Wang Wei, Yang Weizhen, Li Maozhen, Zhang Zipeng, Du Wenbin
School of Information and Communication Engineering, North University of China, Taiyuan 030051, China.
Department of Electronic and Electrical Engineering, Brunel University London, Uxbridge UB8 3PH, UK.
Sensors (Basel). 2023 Jul 17;23(14):6476. doi: 10.3390/s23146476.
Apple is an important cash crop in China, and the prediction of its freshness can effectively reduce its storage risk and avoid economic loss. The change in the concentration of odor information such as ethylene, carbon dioxide, and ethanol emitted during apple storage is an important feature to characterize the freshness of apples. In order to accurately predict the freshness level of apples, an electronic nose system based on a gas sensor array and wireless transmission module is designed, and a neural network prediction model using an improved Sparrow Search Algorithm (SSA) based on chaotic sequence (Tent) to optimize Back Propagation (BP) is proposed. The odor information emitted by apples is studied to complete an apple freshness prediction. Furthermore, by fitting the relationship between the prediction coefficient and the input vector, the accuracy benchmark of the prediction model is set, which further improves the prediction accuracy of apple odor information. Compared with the traditional prediction method, the system has the characteristics of simple operation, low cost, reliable results, mobile portability, and it avoids the damage to apples in the process of freshness prediction to realize non-destructive testing.
苹果是中国重要的经济作物，对其新鲜度进行预测能够有效降低存储风险，避免经济损失。苹果在储存过程中所释放的乙烯、二氧化碳、乙醇等气味信息浓度的变化是表征苹果新鲜度的重要特征。为准确预测苹果的新鲜度水平，设计了一种基于气体传感器阵列和无线传输模块的电子鼻系统，并提出了一种基于混沌序列（帐篷映射）改进麻雀搜索算法（SSA）优化反向传播（BP）神经网络的预测模型。通过研究苹果释放的气味信息来完成苹果新鲜度预测。此外，通过拟合预测系数与输入向量之间的关系，设定了预测模型的精度基准，进一步提高了苹果气味信息的预测精度。与传统预测方法相比，该系统具有操作简单、成本低、结果可靠、便于移动携带的特点，并且在新鲜度预测过程中避免了对苹果的损伤，实现了无损检测。
