藜麦产地的红外光谱鉴别

种植环境差异导致不同产地的藜麦有差异,故对不同产地的藜麦进行区分鉴别对商家、消费者具有重要参考价值。将中红外光谱与主成分分析(PCA)、线性判别分析(LDA)及混淆矩阵结合对不同产地藜麦进行鉴别研究。结果显示:藜麦的红外光谱主要由淀粉、蛋白质和脂质谱峰组成,且在蛋白质和糖类谱峰上有差异。用600~4000 cm^-1范围的原始光谱进行PCA分析,前两个主成分(PC)取得了92%的累计方差贡献率,基于PCA分析生成的PC进行LDA分析,取得了96.25%的分类精度。基于预测结果的混淆矩阵作为综合评价指标,得到PCA-LDA分类模型的精确度、召回率及特异性分别为96.25%、96.59%和99.48%,说明使用PCA-LDA模型可以对藜麦产地进行有效鉴别。研究表明红外光谱结合多元统计分析方法是鉴别藜麦产地的有效方法。     

不同地区八角莲的红外光谱分析

利用光谱学在不同样品中的特征吸收为基础,建立红外光谱学对不同产地的中药材快速鉴定方法,从而实现快速鉴别不同产地的同种中药材提供可高的依据。利用八角莲各种成分对红外光谱的吸收特征峰为基础,对5个不同产地的八角莲进行光谱照射,比较各产地八角莲的红外光谱特征峰,并寻找其差异。结果表明:不同产地八角莲药材红外光谱特征吸收峰的二阶导数图,表现出较为明显的差异,除了黔南布依族苗族自治州长顺县的八角莲在1400.32cm-1处没有吸收峰以外,其他四个地区的八角莲在此处均有吸收。广西桂林的八角莲在1200~1000cm-1范围内有5个吸收峰,而云南昆明的在此范围内只有3个吸收峰;四川凉山自治州冕宁县的八角莲在原图中就可以看出其C-O的伸缩振动吸收峰比较弱,且在1200~1000cm-1范围内只有两个吸收峰。梵净山的八角莲在此范围内的吸收峰最多,共有10个;原谱图中3143.97cm-1及1400.32cm-1峰的吸收峰基本不受其他成分的影响。因此,建立的红外光谱技术可以为不同地区八角莲药材的鉴别提供一种简便快捷的方法。     

不同产地、不同部位的石菖蒲与伪品水菖蒲的红外光谱鉴别研究

以石菖蒲为研究对象,采集了8个不同产地的石菖蒲,采用傅里叶红外光谱技术对石菖蒲的叶、根茎和根3个部位进行检测。检测结果表明石菖蒲主要吸收峰在3 366、2 925、1 738、1 643、1 411、1 152、1 078和1 018cm~(-1)附近,不同产地的石菖蒲的红外光谱的整体峰形相似,但吸收峰的强度和位置有差异。同一产地石菖蒲的不同部位的红外光谱及其特征性化学成分有差异,其中根茎在1 152、1 078和1 021cm~(-1)处,淀粉的特征吸收峰比较显著;根在1 640和1 512cm-1处,蛋白质的特征吸收峰更加明显;叶在763cm-1处吸收峰不明显,说明叶中草酸钙含量少或无。同时研究了石菖蒲和伪品水菖蒲,通过比较1 640和1 512cm-1附近吸收峰可知,水菖蒲中蛋白质的含量较石菖蒲高。红外光谱在石菖蒲不同产地、不同部位和真伪的鉴别中,揭示了整体化学成分,提供了有机大分子、无机小分子等特征性成分信息。     

基于近红外和中红外光谱的杜仲产地溯源

杜仲是一种重要而有价值的中药,具有多种医疗功能,多年来在中国、日本和韩国等亚洲国家被广泛用作保健食品。杜仲的功效和质量与产地密切相关。采用近中红外光谱与化学计量学相结合的方法,用于简便、快速和准确地鉴别杜仲的产地。使用k-最邻近分析(kNN)、主成分分析-线性判别分析(PCA-LDA)和偏最小二乘判别分析(PLS-DA)模型对杜仲样品进行了产地来源分类。结果表明,kNN模型更适合基于近红外光谱的不同省份杜仲样品的识别,kNN模型对来自8个省份的杜仲样本在训练集和测试集上的识别率均达到100%,交叉验证识别率为100%;PLS-DA模型更适合基于中红外光谱的不同省份杜仲样品的识别,PLS-DA模型在训练集和测试集中对来自8个省份的杜仲样品的识别率分别达到99.40%和98.61%,交叉验证识别率为99.11%。该方法可以快速、准确地确定杜仲的省份来源,有望应用于市场监督领域。     

不同产地桔梗的红外光谱整体成分鉴别研究

采用傅里叶变换红外光谱法和显微红外光谱技术对不同产地桔梗进行鉴别.结果表明,不同产地桔梗的一阶红外光谱图整体上差别并不明显,二阶导数处理后,各产地的特征峰差异得以显现,显微红外光谱图则进一步直观清晰地显现出桔梗皂苷d的含量多少与分布区域,进一步佐证了不同产地桔梗间桔梗皂苷d的含量差异的结果.根据不同产地地理远近来分析,...     

