Partial least square analysis of lysozyme near-infrared spectra

Proteins possess strong absorption features in the combination range (5000-4000 cm⁻¹) of the near infrared (NIR) spectrum. These features can be used for quantitative analysis. Partial least squares (PLS) regression was used to analyze NIR spectra of lysozyme with the leave-one-out, full cross-validation method. A strategy for spectral range optimization with cross-validation PLS calibration was presented. A five-factor PLS model based on the spectral range between 4720 and 4540 cm⁻¹ provided the best calibration model for lysozyme in aqueous solutions. For 47 samples ranging from 0.01 to 10 mg/mL, the root mean square error of prediction was 0.076 mg/mL. This result was compared with values reported in the literature for protein measurements by NIR absorption spectroscopy in human serum and animal cell culture supernatants.     

Partial least squares-near infrared determination of pesticides in commercial formulations

A solvent free, fast and environmentally friendly near infrared-based methodology (NIR) was developed for pesticide determination in commercially available formulations. This methodology was based on the direct measurement of the diffuse reflectance spectra of solid samples and a multivariate calibration model (partial least squares, PLS) to determine the active principle concentration in commercial formulations. The PLS calibration set was built on using the spiked samples by mixing different amounts of pesticide standards and powdered samples. Buprofezin, Diuron and Daminozide were used as test analytes. Concentration of Buprofezin in the samples was calculated employing a 4-factors PLS calibration using the spectral information in the range between 2231–2430 and 1657–1784 nm. For Diuron determination a 1-factor PLS calibration model using the spectral range 1110–2497 nm, after a linear removed correction. Daminozide determination was carried out employing a 4-factors PLS model using the spectral information in the ranges 1644–1772 and 2014–2607 nm without baseline correction. The root mean square errors of prediction (RMSEP) found were 1.1, 1.7 and 0.7% (w/w) for Buprofezin, Diuron and Daminozide determination, respectively. The developed PLS-NIR procedure allows the determination of 120 samples/h, does not require any sample pre-treatment and avoids waste generation.     

Determination of human serum albumin and γ-globulin in a control serum solution by near-infrared spectroscopy and partial least squares regression

Near infrared (NIR) spectra in the 1300– 1850 nm region were measured for control serum solutions containing both albumin and γ-globulin of various concentrations. Partial least squares two (PLS2) regression was applied to the NIR spectra to determine simultaneously the concentrations of both proteins. For albumin, the correlation coefficient (R) of 0.988, the standard error of calibration (SEC) of 1.61 g/L, the standard error of prediction (SEP) of 1.29 g/L, the relative standard deviation (RSD) of 0.026 and the ratio of standard deviation of reference data in prediction to SEP (RPD) of 12.2 were obtained. For γ-globulin, the corresponding values were 0.997, 1.36 g/L, 1.35 g/L, 0.0365 and 8.66, respectively. The regression coefficients (RCs) of PLS factors were compared between albumin and γ-globulin, and the observed differences in the RCs were discussed based upon the differences in the hydration between albumin and γ-globulin. In order to explore the effects of various metabolites such as glucose, and cholesterol on the chemometrics models, the RCs for albumin and γ-globulin in the control serum solutions were also compared with those for albumin and γ-globulin in phosphate buffer solutions previously studied. The results of our experiments show that NIR spectroscopy with the use of PLS2 regression has considerable promise in nondestructive determination of the concentrations of blood serum proteins. Received: 31 December 1997 / Revised: 9 April 1998 / Accepted: 27 April 1998     

Combination of mid- and near-infrared spectroscopy for the determination of the quality properties of beers

The combination of infrared (MIR) and near-infrared (NIR) spectroscopy has been employed for the determination of important quality parameters of beers, such as original and real extract and alcohol content. A population of 43 samples obtained from the Spanish market and including different types of beer, was evaluated. For each technique, spectra were obtained in triplicate. In the case of NIR a 1 mm pathlength quartz flow cell was used, whereas attenuated total reflectance measurements were used in MIR. Cluster hierarchical analysis was employed to select calibration and validation data sets. The calibration set was composed of 15 samples, thus leaving 28 for validation. A critical evaluation of the prediction capability of multivariate methods established from the combination of NIR and MIR spectra was made. Partial least squares (PLS) and artificial neural networks (ANN) were evaluated for the treatment of data obtained in each individual technique and the combination of both. Different parameters of each methodology were optimized. A slightly better predictive performance was obtained for NIR-MIR combined spectra, and in all the cases ANN performs better than PLS, which may be interpreted from the existence of some non-linearity in the data. The root-mean-sqare-error of prediction (RMSEP) values obtained for the combined NIR-MIR spectra for the determination of real extract, original extract and ethanol were 0.076% w/w, 0.14% w/w and 0.091% v/v.     

