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

Abstract

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 %.