激光诱导击穿光谱联合UVE变量优选检测大豆油中的铬含量

利用激光诱导击穿光谱(LIBS)技术对大豆油中的重金属Cr进行检测研究。以松木木片对重金属Cr进行富集,采用AvaSpec双通道高精度光谱仪在206.28~481.77 nm波段范围内采集松木木片样本的LIBS光谱,利用无信息变量消除(UVE)方法筛选与重金属Cr相关的波长变量,应用偏最小二乘(PLS)回归建立大豆油中重金属Cr的定标模型,并与单变量及全波段PLS定标模型进行比较。结果表明,相比单变量及全波段PLS定标模型,UVE-PLS定标模型的性能更优,其相关系数、校正均方根误差、交互验证均方根误差及预测均方根误差分别为0.990,0.045,0.050及0.054 mg·g-1。经UVE变量筛选后,UVE-PLS定标模型所用的波长变量数仅为全波段PLS的2%。由此可见,UVE是一种有效的波长变量筛选方法,能有效筛选出与重金属Cr相关的波长变量。     

Adulteration of sesame oil with corn oil detected by use of two-dimensional infrared correlation spectroscopy and multivariate calibration

An innovative methodology was developed to detect adulteration of sesame oil with corn oil based on two-dimensional mid-infrared correlation spectroscopy with multivariate calibration. Forty pure sesame oils and 40 adulterated sesame oils with corn oil were prepared and the infrared absorption spectra were measured at room temperature, respectively. The synchronous two-dimensional mid-infrared correlation spectra were calculated to develop multivariate calibration models for adulteration of sesame oil with corn oil. The results showed the higher classification accuracy of 96.3% for the prediction set using two-dimensional mid-infrared correlation spectra and N-way partial least square discriminant analysis, versus 88.9% using traditional one-dimensional mid-infrared spectra and partial least squares discriminant analysis. Also, the multivariate calibration models were developed for quantitative analysis of sesame oil adulteration with corn oil. The root mean square error of prediction was 0.98% v/v using two-dimensional mid-infrared correlation spectra and N-PLS, and 1.15% v/v using traditional one-dimensional mid-infrared spectra and PLS. The results of our analyses indicated that the proposed method could provide better predictive results than traditional one-dimensional mid-infrared spectra and multivariate calibration.     

梨可溶性固形物含量的在线近红外光谱检测

应用近红外透射检测技术在线检测梨的可溶性固形物(SSC)。在实验台上以0.5 m·s-1的速度,300 W的光照强度,采用半透射方式检测梨的光谱。实验采用的梨样品为187个,其中147个样品为校正集,40个样品为预测集,应用偏最小二乘回归(PLS)和主成分回归(PCR)建立梨可溶性固形物的在线预测模型。选取550～700 nm, 700～850 nm, 550～850 nm为建模波段范围,发现无论对于PLS还是PCR,都是550～850 nm波段的建模结果好。本实验还研究对比不同的光谱预处理方法(光谱平滑,一阶微分,二阶微分等)对预测模型性能的影响,其中5点S-G(Savitzky-Golay)光谱平滑能有效地提高光谱的信噪比,改善模型预测精度,而一阶微分、二阶微分对模型性能改善基本上没有影响;最好的预测模型相关系数r=0.948 8, 校正标准差RMSEC=0.236,预测标准差RMSEP=0.548。结果表明：PLS模型预测性能较好,梨可溶性固形物的在线检测具有可行性。     

Near-infrared spectroscopy to assess typhaneoside and isorhamnetin-3-O-glucoside in different processed products of pollen typhae

