Research Progress of Statistical Analysis Methods for Physicochemical Parameters of Edible Frying Oil
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摘要: 煎炸食品在愉悦味蕾的同时,也面临着油种类的最优抉择和质量安全问题,众多学者在选择何种类型的油,以及煎炸的时间长短方面进行了研究。本文从统计学的角度出发,概述了常用统计分析方法在煎炸油理化指标数据分析中的研究进展。一是,表格分析方式,大多数是将多次重复实验的数据以均值加减标准差的形式放到表格里进行研究,而且分析较多的理化指标是酸值、脂肪酸和极性化合物这三个指标。二是,作图可视化方式:直条图、折线图和散点图;其中,直条图主要用来展现在不同煎炸油、不同煎炸时间、不同煎炸温度、不同加热功率等条件下理化指标的含量情况;折线图主要用来分析理化指标随着煎炸时间、煎炸温度、不同煎炸油、不同煎炸负荷下理化指标的变化情况;散点图主要用来分析理化指标受煎炸时间、煎炸次数、油样密度等的影响,以及理化指标相互之间的关系和理化指标在不同废弃油样中的分布情况等。三是,拟合方式:线性拟合和非线性拟合;其中,线性拟合主要用来分析理化指标随煎炸时间、煎炸次数、介电常数变化等的线性关系,以及用来分析理化指标和近红外光谱之间的线性关系,分析理化指标实测值和其他模型预测值之间的一致性等;非线性拟合主要涉及到多项式拟合、指数函数拟合和逻辑回归的拟合,用来分析某些理化指标随煎炸时间变化的复杂关系,以及根据理化指标将油进行好坏分类等。四是,理化指标间的相关性分析,绝大多数文献是通过计算指标间的皮尔逊相关系数进行的,少部分文献使用了指标间的线性或者非线性拟合方式来分析。五是,针对理化指标数据进行的方差分析,主要用于研究不同煎炸因素下理化指标的变化情况。六是,针对理化指标进行的主成分分析,主要用来分析各个理化指标对煎炸油质量监控的重要性程度等等。通过以上对于煎炸油理化指标的统计分析方法的进展描述,给研究者们提供对于研究方法和研究手段的选择提供思路。Abstract: Frying food is not only pleasant to taste buds, but also faced with the optimal choice of oil type and quality and safety issues. Many scholars have studied the choice of oil type and the frying time. From the perspective of statistics, this paper summarizes the research progress of common statistical data analysis methods in the analysis of frying oil’s physicochemical parameters. First, with table analysis, data of repeated experiments are put into the table in the form of mean value plus minus standard deviation in most research cases, and the three common analyzed physicochemical parameters are acid value, fatty acid and total polar compounds. Second, with visualization methods of straight bar charts, line charts and scatter plots, the straight bar charts are mainly used to show the content of physicochemical parameters under different frying oil, frying time, frying temperature or power levels of heating; the line charts are mainly used to analyze the changes of physicochemical parameters with frying time, frying temperature, frying oil or frying load; the scatter plots are mainly used to analyze the influence of frying time, frying times or oil density on physicochemical parameters, as well as the relationship between physicochemical parameters or the distribution of physicochemical parameters in different waste oil samples. Third, with fitting methods of linear fittings and nonlinear fittings, the linear fittings are mainly used to analyze the linear relationships of physicochemical parameters with frying time, frying times or dielectric constants, as well as the linear relationships between physicochemical parameters and near infrared spectrum, and to analyze the consistency between the measured values of physicochemical parameters and the predicted values of other models; The nonlinear fittings mainly involve the methods of polynomial fitting, exponential function fitting and logistic regression and they are used to analyze the complex relationships between some physicochemical parameters and frying time, and to classify oil according to physicochemical parameters. Fourth, about the correlation analysis of physicochemical parameters, most of the literatures are carried out by calculating the Pearson correlation coefficients between physicochemical parameters, and a small number of literatures use the linear or nonlinear fitting methods between physicochemical parameters to analyze the correlation. Fifth, Analysis of variance is mainly used to study the changes of physicochemical parameters under different frying factors. Sixth, the principal component analysis is mainly used to analyze the importance of each physicochemical parameter to the quality control of frying oil, and so on. Through the above description of the progress of the statistical analysis methods for the physicochemical parameters of frying oil, it provides the researchers with some ideas for the selection of research methods and means.
