Abstract：
To improve the classification and regression performance of the total volatile basic nitrogen (TVB-N) and acid value (AV) of different freshness fish meal samples detected by a metal-oxide semiconductor electronic nose (MOS e-nose), 402 original features, 62 manually extracted features, manually extracted and selected features by the RFRFE method, and the features extracted by the long short-term memory (LSTM) network were used as inputs to identify the freshness. The classification performance of the freshness grades and the estimation performance of the TVB-N and AV values of fish meal with different freshness were compared. According to the sensor response curve, preprocessing and feature extraction steps were first applied to the original data. Then, five classification algorithms and four regression algorithms were used for modeling. The results showed that a total of 30 features were extracted using the LSTM network, and the number of extracted features was significantly reduced. In the classification, the highest accuracy rate of 95.4% was obtained using the support vector machine method. In the regression, the least squares support vector regression method obtained the best root mean square error (RMSE). The coefficient of determination (R2), RMSE, and relative standard deviation (RSD) between the predicted value of TVBN and the actual value were 0.963, 11.01, and 7.9%, respectively. The R2, RMSE, and RSD between the predicted value of AV and the actual value were 0.972, 0.170, and 6.05%, respectively. The LSTM feature extraction method provided a new method and reference for feature extraction using an E-nose to identify other animal-derived material samples.
摘要：
为了提高金属氧化物半导体电子鼻(MOS电子鼻)检测不同新鲜度鱼粉样品的总挥发性碱性氮(TVB-N)和酸值(AV)的分类和回归性能,402个原始特征，62个手动提取的特征，通过RFRFE方法手动提取和选择特征，并将长短期记忆(LSTM)网络提取的特征作为输入来识别新鲜度。比较了具有不同新鲜度的鱼粉的新鲜度等级的分类性能以及TVB-N和AV值的估计性能。根据传感器响应曲线,预处理和特征提取步骤首先应用于原始数据。然后，采用五种分类算法和四种回归算法进行建模。结果表明，使用LSTM网络总共提取了30个特征，并且提取的特征数显着减少。在分类中,用支持向量机方法获得了95.4%的最高准确率。在回归中，最小二乘支持向量回归法获得了最好的均方根误差(RMSE)。决定系数(R2)，RMSE,TVBN预测值与实际值的相对标准偏差(RSD)分别为0.963、11.01和7.9%,分别。R2，RMSE，AV预测值与实际值的RSD分别为0.972、0.170和6.05%,分别。LSTM特征提取方法为使用电子鼻进行特征提取以识别其他动物来源的材料样本提供了新的方法和参考。
