基于多光谱特征融合技术的面粉掺杂定量分析方法

提出了一种基于拉曼光谱技术(Raman)和激光诱导击穿光谱技术(LIBS)的多光谱特征融合技术(MFFT),利用拉曼光谱中分子组分信息和激光诱导击穿光谱中原子组分信息之间的互补特性,采用自适应小波变换(AWT)-竞争性自适应加权(CARS)-偏最小二乘回归(PLS)建模技术,获取了面粉体系更为全面的特征信息。在多光谱特征融合技术中,首先采用AWT-CARS方法分别提取拉曼光谱和激光诱导击穿光谱中的特征变量,然后将两者的特征变量融合为一个向量,采用PLS方法构建MFFT模型,实现了面粉掺杂物的定量分析。通过对二氧化钛、硫酸铝钾等面粉掺杂体系建模分析,考察MFFT模型的有效性。结果表明,与单一拉曼光谱技术或激光诱导击穿光谱技术建立的预测模型相比,MFFT模型显著提升了模型的预测性能,二氧化钛和硫酸铝钾预测模型的线性相关系数分别从相对较差的Raman模型的0.884、0.877提升到0.981、0.980,其预测均方根误差分别从相对较差的Raman模型的0.151、0.154降低到0.069、0.068。表明多光谱特征融合技术可以准确提取Raman光谱中的分子信息和LIBS光谱中的元素信息,使其互为补充、互为校正,进而有效克服面粉基质对掺杂组分定量分析的干扰,显著提高模型的预测精度。     

基于高光谱的马铃薯微型种薯分类检测

采用高光谱分析技术结合模式识别,建立了8种马铃薯微型种薯（大西洋、荷兰-14、荷兰十五041、荷兰十五Q8、冀张薯12号、冀张薯8号、兴佳2号和Y2）的分类检测方法。采集276个种薯样本,对860~1 700 nm的原始光谱进行标准化、11点Savitzky-Golay平滑和4点差分一阶导数光谱预处理,将预处理后的光谱数据进行主成分分析,发现前3个主成分的累积贡献率为95.12%,包含了原始光谱的大部分信息,可作为分类变量。再分别使用线性判别分析、BP神经网络和支持向量机进行分类建模。最终通过分层、分步骤建立了8种马铃薯微型种薯的分类模型。首先采用线性判别分析模型区分大西洋、荷兰-14、荷兰十五041和其它品种,平均正确识别率达88.79%。再建立BP神经网络模型将其它品种样本区分为两类,一类为冀张薯8号和Y2,另一类为荷兰十五Q8、冀张薯12号和兴佳2号,平均正确识别率达93.24%。最后以BP神经网络模型区分冀张薯8号和Y2,平均正确识别率为77.78%;以支持向量机分类模型区分荷兰十五Q8、冀张薯12号和兴佳2号,平均正确识别率为87.23%。该研究建立的8种马铃薯种薯分步骤、分层分类识别模型的平均正确识别率达8975%,表明高光谱光谱分析技术可用于马铃薯微型种薯的分类检测。     

激光诱导击穿光谱在煤质检测中的应用现状

我国当前主要能源仍是煤炭资源,煤质快速检测有利于其清洁高效利用。激光诱导击穿光谱（Laser-Induced Breakdown Spectroscopy,LIBS）作为一种快速光谱检测技术,具有样品需求量小、制样简便、可多元素同时测量等优点,其在煤质快速检测中的应用潜力已得到广泛认可。本文从激光诱导击穿光谱仪器（实验室台式、在线式和便携式）的研发现状、激光诱导击穿光谱对煤质（金属元素、非金属元素和工业指标）的检测现状、煤质分析性能提升方法,以及激光诱导击穿光谱定量分析模型研究等方面介绍了近几年来LIBS技术在煤质检测中的应用现状及未来展望。     