基于Fisher判别⁃支持向量机的汽车灯罩显微激光拉曼光谱研究

为实现对司法鉴定工作中经常遇到的汽车灯罩类物证进行数据化、可视化的无损高效率鉴别,采用PCA主成分分析前处理结合FDA-SVM（RBF）组合分析鉴别物证的方法,对获取的“奥迪”“别克”等18个品牌的173组拉曼红外光谱数据进行了实验和理论分析。借助Pearson相关性分析和PCA主成分分析的结果选择特征位移,分别建立基于Fisher判别分析和SVM支持向量机的数据分类模型。结果表明,FDA模型和SVM（RBF）模型对灯罩样本的综合区分准确率分别为97 %和51.85 %,SVM模型对“奔驰”“别克”等8个品牌的区分准确率达到了100 %,FDA与SVM模型互相补充的FDA-SVM（RBF）模型可对不同品牌灯罩拉曼红外光谱进行准确区分,分类效果较好。该方法高效、准确,对侦查破案中借助灯罩物证鉴定缩小侦察范围有一定的参考意义。     

不同产地、不同部位的丹参与南丹参的红外光谱研究

以丹参为研究对象,采用傅里叶红外光谱技术结合二阶导数红外光谱对不同产地、不同部位的丹参药材进行区分。结果表明,丹参的红外光谱图中主要吸收峰在3 271、2 929、1 607、1 510、1 397、1 260、1 143、1 026、872cm~(-1)附近,不同产地丹参的吸收峰基本相似,但吸收峰的位置及强度存在一定差异;同一产地丹参的不同部位红外光谱及其特征吸收峰有差异,在1 800～800cm~(-1)范围,须根的吸收峰强度根头部主根,推测须根中丹参酮类、丹酚酸类及糖类含量较根头部和主根中高。同时研究了丹参与其伪品南丹参,在1 607与1 026cm~(-1)附近,丹参吸收峰强度均明显高于南丹参,说明丹参中丹酚酸类和糖类的含量较南丹参高。对不同产地丹参进行系统聚类分析,结果发现,有效成分的分布与丹参的生态和产地呈一定的相关性,样品聚为两大类。红外光谱结合聚类分析法在丹参的鉴别中,不仅可以提供丹参主要化学成分的相关信息,还可以对不同产地的丹参进行分类鉴别。     

拉曼光谱结合化学计量学在不同品位磷矿快速分类中的应用

采用拉曼光谱技术结合化学计量学探讨不同品位磷矿快速鉴别和分类的可行性。采用共聚焦显微拉曼光谱系统分析了高、中、低三类不同品位的4种磷矿样品在200～1 950 cm^(-1)范围内的拉曼光谱特性,并对经过自适应迭代重加权惩罚最小二乘(airPLS)算法校正、一阶导和二阶导3种光谱预处理方法处理后的拉曼光谱结合主成分分析(PCA)和系统聚类分析(HCA)建立判别模型。结果显示,在主成分分析(PCA)中,经过3种预处理方法后的拉曼光谱均能实现对4种磷矿样本的聚类,且前两种预处理方式中,在第1主成分上,4种样品随品位值呈规律分布。使用PCA降维后的一阶导数光谱结合系统类分析(HCA)对4种磷矿样品进行分类,准确率为98.75%。结果表明,利用拉曼光谱技术结合化学计量学能够实现不同品位磷矿的快速鉴别和分类,为磷矿品位现场快速检测和评估打下基础。     

拉曼光谱结合化学计量学在不同品位磷矿快速分类中的应用

采用拉曼光谱技术结合化学计量学探讨不同品位磷矿快速鉴别和分类的可行性。采用共聚焦显微拉曼光谱系统分析了高、中、低三类不同品位的4种磷矿样品在200～1 950 cm~(-1)范围内的拉曼光谱特性,并对经过自适应迭代重加权惩罚最小二乘(airPLS)算法校正、一阶导和二阶导3种光谱预处理方法处理后的拉曼光谱结合主成分分析(PCA)和系统聚类分析(HCA)建立判别模型。结果显示,在主成分分析(PCA)中,经过3种预处理方法后的拉曼光谱均能实现对4种磷矿样本的聚类,且前两种预处理方式中,在第1主成分上,4种样品随品位值呈规律分布。使用PCA降维后的一阶导数光谱结合系统类分析(HCA)对4种磷矿样品进行分类,准确率为98.75%。结果表明,利用拉曼光谱技术结合化学计量学能够实现不同品位磷矿的快速鉴别和分类,为磷矿品位现场快速检测和评估打下基础。     

