The method for storage time prediction of chilled-stored tilapia was explored by electronic nose combined with physical and chemical examination. According to national standard,pH and total volatile base nitrogen( TVBN) was examined. Electronic nose responses to the samples were measured. Principal component analysis(PCA)and stochastic resonance( SR) analysis were conducted on electronic nose measurement data. Compared with PCA result, SR output signal-to-noise ratio( SNR) discriminated storage time of tilapia samples qualitatively and quantitatively. Tilapia freshness threshold is -61. 168 8 dB according to TVBN national standard. Tilapia storage time predicting model was developed using SR SNR eigen value linear fitting regression. The predicting coefficient was R2=0. 910. Validating experiment results demonstrated that this model presented good predicting accuracy. This method is promising in aquatic product quality analysis applications.%采用电子鼻结合理化检验方法建立了一种预测低温贮藏罗非鱼储存时间的新方法。依据国家标准检验了罗非鱼样品低温储存过程中的pH值和挥发性盐基氮( TVBN)指标的变化,同时测量了电子鼻响应。采用主成分分析和非线性随机共振分析电子鼻检测数据,对比主成分分析结果,随机共振输出信噪比可以定性和定量的区分罗非鱼样品。依据TVBN国家标准计算得到罗非鱼电子鼻检测信噪比新鲜度阈值为-61.1688 dB。选取信噪比曲线特征值经线性拟合回归建立了罗非鱼储存时间预测模型,该模型的预测系数R2=0.910,验证实验结果表明可以准确预测罗非鱼的储存时间。该方法有望于在水产品品质快速分析中得到应用。
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