Rapid Determination of Betulin in Betula platyphylla Outer Bark Using Near-Infrared Spectroscopy

A simple, rapid, and nondestructive method for the determination of betulin in the outer birch bark was developed using near infrared spectroscopy (NIRS). NIRS data of the outer birch bark collected throughout the year was preprocessed and analyzed by principal component analysis, which led to clear discrimination of the samples according to their harvest times. The reference content of betulin, a major constituent of the outer birch bark, was determined using ultra performance liquid chromatography with a diode array detector (UPLC-DAD). The optimized and validated analytical conditions of UPLC-DAD provided better separation and faster analysis time compared to a conventional HPLC method. Betulin content also showed seasonal variation and was higher in the samples collected during the summer season. Partial least squares calibration techniques were employed to estimate the relationship between the NIRS data and betulin contents. The spectral data showed high correlation coefficients (over 0.700) with betulin content. These results indicate that NIRS combined with UPLC can be used to determine the quality and to quantify the betulin content of the outer birch bark.     

Noncontact analysis of the fiber weight per unit area in prepreg by near-infrared spectroscopy

The fiber weight per unit area in prepreg is an important factor to ensure the quality of the composite products. Near-infrared spectroscopy (NIRS) technology together with a noncontact reflectance sources has been applied for quality analysis of the fiber weight per unit area. The range of the unit area fiber weight was 13.39–14.14 mg cm⁻². The regression method was employed by partial least squares (PLS) and principal components regression (PCR). The calibration model was developed by 55 samples to determine the fiber weight per unit area in prepreg. The determination coefficient (R²), root mean square error of calibration (RMSEC) and root mean square error of prediction (RMSEP) were 0.82, 0.092, 0.099, respectively. The predicted values of the fiber weight per unit area in prepreg measured by NIRS technology were comparable to the values obtained by the reference method. For this technology, the noncontact reflectance sources focused directly on the sample with neither previous treatment nor manipulation. The results of the paired t-test revealed that there was no significant difference between the NIR method and the reference method. Besides, the prepreg could be analyzed one time within 20 s without sample destruction.     

GA-ACE: Alternating conditional expectations regression with selection of significant predictors by genetic algorithms

The non-linear regression technique known as alternating conditional expectations (ACE) method is only applicable when the number of objects available for calibration is considerably greater than the number of considered predictors. Alternating conditional expectations regression with selection of significant predictors by genetic algorithms (GA-ACE), the non-linear regression technique presented here, is based on the ACE algorithm but introducing several modifications to resolve the applicability limitations of the original ACE method, thus facilitating the practical implementation of a very interesting calibration tool. In order to overcome the lack of reliability displayed by the original ACE algorithm when working on data sets characterized by a too large number of variables and prior to the development of the non-linear regression model, GA-ACE applies genetic algorithms as a variable selection technique to select a reduced subset of significant predictors able to accurately model and predict a considered variable response. Furthermore, GA-ACE actually provides two alternative application approaches, since it allows either the performance of prior data compression computing a number of principal components to be subsequently subjected to GA-selection, or working directly on original variables.In this study, GA-ACE was applied to two real calibration problems, with a very low observation/variable ratio (NIR data), and the results were compared with those obtained by several linear regression techniques usually employed. When using the GA-ACE non-linear method, notably improved regression models were developed for the two response variables modeled, with root mean square errors of the residuals in external prediction (RMSEP) equal to 11.51 and 6.03% for moisture and lipid contents of roasted coffee samples, respectively. The improvement achieved by applying the new non-linear method introduced is even more remarkable taking into account the results obtained with the best performance linear method (IPW-PLS) applied to predict the studied responses (14.61 and 7.74% RMSEP, respectively).     

Development of a univariate calibration model for pharmaceutical analysis based on NIR spectra

