基于光子传输模拟的苹果品质高光谱检测源探位置研究
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摘要
光谱无损的检测方法是质检测最常用的方法之一。传统的光谱仪光源探头位置和源探距离相对固定,导致品质检测精度受限。为解决这个问题,提出基于蒙特卡洛的苹果多层组织的光子传输模拟,分析了光子入射最佳位置和源探距离,并用点光源高光谱仪实际拍摄红富士苹果进行验证。分析表明,光子在苹果赤道位置入射,具有73.12%概率到达更深的深度。源探距离与苹果的光学参数有关,形状为圆环,源探距离内外半径为1.5～10.15 mm。点光源高光谱仪采集红富士苹果的光谱信息,光子入射位置为赤道,源探距离为距离光源点半径2.7～11.7mm的圆环,与模拟数据分析结果基本一致。蒙特卡洛光子传输模拟方法为研究高光谱苹果品质无损检测开辟了新思路,分析结果可以为研究高光谱品质检测试验设计和苹果便携式品质检测光学仪器设计提供理论基础。
Due to its high nutritional value, strong ecological adaptability and storage resistance, apple has become one of the world's most consumed fruits. At present, most of the tools mostly used in non-destructive testing of apples at home and abroad are hyperspectral instruments, but the hyperspectral apparatus has the following problems: First, the number of samples actually measured is extremely limited, the speed is slow, the cost is high, the coverage is small, and the error is inevitable. Second, the actual collection process consumes a lot of manpower and material resources. In view of the above problems, this paper proposes a new idea of improvement. Firstly, the Monte Carlo simulation of photon transmission on a multi-layer apple model based on ellipsoidal surface equation is implemented. Secondly, the trace of photons in the multi-layer apple model is analyzed. Based on this, the optimal incident position of the light source and the optimal detection position of the probe during the detection of the hyperspectral quality of the apple were analyzed and verified. The main works of this paper are as follows: 1) on the basis of realizing the Monte Carlo simulation algorithm of photon transmission, firstly, the multi-layer apple model based on ellipsoidal surface is studied. The apple model simulation based on ellipsoidal surface is realized by studying the motion behavior of photons on the surface, including normal vector solution, intersection point calculation and inverse refraction calculation. Finally, the two algorithms are optimized to a certain extent, and the parallelization of the two algorithms is realized by using MATLAB parallel computing toolbox, which improves the computing speed of the algorithm. 2) Based on the realization of the apple model photon transmission simulation, the optimal incident position of the apple hyperspectral quality detection source and the optimal detection position of the probe are further analyzed. Firstly, the photon motion of the light source at different positions of the apple model is simulated. The relationship between the incident depth and the different incident positions when photons are transmitted in the apple tissue is analyzed, and the optimal incident position is determined. By statistically analyzing the maximum incident depth of photons under different optical parameters and model sizes, it is found that photons have a better probability for reaching near the equatorial position of the apple to reach a deeper depth. At the same time, the effective photon ratio standard of photon scattering from the surface of apple model is defined, and the optimal range of source and probe distance when using applet to detect apple quality is proposed. The optimal detection range and apple optical parameters and apple model size parameters are analyzed. The correlation determines the factors that influence the change in the optimal detection area. The source distance is related to the optical parameters of the apple, and the shape is a ring. The radius of the inner and outer diameter of the source is 1.5～10.15 mm. The point source hyperspectral collects the spectral information of the red Fuji apple. The photon incident position is the equator, and the source distance is a circle with a radius of 2.7-11.7 mm, which is basically consistent with the simulation data analysis results. The Monte Carlo photon transmission simulation method opens up new ideas for studying non-destructive testing of high-spectrum apple quality.
