近红外反射光谱快速测定玉米DDGS营养成分的研究

试验探讨了近红外反射光谱测定玉米DDGS的8项重要营养成分,包括水分、蛋白、粗脂肪、粗灰分、总磷、中性洗涤纤维、酸性洗涤纤维、粗纤维的可行性.从国内18个工厂采集93个玉米DDGS样品,使用偏最小二乘法,采取15种不同的导数和去散射光谱预处理方法建立近红外模型.在光谱预处理中,二阶导数产生了较好的预测效果.使用70个与...     

基于KPCA的氧碘激光器出光过程监控方法

根据氧碘化学激光器运行过程基本稳定的特点,利用核主元分析(KPCA)方法监控非线性过程的优势,提出了基于KPCA的激光器出光过程监控方法。该方法通过沿时间轴方向展开激光器正常出光的历史数据,建立了激光器出光过程中各时刻的KPCA模型,并在特征空间构建T2统计量和平方预测误差统计量对激光器出光过程进行监控。仿真计算和试验表明,该方法具有可靠的监控性能,能及时、准确地发现激光器出光过程中的异常状态。     

蔬菜表面农药残留可见-近红外光谱探测与分类识别研究

利用在600～1 100nm波段范围内可见-近红外反射光谱分析技术,对常见的高残留农药在绿色植物活体上的无损检测进行了研究。首先将采集到的漫反射光谱数据进行小波变换提取光谱特征,然后再利用主成分分析方法进一步对光谱特征进行分析,最后把这些光谱的前两个主成分得分作为神经网络的输入信息,建立了多神经元的神经网络感知器。对农药残留检测的结果表明,该方法可有效甄别农药残留和种类,识别得到较好的分类效果。总之,该研究为蔬菜和瓜果表面的农药残留快速无损检测和识别提供了一条新途径。     

Near-Infrared spectroscopy and hyperspectral imaging for non-destructive quality assessment of cereal grains

Hyperspectral imaging (HSI) combines spectroscopy and imaging, providing information about the chemical properties of a material and their spatial distribution. It represents an advance of traditional Near-Infrared (NIR) spectroscopy. The present work reviews the most recent applications of NIR spectroscopy for cereal grain evaluation, then focuses on the use of HSI in this field. The progress of research from ground material to whole grains and single kernels is detailed. The potential of NIR-based methods to predict protein content, sprout damage and α-amylase activity in wheat and barley is shown, in addition to assessment of quality parameters in other cereals such as rice, maize and oats, and the estimation of fungal infection. This analytical technique also offers the possibility to rapidly classify grains based on properties such as variety, geographical origin, kernel hardness, etc. Further applications of HSI are expected in the near future, for its potential for rapid single-kernel analysis.     

血清中胆固醇的近红外光谱测量及模型适用性研究

使用近红外光谱分析方法进行了血清中胆固醇含量的快速、无试剂测最研究,并考察、分析了模型的适用性.利用傅里叶光谱仪测量1,2和6.5mm 厚血清的近红外光谱,在合频与第一、第二倍频波段内,结合偏最小二乘方法(PLS)对胆固醇建立了模型.1,2,6.5mm 厚血清模型的预测标准差(RMSEP)分别是0.15,0.16,0....     

Comparison of connective tissue invaded by Lewis lung carcinoma to healthy connective tissue by means of micro‐Raman spectroscopy

We have measured the micro‐Raman spectra of mouse tissues invaded by Lewis lung carcinoma (LLC). We have also carried out categorical principal component analysis (CATPCA) on the acquired spectra. The results indicate that the tumor tissues can be well discriminated from normal tissues by the first two principal components extracted from the spectra. Furthermore, we have found that the concentrations of nucleic acids and lipids/fatty acids in the tumor are considerably higher than those in the normal tissue, whereas the collagen concentration is lower. These differences can be detected and characterized by Raman images using the 788 cm⁻¹ DNA/RNA band and the 1301 cm⁻¹ lipid/fatty acid band. Copyright © 2009 John Wiley & Sons, Ltd.     

