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.
Keywords: Emblic medicine; Geographical traceability; Quality assessment; Spectroscopic analysis process.
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