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Eucommia ulmoides Oliver, a single species of Eucommia genus belonging to the Eucommiaceae family, is an endemic in China and has been used in traditional Chinese medicine for nearly two thousand years. Records from different historical periods highlight E. ulmoides and its officinal botanical parts, usefulness in adaptation to disease and its central role in Chinese medicine theory. There are also historical collection documents for minorities in China. Tearing the leaves, bark and fruit produces strands of latex; a description of E. ulmoides's morphological features is recorded in this paper. This review summarizes 204 natural compounds isolated from this plant, which are divided into seven categories: lignans, iridoids, flavonoids, phenols, steroids, terpenes and others. These components possess wide-ranging pharmacological efficacies, such as antihypertensive, antihyperglycemic, antihyperlipidemia, anti-oxidative, anti-osteoporosis, antitumor, immunomodulatory and neuroprotective activities. This review aims to provide a reference for extensive researches of E. ulmoides crude drugs, especially for quality control, biosynthesis and structure modification of active ingredients and pharmacological mechanism. [ABSTRACT FROM AUTHOR]

• Information fusion combined with chemometrics was firstly applied for geo-traceability of Eucommia ulmoides leaves. • Spectra data mining was successfully applied for improvement of PLS-DA and robustness of SVM models. • The comprehensive utilization model under the circular industry economy should be recommended. Eucommia ulmoides is one of valuable cash crops and its leaves are a high-quality raw industrial material with great development potential. Geographical variation is the main factor leading to differences in chemical composition of Eucommia ulmoides leaves (EULs). In this study, a total of 159 samples from 13 provinces in China including male and female individuals as well as various elevation ranges were systematically conducted using fusion data, attenuated total reflection Fourier transformation mid-infrared (ATR-FTIR) and ultraviolet-visible (UV–vis) spectra, coupled to chemometrics. Two classification models, partial least squares discrimination analysis (PLS-DA) and support vector machine (SVM), were established based on individual spectra and multi-spectral fused information, respectively. Comparatively, the SVM model based on genetic algorithm (GA) searching for optimal parameters had the best performance for distinguishing different origin samples with 100 % accuracy rates in calibration and validation sets. Furthermore, hierarchical cluster analysis (HCA) was used for investigating the influence caused by the difference in gender and altitude based on low-level fusion data. The result showed that the effect of individual gender on chemical information of EULs was less than the influence of geographical factors. Meanwhile, an interesting focus was that the PLS-DA scores plot and dendrogram suggested that the chemical profiles of these samples in Jiangxi Province (region 8) was significantly different from other regions because of the green circular economy mode. This study indicated that the PLS-DA and GA-SVM algorithm could be developed as an excellent model in geographical traceability on the basis of mid-level (latent variables, LVs) data fusion with two spectral datasets. Such comprehensive utilization model under the circular industry economy should be recommended. [ABSTRACT FROM AUTHOR]