基于红外光谱的在用润滑油衰变信息的快速确定和关联性分析

分析了某合成的在用航空润滑油的组成和需检测的指标项目,研究了润滑油红外光谱信息与检测项目之间的关系。采用偏最小二乘法、人工神经网络法等建立红外光谱和运动粘度、闪点、酸值和水分等理化指标的多元校正分析模型。根据朗伯-比尔定律,建立红外光谱峰面积或峰高与被测组分的工作曲线,实现了燃油、抗氧剂、抗磨剂的定量分析。针对基础油酯基降解程度无法定量检测的特点,将酯基降解与酸值、粘度值进行关联性研究,酸值与酯基降解红外谱峰相关性很好,可通过酸值变化确定酯基降解程度。     

基于FTIR的在用润滑油燃油污染的定量检测

为定量、快速检测在用油的燃油污染程度,通过定量加入法配制一定燃油浓度梯度的润滑油样品,建立燃油浓度和804~759 cm-1谱区峰面积的标准曲线,线性相关性达0.987 7,此曲线可用于在用润滑油燃油污染的定量分析。对配制的样品和在用油样品的红外光谱进行Savitzky-Golay多项式法平滑和一阶导数处理,采用偏最小二乘法(PLS)建立润滑油中燃油污染的红外光谱定量分析模型。模型校正偏差为0.144,相关系数达到0.986,预测集的标准偏差为0.161,具有很好的精密度和准确性,可用于在用油燃油稀释污染的快速定量检测分析。     

基于中红外光谱法的某航空润滑油酸值的测定

利用中红外光谱法能快速测得油品氧化面积的优点,将红外氧化面积与实验室常规酸值测定法测得的润滑油酸值进行相关性分析,提出一种利用红外氧化面积表征润滑油酸值的方法。采用该方法对配制的某航空润滑油新油样及在用油油样进行红外光谱分析和常规酸值分析,选择红外谱图中1 800～1 670 cm-1区域作为测定润滑油酸值的氧化区域,对红外峰面积与标准油和在用油的酸值进行相关性分析,给出回归方程并进行了较正。结果表明,利用傅立叶变换中红外光谱法测定某航空润滑油新油及在用油的酸值是可行的,测定结果是可靠的。     

发动机润滑油含水量近红外特征波段选取

应用近红外光谱技术结合间隔偏最小二乘法,探讨发动机润滑油中含水率检测的特征波段及检测方法。采用间隔偏最小二乘法对发动机润滑油含水量的近红外光谱特征波段进行选择,结合偏最小二乘回归建模方法对所选波段进行验证。结果表明:选用7 432～6 321 cm-1和5 583～4 846 cm-1 2个波段建模可以得到与全谱一致的预测结果,从而实现了模型的简化,为润滑油中含水率近红外光谱分析提供理论依据。     

中红外光谱法快速测定航空润滑油50—1—4Ф的运动粘度

对11个不同基础油含量的50—1—4Ф润滑油样品（标准油）和从现场采集的28个在用50—1—4Ф润滑油样品（在用油）进行傅立叶变换中红外光谱分析。选择1465cm。作为测定其运动粘度的特征峰,分别对标准油和在用油样品的运动粘度和相应红外光谱的特征峰处的吸光度进行了相关性分析,给出了回归方程。用另外20个新油样品较正了标准油的回归方程。结果表明,利用傅立叶变换中红外光谱法测定未用及在用50—1—49Ф润滑油的运动粘度是可行的,测定结果是可靠的。     

实际工况下红外光谱法测润滑油总酸值的研究

针对实际工况下采集的在用润滑油,依据ASTM标准试验方法与条件,采用傅里叶红外光谱法与自动电位滴定法分别测得氧化度与总酸值,并研究得到二者之间的相关关系模型。结果表明:从整体上看氧化度与总酸值高度相关,说明用红外光谱测定的氧化度代替电位滴定法测定的总酸值是可行的。但不同类型的油品总酸值与红外氧化度之间的关系存在较大差异,以氧化度理论计算总酸值需要有足够多的样品实测数据基础。     

