基于蒙特卡洛的柚果多组织层全透射光传输仿真与内部品质无损检测试验

doi:10.3964/j.issn.1000-0593(2025)07-2026-08

摘要
参考文献
相关文章 (15)

全文: PDF (9563 KB)
输出: BibTeX | EndNote (RIS)

摘要：柚果皮厚、体积大，造成光谱信号强度低，导致内部品质无损检测效果差。该研究提出了基于蒙特卡洛的柚果多组织层全透射光传输仿真与试验内部品质无损检测，通过测定柚果多组织层光学特性参数对柚果多组织层光子飞行距离和概率进行模拟，得到光子在柚果多组织层相互作用后光传输结果，再根据柚果多组织层光学特性参数建模仿真，改变光源入射角度及探测器旋转角度对柚果多组织层光传输进行仿真，寻找最优光源入射角度和探测器旋转角度，最后搭建试验平台进行验证，可见/近红外光谱数据经过平滑处理（SG）, 标准正态变换（SNV）预处理，竞争自适应重加权采样（CARS）特征提取，偏最小二乘回归（PLSR）建模。研究结果表明，光子在果皮油胞层飞行距离最长，衰减最严重，存活概率最低，在果肉瓣中飞行距离最短，存活概率最大。光源入射角度36°，探测器绕Z轴旋转10°是最优光传输参数，最优光传输参数训练建模结果显示，训练集R²和RMSEC分别为0.89和0.25，预测集R²和RMSEP分别为0.84和0.38，未经参数优化的光传输训练建模结果显示，光源入射角度0°，探测器旋转0°，训练集R²和RMSEC分别为0.85和0.27，预测集R²和RMSEP分别为0.80和0.34，光源入射角度18°，探测器绕Z轴旋转10°，训练集R²和RMSEC分别为0.80和0.34，预测集R²和RMSEP分别为0.73和0.74，光源入射角度36°，探测器绕Y轴旋转10°，训练集R²和RMSEC分别为0.69和0.25，预测集R²和RMSEP分别为0.60和0.83。本研究得到的柚果多组织层全透射光传输参数可柚果内部品质的无损检测效果，同时为其他多组织层水果全透射光传输仿真与试验内部品质无损检测研究提供了参考。

关键词：柚果；蒙特卡洛；光传输；无损检测；建模仿真

Abstract：The thick peel and large volume of pomelos result in low spectral signal intensity, leading to poor performance in nondestructive internal quality detection. This study proposes a Monte Carlo-based simulation and experimental verification for the full-transmission light transport and nondestructive internal quality detection of pomelos with multiple tissue layers. By measuring the optical properties of the multiple tissue layers of pomelos, we simulated the photon travel distance and interaction probabilities within these layers. The light transport results were obtained after simulating the photon interactions with the multilayer tissues. Following this, modeling simulations were conducted based on the optical properties of these layers. We varied the incident angle of the light source and the rotation angle of the detector to find the optimal angles for light transmission. Finally, an experimental platform was constructed to verify the findings. The near-infrared spectral data underwent preprocessing steps such as Savitzky-Golay (SG) smoothing, standard normal variate (SNV) transformation, and competitive adaptive reweighted sampling (CARS) for feature extraction, followed by partial least squares regression (PLSR) modeling. The study results indicated that photons traveled the longest distance and experienced the greatest attenuation with the lowest survival probability in the epicarp oil cell layer, whereas photons traveled the shortest distance and had the highest survival probability in the pulp lobes. The optimal parameters for light transmission were a light source incident angle of 36° and a detector rotation angle of 10° around the Z-axis. The modeling results with these optimal parameters showed R² and RMSEC values of 0.89 and 0.25 for the training set, and R² and RMSEP values of 0.84 and 0.38 for the prediction set. In contrast, without parameter optimization, the results showed a light source incident angle of 0° and a detector rotation of 0°, with the training set R² and RMSEC being 0.85 and 0.27, and the prediction set R² and RMSEP being 0.80 and 0.34. For a light source incident angle of 18° and a detector rotation of 10° around the Z-axis, the training set R² and RMSEC were 0.80 and 0.34, while the prediction set R² and RMSEP were 0.73 and 0.74. For a light source incident angle of 36° and a detector rotation of 10° around the Y-axis, the training set R² and RMSEC were 0.69 and 0.25, while the prediction set R² and RMSEP were 0.60 and 0.83. The findings of this study on the light transport parameters for multiple tissue layers of pomelos can improve the effectiveness of nondestructive internal quality detection. Additionally, these results provide a reference for the simulation and experimental nondestructive internal quality detection of other multi-tissue layer fruits using full-transmission light transport.