Near-infrared spectroscopy (NIRS) has been widely used in the pharmaceutical field because of its ability to provide quality information about drugs in near-real time. In practice, however, the NIRS technique requires construction of multivariate models in order to correct collinearity and the typically poor selectivity of NIR spectra. In this work, a new methodology for constructing simple NIR calibration models has been developed, based on the spectrum for the target analyte (usually the active principle ingredient, API), which is compared with that of the sample in order to calculate a correlation coefficient. To this end, calibration samples are prepared spanning an adequate concentration range for the API and their spectra are recorded. The model thus obtained by relating the correlation coefficient to the sample concentration is subjected to least-squares regression. The API concentration in validation samples is predicted by interpolating their correlation coefficients in the straight calibration line previously obtained. The proposed method affords quantitation of API in pharmaceuticals undergoing physical changes during their production process (e.g. granulates, and coated and non-coated tablets). The results obtained with the proposed methodology, based on correlation coefficients, were compared with the predictions of PLS1 calibration models, with which a different model is required for each type of sample. Error values lower than 1-2% were obtained in the analysis of three types of sample using the same model; these errors are similar to those obtained by applying three PLS models for granules, and non-coated and coated samples. Based on the outcome, our methodology is a straightforward choice for constructing calibration models affording expeditious prediction of new samples with varying physical properties. This makes it an effective alternative to multivariate calibration, which requires use of a different model for each type of sample, depending on its physical presentation.     

Interference-free determination of Sudan dyes in chilli foods using second-order calibration algorithms coupled with HPLC-DAD

This paper presents a new method for the determination of Sudan dyes contained in hot chilli samples. The method employs second-order calibration algorithms to handle the recorded data. The second-order calibration algorithms are based on the popular parallel factor analysis (PARAFAC), alternating trilinear decomposition (ATLD) and self-weighted alternating trilinear decomposition (SWATLD), respectively. These chemometric methodologies have the second-order advantage, which is the ability to get accurate concentration estimates of interested analytes even in the presence of uncalibrated interfering components. The results on a set of spiked chilli test shows that low contents of Sudan I and Sudan II in complex chilli mixtures can be accurately determined using the new method. The sample preparation was based on solvent extraction, and internal standard was not required. Quantification was carried out with simple mobile phase.     

中药材三七提取液近红外光谱的支持向量机回归校正方法

提出近红外光谱的支持向量机回归校正建模方法.以中药材三七渗漉提取液为实际分析对象,对其近红外光谱数据进行预处理和主成分分析后,用支持向量机回归算法建立人参皂苷R_g1,R_b1和R_d以及三七总皂苷的近红外光谱校正模型.以R_g1,R_b1和R_d的HPLC测定值及三七总皂苷的比色法测定值为参照,将本文方法与偏最小二乘回归和径向基神经网络建模方法相比较,结果表明,本文所建模型的预测准确性优于后两者,可推广应用于中药提取过程的近红外光谱分析.     

Analysis of amoxicillin in human urine by photo-activated generation of fluorescence excitation-emission matrices and artificial neural networks combined with residual bilinearization

Fluorescence excitation-emission data recorded for amoxicillin after photo-activated reaction with periodate have been processed by a novel second-order multivariate method based on the combination of artificial neural networks and residual bilinearization (ANN/RBL), since the signals bear a strong non-linear relation with the analyte concentration. The selected chemometric methodology is employed for the first time to evaluate experimental non-linear second-order spectral information. Due to severe overlapping between the emission profiles for the analyte reaction product and for the urine background, calibration was done using different spiked urine samples. This allowed for the determination of amoxicillin in test spiked urines, other than those employed for calibration. When new urine samples containing a fluorescent anti-inflammatory were analyzed, accurate prediction in the presence of unexpected components required the achievement of the second-order advantage, which is provided by the post-training RBL procedure. Amoxicillin was also determined by ANN/RBL in a series of real urine samples, which allowed one to perform a comparison study with the reference high-performance liquid chromatographic technique.     

Comparison of supervised pattern recognition methods with McNemar’s statistical test: Application to qualitative analysis of sugar beet by near-infrared spectroscopy

The application of supervised pattern recognition methodology is becoming important within chemistry. The aim of the study is to compare classification method accuracies by the use of a McNemar’s statistical test. Three qualitative parameters of sugar beet are studied: disease resistance (DR), geographical origins and crop periods. Samples are analyzed by near-infrared spectroscopy (NIRS) and by wet chemical analysis (WCA). Firstly, the performances of eight well-known classification methods on NIRS data are compared: Linear Discriminant Analysis (LDA), K-Nearest Neighbors (KNN) method, Soft Independent Modeling of Class Analogy (SIMCA), Discriminant Partial Least Squares (DPLS), Procrustes Discriminant Analysis (PDA), Classification And Regression Tree (CART), Probabilistic Neural Network (PNN) and Learning Vector Quantization (LVQ) neural network are computed. Among the three data sets, SIMCA, DPLS and PDA have the highest classification accuracies. LDA and KNN are not significantly different. The non-linear neural methods give the less accurate results. The three most accurate methods are linear, non-parametric and based on modeling methods. Secondly, we want to emphasize the power of near-infrared reflectance data for sample discrimination. McNemar’s tests compare classification developed with WCA or with NIRS data. For two of the three data sets, the classification results are significantly improved by the use of NIRS data.     

