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Nondestructive Textural Assessment of Fish Freshness: A Stochastic Model‐Based Approach Robust to Fish Size Variations
Author(s) -
Dimogianopoulos D.,
Grigorakis K.
Publication year - 2014
Publication title -
journal of texture studies
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.593
H-Index - 54
eISSN - 1745-4603
pISSN - 0022-4901
DOI - 10.1111/jtxs.12072
Subject(s) - benchmark (surveying) , fish <actinopterygii> , computer science , organoleptic , statistics , environmental science , mathematics , fishery , biology , food science , geodesy , geography
This paper aims at proposing a nondestructive scheme for assessing freshness of whole raw fish of various sizes/weights, using a stochastic model‐based fault diagnosis framework. Unlike most alternatives, the scheme detects early postmortem texture alterations linked to freshness reductions, thus facilitating the accurate estimation of high‐quality shelf life of fish. The method involves the experimental testing of fish samples via vibration‐like procedures, and the evaluation of differences between a sample of unknown freshness and a whole group of fresh fish of possibly different sizes/weights. Freshness degree is reliably concluded via statistical decision‐making hypothesis tests, which also quantify the uncertainty of the process. Eleven whole raw sea bass ( D icentrarchus labrax ) of various sizes/weights and freshness degrees were tested, with freshness results correctly assessed as confirmed by organoleptic freshness evaluation. Practical Applications The proposed diagnostic scheme introduces a practical, nondestructive method for assessing fish freshness by evaluating texture alterations based on a stochastic model‐based fault diagnosis framework. The scheme in its current stage of development detects differences between a sample of unknown freshness and a group of fresh fish, without requiring similar sizes/weights of the tested fish samples. Freshness degree is reliably concluded via statistical decision‐making hypothesis tests, which also quantify the uncertainty of the process. These characteristics make it ideal for assessing freshness in fisheries and aquaculture industry, with tested fish samples of unknown freshness being compared with a (potentially expanding) database of “benchmark” fresh fish.