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Rapid Determination of Total Polar Compounds in Frying Oil Using ATR‐FTIR Combined with Extended Partial Least Squares Regression

机译：使用ATR-FTIR结合延伸部分最小二乘回归快速测定油炸油中的总极化物化合物

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Abstract > A method of rapidly determining the total polar compounds (TPCs) in frying oils using attenuated total reflectance‐Fourier transform infrared spectroscopy combined with partial least squares (PLS) regression is developed. Oils of various types and geographic origins are used to ensure that the proposed model is robust. The first derivative spectrum is selected as the spectral processing method. The interval PLS, forward interval PLS, and backward interval PLS algorithms are compared in terms of their performance. A correlation coefficient ( R 2 ) of 0.9942, a root mean square error of calibration (RMSEC) of 1.1, a root mean square error of prediction (RMSEP) of 2.30, a residual predictive deviation (RPD) of 4.1, and a limit of detection (LOD) of 1.65% are obtained by the fiPLS33 model with fewer latent variables and a lower spectral interval number. In addition, sub‐models using a single type of oil showed higher performance ( R 2 0.9957–0.9998, RMSEC 0.12–0.92, RMSEP 0.79–1.58, RPD 4.79–9.64, LOD 0.66–1.26%) than the general model. The TPC models developed are accurate, stable, and adaptable, and they can be used to analyze general frying oil samples quickly, regardless of the oil type, and to analyze samples of specific oil types accurately. > Practical applications : The content of TPCs is an important indicator of whether the oil has been overused and whether it will be harmful during the frying process. However, traditional chemical methods are time‐consuming, and they have not been used to determine large‐sized samples. In addition, due to a lack of regional optimization, most studies on determining TPCs with FTIR give unsatisfactory model performance. A general TPC model that incorporates several oil types and regional optimization is expected to improve prediction performance. Therefore, the proposed method represents a rapid and accurate tool for measuring TPCs in edible fats and oils. </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> > 开发了一种快速确定使用减毒的总反射率 - 傅里叶变换红外光谱与偏最小二乘（PLS）回归结合煎炸油的总极化油脂总体化合物（TPC）的方法。各种类型和地理起源的油用于确保所提出的模型是强大的。选择第一衍生光谱作为光谱处理方法。在其性能方面比较间隔PLS，前向间隔PLS和向后间隔PLS算法。相关系数（ R </ I> 2 </ sup> 0.9942，校准的根均方误差为1.1，预测（RMSEP）的根均方误差为2.30，残余预测偏差（RPD）为4.1，以及1.65的检测极限（LOD）％通过FIPLS33模型获得，具有较少的潜在变量和较低的频谱间隔数。此外，使用单一类型的油的子模型显示出更高的性能（ R </ I> 2 </ sup> 0.9957-0.9998，RMSEC 0.12-0.92，RMSEP 0.79-1.58，RPD 4.79-9.64，LOD 0.66-1.26％）比一般模型。开发的TPC型号精确，稳定和适应性，它们可用于分析普通油炸油样品，无论油式如何，都准确地分析特定油片样品。 </ p> > 实际应用</ i> ：TPC的内容是石油是否过度使用的重要指标以及在煎炸过程中是否会有害。然而，传统化学方法是耗时的，并且他们未被用来确定大型样本。此外，由于缺乏区域优化，大多数关于使用FTIR确定TPC的研究都提供了不令人满意的模型性能。预计包含多种油类和区域优化的一般TPC模型将提高预测性能。因此，所提出的方法代表了一种用于测量可食用脂肪和油中TPC的快速准确的工具。 </ 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-23377/'>《European Journal of Lipid Science and Technology》</a> <b style="margin: 0 2px;">|</b><span>2020年第5期</span><b style="margin: 0 2px;">|</b><span>共7页</span> </div> </li> <li> <div class="author"> <span class="lefttit">作者</span> <p id="fAuthorthree" class="threelineshidden zhankaihshouqi"> <a href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=Liu Xiaoying&option=202" target="_blank" rel="nofollow">Liu Xiaoying;</a> <a href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=Ye Qin&option=202" target="_blank" rel="nofollow">Ye Qin;</a> <a href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=Xiao Chaogeng&option=202" target="_blank" rel="nofollow">Xiao Chaogeng;</a> <a href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=Zou Ying&option=202" target="_blank" rel="nofollow">Zou Ying;</a> <a href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=Meng Xianghe&option=202" target="_blank" rel="nofollow">Meng Xianghe;</a> </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"> <p>College of Food Science and TechnologyZhejiang University of TechnologyHangzhou 310014 P. R. China;</p> <p>Institute of Food SciencesZhejiang Academy of Agricultural SciencesHangzhou 310014 P. R. China;</p> <p>Institute of Food SciencesZhejiang Academy of Agricultural SciencesHangzhou 310014 P. R. China;</p> <p>Wenzhou Vocational College of Science and TechnologyWenzhou 325006 P. R. China;</p> <p>College of Food Science and TechnologyZhejiang University of TechnologyHangzhou 310014 P. R. China;</p> </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/8004.html" title="油脂工业产品及副产品">油脂工业产品及副产品;</a><a href="https://www.zhangqiaokeyan.com/clc/8042.html" title="食用油脂加工工业">食用油脂加工工业;</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=attenuated total reflectance‐FTIR spectroscopy&option=203" rel="nofollow">attenuated total reflectance‐FTIR spectroscopy;</a> <a style="color: #3E7FEB;" href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=forward interval partial least squares regression&option=203" rel="nofollow">forward interval partial least squares regression;</a> <a style="color: #3E7FEB;" href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=frying oil&option=203" rel="nofollow">frying oil;</a> <a style="color: #3E7FEB;" href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=total polar compounds&option=203" rel="nofollow">total polar compounds;</a> </p> <div class="translation"> 机译：减弱总反射率-FTIR光谱;向前间隔部分最小二乘回归;煎炸油;总极化的总体化合物; </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/0704023377998.html">Rapid Determination of Total Polar 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