苹果内部品质分级机械手设计与试验
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摘要
为检测苹果内部品质,基于可见/近红外光谱检测技术并结合分拣机械手,设计了苹果内部品质分级机械手。该装置主要由夹持机构、近红外光谱采集系统、控制系统等组成。机械手稳定夹持苹果后采集苹果的近红外光谱数据,上位机软件中的预测模型对光谱数据进行分析处理,并显示光谱曲线和预测结果。为建立苹果可溶性固形物含量预测模型,基于该装置采集了苹果在650～1 100 nm波长范围内的光谱数据,通过国家标准测量法测得苹果样本的可溶性固形物含量,采用SG卷积平滑(SG-smooth)、标准正态变量变换(SNV)和多元散射校正(MSC)对光谱数据进行预处理,并结合可溶性固形物含量测量值建立偏最小二乘(PLSR)模型。结果表明,采用多元散射校正方法预处理后的建模效果最优,其预测模型的校正集和预测集相关系数分别为0. 978 2、0. 970 1,均方根误差分别为0. 274 6、0. 326 3°Brix。选取20个同品种苹果样本对该装置的稳定性和准确性进行了测试,可溶性固形物含量预测值与测量值相关系数为0. 957 3,均方根误差为0. 422 4°Brix。试验结果表明,苹果内部品质分级机械手在夹持苹果的同时可以实现对苹果可溶性固形物含量的准确预测。
In order to meet the requirements of apple internal quality inspection,the apple internal quality sorting manipulator was designed based on the visible/near infrared spectroscopy detection technology and the sorting manipulator. The device consisted of three parts: clamping mechanism,near infrared spectroscopy acquisition system and control system. The manipulator clamped the apple and collected the near-infrared spectral data of the apple. The spectral data was analyzed by the predictive model in the upper computer software,and the spectral curve and predicted result were displayed. Based on this device,the visible and near-infrared reflection spectra of apple in the range of 650 ～ 1 100 nm were collected. Totally 200 apples were used for the experiment,including 150 apples in the prediction set and 50 apples in the verification set. The soluble solids content of the apples was measured by temperature-compensated refractometer after the collection of spectral information. The collected spectra were pretreated by Savitzky-Golay smooth( SG-smooth),standard normal variable transformation( SNV)and multiplication scattering correction( MSC). The partial least-squares prediction model of the apple's SSC was established with spectral data as independent variable and soluble solids as dependent variable. The prediction result that preprocessed with the multi-scattering correction( MSC) method was the best. The correlation coefficients of the calibration set and the verification set of the prediction model were 0. 978 2 and 0. 970 1,respectively,and the root mean square errors were 0. 274 6° Brix and0. 326 3°Brix,respectively. Finally,the accuracy of models was tested. The reflect spectra of 20 samples were collected,and then the real values of these samples' SSC were measured. The prediction model could give satisfactory results with the correlation coefficient of 0. 957 3 and the root mean square error of prediction of 0. 422 4° Brix. The results indicated that this device can satisfy the requirements of appleinternal quality detection with high accuracy and good performance.
In order to meet the requirements of apple internal quality inspection,the apple internal quality sorting manipulator was designed based on the visible/near infrared spectroscopy detection technology and the sorting manipulator. The device consisted of three parts: clamping mechanism,near infrared spectroscopy acquisition system and control system. The manipulator clamped the apple and collected the near-infrared spectral data of the apple. The spectral data was analyzed by the predictive model in the upper computer software,and the spectral curve and predicted result were displayed. Based on this device,the visible and near-infrared reflection spectra of apple in the range of 650 ～ 1 100 nm were collected. Totally 200 apples were used for the experiment,including 150 apples in the prediction set and 50 apples in the verification set. The soluble solids content of the apples was measured by temperature-compensated refractometer after the collection of spectral information. The collected spectra were pretreated by Savitzky-Golay smooth( SG-smooth),standard normal variable transformation( SNV)and multiplication scattering correction( MSC). The partial least-squares prediction model of the apple's SSC was established with spectral data as independent variable and soluble solids as dependent variable. The prediction result that preprocessed with the multi-scattering correction( MSC) method was the best. The correlation coefficients of the calibration set and the verification set of the prediction model were 0. 978 2 and 0. 970 1,respectively,and the root mean square errors were 0. 274 6° Brix and0. 326 3°Brix,respectively. Finally,the accuracy of models was tested. The reflect spectra of 20 samples were collected,and then the real values of these samples' SSC were measured. The prediction model could give satisfactory results with the correlation coefficient of 0. 957 3 and the root mean square error of prediction of 0. 422 4° Brix. The results indicated that this device can satisfy the requirements of appleinternal quality detection with high accuracy and good performance.