Key words：Pomelo fruit; Monte Carlo; Optical transmission; Non-destructive testing; Modeling and simulation

收稿日期: 2024-08-22 修订日期: 2025-01-22

中图分类号:

TP29

基金资助: 广东省乡村振兴战略专项（2025TS-1-2），国家重点领域研发计划项目（2022YFD2002203），广东省国际科技合作项目（2023A0505050129），科技创新战略（农业科研主力军建设）专项（R2023PY-QN002）资助

通讯作者: 徐赛 E-mail: xusai@gdaas.cn

作者简介: 陈鑫，1999年生，华南农业大学工程学院硕士研究生 e-mail: 979972806@qq.com

引用本文:

陈鑫，徐赛，陆华忠，梁鑫. 基于蒙特卡洛的柚果多组织层全透射光传输仿真与内部品质无损检测试验[J]. 光谱学与光谱分析, 2025, 45(07): 2026-2033.
CHEN Xin, XU Sai, LU Hua-zhong, LIANG Xin. Monte Carlo-Based Full Transmission Light Transmission Simulation of Multi-Tissue Layers of Pomelo Fruit and Non-Destructive Testing of Internal Quality. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2025, 45(07): 2026-2033.

链接本文:

https://www.gpxygpfx.com/CN/10.3964/j.issn.1000-0593(2025)07-2026-08 或 https://www.gpxygpfx.com/CN/Y2025/V45/I07/2026

[1] Xiao L, Ye F Y, Zhou Y, et al. Food Chemistry, 2021, 351: 129247.
[2] Tuan N T, Dang L N, Huong B T C, et al. Chemical Engineering and Processing-Process Intensification, 2019, 142: 107550.
[3] Wang H X, Wang P, Kasapis S, et al. Journal of Food Engineering, 2024, 370: 111966.
[4] Xu S, Lu H Z, Wang X, et al. HortScience, 2021, 56(11): 1325.
[5] Yang S H, Tian Q J, Wang Z W, et al. Postharvest Biology and Technology, 2024, 213: 112935.
[6] SHI Shu-ning, TAN Zuo-jun, XIE Jing, et al(石舒宁，谭佐军，谢静，等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2015, 35(7): 1817.
[7] Fang Z H, Fu X P, He X M. Journal of Zhejiang University-SCIENCE B, 2016, 17(6): 484.
[8] Lu R F, Van Beers R, Saeys W, et al. Postharvest Biology and Technology, 2020, 159: 111003.
[9] Wu N Q, Pitts M J. Postharvest Biology and Technology, 1999, 16(1): 1.
[10] Wang W L, Li C Y, Gitaitis R D. Transactions of the ASABE, 2014, 57: 1771.
[11] Watté R, Aernouts B, Van Beers R, et al. Optics Express, 2015, 23(13): 17467.
[12] Morris T, White L, Crowther M. Statistics in Medicine, 2019, 38: 2074.
[13] Huang Y P, Lu R F, Chen K J. Postharvest Biology and Technology, 2017, 133: 88.
[14] Ncama K, Tesfay S Z, Fawole O A, et al. Scientia Horticulturae, 2018, 231: 265.
[15] Teerachaichayut S, Ho H T. Postharvest Biology and Technology, 2017, 133: 20.
[16] Sun C J, Aernouts B, Van Beers R, et al. Journal of Food Engineering, 2021, 291: 110225.
[17] Xu S, Lu H Z, He Z H, et al. Postharvest Biology and Technology, 2024, 214: 112990.
[18] Tian H, Xu H R, Ying Y B. Biosystems Engineering, 2022, 214: 152.
[19] Askoura M L, Vaudelle F, Huillier J-P. Photonics, 2015, 3: 2.
[20] Ren N N, Liang J M, Qu X C, et al. Optics Express, 2010, 18(7): 6811.
[21] Fang Q Q, Boas D A. Optics Express, 2009, 17(22): 20178.
[22] CHEN Xin，XU Sai，LU Hua-zhong，et al（陈鑫，徐赛，陆华忠，等）. Journal of South China Agricultural University（华南农业大学学报）, 2024, 45(4): 618.
[23] Cai S C, Zhang S, Tan Z J, et al. Optik, 2023, 287: 171121.
[24] López-Maestresalas A, Aernouts B, Van Beers R, et al. Food and Bioprocess Technology, 2016, 9: 463.
[25] Sun C J, Van Beers R, Aernouts B, et al. Postharvest Biology and Technology, 2020, 163: 111127.
[26] Janeeshma E, Johnson R, Amritha M, et al. International Journal of Molecular Sciences, 2022, 23: 5599.
[27] Jacques S L. Physics in Medicine & Biology, 2013, 58(11): R37.
[28] Qin J W, Lu R F. Postharvest Biology and Technology, 2008, 49(3): 355.
[29] Pozhar K V, Mikhailov M O, Litinskaia E L, et al. Biomedical Engineering, 2022, 56(1): 64.
[30] Brescia G, Moreira R, Braby L, et al. Journal of Food Engineering, 2003, 60(1): 31.
[31] Fraser D G, Jordan R B, Künnemeyer R, et al. Postharvest Biology and Technology, 2003, 27(2): 185.
[32] Hayashi T, Kashio Y, Okada E. Applied Optics, 2003, 42(16): 2888.
[33] Bi Y, Yuan K L, Xiao W Q, et al. Analytica Chimica Acta, 2016, 909: 30.
[34] Li H D, Liang Y Z, Xu Q S, et al. Analytica Chimica Acta, 2009, 648(1): 77.
[35] Liu Y D, Sun X D, Ouyang A G. LWT-Food Science and Technology, 2010, 43(4): 602.