Horizontal attenuated total reflection applied to simultaneous determination of ash and protein contents in commercial wheat flour

Partial least-squares (PLS) regression was used to generate various models for the determination of both the protein and the ash contents of wheat flours by using spectroscopic data in the mid-infrared region obtained with a horizontal attenuated total reflectance (HATR) accessory. One hundred samples of wheat flour were used as purchased in the market: 55 for constructing the calibration model and 45 as external samples. The protein content varied between 8.85 and 13.23% and the ash content, between 0.330 and 1.287%, as determined by reference methods. Raw spectra and those corrected by multiplicative signal correction (MSC), first and second derivative spectra, were used as data for building the models. Different pre-treatments, such as mean centered and/or variance scaled (VS) methods, were tested and compared. Very good models were built as judged by the correlation coefficients (R²), root mean square error of calibration (RMSEC), root mean square error of validation (RMSEV) and root mean square error of prediction (RMSEP) that were obtained. Best results were achieved with MSC treated spectra.     

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．     

偏最小二乘近红外光谱法测定瘦肉脂肪酸组成的研究

利用偏最小二乘将瘦肉的近红外光谱数据分别与其棕榈酸、棕榈油酸、硬脂酸、油酸、亚油酸含量建立校正模型,并用交互校验和外部检验来考查模型的可靠性.各脂肪酸模型的校正相关系数分别为0.9998、0.9844、0.9963、0.9754、0.9969,均方估计残差(RMSEC)分别为0.0231、0.0485、0.111、0.373、0.311,交互校验均方残差(RMSECV)分别为0.509、0.115、0.225、0.848、0.649.应用所建立的各脂肪酸近红外模型对瘦肉脂肪酸组成进行预测,并对各脂肪酸的预测值与气相色谱法测定值进行配对t-检验,结果表明两者差异均不显著(p>0.05).     

过程分析技术(PAT)在原料药生产中的应用

该文通过采用近红外光谱分析技术对原料药(API)的浓度调节过程进行实时监控,介绍了在良好生产规范条件下过程分析技术(PAT)的实施过程。利用偏最小二乘算法开发出两个校正模型分别用以监控原料药和水分含量,并通过模型校正均方根误差(RMSEC)、交叉检验均方根误差(RMSECV)和预测均方根误差(RMSEP)以及对应的决定系数(R~2)来评估模型的性能。为保证模型性能,按照分析方法验证要求对模型的线性和范围、准确性、精密度(重复性)、专属性以及稳健性指标进行验证。最后通过系统性能测试确认检测系统满足商业化运行的要求。结果显示,采用过程分析技术控制浓度调节过程,可以大幅度缩短浓度调节时间,节约蒸汽能耗和检测费用,减少生产过程中的偏差,提升产品工艺水平和批次间一致性。     

Simultaneous determination of three surfactants and water in shampoo and liquid soap by ATR-FTIR

It is demonstrated for the first time that the principal constituents of a shampoo as well as of a liquid soap -three surfactants and water- can be determined directly, simultaneously and quickly in undiluted samples by attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy in the middle infrared region, despite the broad absorption bands of the solvent. Two of the surfactants, sodium lauryl ether sulfate (SLES) and cocoamidopropyl betaine (CAPB), are common to both formulations; alkylpolyglucoside (APG) is the third surfactant of the liquid soap and cocodiethanolamide (CDEA), the corresponding ingredient of the shampoo. Absorbance data of the undiluted samples and of the calibration standards was collected in the middle infrared region of the spectrum (800-1600 and 1900-3000 cm⁻¹). Two methods of multivariate quantification were compared: classical least squares (CLS), where absorbance data measured at 200 wavenumbers was processed, and inverse least squares (ILS), where data at 10 selected wavenumbers was analyzed. A spectra normalization procedure, based on a dominating water band, was examined. Twenty-seven standard mixtures were used for each application, consisting of all combinations at three concentration levels of each surfactant, respectively the lower limit, the expected value and the upper limit accepted in quality control. By favoring wavenumbers where absorption bands of the minor components (APG in the liquid soap and CDEA in the shampoo) are more intense, good results were obtained for 18 simulated samples of shampoo and 18 samples of liquid soap, no matter if calculations were made by CLS or ILS. The relative errors for water (major component, 84-88%) and SLES (7-10%) were always below 2%; for CAPB (2-4%), APG (<2%) and CDEA (<2%), they occasionally reached 5% of the component, an uncertainty of less than 0.07% in terms of the sample weight.     

