基于近红外光谱技术与化学计量学的绿茶无损鉴别方法研究

该文利用近红外光谱技术结合化学计量学方法开发了不同品种绿茶的无损鉴别方法。通过近红外光谱技术得到了8个品种绿茶样品的近红外光谱,比较了单一以及优化组合光谱预处理方法对光谱的影响,利用无监督的主成分分析(PCA)与有监督的线性判别分析方法(LDA)分别构建了茶叶品种鉴别模型。结果表明：对比单一预处理方法,优化组合预处理具有更优的鉴别准确性。标准正态变量变换预处理消除了茶叶样品大小不均造成的光谱散射影响,一阶导数预处理实现了变动背景的消除,减少了基线漂移的影响,突出了图谱中的有效信息,采用二者相结合的预处理方式并结合无监督的主成分分析法可实现较为准确的绿茶样品种类鉴别分析,准确率达75.0%。此外,采用有监督的线性判别分析方法处理原始光谱数据,可达到100%的鉴别准确率,但该方法需提供类别的先验知识。因此,采用近红外光谱技术和化学计量学相结合的手段可实现不同品种绿茶的快速无损鉴别。     

基于近红外光谱分析技术的食品包装塑料的定性分析

该文基于近红外漫反射光谱分析技术对食品包装材料聚乙烯、聚丙烯进行定性判别试验研究,选取不同波段范围、采用不同光谱预处理方法,使用主成分分析法(Principal component analysis,PCA)结合SIMCA、贝叶斯判别、K-近邻3种模式识别方法建立定性预测模型,并根据正确识别率比较了各模型预测性能。结果表明：使用SIMCA方法、贝叶斯判别、K-近邻3种方法建立的定性校正模型均在1 050~1 550 nm波长范围内效果较好;采用矢量归一化、标准正态变量变换、中心化、滑动均值滤波、多项式平滑滤波、一阶微分6种光谱预处理方法和上述3种模式识别方法对塑料样品近红外光谱进行了数据处理,其中在1 050~1 550 nm范围内,主成分因子数为3,采用原始光谱建立的K-近邻定性校正模型较优,对样品校正集和预测集的正确识别率均为100%。可为食品包装材料聚乙烯、聚丙烯的快速鉴别研究提供参考。     

Spectroscopic evaluation of living murine macrophage cells before and after activation using attenuated total reflectance infrared spectroscopy

Infection activates immune response pathways in host macrophages and lymphocytes that might be of sufficient magnitude to facilitate early diagnoses of infections through a host immune biosignature. Attenuated total reflectance (ATR) infrared spectroscopy was used to examine the spectroscopic signatures of living mouse macrophage cells before and after activation. Cells were prepared as control samples, or activated with a combination of lipopolysaccharide and interferon-gamma (IFN-γ) and analyzed 21 h after treatment. Resulting ATR/IR spectra collected from the living cells were analyzed using principal components analysis (PCA) and other classification methods. Plotting the scores from the first two principal components against one another provides good separation between activated and control samples. Interpretation of the loadings plots indicated that cellular activation was associated with changes in nucleic acid, protein and lipid infrared bands. Spectral samples were used to develop classification models based on activation status. Linear discriminant analysis (LDA) and K-nearest neighbor (K-NN) models were developed with 100% classification rates, using leave-one-out cross-validation procedures. Activated macrophages can be distinguished from macrophages in the resting state by their ATR spectroscopy biosignatures.     

Classification and quantification analysis of Radix scutellariae from different origins with near infrared diffuse reflection spectroscopy