Due to its high nutritional value, strong ecological adaptability and storage resistance, apple has become one of the world's most consumed fruits. At present, most of the tools mostly used in non-destructive testing of apples at home and abroad are hyperspectral instruments, but the hyperspectral apparatus has the following problems: First, the number of samples actually measured is extremely limited, the speed is slow, the cost is high, the coverage is small, and the error is inevitable. Second, the actual collection process consumes a lot of manpower and material resources. In view of the above problems, this paper proposes a new idea of improvement. Firstly, the Monte Carlo simulation of photon transmission on a multi-layer apple model based on ellipsoidal surface equation is implemented. Secondly, the trace of photons in the multi-layer apple model is analyzed. Based on this, the optimal incident position of the light source and the optimal detection position of the probe during the detection of the hyperspectral quality of the apple were analyzed and verified. The main works of this paper are as follows: 1) on the basis of realizing the Monte Carlo simulation algorithm of photon transmission, firstly, the multi-layer apple model based on ellipsoidal surface is studied. The apple model simulation based on ellipsoidal surface is realized by studying the motion behavior of photons on the surface, including normal vector solution, intersection point calculation and inverse refraction calculation. Finally, the two algorithms are optimized to a certain extent, and the parallelization of the two algorithms is realized by using MATLAB parallel computing toolbox, which improves the computing speed of the algorithm. 2) Based on the realization of the apple model photon transmission simulation, the optimal incident position of the apple hyperspectral quality detection source and the optimal detection position of the probe are further analyzed. Firstly, the photon motion of the light source at different positions of the apple model is simulated. The relationship between the incident depth and the different incident positions when photons are transmitted in the apple tissue is analyzed, and the optimal incident position is determined. By statistically analyzing the maximum incident depth of photons under different optical parameters and model sizes, it is found that photons have a better probability for reaching near the equatorial position of the apple to reach a deeper depth. At the same time, the effective photon ratio standard of photon scattering from the surface of apple model is defined, and the optimal range of source and probe distance when using applet to detect apple quality is proposed. The optimal detection range and apple optical parameters and apple model size parameters are analyzed. The correlation determines the factors that influence the change in the optimal detection area. The source distance is related to the optical parameters of the apple, and the shape is a ring. The radius of the inner and outer diameter of the source is 1.5～10.15 mm. The point source hyperspectral collects the spectral information of the red Fuji apple. The photon incident position is the equator, and the source distance is a circle with a radius of 2.7-11.7 mm, which is basically consistent with the simulation data analysis results. The Monte Carlo photon transmission simulation method opens up new ideas for studying non-destructive testing of high-spectrum apple quality.
引文
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[5]Peng Yankun,Lu Renfu.Improving apple fruit firmness predictions by effective correction of multispectral scattering images[J].Postharvest Biology and Technology,2006,41(3):266-274.
[6]Lu Renfu.Nondestructive measurement of firmness and soluble solids content for apple fruit using hyperspectral scattering images[J].Sensing and Instrumentation for Food Quality and Safety,2007,1(1):19-27.
[7]Qin Jianwei,Lu Renfu,Peng Yankun.Prediction of apple internal quality using spectral absorption and scattering properties[J].Transactions of the ASABE,2009,52(2):499-486.
[8]杨昆程,孙梅,陈兴海.水果成熟度的高光谱成像无损检测研究[J].食品科学技术学报,2015,33(4):63-67.
[9]Li Jianglin,Sun Dawen,Cheng Junhu.Recent advances in nondestructive analytical techniques for determining the total soluble solids in fruits:A review[J].Comprehensive Reviews in Food Science&Food Safety,2016,15(5):897-911.
[10]Manuel L,Andres G,Eduardo A.A multipurpose portable instrument for determining ripeness in wine grapes uising NIR spectroscopy[J].IEEE Transactions on Instrumentation and Measurement,2008,57(2):294-302.