近红外光谱混合模型定量分析不同物理状态样品的研究

近红外光谱(NIRS)以漫反射模式对非均质样本进行测量时,由于其光谱散射和吸收系数差异较大,建立的校正模型准确性和稳健性较低,因此,本研究提出了一种基于均质样本和模型转移方法建立混合模型的策略,解决非均质样本近红外光谱检测的问题.以烟叶样本为研究对象,分别建立了基于Shenk专利算法(Shenk′s)、分段直接标准化(PDS)和基于典型相关分析的模型转移算法(CTCCA)的烟粉+烟丝、烟粉+烟片混合模型,用于烟丝和烟片样本中烟碱含量的预测.结果表明,混合模型对烟丝和烟片样本的预测均方误差(RMSEP)较直接建模分别降低了1.39%和2.73%,预测结果有一定的改善,稳健性提高,3种方法中CTCCA表现最优.因此,采用近红外光谱均质模型和模型转移方法建立的混合模型对非均质样本的测定具有可行性,有利于在线近红外光谱分析技术的发展,可为近红外光谱模型的共享提供参考.     

校正集样品特性对鼠药中毒鼠强近红外光谱分析的影响

根据市售鼠药样品成分各异且相对复杂,建立6种不同成分体系和9个不同样本容量的校正集,运用小波变换压缩鼠药的近红外透射光谱数据,结合BP反向神经网络算法对压缩的数据进行建模,考察校正集样品特性对模型预测能力的影响。试验结果表明:采用BP神经网络算法建立定量模型时,只要校正集样品中包含了与预测样品性质相似的样本,就能准确地对复杂样品进行近红外定量分析。当校正集容量分别为72和84时,模型预测结果趋于平稳。当校正集数量为96时,模型的最大相关系数为0.959 8,预测最小标准差和平均相对误差分别为1.893%和1.92%。     

Calibration of reference materials for total-reflection X-ray fluorescence analysis by heavy ion backscattering spectrometry

Total-reflection X-ray fluorescence (TXRF) is widely used for the control of metallic contamination caused by surface preparation processes and silicon materials. At least three companies supply a variety of TXRF systems to the silicon integrated circuit (IC) community, and local calibration of these systems is required for their day to day operation. Differences in local calibration methods have become an issue in the exchange of information between IC manufacturers' different FABs (Fabrication Facility) and also between silicon suppliers and IC FABs. The question arises whether a universal set of fluorescence yield curves can be used by these different systems to scale system sensitivity from a single element calibration for calculation of elemental concentrations. This is emphasized by the variety of experimental conditions that are reported for TXRF data (e.g. different angles of incidence for the same X-ray source, different X-ray sources, etc.). It appears that an instrumental factor is required. We believe that heavy ion backscattering spectrometry (HIBS) provides a fundamental method of calibrating TXRF reference materials, and can be used in calculating this instrumental factor. In this paper we briefly describe the HIBS system at the Sandia National Laboratories HIBS User Facility and its application to the calibration of TXRF reference materials. We will compare HIBS and TXRF mapping capabilities and discuss the issues associated with the restrictions of some older TXRF sample stages. We will also discuss Motorola's cross-calibration of several TXRF systems using different elements as references.     

The use of FT-NIR for API content assay in organic solvent media: a single calibration for multiple downstream processing streams

The use of near infrared spectroscopy (NIRS) in downstream solvent based processing steps of an active pharmaceutical ingredient (API) is reported. A single quantitative method was developed for API content assessment in the organic phase of a liquid–liquid extraction process and in multiple process streams of subsequent concentration and depuration steps. A new methodology based in spectra combinations and variable selection by genetic algorithm was used with an effective improvement in calibration model prediction ability. Root mean standard error of prediction (RMSEP) of 0.05 in the range of 0.20–3.00% (w/w) was achieved. With this method, it is possible to balance the calibration data set with spectra of desired concentrations, whenever acquisition of new spectra is no longer possible or improvements in model's accuracy for a specific selected range are necessary. The inclusion of artificial spectra prior to genetic algorithms use improved RMSEP by 10%. This method gave a relative RMSEP improvement of 46% compared with a standard PLS of full spectral length.     

