光谱学与光谱分析, 2019, 39 (12): 3904, 网络出版: 2020-01-07  

小麦呕吐毒素污染可见/近红外光谱快速筛查方法研究

Screening of DON Contamination in Wheat Based on Visible/Near Infrared Spectroscopy
作者单位
1 南京林业大学机械电子工程学院, 江苏 南京 210037
2 南京财经大学食品科学与工程学院, 江苏 南京 210023
3 浙江农林大学农业与食品科学学院, 浙江 杭州 311300
摘要
小麦不仅是我国主要的粮食品种, 也是一种重要的饲料和工业原料。 小麦易受赤霉病感染从而产生呕吐毒素, 学名脱氧雪腐镰刀菌烯醇(DON), 具有一定致癌性, 对人畜健康构成严重威胁。 尤其近年来极端异常气候频发, 小麦DON污染风险呈不断上升趋势, 已成为影响其产品质量安全的主要因素。 然而, 传统DON检测方法过程繁琐、 耗时费力, 因此发展一种快速、 低成本且适用于在线的检测方法对小麦安全生产及加工具有重要意义。 首先从江苏各地收集不同赤霉病感染程度的小麦样品200份, 磨粉后利用超高效液相色谱-串联质谱联用法(UPLC-MS/MS)测定小麦中DON含量, 再利用光谱仪在线采集小麦的可见/近红外光谱。 数据处理步骤为: 采用多元散射校正以及二阶导数对光谱进行预处理, 同时根据竞争性自适应权重取样算法提取特征波长, 最后利用线性判别分析(LDA)与偏最小二乘判别分析法(PLS-DA)建立小麦粉样品的定性分析模型(以国家标准1 000 μg·kg-1为界限), 根据偏最小二乘回归(PLSR)建立小麦粉样品DON含量定量分析模型。 UPLC-MS/MS结果表明小麦DON污染风险较高, 所测样品超标率约为50%。 可见/近红外光谱分析表明不同DON含量小麦样品光谱特征具有一定的差异, 原始光谱和二阶导数谱图可看出1 420 nm处DON含量越高, 吸光度越低。 由于DON绝对含量低而光谱仪的检测限有限, 通过主成分分析未能发现明显的聚类趋势, 但根据全光谱以及特征光谱所构建的LDA与PLS-DA判别模型均能够对超标和未超标样品进行快速识别与筛查, 最佳识别率达87.69%。 从定量分析结果来看, 所构建的小麦样品DON含量的PLSR模型结果不太理想, 最优模型结果: 预测集相关系数(rp)为0.688, 均方根误差(RMSEP)为727 μg·kg-1, 相对分析偏差(RPD)值为1.38, 模型精度和稳健性有待进一步提升。 利用可见/近红外光谱和化学计量学方法, 实现小麦DON含量超标与否的在线判别与筛查, 为我国小麦产品质量安全快速检测提供了技术参考。 但对DON含量的定量分析还需要进一步研究, 探究外部因素对模型的影响, 并拟扩大样品量, 收集不同地区、 不同品种的小麦样品, 提高模型的精度及普适性。
Abstract
Wheat is not only the main grain in China, but also is an important feed and industrial raw material. Wheat is susceptible to scab, which can produce vomitoxin whose scientific name is Deoxynivalenol (DON). Vomitoxin is carcinogenic and pose a serious threat to human and animal health. In recent years, due to the frequent occurrence of extreme and abnormal weather, the risk of DON infection is on the rise, which has become the main factor affecting the quality and safety of wheat products. However, traditional methods for detecting DON content have obvious problems such as cumbersome and time-consuming detection process. Therefore, developing a fast, low-cost and online detection method is of great significance for the safe production and processing of wheat. Firstly, 200 wheat samples with different degrees of scab infection were collected from all parts of Jiangsu. After milling, the content of DON in wheat was determined by ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS), and then the visible/near-infrared spectral of wheat were collected online. The data processing steps are: pre-processing the spectrum by multi-scattering correction and second derivative, and extracting the characteristic wavelength according to the competitive adaptive reweighted sampling algorithm, then using linear discriminant analysis (LDA) and partial least squares discriminant analysis (PLS-DA) was used to establish a qualitative analysis model of wheat flour samples (with a national standard of 1 000 μg·kg-1), and a quantitative analysis model of DON content in wheat flour samples was established according to partial least squares regression (PLSR). UPLC-MS/MS results showed that the risk of wheat DON contamination was higher, and the over-standard rate of the tested samples was 50%. Visible/near-infrared spectroscopy analysis showed that the spectral characteristics of different DON content wheat samples had some differences. The original spectrum and the second derivative spectrum showed that the higher the DON content, the lower the absorbance at 1 420 nm. Due to the low absolute content of DON and the limited detection limit of spectroscopy, the obvious clustering trend could not be found by principal component analysis. However, the LDA and PLS-DA discriminant models constructed according to the full spectrum and the characteristic spectrum can quickly identify and screen sound and infection samples, and the best recognition rate was 87.69%. According to the quantitative analysis results, the PLSR model of DON content in wheat samples was not ideal. The optimal model results: the correlation coefficient (rp) of the prediction set was 0.688, the root mean square error (RMSEP) was 727 μg·kg-1, and the relative analysis deviation (RPD) was 1.38. The accuracy and robustness of the model needed to be further improved. It is feasible to use visible/near-infrared spectroscopy and chemometrics methods to achieve on-line discrimination and screening of wheat DON content exceeding the standard, which provides a technical reference for the rapid and quality detection of wheat products in China. However, the quantitative analysis of DON content needs further research to explore the influence of external factors on the model, and it is planned to expand the sample size, collect wheat samples from different regions and different varieties, and improve the accuracy and universality of the model.

蒋雪松, 张斌, 赵天霞, 熊超平, 沈飞, 何学明, 刘琴, 周宏平, 刘兴泉. 小麦呕吐毒素污染可见/近红外光谱快速筛查方法研究[J]. 光谱学与光谱分析, 2019, 39(12): 3904. JIANG Xue-song, ZHANG Bin, ZHAO Tian-xia, XIONG Chao-ping, SHEN Fei, HE Xue-ming, LIU Qin, ZHOU Hong-ping, LIU Xing-quan. Screening of DON Contamination in Wheat Based on Visible/Near Infrared Spectroscopy[J]. Spectroscopy and Spectral Analysis, 2019, 39(12): 3904.

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