人参的地理来源显著影响其营养价值和化学成分,进而影响其市场价格。分析这些差异的传统方法通常很耗时,并且需要大量的试剂,使他们效率低下。因此,高光谱成像(HSI)结合X射线技术用于人参产地的快速和无损可追溯性。最初,通过采用组合的孤立森林算法和密度峰值聚类(DPC)算法,可以有效地拒绝离群样本。随后,利用高光谱数据构建随机森林(RF)和支持向量机(SVM)分类模型。通过应用72种预处理方法及其组合,进一步优化了这些模型。此外,为了提高模型的性能,采用了四种变量筛选算法:SelectKBest,遗传算法(GA),最小绝对收缩和选择运算符(LASSO),和排列特征重要性(PFI)。优化后的模型,利用二阶导数,自动缩放,排列特征重要性,和支持向量机(2ndDer-AS-PFI-SVM),实现了93.4%的预测精度,在独立预测集上,Kappa值为0.876,Brier评分为0.030,F1评分为0.932,AUC为0.994。此外,从彩色和X射线图像中提取的图像数据(包括颜色信息和纹理信息)用于构建分类模型并评估其性能。其中,利用X射线图像的纹理信息构建的SVM模型表现最好,在验证集上实现了63.0%的预测精度,Brier评分为0.181,F1评分为0.518,AUC为0.553。通过基于Stacking策略实现中层融合和高层数据融合,发现采用高光谱光谱信息和X射线图像纹理信息的高级融合的模型明显优于仅使用高光谱光谱信息的模型。这种先进的模型达到了95.2%的预测精度,在独立预测集上,Kappa值为0.912,Brier评分为0.027,F1评分为0.952,AUC为0.997。总之,本研究不仅为人参产地的快速、无损溯源提供了一条新的技术路径,同时也展示了HSI和X射线技术在医药和食品可追溯性领域的联合应用的巨大潜力。
The geographical origin of Panax ginseng significantly influences its nutritional value and chemical composition, which in turn affects its market price. Traditional methods for analyzing these differences are often time-consuming and require substantial quantities of reagents, rendering them inefficient. Therefore, hyperspectral imaging (HSI) in conjunction with X-ray technology were used for the swift and non-destructive traceability of Panax ginseng origin. Initially, outlier samples were effectively rejected by employing a combined isolated forest algorithm and density peak clustering (DPC) algorithm. Subsequently, random forest (RF) and support vector machine (SVM) classification models were constructed using hyperspectral spectral data. These models were further optimized through the application of 72 preprocessing methods and their combinations. Additionally, to enhance the model\'s performance, four variable screening algorithms were employed: SelectKBest, genetic algorithm (GA), least absolute shrinkage and selection operator (LASSO), and permutation feature importance (PFI). The optimized model, utilizing second derivative, auto scaling, permutation feature importance, and support vector machine (2nd Der-AS-PFI-SVM), achieved a prediction accuracy of 93.4 %, a Kappa value of 0.876, a Brier score of 0.030, an F1 score of 0.932, and an AUC of 0.994 on an independent prediction set. Moreover, the image data (including color information and texture information) extracted from color and X-ray images were used to construct classification models and evaluate their performance. Among them, the SVM model constructed using texture information from X -ray images performed the best, and it achieved a prediction accuracy of 63.0 % on the validation set, with a Brier score of 0.181, an F1 score of 0.518, and an AUC of 0.553. By implementing mid-level fusion and high-level data fusion based on the Stacking strategy, it was found that the model employing a high-level fusion of hyperspectral spectral information and X-ray images texture information significantly outperformed the model using only hyperspectral spectral information. This advanced model attained a prediction accuracy of 95.2 %, a Kappa value of 0.912, a Brier score of 0.027, an F1 score of 0.952, and an AUC of 0.997 on the independent prediction set. In summary, this study not only provides a novel technical path for fast and non-destructive traceability of Panax ginseng origin, but also demonstrates the great potential of the combined application of HSI and X-ray technology in the field of traceability of both medicinal and food products.