Optical simulation model of the diffuse reflectance near-infrared spectroscopy for predicting fresh maize quality

Authors

  • Yongli Zhang 1. Academy of Agricultural Planning and Engineering, Ministry of Agriculture and Rural Affairs, Beijing 100125, China 2. Key Laboratory of Agro-Products Primary Processing, Ministry of Agriculture and Rural Affairs, Beijing 100125, China
  • Meipan Wang 2. Key Laboratory of Agro-Products Primary Processing, Ministry of Agriculture and Rural Affairs, Beijing 100125, China 3. College of Electronic Information Engineering, Changchun Science and Technology University, Changchun 130000, China
  • Guanghui Yang 2. Key Laboratory of Agro-Products Primary Processing, Ministry of Agriculture and Rural Affairs, Beijing 100125, China 3. College of Electronic Information Engineering, Changchun Science and Technology University, Changchun 130000, China
  • Li Jian 1. Academy of Agricultural Planning and Engineering, Ministry of Agriculture and Rural Affairs, Beijing 100125, China 2. Key Laboratory of Agro-Products Primary Processing, Ministry of Agriculture and Rural Affairs, Beijing 100125, China
  • Guangfei Zhu 1. Academy of Agricultural Planning and Engineering, Ministry of Agriculture and Rural Affairs, Beijing 100125, China 2. Key Laboratory of Agro-Products Primary Processing, Ministry of Agriculture and Rural Affairs, Beijing 100125, China
  • Jianfang Shi 1. Academy of Agricultural Planning and Engineering, Ministry of Agriculture and Rural Affairs, Beijing 100125, China 2. Key Laboratory of Agro-Products Primary Processing, Ministry of Agriculture and Rural Affairs, Beijing 100125, China
  • Tailin Han 3. College of Electronic Information Engineering, Changchun Science and Technology University, Changchun 130000, China
  • Liu Xuan 3. College of Electronic Information Engineering, Changchun Science and Technology University, Changchun 130000, China

DOI:

https://doi.org/10.25165/ijabe.v18i6.9484

Keywords:

fresh maize cob, optical properties, Monte Carlo simulation, diffuse reflectance spectrum, optical structure

Abstract

The optical properties of fresh maize tissues determine how light interacts with fresh maize cobs, which in turn affects the measured spectral signals and model accuracy. In this paper, a simulation model was developed to invert the optical properties of fresh maize cobs and evaluate the effects of different optical layouts on the accuracy of modeling predictions. First, the uniformity of detector irradiation at various distances (10 mm, 20 mm, 30 mm, 40 mm, 50 mm) and angles (30°, 45°, 60°) with different optical properties was analyzed using optical simulation methods. Then, the spectra of fresh maize cobs were collected at different light source angles and detection distances, and the spectral area polarization was calculated. Finally, the optical properties of the cob were estimated by establishing a link between irradiation uniformity and spectral area polarization, which resolved the distribution of light flux in edible maize cobs under different optical structures. The results show that the model of light transport mimicking the organizational structure of maize cob has been successfully simulated. The estimated optical properties of the cob are: absorption A=37%, transmission T=20%, and diffuse reflectance D=40%. This verifies that the accuracy and precision of the prediction model for the water content of fresh maize are best achieved under an optical structure with a detection distance of 40 mm and a light source angle of 45°. The establishment of the simulation model provides theoretical support for near-infrared detection of the intrinsic quality of fresh maize. Key words: fresh maize cob; optical properties; Monte Carlo simulation; diffuse reflectance spectrum; optical structure DOI: 10.25165/j.ijabe.20251806.9484 Citation: Zhang Y L, Wang M P, Yang G H, Li J, Zhu G F, Shi J F, et al. Optical simulation model of the diffuse reflectance near-infrared spectroscopy for predicting fresh maize quality. Int J Agric & Biol Eng, 2025; 18(6): 250–259.

References

An L, Cheng Y, Luo S K, Zhou G Q, Han X A, Song B. Research progress on nutritional quality of fresh waxy corn and its influencing factors. Journal of Mountain Agriculture and Biology, 2023; 42(5): 40–45. (in Chinese)

Xu L, Zhao J R, Lu B S, Shi Y X, Fan Y L. Current situation and development trend of fresh corn seed industry in China. China Seed Industry, 2020(10): 14–18. (in Chinese)

Yang X H, Zhu L C, Huang X, Zhang Q, Li S, Chen Q L, et al. Determination of the soluble solids content in korla fragrant pears based on visible and near-infrared spectroscopy combined with model analysis and variable selection. Frontiers in Plant Science, 2022; 13: 938162.