不同光谱预处理对激光诱导击穿光谱检测猪肉中铅含量的影响

为了研究适合激光诱导击穿光谱(LIBS)检测猪肉中重金属铅(Pb)元素含量的光谱预处理方法,将配制的84个猪肉腿肌样品分为校正集和预测集,以相关系数(R)、内部交叉验证均方差(RMSECV)和预测均方根误差(RMSEP)作为评价指标,比较了5种光谱预处理方法对偏最小二乘法(PLS)建模预测效果的影响.结果表明,多元散射校正(MSC)预处理效果最好,定标模型预测值与实验室分析元素检测值的相关系数(R)达到0.9908,RMSECV为0.302,RMSEP为0.282,主成分数为16,18个预测集样品的验证结果的平均相对预测误差(ARPE)为7.8％.说明MSC是LIBS检测猪肉Pb含量的有效光谱预处理方法,该研究为进一步实现食品中重金属快速定量分析提供了方法和数据参考.     

基于激光诱导击穿光谱(LIBS)的煤灰熔点快速检测

炉内结渣是影响火电机组和气化工艺可靠运行的关键因素之一，准确预测灰熔点可以提前调整炉膛出口温度以避免结渣。本论文采用激光诱导击穿光谱（LIBS）采集煤灰样中金属元素的光谱，分别建立煤灰中的金属元素的谱线强度与煤灰熔点的随机森林模型、支持向量机回归模型和线性回归模型，直接预测煤灰熔点温度。采用基于马氏距离(MD)的异常数据剔除算法和基于稀疏矩阵的基线估计与降噪算法（BEADS），对粉煤灰样的全光谱数据进行了预处理。随机森林模型对粉煤灰熔点的预测平均相对误差（MRE）为54.74%，支持向量机回归模型的预测平均相对误差为60.08%，而线性回归模型的预测平均相对误差达到了9.78%。研究结果表明，线性回归模型对煤灰熔点的预测结果更准确。     

基于激光诱导击穿光谱的微生物种类鉴别研究

建立了激光诱导击穿光谱(Laser induced breakdown spectroscopy,LIBS)全光学诊断方法,对微生物种类进行快速鉴别。制取10种微生物样品,优选滤纸为富集载体,采集等离子体羽时间演变形貌图及LIBS光谱指纹图分析了鉴别微生物种类的可行性;运用九点平滑(Nine smooth,9SM)、多元散射校正(Multiple scatter correction,MSC)对波长范围200~420 nm和560~680 nm微生物LIBS全谱数据进行了预处理;分析比较了主成分分析(Principal component analysis,PCA)、随机森林结合主成分分析(Random forest combined with principal component analysis,PCA-RF)两种方法对微生物种类的鉴别结果。结果表明,运用一定的数据预处理方法,采用PCA-RF算法对10类微生物种类鉴别,训练集总准确率为99.6%,预测集总准确率为96.7%,说明选择合适的LIBS光谱预处理及模型构建方法,对微生物种类的快速准确鉴别具有可行性。     

主成分分析-支持向量机用于肝病分类模型

将主成分分析(PCA)用于肝功能检测数据特征提取,然后用支持向量机(SVM)对乙肝、丙肝、肝硬化、正常人样本建立分类模型。采用高斯径向基函数(RBF)为核函数,调节核函数参数C及σ以建立最佳支持向量机模型。该模型对训练集的识别率为99.3%,对预测集的预测率为96.4%。结果表明:PCA-SVM法建立的肝病分类模型能较好的区分乙肝、丙肝、肝硬化及正常人,且分类效果优于传统支持向量机及人工神经网络(ANN)分类模型。     