With the ever increasing importance of testing drug quality, rapid analytical methods are needed for supervision of Chinese herbal medicines. Near-infrared spectroscopy is one of the most powerful tools in quality assessment of Chinese herbal medicines. In this work, near-infrared spectroscopy was applied to develop a rapid method for quantitative determination of typhaneoside and isorhamnetin-3-O-glucoside in different processed products of Pollen Typhae. A total of 71 batches of samples were collected from different regions in China. After acquisition of near-infrared spectra, different pre-processing methods were compared, and a competitive adaptive reweighted sampling algorithm was used to perform the variable selection. Then a partial least squares regression algorithm was applied to build the quantitative models. The root mean square error of calibration, root mean square error of cross validation, and root mean square error of prediction were 0.0190, 0.0364, and 0.0158%, respectively, for a quantitative model of typhaneoside. The root mean square error of calibration, root mean square error of cross validation, and root mean square error of prediction were 0.0190, 0.0377, and 0.0170%, respectively, for a quantitative model of isorhamnetin-3-O-glucoside. Moreover, the relative prediction deviation values of both quantitative models were larger than 3, indicating good performance of the partial least squares (PLS) models. The results demonstrated that high accuracy prediction of typhaneoside and isorhamnetin-3-O-glucoside could be obtained by near-infrared spectroscopy, to allow an alternative method for quality assessment of different processed products of Pollen Typhae.     

基于二维相关荧光谱土壤中PAHs检测方法研究

传统荧光光谱技术已被用于土壤中多环芳烃(PAHs)的检测,但由于土壤体系的复杂性、PAHs污染物的多样化和微量化,传统的荧光光谱技术无法有效提取土壤中PAHs的特征信息。为了解决上述问题,提出并建立一种基于二维相关荧光谱土壤中多环芳烃的检测方法。以土壤中典型的多环芳烃蒽和菲为研究对象,配置38个蒽菲混合标准土壤样品(蒽和菲的浓度范围均为0.000 5～0.01 g·g-1),在激发波长265～340 nm,发射波长350～500 nm范围内采集了所有样品的三维荧光谱。以激发波长为外扰,对外扰变化的动态一维荧光谱进行相关计算,得到每一样品的同步二维相关荧光谱。研究了浓度均为0.005 g·g-1蒽菲混合土壤样品的三维荧光谱和同步二维相关荧光谱特性,在同步谱主对角线398,419,444和484 nm处存在自相关峰,其中,398和484 nm荧光峰来自土壤中的菲,419和444 nm荧光峰来自土壤中的蒽;在主对角线外侧,蒽和菲两组荧光峰之间存在负的交叉峰,进一步验证了其来源不同;同时,在(408,434) nm和(434,467) nm处出现交叉峰,其中408和434 nm荧光峰来自土壤中的菲,467 nm荧光峰来自土壤中的蒽。指出与三维荧光谱表征的信息相比,二维相关荧光谱不仅能提取更多的特征信息(408和467 nm的特征峰在三维荧光谱中未被表征),而且还能提供荧光峰之间的相互关系,对其来源进行有效解析。在上述研究二维相关荧光谱特性的基础上,基于同步相关谱矩阵(38×151×151)建立了定量分析土壤中蒽和菲污染物浓度的多维偏最小二乘(N-PLS)模型,对蒽的校正和预测相关系数分别为0.986和0.985,校正均方根误差(RMSEC)和预测均方根误差(RMSEP)分别为4.33×10-4和5.55×10-4 g·g-1;对菲的校正和预测相关系数分别为0.981和0.984,RMSEC和RMSEP分别为5.20×10-4和4.80×10-4 g·g-1。为了比较,基于三维荧光光谱矩阵(38×16×151)建立了定量了分析土壤中蒽和菲的N-PLS模型,对蒽的校正和预测相关系数分别为0.981和0.972,RMSEC和RMSEP分别为5.09×10-4和6.74×10-4 g·g-1;对菲的校正和预测相关系数分别为0.957和0.956,RMSEC和RMSEP分别为7.36×10-4和7.77×10-4 g·g-1。指出,对于土壤中的蒽和菲检测,基于二维相关荧光谱的N-PLS模型的相关系数r,RMSEC和RMSEP都要优于基于三维荧光谱的N-PLS模型。研究结果表明：所提出和建立的方法-二维相关荧光谱直接检测土壤中PAHs污染物不仅可行,而且能提供更好的分析结果。该研究为激光诱导荧光结合相关谱技术现场直接检测土壤中多环芳烃污染物提供了理论和实验基础,具有较好的应用前景。     