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[12]Liu Yande,Gao Rongjie,Hao Yong,et al.Improvement of near-infrared spectral calibration models for brix prediction in‘Gannan’navel oranges by a portable near-infrared device[J].Food and Bioprocess Technology,2012,5(3):1106-1112.(in Chinese with English abstract)
[13]袁雷明,蔡健荣,孙力,等.可见-近红外光谱用于鲜食葡萄感官偏好的检测[J].光谱学与光谱分析,2017(4):1220-1225.Yuan Leiming,Cai Jianrong,Sun Li,et al.Inspection of the sensory preference for table grape with visual-near infrared spectroscopy[J].Spectroscopy and Spectral Analysis,2017(4):1220-1225.(in Chinese with English abstract)
[14]袁雷明,郭珍珠,陈孝敬,等.基于可见/近红外光谱技术的便携分析仪的应用[J].食品安全质量检测学报,2017,8(9):3455-3460.Yuan Leiming,Guo Zhenzhu,Chen Xiaojing,et al.Application of a portably analyzer based on visual/near infrared spectroscopy[J].Journal of Food Safety and Quality,2017,8(9):3455-3460.(in Chinese with English abstract)
[15]郭志明,陈全胜,张彬,等.果蔬品质手持式近红外光谱检测系统设计与试验[J].农业工程学报,2017,33(8):245-250.Guo Zhiming,Chen Quansheng,Zhang Bin,et al.Design and experiment of handheld near-infrared spectrometer for determination of fruit and vegetable quality[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2017,33(8):245-250.(in Chinese with English abstract)
[16]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 Chinese Society of Agricultural Engineering(Transactions of the CSAE),2017,33(19):295-300.(in Chinese with English abstract)
[17]Zaccanti Giovanni.Monte Carlo study of light propagation in optically thick media:Point source case[J].Applied Optics,1991,30(15):2031-2041.
[18]Wang Lihong,Jacques Li,Zheng Liqiong.MCML-Monte Carlo modeling of light transport in multi-layered tissues[J].Computer Methods&Programs in Biomedicine,1995,47(2):131-146.
[19]Maxime Roger,Cyril Caliot,Nicolas Crouseilles,et al.Ahybrid transport-diffusion model for radiative transfer in absorbing and scattering media[J].Journal of Computational Physics,2014,275(15):346-362.
[20]Zhu Caigang,Liu Quan.Review of Monte Carlo modeling of light transport in tissues[J].Journal of Biomedical Optics2013,18(5):50-62.
[21]Wei Li,Lin Ling,Bao Lei,et al.Monte Carlo simulation of photon migration in multi-component media[J].Optical&Quantum Electronics,2015,47(7):1919-1931.
[22]Guy Yona,Nizan Meitav,Itamar Kahn,et al.Realistic numerical and analytical modeling of light scattering in brain tissue for optogenetic applications[J].Eneuro,2016,3(1):22-35.
[23]Liu Yan,Yang Xue,Li Gang,et al.Optimizing Monte Carlo simulation for detecting the internal information in a fat-muscle media[J].Optical&Quantum Electronics,201648(6):1-11.
[24]Qin Jianwei,Lu Renfu.Monte Carlo simulation for quantification of light transport features in apples[J]Computers&Electronics in Agriculture,2009,68(1):44-51.
[25]Askoura Lamine,Vaudelle Fabrice,L'Huillier Pierre Numerical study of light transport in apple models based on monte carlo simulations[J].Photonics,2015,3(11):26-41.
[26]Ding Chizhu,Shi Shuning,Chen Jianjun,et al.Analysis of light transport features in stone fruits using monte carlo simulation[J].Plos One,2015,10(10):10-12.
