Design on electronic nose for detecting the freshness of Larimichthys polyactis based on sensor array

doi:10.14088/j.cnki.issn0439-8114.2024.08.042

Abstract

Abstract: To develop an electronic nose that can be used to determine the freshness of agricultural products, taking the Larimichthys polyactis as the research object, the corruption test was carried out under constant conditions, and the data were collected. The sensor response value was taken as the independent variable and the total volatile basic nitrogen （TVB-N） value of Larimichthys polyactis was taken as the dependent variable. The multiple linear regression, partial least squares and BPNN were used to establish a prediction model of Larimichthys polyactis meat quality grade, and the performance of the model was analyzed by comparing the correlation coefficient R and the average error percentage RE-mean of the three models for TVB-N value prediction. The results showed that the electronic nose could distinguish the fresh rotten samples of Larimichthys polyactis by using the prediction model. It could be seen that the prediction algorithm of BP neural network could achieve the best prediction for the samples, and the performance of the multiple linear regression model and the least square method was poor.

Key words: electronic nose, freshness, total volatile basic nitrogen, multiple linear regression, partial least squares, BPNN

CLC Number:

HUANG Can-can, CHEN Ya-long, CHEN Hai-gen. Design on electronic nose for detecting the freshness of Larimichthys polyactis based on sensor array[J]. HUBEI AGRICULTURAL SCIENCES, 2024, 63(8): 252-256.

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