Peng D, Liu Y L, Yang J S, Bi Y L, Chen J N. Nondestructive detection of moisture content in walnut kernel by near‐infrared diffuse reflectance spectroscopy. Journal of Spectroscopy, 2021; 2021(1): 9986940.

Yang Q N, Yang X H, Zhang Q L, Wang Y B, Song H, Huang F R. Quantifying soluble sugar in super sweet corn using near-infrared spectroscopy combined with chemometrics. Optik, 2020; 220: 165128.

Semyalo D, Kwon O, Wakholi C, Min H J, Cho B K. Nondestructive online measurement of pineapple maturity and soluble solids content using visible and near-infrared spectral analysis. Postharvest Biology and Technology, 2024; 209: 112706.

An M, Cao C, Wang S, Zhang X C, Ding W Y. Non-destructive identification of moldy walnut based on NIR. Journal of Food Composition & Analysis, 2023; 121: 105407.

Li X, Zhong Y, Li J, Lin Z Z, Pei Y L, Dai S Y, et al. Rapid identification and determination of adulteration in medicinal Arnebiae Radix by combining near infrared spectroscopy with chemometrics. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 2024; 318: 124437.

Hu D, Fu X P, Wang A C, Ying Y B. Measurement methods for optical absorption and scattering properties of fruits and vegetables. Transactions of the ASABE, 2015; 58(5): 1387–1401.

Wang Z, Hu D, Sun Z Z, Ying Y Y. Application status and perspective of spatial-frequency domain imaging in quality evaluation of agricultural products. Agricultural Engineering, 2021; 37(15): 275–288.

Sun Z Z, Hu D, Zhou T T, Sun X L, Xie L J, Ying Y B. Development of a multispectral spatial-frequency domain imaging system for property and quality assessment of fruits and vegetables. Computers and Electronics in Agriculture, 2023; 214: 108251.

Tian S J, Xu H R. Nondestructive methods for the quality assessmen.t of fruits and vegetables considering their physical and biological variability. Food Engineering Reviews, 2022; 14(3): 380–407.

Yang S H, Tian Q J, Wang Z W, Guo W C. Relationship between optical properties and internal quality of melon tissues during storage and simulation-based optimization of spectral detection in diffuse reflectance mode. Postharvest Biology and Technology, 2024; 213: 112935.

Tang C X, Wang Z R, Li E B. Study on the distribution characteristics of scattered light around peach pit. Journal of Agricultural Science and Technology, 2016; 18(4): 79–86. (in Chinese)

Teet S E, Hashim N. Recent advances of application of optical imaging techniques for disease detection in fruits and vegetables: A review. Food Control, 2023; 152: 109849.

Pan L Q, Fang L, Zhou B J, Zhang B, Ping J, Tu K. System and principle of optical properties measurement and advances on quality detection of fruits and vegetables. Journal of Nanjing Agricultural University, 2021; 44(3): 401–411.

Lv C Y, Zhan R J. Measurement method of optical property parameters of biological tissue. Laser & Optoelectronics Progress, 2021; 58(3): 0300004–30000458.

Shi Z, Anderson C A. Application of Monte Carlo simulation-based photon migration for enhanced understanding of near-infrared (NIR) diffuse reflectance. Part I: Depth of penetration in pharmaceutical materials. Journal of Pharmaceutical Sciences, 2010; 99(5): 2399–2412.

Zhou Y H, Guo W C, Ji T K, Du R Y. Low-cost and handheld detector on soluble solids content and firmness of kiwifruit. Infrared Physics & Technology, 2023; 131: 104641.

Zhang S X, Xu H L, Wang J B, Sun Y X, Wang H Y. Detection of watercore disease in apple based on inversion of optical characteristic parameters. Journal of Nanjing Agricultural University, 2023; 46(5): 986–994. (in Chinese)

Tan Z J, Ding C Z. Semi-empirical model for diffuse reflectance from homogeneous spherical turbid media measured via small diameter detectors. Journal of Modern Optics, 2017; 64(18): 1885–1891.

Light tools software introduction[EB/OL]. Available: https://www.synopsys.com/. Accessed on [2025-06-25].

Vaudelle F, L’Huillier J P. Influence of the size and skin thickness of apple varieties on the retrieval of internal optical properties using Vis/NIR spectroscopy: A Monte Carlo-based study. Computers and Electronics in Agriculture, 2015; 116: 137–149.

He X M, Fu X, Li T W, Rao X Q. Spatial frequency domain imaging for detecting bruises of pears. 2018; 12: 1266–1273. doi: 10.1007/s11694-018-9740-5

Özcan M K, Gökçe M C, Baykal Y. Transmittance of Gaussian beams in biological tissues. Journal of Quantitative Spectroscopy and Radiative Transfer, 2025; 333: 109312.