Characterisation and classification of automotive clear coats with Raman spectroscopy and chemometrics for forensic purposes

The clear coats from a collection of automotive paint samples of 139 vehicles, covering a range of Australian and international vehicle manufacturers and sold in Western Australia, were characterised using FT‐Raman spectroscopy. Principal component analysis (PCA) revealed 19 distinct classes that were associated with the vehicles' manufacturer and model, and in the case of Australian manufacturers, the years of manufacture. Linear discriminant analysis based on the PCA groupings gave excellent discrimination between the groups with 96.9% of the calibration set and 97.6% of the validation set being correctly classified. Although the sample set comprised only vehicles available in Australia, the methodology used is universal and hence applicable in any jurisdiction that is willing and able to generate a statistically significant data set and maintain and update it as new vehicles appear on the market. A FT‐Raman spectroscopy‐based database would rapidly provide information regarding vehicle origin and manufacture and hence generate investigative leads for questioned paint samples found at incident sites. Copyright © 2016 John Wiley & Sons, Ltd.     

Identification of wheat grain in different states based on hyperspectral imaging technology

Abstract

Nondestructive identification of wheat grains in different states plays an important role in improving the quality of wheat products. This study investigated the possibility of using hyperspectral imaging techniques to discriminate healthy wheat grain, germinated wheat grain, mildewed wheat grain, and shriveled wheat grain (wheat grain infected with fusarium head blight). Both sides of individual wheat kernels were subjected to hyperspectral imaging (866.4–1701.0?nm) to acquire hyperspectral cube data. Spectral data were preprocessed by using standardization and multiple scattering correction. In addition, the principal component loading method was used to extract the characteristic wavelengths of both sides of wheat grains. The sample is divided into calibration set, test set, and validation set. The data of the calibration set are used to train the partial least squares discriminant analysis model, K-nearest neighbor model, and the support vector machine model, and the test set data are used to test the model. The results show that spectral data of both sides can achieve good classification results, while the reverse spectral data perform better. By comparing with each other, the support vector machine model is selected as the best classification model. Finally, using two hyperspectral images (reverse side) that are not involved in training and testing to verify the accuracy of the established support vector machine model, and the classification effect maps of the four wheat grains were visualized. The results indicate that nondestructive classification of wheat grains in different states is feasible based on hyperspectral imaging technology.     

不同生长期当归1H NMR指纹图谱的研究

利用¹H NMR法测定27个不同生长期的当归提取物,对当归的主要活性成分阿魏酸、藁本内酯、当归多糖进行了初步归属,用特征峰相对峰面积表示3种活性成分的相对含量,并对它们在生长过程中的含量变化进行分析. 数据分析显示,当归中阿魏酸、藁本内酯、当归多糖的含量在第1年变化较为平稳,第2年和第3年变化相对较为活跃,而在当归的生长周期内,当归多糖的含量变化明显大于其他两种活性成分,为参与代谢活动的主要活性成分. 结果证实,通过对照¹H NMR指纹图谱, 将相对含量法与主成分分析法相结合,能对不同生长期的当归药材中的活性成分进行宏观评价和分析,可作为植物药材代谢组学的研究方法.     

A comparison of noise models in a hybrid reference spectrum and principal components analysis algorithm for Raman spectroscopy

