基于Trace Pro仿真的橙果品质光谱检测托盘参数设计与试验

为了实现橙果的内部品质可见/近红外光谱无损检测,采用Trace Pro软件对设计的橙果在线检测传送托盘模型进行光学仿真分析,参考仿真结果中的辉度/照度值,对值较高的托盘模型进行实物加工,结合实际光谱检测平台进行试验验证。仿真结果表明,成果传送托盘的最优外形参数为:外径80 mm、内横径55 mm,内纵径50mm、厚度20mm。采用不同材料对托盘进行加工,用于实际橙果可溶性固形物含量(SSC)检测,光谱数据经预处理后,建立偏最小二乘回归法(PLSR)的预测模型,其中亚克力托盘预测结果最优。为进一步优化检测模型,分别用遗传算法(GA)、稳定性竞争自适应重加权采样(SCARS)算法提取光谱特征波段,建立橙果SSC的PLSR的预测模型,其中SCARS算法特征提取方法最佳,预测决定系数R_(pre)~2为0.920 9;预测均方根误差(RMSEP)为0.468 3。

Parameter design and experiment of spectral detection tray for orange fruit quality based on Trace Pro simulation

In order to realize the visible/near infrared spectroscopy nondestructive testing of the internal quality of orange fruits, Trace pro software is used to carry out optical simulation analysis on the designed orange fruit online testing and conveying tray model. referring to the luminance/illuminance value in the simulation result, the tray model with higher value is processed in kind, and is tested and verified by combining with the actual spectrum testing platform.The simulation results show that the optimal shape parameters of the achievement transfer tray were: outer diameter 80 mm, inner transverse diameter 55 mm, inner longitudinal diameter 50 mm, and thickness 20 mm. Different materials were used to process the tray for the detection of soluble solids content (SSC) of actual orange fruits. After the spectral data were preprocessed, the partial least squares regression (PLSR) prediction model was used, among which acrylic tray had the best prediction result. In order to further optimize the detection model, genetic algorithm (GA) and stability competition adaptive re-weighted sampling (SCARS) algorithms are used to extract spectral characteristic bands respectively, and a prediction model of PLSR of orange SSC is established. The SCARS algorithm has the best feature extraction method, and the prediction decision coefficient R is 0.920 9. The predicted rms error (RMSEP) is 0.468 3.