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。     

Orthogonal projection to latent structures combined with artificial neural networks in non-destructive analysis of Ampicillin powder

A new method orthogonal projection to latent structures (O-PLS) combined with artificial neural networks is investigated for non-destructive determination of Ampicillin powder via near-infrared (NIR) spectroscopy. The modern NIR spectroscopy analysis technique is efficient, simple and non-destructive, which has been used in chemical analysis in diverse fields. Be a preprocessing method, O-PLS provides a way to remove systematic variation from an input data set X not correlated to the response set Y, and does not disturb the correlation between X and Y. In this paper, O-PLS pretreated spectral data was applied to establish the ANN model of Ampicillin powder, in this model, the concentration of Ampicillin as the active component was determined. The degree of approximation was employed as the selective criterion of the optimum network parameters. In order to compare the OPLS-ANN model, the calibration models that using first-derivative and second-derivative preprocessing spectra were also designed. Experimental results showed that the OPLS-ANN model was the best.     

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

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

Characterization of green copper phase pigments in Egyptian artifacts with X-ray absorption spectroscopy and principal components analysis

The characterization of materials in historical artifacts can contribute significantly to their preservation and understanding; however, sampling and characterization are ideally performed using non-destructive approaches. The analysis of green pigments from Egyptian artifacts presents a further challenge as responses to laboratory based techniques have proven unsuccessful in many cases. An alternative approach is the use of non-destructive X-ray absorption near-edge structure spectroscopy, which was performed on a reference set of copper-containing green minerals and other compounds. Data projection using principal component analysis was used to explore the spectral data structures and to illustrate the relationship between the spectra and copper speciation, resulting in a calibration or training set of the reference materials used. Data from the training set were compared with samples from Egyptian artifacts. The combination of X-ray absorption spectroscopy with principal components analysis provides a novel approach in archeometry and the characterization of objects of cultural heritage.     

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.     

近红外光谱结合CARS变量筛选方法用于液态奶中蛋白质与脂肪含量的测定

采用CARS(Competitive adaptive reweighted sampling)变量筛选方法建模,显著提高了液态奶中蛋白质与脂肪近红外模型的预测精度。用蒙特卡罗采样(Monte-Carlo sampling)方法先剔除奇异样本,再对光谱进行中心化与Karl Norris滤波降噪处理,通过CARS方法筛选出与样本性质密切相关的变量,建立预测蛋白质与脂肪含量的偏最小二乘法(PLS)校正模型,并与未选变量的PLS模型进行比较。以定标集相关系数(r2)及交互验证均方残差(RMSECV)和预测误差均方根(RMSEP)作为判定依据,确定了蛋白质与脂肪的最佳建模条件。蛋白质与脂肪校正模型的相关系数分别为0.975 0、0.995 1,RMSECV分别为0.194 8、0.136 3,RMSEP分别为0.113 3、0.140 1,预测结果优于未选变量的PLS模型及其他选变量方法,有效简化了模型,适于液态奶中脂肪和蛋白质的快速、无损检测。     

Characterization of fresh and aged natural ingredients used in historical ointments by molecular spectroscopic techniques: IR,Raman and fluorescence

Natural organic materials used to prepare pharmaceutical mixtures including ointments and balsams have been characterized by a combined non-destructive spectroscopic analytical approach. Three classes of materials which include vegetable oils (olive, almond and palm tree), gums (Arabic and Tragacanth) and beeswax are considered in this study according to their widespread use reported in ancient recipes. Micro-FTIR, micro-Raman and fluorescence spectroscopies have been applied to fresh and mildly thermally aged samples. Vibrational characterization of these organic compounds is reported together with tabulated frequencies, highlighting all spectral features and changes in spectra which occur following artificial aging. Synchronous fluorescence spectroscopy has been shown to be particularly useful for the assessment of changes in oils after aging; spectral difference between Tragacanth and Arabic gum could be due to variations in origin and processing of raw materials. Analysis of these materials using non-destructive spectroscopic techniques provided important analytical information which could be used to guide further study.     

Quantification of paracetamol in intact tablets using near-infrared transmittance spectroscopy.