A rapid near infrared spectroscopy analysis method was developed for the geographical origin discrimination and content determination of Radix scutellariae, a kind of Traditional Chinese Medicine (TCM). 81 R. scutellariae samples from six different origins were analyzed with HPLC-UV as reference method. The NIR spectra were collected in integrating-sphere diffused reflection mode and processed with different spectra pretreated methods. Discriminant analysis (DA) and discriminant partial least squares (DPLS) were applied to classify the geographical origins of those samples, and the latter had a better predictive ability with 100% accuracy after two exceptional samples eliminated from the calibration set. For the quantitative calibration, the samples were divided into calibration set and validation set by Kennard-Stone algorithm. The models of baicalin, wogonoside, baicalein, wogonin were established with partial least squares (PLS) algorithm and the optimal principal component (PC) numbers were selected with Leave-One-Out (LOO) cross-validation. The established models were evaluated with the root mean square error of prediction (RMSEP) and corresponding correlation coefficients. The correlation coefficients of all the four calibration models are above 0.920, and the RMSEPs of baicalin, wogonoside, baicalein and wogonin are 0.752%, 0.094%, 0.418% and 0.139%, respectively. This research indicated that the NIR diffuse reflection spectroscopy could be used for the rapid analysis of R. scutellariae, which is beneficial to the quality control of this raw material in TCM pharmaceutical factory, and will also help to solve analogous problems.     

PLS-DA Model for the Evaluation of Attention Deficit and Hyperactivity Disorder in Children and Adolescents through Blood Serum FTIR Spectra

Attention deficit and hyperactivity disorder (ADHD) is one of the most common neurodevelopmental disorders of childhood. It affects ~10% of the world’s population of children, and about 30–50% of those diagnosed in childhood continue to show ADHD symptoms later, with 2–5% of adults having the condition. Current diagnosis of ADHD is based on the clinical evaluation of the patient, and on interviews performed by clinicians with parents and teachers of the children, which, together with the fact that it shares common symptoms and frequent comorbidities with other neurodevelopmental disorders, makes the accurate and timely diagnosis of the disorder a difficult task. Despite the large effort to identify reliable biomarkers that can be used in a clinical environment to support clinical diagnosis, this goal has never been achieved hitherto. In the present study, infrared spectroscopy was used together with multivariate statistical methods (hierarchical clustering and partial least-squares discriminant analysis) to develop a model based on the spectra of blood serum samples that is able to distinguish ADHD patients from healthy individuals. The developed model used an approach where the whole infrared spectrum (in the 3700–900 cm⁻¹ range) was taken as a holistic imprint of the biochemical blood serum environment (spectroscopic biomarker), overcoming the need for the search of any particular chemical substance associated with the disorder (molecular biomarker). The developed model is based on a sensitive and reliable technique, which is cheap and fast, thus appearing promising to use as a complementary diagnostic tool in the clinical environment.     

Identification of origin of coal from the Ostrava-Karvina Mining District by infrared spectroscopy and discriminant analysis

Infrared (IR) spectra of coal from five mines of the Ostrava-Karvina Mining District were collected using diffuse reflectance techniques. The spectral data were classified by the application of discriminant analysis. The aim of this paper is to test out identification of coal origin by IR spectroscopy and discriminant analysis. The results of this study confirm that infrared spectroscopy together with multivariate statistical methods could provide a powerful discriminating tool for the identification of origin of the most of coal from the Ostrava-Karvina Mining District.     

Boosting partial least‐squares discriminant analysis with application to near infrared spectroscopic tea variety discrimination

In the present study, boosting has been combined with partial least‐squares discriminant analysis (PLS‐DA) to develop a new pattern recognition method called boosting partial least‐squares discriminant analysis (BPLS‐DA). BPLS‐DA is implemented by firstly constructing a series of PLS‐DA models on the various weighted versions of the original calibration set and then combining the predictions from the constructed PLS‐DA models to obtain the integrative results by weighted majority vote. Coupled with near infrared (NIR) spectroscopy, BPLS‐DA has been applied to discriminate different kinds of tea varieties. As comparisons to BPLS‐DA, the conventional principal component analysis, linear discriminant analysis (LDA), and PLS‐DA have also been investigated. Experimental results have shown that the inter‐variety difference can be accurately and rapidly distinguished via NIR spectroscopy coupled with BPLS‐DA. Moreover, the introduction of boosting drastically enhances the performance of an individual PLS‐DA, and BPLS‐DA is a well‐performed pattern recognition technique superior to LDA. Copyright © 2012 John Wiley & Sons, Ltd.     

