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

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• 作者：Huang Zhiyi ; Huang Chenchao ; Zhou Jiajia…
• 关键词：Freshness prediction ; Large yellow croaker ; Electronic nose ; Stochastic resonance ; Non ; linear
• 刊名：Journal of Food Measurement and Characterization
• 出版年：2017
• 出版时间：March 2017
• 年：2017
• 卷：11
• 期：1
• 页码：33-40
• 全文大小：
• 刊物类别：Food Science; Chemistry/Food Science, general; Engineering, general;
• 刊物主题：Food Science; Chemistry/Food Science, general; Engineering, general;
• 出版者：Springer US
• ISSN：2193-4134
• 卷排序：11

文摘

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 %.