Orthogonal signal correction applied to the classification of wine and molasses vinegar samples by near-infrared spectroscopy. Feasibility study for the detection and quantification of adulterated vinegar samples

The most common fraudulent practice in the vinegar industry is the addition of alcohol of different origins to the base wine used to produce wine vinegar with the objective of reducing manufacturing costs. The mixture is then sold commercially as genuine wine vinegar, thus constituting a fraud to consumers and an unfair practice with respect to the rest of the vinegar sector. A method based on near-infrared spectroscopy has been developed to discriminate between white wine vinegar and alcohol or molasses vinegar. Orthogonal signal correction (OSC) was applied to a set of 96 vinegar NIR spectra from both original and artificial blends made in the laboratory, to remove information unrelated to a specific response. The specific response used to correct the spectra was the extent of adulteration of the vinegar samples. Both raw and corrected NIR spectra were used to develop separate classification models using the potential functions method as a class-modeling technique. The previous models were compared to evaluate the suitability of near-infrared spectroscopy as a rapid method for discrimination between vinegar origin. The transformation of vinegar NIR spectra by means of an orthogonal signal-correction method resulted in notable improvement of the specificity of the constructed classification models. The same orthogonal correction approach was also used to perform a calibration model able to detect and quantify the amount of exogenous alcohol added to the commercial product. This regression model can be used to quantify the extent of adulteration of new vinegar samples.     

中药口服固体制剂原辅料近红外光谱数据库的构建及应用

建立了中药口服固体制剂原辅料近红外(NIR)光谱数据库,采用模式识别方法研究了NIR光谱数据在物料分类和物性预测中的应用。使用便携式近红外光谱仪快速测量149批原辅料粉末的NIR漫反射光谱数据,并录入iTCM数据库。利用主成分分析(PCA)法探究NIR光谱数据对已知结构物料的分类能力,采用偏最小二乘(PLS)法研究了NIR光谱对原辅料物性参数和直接压片片剂性能的预测能力。经标准正态变量变换(SNV)+Savitzky-Golay(SG)平滑+一阶导数处理后的NIR光谱数据对微晶纤维素、乳糖、乙基纤维素、交联聚维酮和羟丙基甲基纤维素这5类辅料的区分能力较好。NIR光谱数据与原辅料粉末粒径、密度和吸湿性的相关性较强。NIR光谱信息作为物料物理性质的补充,可提高粉末直接压片片剂性能预测模型的性能。NIR光谱数据是iTCM数据库物性参数数据的补充,物性参数与NIR光谱数据的结合能更全面地表征原辅料的性质。     

Near-infrared spectroscopy and chemometric modelling for rapid diagnosis of kidney disease

Rapid diagnosis is important for efficient treatment in clinical medicine. This study aimed at development of a method for rapid and reliable diagnosis using near-infrared (NIR) spectra of human serum samples with the help of chemometric modelling. The NIR spectra of sera from 48 healthy individuals and 16 patients with suspected kidney disease were analyzed. Discrete wavelet transform (DWT) and variable selection were adopted to extract the useful information from the spectra. Principal component analysis (PCA), linear discriminant analysis (LDA) and partial least squares discriminant analysis (PLSDA) were used for discrimination of the samples. Classification of the two-class sera was obtained using LDA and PLSDA with the help of DWT and variable selection. DWT-LDA produced 93.8% and 83.3% of the recognition rates for the validation samples of the two classes, and 100% recognition rates were obtained using DWT-PLSDA. The results demonstrated that the tiny differences between the spectra of the sera were effectively explored using DWT and variable selection, and the differences can be used for discrimination of the sera from healthy and possible patients. NIR spectroscopy and chemometrics may be a potential technique for fast diagnosis of kidney disease.     

Chemometric and microscopic analyses for the size growth of monolayer-protected gold nanoparticles during their superlattice formation

Superlattices of gold nanoparticles have been produced at an air/solution interface under a highly acidic condition. The nanoparticle surface is protected by N-acetylglutathione (NAG). During the course of the superlattice formation, size growth of nanoparticles was observed: The superlattices were composed of nanoparticles of 6.6 nm in core diameter, whereas the as-prepared nanoparticles had the core diameter of 1.4 nm. The growth kinetics was pursued by the time evolution of the UV-vis absorption spectra for the sample solution. The change in the absorption spectral profiles was so small that we conducted principal-component analysis (PCA), which is known as a chemometric technique to resolve (or extract) spectra of minute chemical species submerged in the original spectra. Scanning transmission electron microscopy (STEM) corroborated the PCA results, yielding a successful explanation of the growth scheme of the NAG-protected gold nanoparticles.     

