matlab [J]. Computer technology and development, 2012, (11): 169-171.
[18]	Cheng-cheng Gao, Xiao-wei Hui. GLCM-Based Texture Feature Extraction, 2010, 19(6): 195-198. DOI: 10.3969/j.issn.1003-3254.2010.06.047.
[19]	Haralick R M, Shanmugam K. Texture features for image classification. IEEE Trans. on Sys, Man, and Cyb, 1973, SMC-3(6): 610－621.
[20]	Ulaby FT, Kouyate F, Brisco B, et al. Textural information in SAR Images. IEEE Transactions on Geoscience and Remote Sensing, 1986, 24(2): 235－245.
[21]	Tian-shu Liu. The Research and Application on BP Neural Network Improvement [D]. Northeast Agricultural University, 2011.

Cite This Article

Plain Text BibTeX RIS

APA Style

Xiaoyan Fang, Jiaxu Dong, Huihui Wang, Xu Zhang, Xueheng Tao. (2017). Intelligent Prediction of Abalone Boiling Time and Temperature Using Apparent Characteristics. International Journal of Nutrition and Food Sciences, 6(6), 221-227. https://doi.org/10.11648/j.ijnfs.20170606.12

Copy | Download

ACS Style

Xiaoyan Fang; Jiaxu Dong; Huihui Wang; Xu Zhang; Xueheng Tao. Intelligent Prediction of Abalone Boiling Time and Temperature Using Apparent Characteristics. Int. J. Nutr. Food Sci. 2017, 6(6), 221-227. doi: 10.11648/j.ijnfs.20170606.12

Copy | Download

AMA Style

Xiaoyan Fang, Jiaxu Dong, Huihui Wang, Xu Zhang, Xueheng Tao. Intelligent Prediction of Abalone Boiling Time and Temperature Using Apparent Characteristics. Int J Nutr Food Sci. 2017;6(6):221-227. doi: 10.11648/j.ijnfs.20170606.12

Copy | Download

@article{10.11648/j.ijnfs.20170606.12,
  author = {Xiaoyan Fang and Jiaxu Dong and Huihui Wang and Xu Zhang and Xueheng Tao},
  title = {Intelligent Prediction of Abalone Boiling Time and Temperature Using Apparent Characteristics},
  journal = {International Journal of Nutrition and Food Sciences},
  volume = {6},
  number = {6},
  pages = {221-227},
  doi = {10.11648/j.ijnfs.20170606.12},
  url = {https://doi.org/10.11648/j.ijnfs.20170606.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijnfs.20170606.12},
  abstract = {In this study, changes in the apparent characteristics of abalone under a range of boiling temperatures and times were assessed. The trends of shape, color, and texture were statistically analyzed, while the Back Propagation (BP) neural network model was established by monitoring these 3 characteristics under different times and temperatures. This achieved a model of the characteristic parameters to predict the optimum boiling time and temperature, which can be used as a reference for abalone-processing technology. The results show that, although the model is acceptable, the BP neural network model with color feature offered the best predictor rate at 81.74%.},
 year = {2017}
}

Copy | Download

TY  - JOUR
T1  - Intelligent Prediction of Abalone Boiling Time and Temperature Using Apparent Characteristics
AU  - Xiaoyan Fang
AU  - Jiaxu Dong
AU  - Huihui Wang
AU  - Xu Zhang
AU  - Xueheng Tao
Y1  - 2017/09/26
PY  - 2017
N1  - https://doi.org/10.11648/j.ijnfs.20170606.12
DO  - 10.11648/j.ijnfs.20170606.12
T2  - International Journal of Nutrition and Food Sciences
JF  - International Journal of Nutrition and Food Sciences
JO  - International Journal of Nutrition and Food Sciences
SP  - 221
EP  - 227
PB  - Science Publishing Group
SN  - 2327-2716
UR  - https://doi.org/10.11648/j.ijnfs.20170606.12
AB  - In this study, changes in the apparent characteristics of abalone under a range of boiling temperatures and times were assessed. The trends of shape, color, and texture were statistically analyzed, while the Back Propagation (BP) neural network model was established by monitoring these 3 characteristics under different times and temperatures. This achieved a model of the characteristic parameters to predict the optimum boiling time and temperature, which can be used as a reference for abalone-processing technology. The results show that, although the model is acceptable, the BP neural network model with color feature offered the best predictor rate at 81.74%.
VL  - 6
IS  - 6
ER  -