基于中红外光谱法的航空润滑油黏度测定

提出一种利用傅立叶变换中红外光谱法的在用润滑油黏度快速测定方法。采用该方法对标准油样和某润滑油在用油样进行傅立叶变换中红外光谱分析,选择1 465 cm-1作为测定其黏度的特征峰,分别对标准油和在用油样的黏度和相应红外光谱的特征峰处的吸光度进行相关性分析,给出回归方程。并用该润滑油的新油样品较正了标准油的回归方程。结果表明,利用傅立叶变换中红外光谱法测定在用某润滑油的黏度是可行的,测定结果是可靠的。     

液体奶和奶粉中三聚氰胺含量红外光谱分析方法研究

本文研究了三聚氰胺和奶粉的红外光谱特征,并用偏最小二乘法定量分析牛奶中三聚氰胺含量。掺三聚氰胺奶粉与空白奶粉的红外光谱差异比较明显,主要体现在三聚氰胺的1551cm^-1- 1436cm^-1的三嗪环伸缩振动,该振动特征可以用于三聚氰胺定性分析。当三聚氰胺含量高于5μg/g时,其红外光谱信号强于红外光谱重复性噪音,可以被检测。在液态奶中添加0-200μg/g的三聚氰胺,采用衰减全反射（ATR）测定红外光谱,借助于偏最小二乘法（PLS）,可以准确测定三聚氰胺含量,分析精度高,重复性好,测定结果与实际值相关系数高达0.99以上,分析偏差低于1.5μg/g,所有样品分析误差均小于5μg/g。红外光谱技术无需预处理,速度快,分析精度高,仪器可机动,可实现现场三聚氰胺快速检测,提高奶粉和液体奶质量监控能力。     

基于近红外光谱分析技术的绿茶生产日期快速鉴别（英文）

针对现有方法检测绿茶生产日期的不足,采用控制生产日期单一变量的方法,利用近红外光谱分析技术结合偏最小二乘法对其进行无损伤检测。首先对原始光谱进行五点平滑和一阶微分预处理,并利用移动窗口-BP神经网络算法(MW-BP-ANN)提取特征光谱变量。然后采用偏最小二乘算法验证方式建立校正模型,并采用预测均方根误差(εRMSEP)、相关系数(Cp)和相对分析误差(σRPD)来评价模型质量。当主成分数为9时获得最优模型,3个评价指标分别为19.965,0.943和3.07。研究结果表明,近红外光谱结合偏最小二乘法可用于对绿茶生产日期的快速无损伤检测。     

特征谱区筛选在近红外光谱检测茶叶游离氨基酸含量中的应用研究

在近红外光谱快速检测茶叶游离氨基酸含量过程中,为了提高检测的精度和稳定性,研究利用特征谱区结合偏最小二乘法建立预测模型。研究分别尝试联合区间偏最小二乘法和遗传偏最小二乘法等特征谱区筛选方法,通过交互验证法确定偏最小二乘模型的主成分因子数和筛选区间,以预测均方根误差RMSEP和相关系数R作为模型的评价指标。试验结果表明：两种方法建立模型的预测能力都好于传统PLS模型;利用联合区间偏最小二乘法建立的预测模型最佳,预测时的相关系数(R)和预测均方根误差(RMSEP)分别为0.9542和0.2560。研究结果表明,近红外光谱结合特征谱区筛选方法可以快速准确地测定茶叶中游离氨基酸含量。     

变量优选在纺织品棉含量近红外分析中的应用

为实现用较少的变量建立适当的模型,以准确预测未知棉涤样品的棉含量,用NIRFlex N-500近红外光谱仪采集297个棉涤样品的光谱,用蒙特卡罗无信息变量消除法(MC-UVE)对变量进行筛选,偏最小二乘法(PLS)建立棉含量的定标模型,根据各个模型所用的样品数、交叉验证均方根误差(RMSECV)、预测集均方根误差(RMSEP)和预测相关系数(r)评价定标模型的精度和稳定性。结果表明:通过上述数据预处理方法进行变量筛选后,用PLS建立的校正模型不仅使数据的运算量大幅度降低,还能很好地预测未知样品的棉含量,使得基于近红外光谱的棉涤样品中棉含量的定量分析方法进一步简化。     

Three-way PLS regression and dual energy gamma densitometry for prediction of total volume fractions and enhanced flow regime identification in multiphase flow

