Determination of Fipronil in Acetamiprid Formulation by Attenuated Total Reflectance-Mid-Infrared Spectroscopy Combined with Partial Least Squares Regression

There is a high demand for rapid determination of fipronil in pesticide preparations because it has been restricted and even prohibited in many countries. An infrared-based methodology was developed for this analyte in acetamiprid formulations by attenuated total reflectance mid-infrared spectroscopy. The quantitative calibration models of fipronil were established by partial least squares regression. The determination coefficients (R²) of the model were above 0.99 while both the root mean square error of prediction and root mean square error of calibration were below 0.0011, which showed the partial least squares model accurately predicted fipronil concentrations in acetamiprid. The accuracy was further demonstrated by comparison with another two models' results of low (<1.0%, w/w) and high concentration sample sets (1.0%–4.5%, w/w). These results demonstrate the potential of infrared spectroscopy to quickly detect fipronil in acetamiprid.     

Determination of polyphenols in oats by near-infrared spectroscopy (NIRS) and two-dimensional correlation spectroscopy

Two-dimensional correlation spectroscopy (2DCOS) and near-infrared spectroscopy (NIRS) were used to determine the polyphenol content in oat grain. A partial least squares (PLS) algorithm was used to perform the calibration. A total of 116 representative oat samples from four locations in China were prepared and the corresponding near-infrared spectra were measured. Two-dimensional correlation spectroscopy was employed to select wavelength bands for the PLS regression model for the polyphenol determination. The number of PLS components and intervals was optimized according to the coefficients of determination (R²) and root mean square error of cross validation (RMSECV) in the calibration set. The performance of the final model was evaluated using the correlation coefficient (R) and the root mean square error of validation (RMSEV) in the prediction set. The results showed the band corresponding to the optimal calibration model was between 1350 and 1848?nm and the optimal spectral preprocessing combination was second derivative with second smoothing. The optimal regression model was obtained with an R² of 0.8954 and an RMSECV of 0.06651 in the calibration set and R of 0.9614 and RMSEV of 0.04573 in the prediction set. These measurements reveal the calibration model had qualified predictive accuracy. The results demonstrated that the 2DCOS with PLS was a simple and rapid method for the quantitative determination of polyphenols in oats.     

Determination of Iridoids in Gentiana rigescens by Infrared Spectroscopy and Multivariate Analysis

Gentiana rigescens is a famous herbal medicine in China for treatment of convulsion, rheumatism, and jaundice. Here, the infrared determination of gentiopicroside, swertiamarin, sweroside, and loganic acid in G. rigescens from different areas and varieties was presented for the first time. Reference information for the iridoids were obtained by high-performance liquid chromatography. Partial least squares was used to characterize the relationship between spectra matrix and concentration vector for the determination of the analytes. For determination of gentiopicroside, the appropriate performance of partial least squares model was acquired with coefficient of determination of calibration and coefficient of determination of prediction values of 0.965 and 0.868. The root mean square error of estimation (RMSEE), root mean square error of cross validation (RMSECV), root mean square error of prediction (RMSEP), and residual predictive deviation (RPD) values were 2.612, 5.292, 5.239?mg g^?1, and 2.701, respectively, based on the first derivative and multiplicative scatter correction. For determination of the total iridoids, the best results were obtained using the coefficient of determination of calibration and coefficient of determination of prediction of 0.943 and 0.834, RMSEE, RMSECV, RMSEP and RPD of 3.896, 7.536, 6.543?mg g^?1 and 2.438, respectively, based on the first derivative. Both models were reliable and robust. The results demonstrated that infrared spectroscopy provided a rapid, low-cost tool to monitor the quality of G. rigescens by the determination of the iridoids.     

Characterization of the Release of Urea from Coated Fertilizer by Fourier Transform Infrared Spectroscopy with Attenuated Total Reflectance

The development of a simple and cost-effective method for the determination of the release of coated urea has significant implications. Fourier-transform infrared spectroscopy with attenuated total reflectance was employed to determine the release of urea through univariate and multivariate calibration. The results indicated that univariate calibration did not accurately predict the release of urea, whereas partial least squares based on multivariate calibration performed significantly better. Partial least squares had the highest accuracy when the band located at 1420–1520 per centimeter was employed as the input. Moreover, the accuracy was further improved when segmented partial least squares models were developed at low and high urea concentrations. Unsegmented and segmented partial least squares models were employed, and release values were comparable to those measured by colorimetry. This work demonstrated the use of infrared spectroscopy and partial least squares to characterize the release of coated urea.     

