论文部分内容阅读
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-61.168 8 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。
A new method for predicting the storage time of tilapia at low temperature was established by electronic nose combined with physical and chemical test. The changes of pH value and volatile TVBN index of tilapia samples were tested according to the national standards at the same time, and the electronic nose response was measured. Principal component analysis and nonlinear stochastic resonance analysis of electronic nose detection data, compared with the results of principal component analysis, stochastic resonance output signal-to-noise ratio can qualitatively and quantitatively distinguish tilapia samples. According to the TVBN national standard, the freshness threshold of tilapia electronic nose detection is -61.168 8 dB. The triterpene storage time prediction model was established by linear regression fitting of signal-noise ratio (EISR) curve eigenvalue. The prediction coefficient of the model was R2 = 0.910. The validation results showed that the tilapia storage time can be accurately predicted. This method is expected to be applied in the rapid analysis of aquatic product quality.