Dual energy gamma densitometry and 3-way partial least squares regression were applied to quantify the total volume fractions and improve flow regime identification in multiphase flow. Multiphase flow experiments were carried out with formation water, crude oil and gas from different North Sea gas fields in Statoil׳s High Pressure Multiphase Flow Loop in Porsgrunn, Norway. Four different flow regimes were investigated (stratified wavy, slug, dispersed and annular). A traversable dual energy gamma densitometer was used to measure the fluid densities in the pipe. Partial least squares regression was previously applied to identify multiphase flow regimes and quantify volume fractions of gas, oil and water. That study showed promising results for flow regime identification but the predictions of the total volume fractions were not acceptable. In this study a new method combining gamma densitometry and 3-way partial least squares regression was applied in order to improve the quantitative estimation of the total volume fractions gained in the previous study. The proposed 3-way regression approach allows prediction of the total volume fractions directly using one model instead of multiple models which was reported earlier. The improved quantification of the volume fractions of gas, oil and water was used to improve the flow regime identification plots and increase the interpretability.The new 3-way prediction results for the volume fractions were significantly better than what was found earlier based on individual PLS models. The root mean square error of prediction for gas, oil and water from the 3-way PLS models were 4.1 %, 4.3 % and 4.6% respectively. All models reported were validated based on independent data (test set validation).     

Flow monitoring of underground aquifers by interfacial tensiometry

The aim of this study is online specification of water productivity of crude oil wells based on the multidimensional analysis of the dynamic surface tension (DST) of produced water from underground sub–layers, assuming the impacts of asphaltenes emulsification. The novelties are the discriminant analysis of produced water streams from one aquifer but via different sub–layers, enhancement of tensiometery characteristics to improve the validity and reliability of algorithm, and smoothing the uncertainties and error function or sources. The experimental tests were conducted by the pendant–drop tensiometer via computation of water–air DST of samples obtained from distinct wells exposed to individual sub–layers. DST–partial least squares (PLS) and elasticity–PLS algorithms was computed and the results revealed that the production ratio of L₁–L₄ sub–layers of aquifer were set in the range of 1–53% (for DST–PLS) and 10–50% (for elasticity–PLS) while the conventional method had reported an equal 25% productivity for each layer. The algorithm of elasticity–PLS was developed to determine the production ratio of four sub–layers as that of DST–PLS; however the validity of this algorithm can be improved to higher frequencies more than 5.0 Hz. Both models of DST–PLS and elasticity–PLS were applied successfully for accurate and fast determination of layers productivity in an oil well exposed to multi layers of aquifer and error limit of DST–PLS was determined to be in the range of 1.0–3.2%.     

小型近红外玉米蛋白质成分分析 仪器设计的波段选择

采用傅里叶变换近红外漫反射光谱技术和偏最小二乘法(PLS)建立了玉米蛋白质含量的定标模型。按照预测效果优选光谱波段,为设计小型近红外玉米蛋白质成分分析仪器提供依据。采用多元散射校正方法对光谱进行预处理,然后利用Savitzky-Golay平滑法对原谱、一阶导数谱和二阶导数谱进行平滑处理。选取全谱、合频、一倍频、二倍频和蛋白质基团等5个波段,每个波段分别采用原光谱、一阶导数谱、二阶导数谱,共建立15个定标模型。同时调整Savitzky-Golay平滑点数和PLS因子数,通过多次PLS数值实验比较,按照预测效果确定每个模型的最优平滑点数、因子数,再从中选优。结果表明,采用一阶导数谱的一倍频波段(7 000~5 500 cm^－1)的定标效果最好,模型的预测相关系数、预测均方根偏差、相对预测均方根偏差分别为0.945,0.357,3.340%。一倍频波段可以代替全谱波段并得到更好的定标效果。     

基于拉曼光谱的三组分食用调和油快速定量检测

应用激光拉曼光谱技术结合化学计量学方法实现了三组分食用调和油中菜籽油、花生油和芝麻油的快速定量检测。分别采用标准正态变量变换(SNV)+去趋势(de-trending)算法和正交信号校正(OSC)算法对600~3 000cm-1波段的原始拉曼光谱进行预处理。建立了基于非线性支持向量机(SVM)和线性偏最小二乘(PLS)回归算法的定量分析模型,并采用19个预测集通过外部交叉验证法对模型进行验证。实验结果显示:对含有菜籽油、花生油和芝麻油的三组分食用调和油,以OSC预处理后建立的线性PLS模型预测效果最好,其验证集决定系数R2p分别为0.990 4,0.965 8,0.977 1,均方根误差(RMSEP)分别为0.018 8,0.037 9,0.026 2。研究结果表明,利用激光拉曼光谱结合化学计量学方法快速定量检测三组分食用调和油中菜籽油、花生油和芝麻油的含量具有可行性,并获得了较高的预测精度。     