基于LIBS的GdFe合金材料快速检测技术研究

为了明晰LIBS（激光诱导击穿光谱）技术检测稀土合金材料的光谱特点，进一步开发基于LIBS的快速检测方法，推动LIBS技术在稀土领域的应用。本文利用激光诱导击穿光谱仪对GdFe合金材料的激发光谱，结合信号强度及背景噪音，研究确定了最佳设置参数。分别考察激光器和光谱仪的参数设置对光谱的影响，根据激发光谱中相关谱线的变化趋势，探讨GdFe合金材料的LIBS检测方法。实验发现，GdFe合金样品与激光孔(Laser aperture)的距离(Z值)、激光能量和积分时间是影响 LIBS光谱信号强度和背景噪音的关键参数。因金属和合金样品表面常覆盖氧化层，考察了脉冲激光的预剥蚀次数对光谱稳定性的影响，研究得出，在双脉冲均为164 mJ激光能量的激发下，预剥蚀1次可保证光谱效果。同时研究了两个激光器同时激发和延迟激发对GdFe合金光谱成因的影响，发现单脉冲激发与双脉冲同时激发所得光谱的信号强度并非两束脉冲单独激发的累加信号。通过光谱分析，确定了Z值0.8 mm、两个激光器的脉冲能量164 mJ、积分时间12 μs、脉冲延时时间0 μs，GdFe合金材料的光谱效果最佳，可为GdFe合金材料的LIBS检测方法建立提供参考，拓展LIBS技术在稀土领域的应用，为稀土合金产品的快速检测技术发展提供基础研究数据。     

基于遗传算法和偏最小二乘法的土壤激光诱导击穿光谱定量分析研究

在空气环境下,采用激光诱导击穿光谱(LIBS)技术对土壤成分进行检测,建立了基于遗传算法(GA)和偏最小二乘法(PLS)的定量分析模型。将配制的58个土壤样品分为定标集、监控集和预测集,对11种组分Mn,Cr,Cu,Pb,Ba,Al2O3,Ca O,Fe2O3,Mg O,Na2O和K2O的含量分别进行预测。结果表明,GA作为一种谱线选择的预处理方法,可以有效减少用于PLS建模的光谱谱线的数目,从而简化模型。对于土壤中的大部分组成成分,GA-PLS模型能够显著改善传统PLS模型的预测能力。以Mn元素为例,浓度预测均方根误差(RMSEP)从0.0215%降低至0.0167%,平均百分比误差(MPE)从8.10%降低至5.20%。本研究为进一步提高土壤的LIBS定量分析准确度提供了方法参考。     

光谱滤波法提高激光诱导击穿光谱对蔬菜中元素Pb的检测精度

激光诱导击穿光谱(Laser induced breakdown spectroscopy, LIBS)原始光谱中包含较多噪声信号, 为探究不同滤波方法对LIBS光谱预处理的影响, 本研究以实验室Pb污染处理的蔬菜为研究对象, 采集波长范围在400.45~410.98 nm的LIBS谱线信息, 分别利用相邻平均(Adjacent averaging)、Savitzky-Golay(S-G)滤波器、快速傅里叶变换(Fast Fourier transformation, FFT)对采集的LIBS光谱进行平滑、去噪, 并结合偏最小二乘法(PLS)定量分析模型对光谱处理效果进行评价.结果表明, S-G平滑效果最优, 当S-G滤波器窗口宽度为15, 拟合阶次为3时, PLS定量模型效果最佳, 其验证集均方根误差(RMSEP)为0.26、平均相对误差(ARE)为3.7%.结果表明, 选择适合的滤波方法有助于提高LIBS光谱质量以及检测模型的精度.     

Multivariate analysis of laser-induced breakdown spectroscopy chemical signatures for geomaterial classification

A large suite of natural carbonate, fluorite and silicate geological materials was studied using laser-induced breakdown spectroscopy (LIBS). Both single- and double-pulse LIBS spectra were acquired using close-contact benchtop and standoff (25 m) LIBS systems. Principal components analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were used to identify the distinguishing characteristics of the geological samples and to classify the materials. Excellent discrimination was achieved with all sample types using PLS-DA and several techniques for improving sample classification were identified. The laboratory double-pulse LIBS system did not provide any advantage for sample classification over the single-pulse LIBS system, except in the case of the soil samples. The standoff LIBS system provided comparable results to the laboratory systems. This work also demonstrates how PCA can be used to identify spectral differences between similar sample types based on minor impurities.     

