Carbonization mechanism of bamboo (phyllostachys) by means of Fourier Transform Infrared and elemental analysis

通过测定在200-600℃炭化竹材得到的固体产物的碳、氢、氧元素的含量及它们的红外光谱,研究了在炭化过程中竹材中半纤维素、纤维素及木素的变化规律。结果表明,结合元素分析,红外光谱分析方法是研究竹材炭化机理的有效手段。在200℃以前,竹材中的半纤维素和纤维素的大量羟基断裂,并结合成水而失去。在200-250℃之间,竹材中的纤维素被降解,其中的吡喃型环也遭到破坏。并且木素中的甲氧基也被脱去。竹材中的木素网状结构在250-400℃之间遭到完全的破坏。竹炭中的碳原子在600℃已基本上完成了芳环化。图3表2参15。

Mathematic Models for Analysis of Quality Components in Sugarcane Juice with Fourier Transform Near Infrared Spectroscopy

With the technique of Fourier transform near infrared (FT-NIR) spectroscopy, the calibration models for quantitative analysis of sucrose and polarization in sugarcane juice were developed by using transmission mode and calibrating with partial least square (PLS) algorithm. The determination coefficients (R2) of the predicted models for sucrose and polarization in juice were 0. 9980 and 0. 9979 respectively; the root mean square errors of cross validation (RMSECV) were 0. 143 and 0. 155% for sucrose and polarization in juice respectively. The predictive errors measured by FT-NIR were close to those by routine laboratory methods. The results demonstrated that the FT-NIR methods had high accuracy and they were able to replace the routine laboratory analysis. It was also demonstrated that as a rapid and accurate measurement, the FT-NIR technique had potential applications in quality control of mill sugarcane, establishment of payment system based on sugarcane quality, and selection of clones in sugarcane breeding.

Comparative study on identi¯cation of healthy and osteoarthritic articular cartilages by fourier transform infrared imaging and chemometrics methods

Two discriminant methods,partial least squares-discriminant analysis(PLS-DA)and Fisher's discriminant analysis(FDA),were combined with Fourier transform infrared imaging(FTIRI)to differentiate healthy and osteoarthritic articular cartilage in a canine model.Osteoarthritic cartilage had been developed for up to two years after the anterior cruciate ligament(ACL)transection in one knee.Cartilage specimens were sectioned into 10μm thickness for FTIRI.A PLS-DA model was developed after spectral pre-processing.All IR spectra extracted from FTIR images were calculated by PLS-DA with the discriminant accuracy of 90%.Prior to FDA,principal component analysis(PCA)was performed to decompose the IR spectral matrix into informative princi pal component matrices.Based on the different discriminant mechanism,the discriminant accuracy(96%)of PCA-FDA with high convenience was higher than that of PLS-DA.No healthy cartilage sample was mis assigned by these two methods.The above mentioned suggested that both integrated technologies of FTIRI-PLS-DA and,especially,FTIRI-PCA-FDA could become a promising tool for the discrimination of healthy and osteoarthritic cartilage specimen as well as the diagnosis of cartilage lesion at microscopic level.The results of the study would be helpful for better understanding the pathology of osteoarthritics.

Discrimination of Wild-Grown and Cultivated <i>Ganoderma lucidum</i>by Fourier Transform Infrared Spectroscopy and Chemometric Methods

Wild-grown Ganoderma lucidum (G. lucidum), a traditional Chinese herbal medicine, is highly cherished and expensive for its medicinal efficiency. This study targets the development of an accurate and effective analytical method to distinguish wild-grown G. lucidum from cultivated ones, which are of essential importance for the quality assurance and estimation of its medicinal value. Furthermore, different parts of G. lucidum have been studied to examine the differences between wild-grown and cultivated ones. Fourier transform infrared (FTIR) diffuse reflectance spectroscopy combined with the appropriate chemometric method has been proven to be a rapid and powerful tool for discrimination of wild-grown and cultivated G. lucidum with classification accuracy of 98%. The informative spectral absorption bands for discrimination emphasized by the linear diagnostic rule have provided quantitative interpretations of the chemical constituents of wild-grown G. lucidum regarding its anticancer effects.