Determination of Ni2+ using an equilibrium ion exchange technique: Important chemical factors and applicability to environmental samples

The main purpose of this study was to investigate the relationship between some coffee roasting variables (weight loss, density and moisture) with near infrared (NIR) spectra of original green (i.e. raw) and differently roasted coffee samples, in order to test the availability of non-destructive NIR technique to predict coffee roasting degree. Separate calibration and validation models, based on partial least square (PLS) regression, correlating NIR spectral data of 168 representatives and suitable green and roasted coffee samples with each roasting variable, were developed. Using PLS regression, a prediction of the three modelled roasting responses was performed. High accuracy results were obtained, whose root mean square errors of the residuals in prediction (RMSEP) ranged from 0.02 to 1.23%. Obtained data allowed to construct robust and reliable models for the prediction of roasting variables of unknown roasted coffee samples, considering that measured vs. predicted values showed high correlation coefficients (r from 0.92 to 0.98). Results provided by calibration models proposed were comparable in terms of accuracy to the conventional analyses, revealing a promising feasibility of NIR methodology for on-line or routine applications to predict and/or control coffee roasting degree via NIR spectra.     

近红外光谱结合膜富集技术测定饮料中微量邻苯二甲酸二异辛酯的含量

提出了一种基于在线膜富集的近红外漫反射光谱技术,对饮料中的微量塑化剂邻苯二甲酸二异辛酯(DEHP)进行快速检测。采用聚醚砜膜对饮料中的DEHP进行富集,将富集DEHP的膜直接进行近红外漫反射检测。参考DEHP的透射近红外光谱,对波数进行选择,以4 420～4 060、4 700～4 540、6 040～5 600cm-1作为建模的波数区间。通过比较原始光谱、多元散射校正、一阶求导、二阶求导及其组合,考察了光谱预处理方法对模型的影响,用去一交互验证法建立了偏最小二乘(PLS)模型,并用所建立的校正模型对校正集样品进行了预测。结果表明,在选定的波数区间,当用一阶求导对校正集光谱进行预处理时,所建立的模型对校正集的预测效果最佳,在隐变量数为7时,对校正集所有样品的校正均方根误差(RMSEC)为0.188 7mg/L。用此模型对预测集样品进行预测时,DEHP的质量浓度在0.5～5.0 mg/L范围内,预测均方根误差(RMSEP)为0.232 4 mg/L,平均相对预测误差为6.29%。     

Electrospray ionization mass spectrometry of terrestrial humic substances and their size fractions

Electrospray ionization mass spectrometry (ESI-MS) was used to evaluate the average molecular mass of terrestrial humic substances, such as humic (HA) and fulvic (FA) acids from a soil, and humic acid from a lignite (NDL). Their ESI mass spectra, by direct infusion, gave average molecular masses comparable to those previously obtained for aquatic humic materials. The soil HA and FA were further separated in size-fractions by preparative high performance size exclusion chromatography (HPSEC) and analyzed with ESI-MS by both direct infusion and a further on-line analytical HPSEC. Unexpectedly, their average molecular mass was only slightly less than for the bulk sample and, despite different nominal molecular size, did not substantially vary among size-fractions. The values increased significantly (up to around 1200 Da) after on-line analytical HPSEC for the HA bulk sample, at both pH 8 and 4, and for the HA size-fractions when pH was reduced from 8 to 4. It was noticed that HA size-fractions at pH 8 were separated by on-line HPSEC in further peaks showing average masses which progressively increased with elution volume. Furthermore, when the HA and NDL bulk samples were sequentially ultracentrifuged at increasing rotational speed, their supernatants showed mass values which were larger than bulk samples and increased with rotational speed. These variations in mass values indicate that the electrospray ionization is dependent on the composition of the humic molecular mixtures and increases when their heterogeneity is progressively reduced. It is suggested that the dominance of hydrophobic compounds in humic supramolecular associations may inhibit the electrospray ionization of hydrophilic components. Our results show that ESI-MS is reasonably applicable to humic substances only after an extensive reduction of their chemical complexity.     

