基于近红外稳态空间的苹果病变检测系统设计与试验

Design and Experiment of Apple Lesion Detection System Based on Near-infrared Steady-state Spatial Resolution

为了实现苹果内部病变的无损检测,设计了基于近红外稳态空间分辨技术的苹果病变检测系统。系统选用波长830 nm的半导体激光器作为光源,选择感光面积大、感光系数灵敏的FDS1010光电探测器实现苹果内部病变无损检测。搭建试验平台对检测系统的性能进行了分析,包括电流稳定性、温度稳定性和光强稳定性,系统稳定性满足试验需求。选择Evans Blue固体配制不同质量分数的纯吸收溶液,对检测装置的准确性和灵敏度进行验证,发现随着溶液浓度的升高,吸光度呈现线性变化的趋势,在纯吸收溶液中系统表现出了稳定的检测性能,且对不同质量分数也表现出良好的分辨性能。根据稳态空间分辨检测技术原理,设计苹果病变替换试验,通过改变光源的位置,并记录"源-探"之间的距离,依据漫射传输方程求出苹果病变位置的光学参数,与目前已知的苹果内部组织光学参数一致。研究结果表明该系统可以实现苹果内部病变无损检测。

In order to realize the nondestructive detection of apple lesions,an apple lesions detection system based on near-infrared steady-state spatial resolution technology was designed.The system used 830 nm semiconductor laser as the light source,and FDS1010 photoelectric detector with large sensitive area and sensitive sensitivity coefficient was selected to realize the nondestructive detection of internal lesions in apple.An experimental platform was built to analyze the performance of the detection system,including current stability,temperature stability and light intensity stability.The system stability met the experimental requirements.Choosing Evans Blue solid preparation of different mass fraction of pure absorption solution,the accuracy and sensitivity of detection device were validated,it was found that with the increase of solution concentration,the absorbance showed linear change trends,and in the pure absorption system in the solution it showed stable detection performance,and with different mass fraction also showed a good resolution performance.According to the principle of spatial resolution detection technology,apple lesions replacing experiment was designed,by changing the position of the light source,and recording the distance between the source-agent,on the basis of the diffuse transmission equation of the optical parameters of apple lesion location,it was consistent with the known apple internal tissue optical parameters,the results showed that the system can realize the nondestructive detection of apple internal lesions.