基于GC-MS与MALDI-MSI多维技术解析抑霉唑在作物中的时空迁移与分布特征

李晓萍 ,  赵翌帆 ,  裘雅渔 ,  黄琼 ,  何巧红

高等学校化学学报 ›› 2026, Vol. 47 ›› Issue (7) : 63 -70.

PDF (7211KB)
高等学校化学学报 ›› 2026, Vol. 47 ›› Issue (7) : 63 -70. DOI: 10.7503/cjcu20250353
研究论文

基于GC-MS与MALDI-MSI多维技术解析抑霉唑在作物中的时空迁移与分布特征

作者信息 +

Multi-dimensional Analysis of the Spatiotemporal Migration and Distribution of Imazalil in Crops Based on GC-MS and MALDI-MSI

Author information +
文章历史 +
PDF (7383K)

摘要

建立了一种基于高分辨气相色谱-质谱联用(GC-MS)与基质辅助激光解吸电离质谱成像技术(MALDI-MSI)相结合的多维分析方法,用于解析三唑类杀菌剂抑霉唑(Imazalil)在块根类作物内部的时空迁移与分布特征。以樱桃萝卜为模型作物,首先通过逐层精细切片(每层厚度约1.5 mm),结合QuECHERS方法进行样品前处理,然后采用高分辨GC-MS并利用基质匹配外标法进行定性定量分析。结果表明,抑霉唑在萝卜中的纵深分布随暴露时间呈现显著差异:暴露2 h,仅在表层检测到较高浓度(约30.6 mg/kg),向内迅速递减;随着暴露时间延长至5,10和24 h,抑霉唑逐渐向深层组织渗透,内层浓度明显上升,整体浓度梯度趋于平缓,展现出“表皮吸附、逐层渗透”动态迁移特征。进一步利用MALDI-MSI技术对萝卜横切面进行高空间分辨率质谱成像分析,直观呈现了抑霉唑在组织结构中的二维分布特征。成像结果显示,外皮与皮层区域信号最强,向内逐渐减弱,反映了抑霉唑在组织中的缓慢扩散与积累过程。MALDI成像所得的空间分布趋势与GC-MS定量结果高度一致,两者从空间与定量两个维度共同验证了抑霉唑在萝卜中“由表及里、逐层递减、逐步渗透”的迁移机制。本研究实现了农药在块根类果蔬中的时空分布可视化与纵深定量一体化分析,为揭示农药在植物体内的迁移行为提供了新的技术路径与实验依据,对农产品安全评估与农药残留风险控制提供了科学支撑。

Abstract

A multidimensional analytical method based on high-resolution gas chromatography-mass spectrometry (GC-MS) combined with matrix-assisted laser desorption ionization mass spectrometry imaging (MALDI-MSI) was established for the systematic analysis of the spatiotemporal migration and distribution characteristics of the triazole fungicide Imazalil within root tuber crops. Using cherry radish as the model crop, a quantitative analysis was first performed through fine layer-by-layer sectioning (ca. 1.5 mm per layer), combined with the QuECHERS pretreatment technique and a matrix-matched external standard method. The results revealed significant differences in the depth distribution of Imazalil in radishes depending on the exposure time: after exposure of 2 h, a high concentration (ca. 30.6 mg/kg) was detected in the surface layer, decreasing rapidly inward. As the exposure time extended to 5, 10 and 24 h, Imazalil gradually penetrated into deeper tissues, with the inner layer concentration increasing significantly and the overall concentration gradient becoming more gradual, demonstrating a dynamic migration characterized by "epidermal adsorption; layer-by-layer penetration". Furthermore, MALDI-MSI was utilized for high spatial resolution imaging analysis of radish cross-sections, visually presenting the two-dimensional distribution characteristics of Imazalil within the tissue structure. The imaging results showed the strongest signals in the epidermis and cortex regions, gradually weakening inward, reflecting the slow diffusion and accumulation process of Imazalil in tissues. The spatial distribution trend obtained from MALDI imaging was highly consistent with the quantitative results from GC-MS. Together, these two approaches validated the migration mechanism of Imazalil in radish from both quantitative and spatial dimensions, characterized by "from surface to inside, decreasing layer by layer, and gradually penetrating." This study achieved the dynamic visualization and in-depth quantitative analysis of the spatiotemporal distribution of pesticides in root crops, providing a new technical approach and experimental basis for revealing pesticide migration behavior in plants, and offering scientific support for agricultural product safety assessment and pesticide residue risk control.