Identification of Plant-Pathogenic Fungi Using Fourier Transform Infrared Spectroscopy Combined with Chemometric Analyses

Identification of plant-pathogenic fungi is time-consuming due to cultivation and microscopic examination and can be influenced by the interpretation of the micro-morphological characters observed.The present investigation aimed to create a simple but sophisticated method for the identification of plant-pathogenic fungi by Fourier transform infrared(FTIR)spectroscopy.In this study,FTIR-attenuated total reflectance(ATR)spectroscopy was used in combination with chemometric analysis for identification of important pathogenic fungi of horticultural plants.Mixtures of mycelia and spores from 27fungal strains belonging to nine different families were collected from liquid PD or solid PDA media cultures and subjected to FTIR-ATR spectroscopy measurements.The FTIR-ATR spectra ranging from 4 000to 400cm-1 were obtained.To classify the FTIRATR spectra,cluster analysis was compared with canonical vitiate analysis(CVA)in the spectral regions of3 050~2 800and 1 800~900cm-1.Results showed that the identification accuracies achieved 97.53%and99.18%for the cluster analysis and CVA analysis,respectively,demonstrating the high potential of this technique for fungal strain identification.

Identification of Eight Carnation Cultivars by Fourier Transform Infrared(FTIR) Spectroscopy

In order to distinguish eight carnation cultivars, 40 samples were analyzed by Fourier transform infrared （FI＇IR） spectroscopy combined with principal component analysis （PCA） and hierarchical cluster analysis （HCA）. According to the results, infrared spectra of eight carnation cuhivars were similar, but signifi- cant differences were observed in wave numbers and absorption peak intensities in the range of 1 800 -700cm-1. The second order derivative spectra in the range of 1 800 -700 cm -l were selected to perform principal component analysis （PCA） and hierarchical cluster analysis （HCA）. The cumulative contribution rate of the first three principal components reached 96.2%. The classification accuracy rate of PCA and HCA was 95% and 100%, respectively. The results demonstrated that Fourier transform infrared （FTIR） spectroscopy combined with principal component analysis （PCA） and hierarchical cluster analysis （HCA） could be used for identification of different carnation cultivars.

Evaluation of Fourier Transform Infrared Spectroscopy for Diagnosis of Peroxisomal Diseases with Abnormal Very-Long-Chain Fatty Acid Metabolism

Very long chain fatty acids (VLCFAs) are accumulated in cells and blood in patients with peroxisomal diseases, such as adrenoleukodystrophy (ALD) and Zellwger Syndrome (ZS). The purpose of this study is to investigate usefulness of Fourier transform infrared spectroscopy (FTIR) with attenuated total reflection (ATR) analysis method for clinical diagnosis of those diseases, thereby we measured the infrared spectra of the sera of patients and healthy controls. Correlation coefficients between 2nd derivative FTIR spectra of the serum samples and the VLCFA content ratio which is used as a clinical parameter to date were comprehensively calculated to investigate which wavenumber showed high correlation with the VLCFA ratio. Multiple regression analysis using the serum FTIR spectra showed that high correlations were observed with VLCFA ratios (C26:0/C22:0 ratio), and we could construct a suitable regression model (R2 = 0.97, p ﹣19). In addition, the model system using various VLCFAs in newborn bovine serum also showed that several FTIR peaks in 800 ~ 900 cm﹣1 region were found to have good correlation with VLCFA ratios. Our results support that FTIR analysis is useful for diagnosis of peroxisomal diseases.

Determination of creatinine level in patient blood samples by Fourier NIR spectroscopy and multivariate analysis in comparison with biochemical assay

