Image-based Analysis for Characterization of Chicken Nugget Quality

Authors

  • Chumpol Yuangyai Faculty of Engineering, Department of Industrial Engineering, King Mongkut’s Institute of Technology, Chalongkrung Road, Ladkrabang, Bangkok 10520
  • Piyaphorn Matvises Faculty of Engineering, Department of Industrial Engineering, King Mongkut’s Institute of Technology, Chalongkrung Road, Ladkrabang, Bangkok 10520
  • Udom Janjarassuk Faculty of Engineering, Department of Industrial Engineering, King Mongkut’s Institute of Technology, Chalongkrung Road, Ladkrabang, Bangkok 10520

DOI:

https://doi.org/10.9744/jti.15.2.125-130

Keywords:

Chicken nugget quality, gage repeatability and reproducibility, image-based analysis, principal component analysis.

Abstract

Appearance, colors and adhesion characteristics of chicken nugget are important to customer satisfaction and buying decision. These characteristics are generally inspected by hu-man, thus, the inspectors might incorrectly judge. In addition, the results are not quantitatively recorded for further analysis and improvement. Therefore, this study focuses on constructing a measurement instrument for detecting the qualities of chicken nugget, then gage repeatability and reproducibility (GR&R) study is used to ensure that the instrument is capable of dis-tinguishing nugget differences. Since, there are eleven characteristics of chicken nugget are analyzed. The principal component analysis is applied to reduce the number of characteristics from eleven dimensions to only four dimensions. The experiments and data analysis show that the dimension reduction is useful for rapidly detect the abnormality of nuggets and finally help practitioners to improve the process.

References

AIAG, Automative Industry Action Group, Measurement System Analysis (MSA), 3rd edition, Michigan, 2002.

Baldevbhai, P. J. and Anand, R. S., Color Image Segmentation for Medical Images using L*a*b* Color Space, Journal of Electronics and Communication Engineering, 1(2), 2012, pp. 24-45.[CrossRef]

Barni, M., Cappellini, V., and Mecocci, A., Colour-based Detection of Defects on Chicken Meat, Image and Vision Computing, 15, 1997, pp. 549–556.[CrossRef]

Borggaard, C., Madsen, N.T. and Thodberg, H.H., In-line Image Analysis in the Slaughter Industry, Illustrated by Beef Carcass Classification, Meat Science, 43(1), 1996, pp. 151-163.[CrossRef]

Brosnan, T., and Sun, D.W., Improving Quality Inspection of Food Products by Computer Vision-a Review. Journal of Food Engineering, 61(1), 2004, pp. 3-16.[CrossRef]

Du, C.J and Sun, D.W., Recent Developments in the Applications of Image Processing Techniques for Food Quality Evaluation, Trends in Food Science & Technology, 15, 2004, pp. 230–249.[CrossRef]

Fortin, A., Tong, A. K. W., Robertson, W. M., Zawadski, S. M., Landry, S. J., Robinson, D. J., Liu, T., and Mockford, R. J., A Novel Approach to Grading Pork Carcasses: Computer Vision and Ultrasound. Meat Science, 63, 2003, pp. 451–462.[CrossRef]

Johnson, A. and Wichern D. W., Applied Multivariate Statistical Analysis, 6th, Pearson, 2007.

Karplus, I., Alchanatis, V., Zion, B., Guidance of Groups of Guppies (Poeciliareticulata) to Allow Sorting by Computer Vision, Aquacultural Engineering, 32(3–4), 2005, pp. 509-520.[CrossRef]

Li, Q. Z., Wang, M. H., and Gu, W. K., Computer Vision Based System for Apple Surface Defect Detection, Computers and Electronics in Agriculture, 36(2–3), 2002, pp. 215–223.[CrossRef]

Montgomery, D.C. and Runger, G.C., Applied Statistics and Probability for Engineers, 4th, New York: John Wiley and Sons, 2007.

Pedreschi, F., LeÓn, J., Mery, D. and Moyano, P., Development of a Computer Vision System to Measure the Color of Potato Chips, Food Research International, 39(10), 2006, pp. 1092–1098.[CrossRef]

Yuangyai, C., Kaewsuwan, P., and Cheng, C.Y., Color-based Image Analysis for Statistical Sausage Production Control, Proceedings of the Asia Pacific Industrial Engineering & Management Systems Conference 2012.

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Published

2013-12-04