Abstract
In the process of growth, apples’ ripening, storage, transportation, and processing, the appearance and internal physiological characteristics will have some changes because of the effect of time and physical attributes. A series of problems, such as the false ripeness and putrefaction of fruit, will bring huge economic losses to fruit vendors and harm to consumers. In this paper, an odor recognition system has been designed for the fast evaluation of the freshness characteristics of apples, which is based on the freshness characteristics of Fuji apple. A series of apple-air mixture with equivalent model was established by studying the change of gas concentration during the growth and storage of apples. The continuous projection algorithm (Successive Projections Algorithm, SPA) is used to optimize the sensor array to solve the problems of collinearity and overlap and also to eliminate the abnormal and redundant sensors. ZigBee wireless sensor network is adopted to send data to host computer, and BP (Error Backpropagation) neural network algorithm optimized by SFLA (shuffled complex evolution, SCE + Particle Swarm Optimization, PSO) algorithm is used to recognize gas data, which greatly improves the training speed and precision of neural network. The experimental results show that the detection accuracy of the Fuji apples freshness is 98.67% and can quickly and comprehensively identify the freshness of apples.
Highlights
In 1982, Persaud and Dodd proposed the concept of an electronic nose, which consisted of two main components: an odor sensor array and a pattern recognition system. e odor sensor is the core component of the electronic nose system
The MOS sensor based on changes operates in its conductivity and resistance, which are caused by the measured redox between the odor and the odor sensing component. is is the most widely used sensor
Based on the data collected by the sensor array, simple and complex odors can be identified by an appropriate pattern recognition system
Summary
In 1961 Moncrieff, the first to complete the study of odor recognition technology, developed a mechanical odor detection device. In 1982, Persaud and Dodd proposed the concept of an electronic nose, which consisted of two main components: an odor sensor array and a pattern recognition system. Brezmes et al [2] used smell recognition to detect the ripeness of pear, apple, and peach, adopted neural network analysis to identify fruit samples, and found that there is a close relationship. The study of odor recognition technology in scientific research institutions is still in the laboratory stage, which is suitable for on-the-spot real-time detection and network remote monitoring. A high-precision identification and matching algorithm is proposed to transmit detection data by wireless communication in the open environment, in which the self-made gas sensor array has a great price advantage over the finished electronic nose
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