白酒基酒中酮类物质的近红外光谱检测方法

应用近红外光谱技术建立了白酒基酒中2,3-丁二酮和3-羟基-2-丁酮的快速检测模型。从洛阳杜康酒厂选取182个白酒基酒样品为材料,运用气相色谱法测得两种物质的化学值,同时采集其在12 000~4 000 cm-1范围内的光谱数据,采用偏最小二乘法(PLS)结合内部交叉验证建立校正模型。通过对比不同光谱预处理下PLS模型效果对其进行优化,确定2,3-丁二酮和3-羟基-2 丁酮的最佳预处理方法分别为一阶导数+多元散射校正和二阶导数,最佳光谱区间分别为9 403.2~7 497.9 cm-1和9 403.2~7 497.9 cm-1+6 101.7~5 449.8 cm-1。优化后2,3-丁二酮和3 羟基-2-丁酮校正集样品的化学值和近红外预测值的决定系数(R2)分别为0.960 2和0.963 2,交叉验证均方根误差(RMSECV)分别为0.39、0.22 mg/100 mL;通过外部检验,验证集样品的R2分别为0.957 6和0.957 8,预测均方根误差(RMSEP)分别为0.40、0.24 mg/100 mL。结果表明,应用近红外光谱技术结合化学计量学方法所建立的模型有较高的准确度,能够满足白酒生产中酮类物质的快速检测需要。     

Determination of biodiesel content when blended with mineral diesel fuel using infrared spectroscopy and multivariate calibration

In this work, multivariable calibration models based on middle- and near-infrared spectroscopy were developed in order to determine the content of biodiesel in diesel fuel blends, considering the presence of raw vegetable oil. Soybean, castor and used frying oils and their corresponding esters were used to prepare the blends with conventional diesel. Results indicated that partial least squares (PLS) models based on MID or NIR infrared spectra were proven suitable as practical analytical methods for predicting biodiesel content in conventional diesel blends in the volume fraction range from 0% to 5%. PLS models were validated by independent prediction set and the RMSEPs were estimated as 0.25 and 0.18 (%, v/v). Linear correlations were observed for predicted vs. observed values plots with correlation coefficient (R) of 0.986 and 0.994 for the MID and NIR models, respectively. Additionally, principal component analysis (PCA) in the MID region 1700 to 1800 cm^− 1 was suitable for identifying raw vegetable oil contaminations and illegal blends of petrodiesel containing the raw vegetable oil instead of ester.     

Trace humic and fulvic acid determination in natural water by cloud point extraction/preconcentration using non-ionic and cationic surfactants with FI-UV detection

A preconcentration and determination method for humic and fulvic acids at trace levels in natural water samples was developed. Cloud point extraction was successfully employed for the preconcentration of humic acid (HA) and fulvic acid (FA) prior to the determination by using a flow injection (FI) system coupled to a spectrophotometric UV-Vis detector. The quantitative extraction of HA and FA within the pH range 1-12 was obtained by neutralization of the anionic charge on the humic substances with a cationic surfactant, hexadecyltrimethylammonium bromide (CTAB). This generated a hydrophobic species that was subsequently incorporated (solubilized) into the micelles of a non-ionic surfactant polyethylene glycol, tert-octylphenyl ether (Triton X-114). The FI method for HA and FA determination was developed by injection of 100 microl of the extracted surfactant-rich phase using an HPLC pump with spectrophotometric detection at 350 nm. A 50 ml sample solution preconcentration allowed an enrichment factor of 167. The limit of detection (LOD) obtained under the optimal conditions was 5 microg l(-1). The precision for ten replicate determinations at 0.2 mg l(-1) HA was 3.1% relative standard deviation (RSD), calculated from the peak heights. The calibration using the preconcentration system for HA and FA was linear with a correlation coefficient (r2) of 0.9997 at levels near the detection limits up to at least 1 mg l(-1). The method was successfully applied to the determination of HA and FA in natural water samples (river water).     