关键词

高分辨气相色谱‐质谱联用 / 基质辅助激光解吸电离质谱成像 / 抑霉唑 / 农药残留 / 时空分布

Key words

High-resolution gas chromatography-mass spectrometry (GC-MS) / Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) / Imazalil / Pesticide residues / Spatial and temporal distribution

引用本文

引用格式 ▾
李晓萍,赵翌帆,裘雅渔,黄琼,何巧红. 基于GC-MS与MALDI-MSI多维技术解析抑霉唑在作物中的时空迁移与分布特征[J]. 高等学校化学学报, 2026, 47(7): 63-70 DOI:10.7503/cjcu20250353

登录浏览全文

4963

注册一个新账户 忘记密码

参考文献

[1]

Sergent T., Dupont I., Jassogne C., Ribonnet L., van der Heiden E., Scippo M. L., Muller M., McAlister D., Pussemier L., Larondelle Y., Toxicol. lett., 2009, 184(3), 159-168

[2]

Zega G., de Bernardi F., Groppelli S., Pennati R., Aquat. Toxicol., 2009, 91(3), 255-261

[3]

Zhang R., Zhang Y. C., Xu Q. R., Li J. H., Zhu F. X., Plant Dis., 2019, 103(3), 546-554

[4]

Vinggaard A. M., Hass U., Dalgaard M., Andersen H. R., Bonefeld‐Jørgensen E., Christiansen S., Laier P., Poulsen M. E., Int. J. Androl., 2006, 29(1), 186-192

[5]

Lamberth C., Dinger J., Bioactive Heterocyclic Compound Classes: Argochemicals, Wiley-VCH Press, Weinheim, 2012, 103-118

[6]

Bai S., Zhang M., Tang S., Li M., Wu R., Wan S., Chen L., Wei X., Li F., Molecules, 2024, 29(6), 1218

[7]

Jin C., Luo T., Fu Z., Jin Y., Environ. Toxicol., 2018, 33(6), 650-658

[8]

Tisza B. B., Járomi L., Háhn J., Bérczi B., Horváth‐Sarródi A., Gubicskóné Kisbenedek A., Gerencsér G., Foods, 2025, 14(7), 1264

[9]

Liu Z. L., Pan C. P., Han Y. T., Zou N., J. Food Saf. Qual., 2016, 7(12), 4858-4863

[10]

(刘振龙, 潘灿平, 韩永涛, 邹楠. 食品安全质量检测学报, 2016, 7(12), 4858-4863)

[11]

Wang Z. W., Li F. L., He A. F., Tang Y. L., Qiu Y. P., J. Agro-Environ. Sci., 2011, 30(6), 1076-1081

[12]

(汪志威, 李非里, 何岸飞, 汤宇恋, 邱宇平. 农业环境科学学报, 2011, 30(6), 1076-1081)

[13]

Ju C., Zhang H. C., Wu R. L., Dong S. X., Yao S. J., Wang F. Y., Cao D. T., Xu S. J., Fang H., Yu Y. L., Sci.Total Environ., 2020, 703

[14]

Ju C., Dong S. X., Zhang H. C., Yao S. J., Wang F. Y., Cao D. T., Xu S. J., Fang H., Yu Y. L., Chemosphere, 2020, 248

[15]