Production batch samples of paracetamol tablets and specially prepared out-of-specification batches covering the range 90-110% of the stated amount (500 mg) were analysed by the BP official UV assay and by NIR transmittance spectroscopy. NIR measurements were made on 20 intact tablets from each batch, scanned five times each (10 min measurement time per batch) over the spectral range 6000-11,520 cm-1. An average spectrum was calculated for each batch. Partial least squares (PLS) regression models were set up using a calibration set (20 batches) between the NIR response and the reference tablet paracetamol content (UV). Various pre-treatments of the spectra were examined; the smallest relative standard error of prediction (0.73%) was obtained using the first derivative of the absorbance over the full spectrum. Only two principal components were required for the PLS model to give a good relationship between the spectral information and paracetamol content. Applying this model to the validation set (15 batches) gave a mean bias of -0.08% and a mean accuracy of 0.59% with relative standard deviations of 0.75 and 0.44%, respectively. The proposed method is non-destructive and therefore lends itself to on-line/at-line production control purposes. The method is easy to use and does not require a knowledge of the mass of the tablets.     

Detector comparison for sulfur and chlorine detection with laser induced breakdown spectroscopy in the near-infrared-region

Laser-induced breakdown spectroscopy has been employed for the investigation of the sulfur and chlorine content of building materials. Both, chloride and sulfate ions are major damaging species affecting the stability and lifetime of a structure. Chlorine and sulfur are mostly detected in the VUV and the NIR. In case of building materials the main elements like calcium or iron have many strong spectral lines over the whole spectral range, so that trace elements can only be detected in spectral windows unaffected from these lines. With regard to a preferably simply, robust against dust and vibrations and portable setup only the NIR spectral features are used for civil engineering applications.

Most detectors, mainly CCD cameras have rapidly decreasing quantum efficiency in the NIR. Also the quantum efficiency of the photocathode of CCD-Detectors with image intensifier is decreasing in the NIR. Different CCD-detectors were tested with respect to high quantum efficiency and high dynamic range, which is necessary for simultaneous detection of weak spectral lines from trace elements and intense spectral lines from main elements.

The measurements are made on reference samples consisting of cement, hydrated cement, cement mortar and concrete with well-defined amounts of the trace elements. Experimental conditions are chosen for an optimum intensity of the trace element spectral lines. The detector systems are compared by limit of detections and the signal to noise ratio.     

Illuminating the flesh of bone identification – An application of near infrared spectroscopy

Near infrared (NIR) reflectance spectroscopy coupled with chemometric analysis was evaluated as a non-destructive tool to discriminate skull bone samples from different animal species. In total 70 skull bones from animals of three classes (mammalians, avian and reptiles) were scanned in the wavelength range between 950 to 1650 nm. Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were used to analyse the NIR spectra of the skull samples. Correct classification rates of 96% and 81% were obtained for the classification of skull bone samples according to avian and mammalian classes, respectively. Overall, a 91% correct classification rate was obtained for the classification of skull samples according to the class (mammalian and avian). This study demonstrates the potential of NIR spectroscopy coupled with chemometric as data processing, as a means of a rapid, non-destructive classification technique for skull bone samples.     

大豆蛋白的中红外和近红外光谱研究*

大豆蛋白在各领域的应用已得到广泛的关注,因此大豆蛋白及其改性材料在结构性能方面的研究显得越来越重要。中红外光谱(mid-infrared spectroscopy,MIR)和近红外光谱(near-infrared spectroscopy,NIR)正是对蛋白质进行定性定量分析的有力手段。中红外光谱可以有效地分析大豆蛋白在溶液和薄膜中的二级结构以及大豆衍生材料内蛋白质的结构变化情况。近红外光谱则在蛋白质定量分析方面有着独特的优势。本文介绍了运用这两种光谱技术进行研究的一些工作,这些实例表明了中红外和近红外光谱在大豆蛋白研究领域的重要应用价值。     

Identification of transgenic foods using NIR spectroscopy: A review

The utilization of chemometric methods in the quantitative and qualitative analysis of feeds, foods, medicine and so on has been accompanied with the great evolution in the progress and in the near infrared spectroscopy (NIRS). Hence, recently the application of NIR spectroscopy has extended on the context of genetics and transgenic products. The aim of this review was to investigate the application of NIR spectroscopy to identificate transgenic products and to compare it with the traditional methods. The results of copious researches showed that the application of NIRS technology was successful to distinguish transgenic foods and it has advantages such as fast, avoiding time-consuming, non-destructive and low cost in relation to the antecedent methods such as PCR and ELISA.     

