In this paper, rapid freshness analysis method of mantis shrimps (MSs) (Oratosquilla oratoria) by using electronic nose (e-nose) was investigated. Shrimps were stored at 4°and -20°. E-nose responses to samples were measured. Meanwhile, appearance of MS was recorded. Total volatile basic nitrogen (TVB-N) index was examined to provide a standard freshness indicator for samples according to Chinese national standard protocol. E-nose measurement data was processed by principal component analysis (PCA) and non-linear bistable stochastic resonance (SR). Experimental results demonstrated that e-nose sensitively responded to shrimps. PCA results failed to discriminate all shrimps. SR signal-to-noise ratio (SNR) spectrum successfully discriminated all shrimp samples stored at 4°and -20°. E-nose MS freshness degree forecasting models were developed using SNR maximums. Combining TVB-N examination results, the forecasting accuracy of developed freshness analysis model is 91.67 %. The proposed method had some advantages including rapid analysis, easy operating, non-destructive, low cost, etc.

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A bayberry quality predicting model (R = 0.98644) is developed via linear fitting regression of SR SNR-Max values. The validation experiment results demonstrate that the developed model presents a predicting accuracy of 95% for Chinese bayberry quality.