独立分量分析预处理法提高苹果糖度模型预测精度研究

为了提高苹果近红外光谱糖度预测模型精度,利用独立分量分析方法(ICA)对苹果近红外光谱进行了预处理,并且建立了糖度的偏最小二乘(PLS)预测模型。结果表明,独立分量分析不但能分离出噪声信号,而且所分离出来的光谱信号也比原始光谱信号光滑。在预处理后的最佳PLS糖度模型校正时的相关系数rc和标准偏差SEC分别为0.9549和0.3361,用于预测时的相关系数rp和标准偏差SEP分别为0.9071和0.4355。与普通的平均处理法的PLS模型相比,其精度有所提高,且模型更加简洁。     

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。     

Application of two-dimensional near-infrared correlation spectroscopy to the discrimination of Chinese herbal medicine of different geographic regions

Fructus Lycii is a traditional Chinese medicinal herb. The objective of this paper was to apply two-dimensional (2D) near-infrared (NIR) correlation spectroscopy to the discrimination of Fructus Lycii of four different geographic regions. Generalized 2D-NIR correlation spectroscopy was able to enhance spectral resolution, simplify the spectrum with overlapped bands, and provide information about temperature-induced spectral intensity variations that was hard to obtain from one-dimensional NIR spectroscopy. The 2D synchronous and asynchronous spectra showed remarkable differences within the range of 4950-5700cm(-1) between samples of different geographic regions. Using NIR instead of IR made the 2D approach more convenient and fast, and it can be applied to more area like process control. This approach can also be applied analogously to the discrimination of other Chinese herbal medicine of different geographic regions.     

基于近红外光谱分析技术的栽培香菇产地快速鉴别

建立了一种基于近红外光谱分析技术的香菇产地鉴别方法。利用近红外光谱仪扫描不同主产地的香菇干样,获得样品的近红外漫反射光谱。利用偏最小二乘判别分析(PLSDA)分别建立了吉林、湖北、福建3个省份栽培香菇的产地判别模型,同时使用光谱预处理和波长筛选技术对判别模型进行优化,最后使用预测样品对模型进行验证。结果表明,使用原始光谱建立的模型能够初步实现对产地的判别,使用光谱预处理技术扣除光谱中的背景信息,同时利用波长筛选技术选择特定波长对模型进行优化后,可进一步提高预测正确率。该方法为香菇产地真实性溯源提供了一种新方法,对香菇产业发展具有重要的实际意义。     

Systematization method for distinguishing plastic groups by using NIR spectroscopy.

A systematic classification method for polymers is not yet available in case of using near infrared spectra (NIR). That is why we have been searching for a systematic method. Because raw NIR spectra usually have few obvious peaks, NIR spectra have been pretreated by 2nd derivation for taking well modulated spectra. After the pretreatment, we applied classification and regression trees (CART) to the discrimination between the spectra and the species of polymers. As a result, we obtained a relatively simple classification tree. Judging from the obtained splitting conditions and the classified polymers, we concluded that obtained knowledge on the chemical function groups estimated by the important wavelength regions is not always applicable to this classification tree. However, we clarified the splitting rules for polymer species from the NIR spectral point of view.     

Applications of maximum likelihood principal component analysis: incomplete data sets and calibration transfer

The application of a new method to the multivariate analysis of incomplete data sets is described. The new method, called maximum likelihood principal component analysis (MLPCA), is analogous to conventional principal component analysis (PCA), but incorporates measurement error variance information in the decomposition of multivariate data. Missing measurements can be handled in a reliable and simple manner by assigning large measurement uncertainties to them. The problem of missing data is pervasive in chemistry, and MLPCA is applied to three sets of experimental data to illustrate its utility. For exploratory data analysis, a data set from the analysis of archeological artifacts is used to show that the principal components extracted by MLPCA retain much of the original information even when a significant number of measurements are missing. Maximum likelihood projections of censored data can often preserve original clusters among the samples and can, through the propagation of error, indicate which samples are likely to be projected erroneously. To demonstrate its utility in modeling applications, MLPCA is also applied in the development of a model for chromatographic retention based on a data set which is only 80% complete. MLPCA can predict missing values and assign error estimates to these points. Finally, the problem of calibration transfer between instruments can be regarded as a missing data problem in which entire spectra are missing on the ‘slave’ instrument. Using NIR spectra obtained from two instruments, it is shown that spectra on the slave instrument can be predicted from a small subset of calibration transfer samples even if a different wavelength range is employed. Concentration prediction errors obtained by this approach were comparable to cross-validation errors obtained for the slave instrument when all spectra were available.     