Copy | Download

Author Information

Xiaoyan Fang

Key Laboratory for Seafood Processing Technology and Equipment of Liaoning Province, Dalian Polytechnic University, Dalian, China
Jiaxu Dong

Key Laboratory for Seafood Processing Technology and Equipment of Liaoning Province, Dalian Polytechnic University, Dalian, China
Huihui Wang

Key Laboratory for Seafood Processing Technology and Equipment of Liaoning Province, Dalian Polytechnic University, Dalian, China
Xu Zhang

Key Laboratory for Seafood Processing Technology and Equipment of Liaoning Province, Dalian Polytechnic University, Dalian, China
Xueheng Tao

Key Laboratory for Seafood Processing Technology and Equipment of Liaoning Province, Dalian Polytechnic University, Dalian, China

Download PDF

Sections

Plain Text BibTeX RIS

APA Style

Xiaoyan Fang, Jiaxu Dong, Huihui Wang, Xu Zhang, Xueheng Tao. (2017). Intelligent Prediction of Abalone Boiling Time and Temperature Using Apparent Characteristics. International Journal of Nutrition and Food Sciences, 6(6), 221-227. https://doi.org/10.11648/j.ijnfs.20170606.12

Copy | Download

ACS Style

Xiaoyan Fang; Jiaxu Dong; Huihui Wang; Xu Zhang; Xueheng Tao. Intelligent Prediction of Abalone Boiling Time and Temperature Using Apparent Characteristics. Int. J. Nutr. Food Sci. 2017, 6(6), 221-227. doi: 10.11648/j.ijnfs.20170606.12

Copy | Download

AMA Style

Xiaoyan Fang, Jiaxu Dong, Huihui Wang, Xu Zhang, Xueheng Tao. Intelligent Prediction of Abalone Boiling Time and Temperature Using Apparent Characteristics. Int J Nutr Food Sci. 2017;6(6):221-227. doi: 10.11648/j.ijnfs.20170606.12

Copy | Download

@article{10.11648/j.ijnfs.20170606.12,
  author = {Xiaoyan Fang and Jiaxu Dong and Huihui Wang and Xu Zhang and Xueheng Tao},
  title = {Intelligent Prediction of Abalone Boiling Time and Temperature Using Apparent Characteristics},
  journal = {International Journal of Nutrition and Food Sciences},
  volume = {6},
  number = {6},
  pages = {221-227},
  doi = {10.11648/j.ijnfs.20170606.12},
  url = {https://doi.org/10.11648/j.ijnfs.20170606.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijnfs.20170606.12},
  abstract = {In this study, changes in the apparent characteristics of abalone under a range of boiling temperatures and times were assessed. The trends of shape, color, and texture were statistically analyzed, while the Back Propagation (BP) neural network model was established by monitoring these 3 characteristics under different times and temperatures. This achieved a model of the characteristic parameters to predict the optimum boiling time and temperature, which can be used as a reference for abalone-processing technology. The results show that, although the model is acceptable, the BP neural network model with color feature offered the best predictor rate at 81.74%.},
 year = {2017}
}

Copy | Download

TY  - JOUR
T1  - Intelligent Prediction of Abalone Boiling Time and Temperature Using Apparent Characteristics
AU  - Xiaoyan Fang
AU  - Jiaxu Dong
AU  - Huihui Wang
AU  - Xu Zhang
AU  - Xueheng Tao
Y1  - 2017/09/26
PY  - 2017
N1  - https://doi.org/10.11648/j.ijnfs.20170606.12
DO  - 10.11648/j.ijnfs.20170606.12
T2  - International Journal of Nutrition and Food Sciences
JF  - International Journal of Nutrition and Food Sciences
JO  - International Journal of Nutrition and Food Sciences
SP  - 221
EP  - 227
PB  - Science Publishing Group
SN  - 2327-2716
UR  - https://doi.org/10.11648/j.ijnfs.20170606.12
AB  - In this study, changes in the apparent characteristics of abalone under a range of boiling temperatures and times were assessed. The trends of shape, color, and texture were statistically analyzed, while the Back Propagation (BP) neural network model was established by monitoring these 3 characteristics under different times and temperatures. This achieved a model of the characteristic parameters to predict the optimum boiling time and temperature, which can be used as a reference for abalone-processing technology. The results show that, although the model is acceptable, the BP neural network model with color feature offered the best predictor rate at 81.74%.
VL  - 6
IS  - 6
ER  -

Copy | Download