偏最小二乘法在红外光谱识别茶叶中的应用

采用漫反射傅立叶变换红外光谱(FTIR)法结合主成分分析(PCA)、偏最小二乘法(PLS)、簇类的独立软模式(SIMCA)识别法对十三种茶叶进行了分类判别研究。研究结果表明,通过多元散射校正(MSC)对原始光谱进行预处理,可以提高模式识别技术的分类判别效果。在此基础上,选取1 900~900 cm-1波长范围内的茶叶红外光谱建立识别模型,三种方法都得到了满意的分类判别效果。在对检验集中全部130个样本的判别中,PCA仅有两类样本无法判别,SIMCA的识别率和拒绝率都在90%以上,而PLS的识别效果最佳,全部样本都得到了正确的归类。这一研究结果表明傅立叶变换红外光谱法与化学计量学方法相结合可以实现茶叶品种的快速鉴别,这为茶叶的客观评审提供了一种新思路。     

Application to classification of mulberry leaves using multivariate analysis of proton NMR metabolomic data

Recently, NMR-based metabolomic analysis has been used to acquire information based on differentiation among biological samples. In the present study, we examined whether multivariate analysis was able to be applied to natural products and/or material field. Each extraction of 24 leaf samples, divided into six locations from the tip of the stem in each of four strains, was analyzed by pattern recognition methods, known as Principal Component Analysis (PCA) and Soft Independent Modeling of Class Analogy (SIMCA). Twenty-four extracts from mulberry leaf showed independent spectra by 1H NMR. The separation of leaf extraction data due to the difference at six locations was achieved in the PCA score plot as correlation PC1 (86.1%) and PC3 (4.6%) and showed two loading plots, suggesting classification by leaf position as an independent variable in the loading plot. Moreover, the difference among six locations clarified the seven highest discrimination powers by the SIMCA method. Meanwhile, the PCA score plot obtained classification by the variety of mulberry strains with three loading plots, but the SIMCA method did not give a peak by classification.     

Pattern recognition analysis for classification of hypertensive model rats and diurnal variation using 1H-NMR spectroscopy of urine.

Urine samples were collected during the daytime and nighttime from spontaneously hypertensive model rats and normal rats without dosing. The 1H NMR spectra were measured for their urine samples, and analyzed by a pattern recognition method, known as Principal Component Analysis (PCA) and Soft Independent Modeling of Class Analogy (SIMCA). The separation of urinary data due to the diurnal variation (daytime and nighttime) and also to the difference between the two strains of rat was achieved in the PCA score plot. Differences of the urinary profiles in the respective separation were effectively extracted as marker variables by the SIMCA method. NMR measurements coupled with pattern recognition methods provide a straightforward approach to inspect the disease metabolic status and the preliminary screening tool of marker candidates for further development.     

傅里叶变换红外光谱结合模式识别法快速鉴别食用油的真伪

采用傅里叶变换红外光谱(FTIR)结合簇类独立软模式识别技术(SIMCA)建立了真伪食用油的快速鉴别方法. 该方法依据FTIR 的指纹特性, 收集并分析了53 个合格食用油和13 个伪造食用油的FTIR 谱图; 通过对谱图取二阶导数和标准化处理, 主成分分析(PCA)提取特征变量; 采用SIMCA 方法分别随机选取43 个合格食用油和9 个伪食用油样品的FTIR 谱图组成训练集, 构建得到真伪食用油的SIMCA 分类模型. 该模型经过剩余10 个合格食用油和4 个伪食用油的验证, 正确识别率达到了100%. 说明FTIR 结合SIMCA 可能成为快速鉴别食用油真伪的一种新方法.     