啤酒主要成分的近红外光谱法测定

根据近红外光谱的振动吸收强度与有机分子官能团含量的线性关系,用偏最小二乘法,对啤酒的近红外光谱与其中的酒精度、原麦汁浓度以及总酸含量等3种主要成分进行了线性回归,并建立起相关的模型。用该模型对未知啤酒样品中的上述3种成分的含量进行预测,取得了令人非常满意的结果。可望作为啤酒厂的一种快捷而准确的检测方法予以推广。     

Chemometrics: An Excavator in Temperature-Dependent Near-Infrared Spectroscopy

Temperature-dependent near-infrared (NIR) spectroscopy has been developed and taken as a powerful technique for analyzing the structure of water and the interactions in aqueous systems. Due to the overlapping of the peaks in NIR spectra, it is difficult to obtain the spectral features showing the structures and interactions. Chemometrics, therefore, is adopted to improve the spectral resolution and extract spectral information from the temperature-dependent NIR spectra for structural and quantitative analysis. In this review, works on chemometric studies for analyzing temperature-dependent NIR spectra were summarized. The temperature-induced spectral features of water structures can be extracted from the spectra with the help of chemometrics. Using the spectral variation of water with the temperature, the structural changes of small molecules, proteins, thermo-responsive polymers, and their interactions with water in aqueous solutions can be demonstrated. Furthermore, quantitative models between the spectra and the temperature or concentration can be established using the spectral variations of water and applied to determine the compositions in aqueous mixtures.     

An adaptive strategy for selecting representative calibration samples in the continuous wavelet domain for near-infrared spectral analysis

Sample selection is often used to improve the cost-effectiveness of near-infrared (NIR) spectral analysis. When raw NIR spectra are used, however, it is not easy to select appropriate samples, because of background interference and noise. In this paper, a novel adaptive strategy based on selection of representative NIR spectra in the continuous wavelet transform (CWT) domain is described. After pretreatment with the CWT, an extension of the Kennard–Stone (EKS) algorithm was used to adaptively select the most representative NIR spectra, which were then submitted to expensive chemical measurement and multivariate calibration. With the samples selected, a PLS model was finally built for prediction. It is of great interest to find that selection of representative samples in the CWT domain, rather than raw spectra, not only effectively eliminates background interference and noise but also further reduces the number of samples required for a good calibration, resulting in a high-quality regression model that is similar to the model obtained by use of all the samples. The results indicate that the proposed method can effectively enhance the cost-effectiveness of NIR spectral analysis. The strategy proposed here can also be applied to different analytical data for multivariate calibration.     

Vibrational overtone combination spectroscopy (VOCSY)—a new way of using IR and NIR data

This work explores a novel method for rearranging 1st order (one-way) infra-red (IR) and/or near infra-red (NIR) ordinary spectra into a representation suitable for multi-way modelling and analysis. The method is based on the fact that the fundamental IR absorption and the first, second, and consecutive overtones of NIR absorptions represent identical chemical information. It is therefore possible to rearrange these overtone regions of the vectors comprising an IR and NIR spectrum into a matrix where the fundamental, 1st, 2nd, and consecutive overtones of the spectrum are arranged as either rows or columns in a matrix, resulting in a true three-way tensor of data for several samples. This tensorization facilitates explorative analysis and modelling with multi-way methods, for example parallel factor analysis (PARAFAC), N-way partial least squares (N-PLS), and Tucker models. The vibrational overtone combination spectroscopy (VOCSY) arrangement is shown to benefit from the “order advantage”, producing more robust, stable, and interpretable models than, for example, the traditional PLS modelling method. The proposed method also opens the field of NIR for true peak decomposition—a feature unique to the method because the latent factors acquired using PARAFAC can represent pure spectral components whereas latent factors in principal component analysis (PCA) and PLS usually do not.     

Near infrared reflectance spectroscopy as a process signature in uranium oxides

Near-infrared (NIR) reflectance spectroscopy was examined as a potential tool for the determination of forensic signatures indicative of the chemical process history of uranium oxides. The ability to determine the process history of nuclear materials is a desired, but underdeveloped, area of technical nuclear forensics. Application of the NIR technique potentially offers a quick and non-destructive tool to serve this need; however, few data have been published on the compounds of interest. The viability of NIR was investigated through the analysis of a combination of laboratory-derived and real-world uranium precipitates and oxides. A set of reference uranium materials was synthesized in the laboratory using the commonly encountered aqueous precipitation reactions for uranium ore concentration and chemical separation processes (ammonia, hydrogen peroxide, sodium hydroxide, ammonium carbonate, and magnesia). NIR spectra were taken on a range of samples heat treated in air between 85 and 750 °C. X-ray diffraction patterns were also obtained to complement the NIR analysis with crystal phase information. Similar analyses were performed using a set of real-world samples, with process information obtained from the literature, to provide a comparison between materials synthesized in the laboratory and samples representative of industrial processes.     

