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 (R
pre
). The results revealed that SVR model was superior
to PLS model with RMSEP = 5.65 mg/100 g, R
pre
= 0.9491 for TVB-N prediction and RMSEP = 0.73 log CFU/g, R
pre
=
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