Determination of Basic Nitrogen in Residues of Crude Oil Distillation Using ATR-FTIR and Chemometric Methods

A method for basic nitrogen determination in residues of crude oil distillation using infrared spectroscopy and chemometrics algorithms was developed. Interval partial least squares, synergy interval partial least squares, and backward interval partial least squares were evaluated for calibration model construction. The samples were divided into a calibration and prediction set containing 40 and 15 samples, respectively. The first derivative with a Savitzky-Golay filter and the mean centered data showed the best results and were used in all calibration models. The backward interval partial least squares algorithm with spectra divided in 60 intervals and combinations of 4 intervals (1407 to 1372; 1117 to 1082; 971 to 936; 914 to 879 cm^?1) showed the best root mean square error of prediction of 0.016 wt%. This calibration model displayed a suitable correlation coefficient between reference and predicted values.     

应用遗传算法和PLS的近红外光谱预测玉米中淀粉含量的研究

以普通玉米籽粒为试验材料,在应用遗传算法结合偏最小二乘回归法对近红外光谱数据进行特征波长选择的基础上,应用偏最小二乘回归法建立了特征波长测定玉米籽粒中淀粉含量的校正模型．试验结果表明,基于11个特征波长所建立的校正模型,其校正误差（RMSEC）、交叉检验误差（RMSECV）和预测误差（RMSEP）分别为0.30％、0.35％和0.27％,校正数据集和独立的检验数据集的预测值与实际测定值之间的相关系数分别达到0.9279和0.9390,与全光谱数据所建立的预测模型相比,在预测精度上均有所改善,表明应用遗传算法和PLS进行光谱特征选择,能获得更简单和更好的模型,为玉米籽粒中淀粉含量的近红外测定和红外光谱数据的处理提供了新的方法与途径．     

Determination of soil properties using Fourier transform mid-infrared photoacoustic spectroscopy

Photoacoustic spectroscopy (PAS) is based on the absorption of electromagnetic radiation by analyte molecules, and this technique has emerged as a valuable tool for the study of materials like biological, chemical and geological samples. In this paper, Fourier transform mid-infrared photoacoustic spectroscopy (FTIR-PAS) was used in the prediction of soil properties. Air-dried soil samples (n = 56) from Fengqiu Ecology Experimental Station Chinese Academy of Sciences were involved in this experiment, and FTIR-PAS spectra of these soil samples were recorded. These FTIR-PAS spectra indicated abundant soil information, but overlapping of absorption made it difficult to make direct measurement of soil properties. Partial least squares (PLS) models based on soil FTIR-PAS spectra was developed to predict available nitrogen (N), phosphorus (P), potassium (K) and organic matter content of soil. 42 soil samples were firstly used in leave-one-out cross-validation, and calibration error, calibration coefficient, validation error and ratio of standard deviation to prediction error (RPD) were obtained to optimize the PLS factor number; then based on the optimized PLS models the soil properties of the other 14 soil samples were predicted. The calibration statistics showed that the PLS model was suitable to use in the prediction of available N, P, K and organic matter content of soil. This prediction technique was non-destructive, and no sample pre-treatment was needed, which made FTIR-PAS a very promising method for fast evaluation of soil properties as well as soil quality.     

近红外光谱法快速测定涂料固化剂中游离甲苯二异氰酸酯含量

用气相色谱分析值为参照,采用近红外透射光谱（NIR）技术采集相应样品的NIR光谱,研究了涂料固化剂中游离甲苯二异氰酸酯（TDI）含量的快速测定分析方法。 并从120个固化剂样品中挑选出109个代表性的样品建模,选择7320~7250 cm-1和8485~8370 cm-1波段区间,用偏最小二乘法(PLS)和完全交互验证方式建立TDI含量的预测模型。 结果表明,固化剂中游离甲苯二异氰酸酯含量和近红外光谱之间存在较好的相关性,其预测模型的校正集均方差（RMSEC）为0.0815,验证集均方差（RMSEP）为0.0715,模型性能良好。 近红外光谱法可快速准确测定游离甲苯二异氰酸酯（TDI）含量,用于固化剂样品快速分析。     