Adulteration of the anthocyanin content of red wines: perspectives for authentication by Fourier transform-near infrared and 1H NMR spectroscopies

In the Italian oenological industry, the regular practice used to naturally increase the colour of red wines consists in blending them with a wine very rich in anthocyanins, namely Rossissimo. In the Asian market, on the other hand, anthocyanins extracted by black rice are frequently used as correctors for wine colour. This practice does not produce negative effects on health; however, in many countries, it is considered as a food adulteration. The present study is therefore aimed to discriminate wines containing anthocyanins originated from black rice and grapevine by using reliable spectroscopic techniques requiring minimum sample preparation. Two series of samples have been prepared from five original wines, that were added with different amounts of Rossissimo or of black rice anthocyanins solution, until the desired Colour Index was reached. The samples have been analysed by FT-NIR and (1)H NMR spectroscopies and the resulting spectra matrices were subjected to multivariate classification. Initially, PLS-DA was used as classification method, then also variable selection/classification methods were applied, i.e. iPLS-DA and WILMA-D. The classification with variable selection of NIR spectra permitted to classify the test set samples with an efficiency of about 70%. Probably these not excellent performances are due to the matrix effect, together with the lack of sensitivity of NIR with respect to minor compounds. On the contrary, very satisfactory results were obtained on NMR spectra in the aromatic region between 6.5 and 9.5 ppm. The classification method based on wavelet-based variables selection, permitted to reach an efficiency in validation greater than 95%. Finally, 2D correlation analysis was applied to FT-NIR and (1)H NMR matrices, in order to recognise the spectral zones bringing the same chemical information.     

Classification of cultivation area of ginseng by near infrared spectroscopy and ICP-AES

A rapid and nondestructive near infrared (NIR) method using soft independent modeling of class analogy (SIMCA) for the classification of cultivation area (Korea and China) was evaluated and confirmed. Raw, first, and second derivative NIR spectra were compared to develop a robust classification rule. The chemical properties of ginseng samples were also investigated to find out the differences between Korean samples and Chinese samples. These differences make NIR spectroscopic method viable. The average value of each Korean and Chinese ginseng sample for crude fiber, crude protein, starch, and 10 inorganic constituents were measured and compared with F-test and t-test. The inorganic constituents were also measured by induced coupled plasma-atomic emission spectroscopy (ICP-AES). It could be found that the amount of starch and ten inorganic elements for example Na, Mg, P, K, Ca, Mn, Fe, Ni, Cu and Zn in ginseng samples are considerably different based on cultivation area. SIMCA has been applied to the inorganic data to investigate the possibility of ICP-AES as classification tool. However, it was observed that the result was not equal to than NIR spectra data. The overall results showed the availability of NIR method using SIMCA would be adequate for classification of cultivation of ginseng, since NIR spectra includes useful and various information on chemical properties in spite of broad and overlapped bands.     

Rapid recognition of Chinese herbal pieces of Areca catechu by different concocted processes using Fourier transform mid-infrared and near-infrared spectroscopy combined with partial least-squares discriminant analysis

Rapid and sensitive recognition of herbal pieces according to different concocted processing is crucial to quality control and pharmaceutical effect.Near-infrared(NIR) and mid-infrared(MIR) technology combined with supervised pattern recognition based on partial least-squares discriminant analysis (PLSDA) was attempted to classify and recognize six different concocted processing pieces of 600 Areca catechu L.samples and the influence of fingerprint information preprocessing methods on recognition performance was also investigated in this work.Recognition rates of 99.24%,100%and 99.49%for original fingerprint,multiple scatter correct(MSC) fingerprint and second derivative(2nd derivative) fingerprint of NIR spectra were achieved by PLSDA models,respectively.Meanwhile,a perfect recognition rate of 100%was obtained for the above three fingerprint models of MIR spectra.In conclusion.PLSDA can rapidly and effectively extract otherness of fingerprint information from NIR and MIR spectra to identify different concocted herbal pieces of A.catechu.     

一种基于小波变换的近红外化学指纹图谱分析方法

提出了一种快速无损的化学指纹图谱分析新方法. 根据小波变换多分辨分析的特点, 对近红外(NIR)光谱进行小波分解, 从中提取被测样品的化学特征信息, 并作数字信息可视化处理, 构建形成可直观识别样品模式特征的NIR指纹图谱. 将该方法用于中药材丹参的质量检测, 检测结果与色谱指纹图谱检测结果相符, 能快速有效地识别丹参质量模式间的差异, 有望发展成为一种快速分析检测天然产物质量的方法.