Using combinations of principal component scores from different spectral ranges in near-infrared region to improve discrimination for samples of complex composition

Principal component analysis (PCA) is widely used as an exploratory data analysis tool in the field of vibrational spectroscopy, particularly near-infrared (NIR) spectroscopy. PCA represents original spectral data containing large variables into a few feature-containing variables, or scores. Although multiple spectral ranges can be simultaneously used for PCA, only one series of scores generated by merging the selected spectral ranges is generally used for qualitative analysis. Alternatively, the combined use of an independent series of scores generated from separate spectral ranges has not been exploited.The aim of this study is to evaluate the use of PCA to discriminate between two geographical origins of sesame samples, when scores independently generated from separate spectral ranges are optimally combined. An accurate and rapid analytical method to determine the origin is essentially required for the correct value estimation and proper production distribution. Sesame is chosen in this study because it is difficult to visually discriminate the geographical origins and its composition is highly complex. For this purpose, we collected diffuse reflectance near-infrared (NIR) spectroscopic data from geographically diverse sesame samples over a period of eight years. The discrimination error obtained by applying linear discriminant analysis (LDA) was improved when separate scores from two spectral ranges were optimally combined, compared to the discrimination errors obtained when scores from singly merged two spectral ranges were used.     

Chemometric determination of arsenic and lead in untreated powdered red paprika by diffuse reflectance near-infrared spectroscopy

It has been evaluated the potential of near-infrared (NIR) diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS) as a way for non-destructive measurement of trace elements at μg kg⁻¹ level in foods, with neither physical nor chemical pre-treatment. Predictive models were developed using partial least-square (PLS) multivariate approaches based on first-order derivative spectra. A critical comparison of two spectral pre-treatments, multiplicative signal correction (MSC) and standard normal variate (SNV) was also made. The PLS models built after using SNV provided the best prediction results for the determination of arsenic and lead in powdered red paprika samples. Relative root-mean-square error of prediction (RRMSEP) of 23% for both metals, arsenic and lead, were found in this study using 20 well characterized samples for calibration and 13 additional samples as validation set. Results derived from this study showed that NIR diffuse reflectance spectroscopy combined with the appropriate chemometric tools could be considered as an useful screening tool for a rapid determination of As and Pb at concentration level of the order of hundred μg kg⁻¹.     

Qualitative and quantitative analysis of oxytetracycline by near-infrared spectroscopy

Near-infrared (NIR) spectroscopy, in combination with chemometrics, enable the analysis of raw materials without time-consuming sample preparation methods. The aim of our work was to estimate critical parameters in the analytical specification of oxytetracycline, and consequently the development of a method for quantification and qualification of these parameters by NIR spectroscopy. A Karl Fischer (K.F.) titration to determine the water content, a colorimetric assay method, and Fourier transform-infrared (FT-IR) spectroscopy to identify the oxytetracycline base, were used as reference methods, respectively. Multivariate calibration was performed on NIR spectral data using principal component analysis (PCA), partial least-squares (PLS 1) and principal component regression (PCR) chemometric methods. Multivariate calibration models for NIR spectroscopy have been developed. Using PCA and the Soft Independent Modelling of Class Analogy (SIMCA) approach, we established the cluster model for the determination of sample identity. PLS 1 and PCR regression methods were applied to develop the calibration models for the determination of water content and the assay of the oxytetracycline base. Comparing the PLS and PCR regression methods we found out that the PLS is better established by NIR, especially as the spectroscopic data (NIR spectra) are highly collinear and there are many wavelengths due to non-selective wavelengths. The calibration models for NIR spectroscopy are convenient alternatives to the colorimetric method and to the K.F. method, as well as to FT-IR spectroscopy, in the routine control of incoming material.     

Application of two‐dimensional near‐infrared (2D‐NIR) correlation spectroscopy to the discrimination of three species of Dendrobium

The objective of this paper was to apply two‐dimensional (2D) near‐infrared (NIR) correlation spectroscopy to the discrimination of three species of Dendrobium. 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 FT‐NIR spectra were measured over a temperature range of 30–140°C. The 2D synchronous and asynchronous spectra showed remarkable differences within the range of 5600–4750 cm⁻¹ between different species of Dendrobium. Copyright © 2009 John Wiley & Sons, Ltd.     