近红外光谱技术定量分析连作滁菊土壤中的阿魏酸含量

应用近红外光谱(NIRS)技术定量分析连作滁菊土壤样品中阿魏酸的含量.通过标准杠杆值、学生残差和马氏距离判断异常光谱,经二阶导数和Norris平滑滤噪预处理后,在6000~4000 cm-1范围,最佳因子数为7,采用偏最小二乘法(PLS)构建数学模型.结果表明,模型校正集和验证集与高效液相色谱仪(HPLC)测定的参考值之间均呈现良好相关关系,校正相关系数Rc为0.9914,交叉验证相关系数Rcv为0.9935,校正集误差均方根(RMSEC)为0.484,预测误差均方根(RMSEP)为0.539,交叉验证误差均方根(RMSECV)为0.615.研究结果表明,NIRS分析技术能够实现连作土壤中阿魏酸的快速检测,结果准确可靠.     

Sweeteners determination in table top formulations using FT-Raman spectrometry and chemometric analysis

A partial least squares (PLS) Fourier transform Raman spectrometry procedure based on the measurement of solid samples contained inside standard glass vials, has been developed for direct and reagent-free determination of sodium saccharin and sodium cyclamate in table top sweeteners. A classical 2² design for standards was used for calibration, but this system provides accuracy errors higher than 13% w/w for the analysis of samples containing glucose monohydrate. So, an extended model incorporating glucose monohydrate (2³ standards) was assayed for the determination of sodium saccharin and sodium cyclamate in all the samples. Mean centering spectra data pre-treatment has been employed to eliminate common spectral information and root mean square error of calibration (RMSEC) of 0.0064 and 0.0596 was obtained for sodium saccharin and sodium cyclamate, respectively. A mean accuracy error of the order of 1.1 and 1.9% w/w was achieved for sodium saccharin and sodium cyclamate, in the validation of the method using actual table top samples, being lower than those obtained using an external monoparametric calibration. FT-Raman provides a fast alternative to the chromatographic method for the determination of the sweeteners with a three times higher sampling throughput than that obtained in HPLC. On the other hand, FT-Raman offers an environmentally friendly methodology which eliminates the use of solvents. Furthermore, the stability of samples and standards into chromatographic standard glass vials allows their storage for future analysis thus avoiding completely the waste generation.     

Cellulose I crystallinity determination using FT–Raman spectroscopy: univariate and multivariate methods

Two new methods based on FT–Raman spectroscopy, one simple, based on band intensity ratio, and the other using a partial least squares (PLS) regression model, are proposed to determine cellulose I crystallinity. In the simple method, crystallinity in cellulose I samples was determined based on univariate regression that was first developed using the Raman band intensity ratio of the 380 and 1,096 cm^?1 bands. For calibration purposes, 80.5% crystalline and 120-min milled (0% crystalline) Whatman CC31 and six cellulose mixtures produced with crystallinities in the range 10.9–64% were used. When intensity ratios were plotted against crystallinities of the calibration set samples, the plot showed a linear correlation (coefficient of determination R ² = 0.992). Average standard error calculated from replicate Raman acquisitions indicated that the cellulose Raman crystallinity model was reliable. Crystallinities of the cellulose mixtures samples were also calculated from X-ray diffractograms using the amorphous contribution subtraction (Segal) method and it was found that the Raman model was better. Additionally, using both Raman and X-ray techniques, sample crystallinities were determined from partially crystalline cellulose samples that were generated by grinding Whatman CC31 in a vibratory mill. The two techniques showed significant differences. In the second approach, successful Raman PLS regression models for crystallinity, covering the 0–80.5% range, were generated from the ten calibration set Raman spectra. Both univariate-Raman and WAXS determined crystallinities were used as references. The calibration models had strong relationships between determined and predicted crystallinity values (R ² = 0.998 and 0.984, for univariate-Raman and WAXS referenced models, respectively). Compared to WAXS, univariate-Raman referenced model was found to be better (root mean square error of calibration (RMSEC) and root mean square error of prediction (RMSEP) values of 6.1 and 7.9% vs. 1.8 and 3.3%, respectively). It was concluded that either of the two Raman methods could be used for cellulose I crystallinity determination in cellulose samples.     