Discrimination of ethylene/vinyl acetate copolymers with different composition and prediction of the content of vinyl acetate in the copolymers and their melting points by near-infrared spectroscopy and chemometrics

Near-infrared (NIR) diffuse reflectance spectra have been measured by use of a rotating drawer for pellets of 12 kinds of ethylene/vinyl acetate (EVA) copolymers with vinyl acetate (VA, the comonomer) varying in the 7–44 wt % range. They are unambiguously discriminated from one another by a score plot of the principal component analysis (PCA) Factor 1 and 2, based upon the NIR spectra pretreated by multiplicative scatter correction (MSC). Principal component (PC) weight loadings for Factor 1 show that the discrimination relies largely upon bands due to the overtone and combination modes arising from the VA unit. We have found one “outlier” in the score plot and elucidated its spectral characteristics based upon PC weight loadings for Factor 2. Partial least-squares (PLS) regression has been applied to propose calibration models which predict the VA content in EVA. The models have been prepared for three kinds of pretreatment, the first derivative, the second derivative, and MSC; and four kinds of wavelength regions. The NIR spectra in the 1100–2200 nm region after the MSC treatment has given the best correlation coefficient and standard error of prediction (SEP) of 0.998 and 0.70%, respectively. The calibration models, prepared by NIR diffuse reflectance spectroscopy for the pellet samples, are compared with previously reported models by NIR transmission spectroscopy for the flowing molten samples, and with those by Raman spectroscopy for the pellet samples. PLS regression has also allowed us to predict melting points of the copolymers with the correlation coefficient and SEP of 0.997 and 0.78°C, respectively. © 1998 John Wiley & Sons, Inc. J Polym Sci B: Polym Phys 36: 1529–1537, 1998     

Construction of global and robust near-infrared calibration models based on hybrid calibration sets using Partial Least Squares (PLS) regression

Near-infrared spectroscopy (NIR) models built on a particular instrument are often invalid on other instruments due to spectral inconsistencies between the instruments. In the present work, global and robust NIR calibration models were constructed by partial least square (PLS) regression based on hybrid calibration sets, which are composed of both primary and secondary spectra. Three datasets were used as case studies. The first consisted of 72 radix scutellaria samples measured on two NIR spectrometers with known baicalin content. The second was composed of 80 corn samples measured on two instruments with known moisture, oil, and protein concentrations. The third dataset included 279 primary samples of tobacco with known nicotine content and 78 secondary samples of tobacco with known nicotine concentrations. The effect of the number of secondary spectra in the hybrid calibration sets and the methods for selecting secondary spectra on the PLS model performance were investigated by comparing the results obtained from different calibration sets. This study shows that the global and robust calibration models accurately predicted both primary and secondary samples as long as the ratios of the number of primary spectra to the number of secondary spectra were less than 22. The models performance was not influenced by the selection method of the secondary spectra. The hybrid calibration sets included the primary spectral information and also the secondary spectra; information, rendering the constructed global and robust models applicable to both primary and secondary instruments.     

Strong association of fallout plutonium with humic and fulvic acid as compared to uranium and137Cs in Nishiyama soils from Nagasaki, Japan

To investigate the formation of mobile organic plutonium, we analyzed the plutonium contents of the fulvic (FA) and humic (HA) acids from the soil samples obtained at Nishiyama, Nagasaki, Japan. The percentages of the plutonium bound strongly to HA and to FA vs. the total plutonium in the soil were 5–10% and 1%, respectively, at the depth of 0–0.1 m, much higher values than those of¹³⁷Cs and uranium. After being weathered for 51 years under a temperate climate, the initial highfired oxides of fallout plutonium have become as chemically reactive plutonium from nuclear fuel reprocessing plants.     

Determination of human serum albumin and γ-globulin in a control serum solution by near-infrared spectroscopy and partial least squares regression