Comparison of near-infrared and mid-infrared spectroscopy for the determination of distillation property of kerosene

Near-infrared (NIR) and mid-infrared (MIR) spectroscopy have been compared and evaluated for the determination of the distillation property of kerosene with the use of partial least squares (PLS) regression. Since kerosene is a complex mixture of similar hydrocarbons, both spectroscopic methods will be best evaluated with this complex sample matrix. PLS calibration models for each percent recovery temperature have been developed by using both NIR and MIR spectra without spectral pretreatment. Both methods have shown good correlation with the corresponding reference method, however NIR provided better calibration performance over MIR. To rationalize the improved calibration performance of NIR, spectra of the same kerosene sample were continuously collected and the corresponding spectral reproducibility was evaluated. The greater spectral reproducibility including signal-to-noise ratio of NIR led to the improved calibration performance, even though MIR spectroscopy provided more qualitative spectral information. The reproducibility of measurement, signal-to-noise ratio, and richness of qualitative information should be simultaneously considered for proper selection of a spectroscopic method for quantitative analysis.     

近红外光谱法快速测定附子中6个生物碱的含量

应用近红外光谱(NIRS)技术结合偏最小二乘(PLS)和最小二乘支持向量机(LS-SVM)建立了附子中多指标成分的快速无损检测方法。选取38批样品建立了同时测定附子样品中6种成分含量的高效液相色谱(HPLC)方法;通过采集附子样品的NIRS图,分别采用PLS和LS-SVM建立了各个成分HPLC测定值与NIRS图的定量校正模型。所建立的苯甲酰新乌头原碱、苯甲酰乌头原碱、苯甲酰次乌头原碱、新乌头碱、次乌头碱、乌头碱、单酯型生物碱总量和双酯型生物碱总量LS-SVM模型的相对预测偏差(RPD)分别为3.3、3.2、4.1、7.7、8.8、7.6、4.0和8.6;验证集相关系数(rpre)分别为0.9486、0.9475、0.9668、0.9909、0.9946、0.9969、0.9669和0.9927,且LS-SVM模型优于PLS模型,说明NIRS模型验证集与HPLC测定值具有良好的非线性关系,模型预测效果良好。采用NIRS技术结合LS-SVM模型可以快速对附子中的上述6个生物碱含量以及单酯型生物碱总量和双酯型生物碱总量进行检测,方法操作简便,对控制附子中的生物碱含量具有一定的指导作用。     

阴离子交换树脂结合近红外漫反射光谱测定碳酸饮料中日落黄

采用强碱性阴离子交换树脂富集饮料中的合成食用色素日落黄,用近红外漫反射光谱技术直接测定富集有色素的树脂.将34个模拟样品建模,日落黄浓度范围为0.05～1.2g/L.以柠檬黄和亮丽春红5R为干扰,经偏最小二乘回归建模,得到决定系数为0.9883,标准偏差为0.0187的稳健模型.定量预测3种不同市售饮料中的日落黄,回收...     

Rapid Determination of the Main Compounds in Crude and Processed Atractylodes macrocephala Using Fourier Transform Infrared Spectroscopy with Attenuated Total Reflectance

Infrared spectroscopy with attenuated total reflectance was investigated for the rapid determination of atractylenolide I and atractylenolide III contained in crude and processed Atractylodes macrocephala. High performance liquid chromatography was used as a reference method for the analysis, and a multivariate calibration model based on a partial least squares algorithm was developed to correlate the spectra and values determined by the reference method. The results suggested that the relative deviation of the predicted contents of atractylenolide I and atractylenolide III in the samples were less than 5.0%, which proved the stability and reliability of the models used. The proposed method is fast and nondestructive, and provides a novel, efficient, and environmentally-friendly approach for the rapid determination of the active components in crude and processed traditional Chinese herbal medicines.     