近红外光谱技术用于花生油中棕榈油含量的测定

本文采用近红外光谱技术采集样品的近红外光谱数据,光谱经一阶求导后,采用偏最小二乘法(PLS)建立花生油中棕榈油含量的定标模型,并用交互验证法对模型进行了验证。模型相关系数为0.9963,校正均方根(RMSEC)为0.937。该模型应用于实际样品的检测,结果令人满意。     

Near-infrared imaging for quantitative analysis of active component in counterfeit dimethomorph using partial least squares regression

Near-infrared (NIR) imaging systems simultaneously record spectral and spatial information. Near-infrared imaging was applied to the identification of (E,Z)-4-(3-(4-chlorophenyl)-3-(3,4-dimethoxyphenyl)acryloyl)morpholine (dimethomorph) in both mixed samples and commercial formulation in this study. The distributions of technical dimethomorph and additive in the heterogeneous counterfeit product were obtained by the relationship imaging (RI) mode. Furthermore, a series of samples which consisted of different contents of uniformly distributed dimethomorph were prepared and three data cubes were generated for each content. The spectra extracted from these images were imported to establish the partial least squares model. The model??s evaluating indicators were: coefficient of determination (R ²) 99.42 %, root mean square error of calibration (RMSEC) 0.02612, root mean square error of cross-validation (RMSECV) 0.01693, RMSECV_mean 0.03577, relative standard error of prediction (RSEP) 0.01999, and residual predictive deviation (RPD) 15.14. Relative error of prediction of the commercial formulation was 0.077, indicating the predicted value correlated with the real content. The chemical value reconstruction image of dimethomorph formulation products was calculated by a MATLAB program. NIR microscopy imaging here manifests its potential in identifying the active component in the counterfeit pesticide and quantifying the active component in its scanned image.     

基于近红外光谱的食用植物油中反式脂肪酸含量快速定量检测及模型优化研究

利用近红外光谱技术对食用植物油中反式脂肪酸(Trans fatty acids,TFA)含量进行快速定量检测,并通过波段选择、预处理方法、变量筛选及建模方法对TFA含量预测模型进行优化.采用AntarisⅡ傅里叶变换近红外光谱仪在4000~10000 cm-1光谱范围采集98个食用植物油样本的近红外透射光谱,然后采用气相色谱法测定TFA的真实含量.首先,对样本原始光谱进行波段、预处理方法优选;在此基础上,采用竞争自适应重加权法(Competitive adaptive reweighted sampling,CARS)筛选TFA相关的重要变量,最后应用主成分回归、偏最小二乘和最小二乘支持向量机方法分别建立食用植物油中TFA含量的预测模型.研究结果表明,近红外光谱技术检测食用植物油中的TFA含量是可行的,优化后的最佳预测模型的校正集和预测集R2分别为0.992和0.989,RMSEC和RMSEP分别为0.071%和0.075%.最佳预测模型所用的变量仅26个,占全波段变量的0.854%.此外,与全波段偏最小二乘预测模型相比,其预测集R2由0.904上升为0.989,RMSEP由0.230%下降为0.075%.由此表明,模型优化非常必要,CARS能有效筛选TFA相关的重要变量,极大减少建模变量数,从而简化预测模型,并较大提高预测模型的精度和稳定性.     

QSAR modeling based on the bias/variance compromise: a harmonious

Modeling quantitative structure-activity relationships (QSAR) is considered with an emphasis on prediction. An abundance of methods are available to develop such models. Using a harmonious approach that balances the bias and variance of predictions, the best calibration models are identified relative to the bias and variance criteria used. Criteria utilized to determine the adequacy of models are the root mean square error of calibration (RMSEC) and validation (RMSEV), respective R2 values, and the norm of the regression vector. QSAR data from the literature are used to demonstrate concepts. For these data sets and criteria used, it is suggested that models obtained by ridge regression (RR) are more harmonious and parsimonious than models obtained by partial least squares (PLS) and principal component regression (PCR) when the data is mean-centered. The most harmonious RR models have the best bias/variance tradeoff, reflected by the smallest RMSEC, RMSEV, and regression vector norms and the largest calibration and validation R2 values. The most parsimonious RR models have the smallest effective rank.     

