为实现对鳓鱼固态发酵过程的监测，用常规分析方法和电子舌技术分别测定了鳓鱼发酵过程中水分、pH、总酸、氨基态氮（ANN）、挥发性盐基氮（TVB-N）和味觉指纹的变化；基于电子舌数据，采用主成分分析（PCA）和判别分析（DA）对不同发酵时间的鳓鱼样品进行识别；采用偏最小二乘回归分析（PLSR）建立电子舌数据与相关理化指标之间的预测模型，并对模型进行评价。结果表明：发酵过程中鳓鱼理化指标和滋味特征均有显著变化；主成分分析提取的3个主成分的累积贡献率可达94.49%，判别分析的判别符合率为100%，不同发酵时间的鳓鱼能被有效识别; 基于电子舌响应信号建立的5种理化指标预测模型中，水分和ANN模型的相对分析误差（RPD）均为1.80，可用于定性分析。TVB-N 模型的RPD为2.47，具有一定定量检测分析能力。pH和总酸的PLSR预测模型的RPD大于5，定量效果良好，稳定性优良，预测精度高。因此，利用电子舌结合相关化学计量方法对鳓鱼固态发酵过程进行识别和监控可行。

To monitor the process of solid-state fermentation of Chinese herring, parameters including water content, pH, total acid content, amino nitrogen (ANN) content, total volatile basic nitrogen (TVB-N) content, and changes in the taste fingerprint were measured by conventional analytical methods and an electronic tongue. The principal component analysis (PCA) and discriminant analysis (DA) were used to identify fermented Chinese herring samples undergoing different durations of fermentation, while partial least-squares regression (PLSR) prediction models for the electric tongue and related physicochemical indicators were also established and evaluated. The results indicated that the physical indicators and taste of Chinese herring samples changed significantly during fermentation. The accumulative contribution rate of three extracted principal components was 94.49%, the discrimination coincidence rate was 100%, and the Chinese herring samples with different fermentation durations could be identified effectively by both, PAC and DA. In five prediction models based on electronic tongue signals, both relative percent deviation (RPD) values of water content and ANN models were 1.80, and the RPD value of TVB-N model was 2.47, which could be used for qualitative analysis. The RPD values of pH and total acids models were both > 5, which indicated good quantitative effect, good stability, and high prediction accuracy. Thus, it is feasible to identify and monitor the process of solid-state fermentation of Chinese herring using the electric tongue coupled with related chemometric methods.

国家自然科学基金（31271890）；国家科技部星火计划（2012GA701063）；教育部留学回国人员科研启动；浙江省教育厅中青年学科带头人学术攀登项目（pd2013100）；“水产”浙江省重中之重学科开放基金（xkzsc1428）