In this paper,in vivo spectra from 23 patients'blood samples with various Creatinine(Cr)concentration levels ranging from 0.96 to 12.5 mg/dL were measured using Fourier transform near-infrared spectrometer(FT-NIRS)and spectrum quantitative analysis method.Since Cr undergoes passive filtration,it serves as a key biomarker of kidneys function via the estimation of glomerular filtration rate.Thus,increased blood Cr concentration reflects impaired renal func-tion.After spectra pre processing and outlier exclusion,a spectral model was developed based on partial least squares regression(PLSR)method,wherein Cr concentrations correlated with filtered NIR spectra across several peaks,where Cr is know n to absorb NIR light.Several statistical metrics were applied to estimate the model efficiency during data analysis.Comparison of spectra-derived concentrations to reference Cr measurements by the current gold-standard Jaffe's method held in hospital lab revealed a Cr prediction accuracy of 1.64 mg/dL with good corre-lation of R=0.9.Bland-Altman plots were used to compare between our calculations and ref-erence lab values and reveal minimal bias between the two.The finding presented the potential of FT-NIRS coupled with PLSR technique for Cr determination.

Study on FTIR Spectra of Corn Germs and Endosperms of Three Different Colors Combining with Cluster Analysis

[Objective] This research aimed to study the FTIR spectra of corn germs and endosperms so as to provide a scientific way for identifying corn of different types. [Method] The corn germs and endosperms of three types were studied by using Fourier transform infrared spectroscopy（FTIR） technology, combined with cluster analysis. [Result] The overall characteristics of original FTIR spectra were basically similar within the range of 700-1 800 cm^-1. The FTIR spectra were mainly composed by the absorption peaks of polysaccharides, proteins and lipids. Within the wavelength range of 700-1 800 cm^-1, there were only tiny differences in original FTIR spectra among the corn germs and endosperms of three different types. The spectra were then processed by using first derivative and second derivative. The second derivative spectra were used for hierarchical cluster analysis（HCA）. The results showed that with the wavelength range of 700-1 800 cm^-1, the second derivative spectra of the 52 samples could be better clustered according to the tree types and corn germ and corn endosperm. The clustering correct rate reached 96.1%.[Conclusion] FTIR technology, combined with cluster analysis, can be used to identify different types of corn germs and endosperms, and it is characterized by convenience and rapidness.

Comparative Study on the Infrared Spectra of Straight Guard Hairs of Leopard Cat and Golden Cat

[Objective] The paper was to compare the infrared spectra of straight guard hairs of leopard cat and golden cat. [Method] The straight guard hairs of leopard cat and golden cat were detected and analyzed by FTIR （Fourier Transform Infrared Spectroscopy）. [Result] There was significant interspecific difference in the infrared spectra and second derivative spectra for the middle part of guard hair. An evident M-shaped bimodal absorption peak appeared in golden cat at 648 and 654 cm-1, respectively, while did not appear in leopard cat. The second derivative spec-tra of golden cat at wave numbers from 709 to 763 cm -1 were mainly unimodal peaks with strong peak intensity, while that of leopard cat mainly was M-shaped bi-modal absorption peak with weak peak intensity. It indicated that there was obvious difference in the structure of protein side chain in guard hairs of these two animals. [Conclusion] The FTIR analysis showed great application foreground in the study of animal hairs for interspecific identification.

Characteristic wavelength selection of volatile organic compounds infrared spectra based on improved interval partial least squares

As important components of air pollutant,volatile organic compounds(VOCs)can cause great harm to environment and human body.The concentration change of VOCs should be focused on in real-time environment monitoring system.In order to solve the problem of wavelength redundancy in full spectrum partial least squares(PLS)modeling for VOCs concentration analysis,a new method based on improved interval PLS(iPLS)integrated with Monte-Carlo sampling,called iPLS-MC method,was proposed to select optimal characteristic wavelengths of VOCs spectra.This method uses iPLS modeling to preselect the characteristic wavebands of the spectra and generates random wavelength combinations from the selected wavebands by Monte-Carlo sampling.The wavelength combination with the best prediction result in regression model is selected as the characteristic wavelengths of the spectrum.Different wavelength selection methods were built,respectively,on Fourier transform infrared(FTIR)spectra of ethylene and ethanol gas at different concentrations obtained in the laboratory.When the interval number of iPLS model is set to 30 and the Monte-Carlo sampling runs 1000 times,the characteristic wavelengths selected by iPLS-MC method can reduce from 8916 to 10,which occupies only 0.22%of the full spectrum wavelengths.While the RMSECV and correlation coefficient(Rc)for ethylene are 0.2977 and 0.9999 ppm,and those for ethanol gas are 0.2977 ppm and 0.9999.The experimental results show that the iPLS-MC method can select the optimal characteristic wavelengths of VOCs FTIR spectra stably and effectively,and the prediction performance of the regression model can be significantly improved and simplified by using characteristic wavelengths.