Multivariate pattern recognition of petroleum-based accelerants by solid-phase microextraction gas chromatography with flame ionization detection

A novel method has been developed for the extraction, analysis and identification of petroleum-based fuels using solid-phase microextraction with analysis by GC-FID. Multivariate data analysis is employed to simplify these data allowing for more accurate classification. Principal component analysis (PCA) and soft independent modeling of class analogy (SIMCA) are explored for their effectiveness in establishing accelerant groupings based on the current and previous ASTM International guidelines. The SIMCA models developed for the previous and current ASTM system were 98.5% and 97.2% accurate in unknown sample class prediction. SPME in conjunction with multivariate data analysis is a new approach in accelerant sampling and classification.     

基于质子转移反应-飞行时间质谱快速鉴别不同产地闽北水仙茶

采用质子转移反应-飞行时间质谱仪(PTR-TOF-MS), 构建了3个产地(武夷山、建阳、建瓯)113个闽北水仙茶样品香气的化学指纹图谱, 对所得的闽北水仙茶香气指纹图谱进行主成分分析(PCA), 获得了不同产地闽北水仙茶样品的质谱信息特征, 然后采用软独立建模分类法(SIMCA)、K最邻近结点算法(KNN)、偏最小二乘判别分析法(PLS-DA)对闽北水仙茶的质谱信息进行了模式识别.结果表明, PTR-TOF-MS结合分类识别模式能有效区分不同产地的闽北水仙茶.PCA 提取了3个主成分, 累计贡献率为84.66%;3个识别模型的校正集判别正确率分别为89.38%、100.00%和100.00%, 预测集的判别正确率分别为83.18%、 96.46%和95.57%.基于此成功建立了不同产地的闽北水仙茶识别模型.本方法无需样品预处理、分析速度快、灵敏度高、对茶叶无损伤, 为茶叶产地溯源提供了新方法.     

Classification of rapeseed and soybean oils by use of unsupervised pattern-recognition methods and neural networks

Unsupervised pattern-recognition methods and Kohonen neural networks have been applied to the classification of rapeseed and soybean oil samples according to their type and quality by use of chemical and physical properties (density, refractive index, saponification value, and iodine and acid numbers) and thermal properties (thermal decomposition temperatures) as variables. A multilayer feed-forward (MLF) neural network (NN) has been used to select the most important variables for accurate classification of edible oils. To accomplish this task different neural networks architectures trained by back propagation of error method, using chemical, physical, and thermal properties as inputs, were employed. The network with the best performance and the smallest root mean squared (RMS) error was chosen. The results of MLF network sensitivity analysis enabled the identification of key properties, which were again used as variables in principal components analysis (PCA), cluster analysis (CA), and in Kohonen self-organizing feature maps (SOFM) to prove their reliability.     

Screening Brazilian commercial gasoline quality by hydrogen nuclear magnetic resonance spectroscopic fingerprintings and pattern-recognition multivariate chemometric analysis

The identification of gasoline adulteration by organic solvents is not an easy task, because compounds that constitute the solvents are already in gasoline composition. In this work, the combination of Hydrogen Nuclear Magnetic Resonance (¹H NMR) spectroscopic fingerprintings with pattern-recognition multivariate Soft Independent Modeling of Class Analogy (SIMCA) chemometric analysis provides an original and alternative approach to screening Brazilian commercial gasoline quality in a Monitoring Program for Quality Control of Automotive Fuels. SIMCA was performed on spectroscopic fingerprints to classify the quality of representative commercial gasoline samples selected by Hierarchical Cluster Analysis (HCA) and collected over a 6-month period from different gas stations in the São Paulo state, Brazil. Following optimized the ¹H NMR-SIMCA algorithm, it was possible to correctly classify 92.0% of commercial gasoline samples, which is considered acceptable. The chemometric method is recommended for routine applications in Quality-Control Monitoring Programs, since its measurements are fast and can be easily automated. Also, police laboratories could employ this method for rapid screening analysis to discourage adulteration practices.     