Near infrared (NIR) spectra in the 1300– 1850 nm region were measured for control serum solutions containing both albumin and γ-globulin of various concentrations. Partial least squares two (PLS2) regression was applied to the NIR spectra to determine simultaneously the concentrations of both proteins. For albumin, the correlation coefficient (R) of 0.988, the standard error of calibration (SEC) of 1.61 g/L, the standard error of prediction (SEP) of 1.29 g/L, the relative standard deviation (RSD) of 0.026 and the ratio of standard deviation of reference data in prediction to SEP (RPD) of 12.2 were obtained. For γ-globulin, the corresponding values were 0.997, 1.36 g/L, 1.35 g/L, 0.0365 and 8.66, respectively. The regression coefficients (RCs) of PLS factors were compared between albumin and γ-globulin, and the observed differences in the RCs were discussed based upon the differences in the hydration between albumin and γ-globulin. In order to explore the effects of various metabolites such as glucose, and cholesterol on the chemometrics models, the RCs for albumin and γ-globulin in the control serum solutions were also compared with those for albumin and γ-globulin in phosphate buffer solutions previously studied. The results of our experiments show that NIR spectroscopy with the use of PLS2 regression has considerable promise in nondestructive determination of the concentrations of blood serum proteins.     

An expeditious method for determining particle size distribution by near infrared spectroscopy: Comparison of PLS2 and ANN models

The particle size distribution of a solid product can be crucial parameter considering its application to different kinds of processes. The influence of particle size on near infrared (NIR) spectra has been used to develop effective alternative methods to traditional ones in order to determine this parameter. In this work, we used the chemometrical techniques partial least squares 2 (PLS2) and artificial neural networks (ANNs) to simultaneously predict several variables to the rapid construction of particle size distribution curves. The PLS2 algorithm relies on linear relations between variables, while the ANN technique can model non-linear systems.Samples were passed through sieves of different sieve opening in order to separate several size fractions that were used to construct two types of particle size distribution curves. The samples were recorded by NIR and their spectra were used with PLS2 and ANN to develop two calibration models for each. The correlation coefficients and relative standard errors of prediction (RSEP) have been used to assess the goodness of fit and accuracy of the results.The four calibration models studied provided statistically identical results based on RSEP values. Therefore, the combined use of NIR spectroscopy and PLS2 or ANN calibration models allows determining the particle size distributions accurately. The results obtained by ANN or PLS2 are statistically similar.     

Determination of chlorinated hydrocarbons in water by fiber-optic evanescent wave spectroscopy and partial least-squares regression

An application of the multivariate calibration technique of partial least-squares (PLS) regression to near-infrared spectra of a fiber-optic sensor based on the evanescent wave principle is presented. The sensing element consists of a quartz glass fiber with a silicone cladding which enriches nonpolar water contaminants. Due to the interaction of the extracted molecules with the part of the light which is transmitted in the evanescent wave zone of the cladding, absorbance spectra of the contaminants can be collected. In view of a sensor application for in-situ environmental analysis, aqueous solutions of chlorinated hydrocarbon solvents (CHS), which often can be found as major water contaminants, have been measured. PLS regression was applied to three sets of CHS samples, representing typical features of NIR evanescent wave spectral data. These are, e.g., strong overlapping of the absorption bands of different CHS components, peak distortions due to temperature variations between reference and sample measurement and noisy data at analyte concentrations near to the limit of detection, respectively. For trichloroethene and 1,1-dichloroethene, where the calibration model was built for samples within a small concentration range of 1–9 mg l^–1, satisfactory prediction results could be obtained with a relatively small root-mean-square error of 0.3 mg l^–1 compared to analytical reference measurements. In contrast to this, for a three component system of dichloromethane, trichloromethane and trichloroethene with strongly overlapping absorption bands, where samples over a very broad concentration range from 3–4940 mg l^–1 were included in the PLS model, the prediction accuracy decreased enormously and for some samples strong deviations between real and predicted data occurred. Nevertheless, applying multivariate calibration to this difficult system with similar spectral features and huge differences in the concentration of the species allowed an acceptable spectral distinction and at least a semi-quantitative determination of the CHS species.     

Improving the sensitivity of NIR spectroscopy with an enrichment technique: determining a trace analyte of ethyl carbamate.

An exploration was made to develop a determination method of a low-concentration analyte by NIR spectroscopy. An absorber, silica gel was employed to extract and enrich a low-concentration analyte of ethyl carbamate. The solid absorber with the enriched analyte was measured by NIR spectroscopy in the range of 800 - 2500 nm. Afterwards, PLS regression was performed between the NIR spectra and the concentrations of the analyte for quantitative analysis of the low-concentration analyte. The spectra of 20 solid samples of analyte-absorbed silica gel showed a good correlation with the concentrations of ethyl carbamate in the samples. A leave-one-out cross validation was applied to evaluate the prediction ability of PLS models built with the full spectra, spectra in the region of 1920 - 1970 nm and the region of 2250 - 2430 nm, respectively. The values of the root-mean-square error of the cross validation (RMSECV) were about 0.1 mg L(-1) (0.1 ppm).     