红外指纹图谱结合化学计量学在不同产地姜黄药材鉴别中的应用

目的:应用红外指纹图谱法检测来自于广东、广西、山东、安徽和四川的姜黄样品指纹图谱,采用多种化学计量学方法分析,以鉴别不同产地的姜黄药材。方法:利用共有峰率和变异峰率双指标模型,鉴别了不同产地的姜黄样品的红外指纹图谱,并同时运用模式识别对姜黄样品共有特征红外光谱吸收峰峰数据进行处理。结果:模式识别与双指标模型分析结果相同,广西的姜黄综合得分最高,为1.12,山东和安徽的姜黄共有峰率最高,达到100%。结论是红外指纹图谱与化学计量学结合,可以对中药进行简单快速准确鉴别,为中药品质的评价与产地的分类提供了另一种思路。     

Chemical Markers of Human Tendon Health Identified Using Raman Spectroscopy: Potential for In Vivo Assessment

The purpose of this study is to determine whether age-related changes to tendon matrix molecules can be detected using Raman spectroscopy. Raman spectra were collected from human Achilles (n = 8) and tibialis anterior (n = 8) tendon tissue excised from young (17 ± 3 years) and old (72 ± 7 years) age groups. Normalised Raman spectra underwent principal component analysis (PCA), to objectively identify differences between age groups and tendon types. Certain Raman band intensities were correlated with levels of advanced glycation end-product (AGE) collagen crosslinks, quantified using conventional destructive biochemistry techniques. Achilles and tibialis anterior tendons in the old age group demonstrated significantly higher overall Raman intensities and fluorescence levels compared to young tendons. PCA was able to distinguish young and old age groups and different tendon types. Raman intensities differed significantly for several bands, including those previously associated with AGE crosslinks, where a significant positive correlation with biochemical measures was demonstrated. Differences in Raman spectra between old and young tendon tissue and correlation with AGE crosslinks provides the basis for quantifying age-related chemical modifications to tendon matrix molecules in intact tissue. Our results suggest that Raman spectroscopy may provide a powerful tool to assess tendon health and vitality in the future.     

Evaluating the Performance of Detergent Powder Formulations Containing Nano Alumina Using Diffuse Reflectance Infrared Spectroscopy and Pattern Recognition Techniques

An analytical method has been introduced based on diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS) coupled with pattern recognition techniques to determine the efficiency of various detergent powder formulations containing nano alumina. In order to reach this aim, different multivariate classification methods such as principal component analysis, hierarchical cluster analysis and linear discriminate analysis (LDA) were utilized for diffuse reflectance spectra to evaluate the classification approach. The best model was predicted by LDA, with a correct classification rate (%CC) of 93.55 %. Furthermore, sensitivity and specificity for the test set were 0.90 and 0.95, respectively. These results confirm the capability of DRIFTS coupled with chemometric techniques for estimating the performance of detergent powder formulations containing nano alumina.     

A Systematic Chemometric Approach to Identify the Geographical Origin of Olive Oils

The verification of the geographical origin of olive oils by analytical techniques is still a challenge. The goal of this work is to explore the application and accuracy of different chemometric tools combined with near infrared spectroscopy (NIR) based analytical methods in the field of geographical authenticity of olive oils. As olive oils associated with different geographical origins are mainly characterized by different fatty acid (FA) and triacylglycerol (TAG) compositions, NIR methods for the fast and reliable determination of these parameters are developed. Next, these NIR methods are used to characterize a comprehensive set of olive oils (n > 5000) derived from 19 different countries. This set of data is used to build a statistical workflow, which allows the determination of the geographical origin of unknown olive oil samples. First of all, the untreated data set is pretreated by k‐means clustering and the selection of the relevant analytical variables by principal component analysis (PCA) and linear discriminant analysis (LDA) and min/max normalization of all parameters. Subsequently, classification is performed with a reduced sample set of the 200 most similar samples identified by k‐nearest neighbor tool (kNN). For classification purpose kNN, LDA, naïve Bayes classifier, and logit regression are applied. Practical Applications: The established statistical workflow can be used to verify the geographical origin of olive oils. The application and usage of up to four different statistical models for classification purpose results in a superior probability of the predicted origin in comparison to the application of only one single statistical classification test. As standardized methods are used as reference methods for building the NIR methods, the FA and TAG composition and the iodine value can be either determined by the standard methods or by the described NIR method. The presented statistical approach will help to build up a system for the verification of the geographical origin of olive oils.     