Discrimination of natural gas-related bacteria by means of micro-Raman spectroscopy

Methane-oxidizing bacteria (MOB) are a unique group of gram-negative bacteria that are proved to be biological indicator for gas prospecting since they utilize methane as a sole source of carbon and energy. Herein the feasibility of a novel and efficient gas prospecting method using Raman spectroscopy is studied. Confocal Raman spectroscopy is utilized to establish a Raman database of 11 species of methanotrophs and other closely related bacteria with similar morphology that generally coexist in the upper soil of natural gas. After strict and consistent spectral preprocessing, Raman spectra from the whole cell area are analyzed using the combination of principal component analysis (PCA) and Mahalanobis distance (MD) that allow unambiguous classification of the different cell types with an accuracy of 95.91%. The discrimination model based on multivariate analysis is further evaluated by classifying Raman spectra from independently cultivated bacteria, and achieves an overall accuracy of 94.04% on species level. Our approach using Raman spectroscopy in combination with statistical analysis of various gas reservoirs related bacteria provides rapid distinction that can potentially play a vital role in gas exploration.     

基于非接触式拉曼光谱分析人血与犬血的PCA-LDA鉴别方法

将拉曼光谱分析法与数理统计方法有机结合,构建人血与犬血种属判别模型,实现了不同种属血液样本的高效无损鉴别.采用拉曼光谱的无损测试模式对血液样本进行测试,考察了抗凝管管材、聚焦位置及曝光时间等对血液样本拉曼光谱的影响,在激发波长为632.8 nm,光谱扫描范围为200~1800 cm-1,功率衰减率50%,曝光时间5 s及累加次数为2次的优化条件下,获得了无损检测条件下的血液样本拉曼光谱图.针对血液样本组分复杂、拉曼光谱信号基底背景高等问题,提出了基于小波变换去噪,进行分段多项式基线校正的预处理方法,有效解决了血液样本拉曼光谱谱图的高噪音和基线漂移问题.实验选择30例正常人血和33例比格犬血为样本训练集,5例正常人血和5例比格犬血为测试集,基于主成分分析法(PCA)联合线性判别法(LDA)模型,训练集分类正确率达到95.23%,盲测集分类正确率达90.00%.这种基于非接触式血液样本拉曼光谱和PCA-LDA判断模型的测试方法在进出口检验检疫等涉及血液无损鉴别的领域具有广泛的应用价值和前景.     

偏最小二乘法在红外光谱识别茶叶中的应用

采用漫反射傅立叶变换红外光谱(FTIR)法结合主成分分析(PCA)、偏最小二乘法(PLS)、簇类的独立软模式(SIMCA)识别法对十三种茶叶进行了分类判别研究。研究结果表明,通过多元散射校正(MSC)对原始光谱进行预处理,可以提高模式识别技术的分类判别效果。在此基础上,选取1 900~900 cm-1波长范围内的茶叶红外光谱建立识别模型,三种方法都得到了满意的分类判别效果。在对检验集中全部130个样本的判别中,PCA仅有两类样本无法判别,SIMCA的识别率和拒绝率都在90%以上,而PLS的识别效果最佳,全部样本都得到了正确的归类。这一研究结果表明傅立叶变换红外光谱法与化学计量学方法相结合可以实现茶叶品种的快速鉴别,这为茶叶的客观评审提供了一种新思路。     

Error removal by orthogonal subtraction (EROS): a customised pre‐treatment for spectroscopic data

In some applications of diffuse reflectance spectroscopy there may be substantial variability between the spectra from replicate measurements of what is nominally the same sample. A method called error reduction by orthogonal subtraction (EROS) is proposed to ameliorate the effects of this. The first step is to use principal component analysis (PCA) to identify the structure in the variability of replicate measurements. This is followed by subtraction of the modelled effects from the original spectral data matrix X by projection onto the subspace orthogonal to factors derived from the PCA. An application to the clinical diagnosis of colon lesions is presented, in which pre‐treatment of spectra using the proposed method is successful in reducing the complexity and increasing both the accuracy and interpretability of the subsequent classification model. Copyright © 2008 John Wiley & Sons, Ltd.     