Raman spectroscopy exploits the Raman scattering effect to analyze chemical compounds with the use of laser light. Raman spectra are most commonly analyzed using the ordinary least squares (LS) method. However, LS is known to be sensitive to variability in the spectra of the analyte and background materials. In a previous paper, we addressed this problem by proposing a novel algorithm that models expected variations in the analyte as well as background signals. The method was called the hybrid LS and principal component analysis (HLP) algorithm and used an unweighted Gaussian distribution to model the noise in the measured spectra. In this paper, we show that the noise in fact follows a Poisson distribution and improve the noise model of our hybrid algorithm accordingly. We also approximate the Poisson noise model by a weighted Gaussian noise model, which enables the use of a more efficient solver algorithm. To reflect the generalization of the noise model, we from hereon call the method the hybrid reference spectrum and principal components analysis (HRP) algorithm. We compare the performance of LS and HRP with the unweighted Gaussian (HRP‐G), Poisson (HRP‐P), and weighted Gaussian (HRP‐WG) noise models. Our experiments use both simulated data and experimental data acquired from a serial dilution of Raman‐enhanced gold‐silica nanoparticles placed on an excised pig colon. When the only signal variability was zero‐mean random noise (as examined using simulated data), HRP‐P consistently outperformed HRP‐G and HRP‐WG, with the latter coming in as a close second. Note that in this scenario, LS and HRP‐G were equivalent. In the presence of random noise as well as variations in the mean component spectra, the three HRP algorithms significantly outperformed LS, but performed similarly among themselves. This indicates that, in the presence of significant variations in the mean component spectra, modeling such variations is more important than optimizing the noise model. It also suggests that for real data, HRP‐WG provides a desirable trade‐off between noise model accuracy and computational speed. Copyright © 2013 John Wiley & Sons, Ltd.     

Improving the Robustness of Particle Size Analysis by Multivariate Statistical Process Control

The robustness of online particle size analysis in wet processes is improved by applying data based modeling methods to the control of the sample preparation and measurement sequence of the particle size analyzer. The aim is to find a more accurate and reliable method of determining the end of the particle size integration period using multivariate statistical process control (MSPC). The studied approach is tested on analyzers installed at two mineral processing plant sites and validated using two validation tests. Research shows that the proposed method works with two very different slurry types. The main advantage of the adapted approach is that there are no adjustable parameters that have to be set by the user.     

黄桃碰伤和可溶性固形物高光谱成像无损检测

黄桃在线分级时,表面损伤和可溶性固形物同时在线检测。损伤和可溶性固形物是评价黄桃品质好坏的重要指标。采用高光谱成像技术,尝试对黄桃损伤和可溶性固形物进行同时检测。利用主成分分析法,首先对高光谱图像进行主成分分析得到最佳PC(principal component)图像,其次根据PC图像中各波长对其贡献率的大小确定最佳特征波长(550和720 nm)并结合二值化,图像掩膜和阈值分割以及相关的图像处理技术对最佳光谱图像进行定性判别。其准确率最高达到94.6%,同时建立偏最小二乘定量回归模型对正常样品SSC(soluble solid content)含量进行预测,通过对模型的不断优化,实现了基于高光谱成像技术对黄桃碰伤和可溶性固形物同时检测。可溶性固形物分选准确率为79.2%。实验结果表明,利用高光谱成像技术可以实现对黄桃碰伤和可溶性固形物同时检测,该研究可以为实际在线分选提供理论依据和参考。     

用主量化学组成研究汝官瓷和钧官瓷的原料来源

将84个清凉寺窑汝官瓷和钧台窑钧官瓷样品进行质子激发X射线荧光分析,得到每个样品胎和釉的7种主量化学组分。将所有样品的7种主量化学组成数据进行散布分析,以确定汝官瓷和钧官瓷原料来源是否相同。从散布分析图可以看出,汝官瓷釉和 钧官瓷釉样品的原料产地和配方明显不同,汝官瓷胎和钧官瓷胎样品的原料产地和成分接近,但有所不同。The seven main chemical components of the body and glaze samples of 84 RuGuan porcelains from Qingliangsi kiln and JunGuan porcelains from Juntai kiln are determined by the proton induced X-ray emission （ PIXE）. Then these data are analyzed by scatter analysis to confirm whether the sources of the raw materials of Ru-Guan porcelain from Qingliangsi kiln and JunGuan porcelain from Juntai kiln are the same or not. The figure of the scatter diagram shows that the sourcing area of the raw material and batch formula of RuGuan porcelain and Jun-Guan porcelain glaze are obviously different and the sources of the raw materials and components of their body samples are close but a bit different.     

