欧亚国际

近期，科学家利用VideometerLab多光谱成像系统发表了题为Monitoring the growth of Fusarium graminearum in wheat kernels using multispectral imaging with chemometric methods的文章。多光谱成像技术是功能强大的图谱合一技术，在植物病理学研究领域有着巨大的应用前景。

详细应用介绍见以下链接http://www.010wangxiao.com/Wenzhang/detail/id/1043.html

利用化学计量学多光谱成像技术监测小麦籽粒中禾谷镰刀菌的生长

摘要 

小麦是为人类提供能量和营养的重要农业经济作物。然而，小麦籽粒容易受到禾谷镰刀菌的污染，对人类健康有害。本研究开发了一种利用多光谱成像技术快速无损检测小麦籽粒中禾谷镰刀菌污染程度和数量的方法。基于遗传算法（GA）和主成分分析（PCA）数据预处理方法，结合偏最小二乘（PLS）、支持向量机（SVM）和反向传播神经网络（BPNN）化学计量学方法，建立了禾谷镰刀菌识别和定量测定模型。GA-BPNN方法在不同污染期的小麦籽粒污染程度识别中获得了最佳结果，准确率高达100%。不同方法的结果比较表明，GA-SVM对禾谷镰刀菌数量的预测效果最好，校正集和预测集的相关系数（R）分别为0.9663和0.9292，校准集和预测集的均方根误差（RMSE）分别为0.5992和0.6725 CFU g−1。可以得出结论，多光谱成像和化学计量学方法的结合在实际应用中对谷物真菌的快速无损检测具有潜在的实用价值。

Monitoring the growth of Fusarium graminearum in wheat kernels using multispectral imaging with chemometric methods

Abstract

Wheat is an important agricultural economic crop providing energy and nutrition for human beings. However, wheat kernels are easily contaminated with Fusarium graminearum that is harmful to human health. In this study, a rapid and nondestructive detection method has been developed to identify the degree of contamination and determine the count of Fusarium graminearum in wheat kernels using multispectral imaging technology. Based on genetic algorithm (GA) and principal component analysis (PCA) data preprocessing methods combined with partial least squares (PLS), support vector machine (SVM) and back propagation neural network (BPNN) chemometric methods, identification and quantitative determination models were established. The best result was obtained by GA-BPNN with an accuracy of up to 100% in the identification of the degree of contamination in wheat kernels at different contamination periods. Comparison of the results from different methods revealed that the best prediction of the count of Fusarium graminearum was obtained by GA-SVM with the correlation coefficient (R) in the calibration set and prediction set being 0.9663 and 0.9292, while the root mean square error (RMSE) in the calibration set and prediction set was 0.5992 and 0.6725 CFU g−1, respectively. It can be concluded that the combination of multispectral imaging and chemometric methods was potentially useful for rapid and nondestructive detection of cereal fungi in practice.