基于可见/近红外光谱的柑橘糖度在线检测分选系统的设计与试验  

Design and experiment of an online detection and sorting system for citrus sugar content based on visible/near infrared spectroscopy

作　　者：刘豪 龚中良[1] 文韬[1] 王志宇 代兴勇 LIU Hao;GONG Zhongliang;WEN Tao;WANG Zhiyu;DAI Xingyong(School of Mechanical and Electrical Engineering,Central South University of Forestry and Technology,Changsha,Hunan 410004,China)

机构地区：[1]中南林业科技大学机电工程学院,湖南长沙410004

出　　处：《湖南农业大学学报（自然科学版）》2023年第4期497-502,共6页Journal of Hunan Agricultural University(Natural Sciences)

基　　金：湖南省科学技术厅研发计划项目(2018NK2066);湖南省自然科学基金项目(2020JJ4142);湖南省教育厅林业杰青培养科研项目(XLK202108-7);湖南省教育厅重点项目(20A515)。

摘　　要：在6GF-4型林果无损检测与分选成套设备中,设计了基于可见/近红外光谱的柑橘糖度在线检测分选系统,系统主要包括传输装置、光谱采集装置、控制系统以及分选装置。系统在柑橘果实运动状态中采集其光谱信息,并通过所建立的果实糖度模型进行同步计算,根据所得糖度值对柑橘果实实现在线分选。在光谱采集装置中设计了双透镜式光路,可改变投射于柑橘果实上的光斑大小,通过研究比较试验参数积分时间和光斑尺寸大小,得出系统的最佳采集参数为积分时间100 ms,光斑尺寸设置为小,样本移动速率为5个/s。建立的SPXY-CARS-PLSR柑橘糖度在线检测模型校准集和预测集的决定系数分别为0.938和0.836,校准集和预测集的均方根误差分别为0.273°Brix和0.418°Brix。使用未参与建模的25个柑橘果实样本进行外部验证集的在线检测和分选,结果在1°Brix的误差范围内,检测糖度的准确率为92%;当样本分为4个等级时,系统分选正确率为92%;当样本分为3个等级时,系统分选正确率可达100%。In the 6GF-4 forest fruit non-destructive testing and sorting equipment,an online citrus sugar content detection and sorting system was designed based on visible/near-infrared spectroscopy technology.The system mainly includes transmission devices,spectral acquisition devices,control systems,and sorting devices,which achieves spectral information collection during citrus movement,and synchronously calculates through the established sugar content model,achieving online sorting based on the obtained sugar content values.A dual lens optical path has been designed in the spectral acquisition device,to change the size of the light spot transmitted on citrus fruits.By comparing and analyzing the integration time and spot size of the experimental parameters,it is found that the optimal collection parameters for the system were the integration time of 100 ms,the spot size set to small,and the sample movement rate of 5 pieces/s.An SPXY-CARS-PLSR online detection model was established for citrus sugar content.The determination coefficients is 0.938and 0.836 for the calibration and prediction sets of the model,respectively.The root mean square error is 0.273 °Brix and 0.418 °Brix for the calibration and prediction sets,respectively.25 citrus samples not involved in modeling were used for online detection and sorting of external validation sets.It is found that the detecting sugar contents are within the error range of 1 °Brix with the accuracy of 92%.When the sample is divided into 4 levels,the system's sorting accuracy is 92%;When the samples are divided into three levels,the system's sorting accuracy can reach 100%.

关 键 词：柑橘 糖度 在线检测 可见/近红外光谱 积分时间 光斑尺寸 

分 类 号：TP271.4[自动化与计算机技术—检测技术与自动化装置]