Laser-induced breakdown spectroscopy applied to the characterization of rock by support vector machine combined with principal component analysis

Laser-induced breakdown spectroscopy(LIBS) is a versatile tool for both qualitative and quantitative analysis.In this paper,LIBS combined with principal component analysis(PCA) and support vector machine(SVM) is applied to rock analysis.Fourteen emission lines including Fe,Mg,Ca,Al,Si,and Ti are selected as analysis lines.A good accuracy(91.38% for the real rock) is achieved by using SVM to analyze the spectroscopic peak area data which are processed by PCA.It can not only reduce the noise and dimensionality which contributes to improving the efficiency of the program,but also solve the problem of linear inseparability by combining PCA and SVM.By this method,the ability of LIBS to classify rock is validated.     

Fast detection and identification of counterfeit antimalarial tablets by Raman spectroscopy

During the last decade there has been an apparent increase in the prevalence of counterfeit medicines in developing as well as developed countries. The pivotal antimalarial artesunate has been counterfeited on a large scale in SE Asia. In this work, the possibilities of Raman spectroscopy are explored as a fast and reliable screening method for the detection of counterfeit artesunate tablets. In this study, 50 ‘artesunate tablets’, purchased in SE Asia, were examined. This spectroscopic method was able to distinguish between genuine and counterfeit artesunate and to identify the composition of the counterfeit tablets. These contained no detectable levels of artesunate, but consisted mostly of starch, calcite (CaCO₃), and paracetamol (4‐acetamidophenol). In one particular case an admixture of rutile (TiO₂) and artesunate was detected. The results of the investigation by Raman spectroscopy were in agreement with those of colorimetric tests and of liquid chromatography‐mass spectrometry on the artesunate. Moreover, principal components analysis (PCA) was combined with hierarchical cluster analysis to establish an automated approach for the discrimination between different groups of counterfeits and genuine artesunate tablets. These results demonstrate that Raman spectroscopy combined with multivariate analysis is a promising and reliable methodology for the fast characterization of genuine and counterfeit artesunate antimalarial tablets. Copyright © 2006 John Wiley & Sons, Ltd.     

Study of both fingerprint and high wavenumber Raman spectroscopy of pathological nasopharyngeal tissues

High wavenumber (HW) Raman spectroscopy has weaker fluorescence background compared with fingerprint (FP) region. This study aims to evaluate the discrimination feasibility of nasopharyngeal non‐cancerous and nasopharyngeal cancer (NPC) tissue with both FP and HW Raman spectroscopy. HW Raman spectra of nasopharyngeal tissue were obtained for the first time. Raman spectra were collected to differentiate nasopharyngeal non‐cancerous (n = 37) from NPC (n = 41) tissues in FP (800–1800cm⁻¹), HW (2700–3100cm⁻¹), and integrated FP/HW region. First, to assess the utility of this method, the averaged Raman spectral intensities and intensity ratios of corresponding Raman bands were analyzed in HW and FP regions, respectively. The results show that intensities as well as the ratios of specific Raman peaks might be helpful in distinguishing nasopharyngeal non‐cancerous from NPC tissue with the HW Raman spectroscopy, as with FP Raman reported before. The multivariate statistical method based on the combination of principal component analysis–liner discriminant analysis (PCA‐LDA), together with leave‐one‐patient‐out, cross‐validation diagnostic algorithm, was used for discriminating nasopharyngeal non‐cancerous from NPC tissue, generating sensitivities of 87.8%, 85.4%, and 95.1% and specificities of 86.5%, 91.9%, and 89.2%, respectively, with Raman spectroscopy in the FP, HW, and integrated FP/HW regions. The posterior probability of classification results and receiver operating characteristic curves were utilized to evaluate the discrimination of PCA‐LDA algorithm, verifying that HW Raman spectroscopy has a positive effect on the differentiation for the diagnosis of NPC tissue by integrated FP/HW Raman spectroscopy. What's more, the potential of Raman spectroscopy used for differentiating different pathology NPC tissues was also discussed. The results demonstrate that both FP and HW Raman spectroscopy have the potential for diagnosis and detection in early nasopharyngeal carcinoma, and HW Raman spectroscopy may improve the discrimination of NPC tissue compared with FP region alone, providing a promising diagnostic tool for the diagnosis of NPC tissue. Copyright © 2015 John Wiley & Sons, Ltd.     