引文
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[6]满尊.鲜枣品质在线光谱无损检测与分选系统研究[D].太谷:山西农业大学,2016.MAN Zun. The study of on-line nondestructive detecting and sorting system of the quality of fresh jujube based on near infrared spectroscopy[D]. Taigu:Shanxi Agricultural University,2016.(in Chinese)
[7] SOFU M M,ER O,KAYACAN M C. Design of an automatic apple sorting system using machine vision[J]. Computers&Electronics in Agriculture,2016,127(C):395-405.
[8] BANEH N M,NAVID H,KAFASHAN J. Mechatronic components in apple sorting machines with computer vision[J]. Journal of Food Measurement&Characterization,2018(2):1-21.
[9] BLASCO J,ALEIXOS N,MOLTE. Machine vision system for automatic quality grading of fruit[J]. Biosystems Engineering,2003,85(4):415-423.
[10] ISHII T,TOITA H,KONDO N,et al. Deciduous fruit grading robot(part 1):development of fruit providing and grading robots[J]. Journal of JSAM,2003,65:163-172.
[11]宋雪健,王洪江,张东杰,等.基于近红外光谱技术的水果品质检测研究进展[J].无损检测,2017,39(10):71-75.SONG Xuejian,WANG Hongjiang,ZHANG Dongjie,et al. Research progress of fruit quality detection based on near infrared spectroscopy[J]. Nondestructive Testing,2017,39(10):71-75.(in Chinese)
[12]潘圆媛,刘燕德,孙旭东.水果内部品质近红外动态在线检测研究进展[J].激光与红外,2010,40(8):805-810.PAN Yuanyuan,LIU Yande,SUN Xudong. Research progress of on-line detection of fruit quality using near infrared spectroscopy[J]. Laser and Infrared,2010,40(8):805-810.(in Chinese)
[13]苏东林,李高阳,何建新,等.近红外光谱分析技术在我国大宗水果品质无损检测中的应用研究进展[J].食品工业科技,2012,33(6):460-464.SU Donglin,LI Gaoyang,HE Jianxin,et al. Progress in application of near infrared spectroscopy to nondestructive detection of big yield fruit[J]. Science and Technology of Food Industry,2012,33(6):460-464.(in Chinese)
[14]孙旭东,龚志远,蔡丽君,等.水果糖度近红外光谱在线检测装置[J].中国农机化学报,2012,33(2):116-120.SUN Xudong,GONG Zhiyuan,CAI Lijun,et al. Device and method of infrared thermography in surface quality detection of apples[J]. Journal of Chinese Agricultural Mechanization,2012,33(2):116-120.(in Chinese)
[15]王凡,李永玉,彭彦昆,等.便携式番茄多品质参数可见/近红外检测装置研发[J].农业工程学报,2017,33(19):295-300.WANG Fan,LI Yongyu,PENG Yankun,et al. Development of portable device for simultaneous detection on multi-quality attributes of tomato by visible and near-infrared[J]. Transactions of the CSAE,2017,33(19):295-300.(in Chinese)
[16]王转卫,迟茜,郭文川,等.基于近红外光谱技术的发育后期苹果内部品质检测[J/OL].农业机械学报,2018,49(5):348-354. http:∥www. j-csam. org/jcsam/ch/reader/view_abstract. aspx? flag=1&file_no=20180541&journal_id=jcsam.DOI:10. 6041/j. issn. 1000-1298. 2018. 05. 041.WANG Zhuanwei,CHI Qian,GUO Wenchuan,et al. Internal quality detection of apples during late developmental period based on near-infrared spectral technology[J/OL]. Transactions of the Chinese Society for Agricultural Machinery,2018,49(5):348-354.(in Chinese)
[17]刘燕德,周延睿.便携式近红外水果内部品质检测仪原理及应用进展[J].中国农机化学报,2013,34(4):204-209.LIU Yande,ZHOU Yanrui. Principle and application of nondestructive evaluation for fruit internal quality using portable nearinfrared spectrometry[J]. Journal of Chinese Agricultural Mechanization,2013,34(4):204-209.(in Chinese)
[18]宫元娟,裴军强,李宏博,等.便携式苹果品质快速无损检测系统设计[J].沈阳农业大学学报,2017,48(2):238-243.GONG Yuanjuan,PEI Junqiang,LI Hongbo,et al. Design of portable quickly nondestructive detection system apple quality[J]. Journal of Shenyang Agricultural University,2017,48(2):238-243.(in Chinese)
[19]郅建军,陈通,孙武坚,等.微型近红外光谱仪检测水果可溶性固形物[J].分析仪器,2016(1):71-76.ZHI Jianjun,CHEN Tong,SUN Wujian,et al. Detection of the soluble solids content(SSC)in fruit using miniature NIR spectrometry[J]. Analytical Instrumentation,2016(1):71-76.(in Chinese)
[20] CROOKS W,VUKASIN G,O'SULLIVAN M,et al. Fin Ray effect inspired soft robotic gripper:from the Robo Soft Grand challenge toward optimization[J]. Frontiers in Robotics and AI,2016,70(3):1-9.