近红外漫反射光谱法测定玉米秸秆NDF与ADF含量

应用主成分空间和傅里叶变换近红外光谱技术,采用偏最小二乘回归法(PLS),在国内首次建立了适合不同品种类型、不同生长发育时期和不同部位且适配范围广的近红外漫反射光谱(NIRS)测定玉米秸秆中性洗涤纤维(NeutralDetergentFiber,NDF)和酸性洗涤纤维(AcidDetergentFiber,ADF)含量的稳定校正模型。结果表明,采用一阶导数 矢量归一化预处理和一阶导数 多元散射校正预处理,谱区均为7502～5450cm-1和4601～4247cm-1,所建立的NDF与ADF校正模型,其校正和预测效果最佳。其校正决定系数(R2cal)均大于094,交叉验证和外部验证决定系数(R2cv,R2val)为092～096,各项误差(RMSEE,RMSECV和RMSEP)为149%～181%。该结果对青贮玉米秸秆材料快速鉴定和筛选具有重要的意义。     

基于近红外漫反射光谱的丁香蓼叶片重金属铜含量快速检测研究

植物中的重金属离子以一定形式与具有近红外吸收的有机分子基团结合, 因此可以借助近红外光谱技术间接检测其重金属离子含量。研究了基于近红外漫反射光谱技术快速检测丁香蓼叶片中重金属铜含量的方法。通过不同光谱数据预处理方法的对比,结合偏最小二乘法,建立了丁香蓼叶内重金属铜含量近红外光谱检测定量模型。实验结果为,经过平滑处理的光谱建模效果较理想,其建立的校正相关系数为0.950,校正均方根误差为5.99;外部验证相关系数为0.923,预测均方根误差为7.38。研究表明,近红外漫反射光谱技术用于丁香蓼叶片中重金属铜含量的快速检测具有可行性。     

近红外漫反射用于检测苹果糖度及有效酸度的研究

提出了应用近红外漫反射光谱技术并结合光纤传感技术快速检测苹果糖度和有效酸度的新方法。以傅里叶变换光谱仪(12 500～4 000 cm-1)为试验仪器,以120个红富士苹果为标准样品并结合偏最小二乘法,建立了苹果糖度、有效酸度的定量预测数学模型。试验结果为：样品预测值和真实值之间的相关系数分别为0.970,0.906,标准校正误差(SEC)分别为0.261,0.0562,标准预测误差(SEP)分别为0.272,0.0562,偏差(Bias)分别为0.011,0.0115。通过本研究表明：应用近红外光谱漫反射技术在10 341～5 461 cm-1光谱波长范围内对苹果糖度的无损检测和在10 341～3 818 cm-1有效光谱范围内对有效酸度的无损检测具有可行性。     

近红外光谱法快速检测绿茶中儿茶素的含量

提出了一种应用傅里叶近红外漫反射光谱分析技术快速检测茶叶中主要儿茶素含量的新方法.首先获取茶叶在10000～4000 cm<'-1>范围的近红外漫反射光谱,然后以高效液相色谱分析值作参考值.采用偏最小二乘法建立茶叶中表没食子儿茶素没食子酸酯(EGCG)、表儿茶素没食子酸酯(ECG)和表没食子儿茶素(EGC)含量的定量分析模型.通过交互验证方法来优化模型的主成分数和所采用的光谱预处理方法.EGCG、ECG和EGC三个模型预测值和参考值问的相关系数分别为0.9800、0.9763和0.9853,预测均方根误差分别为0.3509、0.1147和0.1365.研究结果表明,近红外光谱技术可成功地检测茶叶中EGCG,ECG和EGC的含量.     

基于可见-近红外光谱技术预测茶鲜叶全氮含量

为快速无损监测茶树氮素营养及其生长状况,基于可见-近红外光谱技术建立了茶鲜叶全氮含量的预测模型。以茶鲜叶为对象,田间试验使用便携式光谱仪采集叶片漫反射光谱信息,通过不同预处理和统计分析,建立茶鲜叶全氮含量预测的光谱模型。试验共采集111个样品,其中86个样品作校正集,25个样品作预测集。通过一阶导数与滑动平均滤波相结合的预处理方法,用7个主成分建立的偏最小二乘模型最好,其校正集均方根误差(RMSEC)为0.097 3,预测集的相关系数为0.888 1,预测均方根误差(RMSEP)为0.130 4,预测的平均相对误差为4.339%。研究结果表明,利用可见-近红外光谱技术可以很好地预测茶鲜叶全氮含量,对于快速实时监测茶树长势和施肥管理具有重要指导意义。     