Discrimination of Cod Liver Oil According to Wild/Farmed and Geographical Origins by GC and <Superscript>13</Superscript>C NMR

The objective of this study was to test the possibility of using lipid profiles obtained by gas chromatography (GC) and ¹³C nuclear magnetic resonance (NMR) in authentication of cod liver oils according to wild/farmed and geographical origin. GC and ¹³C-NMR data of cod liver oil from wild and farmed fish from different locations in Norway and Scotland were obtained, and analyzed by principal component analysis (PCA) and linear discriminant analysis (LDA) to test if it was possible to differentiate oil from wild and cultured cod (Gadus morhua L.), and to further elucidate differences between fish from the different farms/catch area. Cod liver oils of wild and farmed origin were clearly separated in the PCA score plot both from GC and NMR data. From NMR data it was also possible to observe groupings based on geographical origin (farm/catch area) of the different samples. Using LDA with cross validation the wild/farmed classification rates were 97% for GC data and 100% for NMR data. In the classification of cod liver oils according to geographical origin (38 samples from six different farms/catch area), the correct classification rate was 63% for GC data and 95% for NMR data.     

Monitoring the effects of selective laser sintering (SLS) build parameters on polyamide using near infrared spectroscopy

Near infrared spectroscopy has been used to monitor the effects of changing build parameters on the sintering process of selective laser sintering components. The surface roughness of the parts produced has been studied whilst modifying laser scan speed and laser power build parameters. Near infrared spectroscopy is shown to be a powerful tool in detecting subtle variations in the coalescence of particles that form the surface topology of the component. Principal component analysis (PCA) performed on the diffuse reflectance spectra obtained from the surface of the components shows a strong correlation between near infrared (NIR) spectra and build parameters. Using the chemometric model produced from the PCA analysis it is possible to calculate build parameters for unknown components, making NIR a useful aid for quality control of additive manufacturing technologies. © 2011 Wiley Periodicals, Inc. J Appl Polym Sci, 2011     

GMM and optimal principal components-based Bayesian method for multimode fault diagnosis

Principal component analysis (PCA) serves as the most fundamental technique in multivariate statistical process monitoring. However, other than determining contributions to a fault from each variable based on the pre-selected major principal components (PCs), the PCA-based fault diagnosis with an optimal selection of PCs is seldom investigated. This paper presents a novel Gaussian mixture model (GMM) and optimal principal components (OPCs)-based Bayesian method for efficient multimode fault diagnosis. First, the GMM and Bayesian inference is utilized to identify the operating mode, and then local PCA model is established in each mode. Second, given that the various principal components (PCs) may contain distinct fault signatures, the behavior of each PC in local PCA is examined and the OPCs are selected through stochastic optimization algorithm. Based on the OPCs, a Bayesian diagnosis system is then formulated to identify the fault statuses in a probability manner. Performance of GMM–OPC Bayesian diagnosis is examined through a numerical example and the Tennessee Eastman challenge process. The efficiency and feasibility are demonstrated.     

Non–destructive near–infra–red analysis for the identification of dyes on textiles

A pattern–recognition algorithm combined with near–infra–red reflectance spectroscopy has been modified to function as a non–destructive analysis technique for identifying dyes present on textiles. Samples of 261 dyes and textiles were measured in the 1100–2500 nm region to form a near–infrared (reflectance) spectral library. Principal component analysis (PCA) was used to generate an orthonormal reference library from the library of original spectra. The PCA algorithm treats the spectra in the library as an n component quantitative analysis problem in which each spectrum represents a standard mixture having a concentration of 1. 0 for that component. Spectra of dyed textiles were used as an unknown set in a library search. This new method saves time and materials in comparison with traditional methods of analysing dyes present on textile fibres. The library of dye spectra can be developed from measurements made directly on dye powder without interference from inorganic diluents. The method was successfully used to identify the dyes present on five textiles. The technique is particularly well suited for studying forensic, historic and archaeological textiles because of its non–destructive nature and ability to analyse small amounts of sample.     

Differentiation of Lard From Other Edible Fats and Oils by Means of Fourier Transform Infrared Spectroscopy and Chemometrics

Fourier transform infrared (FTIR) spectra at mid infrared regions (4,000–650 cm⁻¹) of lard and 16 edible fats and oils were compared and differentiated. The chemometrics of principal component analysis and cluster analysis (CA) was used for such differentiation using FTIR spectra intensities of evaluated fats and oils. With PCA, an “eigenvalue” of about 90% was achieved using four principal components (PCs) of variables (FTIR spectra absorbances at the selected frequency regions). PC1 accounted for 44.1% of the variation, while PC2 described 30.2% of the variation. The main frequency regions that influence the separation of lard from other evaluated fats and oils based on PC1 are 2,852.8 followed by 2,922 and 1,464.7 cm⁻¹. Furthermore, CA can classify lard into its group based on Euclidean distance.