A nondestructive method for fish freshness determination with electronic tongue combined with linear and non-linear multivariate algorithms

Fangkai Han, Xingyi Huang, Ernest Teye, Haiyang Gu, Huang Dai and Liya Yao
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Fangkai Han: School of Food and Biological Engineering, Jiangsu University, Zhenjiang, Jiangsu, P.R. China
Xingyi Huang: School of Food and Biological Engineering, Jiangsu University, Zhenjiang, Jiangsu, P.R. China
Ernest Teye: School of Food and Biological Engineering, Jiangsu University, Zhenjiang, Jiangsu, P.R. China
Haiyang Gu: School of Food and Biological Engineering, Jiangsu University, Zhenjiang, Jiangsu, P.R. China
Huang Dai: School of Food and Biological Engineering, Jiangsu University, Zhenjiang, Jiangsu, P.R. China
Liya Yao: School of Food and Biological Engineering, Jiangsu University, Zhenjiang, Jiangsu, P.R. China

Czech Journal of Food Sciences, 2014, vol. 32, issue 6, 532-537

Abstract: Electronic tongue coupled with linear and non-linear multivariate algorithms was attempted to address the drawbacks of fish freshness detection. Parabramis pekinensis fish samples stored at 4°C were used. Total volatile basic nitrogen (TVB-N) and total viable count (TVC) of the samples were measured. Fisher liner discriminant analysis (Fisher LDA) and support vector machine (SVM) were applied comparatively to classify the samples stored at different days. The results revealed that SVM model was better than Fisher LDA model with a higher identification rate of 97.22% in the prediction set. Partial least square (PLS) and support vector regression (SVR) were applied comparatively to predict the TVB-N and TVC values. The quantitative models were evaluated by the root mean square error of prediction (RMSEP) and the correlation coefficient in the prediction set (Rpre). The results revealed that SVR model was superior to PLS model with RMSEP = 5.65 mg/100 g, Rpre = 0.9491 for TVB-N prediction and RMSEP = 0.73 log CFU/g, Rpre = 0.904 for TVC prediction. This study demonstrated that the electronic tongue together with SVM and SVR has a great potential for a convenient and nondestructive detection of fish freshness.

Keywords: fish quality; taste sensors; nondestructive detection; support vector machine; support vector regression; chemical and microbiological analyses (search for similar items in EconPapers)
Date: 2014
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Persistent link: https://EconPapers.repec.org/RePEc:caa:jnlcjf:v:32:y:2014:i:6:id:88-2014-cjfs

DOI: 10.17221/88/2014-CJFS

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