Potential of near infrared spectroscopy for the analysis of mycotoxins applied to naturally contaminated red paprika found in the Spanish market

The potential of the near infrared spectroscopy (NIRS) technique for the analysis of red paprika for aflatoxin B(1), ochratoxin A and total aflatoxins is explored. As a reference, the results from a chromatographic method with fluorescence detection (HPLC-FD) following an immunoaffinity cleanup (IAC) were employed. For the NIRS measurement, a remote reflectance fibre-optic probe was applied directly onto the samples of paprika. There was no need for pre-treatment or manipulation of the sample. The modified partial least squares (MPLS) algorithm was employed as a regression method. The multiple correlation coefficients (RSQ) and the prediction corrected standard errors (SEP(C)) were respectively 0.955 and 0.2 microg kg(-1), 0.853 and 2.3 microg kg(-1), 0.938 and 0.3 microg kg(-1) for aflatoxin B(1), ochratoxin A and total aflatoxins, respectively. The capacity for prediction of the developed model measured as ratio performance deviation (RPD) for aflatoxin B(1) (5.2), ochratoxin A (2.8) and total aflatoxins (4.4) indicate that NIRS technique using a fibre-optic probe offers an alternative for the determination of these three parameters in paprika, with an advantageously lower cost and higher speed as compared with the chemical method. Content of aflatoxin B(1) and total aflatoxins are the parameters currently employed by the food regulations to limit the levels of the four aflatoxins in many foodstuffs. In addition, aflatoxin B(1) itself is an excellent indicator for aflatoxins' contamination since it is always the most abundant and toxic.     

Experimental Evaluation of Procedures for Heat Capacity Measurement by Differential Scanning Calorimetry

Experimental evaluation of the procedures adopted for heat capacity measurements employing differential scanning calorimetry (DSC) has been carried out by taking nickel and sapphire as test samples. Among the various methodologies reported in literature, the absolute dual step method was chosen for this purpose due to its simplicity and minimum number of measurements required. By proper temperature and heat flux calibration employing indium as reference, it was possible to obtain the calibration factor independent of temperature. This was ascertained by analysing other pure metals namely Sn, Zn, Cd, and Pb and determining their melting temperatures and heats of melting. Various operator- and sample-dependent parameters such as heating rate, sample mass, the structure of the sample, reproducibility and repeatability in the measurements were investigated. Heat capacities of both nickel and sapphire have been determined using the above method. Further, the heat capacity of nickel has also been determined using the widely employed three-step method taking sapphire as the heat flux calibration standard. Both methods yielded the comparable heat capacity values for nickel. Based on the parameters investigated and their influence, it could be concluded that reasonably precise and accurate heat capacity measurements are possible with DSC. One advantage of this method is the elimination of a separate calibration run using a reference material of known heat capacity. This revised version was published online in August 2006 with corrections to the Cover Date.     

Qualitative Analysis of Maize Haploid Kernels Based on Calibration Transfer by Near-Infrared Spectroscopy

Haploid breeding is one of the most important modern crop selection technologies. Near-infrared spectroscopy (NIRS) has been used to identify haploids rapidly and to non-destructively accelerate the selection process. However, the change of the external environment weakens the performance of the model, as the training and the test spectra may be collected separately from different environments. Thus, a novel calibration transfer method is proposed to calibrate the model in order to reduce the impact of the environment. The near-infrared spectra of 400 maize kernels of two varieties were collected from 9000 to 4000?cm^?1. Principal component analysis was performed to construct a feature space and extract features. In the constructed feature space, the calibration transfer method was used to calibrate test sets. Finally, support vector machine was employed to establish a haploid identification model. The results show that when the spectra of the test set and the training set were collected in the same environment, the corrected acceptance of the model was above 90%. While the spectra of the test set and the training set were collected from different environments, the corrected acceptance was 77.87%. However, when the model used the calibration transfer method, the corrected acceptance increased by 12.46%. Moreover, compared with direct standardization, this calibration transfer method achieved better results without detailed sample chemical information and many standards. The results demonstrate that the calibration transfer method based on NIRS was effective for identifying maize haploid kernels in variable environments.     

