Emblic medicine is a popular natural source in the world due to its outstanding healthcare and therapeutic functions. Our preliminary results indicated that the quality of emblic medicines might have an apparent regional variation. A rapid and effective geographical traceability system has not been designed yet. To trace the geographical origins so that their quality can be controlled, an integrated spectroscopic strategy including spectral pretreatment, outlier diagnosis, feature selection, data fusion, and machine learning algorithm was proposed. A featured data matrix (245 × 220) was successfully generated, and a carefully adjusted RF machine learning algorithm was utilized to develop the geographical traceability model. The results demonstrate that the proposed strategy is effective and can be generalized. Sensitivity (SEN), specificity (SPE) and accuracy (ACC) of 97.65%, 99.85% and 97.63% for the calibrated set, as well as 100.00% predictive efficiency, were obtained using this spectroscopic analysis strategy. Our study has created an integrated analysis process for multiple spectral data, which can achieve a rapid, nondestructive and green quality detection for emblic medicines originating from seventeen geographical origins.

栓塞药因其出色的保健和治疗功能而成为世界上流行的天然来源。我们的初步结果表明，余味药物的质量可能存在明显的区域差异。尚未设计出快速有效的地理可追溯系统。为了追踪地理起源，以便控制其质量，提出了一种综合光谱策略，包括光谱预处理，离群值诊断，特征选择，数据融合和机器学习算法。成功生成了特征数据矩阵（245×220），并使用经过仔细调整的RF机器学习算法来开发地理可追溯性模型。结果表明，所提出的策略是有效的，可以推广。灵敏度（SEN），使用该光谱分析策略可获得的校正组的特异性（SPE）和准确度（ACC）分别为97.65％，99.85％和97.63％，以及100.00％的预测效率。我们的研究为多种光谱数据创建了一个集成的分析过程，该过程可对源自十七个地理起源的栓塞药物实现快速，无损和绿色质量的检测。