Discrimination of Osteonecrosis and Normal Tissues by Near-Infrared Spectroscopy and Successive Projections Algorithm-Linear Discriminant Analysis

Osteonecrosis of femoral head (ONFH) is a disease characterized by an impaired blood flow in the bone. The pathogenesis is still unknown, which makes an exact diagnosis troublesome and heavily dependent on experience. Exploring the information of molecular level by modern spectroscopy may help to discover the underlying pathogenesis and find its diagnostic application in clinical medicine. The study focuses on the combination of near-infrared (NIR) spectroscopy and classification models for discriminating ONFH and normal tissues. A total of 128 surgical specimens was prepared and NIR spectra were recorded by an integrating sphere. The experiment data set was divided into three subsets, i.e., the training set, validation set, and test set. Successive projection algorithm-linear discriminant analysis (SPA-LDA) was used to compress variables and build the diagnostic model. Partial least square-discriminant analysis (PLS-DA) was used as the reference. Principal component analysis (PCA) was used for exploratory analysis. The results showed that compared to PLS-DA, SPA-LDA provided a more parsimonious model using only seven variables and achieved better performance, i.e., sensitivity of 90.5 and 85%, and specificity of 100 and 95.5% for the validation and test sets, respectively. It indicated that NIR spectroscopy combined with SPA-LDA algorithm was a feasible aid tool for discriminating ONFH from normal tissue.     

Two fluoro compounds of main group elements: Synthesis,characterization, theoretical and spectroscopic study

Two new compounds of fluorine: (C₂H₅)₄N[I₂F] and (C₂H₅)₄N[Br₂F], have been easily synthesized in a nearly quantitative by a direct reaction of (C₂H₅)₄NF, I₂ and Br₂. The products were isolated and characterized by elemental analysis and spectroscopic methods such as: Fourier transform infrared spectroscopy (FTIR) and ultraviolet-visible spectroscopy (UV-Vis). These compounds have been studied computationally with the Scalar ZORA relativistic level of theory using the ADF program package. The molecular parameters, and vibrational spectra were calculated. The excitation energies were found by timedependent perturbation density functional theory (TD-DFT). Molecule optimization, frequencies and excitation energies were calculated with standard Slatertype-orbital (STO) basis sets with triple-zeta quality double plus polarization functions (TZ2P) for all atoms. The FTIR, UV-Vis spectra and assignment of principal transitions and total density of state (TDOS) were extracted using the GaussSum 2.2 program. The comparison between experimental and calculated values showes that the experimental results correlate well with the predicted data.     

基于非接触式拉曼光谱分析人血与犬血的PCA-LDA鉴别方法

将拉曼光谱分析法与数理统计方法有机结合,构建人血与犬血种属判别模型,实现了不同种属血液样本的高效无损鉴别.采用拉曼光谱的无损测试模式对血液样本进行测试,考察了抗凝管管材、聚焦位置及曝光时间等对血液样本拉曼光谱的影响,在激发波长为632.8 nm,光谱扫描范围为200~1800 cm-1,功率衰减率50%,曝光时间5 s及累加次数为2次的优化条件下,获得了无损检测条件下的血液样本拉曼光谱图.针对血液样本组分复杂、拉曼光谱信号基底背景高等问题,提出了基于小波变换去噪,进行分段多项式基线校正的预处理方法,有效解决了血液样本拉曼光谱谱图的高噪音和基线漂移问题.实验选择30例正常人血和33例比格犬血为样本训练集,5例正常人血和5例比格犬血为测试集,基于主成分分析法(PCA)联合线性判别法(LDA)模型,训练集分类正确率达到95.23%,盲测集分类正确率达90.00%.这种基于非接触式血液样本拉曼光谱和PCA-LDA判断模型的测试方法在进出口检验检疫等涉及血液无损鉴别的领域具有广泛的应用价值和前景.     

Comparison and improvement of commonly applied statistical approaches for identification of plant species from IR spectra

Fourier transform infrared spectroscopy (FTIR) has been studied many times in the context of identification of plant, fungal and bacterial species. Infrared spectra are commonly analyzed using multivariate statistical methods such as cluster analysis (CA), principal component analysis (PCA), partial least squares analysis (PLS) and discriminant analysis (DA). In this study, a univariate statistical method for analysis of variance (ANOVA) was used to reduce the number of variables before applying the multivariate methods. Analyzing variables using ANOVA or a combination of ANOVA with CA produced better results. Here, experiments were carried out by performing ANOVA using the first derivative of the spectra instead of the original spectra or its second derivative because using the first‐derivative variables led to improved distinction between species. Different results were obtained by applying different validation methods. The leave‐one‐out validation method gave higher results than the validation‐with‐training and validation sample sets, thus indicating the non‐objectivity of the leave‐one‐out validation method. Copyright © 2010 John Wiley & Sons, Ltd.     