中红外、近红外和拉曼光谱法测定商品农药制剂中氰戊菊酯和马拉硫磷的含量

利用近红外、中红外和拉曼光谱法定量分析了商品农药制剂中有效成分氰戊菊酯和马拉硫磷的含量.采用偏最小二乘法(Partial least squares,PLS)建立氰戊菊酯和马拉硫磷的定量模型并进行了优化,用独立检验集对模型适应性进行评价.近红外和中红外法测定氰戊菊酯、马拉硫磷定量模型的相关系数分别是0.9981,0.9994和0.9946,0.9998,外部验证集标准差分别是0.082,0081和0.092,0.075,两种方法的定量效果接近;拉曼法氰戊菊酯和马拉硫磷定量模型的相关系数分别为0.9872和0.9993,外部验证集标准差分别为0.254和0.317,预测精度不及近红外和中红外法高.MIR-ATR,NIR和Raman 3种方法均能满足现场检测农药质量的需要.     

Fluorescence spectroscopic determination of triglyceride in human serum with window genetic algorithm partial least squares

Fluorescence spectrum, as well as the first and second derivative spectra in the region of 220–900 nm, was utilized to determine the concentration of triglyceride in human serum. Nonlinear partial least squares regression with cubic B‐spline‐function‐based nonlinear transformation was employed as the chemometric method. Window genetic algorithms partial least squares (WGAPLS) was proposed as a new wavelength selection method to find the optimized spectra wavelengths combination. Study shows that when WGAPLS is applied within the optimized regions ascertained by changeable size moving window partial least squares (CSMWPLS) or searching combination moving window partial least squares (SCMWPLS), the calibration and prediction performance of the model can be further improved at a reasonable latent variable number. SCMWPLS should start from the sub‐region found by CSMWPLS with the smallest root mean squares error of calibration (RMSEC). In addition, WGAPLS should be utilized within the region of smallest RMSEC whether it is the sub‐region found by CSMWPLS or region combination found by SCMWPLS. Moreover, the prediction ability of nonlinear models was better than the linear models significantly. The prediction performance of the three spectra was in the following order: second derivative spectrum < original spectrum < first derivative spectrum. Wavelengths within the region of 300–367 nm and 386–392 nm in the first derivative of the original fluorescence spectrum were the optimized wavelength combination for the prediction model. Copyright © 2012 John Wiley & Sons, Ltd.     

Sample classification for improved performance of PLS models applied to the quality control of deep-frying oils of different botanic origins analyzed using ATR-FTIR spectroscopy

The selection of an appropriate calibration set is a critical step in multivariate method development. In this work, the effect of using different calibration sets, based on a previous classification of unknown samples, on the partial least squares (PLS) regression model performance has been discussed. As an example, attenuated total reflection (ATR) mid-infrared spectra of deep-fried vegetable oil samples from three botanical origins (olive, sunflower, and corn oil), with increasing polymerized triacylglyceride (PTG) content induced by a deep-frying process were employed. The use of a one-class-classifier partial least squares-discriminant analysis (PLS-DA) and a rooted binary directed acyclic graph tree provided accurate oil classification. Oil samples fried without foodstuff could be classified correctly, independent of their PTG content. However, class separation of oil samples fried with foodstuff, was less evident. The combined use of double-cross model validation with permutation testing was used to validate the obtained PLS-DA classification models, confirming the results. To discuss the usefulness of the selection of an appropriate PLS calibration set, the PTG content was determined by calculating a PLS model based on the previously selected classes. In comparison to a PLS model calculated using a pooled calibration set containing samples from all classes, the root mean square error of prediction could be improved significantly using PLS models based on the selected calibration sets using PLS-DA, ranging between 1.06 and 2.91% (w/w).     

Subspace Regression Ensemble Method Based on Variable Clustering for Near-Infrared Spectroscopic Calibration

Abstract

An ensemble approach, based on the combination of multiple linear regressions in subspace and variable clustering and therefore named VCS-MLR, was proposed for near-infrared spectroscopy (NIRS) calibration. By an experiment involving the determination of five components in tobacco samples, it was shown that VCS-MLR improved the performance by 61.4, 23.3, 10.2, 20.5, and 18, respectively, with respect to partial least squares regression (PLSR). The results confirmed that VCS-MLR can result in a more accurate calibration model but without the increase of computational burden. Moreover, the superiority of VCS-MLR was highlighted for small sample problems.     

