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首页> 外文期刊>Chemometrics and Intelligent Laboratory Systems >Penalized logistic regression for classification and feature selection with its application to detection of two official species of Ganoderma
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Penalized logistic regression for classification and feature selection with its application to detection of two official species of Ganoderma

机译:惩罚分类和特征选择的惩罚物流回归,其应用于检测两个官方的灵芝种类

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AbstractTwo species ofGanoderma,Ganoderma lucidum(G.?lucidum)andGanoderma sinense (G. sinense)have been widely used as traditional Chinese herbal medicine for their high medicinal value. Recent studies show that the two species differ in levels of their main active compounds triterpenoids though both have antitumoral effects. An effective and simple analytical method using attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy to discriminate between the two species is of essential importance for its quality assurance and medicinal value estimation. In this study three penalized logistic regression models, weighted least absolute shrinkage and selection operator (Lasso), elastic net and weighted fusion, using ATR-FTIR spectroscopy have been explored for the purpose of classification and interpretation. The weighted fusion model incorporating spectral correlation structure allowed an automatic selection of a small number of spectral bands and achieved an excellent overall classification accuracy of 99% in discriminating spectra ofG.?lucidumfrom that ofG.?sinense. Its classification performance was superior to that of the weighted Lasso model and elastic net model. The automatic selection of informative spectral features results in substantial reduction in model complexity and improvement of classification accuracy, and it is particularly helpful for the quantitative interpretations of the major chemical constituents ofGanodermaregarding its anti-cancer effects.Highlights?A successful combination of ATR-FTIR and penalized classification to distinguish between two official species ofGanoderma.?An automatic selection of a small number of informative spectral wavenumbers for classification.?The proposed model outperformed its competitors with excellent classification accuracy.?The proposed model provided interpretation of the major bioactive compounds inGanodermaregarding its medicinal effects. .]]>
机译:<![CDATA [ 抽象 两个物种灵芝灵芝> ganoderma lucidum g.?lucidum)灵芝:ganoderma sinense(g. sinense)已被广泛用作中文草药医药的高药物价值。最近的研究表明,两种物种在其主要活性化合物三萜类化合物的水平差异,尽管两者都具有抗肿瘤效应。一种有效且简单的分析方法,使用衰减的总反射傅里叶变换红外线(ATR-FTIR)光谱,以区分两种物种是其质量保证和药物价值估计的重要意义。在这项研究中,针对分类和解释的目的,已经探讨了使用ATR-FTIR光谱的三次惩罚逻辑回归模型,加权最低绝对收缩和选择操作员(套索),弹性网和加权融合。结合光谱相关结构的加权融合模型允许自动选择少量的光谱带,并在 G.?lucidum 斜体>的鉴别光谱中实现了99%的优异整体分类精度。 g.?sinense 。其分类性能优于加权套索模型和弹性净模型。信息性谱特征的自动选择导致模型复杂性和改善分类精度的显着降低,并且对灵芝的主要化学成分的定量解释特别有利,这是它的抗议性的-Cancer效果。 突出显示 a ATR-FTIR和惩罚分类的成功组合,以区分两个官方的灵芝。 自动选择少量的分类频谱波浪管。 所提出的型号优于其竞争对手,具有出色的分类准确性。 所提出的模型提供了在灵芝:斜体>关于其药用作用的主要生物活性化合物的解释。 。 ]]>

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