基于信噪比分析技术的谷物霉变快速检测方法

该文探索了一种基于半导体气敏传感器阵列和随机共振信噪比分析技术的谷物霉变快速检测方法，试验测量了大米、小米、燕麦和红豆的霉变试验数据，输入分析系统处理并输出信噪比谱图，以基准信噪比特征值-82.5所包络的噪声宽度作为4种谷物样品霉变程度的数字化表征手段，可以直观的观察到每类样品的霉变过程。大米和小米样品在第4天检测时，就出现了较明显的霉变，燕麦样品在第7天出现霉变，而红豆样品在检测过程中未发生变化。该分析技术无需信号前处理手段，并且可以克服传感器基线漂移造成的干扰，系统响应速度快、重复性好，具有实际应用价值。

Investigation of moldy corn fast detection based on signal-to-noise ratio spectrum analysis technique

Moldy corn not only causes huge financial losses to our country, but also does great harm to human health and life safety. Stochastic resonance signal-to-noise ratio analysis technique is a novel method in sensor signal feature extraction field. In this paper, a gas detection method using semiconductor gas sensor and stochastic resonance signal-to-noise ratio analysis technique was established for rapid detection of moldy corn. Four kinds of corn samples were detected by the proposed method. The results showed that the signal-to-noise ratio analysis technique could characterize the moldy corn feature, and the detecting signal did not need pre-processing. The system has the advantages such as fast response, shorter desorption time, easy to use, low cost, durable, and so on. The system is promising in practical application.