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.     

Identification of four cornua by ultra‐performance liquid chromatography with time‐of‐flight mass spectrometry coupled with principal component analysis

An ultra‐high performance liquid chromatography with quadrupole time‐of‐flight mass spectrometry method coupled with principal component analysis was developed and applied to the identification of Cornu Antelopis, Cornu Bubali, Cornu Naemorhedi, and Cornu Bovis. The data obtained from the trypsin‐digested samples were subjected to principal component analysis to classify these four cornua. Additionally, marker peptides of the cornua were determined by orthogonal partial least‐squares discriminant analysis, and fragmentation tandem mass spectra of these marker peptides were evaluated. The results from this study indicate that the proposed method is reliable, and it has been successfully applied to the identification of variants of cornua commonly used in traditional Chinese medicine.     

Noninvasive Surface Detection of Papillary Thyroid Carcinoma by Fourier Transform Infrared Spectroscopy

Previously Fourier transform infrared(FTIR) spectroscopy has been applied to detecting thyroid cancer during operations and to discriminating cervical metastatic ones from non-metastatic lymph nodes. This study explored the possibility of establishing a sensitive, accurate and noninvasive screen or diagnosis by preoperative FTIR spectroscopy. 111 patients undergone a thyroid operation and 50 healthy volunteers were enrolled in the study. The FTIR spectra were obtained by two mid-infrared optical fibers with an attenuated total reflectance(ATR) probe closely contacting the subjects' skin on the thyroid nodules. The FTIR spectra obtained from normal thyroid, nodular goiter(NG) and papillary thyroid carcinoma(PTC) patients were compared. A Fisher's discriminant analysis was created based on these data. There were 41 PTC patients and 70 NG patients according to their histopathological examinations. A total of 23(of 39) parameters were statistically different among the three groups(P<0.05). The F₁₃₀₀ and F₁₀₈₀ parameters were significantly different between the three groups. In total, 9 out of 39 FTIR parameters were selected as independent factors by the Wilks' lambda stepwise discriminant analysis. The discrimination accuracy of papillary thyroid carcinoma in the three groups was 88.8%. Surface detection of PTC by FTIR spectroscopy is feasible. FTIR spectroscopy can be used for rapid and noninvasive PTC screen and auxiliary diagnosis.     

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.     

Near-Infrared Raman Spectroscopy for In Vitro Detection of Cervical Precancers

Abstract— In this study, we investigate the potential of near-infrared Raman spectroscopy to differentiate cervical precancers from normal tissues, inflammation and metaplasia and to differentially diagnose low-grade and high-grade precancers. Near infrared Raman spectra were measured from 36 biopsies from 18 patients in vitro. Detection algorithms were developed and evaluated relative to histopathologic examination. Algorithms based on empirically selected peak intensities, ratios of peak intensities and a combination of principal component analysis for data reduction and Fisher discriminant analysis for classification were investigated. Spectral peaks were tentatively identified from measured spectra of potential chromophores. Empirically selected normalized intensities can differentiate precancers from other tissues with an average sensitivity and specificity of 88 ± 4% and 92 ± 4%. Ratios of un-normalized intensities can differentiate precancers from other tissues with a sensitivity and specificity of 82% and 88% and high-grade from low-grade lesions with a sensitivity and specificity of 100%. Using multivariate methods, intensities at eight frequencies can be used to differentiate precancers from all other tissues with a sensitivity and specificity of 82% and 92% in an unbiased test. Raman algorithms can potentially separate benign abnormalities such as inflammation and metaplasia from precancers. Comparison of tissue spectra to published and measured chromophore spectra indicate that the most likely primary contributors to the tissue spectra are collagen, nucleic acids, phospholipids and glucose 1-phos-phate. These results suggest that near-infrared Raman spectroscopy can be used for cervical precancer diagnosis and may be able to accurately separate samples with inflammation and metaplasia from precancer.     