光谱预处理对LIBS检测脐橙中Cu的偏最小二乘定量模型影响

应用激光诱导击穿光谱(LIBS)对脐橙中Cu元素进行快速检测,并结合偏最小二乘法(PLS)进行定量分析,探索光谱数据预处理方法对模型检测精度的影响。针对实验室污染处理后的52个赣南脐橙样品的光谱数据,进行不同数据平滑、均值中心化和标准正态变量变换三种预处理方法。然后选择包含Cu特征谱线的319~338 nm波段进行PLS建模,对比分析了模型的主要评价指标回归系数(r)、交互验证均方根误差(RMSECV)和预测均方根误差(RMSEP)。采用13点平滑、均值中心化的PLS模型3个指标分别达到了0.992 8,3.43和3.4,模型的平均预测相对误差仅为5.55%,即采用该前处理方法模型的校准质量和预测效果都最好。选择合适的数据前处理方法能有效提高LIBS检测果蔬产品PLS定量模型的预测精度,为果蔬产品LIBS快速精准检测提供了新方法。     

近红外透射光谱法检测三组分食用调和油含量的研究

以大豆油、花生油和玉米油三组分食用调和油为研究对象,采集样品在10 000～4 200 cm-1范围内的近红外透射光谱,对光谱进行不同预处理后结合偏最小二乘法分别建立调和油中三组分的定量分析模型,并检验模型预测的准确度和精密度。结果显示,一阶导数结合多元散射校正(FD+MSC),一阶导数结合减去一条直线(FD+SLS)以及一阶导数(FD)进行光谱预处理,可以得到大豆油、花生油以及玉米油含量的最优定标模型,分别是在5 450.1～4 597.7 cm-1,7 521.3～6 098.1 cm-1和9 993.7～7 498.2 cm-1谱区范围内获得的。各预测模型的相关系数R2和预测均方根RMSEP分别为99.89%,1.09%;99.88%,1.17%;99.76%,1.48%;配对t检验值在0.371 9～0.007 9之间;预测相对标准偏差RSD均小于1.50%。表明傅里叶变换近红外透射光谱分析技术可以快速准确可靠地检测三组分食用调和油中大豆油、花生油、玉米油的含量。     

液态样本近红外光谱测量中的光程变化误差消减方法研究

以蔗糖溶液为研究对象,利用近红外光谱分别测量4,5和6 mm光程下不同浓度蔗糖溶液的透反射光谱,研究采用矢量归一化、基线偏移校正、多元散射校正、标准正态变量变换、一阶导数5种预处理方法消除光程差异的影响,并结合PLS方法建立校正集模型。与原始光谱的PLS模型相比,五种预处理方法均对模型的预测精度有不同程度的提高,其中,多元散射校正结合PLS方法建立的模型最优,使原始光谱的主成分数PC由6下降为3,决定系数R2由0.891 278提高到0.987 535,交互验证决定系数R2_CV由0.888 374提高到0.983 343,校正标准偏差RMSEC由1.704%下降到0.89%,交互验证的校正标准偏差RMSECV由1.827%下降到1.05%,预测集样本的相关系数由0.950 89上升到0.976 22,预测标准偏差由0.014 36下降为0.01。结果表明,五种预处理方法中,多元散射校正法能够消除光程差异的干扰,提高模型的预测精度,改善稳定性。     