[27]石舒宁.水果组织的光学描述与光传输规律研究[D].武汉:华中农业大学,2016.Shi Shuning.Optical Description of Fruit Tissue and Study of Optical Transmission Discipline[D].Wuhan:Huazhong Agricultural University,2016.(in Chinese with English abstract)
[28]陆玲,王蕾.基于椭球变形的植物果实造型[J].农业机械学报,2007,38(4):114-117.Lu Ling,Wang Lei.Plant fruit model ing based on the distortion to ellipsoid[J].Transactions of the Chinese Society for Agricultural Machinery,2007,38(4):114-117.(in Chinese with English abstract)
[29]陆玲,周书民.植物果实的几何造型及可视化研究[J].系统仿真学报,2007,19(8):1739-1741.Lu Ling,Zhou Shumin.Study on geometry model and visualization of plant fruit[J].Journal of System Simulation2007,19(8):1739-1741.(in Chinese with English abstract)
[30]刘妍.基于空间漫反射法检测多层生物组织内部信息的MC模拟研究[D].天津:天津大学,2016.Liu Yan.Monte Carlo Simulation of Detecting the Inner Information of Multi-layered Biological Tissue Based on Spatially Resolved Diffuse Reflectance Technology[D].Tianjin:Tianjin University,2016.(in Chinese with English abstract)
[2]公丽艳,孟宪军,刘乃侨,等.基于主成分与聚类分析的苹果加工品质评价[J].农业工程学报,2014,30(13):276-285.Gong Liyan,Meng Xianjun,Liu Naiqiao,et al.Evaluation of apple quality based on principal component and hierarchical cluster analysis[J].Transactions of the Chinese Society of Agricultural Engineerin(Transactions of the CSAE),2014,30(13):276-285.(in Chinese with English abstract)
[3]方振欢.用于水果组织光传输特性检测的单积分球系统研制及应用[D].杭州:浙江大学,2015.Fang Zhenhuan.Development and Application of A Single Intergrating Sphere System for Detecting Optical Properties of Fruit Tissue[D].Hangzhou:Zhejiang University,2015.(in Chinese with English abstract)
[4]Marco Ciotti,Anton de Jager,Herman de Putter,et al.Non-destructive determination of soluble solids in apple fruit by near infrared spectroscopy(NIRS)[J].Postharvest Biology&Technology,1998,14(1):21-27.
[5]Peng Yankun,Lu Renfu.Improving apple fruit firmness predictions by effective correction of multispectral scattering images[J].Postharvest Biology and Technology,2006,41(3):266-274.
[6]Lu Renfu.Nondestructive measurement of firmness and soluble solids content for apple fruit using hyperspectral scattering images[J].Sensing and Instrumentation for Food Quality and Safety,2007,1(1):19-27.
[7]Qin Jianwei,Lu Renfu,Peng Yankun.Prediction of apple internal quality using spectral absorption and scattering properties[J].Transactions of the ASABE,2009,52(2):499-486.
[8]杨昆程,孙梅,陈兴海.水果成熟度的高光谱成像无损检测研究[J].食品科学技术学报,2015,33(4):63-67.
[9]Li Jianglin,Sun Dawen,Cheng Junhu.Recent advances in nondestructive analytical techniques for determining the total soluble solids in fruits:A review[J].Comprehensive Reviews in Food Science&Food Safety,2016,15(5):897-911.
[10]Manuel L,Andres G,Eduardo A.A multipurpose portable instrument for determining ripeness in wine grapes uising NIR spectroscopy[J].IEEE Transactions on Instrumentation and Measurement,2008,57(2):294-302.
[11]陈明林.基于便携式近红外光谱仪的水果内部品质无损检测研究[D].杭州:浙江大学,2010.Chen Minglin.Nondestrctive Detection of Fruit Internal Quality Based on Portable Near Infrared Spectrometer[D].Hangzhou:Zhejiang University,2010.(in Chinese with English abstract)
[12]Liu Yande,Gao Rongjie,Hao Yong,et al.Improvement of near-infrared spectral calibration models for brix prediction in‘Gannan’navel oranges by a portable near-infrared device[J].Food and Bioprocess Technology,2012,5(3):1106-1112.(in Chinese with English abstract)
[13]袁雷明,蔡健荣,孙力,等.可见-近红外光谱用于鲜食葡萄感官偏好的检测[J].光谱学与光谱分析,2017(4):1220-1225.Yuan Leiming,Cai Jianrong,Sun Li,et al.Inspection of the sensory preference for table grape with visual-near infrared spectroscopy[J].Spectroscopy and Spectral Analysis,2017(4):1220-1225.(in Chinese with English abstract)
[14]袁雷明,郭珍珠,陈孝敬,等.基于可见/近红外光谱技术的便携分析仪的应用[J].食品安全质量检测学报,2017,8(9):3455-3460.Yuan Leiming,Guo Zhenzhu,Chen Xiaojing,et al.Application of a portably analyzer based on visual/near infrared spectroscopy[J].Journal of Food Safety and Quality,2017,8(9):3455-3460.(in Chinese with English abstract)
[15]郭志明,陈全胜,张彬,等.果蔬品质手持式近红外光谱检测系统设计与试验[J].农业工程学报,2017,33(8):245-250.Guo Zhiming,Chen Quansheng,Zhang Bin,et al.Design and experiment of handheld near-infrared spectrometer for determination of fruit and vegetable quality[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2017,33(8):245-250.(in Chinese with English abstract)
[16]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 Chinese Society of Agricultural Engineering(Transactions of the CSAE),2017,33(19):295-300.(in Chinese with English abstract)
[17]Zaccanti Giovanni.Monte Carlo study of light propagation in optically thick media:Point source case[J].Applied Optics,1991,30(15):2031-2041.