Pareto calibration with built-in wavelength selection

In multivariate regression, it is often reported that wavelength selection can improve results. Improvement is often solely based on bias measures such as the root mean square error of calibration (RMSEC) and root mean square error of validation (RMSEV), R² for the calibration and validation, etc. In recent studies, it has been shown that when variance measures are included, Pareto optimal models can be determined. However, variance measures used to date do not provide the ability to choose wavelength subset models relative to full wavelength models when wavelength subset models may be the Pareto models. In this paper, simplex optimization is used with a more complete variance measure to generate Pareto optimal models. The standard basis set is used as well a basis set that includes the range and null space of the calibration spectra. Results show that it is possible to identify Pareto optimal models and if a wavelength subset is best, these are the models found. Regression coefficients for non-essential wavelengths are zero to near zero.     

Determination of total sulfur in diesel fuel employing NIR spectroscopy and multivariate calibration

A method for sulfur determination in diesel fuel employing near infrared spectroscopy, variable selection and multivariate calibration is described. The performances of principal component regression (PCR) and partial least square (PLS) chemometric methods were compared with those shown by multiple linear regression (MLR), performed after variable selection based on the genetic algorithm (GA) or the successive projection algorithm (SPA). Ninety seven diesel samples were divided into three sets (41 for calibration, 30 for internal validation and 26 for external validation), each of them covering the full range of sulfur concentrations (from 0.07 to 0.33% w/w). Transflectance measurements were performed from 850 to 1800 nm. Although principal component analysis identified the presence of three groups, PLS, PCR and MLR provided models whose predicting capabilities were independent of the diesel type. Calibration with PLS and PCR employing all the 454 wavelengths provided root mean square errors of prediction (RMSEP) of 0.036% and 0.043% for the validation set, respectively. The use of GA and SPA for variable selection provided calibration models based on 19 and 9 wavelengths, with a RMSEP of 0.031% (PLS-GA), 0.022% (MLR-SPA) and 0.034% (MLR-GA). As the ASTM 4294 method allows a reproducibility of 0.05%, it can be concluded that a method based on NIR spectroscopy and multivariate calibration can be employed for the determination of sulfur in diesel fuels. Furthermore, the selection of variables can provide more robust calibration models and SPA provided more parsimonious models than GA.     

Quality assessment of gasoline using comprehensive two‐dimensional gas chromatography combined with unfolded partial least squares: A reliable approach for the detection of gasoline adulteration

Comprehensive two‐dimensional gas chromatography and flame ionization detection combined with unfolded‐partial least squares is proposed as a simple, fast and reliable method to assess the quality of gasoline and to detect its potential adulterants. The data for the calibration set are first baseline corrected using a two‐dimensional asymmetric least squares algorithm. The number of significant partial least squares components to build the model is determined using the minimum value of root‐mean square error of leave‐one out cross validation, which was 4. In this regard, blends of gasoline with kerosene, white spirit and paint thinner as frequently used adulterants are used to make calibration samples. Appropriate statistical parameters of regression coefficient of 0.996–0.998, root‐mean square error of prediction of 0.005–0.010 and relative error of prediction of 1.54–3.82% for the calibration set show the reliability of the developed method. In addition, the developed method is externally validated with three samples in validation set (with a relative error of prediction below 10.0%). Finally, to test the applicability of the proposed strategy for the analysis of real samples, five real gasoline samples collected from gas stations are used for this purpose and the gasoline proportions were in range of 70–85%. Also, the relative standard deviations were below 8.5% for different samples in the prediction set.     

近红外光谱法快速鉴定油菜杂交种的纯度

建立近红外光谱技术测定油菜杂交种纯度的方法。考察了样品杯类型、光谱预处理方法和波长范围对近红外模型预测性能的影响。结果发现,由不同样品杯采集近红外光谱所建立的校正模型,其预测性能存在较大的差异,旋转杯明显优于安瓿瓶;采用消除常数偏移量对光谱进行预处理能有效地提取光谱信息,选择5 000~8 000 cm–1波数范围作为建模谱区,其包含的有效信息率最高。在最佳条件下建立油菜杂交种纯度的校正模型,其决定系数(R2)为0.980 0,交互验证均方根误差(RMSECV)为0.008 59。利用该模型对预测集进行测定,预期均方根误差(RMSEP)为0.007 59,表明该模型具有很好的预测性能,近红外光谱法用于杂交种纯度的鉴定是可行的。     