Developing a Vis/NIR spectroscopic system for fast and non-destructive pesticide residue monitoring in agricultural product

In this research, an optical system based on fibre optic Vis/NIR spectroscopy combined with chemometrics methods and software as a graphical user interface (GUI) was developed and presented for fast and non-destructive detection and determination of pesticide residues in agricultural products (a case study on diazinon in intact cucumbers). Vis/NIR spectra of cucumber samples without and with different concentrations of diazinon residue were analyzed at the range of 450–1000 nm. Partial least squares (PLS) regression models were developed based on chemical reference measurements and the spectral information of the samples after performing different pre-processing methods. Moreover, partial least squares-discriminant analysis (PLS-DA) models were developed based on different spectral pre-processing techniques to classify cucumbers with contents of diazinon below and above the maximum residue limits (MRL) as safe and unsafe samples, respectively. Finally, user-friendly software as a GUI was created based on the best PLS and PLS-DA models developed for prediction of diazinon contents in the samples and for classification of intact cucumbers by the absence/presence of diazinon residues, respectively. Evaluation of the system and software designed based on the best developed PLS and PLS-DA models indicated good performance for measuring and detection of diazinon residue in cucumbers. It was concluded that the designed system and software based on Vis/NIR spectroscopy combined with chemometrics methods can be utilized for fast and non-destructive safety control of intact cucumbers by the absence/presence of diazinon residues. It can also be generalized for detection of other pesticide residues in agricultural products if developing their appropriate models is feasible.     

TDLAS技术在烯烃生产过程中的多组分检测应用

烯烃工业生产过程中的多组分在线检测是对其工业过程有效控制、提高处理装置综合效益的重要手段。本文以在线检测烯烃裂解炉的清焦过程生成的一氧化碳和二氧化碳为应用案例,采用可调谐二极管激光吸收光谱技术(TDLAS)作为分析平台进行多组分分析。针对清焦过程,设计了检测0~5%量程CO和CO_2的模拟实验。对气体含量随机分布的19组数据分别采用多变量最小二乘算法(CLS)、单组分偏最小二乘算法(PLS1)和多组分偏最小二乘算法(PLS2)进行建模和评估。在后续的多组分交叉干扰实验和CO_2的扩展量程准确性测试实验中,PLS1模型的最大误差小于±0.05%,PLS2的小于±0.10%,CLS的小于±0.20%。因此,TDLAS技术结合PLS1算法在实现化工过程中的多组分在线检测时具有先进性。     

RF fingerprint measurements for the identification of devices in wireless communication networks based on feature reduction and subspace transformation

This document proposes a radiofrequency (RF) fingerprinting strategy for the proper identification of wireless devices in mobile and wireless networks. The proposed identification methods are based on the extraction of the preamble RF fingerprint of a device and its comparison with a set of already known device RF fingerprints. The identification method combines techniques for feature reduction such as Principal Component Analysis (PCA) and Partial Least Squares regression (PLS), both based on subspace transformation, along with a similarity-based analysis. In this work, a complete procedure for RF fingerprint data extraction and analysis is provided. In addition, some experimentation with commercial Wi-Fi devices is carried out for the methods validation.     

短波近红外光谱法非破坏分析双氯酚酸钠粉末药品

本文应用偏最小二乘法（PLS）同近红外漫反射光谱法结合,对双氯酚酸钠粉末药品进行无损非破坏定量分析,建立了最佳的数学校正模型。讨论了光谱的预处理方法和主成分数对PLS定量预测能力的影响,并对预测集样品含量进行预测,得到了较为满意的结果。     

近红外漫反射光谱法非破坏分析颠茄粉末药品质量

本文应用偏最小二乘法(PLS)同近红外漫反射光谱法结合,对颠茄粉末药品进行无损非破坏定量分析,建立了最佳的数学校正模型。讨论了光谱的预处理方法和主成分数对PLS定量预测能力的影响,并对预测集样品含量进行预测,得到了较为满意的结果。