Forensic classification of ballpoint pen inks using high performance liquid chromatography and infrared spectroscopy with principal components analysis and linear discriminant analysis

Several varieties of blue ballpoint pen inks were analyzed by high performance liquid chromatography (HPLC) and infrared spectroscopy (IR). The chromatographic data extracted at four wavelengths (254, 279, 370 and 400 nm) was analyzed individually and at a combination of these wavelengths by the soft independent modeling of class analogies (SIMCA) technique using principal components analysis (PCA) to estimate the separation between the pen samples. Linear discriminant analysis (LDA) measured the probability with which an observation could be assigned to a pen class. The best resolution was obtained by HPLC using data from all four wavelengths together, differentiating 96.4% pen pairs successfully using PCA and 97.9% pen samples by LDA. PCA separated 60.7% of the pen pairs and LDA provided a correct classification of 62.5% of the pens analyzed by IR. The results of this study indicate that HPLC coupled with chemometrics provided a better discrimination of ballpoint pen inks compared to IR. The need to develop a suitable IR method for analysing blue ballpoint pen inks has been emphasized and it is hoped that the development of such a method would indeed provide a valuable tool for the non-destructive analysis of blue ballpoint pen ink samples for forensic purposes.     

基于近红外光谱的维生素C片无损鉴别分析

基于近红外光谱技术与化学计量学方法,建立了一种国内外不同品牌维生素C片的无损鉴别方法。采集了国内外8个品牌的维生素C片共计40个样本的近红外光谱数据,比较了完整样品以及粉末样品的近红外光谱,采用连续小波变换技术消除背景干扰和基线漂移,基于标准偏差与相对标准偏差的变量筛选方法筛选出具有代表性的波数点,结合主成分分析方法对国内外不同品牌维生素C片进行鉴别分析。结果表明:原始光谱存在着明显的背景干扰和基线漂移现象,且粉末样品的重现性要优于完整样品;单纯使用原始光谱无法辨别来自不同品牌的维生素C片;连续小波变换可以有效消除背景干扰,提高模型鉴别能力;完整样品的鉴别准确率优于粉末样品,说明国内外不同品牌维生素C片主要成分基本一致,可能是辅剂和工艺上存在细微差异。通过结合近红外光谱分析技术与化学计量学方法,可实现对国产以及进口不同品牌维生素C片的鉴别分析。     

Multivariate analysis of reflectance spectra from propolis: Geographical variation in Romanian samples

The present study described reflectance spectroscopy as a suitable analytical tool to discriminate the floral origin of 39 Romanian propolis samples. Relevant differences between the UV-vis reflectance spectra of the investigated propolis samples within the 220-850 nm spectral range were found. The results obtained applying cluster analysis, principal component analysis and linear discriminant analysis to the digitized data of zero order, zero order normalized and first order derivative spectra support the reliability of this technique. In addition, the application of the linear discriminant analysis to the score matrices corresponding to the first principal components appeared to be an illuminating solution. Generally, the samples have been assigned to two large groups in a good agreement with their vegetal sampling location, samples originating from predominant forest area and samples originating from meadows. Within the first group, two subgroups were identified according to the dominant type of the forest, deciduous or resinous, while within the last group three subgroups were found according to the extend and variety of the meadow.     

Nondestructive Discrimination of Pharmaceutical Preparations Using Near-Infrared Spectroscopy and Partial Least-Squares Discriminant Analysis

Metronidazole is a widely used antibacterial and amoebicide drug. The feasibility of the classification of metronidazole samples with respect to their brands was investigated by near-infrared (NIR) spectroscopy along with chemometrics. A total of 92 samples of different lots and four brands were collected for measurements. First, principal component analysis was conducted to visualize the difference between metronidazole samples of different brands. Then, based on an effective classifier-independent method, i.e., joint mutual information, only the 30 most important variables were selected for modeling. From the independent test set, the partial least-squares discriminant analysis model based on the reduced variable set was compared with the corresponding full-spectrum model using all variables, which indicates the model based on the reduced variable set outperforms the full-spectrum model. It appears that the combination of NIR spectroscopy, joint mutual information, and partial least-squares discriminant analysis is a potential method for the classification of metronidazole from different brands and can, therefore, be used in the screening of counterfeit pharmaceutical products.