| 刘艮森1, 2, 聂 晓1, 2, 王奥霖2, 贾镇宇1, 2, 危学华1, 2, 徐 飞2, 3, 范洁茹2, 马东方1*, 刘 伟2*, 周益林2.基于电子鼻技术的小麦籽粒DON含量检测[J].植物保护,2024,50(3):137-145. |
| 基于电子鼻技术的小麦籽粒DON含量检测 |
| Detection of deoxynivalenol (DON) content in wheat grain using electronic nose technology |
| 投稿时间：2023-04-18 修订日期：2023-05-29 |
| DOI：10.16688/j.zwbh.2023183 |
| 中文关键词: 电子鼻 小麦籽粒 脱氧雪腐镰刀菌烯醇 毒素检测 回归模型 |
| 英文关键词:electronic nose wheat grain deoxynivalenol (DON) toxin detection regression model |
| 基金项目:国家重点研发计划（2022YFD1400100）；中国农业科学院科技创新工程 |
| 作者 | 单位 | E-mail | | 刘艮森1, 2, 聂 晓1, 2, 王奥霖2, 贾镇宇1, 2, 危学华1, 2, 徐 飞2, 3, 范洁茹2, 马东方1*, 刘 伟2*, 周益林2 | 1. 长江大学农学院, 荆州 434025
2. 中国农业科学院植物保护研究所, 植物病虫害综合治理全国重点实验室, 北京 100193
3. 河南省农业科学院植物保护研究所, 农业农村部华北南部作物有害生物综合治理重点实验室, 郑州 450002 | 马东方madf@yangtzeu.edu.cn；刘伟wliusdau@163.com |
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| 中文摘要: |
| 为探究利用电子鼻技术对小麦籽粒中DON含量定量检测的可行性, 本研究在25℃和40℃平衡温度下对DON含量不同的80个小麦籽粒样品的顶空气体进行电子鼻检测, 并结合由UPLC-MS/MS测得的各样品DON含量进行统计分析, 结果发现, 除4号和5号传感器外, 其余8个传感器的响应值均与样品DON含量具有显著或极显著的相关性, 其中1号传感器的响应值及其参数与样品DON含量的相关性最强, 表明1号传感器可以作为检测小麦籽粒样品DON含量的关键气体传感器。以1号传感器的参数为主, 分别建立了在两平衡温度下样品DON含量的回归模型。对各模型的拟合效果分析发现, 在40℃平衡温度下基于X17(1号传感器21~60 s响应值的和)所建一元回归模型最好；在25℃平衡温度下，基于X1(1号传感器20~40 s响应值的平均值)和X12(8号传感器1~5 s响应值的和)所建二元回归模型拟合效果最好，其次为基于X1所建的一元回归模型。该研究可为电子鼻技术在小麦籽粒中DON含量检测中的应用提供理论依据和技术支撑。 |
| 英文摘要: |
| To explore the potential of electronic nose technology for quantifying deoxynivalenol (DON) content in wheat grains, the detection on the headspace gas of 80 wheat grain samples with different DON contents at equilibrium temperatures of 25℃ and 40℃ were conducted using electronic nose, and alongside the DON content of each wheat sample was measured by UPLC-MS/MS. The statistic analysis results showed that, except for sensors no.4 and no.5, the response values of the remaining eight sensors significantly correlated or extremely significantly correlated with the sample DON content. Notably, sensor no.1 exhibited the strongest correlation, indicating its potential as a principal gas sensor for detecting DON content in wheat grain samples. Regression models for sample DON content were established mainly based on sensor no.1 parameters at each equilibrium temperature. The analysis of the fitting effect of these models revealed that the univariate regression model based on X17 (the sum of response values of sensor no.1 from 21 to 60 seconds) yielded the best fitting effect at equilibrium temperature of 40℃. The bivariate regression model based on X1 (the average response value of sensor no.1 from 20 to 40 seconds) and X12 (the sum of response values of sensor no.8 from 1 to 5 seconds) yielded the best fitting effect at 25℃, the univariate regression model based on X1 also showed promising results. This study provides a theoretical basis and technical support for detecting DON content in wheat grains using electronic nose technology. |
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