同步荧光技术检测鲜牛乳中掺杂复原乳的可行性研究

近年来随着人们对乳制品需求的不断增加,将复原乳冒充或添加在鲜乳中出售的现象也日益严重,亟需简单、快速的检测方法监测掺假行为。利用同步荧光技术,分别对两种鲜牛乳(未经杀菌的生牛乳和低温处理的巴氏杀菌乳)掺杂复原乳的情况进行了定性判别和定量分析。以各类样本及全部样本的判别正确率作为定性判别模型的评价指标;以相关系数(r)、校正均方根误差(RMSEC)和预测均方根误差(RMSEP)作为定量分析模型的评价指标。通过分析牛乳的三维荧光图谱确定同步荧光扫描的固定波长差Δλ值为80 nm;在对图谱进行二阶求导后,偏最小二乘-判别分析法(PLS-DA)对生鲜牛乳、巴氏杀菌乳和复原乳的种类判别总正确率可达100%,并且在判断两种鲜乳中是否添加复原乳时,校正集样品的正确率均可达到100%,预测集样品的正确率分别为75%和81.25%,鲜牛乳和复原乳的种类判别模型,以及鲜乳与掺假乳的定性判别模型均取得了良好的效果;PLS回归对同步光谱值与复原乳含量建立线性关系时,两种鲜乳定量模型的r值分别为0.911 2和0.936 7,RMSEC分别为0.042 2和0.038 4,RMSEP分别为0.054 8和0.057 5,鲜乳中复原乳含量的定量分析模型的r值均可达到0.9以上,能对添加量较高的样品进行预测。因此,同步荧光技术可以较为准确、快速的检测鲜牛乳中是否掺杂复原乳。     

Linear quantification calibration of crystallinity at subpercent and its evaluation based on spectral and spatial information inherited in Raman chemical images

In this paper, the author reported two methods to extract spectral or spatial information inherited in the Raman chemical images for linear quantification calibration of crystallinity. The two approaches reported quantification results according to the spectral mean score of overall pixels or the spatial percentage of the pixels with a score greater than and equal to the threshold of the chemical images, respectively. From this study, it can be concluded that, first, sampling method for data collection in mapping has to be optimized to achieve linear quantification calibration through simple univariate analysis approaches. Second, the ordinary way of evaluating/validating a linear quantification technique by best linear correlation coefficient (R²) and root‐mean‐square error of calibration is disputable and has to be reconsidered. Lastly, with further consideration of root‐mean‐square relative error of calibration and predicted crystallinity at subpercent, it was found that the spectral mean score method cannot generate reliable quantification results at subpercent crystallinity. Copyright © 2014 John Wiley & Sons, Ltd.     

Quantitative Analysis of Simulated Illicit Street-Drug Samples Using Raman Spectroscopy and Partial Least Squares Regression

Modern drug laws require that a seized sample be characterized for both the illegal substances present and the quantity of each of those substances. The goal of this work was to develop a common approach to model development based on Raman spectroscopic analysis followed by partial least squares (PLS) regression that would allow us to obtain quantitative information from simulated street-drug samples. Each drug sample contained one drug surrogate—either isoxsuprine, norephedrine, benzocaine, or lidocaine—and up to 3 different cutting agents. All spectra were acquired on a homebuilt Raman instrument equipped with a rotating sample holder. The same steps were employed for developing separate models for each drug surrogate, including spectral preprocessing by Savitsky-Golay smoothing, differentiation, mean-centering, and autoscaling. PLS models were developed using 2 latent variables that yielded root mean square errors of calibration (RMSEC) values in the 3% range and root mean square error of prediction (RMSEP) values in the 4% range.     

Sequential Injection/Mid-Infrared Spectroscopic Analysis of an Acetone‐Butanol‐Ethanol Fermentation: Analyte Cross‐Correlation Effects

Mid‐infrared spectroscopy together with sequential injection analysis (SIA) and partial least squares (PLS) regression analysis was used to monitor acetone‐butanol‐ethanol (ABE) fermentations under different fermentation conditions. Five analytes were simultaneously predicted (acetone, acetate, butyrate, n‐butanol, and glucose). In order to compare the overall model prediction ability, a relative average of the root mean square error of prediction (RMSEP) across all five analytes was employed. To form a PLS model devoid of any cross‐correlations between analytes, a synthetic calibration data set was created by the SIA system. As a test of their robustness, PLS models from synthetic samples and those from real fermentation samples were compared and used to predict samples from the opposite data set and from independent “acid‐crash” fermentations. The PLS model developed from the synthetic samples proved to be far more robust and accurate and used fewer factors than PLS models from the real fermentations, which were found to contain analyte cross‐correlations. The use of synthetic data enabled more accurate selection of factors and showed the importance of investigating spectral regression coefficients plots to aid and confirm appropriate factor selection. In addition, an alternative method of factor selection was proposed, using a “similarity measure” between the regression coefficient plots of factors for certain analytes and their standard spectra. Predictions using this method of factor selection over the common “minimum from an error vs. factor” plot proved to be more accurate and used far fewer factors.     

