Analysis of visual quality attributes of white shrimp by machine vision

An important component of shrimp quality evaluation is determination of visual attributes by trained inspectors. This is subjective, time consuming, and difficult to quantify. An automated device was developed for repeatable, objective measurement of visual quality of shrimp. The device measured the count, uniformity ratio, color, melanosis, and detected foreign objects. Shrimp area viewed by a camera was used to estimate weight after calibration, and count and uniformity ratio was calculated. The system quantified color changes in white shrimp during iced storage. Melanosis was quantified and correlated with the grading of a trained inspector. The method objectively measured visual quality attributes of white shrimp in less than 1 min per sample.

[1]  Charles N. Satterfield,et al.  Heterogeneous catalysis in practice , 1980 .

[2]  G. H. Brusewitz,et al.  Asparagus Defect Inspection with Machine Vision , 1992 .

[3]  M. Balaban,et al.  Determination of count and uniformity ratio of shrimp by machine vision , 1995 .

[4]  R. R. Wolfe,et al.  Computer vision based system for quality separation of fresh market tomatoes , 1984 .

[5]  L. F. Pau,et al.  Vision Applications in the Fishing and Fish Product Industries , 1988, Int. J. Pattern Recognit. Artif. Intell..

[6]  O. Levenspiel.  Chemical Reaction Engineering , 1972 .

[7]  V. Gökmen,et al.  Rapid reversed-phase liquid chromatographic determination of patulin in apple juice. , 1996, Journal of chromatography. A.

[8]  M. D. Evans,et al.  Maturity Detection in Peanuts (Arachis Hypogaea L.) Using Machine Vision , 1993 .

[9]  Roy E. Martin,et al.  Studies on the use of sulfites to control shrimp melanosis (Blackspot) (Presented at the 11th Annual Meeting of the Tropical and Subtropical Fisheries Technological Conference of the Americas, January 14, 1986, Tampa, Florida) , 1986 .

[10]  Peter Corke,et al.  Automatic grading and packing of prawns , 1993 .

[11]  P. Scott,et al.  Improved Method for the Thin Layer Chromatographic Determination of Patulin in Apple Juice , 1973 .

[12]  Gerald E. Rehkugler,et al.  Image Processing Algorithm for Apple Defect Detection , 1987 .

[13]  A. G. Berlage,et al.  Soybean Seed Coat and Cotyledon Crack Detection by Image Processing , 1988 .

[14]  K. Kobe,et al.  Chemical engineering kinetics , 1956 .

[15]  M. Mutlu,et al.  The adsorption of copper(II) by Z. ramigera immobilized on Ca-alginate in packed bed columns: a dynamic approach by stimulus-response technique and evaluation of adsorption data by moment analysis , 1997 .

[16]  John Howard Perry,et al.  Chemical Engineers' Handbook , 1934 .

[17]  Michael J. Delwiche,et al.  A Color Vision System for Peach Grading , 1988 .

[18]  Alastair R. Allen,et al.  Fish species recognition by shape analysis of images , 1990, Pattern Recognit..

[19]  R. T. Elster,et al.  Detection of cracks in eggs using machine vision , 1991 .

[20]  M. Ogawa,et al.  生理学的な面からみたエビ(Shrimp)の黒変について , 1984 .

[21]  U. Fish.  Fisheries of the United States , 1959 .

[22]  S. W. Searcy,et al.  Feature extraction for a vision based shrimp deheader , 1989 .

[23]  Norval J. C. Strachan,et al.  Length measurement of fish by computer vision , 1993 .