Near-infrared spectroscopic determination of human serum albumin, γ-globulin, and glucose in a control serum solution with searching combination moving window partial least squares

Near-infrared (NIR) spectra in the region of 5000-4000 cm⁻¹ with a chemometric method called searching combination moving window partial least squares (SCMWPLS) were employed to determine the concentrations of human serum albumin (HSA), γ-globulin, and glucose contained in the control serum IIB (CS IIB) solutions with various concentrations. SCMWPLS is proposed to search for the optimized combinations of informative regions, which are spectral intervals, considered containing useful information for building partial least squares (PLS) models. The informative regions can easily be found by moving window partial least squares regression (MWPLSR) method. PLS calibration models using the regions obtained by SCMWPLS were developed for HSA, γ-globulin, and glucose. These models showed good prediction with the smallest root mean square error of predictions (RMSEP), the relatively small number of PLS factors, and the highest correlation coefficients among the results achieved by using whole region and MWPLSR methods. The RMSEP values of HSA, γ-globulin, and glucose yielded by SCMWPLS were 0.0303, 0.0327, and 0.0195 g/dl, respectively. These results prove that SCMWPLS can be successfully applied to determine simultaneously the concentrations of HSA, γ-globulin, and glucose in complicated biological fluids such as CS IIB solutions by using NIR spectroscopy.     

废旧涤/棉混纺织物近红外定量分析模型的建立及预测

将中红外光谱筛选出的598个纯涤、纯棉及涤/棉混纺样本采用GB/T 2910.11-2009法测定其涤、棉准确含量,其中校正集样本252个,验证集样本346个。使用便携式近红外光谱仪获取样本的原始近红外光谱(NIRS)。校正集样本依据回归系数的分布趋势和范围选取最佳建模谱区,并采用差分一阶导、S-G平滑和均值中心化相结合的方法对原始光谱进行预处理,利用偏最小二乘法(PLS)建立涤/棉混纺织物中涤含量的近红外(NIR)定量分析模型。同时分析了样本颜色对NIRS的影响,探讨了斜线光谱样本、奇异样本和不同组织结构织物对模型预测效果的影响。结果表明:利用PLS法建立的涤/棉混纺织物定量分析模型最优组合包含1个光谱区间和9个主成分因子,校正集相关系数(RC)为0.998,标准偏差(SEC)为0.908。为验证所建模型的有效性和实用性,对346个未参与建模的涤棉样本进行了预测,并将预测结果与国标法测定值进行方差分析,两种方法结果无显著差异,预测正确率达97%以上。模型的建立为废旧涤/棉混纺织物快速、无损分拣提供了基础数据库。     

Determination of OH groups in humic acids using methylation with dimethylsulfate

The methylation of humic acids (HA) with dimethylsulfate in acetone and methanol followed by the iodometric determination of the methoxy groups (Zeisel reaction) were applied to determine the contents of –OH groups in solid samples of HA of different origins. For the coal- and peat-derived HA samples, the contents of –OH groups determined after methylation in acetone ranged from 6.6 to 8.7 mmol/g, whereas the contents of –OH groups determined after methylation in methanol ranged from 4.0 to 5.0 mmol/g. These differences may be related to the content of carboxylic groups in the HA molecule that were not methylated in methanol, as confirmed by a comparison with results of conventional titrimetric determinations. Observed differences were interpreted as results of different polarity of both solvents and alkalinity of the reaction mixture during the methylation. The contents of alcoholic groups as well as some other minor –OH groups can be estimated using the –OH group contents obtained after methylation in both solvents together with the results of the conventional determinations of acidic functional groups. A repeatability of the –OH groups determination as estimated from a series of triplicate analyses of different HA samples (n = 7) was in range of 0.15–0.73 mmol/g and 0.08–1.06 mmol/g (standard deviations) for methylation in acetone and methanol, respectively. Thus, the average repeatability of the –OH groups determination was estimated to be 0.38 and 0.50 mmol/g for methylation in acetone and methanol, respectively.