燕麦干草品质的近红外光谱定量分析

应用近红外漫反射光谱（NIRS）分析技术,采用偏最小二乘回归法(PLS),建立了适合不同品种类型和不同生长发育时期的NIRS测定燕麦全株干草的粗蛋白（Crude Protein,CP）、秸秆中性洗涤纤维(Neutral Detergent Fiber,NDF)和酸性洗涤纤维(Acid Detergent Fiber,ADF)含量的稳定校正模型。结果表明,采用二阶导数（2st Deriv）+平滑处理（Norris）、多元散射校正（MSC）+二阶导数（2st Deriv）+平滑处理（Norris）、多元散射校正（MSC）,分析谱区为9 668～4 518,9 550～5 543,8 943～4 042 cm-1建立粗蛋白、中性洗涤纤维和酸性洗涤纤维的校正模型,其校正和预测效果最佳。其中CP与NDF的建标决定系数（r2_cal）和交叉检验的决定系数（R2_cv）均在0.95以上,各项误差均小于3%,RPD值均大于3,逼近了化学分析的精确度,具有较好的预测效果。ADF的建模效果较CP与NDF差,其建标决定系数和交叉检验决定系数分别为0.912 0,0.855 3,建标误差（RMSEC）和检验误差（RMSECV）分别为2.33%,2.62%,接近了化学分析的精确度,且RPD值大于2.5,说明所建的ADF模型也可用于近红外预测。     

玉米秸秆组分近红外漫反射光谱(NIRS)测定方法的建立

玉米秸秆是我国产量最大的秸秆生物质资源,但目前还没有快速高效的组分分析方法,本研究利用傅里叶变换近红外漫反射光谱 (NIRS) 技术,采用偏最小二乘法(PLS),在国内首次建立了NIRS测定玉米秸秆中灰分、半纤维素、纤维素、Klason木质素、酸不溶灰分和水分含量的校正模型,该模型稳定,适合不同地区、不同品种的玉米秸秆及其不同部位。实验结果表明,采用一阶导数+Karl Norris滤波预处理,谱区在4 100～7 500 cm-1,能得到理想的预测模型。该模型对玉米秸秆各组分的交叉验证均方差(RMSECV)范围为0.090 3～1.45,预测误差(RMSEP)范围为0.256 9%～2.581 9%,预测相关系数≥0.871 1。该研究对加速我国秸秆生物质的工业转化具有重要意义。     

基于近红外光谱和变量优选的棉麻混纺织物棉含量快速检测

纺织品纤维成分的快速检测对其生产过程质量监控、贸易和市场监督均具有重要的意义。利用近红外光谱技术联合变量优选对棉麻混纺织物中的棉含量进行快速检测研究。采用NIRFlex N-500型傅里叶近红外光谱仪在4 000～10 000 cm-1光谱范围内采集样本的反射光谱,对样本光谱进行范围初选和预处理分析。在此基础上,利用UVE(uninformative variables elimination),SPA(successive projections algorithm)及CARS (competitive adaptive reweighted sampling)方法对光谱变量进行优选,再应用PLS(partial least squares)建立棉麻混纺织物中的棉含量预测模型。最后,采用最优预测模型对未参与建模的样本进行预测。研究结果表明,4 052～8 000 cm-1光谱范围为棉含量较优的建模光谱范围。CARS变量选择方法能较为有效地提高预测模型的精度,CARS-PLS模型的校正集、预测集相关系数和均方根误差分别为0.903,0.749和8.01%,12.93%。因此,近红外光谱联合CARS变量优选可以用于棉麻混纺织物棉含量的快速检测,CARS方法可以有效简化预测模型,提高预测模型性能。