Metabolome Comparison of Transgenic and Non-transgenic Rice by Statistical Analysis of FTIR and NMR Spectra

Modern biotechnology, based on recombinant DNA techniques, has made it possible to introduce new traits with great potential for crop improvement. However, concerns about unintended effects of gene transformation that possibly threaten environment or consumer health have persuaded scientists to set up pre-release tests on genetically modified organisms. Assessment of ＇substantial equivalence＇ concept that established by comparison of genetically modified organism with a comparator with a history of safe use could be the first step of a comprehensive risk assessment. Metabolite level is the dchest in performance of changes which stem from genetic or environmental factors. Since assessment of all metabolites in detail is very costly and practically impossible, statistical evaluation of processed data of grain spectroscopic values could be a time and cost effective substitution for complex chemical analysis. To investigate the ability of multivariate statistical techniques in comparison of metabolomes as well as testing a method for such comparisons with available tools, a transgenic rice in combination with its traditionally bred parent were used as test material, and the discriminant analysis were applied as supervised method and principal component analysis as unsupervised classification method on the processed data which were extracted from Fourier transform infrared spectroscopy and nuclear magnetic resonance spectral data of powdered rice and rice extraction and badey grain samples, of which the latter was considered as control. The results confirmed the capability of statistics, even with initial data processing applications in metabolome studies. Meanwhile, this study confirms that the supervised method results in more distinctive results.

Cluster Analysis for IR and NIR Spectroscopy: Current Practices to Future Perspectives

Supervised machine learning techniques have become well established in the study of spectroscopy data.However,the unsupervised learning technique of cluster analysis hasn’t reached the same level maturity in chemometric analysis.This paper surveys recent studies which apply cluster analysis to NIR and IR spectroscopy data.In addition,we summarize the current practices in cluster analysis of spectroscopy and contrast these with cluster analysis literature from the machine learning and pattern recognition domain.This includes practices in data pre-processing,feature extraction,clustering distance metrics,clustering algorithms and validation techniques.Special consideration is given to the specific characteristics of IR and NIR spectroscopy data which typically includes high dimensionality and relatively low sample size.The findings highlighted a lack of quantitative analysis and evaluation in current practices for cluster analysis of IR and NIR spectroscopy data.With this in mind,we propose an analysis model or workflow with techniques specifically suited for cluster analysis of IR and NIR spectroscopy data along with a pragmatic application strategy.

FOURIER TRANSFORM FAR INFRARED SPECTRA AND NORMAL COORDINATE ANALYSIS OF FIFTEEN RARE-EARTH OXALATE HYDRATES

The Fourier transform far infrared spectra of oxalate hydrates of whole rare-earth series except Pm and Sc are investigated in the range of 100-400 cm^(-1). The assignment of vibrational frequencies is made on the basis of normal coordinate analysis using our NORVIB program. The model used for the hghter elements of the lanthanide series (La to Eu) is based on the crystal structure of Nd_2(C_2O_4)_3. 10H_2O, in which each lanthanide ion is surrounded by nine O atoms, six from three oxalate ions and three from Water molecules.The model adopted for the heavier elements of the lanthanide series (Gd to Lu) and Ytterbium is based on the crystal structure of Yb_2(C_2O_4)_3· 6H_2O, in which each ion is surrounded by eight atoms, six from three oxalate ions and two from water molecules. The variation of the metal-ligand frequencies and force constants with the atomic numbers of lanthanides is plotted and discussed.

