Electronic nose system fabrication and application in large yellow croaker (Pseudosciaena crocea) fressness prediction

In this paper, an electronic nose (E-nose) system was fabricated, and its application in large yellow croaker (Pseudosciaena crocea) freshness prediction was also explored. E-nose responses to samples stored at 277 K were measured for 8 days. Freshness indexes, such as total viable counts (TVC), total volatile basic nitrogen (TVB-N) and K value, were synchronously examined by chemical examinations. Principal component analysis (PCA) and stochastic resonance (SR) were utilized for e-nose data analysis. Results suggested that PCA showed poor freshness discrimination result. SR signal-to-noise (SNR) spectrum using maximal SNR (Max(SNR)) values quantitatively characterized freshness of all croakers. Multiple variable regression (MVR) result demonstrated that there was good linearity relationship between SR Max(SNR) values and fish freshness indexes. Large yellow croaker freshness predicting model was developed by non-linear fitting regression on Max(SNR) values with high accuracy and repeatability. Therefore, the method proposed in this paper provides a rapid and nondestructive methodology for freshness prediction of large yellow croakers. The predicting error of the developed model is 10 %.