漫反射近红外光谱法同时测定液体咖啡中的速溶咖啡、植脂末、糖含量

采用傅里叶变换近红外光谱仪结合积分球附件对20个液体咖啡样品以漫反射方式采集近红外光谱,分别针对速溶咖啡、植脂末、糖建立定量校正模型。结果表明,速溶咖啡、植脂末、糖的模型因子数分别为4,5和4;测定系数(R2)分别为98.97%,99.94%和99.18%;校正均方根误差(root mean square error ofcalibration,RMSEC)分别为1.62,0.42和1.58;交互验证均方根误差(root mean square error of cross vali-dation,RMSECV)分别为2.12,0.72和2.01;F检验结果表明,三个模型的预测值-化学值之间存在极显著的相关关系。研究表明,近红外光谱法可以快速、准确地对液体咖啡中的三种主要成分同时进行定量测定,可为液体咖啡质量控制以及液体配方食品中具有一定组成的混合物的定量测定提供一定的参考。     

KPCA-聚类分析法和用便携式拉曼仪快速鉴别降糖药

对不同种类的降糖药片进行拉曼光谱的核主成分分析(KPCA)-聚类分析,实现快速、简便的鉴别。KPCA可以有效地避免主成分分析(PCA)只能处理线性问题和降维效果不明显的弊端。它通过一个非线性变换,首先将原变量空间映射到高维特征空间,然后在这个高维特征空间中进行线性主成分分析。采集得到的药片拉曼光谱的KPCA-聚类分析结果表明,采用KPCA提取特征变量的聚类结果比采用PCA提取特征变量后进行聚类分析的效果好,并且未经刮除表面包膜的降糖药片识别准确率为96.5%,经过刮除表面包膜处理的降糖药片的识别准确率为100%。便携式拉曼光谱仪结合该方法以其检测速度快、准确率高、使用简便、无样品前处理等显著优势,为药品的快速检验技术提供一种新的有效的鉴别手段。     

Effect of the target positions on the rapid identification of aluminum alloys by using filament-induced breakdown spectroscopy combined with machine learning

Filament-induced breakdown spectroscopy (FIBS) combined with machine learning algorithms was used to identify five aluminum alloys. To study the effect of the distance between focusing lens and target surface on the identification accuracy of aluminum alloys, principal component analysis (PCA) combined with support vector machine (SVM) and K-nearest neighbor (KNN) was used. The intensity and intensity ratio of fifteen lines of six elements (Fe, Si, Mg, Cu, Zn, and Mn) in the FIBS spectrum were selected. The distances between the focusing lens and the target surface in the pre-filament, filament, and post-filament were 958 mm, 976 mm, and 1000 mm, respectively. The source data set was fifteen spectral line intensity ratios, and the cumulative interpretation rates of PC1, PC2, and PC3 were 97.22%, 98.17%, and 95.31%, respectively. The first three PCs obtained by PCA were the input variables of SVM and KNN. The identification accuracy of the different positions of focusing lens and target surface was obtained, and the identification accuracy of SVM and KNN in the filament was 100% and 90%, respectively. The source data set of the filament was obtained by PCA for the first three PCs, which were randomly selected as the training set and test set of SVM and KNN in 3:2. The identification accuracy of SVM and KNN was 97.5% and 92.5%, respectively. The research results can provide a reference for the identification of aluminum alloys by FIBS.     

血凝素蛋白及抗体相互作用的太赫兹光谱主成分分析

采用太赫兹时域光谱系统, 测量了7种不同浓度的血凝素蛋白及其与特异性抗体、无关抗体对照组反应的透射光谱, 采用光谱预处理及主成分分析法, 对多个太赫兹光谱参数进行分析. 结果显示, 主成分分析在数据降维的同时, 可以突出数据的主要变化趋势; 在原始变量相关性一致的条件下, 约化吸收截面与血凝素蛋白浓度之间表现出最强的相关性, 而介电损耗角正切值更适合于对血凝素蛋白-抗体复合物的聚类效果进行定性分析. 该研究表明主成分分析法对于太赫兹生物光谱的分析及进一步研究蛋白质的结构和功能具有重要的指导意义.