Chemometric Techniques Applied for Classification and Quantification of Binary Biodiesel/Diesel Blends

In this paper, three different types of biodiesel, which were synthesized from peanut, corn, and canola oils, were characterized by positive-ion electrospray ionization (ESI) and Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS). Different biodiesel/diesel blends containing 2–90% (V/V) of each biodiesel type were prepared and analyzed by near infrared spectroscopy (NIR). In the next step, the chemometric methods of hierarchical clusters analysis (HCA), principal component analysis (PCA), and support vector machines (SVM) were used for exploratory analysis of the different biodiesel samples, and the SVM was able to give the best classification results (correct classification of 50 peanut and 50 corn samples, and only one misclassification out of 49 canola samples). Then, partial least squares (PLS) and multivariate adaptive regression splines (MARS) models were evaluated for biodiesel quantification. Both methods were considered equivalent for quantification purposes based on the values smaller than 5% for the root mean square error of calibration (RMSEC) and root mean square of validation (RMSEP), as well as Pearson correlation coefficients of at least 0.969. The combination of NIR to the chemometric techniques of SVM and PLS/MARS was proven to be appropriate to classify and quantify biodiesel from different origins.     

Simultaneous Determination of Eight Bioactive Components of Radix Dipsaci by Near-infrared Spectroscopy

A new method was developed using Fourier transform near-infrared spectroscopy and high-performance liquid chromatography with diode array detection for the identification and determination of eight major compounds in crude and sweated Radix Dipsaci. Partial least square regression was selected for the analysis. Multiplicative scatter correction, first derivative, and a Savitzky–Golay filter were used for the spectral pretreatment of the crude material, while standard normal variation, first derivative, and the Savitzky–Golay filter were used for the sweated samples. The correlation coefficients of the calibration models were above 0.99 and the root mean square error of calibration, the root mean square error of prediction, and root mean square error of cross-validation were under 0.63. The developed models were used to analyze unknown crude and sweated Radix Dipsaci with satisfactory results. The established methods were rapid, simple, nondestructive, and useful for quality control of Radix Dipsaci.     

Simultaneous determination of albumin and immunoglobulin G with fluorescence spectroscopy and multivariate calibration

A method is proposed for the simultaneous determination of albumin and immunoglobulin G (IgG1) with fluorescence spectroscopy and multivariate calibration with partial least squares regression (PLS). The influence of some instrumental parameters were investigated with two experimental designs comprising 19 and 11 experiments, respectively. The investigated parameters were excitation and emission slit, detection voltage and scan rate. When a suitable instrumental setting had been found, a minor calibration and test set were analysed and evaluated. Thereafter, a larger calibration of albumin and IgG1 was made out of 26 samples (0-42 μg ml⁻¹ albumin and 0-12.7 μg ml⁻¹ IgG1). This calibration was validated with a test set consisting of 14 samples in the same concentration range. The precision of the method was estimated by analysing two test set samples for six times each. The scan modes tested were emission scan and synchronous scan Δ60 nm. The results showed that the method could be used for determination of albumin and IgG1 (albumin, root mean square error of prediction (RMSEP) <2, relative standard error of prediction (RSEP) <6% and IgG1, RMSEP <1, RSEP <8%) in spite of the overlapping fluorescence of the two compounds. The estimated precision was relative standard deviation (R.S.D.) <1.7%. The method was finally applied for the analysis of some sample fractions from an albumin standard used in affinity chromatography.     

Sucrose as chiral selector for determining enantiomeric composition of phenylalanine by UV-vis spectroscopy and chemometrics

The determination of enantiomeric composition by partial least squares(PLS) modeling of UV-vis spectral data was investigated for samples of phenylalanine(phe) using sucrose as a chiral auxiliary.And a new data preprocess method,reference band normalization,was introduced to eliminate the spectral variations due to the changes of total concentration of phe.The determination coefficient(R~2) and the standard error of calibration set(SEC) of 13 standard samples are 0.9987 and 0.0128 respectively.The standard error of validation set(SECV) of 7 validation samples is 0.0049.The standard error of predict(SEP) of 6 blind samples for evaluating the robustness of the model is 0.0366.The regression model is robust to determine enantiomeric composition when total concentration varied.It is demonstrated that the reference band normalization is a convenient method of compensating for variations in total concentrations without knowing that in advance.