Inline monitoring of butane isomers adsorption on MFI using near-infrared spectroscopy: drift correction in time based experiments

Near-infrared (NIR) spectroscopy is used to monitor online a large variety of processes. Hydrocarbons with their strong NIR spectral signature are good candidate analytes. For this work, the sorption data are measured in a manometric setup coupled with online NIR spectroscopy, to monitor the bulk composition. The assessment of time based results faces a baseline stability problem. The goal of this article is to study the robustness of different spectral preprocessing methods when dealing with time based data. In this study, it was found that for time based experiments it is necessary to perform drift correction on the spectra combined with a water band correction. For the calibration experiments, which only last few seconds, offset correction and drift correction performed equally well.     

A spectral transfer procedure for application of a single class-model to spectra recorded by different near-infrared spectrometers for authentication of olives in brine

Analytical methods for confirmation of food authenticity claims should be rapid, economic, non-destructive and should not require highly skilled personnel for their deployment. All such conditions are satisfied by spectroscopic techniques. In order to be extensively implemented in routine controls, an ideal method should also give a response independent of the particular equipment used. In the present study, near-infrared (NIR) spectroscopy was used for verifying authenticity of commercial olives in brine of cultivar Taggiasca. Samples were analysed in two laboratories with different NIR spectrometers and a mathematical spectral transfer correction – the boxcar signal transfer (BST) – was developed, allowing to minimise the systematic differences existing between signals recorded with the two instruments. Class models for the verification of olive authenticity were built by the unequal dispersed classes (UNEQ) method, after data compression by disjoint principal component analysis (PCA). Models were validated on an external test set.     

Improving prediction selectivity for on-line near-infrared monitoring of components in etchant solution by spectral range optimization

The components (H₃PO₄, HNO₃, CH₃COOH and water) in an etchant solution have been accurately measured in an on-line manner using near-infrared (NIR) spectroscopy by directly illuminating NIR radiation through a Teflon line. In particular, the spectral features according to the change of H₃PO₄ or HNO₃ concentrations were not mainly from NIR absorption themselves, but from the perturbation (or displacement) of water bands; therefore, the resulting spectral variations were quite similar to each other. Consequently partial least squares (PLS) prediction selectivity among the components should be the most critical issue for continuous on-line compositional monitoring by NIR spectroscopy. To improve selectivity of the calibration model, we have optimized the calibration models by finding selective spectral ranges with the use of moving window PLS. Using the optimized PLS models for each component, the resulting prediction accuracies were substantially improved. Furthermore, on-line prediction selectivity was evaluated by spiking individual pure components step by step and examining the resulting prediction trends. When optimized PLS models were used, each concentration was selectively and sensitively varied at each spike; meanwhile, when whole or non-optimized ranges were used for PLS, the prediction selectivity was greatly degraded. This study verifies that the selection of an optimal spectral range for PLS is the most important factor to make Teflon-based NIR measurements successful for on-line and real-time monitoring of etching solutions.     

Raman spectroscopy in combination with background near-infrared autofluorescence enhances the in vivo assessment of malignant tissues

The diagnostic ability of optical spectroscopy techniques, including near-infrared (NIR) Raman spectroscopy, NIR autofluorescence spectroscopy and the composite Raman and NIR autofluorescence spectroscopy, for in vivo detection of malignant tumors was evaluated in this study. A murine tumor model, in which BALB/c mice were implanted with Meth-A fibrosarcoma cells into the subcutaneous region of the lower back, was used for this purpose. A rapid-acquisition dispersive-type NIR Raman system was employed for tissue Raman and NIR autofluorescence spectroscopic measurements at 785-nm laser excitation. High-quality in vivo NIR Raman spectra associated with an autofluorescence background from mouse skin and tumor tissue were acquired in 5 s. Multivariate statistical techniques, including principal component analysis (PCA) and linear discriminant analysis (LDA), were used to develop diagnostic algorithms for differentiating tumors from normal tissue based on their spectral features. Spectral classification of tumor tissue was tested using a leave-one-out, cross-validation method, and the receiver operating characteristic (ROC) curves were used to further evaluate the performance of diagnostic algorithms derived. Thirty-two in vivo Raman, NIR fluorescence and composite Raman and NIR fluorescence spectra were analyzed (16 normal, 16 tumors). Classification results obtained from cross-validation of the LDA model based on the three spectral data sets showed diagnostic sensitivities of 81.3%, 93.8% and 93.8%; specificities of 100%, 87.5% and 100%; and overall diagnostic accuracies of 90.6%, 90.6% and 96.9% respectively, for tumor identification. ROC curves showed that the most effective diagnostic algorithms were from the composite Raman and NIR autofluorescence techniques.