Flow injection mass spectroscopic fingerprinting and multivariate analysis for differentiation of three Panax species

This study describes the use of spectral fingerprints acquired by flow injection(FI)-MS and multivariate analysis to differentiate three Panax species: P. ginseng, P. quinquefolius, and P. notoginseng. Data were acquired using both high resolution and unit resolution MS, and were processed using principal component analysis (PCA), soft independent modeling of class analogy (SIMCA), partial least squares-discriminant analysis (PLS-DA), and a fuzzy rule-building expert system (FuRES). Both high and unit resolution MS allowed discrimination among the three Panax species. PLS-DA and FuRES provided classification with 100% accuracy while SIMCA provided classification accuracies of 77 and 88% by high- and low-resolution MS, respectively. The method does not quantify any of the sample components. With FI-MS, the analysis time was less than 2 min.     

Application of multivariate methods to scalp hair metal data to distinguish between drug-free subjects and drug abusers

Human scalp hair samples of drug-free subjects and drug abusers (heroin and cocaine-heroin abusers) were analysed for trace metals by flame atomic absorption spectrometry (FAAS), flame atomic emission spectrometry (FAES) and electrothermal atomic absorption spectrometry (ETAAS). The classification of drug-free subjects and drug abuses groups with four multivariate methods using the metal contents in hair samples as discriminant variables has been discussed. Principal component analysis (PCA), cluster analysis (CA), linear discriminant analysis (LDA) and soft independent modelling of class analogy (SIMCA) allow distinguishing the two groups correctly. However, predictions by SIMCA are less satisfactory. Thirteen elements (Ag, Al, Ca, Cd, Cr, Cu, K, Mg, Mn, Na, Ni, Pb, and Zn) were determined by FAAS/FAES/ETAAS in 53 hair samples (16 samples of drug-free people and 37 samples of drug abusers). Human hair samples were prepared as aqueous slurries as sample pre-treatment and they were analysed using the slurry sampling technique. The half-range central value transformation was novelty used as data pre-treatment to homogenise the data. Grouping in the samples (drug-free people and drug abusers) were observed by using PCA and CA (squared Euclidean distance between objects and Ward method as clustering procedure). The application of LDA gave a correct recognition assignation percentage of 91.7 and 100.0% for the drug-free people and drug abusers, respectively, at a significance of 5%, while SIMCA offered recognition percentages of 83.3 and 91.3% for drug-free people and drug abusers, respectively, also at 5%. Finally, some studies were developed to classify heroin abusers and polidrug abusers (cocaine-heroin abusers) by the cited multivariate statistical methods. Recognition percentages of 90.9 and 100.0% were reached for heroin abusers and polidrug abusers groups, respectively, after LDA, while these percentages decreased to percentages lower than 90.0% when SIMCA was applied.     

Screening Brazilian Commercial Gasoline Quality by ASTM D6733 GC and Pattern-Recognition Multivariate SIMCA Chemometric Analysis

The combination of ASTM D6733 gas chromatographic fingerprinting data with pattern-recognition multivariate soft independent modeling of class analogy (SIMCA) chemometric analysis provides an original and alternative approach to screening Brazilian commercial gasoline quality in a monitoring program for quality control of automotive fuels. SIMCA was performed on chromatographic fingerprints to classify the quality of the gasoline samples. Using SIMCA, it was possible to correctly classify 94.0% of commercial gasoline samples, which is considered acceptable. The method is recommended for quality-control monitoring. Quality control and police laboratories could employ this method for rapid monitoring.     

Accurate grading of brain gliomas by soft independent modeling of class analogy based on non‐negative matrix factorization of proton magnetic resonance spectra