Simultaneous kinetic spectrophotometric determination of Cu(II), Co(II) and Ni(II) using partial least squares (PLS) regression

A partial least squares (PLS-1) calibration model based on kinetic—spectrophotometric measurement, for the simultaneous determination of Cu(II), Ni(II) and Co(II) ions is described. The method was based on the difference in the rate of the reaction between Co(II), Ni(II) and Cu(II) ions with 1-(2-pyridylazo)2-naphthol in a pH 5.8 buffer solution and in micellar media at 25°C. The absorption kinetic profiles of the solutions were monitored by measuring the absorbance at 570 nm at 2 s intervals during the time range of 0–10 min after initiation of the reaction. The experimental calibration matrix for the partial least squares (PLS-1) model was designed with 30 samples. The cross-validation method was used for selecting the number of factors. The results showed that simultaneous determination could be performed in the range 0.1-2 μg mL⁻¹ for each cation. The proposed method was successfully applied to the simultaneous determination of Cu(II), Ni(II) and Co(II) ions in water and in synthetic alloy samples.      

Quantitative Analysis of Chromium(VI) in Dilute Solution by Using Adsorption and Diffuse Reflectance Near‐infrared Spectroscopy

A method for quantitative determination of metal element in aqueous solution was developed by using adsorption and diffuse reflectance near‐infrared spectroscopy (DRNIRS). In this method, the analyte is firstly adsorbed onto the resin from the dilute solution, and then the adsorbed analyte is directly determined in the sorbent by using DRNIRS. Enrichment of the analyte is achieved by the adsorption from the dilute solution, and quantitative determination is accomplished by using multivariate calibration technique. Taking chromium(VI) in river water as the analytical target, adsorption conditions and the partial least squares (PLS) model was optimized. The results show that chromium(VI) can be immobilized onto the adsorbent and quantitatively measured by DRNIRS and multivariate calibration. With cross validation and external validation, the correlation coefficient between the reference and predicted concentration was found to be above 0.98 in the range of 0.75–29.90 mg·L⁻¹ for the PLS model, and the interference of the coexisting matrix was eliminated with the aid of multivariate calibration.     

Simultaneous kinetic spectrophotometric determination of cyanide and thiocyanate using the partial least squares (PLS) regression

The partial least squares (PLS-1) calibration model based on spectrophotometric measurement, for the simultaneous determination of CN⁻ and SCN⁻ ions is described. The method is based on the difference in the rate of the reaction between CN⁻ and SCN⁻ ions with chloramine-T in a pH 4.0 buffer solution and at 30 °C. The produced cyanogen chloride (CNCl) reacts with pyridine and the product condenses with barbituric acid and forms a final colored product. The absorption kinetic profiles of the solutions were monitored by measuring absorbance at 578 nm in the time range 20-180 s after initiation of the reaction with 2 s intervals. The experimental calibration matrix for partial least squares (PLS-1) calibration was designed with 31 samples. The cross-validation method was used for selecting the number of factors. The results showed that simultaneous determination could be performed in the range 10.0-900.0 and 50.0-1200.0 ng mL⁻¹ for CN⁻ and SCN⁻ ions, respectively. The proposed method was successfully applied to the simultaneous determination of cyanide and thiocyanate in water samples.     

Quality Evaluation of Ephedrae herba by near Infrared Spectroscopy

ABSTRACT

A novel method was developed for the quality control of Ephedrae herba by near-infrared (NIR) spectroscopy. First, qualitative models established by discriminant analysis and support vector machine were used for the preliminary screening of unqualified samples of E. herba. Then quantitative models of ephedrine and the total alkali (ephedrine and pseudoephedrine) were established by partial least squares regression and particle swarm optimization based least square support vector machine. The contents of test samples were predicted by the established NIR quantitative models. As a result, the accuracies of unqualified identification were 98.9% by discriminant analysis and 100% by support vector machine. The performance of the particle swarm optimization based least square support vector machine models were better than the partial least squares regression models. The correlation coefficients were both more than 0.98 and relative standard errors of calibrations were less than 9% in the calibration sets of particle swarm optimization based least square support vector machine models. As for the test sets, the correlation coefficients were both more than 0.93 and the relative standard errors of prediction were less than 13%, indicating satisfactory predicted results. All of these results demonstrated that NIR spectroscopy may be a powerful tool for the quality control of E. herba.