Quantitative analysis of fish meal freshness using an electronic nose combined with chemometric methods

Total volatile basic nitrogen (TVB-N) and acid value (AV) are the main indicators for characterizing fish meal freshness. To explore the feasibility of fish meal freshness detection based on an electronic nose system, a portable electronic nose measurement system was developed to detect the TVB-N and AV mass fraction in fish meal. The device is mainly composed of a gas acquisition and transmission module, a control and processing storage module with Raspberry Pi as the core, an ARPI600 data acquisition module, and a sensor array module. The system was used to collect the odour signals of fish meal samples with different TVB-N and AV mass fractions and the chemical test was conducted for the TVB-N and AV mass fractions in fish meal samples. The feature selection method of recursive feature elimination based on random forest (RFRFE) was used to select the sensor array features, and models of the TVB-N and AV mass fraction in fish meal were established by random forest regression (RFR) and least squares support vector machine (LSSVM) model based on the particle swarm optimization (PSO) algorithm, and the models were verified by a test sample. The experimental results show that the performance of the LSSVM regression model based on the RFRFE optimized sensor array data set is better than that of the traditional LSSVM model, The R2 and root mean square error (RMSE) between the predicted values and the real values for AV and TVB-N based on the test set are 0.8585, 0.3622 and 0.8789, 20.52, respectively. Therefore, the performance of feature selection using the RFRFE method is better. The results show that the TVB-N and AV of fish meal can be detected effectively by electronic nose technology.