[21] GANDARIAS J M,GMEZDEGABRIEL J M,GARCACEREZO A J. Enhancing perception with tactile object recognition in adaptive grippers for human-robot interaction[J]. Sensors,2018,18(3):692.
[22] XIAO B Z,JIEWEN Z,POVEY M J,et al. Variables selection methods in near-infrared spectroscopy[J]. Analytica Chimica Acta,2010,667(1-2):14-32.
[23]戚淑叶.可见/近红外光谱检测水果品质时影响因素的研究[D].北京:中国农业大学,2016.QI Shuye. Research of influence factors on determination of fruit quality by visible and near infrared spectroscopy[D].Beijing:China Agricultural University,2016.(in Chinese)
[24] GB12295—1990水果、蔬菜制品可溶性固形物含量的测定——折射仪法[S]. 1990.
[25]赵杰文,孙永海.现代食品检测技术[M].北京:中国轻工业出版社,2008.
[2]陈学森,韩明玉,苏桂林,等.当今世界苹果产业发展趋势及我国苹果产业优质高效发展意见[J].果树学报,2010,27(4):598-604.CHEN Xuesen,HAN Mingyu,SU Guilin,et al. Discussion on today's world apple industry trends and the suggestions on sustainable and efficient development of apple industry in China[J]. Journal of Fruit Science,2010,27(4):598-604.(in Chinese)
[3]宋哲,王宏,里程辉,等.我国苹果产业存在的主要问题、发展趋势及解决办法[J].江苏农业科学,2016,44(9):4-8.SONG Zhe,WANG Hong,LI Chenghui,et al. The main problems,development trends and solutions of apple industry in China[J]. Jiangsu Agricultural Sciences,2016,44(9):4-8.(in Chinese)
[4]白小凯.苹果霉心病在线检测分选试验平台设计与实现[D].杨凌:西北农林科技大学,2017.BAI Xiaokai. Design and implementation of sorting test platform for online detection of moldy core in apples[D]. Yangling:Northwest A&F University,2017.(in Chinese)
[5]李龙,彭彦昆,李永玉.苹果内外品质在线无损检测分级系统设计与试验[J].农业工程学报,2018,34(9):267-275.LI Long,PENG Yankun,LI Yongyu. Design and experiment on grading system for online non-destructive detection of internal and external quality of apple[J]. Transactions of the CSAE,2018,34(9):267-275.(in Chinese)
[6]满尊.鲜枣品质在线光谱无损检测与分选系统研究[D].太谷:山西农业大学,2016.MAN Zun. The study of on-line nondestructive detecting and sorting system of the quality of fresh jujube based on near infrared spectroscopy[D]. Taigu:Shanxi Agricultural University,2016.(in Chinese)
[7] SOFU M M,ER O,KAYACAN M C. Design of an automatic apple sorting system using machine vision[J]. Computers&Electronics in Agriculture,2016,127(C):395-405.
[8] BANEH N M,NAVID H,KAFASHAN J. Mechatronic components in apple sorting machines with computer vision[J]. Journal of Food Measurement&Characterization,2018(2):1-21.
[9] BLASCO J,ALEIXOS N,MOLTE. Machine vision system for automatic quality grading of fruit[J]. Biosystems Engineering,2003,85(4):415-423.
[10] ISHII T,TOITA H,KONDO N,et al. Deciduous fruit grading robot(part 1):development of fruit providing and grading robots[J]. Journal of JSAM,2003,65:163-172.
