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Modified PCA and PLS: Towards a better classification in Raman spectroscopy–based biological applications

机译:改进的PCA和PLS:迈向拉曼光谱的生物应用更好地分类

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Abstract >Raman spectra of biological samples often exhibit variations originating from changes of spectrometers, measurement conditions, and cultivation conditions. Such unwanted variations make a classification extremely challenging, especially if they are more significant compared with the differences between groups to be separated. A classifier is prone to such unwanted variations (ie, intragroup variations) and can fail to learn the patterns that can help separate different groups (ie, intergroup differences). This often leads to a poor generalization performance and a degraded transferability of the trained model. A natural solution is to separate the intragroup variations from the intergroup differences and build the classifier based on merely the latter information, for example, by a well‐designed feature extraction. This forms the idea of this contribution. Herein, we modified two commonly applied feature extraction approaches, principal component analysis (PCA) and partial least squares (PLS), in order to extract merely the features representing the intergroup differences. Both of the methods were verified with two Raman spectral datasets measured from bacterial cultures and colon tissues of mice, respectively. In comparison to ordinary PCA and PLS, the modified PCA was able to improve the prediction on the testing data that bears significant difference to the training data, while the modified PLS could help avoid overfitting and lead to a more stable classification. </abstract> </span> <span class="z_kbtn z_kbtnclass hoverxs" style="display: none;">展开▼</span> </div> <div class="translation abstracttxt"> <span class="zhankaihshouqi fivelineshidden" id="abstract"> <span>机译:</span><Abstract Type =“Main”XML:Lang =“en”> <标题类型=“main”>抽象</ title> >生物样品的拉曼光谱通常表现出源自光谱仪,测量条件和栽培条件的变化的变化。这种不需要的变化使分类非常具有挑战性,特别是如果与要分开的组之间的差异相比,它们更为显着。分类器容易出现这种不需要的变化(即,Intragroup变化),并且可以无法学习可以帮助分离不同组的模式(即,帧组差异)。这通常会导致普遍性的性能差和训练有素的模型的降级可转移性。自然解决方案是将Intragous变化与帧组差异分离,并仅基于仅通过精心设计的特征提取来构建分类器。这构成了这一贡献的想法。这里,我们修改了两个常用的特征提取方法,主成分分析(PCA)和局部最小二乘(PLS),以便仅提取代表互连差异的特征。两种方法都用两种拉曼光谱数据集验证,分别从小鼠的细菌培养物和结肠组织测量。与普通PCA和PLS相比,改进的PCA能够改善对对训练数据有显着差异的测试数据的预测,而改进的请避免过度拟合并导致更稳定的分类。</ p> </摘要> </span> <span class="z_kbtn z_kbtnclass hoverxs" style="display: none;">展开▼</span> </div> </div> <div class="record"> <h2 class="all_title" id="enpatent33" >著录项</h2> <ul> <li> <span class="lefttit">来源</span> <div style="width: 86%;vertical-align: text-top;display: inline-block;"> <a href='/journal-foreign-33917/'>《Journal of Chemometrics》</a> <b style="margin: 0 2px;">|</b><span>2020年第4期</span><b style="margin: 0 2px;">|</b><span>共10页</span> </div> </li> <li> <div class="author"> <span class="lefttit">作者</span> <p id="fAuthorthree" class="threelineshidden zhankaihshouqi"> </p> <span class="z_kbtnclass z_kbtnclassall hoverxs" id="zkzz" style="display: none;">展开▼</span> </div> </li> <li> <div style="display: flex;"> <span class="lefttit">作者单位</span> <div style="position: relative;margin-left: 3px;max-width: 639px;"> <div class="threelineshidden zhankaihshouqi" id="fOrgthree"> </div> <span class="z_kbtnclass z_kbtnclassall hoverxs" id="zhdw" style="display: none;">展开▼</span> </div> </div> </li> <li > <span class="lefttit">收录信息</span> <span style="width: 86%;vertical-align: text-top;display: inline-block;"></span> </li> <li> <span class="lefttit">原文格式</span> <span>PDF</span> </li> <li> <span class="lefttit">正文语种</span> <span>eng</span> </li> <li> <span class="lefttit">中图分类</span> <span><a href="https://www.zhangqiaokeyan.com/clc/159.html" title="化学">化学;</a></span> </li> <li class="antistop"> <span class="lefttit">关键词</span> <p style="width: 86%;vertical-align: text-top;"> <a style="color: #3E7FEB;" href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=factor methods&option=203" rel="nofollow">factor methods;</a> <a style="color: #3E7FEB;" href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=feature extraction&option=203" rel="nofollow">feature extraction;</a> <a style="color: #3E7FEB;" href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=PCA&option=203" rel="nofollow">PCA;</a> <a style="color: #3E7FEB;" href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=PLSR&option=203" rel="nofollow">PLSR;</a> <a style="color: #3E7FEB;" href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=Raman spectroscopy&option=203" rel="nofollow">Raman spectroscopy;</a> </p> <div class="translation"> 机译:因子方法;特征提取;PCA;PLSR;拉曼光谱; </div> </li> </ul> </div> </div> <div class="literature cardcommon"> <div class="similarity "> <h3 class="all_title" id="enpatent66">相似文献</h3> <div class="similaritytab clearfix"> <ul> <li class="active" >外文文献</li> <li >中文文献</li> <li >专利</li> </ul> </div> <div class="similarity_details"> <ul > <li> <div> <b>1. </b><a class="enjiyixqcontent" href="/journal-foreign-detail/0704025425503.html">Modified PCA and PLS: Towards a better classification in Raman spectroscopy–based biological applications</a> <b>[J]</b> . <span> <a href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=&option=202" target="_blank" rel="nofollow" class="tuijian_auth tuijian_authcolor"> </a> <a href="/journal-foreign-33917/" target="_blank" rel="nofollow" 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