基于显微共聚焦拉曼光谱技术快速检测3种梭菌中肉毒梭菌

提出了一种基于显微共聚焦拉曼光谱技术的肉毒梭菌快速鉴别方法.利用共聚焦显微拉曼光谱技术(CRM)采集了肉毒梭菌、艰难梭菌和产气荚膜梭菌的拉曼光谱,比较了3种梭菌的平均拉曼光谱,采用基线校正、标准正态变换、Savitzky-Golay 5点平滑和最大最小值归一化预处理后,借助主成分分析(PCA)降维并提取特征变量,对样本...     

基于主成分和支持向量机浓度参量同步荧光光谱油种鉴别

基于浓度参量同步荧光光谱技术,对不同溢油类型不同油源原油样品集、引入外扰相似油源样品集进行光谱数据采集,获取其浓度同步荧光光谱矩阵Concentration-Synchronous-Matrix-Fluorescence(CSMF),利用主成分分析方法对两套不同层次的原油相关样品集进行了多类分类识别。结果表明:主成分载荷图可以很好地反映各个原油相关样品在油源上的相似程度,结合支持向量机可以实现不同溢油类型及不同油源原油的准确分类,对于引入风化和海水外扰相似油源溢油样品集,两类分类区分的结果远远高于多类分类识别的结果。通过详细的主成分分析讨论,为溢油油种鉴别提供了一种利用多类分类识别,逐步缩减嫌疑样本数量,最后通过两两分类实现溢油样品准确识别的新思路。     

Qualitative study of ethanol content in tequilas by Raman spectroscopy and principal component analysis

Using Raman spectroscopy, with an excitation radiation source of 514.5 nm, and principal component analysis (PCA) was elaborated a method to study qualitatively the ethanol content in tequila samples. This method is based in the OH region profile (water) of the Raman spectra. Also, this method, using the fluorescence background of the Raman spectra, can be used to distinguish silver tequila from aged tequilas. The first three PCs of the Raman spectra, that provide the 99% of the total variance of the data set, were used for the samples classification. The PCA1 and PCA2 are related with the water (or ethanol) content of the sample, whereas the PCA3 is related with the fluorescence background of the Raman spectra.     

Evaluation of minor element concentrations in potatoes using laser-induced breakdown spectroscopy

We have performed spectroscopic analysis of the plasma generated by Nd:YAG laser irradiation of flesh and skin of fresh potatoes. From the spectra recorded with an Echelle spectrometer 11 minor elements have been identified. Their relative concentrations were estimated by comparing the measured spectra to the spectral radiance computed for a plasma in local thermal equilibrium. According the moderate plasma temperature of about 6500 K at the time of spectroscopic observation, the electrons are essentially generated by the ionization of the minor metal atoms, making plasma modeling possible although the organic elements may be out of equilibrium. Among the spectral lines selected for the analysis, the Na I 588.99 and 589.59 nm doublet was found to be partially self-absorbed allowing us to estimate the number density of sodium atoms. The value was found to agree with the number density predicted by the plasma model. As a result, the relative concentrations of the detected minor elements have been estimated for both the flesh and skin of the potatoes. Among these, aluminum and silicon were found to have relatively large mass fractions in the potato skin whereas their presence was not detected in the flesh. The present study shows that laser-induced breakdown spectroscopy is a promising tool to measure the elemental composition of fresh vegetables without any sample preparation.     

傅里叶变换红外光谱结合模式识别法快速鉴别食用油的真伪

采用傅里叶变换红外光谱(FTIR)结合簇类独立软模式识别技术(SIMCA)建立了真伪食用油的快速鉴别方法. 该方法依据FTIR 的指纹特性, 收集并分析了53 个合格食用油和13 个伪造食用油的FTIR 谱图; 通过对谱图取二阶导数和标准化处理, 主成分分析(PCA)提取特征变量; 采用SIMCA 方法分别随机选取43 个合格食用油和9 个伪食用油样品的FTIR 谱图组成训练集, 构建得到真伪食用油的SIMCA 分类模型. 该模型经过剩余10 个合格食用油和4 个伪食用油的验证, 正确识别率达到了100%. 说明FTIR 结合SIMCA 可能成为快速鉴别食用油真伪的一种新方法.