[11]宋雪健,王洪江,张东杰,等.基于近红外光谱技术的水果品质检测研究进展[J].无损检测,2017,39(10):71-75.SONG Xuejian,WANG Hongjiang,ZHANG Dongjie,et al. Research progress of fruit quality detection based on near infrared spectroscopy[J]. Nondestructive Testing,2017,39(10):71-75.(in Chinese)
[12]潘圆媛,刘燕德,孙旭东.水果内部品质近红外动态在线检测研究进展[J].激光与红外,2010,40(8):805-810.PAN Yuanyuan,LIU Yande,SUN Xudong. Research progress of on-line detection of fruit quality using near infrared spectroscopy[J]. Laser and Infrared,2010,40(8):805-810.(in Chinese)
[13]苏东林,李高阳,何建新,等.近红外光谱分析技术在我国大宗水果品质无损检测中的应用研究进展[J].食品工业科技,2012,33(6):460-464.SU Donglin,LI Gaoyang,HE Jianxin,et al. Progress in application of near infrared spectroscopy to nondestructive detection of big yield fruit[J]. Science and Technology of Food Industry,2012,33(6):460-464.(in Chinese)
[14]孙旭东,龚志远,蔡丽君,等.水果糖度近红外光谱在线检测装置[J].中国农机化学报,2012,33(2):116-120.SUN Xudong,GONG Zhiyuan,CAI Lijun,et al. Device and method of infrared thermography in surface quality detection of apples[J]. Journal of Chinese Agricultural Mechanization,2012,33(2):116-120.(in Chinese)
[15]王凡,李永玉,彭彦昆,等.便携式番茄多品质参数可见/近红外检测装置研发[J].农业工程学报,2017,33(19):295-300.WANG Fan,LI Yongyu,PENG Yankun,et al. Development of portable device for simultaneous detection on multi-quality attributes of tomato by visible and near-infrared[J]. Transactions of the CSAE,2017,33(19):295-300.(in Chinese)
[16]王转卫,迟茜,郭文川,等.基于近红外光谱技术的发育后期苹果内部品质检测[J/OL].农业机械学报,2018,49(5):348-354. http:∥www. j-csam. org/jcsam/ch/reader/view_abstract. aspx? flag=1&file_no=20180541&journal_id=jcsam.DOI:10. 6041/j. issn. 1000-1298. 2018. 05. 041.WANG Zhuanwei,CHI Qian,GUO Wenchuan,et al. Internal quality detection of apples during late developmental period based on near-infrared spectral technology[J/OL]. Transactions of the Chinese Society for Agricultural Machinery,2018,49(5):348-354.(in Chinese)
[17]刘燕德,周延睿.便携式近红外水果内部品质检测仪原理及应用进展[J].中国农机化学报,2013,34(4):204-209.LIU Yande,ZHOU Yanrui. Principle and application of nondestructive evaluation for fruit internal quality using portable nearinfrared spectrometry[J]. Journal of Chinese Agricultural Mechanization,2013,34(4):204-209.(in Chinese)
[18]宫元娟,裴军强,李宏博,等.便携式苹果品质快速无损检测系统设计[J].沈阳农业大学学报,2017,48(2):238-243.GONG Yuanjuan,PEI Junqiang,LI Hongbo,et al. Design of portable quickly nondestructive detection system apple quality[J]. Journal of Shenyang Agricultural University,2017,48(2):238-243.(in Chinese)
[19]郅建军,陈通,孙武坚,等.微型近红外光谱仪检测水果可溶性固形物[J].分析仪器,2016(1):71-76.ZHI Jianjun,CHEN Tong,SUN Wujian,et al. Detection of the soluble solids content(SSC)in fruit using miniature NIR spectrometry[J]. Analytical Instrumentation,2016(1):71-76.(in Chinese)
[20] CROOKS W,VUKASIN G,O'SULLIVAN M,et al. Fin Ray effect inspired soft robotic gripper:from the Robo Soft Grand challenge toward optimization[J]. Frontiers in Robotics and AI,2016,70(3):1-9.
[21] GANDARIAS J M,GMEZDEGABRIEL J M,GARCACEREZO A J. Enhancing perception with tactile object recognition in adaptive grippers for human-robot interaction[J]. Sensors,2018,18(3):692.
[22] XIAO B Z,JIEWEN Z,POVEY M J,et al. Variables selection methods in near-infrared spectroscopy[J]. Analytica Chimica Acta,2010,667(1-2):14-32.
[23]戚淑叶.可见/近红外光谱检测水果品质时影响因素的研究[D].北京:中国农业大学,2016.QI Shuye. Research of influence factors on determination of fruit quality by visible and near infrared spectroscopy[D].Beijing:China Agricultural University,2016.(in Chinese)
[24] GB12295—1990水果、蔬菜制品可溶性固形物含量的测定——折射仪法[S]. 1990.
[25]赵杰文,孙永海.现代食品检测技术[M].北京:中国轻工业出版社,2008.