[18]Wang Lihong,Jacques Li,Zheng Liqiong.MCML-Monte Carlo modeling of light transport in multi-layered tissues[J].Computer Methods&Programs in Biomedicine,1995,47(2):131-146.
[19]Maxime Roger,Cyril Caliot,Nicolas Crouseilles,et al.Ahybrid transport-diffusion model for radiative transfer in absorbing and scattering media[J].Journal of Computational Physics,2014,275(15):346-362.
[20]Zhu Caigang,Liu Quan.Review of Monte Carlo modeling of light transport in tissues[J].Journal of Biomedical Optics2013,18(5):50-62.
[21]Wei Li,Lin Ling,Bao Lei,et al.Monte Carlo simulation of photon migration in multi-component media[J].Optical&Quantum Electronics,2015,47(7):1919-1931.
[22]Guy Yona,Nizan Meitav,Itamar Kahn,et al.Realistic numerical and analytical modeling of light scattering in brain tissue for optogenetic applications[J].Eneuro,2016,3(1):22-35.
[23]Liu Yan,Yang Xue,Li Gang,et al.Optimizing Monte Carlo simulation for detecting the internal information in a fat-muscle media[J].Optical&Quantum Electronics,201648(6):1-11.
[24]Qin Jianwei,Lu Renfu.Monte Carlo simulation for quantification of light transport features in apples[J]Computers&Electronics in Agriculture,2009,68(1):44-51.
[25]Askoura Lamine,Vaudelle Fabrice,L'Huillier Pierre Numerical study of light transport in apple models based on monte carlo simulations[J].Photonics,2015,3(11):26-41.
[26]Ding Chizhu,Shi Shuning,Chen Jianjun,et al.Analysis of light transport features in stone fruits using monte carlo simulation[J].Plos One,2015,10(10):10-12.
[27]石舒宁.水果组织的光学描述与光传输规律研究[D].武汉:华中农业大学,2016.Shi Shuning.Optical Description of Fruit Tissue and Study of Optical Transmission Discipline[D].Wuhan:Huazhong Agricultural University,2016.(in Chinese with English abstract)
[28]陆玲,王蕾.基于椭球变形的植物果实造型[J].农业机械学报,2007,38(4):114-117.Lu Ling,Wang Lei.Plant fruit model ing based on the distortion to ellipsoid[J].Transactions of the Chinese Society for Agricultural Machinery,2007,38(4):114-117.(in Chinese with English abstract)
[29]陆玲,周书民.植物果实的几何造型及可视化研究[J].系统仿真学报,2007,19(8):1739-1741.Lu Ling,Zhou Shumin.Study on geometry model and visualization of plant fruit[J].Journal of System Simulation2007,19(8):1739-1741.(in Chinese with English abstract)
[30]刘妍.基于空间漫反射法检测多层生物组织内部信息的MC模拟研究[D].天津:天津大学,2016.Liu Yan.Monte Carlo Simulation of Detecting the Inner Information of Multi-layered Biological Tissue Based on Spatially Resolved Diffuse Reflectance Technology[D].Tianjin:Tianjin University,2016.(in Chinese with English abstract)
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