Thermogravimetric coupled with Fourier transform infrared analysis study on thermal treatment of monopotassium phosphate residue

In China,safe disposal of hazardous waste is more and more a necessity,urged by rapid economic development.The pyrolysis and combustion characteristics of a residue from producing monopotassium phosphate(monopotassium phosphate residue),considered as a hazardous waste,were studied using a thermogravimetric,coupled with Fourier transform infrared analyzer(TGFTIR).Both pyrolysis and combustion runs can be subdivided into three stages:drying,thermal decomposition,and final devolatilization.The average weight loss rate during fast thermal decomposition stage in pyrolysis is higher than combustion.Acetic acid,methane,pentane,(acetyl)cyclopropane,2,4,6-trichlorophenol,CO,and CO_(2) were distinguished in the pyrolysis process,while CO_(2) was the dominant combustion product.

基于Fourier变换红外光谱分析咖啡豆成分

采用Fourier变换红外光谱对不同产区、不同海拔的5组浅中烘焙的咖啡豆样品进行分析,根据官能团的红外光谱特征峰分析样品主要成分;为进一步分析5组样品的成分差异,对原始光谱进行二阶导数处理,结合聚类分析理论建立平均偏离分析方法,计算出5组样品间的平均偏离大小并进行分析.结果表明:5组样品的红外光谱特征峰峰形相似,即主要成分相同;根据红外光谱得到平均偏离大小与产区海拔差值呈正相关.研究结果为分析咖啡豆产区和海拔提供鉴别依据,并对红外光谱研究提供一定参考.

Discrimination of Handlebar Grip Samples by Fourier Transform Infrared Microspectroscopy Analysis and Statistics

In this paper,the authors presented a study on the discrimination of handlebar grip samples,to provide effective forensic science service for hit and run traffic cases.50 bicycle handlebar grip samples,49 electric bike handlebar grip samples,and 96 motorcycle handlebar grip samples have been randomly collected by the local police in Beijing(China).Fourier transform infrared microspectroscopy(FTIR)was utilized as analytical technology.Then,target absorption selection,data pretreatment,and discrimination of linked samples and unlinked samples were chosen as three steps to improve the discrimination of FTIR spectrums collected from different handlebar grip samples.Principal component analysis and receiver operating characteristic curve were utilized to evaluate different data selection methods and different data pretreatment methods,respectively.It is possible to explore the evidential value of handlebar grip residue evidence through instrumental analysis and statistical treatments.It will provide a universal discrimination method for other forensic science samples as well.

Diagnosis of colon cancer with Fourier transform infrared ;pectroscopy on the malignant colon tissue samples

Background Fourier transform infrared spectroscopy （FT-IR） combined with chemometrics discriminant analysis technology could improve diagnosis. The present study aimed to evaluate the effects of FT-IR on malignant colon tissue samples in diagnosis of colon cancer. Methods Principal component analysis （PCA） and support vector machine classification were used to discriminate FT-IR spectra from malignant and normal tissue. Colon tissues samples from 85 patients were used to demonstrate the procedure. Results For this set of colon spectral data, the sensitivity and specificity of the support vector machine （SVM） classification were found both higher than 90%. Conclusions FT-IR provided important information about cancerous tissue, which could be used to discriminate malignant from normal tissues. The combination of PCA and SVM classification indicated that FT-IR has a potential clinical application in diagnosis of colon cancer.

A study on the best irradiation dose of X-ray for Hep-2 cells by Fourier transform infrared spectroscopy

Fourier transform infrared spectroscopy （FTIR） was employed to study the human epidermis larynx carcinoma cell lines （Hep-2） which were irradiated by different doses of X-ray. The results show that （1） the irradiation of X-ray damages the structure of the CH3 groups of the thymine in DNA, which restrains the reproduction of Hep-2 cells effectively, （2） the 8 Gy dose of X-ray irradiation changes the framework and the relative contents of some proteins, lipids and the nucleic acid molecules intercellular in the greatest degree, and （3） the 8 Gy dose of X-ray irradiation is the best irradiation dose for lowering the degree of the cancerization of Hep-2 cells according to the criteria for the degree of the cancerization reported recently. Meanwhile, the apoptosis of these cells were detected by using flow cytometry （FCM） primarily. It shows that the apoptotic ratio of the Hep-2 cells depends on the irradiation dose to some extent, but is not linearly. And the apoptotic ratio of the 12 Gy dose group is the maximum （20.36%）, but the apoptotic ratios of the 2 to 8 Gy dose groups change little.

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 Fi300 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.