Wang X. Y., An K., Guo Y. J., Li Q., Liu T. T., Liu Y. C., Feng X. X., J. Agr. Food Chem., 2023, 71(49), 19324-19332

[16]

Wang F. Y., Zhao E. C., Pei T., Pan W., Zhao X. J., Meng Z. H., Li L., J. Agro-Environ. Sci., 2025, 44(5), 1225-1232

[17]

(王富芸, 赵尔成, 裴涛, 潘薇, 赵晓军, 孟致衡, 李莉. 农业环境科学学报, 2025, 44(5), 1225-1232)

[18]

Cui S. Q., Yu Y., Yang S. J., Zhang Z. L., XIANDAI HORTICULTURE, 2015, (22), 7-8

[19]

(崔社强, 于泳, 杨守军, 张忠兰. 现代园艺, 2015, (22), 7-8)

[20]

Chinchkar A. V., Singh A., Singh S. V., Acharya A. M., Kamble M. G., J. Food Process. Pres., 2022, 46(10), e16495

[21]

Lučić M., Onjia A., J. Xenobiotics, 2025, 15(4), 125

[22]

Jan I., Rashied F., Malik N. A., Mukhtar M., Dudwal R., Kataria A., Biomed. Chromatogr., 2025, 39(11), e70224

[23]

Zheng K., Lin R., Liu X., Wu X., Chen R., Yang M., Molecules, 2022, 27(23), 8419

[24]

Vareli C. S., Pizzutti I. R., Gebler L., Cardoso C. D., Fontana M. E., Reichert B., Jänisch B. D., Anal. Methods, 2019, 11(42), 5455-5463

[25]

Pereira I., Banstola B., Wang K., Donnarumma F., Vaz B. G., Murray K. K., Anal. Chem., 2019, 91(9), 6051-6056

[26]

Annangudi S. P., Myung K., Avila Adame C., Gilbert J. R., Environ. Sci. & Technol., 2015, 49(9), 5579-5583

[27]

Yang X., Shi M., Hong M., Hui Z., Pan J., Xiu G., Zhou L., Food Chemistry: X, 2025, 25, 102162

[28]

Wang X. Q., Huang G. M., Chem. J. Chinese Universities, 2020, 41(12), 2673-2680

[29]

(王晓群, 黄光明, 高等学校化学学报, 2020, 41(12), 2673-2680)

[30]

Yang F., Shen J. L., Gong C., Liu Z. X., Guo Q. S., Chinese J. Anal. Chem., 2025, 53(2), 204-215

[31]

(杨帆, 沈钰琳, 龚灿, 刘兆鑫, 郭强胜, 许旭. 分析化学, 2025, 53(2), 204-215)

[32]

Lyu C. C., Jiang Y. T., Hu Y. H., Wu L. T., Qi K. K., Liu C. Y., Pan Y., Chinese J. Anal. Chem., 2024, 52(6), 876-886

[33]

(吕纯纯, 姜余婷, 胡永华, 吴刘天, 戚可可, 刘成园, 潘洋. 分析化学, 2024, 52(6), 876-886)

[34]

Machado I., Gérez N., Pistón M., Heinzen H., Cesio M. V., Food Chem., 2017, 227, 227-236

[35]

Tankiewicz M., Berg A., Microchem. J., 2022, 181, 107794

[36]

Wang M., Tian Q., Li H., Dai L., Wan Y., Wang M., Han B., Huang H., Zhang Y., Chen J., J.Hazard.Mater., 2023, 446, 130665

[37]

Qin R., Li P., Du M., Ma L., Huang Y., Yin Z., Zhang Y., Chen D., Xu H., Wu X., Nanomaterials, 2021, 11(5), 1327

基金资助

浙江大学实验技术研究项目(SYBJS202301)

AI Summary AI Mindmap
PDF (7211KB)

7

访问

0

被引

详细

导航
相关文章

AI思维导图

/