Different approaches to multivariate calibration of nonlinear sensor data

In this study, different approaches to the multivariate calibration of the vapors of two refrigerants are reported. As the relationships between the time-resolved sensor signals and the concentrations of the analytes are nonlinear, the widely used partial least-squares regression (PLS) fails. Therefore, different methods are used, which are known to be able to deal with nonlinearities present in data. First, the Box–Cox transformation, which transforms the dependent variables nonlinearly, was applied. The second approach, the implicit nonlinear PLS regression, tries to account for nonlinearities by introducing squared terms of the independent variables to the original independent variables. The third approach, quadratic PLS (QPLS), uses a nonlinear quadratic inner relationship for the model instead of a linear relationship such as PLS. Tree algorithms are also used, which split a nonlinear problem into smaller subproblems, which are modeled using linear methods or discrete values. Finally, neural networks are applied, which are able to model any relationship. Different special implementations, like genetic algorithms with neural networks and growing neural networks, are also used to prevent an overfitting. Among the fast and simpler algorithms, QPLS shows good results. Different implementations of neural networks show excellent results. Among the different implementations, the most sophisticated and computing-intensive algorithms (growing neural networks) show the best results. Thus, the optimal method for the data set presented is a compromise between quality of calibration and complexity of the algorithm.Electronic Supplementary Material Supplementary material is available for this article at     

三维(二阶)算法在液相色谱分析中的应用

使用"垂线法"、"切线法"或"三角形法"等传统方法对液相色谱重叠峰分辨时经常会遇到误差过大的情况,而使用三维(二阶)算法对重叠和拖尾峰分辨可以最大限度地降低这种因几何分割而人为产生的误差。这样改进的色谱解析方法具有自动化程度高、抗干扰能力强、对重叠/拖尾峰定量准确等优点,甚至可以减少样品前处理和色谱条件优化。该方法的核心是基于化学计量学三维(二阶)算法抽取有效信息和建模的思想,三维色谱数据按照对三线性模型的符合程度有"三线性数据"和"非三线性数据"的区别,相应地将三维(二阶)算法分为"三线性算法"和"非三线性算法"。本文综述了近10年来国内外三维(二阶)算法在复杂体系液相色谱分析中的应用进展,侧重于样品前处理、辅助算法、校正算法间的联用和对比等问题。     

采用多光程长建模方法检测血液成分含量

为了提高近红外光谱血液成分含量分析模型的预测精度,利用多个光程长(optical path length,OPL)共同参与建模的方法进行血糖等6种血液成分的定量分析。通过微米位移机构实现不同光程长血液光谱的测量,由全自动生化分析仪给出生化成分分析结果,并出具化验单。采用偏最小二乘法(PLS2)进行血液的近红外光谱建模及预测。由于血液光谱存在显著的非线性特征,不同光程长的血液样本的等效吸收系数不同,同一波长不同光程长(0.20~1.25 mm)测得的血液光谱互不相关。主动把非线性特性作为一种测量手段引入,不再利用单个的最佳光程长建模,而是用各个血液组分对应的多个最佳光程长的近红外光谱同时参与建立校正模型,进行血液成分的分析预测。研究结果表明,多光程长建模方法用于血液成分含量分析,可提高血液成分校正模型的预测精度。     

Methods of calibration in the direct analysis of solid samples by electrothermal atomic absorption spectrometry

One of the major problems involved in the direct analysis of solid samples by electrothermal atomic absorption spectrometry (ETAAS) lies in the calibration step because non-spectral interference effects are often pronounced. Three standardization techniques have been described and used in solid sampling-ETAAS: (i) standard additions method; (ii) calibration relative to a certified reference material; and (iii) calibration curve technique. However, an adequate statistical evaluation of the uncertainty in the analyte concentration in the solid sample is most frequently neglected, and reported errors may be seriously underestimated. This can be attributed directly to the complexity of the statistical expressions required to accurately account for errors in each of the calibration techniques mentioned above, and the general lack of relevant reference literature. The object of this work has been to develop a computer package which will perform the necessary statistical analyses of solid sampling-ETAAS data; the result is the program “SOLIDS” described here in the form of an electronic publication in Spectrochimica Acta Electronica, the electronic section of Spectrochimica Acta Part B. The program could also be useful in other analytical fields where similar calibration methods are used. The hard copy text, outlining the calibration models and their associated errors, is accompanied by a diskette containing the program, some data files, and a manual. Use of the program is exemplified in the text, with some of the data files discussed included on the diskette which, together with the manual, should enable the reader to become familiarized with the operation of the program, and the results generated.