基于THz时域反射成像技术的玉米种子淀粉分布可视化研究

doi:10.3964/j.issn.1000-0593(2023)09-2722-07

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Abstract To identify the intrinsic relationship between the distribution of seed composition and the change in seed vigour using a nondestructive testing method, this study chooses starch, a major component of corn seeds, as the research object. Combining the terahertz (THz) time-domain spectral reflection imaging technology with moving-window correlation coefficient imaging, a visualised map showing the spatial distribution of starch in corn seeds with different vigour degrees is constructed. Using the Zhengdan 958 corn variety as an example, a test is conducted to apply an artificial ageing method to prepare samples aged 0, 18, 36, 54 and 72 h. Then, the THz spectrometer is used to scan the reflection imaging attachments to obtain THz images of the samples. The THz image at 16.35 cm^-1 was used as a benchmark, and the endosperm and seed embryo regions of the seeds were accurately extracted using the threshold segmentation method. By comparing the average absorbance of THz in different tissue regions, it can be obtained that the endosperm and seed embryo spectra differ significantly, and there is an obvious common absorption peak near 51.96 cm^-1 for endosperm and starch pure substances. The moving window correlation coefficient method (window width 20, moving step 10) was applied to calculate the correlation coefficient between the terahertz time-domain spectrum of the seeds and the pure maize starch spectrum on a pixel-by-pixel basis and to construct a pseudo-colour heat map based on the correlation coefficient values and the coordinate information to visualise the maize seed starch distribution. Statistics on the percentage of pixel points with a correlation coefficient of >0.8 in the starch distribution map and covering five ageing stages and six spectral region windows lead to the conclusion that within the range of 29.83～67.36 cm^-1 of the starch in the endosperm area of the seed exhibits an overall downward trend in vigour decline, presenting a positive correlation with vigour. Test results show that the THz time-domain spectral imaging technology can preliminarily realise the nondestructive detection of the spatial distribution characteristics of starch in corn seeds as seed vigour changes when it is used with the moving-window correlation coefficient pseudo-colour imaging analysis method. This technique provides a new perspective and method for studying the relationship between the chemical composition of seeds and their vitality and for non-destructively analysing changes in the vital activity of seeds and their own physiological and ecological patterns.

Key words：Corn seeds; Terahertz time-domain spectral reflection imaging; Correlation coefficient imaging method; Moving window; Starch

Received: 2022-05-27 Accepted: 2022-10-23

ZTFLH:

O433.4

Corresponding Authors: WU Jing-zhu E-mail: pubwu@163.com

Cite this article:

LI Yang,LI Xiao-qi,YANG Jia-ying, et al. Visualisation of Starch Distribution in Corn Seeds Based on Terahertz Time-Domain Spectral Reflection Imaging Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2722-2728.

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https://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2023)09-2722-07 OR https://www.gpxygpfx.com/EN/Y2023/V43/I09/2722

[1] GUO Ai-liang, ZHOU Xiang-han, YAO Ya-ya, et al(郭爱良, 周湘寒, 姚亚亚, 等). Journal of The Chinese Cereals and Oils Association(中国粮油学报), 2022，(37)：5.
[2] WANG Xin-yu(王欣宇). Modern Animal Husbandry Science & Technology(现代畜牧科技), 2018, 40(4): 37.
[3] ZHAO Yan-hao, ZHANG Qian-xia, XIE Wei(赵燕昊, 张倩霞, 谢炜). Modern Agricultural Science and Technology(现代农业科技), 2019，(15): 1.
[4] LI Wen-bin, YU Xiu-qi, LIU Chun-yu, et al(李文彬, 于秀琪, 刘春宇, 等. ). Journal of Agricultural Catastrophology (农业灾害研究), 2021, 11(6): 3.
[5] Moreira J, Cardoso R R, Braga R A. Optics & Lasers in Engineering, 2014, 61: 8.
[6] Peiponen K E, Zeitler J A, Kuwata-Gonokami M. Terahertz Spectroscopy and Imaging. Berlin Heidelberg: Springer, 2012.
[7] LI Bin, LONG Yuan, LIU Huan, et al(李斌, 龙园, 刘欢, 等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2018, 34(2): 1.
[8] XIE Li-juan, XU Wen-dao, YING Yi-bin, et al(谢丽娟, 徐文道, 应义斌, 等). Transactions of the Chinese Society for Agricultural Machinery(农业机械学报), 2013, 44(7): 246.
[9] Ge H, Jiang Y, Lian F, et al. Sensors, 2015, 15(6): 12560.
[10] Xu W, Xie L, Ye Z, et al. Scientific Reports, 2015, 5: 11115.
[11] Wei L, Liu C, Hu X, et al. Food Chemistry, 2016, 210: 415.
[12] Liu J, Li Z, Hu F，et al. Optical and Quantum Electronics, 2015, 47(2): 313.
[13] BU Zheng-yan, LI Zhen-feng, SONG Fei-hu, et al(步正延, 李臻峰, 宋飞虎, 等). Acta Agriculturae Zhejiangensis(浙江农业学报), 2018, 30(8): 1420.
[14] Nakajima S, Shiraga K, Suzuki T, et al. Food Chemistry, 2019, 294: 203.
[15] XU Hong-mei, WEN Jiang, ZHONG Wen-jie, et al(徐红梅, 文江, 钟文杰, 等). Journal of Jiangsu University (Natural Science Edition)[江苏大学学报(自然科学版)], 2017, 38(3): 295.
[16] ZHANG Ming-dan, WANG Min, CHEN Guang-xuan, et al(张明丹, 王敏, 陈光选, 等). Modern Food Science and Technology(现代食品科技), 2021, 37(2): 129.