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.

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.

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.

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.

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.

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.

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.

应用衰减全反射红外光谱无损鉴别中国传统手工纸的方法研究

纤维种类鉴别是古代纸张保护的重要基础工作。探究中国传统手工纸的无损纤维鉴别的方法对中国古籍、档案及纸质文物的研究和保护具有重要意义。该研究应用衰减全反射傅里叶变换红外光谱(ATRFTIR)对已知纤维成分的17类64种中国传统手工纸标准样品进行了无损分析,参考纤维素和木质素的红外峰位及部分纸样的X射线衍射(XRD)分析结果确认纸样中的所有红外谱峰归属;并采用分波段比较分析的方法对相似度极高的谱图进行比对分析,分别总结各类纸张在4000~1800、1800~1500、1500~1200和1200~600cm^(-1)四个波段的谱图特征;同时对4cm^(-1)精度的谱图进行二阶导数处理,分别总结各类纸张在1500~1200和1200~900cm^(-1)两个波段的二阶导数谱图特征;最后通过红外结晶指数及其他峰高和峰面积比值计算结果实现对纸类更细致的区分。应用上述方法对16个盲测样品进行了有效性测试,红外分析结果与显微纤维鉴别结果一致,初步证明了该方法在中国传统手工纸无损纤维鉴别上的可行性和有效性。实验结果表明,应用ATR-FTIR无损分析法可对麻、桑构皮、檀皮、瑞香皮和竹几大类原料的传统手工纸纤维类型作出快速而准确的鉴别,对于混料的宣纸也同样适用;但对于显微分析也难以鉴别的亲缘关系较近的植物原料如桑、构皮之间的精细鉴别仍有一定局限性。

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.

常压下酸催化多元醇液化纤维素反应机理的研究

开发农林废弃物高值化利用路径与“深入推进能源绿色低碳转型”国家重大战略需求高度契合。实验探索农业废弃物玉米秸秆中纤维素组分常压酸(H3PO4)催化多元醇[丙二醇(PG)与二甘醇(DEG)混合液]液化反应路径为研究目标,了解酸催化多元醇作用条件下的木质生物质液化反应机理。采用傅里叶红外光谱(FTIR)、凝胶渗透色谱(GPC)、核磁共振波谱(NMR)及热重分析(TGA)对不同反应时间下纤维素液化所得生物油的化学基团、分子量及其分布、分子结构、热解等进行了分析。FTIR检测表明,不同反应时间所得生物油具有相似FTIR特征;液化初期,纤维素降解生成了较多的烃类、醚类和含羰基化合物;液化后期,纤维素中的糖类降解产物、羟基或烯烃与PG/DEG反应生成了难溶于1,4-二氧六环的有机物。GPC分析表明,随着液化的进行,纤维素分子链发生断裂程度加剧,降解生成低分子量(LMW)物质越来越多,降解反应占主导地位;而当反应时间达到某一值时,其降解产物与PG/DEG发生聚合反应产生越来越多的大分子量物质,使生物油的分子量不再降低,即聚合反应占主导地位。^(1)H-和^(13)C-NMR表明,纤维素在液化作用下发生降解使得分子链断裂,仍保留了部分葡萄糖单元结构;随着液化反应的进行,这些结构单元再次发生转化产生LMW化合物;反应继续进行时,这些产物之间或与PG/DEG之间可进行聚合反应,形成结构趋于一致、性质逐渐稳定的新物质。TGA分析结果:生物油中含有70%~85%碳数小于25和5%~10%碳数高于25的化合物,最终残炭率随着反应时间的延长先降低再升高。实验通过研究纤维素在多元醇液化过程中的结构变化,揭示其液化的反应历程,为探索秸秆全组分的液化机理奠定理论基础。