Yang M, Sun Q C, Hou H Y. Design of fiber optics probe based on monte carlo simulation and application of the probe in fluorescence spectrum measurement of nicotinamide adenine dinucleotide in Skin. Laser & Optoelectronics Progress, 2021; 58(22): 433–440. (in Chinese)

Qiao W Y, Gao Z S, Yuan Q, Zhu D, Xu N Y, Lun X L, et al. Scattered ray tracing based on rejection sampling method with BRDF discrete numerical value. Acta Photonica Sinica, 2023; 52(4): 0429001.

Misran M A B, Bilgaiyan A, Hattori R. Optical ray tracing simulation by using monte carlo method for reflectance-based photoplethysmography sensor in human skin and fingertip model. Computational And Experimental Research In Materials And Renewable Energy, 2022; 5(2): 78–91.

Sun J, Künnemeyer R, McGlone A, Tomer N. Investigations of optical geometry and sample positioning in NIRS transmittance for detecting vascular browning in apples. Computers and Electronics in Agriculture, 2018; 155: 32–40.

Han C, Yin J F, Hao T, Yan J S, Xu H R. Evaluation of the optical layout and sample size on online detection of apple watercore and SSC using Vis/NIR spectroscopy. Food Composition and Analysis, 2023; 123: 105528.

Sun C, Beers R V, Aernouts B, Saeys W. Bulk optical properties of citrus tissues and the relationship with quality properties. Postharvest Biology and Technology, 2020; 163: 111127.

Xie D D, Liu D Y, Guo W C. Relationship of the optical properties with soluble solids content and moisture content of strawberry during ripening. Postharvest Biology and Technology, 2021; 179: 111569.

Qin J W, Lu R F. Measurement of the optical properties of fruits and vegetables using spatially resolved hyperspectral diffuse reflectance imaging technique. Postharvest Biology and Technology, 2008; 49(3): 355–365.

Sun X L, Zhou T T, Sun Z Z, Li Z M, Hu D. Research progress into optical property-based nondestructive fruit and vegetable quality assessment. Food Research & Development, 2022; 43(4): 209–218. (in Chinese)

Gao Y W, Geng J F, Rao X Q. Non-invasive bruise detection in postharvest fruits and vegetables: A review. 2017; 38(15): 277–287. (in Chinese) doi: 10.7506/spkx1002-6630-201715044.

Xu G, Dong L Q, Kong L G, Zhao Y J, Liu M, Hui M, et al. Parameters inversion algorithm of biological tissues based on a neural network model. Acta Optica Sinica, 2021; 41(11): 1117001. (in Chinese)

Chong W, Lyu W H, Zhang J, Liang J, Yang X T, Liu S, et al. Calibration system of sunshine duration recorder based on Bi-Xenon lamp source integrating sphere. Acta Optica Sinica, 2022; 42(1): 0112004. (in Chinese)

Zhang Y L, Yang G H, Wang M P, Han Z Y, Zhu G F, Shi J F, et al. Factors affecting the non-destructive detection of water contents in fresh corn cobs by near-infrared spectroscopy. Transactions of the Chinese Society of Agricultural Engineering, 2024; 40(15): 262–270. (in Chinese)

Liu W H, Han Y N, Wang N, Zhang Z, Wang Q G, Miao Y P. Apple sugar content non-destructive detection device based on near-infrared multi-characteristic wavelength. Journal of Physics: Conference Series. IOP Publishing, 2022; 2221: 012012.

Shen F, Liu X, Pei F, Li P, Jiang D F, Liu Q. Rapid identification of deoxynivalenol contamination in wheat and its products by attenuated total reflectance-Fourier transform infrared spectroscopy (ATR-FTIR). Food Science, 2019; 40(2): 293–297. (in Chinese)

Downloads

Published

2025-12-26

How to Cite

Zhang, Y., Wang, M., Yang, G., Jian, L., Zhu, G., Shi, J., … Xuan, L. (2025). Optical simulation model of the diffuse reflectance near-infrared spectroscopy for predicting fresh maize quality. International Journal of Agricultural and Biological Engineering, 18(6), 250–259. https://doi.org/10.25165/ijabe.v18i6.9484

Issue

Vol. 18 No. 6 (2025): IJABE

Section

Information Technology, Sensors and Control Systems

License

IJABE is an international peer reviewed open access journal, adopting Creative Commons Copyright Notices as follows.


Authors who publish with this journal agree to the following terms:


  1. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.

  2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.

  3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).