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表 1 数据表格常用场景分类情况
Table 1. Classification of common scenarios in data tables
表 2 一种油中实验数据表格研究的理化指标
Table 2. Physicochemical parameters for the study of one kind of oil
表 3 一种油中以均值加减标准差的数据值形式的表格研究的理化指标
Table 3. Physicochemical parameters for the study of one kind of oil in the form of mean plus or minus standard deviations
表 4 多种油实验单一数据值表格研究的理化指标
Table 4. Physicochemical parameters for the study of various oils in the form of single data value
表 5 多种油以均值加减标准差的数据形式的表格研究的理化指标
Table 5. Physicochemical parameters for the study of various oils in the form of mean plus or minus standard deviations
表 6 直条图使用场景
Table 6. The common scene of straight bars
表 7 折线图常用场景
Table 7. The common scene of line charts
表 8 散点图常用场景
Table 8. The common scene of scatter diagram
表 9 线性拟合常用场景
Table 9. The common scene of linear fit
常用场景 案例 理化指标随煎炸时间
的线性变化关系文献[48]回归了过氧化值、酸值、极性化合物、介电常数和黏度随着煎炸时间变化的线性关系;
文献[81]拟合了羰基价及过氧化值和煎炸时间的线性关系;文献[83]研究了油样的LF-NMR弛豫特性中,
峰起时间和峰面积随着煎炸时间的变化关系。理化指标随煎炸次数
的线性变化关系文献[8]研究了酸值、碘值、极性化合物和羰基价分别随煎炸次数而变化的线性关系;
文献[37]研究了极性化合物与煎炸次数之间的线性关系。理化指标随介电常数
的变化关系文献[28]研究了极性化合物和游离脂肪酸随着介电常数变化的线性关系 某些理化指标相互
之间线性相关关系文献[6]研究了极性甘油三酯与反式-9,10-环氧油酸甲酯的线性关系;文献[11]研究了几种油中TPC(极性化合物)
与TGP(甘油三酯聚合物)、TPC及Ba P (苯并芘)含量关系、TPC与多环芳烃的含量关系。近红外光谱和理化
指标之间的关系文献[12]研究了酸值、极性化合物和近红外光谱预测值之间的关系。 理化指标实测值和某些模型对
该指标的预测值之间的线性关系文献[87]研究了模型预测过氧化物、羰基价和极性化合物和实际测得的极性化合物之间的线性关系;
文献[87]研究了实际测得的皂化值、碘值和模型预测的相应值之间的线性关系;表 10 非线性拟合常用场景
Table 10. The common scene of non-linear fit
常用场景 案例 多项式拟合 文献[82]研究了羰基价、过氧化值分别随煎炸时间变化的关系;文献[83]研究了黏度、
吸光值分别随煎炸时间变化的关系;文献[78]研究了丙烯酰胺分别随煎炸温度、煎炸时间变化的关系;
文献[85]研究了K值、茴香胺值、碘值、极性化合物分别随煎炸时间变化的关系。指数函数拟合 文献[25]研究了黏度和煎炸温度之间的关系;文献[85]研究了K值随煎炸时间变化的关系;
文献[81]研究了运动黏度、极性化合物随煎炸时间变化的关系。逻辑回归 文献[66]用逻辑回归根据P-茴香氨值、酸值、极性化合物、聚合三酰甘油、单体氧化三酰甘油、
碘值等理化指标进行研究,将煎炸油分为质量好坏两大类,用以表示煎炸油的质量变化的可能性。表 11 计算指标间相关性的方法
Table 11. The methods for calculating the correlation between parameters
表 12 不同应用场景的统计方法参考
Table 12. Reference for statistical analysis methods in different application scenarios
实验目的 方法 一种油的理化指标的变化情况 基于实验单一数据值表格分析 基于均值加减标准差的数据表格分析 多种油进行对比分析 基于实验单一数据值表格分析 基于均值加减标准差的数据表格分析 比较不同实验条件下的理化指标含量分布情况 直条图 研究理化指标随时间、温度、煎炸次数等的变化的趋势 折线图 研究理化指标随煎炸次数的变化趋势 折线图 研究理化指标受煎炸时间的影响 散点图 研究理化指标间的相关关系 散点图 找到理化指标和煎炸时间、煎炸温度以及煎炸次数之间的函数关系 线性拟合 非线性拟合 找到理化指标之间的函数关系 线性拟合 非线性拟合 指标间的相关性分析 计算指标间的皮尔逊相关系数 指标间的线性或者非线性拟合 不同煎炸时间和不同煎炸油或不同煎炸时间和煎炸温度相结合对于煎炸质量水平是否存在显著性影响 双因素方差分析 研究某个煎炸实验条件的不同水平是否对煎炸过程中观测变量产生显著影响 单因素方差分析 验证多元回归模型的整体显著性 方差分析 将实验数据集简化,克服原始数据集的重叠性、相关性 主成分分析 -
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