非线性最小二乘法在傅里叶变换红外光谱定量分析中的误差估计

根据统计学的参数估计理论,提出在傅里叶变换红外光谱定量分析中使用非线性最小二乘法进行浓度反演的参数误差估计方法。实验中,通过光谱平均次数来控制噪声水平,以此来评估不同噪声水平下混合气体中各组分反演结果的估计误差。结果表明,对于自研抽取式傅里叶变换红外光谱仪,8次平均光谱即可满足反演误差小于3%的需求,64次平均光谱的反演结果配合估计误差可以实现对平均浓度的覆盖率达到100%。随着噪声水平的降低,仪器、环境等非噪声因素的扰动是估计误差的主要部分。该方法在优化光谱定量分析的参数配置和指导光谱仪器系统设计等诸多方面具有重要的应用前景。

近红外与傅里叶变换红外光谱技术对麦麸固体发酵饲料中不同成分的监测研究

为实现光谱技术对麦麸固体发酵过程中不同成分变化的在线监测,通过国家标准方法测定61份麦麸固体发酵饲料样本的蛋白质、水分、总酚和粗纤维含量,采集样本近红外光谱(NIR)和傅里叶变换红外光谱(FT-IR),经过标准正态变换(standard normal variate transformation,SNV)、多元散射校正(multiplicative scatter correction,MSC)、平滑(smoothing)等9种预处理方法对原始光谱进行校正,结合偏最小二乘法(partial least squares,PLS)建立4种成分的NIR和FT-IR定量分析模型并进行比较分析。结果表明:所建立的4种成分NIR和FT-IR模型的训练集决定系数(Rc^(2))和验证集决定系数(Rp^(2))均大于0.8,交叉验证均方根误差(root mean square error of cross validation,RMSECV)小于2.0,训练集均方根误差(root mean square error of calibration,RMSEC)和验证集均方根误差(root mean square error of prediction,RMSEP)小于1.0。因此,所建立的NIR和FT-IR定量分析模型具有较好的准确性和稳定性,能够对麦麸固体发酵过程中不同成分变化实行快速监测。

红外光谱结合贝叶斯判别对洗发用品的分类研究

建立一种基于红外光谱的快速无损地检验洗发用品的分析方法。利用傅里叶红外光谱对60个常见的洗发用品样品进行检验,分别采用Savitzky-Golay(S-G)平滑、快速傅里叶变换(FFT)、降噪等方法对光谱数据进行预处理,并结合主成分分析法对光谱数据进行降维处理。同时建立多层感知器神经网络和贝叶斯判别分析两种分类模型,对光谱数据进行分析验证。多层感知器神经网络对原始数据、经过S-G平滑、FFT、降噪后的分类准确率分别为86.67%、88.33%、80%、90%,贝叶斯判别的分类准确率为83.33%、85%、83.33%、95%。结果显示,降噪处理效果较佳,贝叶斯判别具有更高的准确率。该方法重现性好、样品用量少、无损样品,可为洗发用品类物证鉴定提供科学依据。

傅里叶变换红外光谱技术在法庭科学中的应用现状

傅里叶变换红外光谱技术因其方法简单快捷、结果准确直观、样品损耗小、检测成本低廉而被认为是适合分析各类物证的技术之一。通过介绍傅里叶变换红外光谱技术的原理及运用傅里叶变换红外光谱技术进行死后间隔时间推断、避孕套有关成分分析、微生物鉴定、土壤分析和车辆物证分析等几个方面阐述了其在法庭科学领域中的研究和应用现状,希望能够为该技术在法庭科学中的应用提供参考,并为鉴定相关案件中的物证提供一种新的思路。

难浸润无烟煤分子模型构建

为了高效治理天地王坡难浸润煤尘污染问题,通过实验精准分析天地王坡煤矿粉尘成分及结构表征。通过元素分析、傅里叶变换红外光谱测试、X射线光电子能谱测试、固体核磁共振碳谱测试等实验方法确定了煤样的元素含量、表面官能团参数、芳香结构组成和脂肪结构的分布等情况,构建和优化了无烟煤分子结构模型。研究结果表明:天地王坡无烟煤分子化学结构式为C1_(91)H_(124)O_(14)N_(2)S_(1),总分子量为2700。经过几何优化、退火驰豫,得到无烟煤分子的能量最低三维结构模型。本研究为煤分子模型构建提供依据,天地王坡无烟煤尘治理提供指导。