Hydrogen magnetic resonance spectroscopy (¹H‐MRS) is a non‐invasive technique which provides a ‘frequency‐signal intensity’ spectrum of biochemical compounds of tissues in the body. Although this method is currently used in human brain studies, accurate classification of in‐vivo ¹H‐MRS is a challenging task in the diagnosis of brain tumors. Problems such as overlapping metabolite peaks, incomplete information on background component and low signal‐to‐noise ratio disturb classification results of this spectroscopic method. This study presents an alternative approach to the soft independent modeling of class analogy (SIMCA) technique, using non‐negative matrix factorization (NMF) for dimensionality reduction. In the adopted strategy, the performance of SIMCA was improved by application of a robust algorithm for classification in the presence of noisy measurements. Total of 219 spectra from two databases were taken by water‐suppressed short echo‐time ¹H‐MRS, acquired from different subjects with different stages of glial brain tumors (Grade II (26 cases), grade III (24 cases), grade IV (41 cases), as well as 25 healthy cases). The SIMCA was performed using two approaches: (i) principal component analysis (PCA) and (ii) non‐negative matrix factorization (NMF), as a modified approach. Square prediction error was considered to assess the class membership of the external validation set. Finally, several figures of merit such as the correct classification rate (CCR), sensitivity and specificity were calculated. Results of SIMCA based on NMF showed significant improvement in percentage of correctly classified samples, 91.4% versus 83.5% for PCA‐based model in an independent test set. Copyright © 2015 John Wiley & Sons, Ltd.     

顶空固相微萃取-全二维气相色谱/飞行时间质谱法分析闽南乌龙茶中的挥发性成分及其在分类中的应用

采用顶空固相微萃取(HS-SPME)结合全二维气相色谱/飞行时间质谱(GC×GC-TOF MS)分析了闽南乌龙茶中的挥发性成分。从48份不同等级和产季的乌龙茶(铁观音、黄金桂、本山、毛蟹和梅占)中获得了2000余种挥发性化合物,经筛选得到51种共有组分,并结合质谱数据库、保留指数与结构谱图等进行了初步鉴定。在此基础上采用主成分分析法(PCA)获得得分投影图,直观给出了不同样品的分类趋势。通过逐步判别获得9种对分类结果有显著影响的组分,并以此为变量通过Fisher判别法(FDA)建立了4个判别函数,对样品的分类准确率达到97.9%。本试验证实了以挥发性成分识别闽南乌龙茶的可行性。     

Classification of biodiesel using NIR spectrometry and multivariate techniques

This article describes the classification of biodiesel samples using NIR spectroscopy and chemometric techniques. A total of 108 spectra of biodiesel samples were taken (being three samples each of four types of oil, cottonseed, sunflower, soybean and canola), from nine manufacturers. The measurements for each of the three samples were in the spectral region between 12,500 and 4000 cm⁻¹. The data were preprocessed by selecting a spectral range of 5000-4500 cm⁻¹, and then a Savitzky-Golay second-order polynomial was used with 21 data points to obtain second derivative spectra. Characterization of the biodiesel was done using chemometric models based on hierarchical cluster analysis (HCA), principal component analysis (PCA) and soft independent modeling of class analogy (SIMCA) elaborated for each group of biodiesel samples (cotton, sunflower, soybean and canola). For the HCA and PCA, the formation of clusters for each group of biodiesel was observed, and SIMCA models were built using 18 spectral measurements for each type of biodiesel (training set), and nine spectral measurements to construct a classification set (except for the canola oil which used eight spectra). The SIMCA classifications obtained 100% accurate identifications. Using this strategy, it was feasible to classify biodiesel quickly and nondestructively without the need for various analytical determinations.     

The Use of UV Spectroscopy and SIMCA for the Authentication of Indonesian Honeys According to Botanical,Entomological and Geographical Origins

As a functional food, honey is a food product that is exposed to the risk of food fraud. To mitigate this, the establishment of an authentication system for honey is very important in order to protect both producers and consumers from possible economic losses. This research presents a simple analytical method for the authentication and classification of Indonesian honeys according to their botanical, entomological, and geographical origins using ultraviolet (UV) spectroscopy and SIMCA (soft independent modeling of class analogy). The spectral data of a total of 1040 samples, representing six types of Indonesian honey of different botanical, entomological, and geographical origins, were acquired using a benchtop UV-visible spectrometer (190–400 nm). Three different pre-processing algorithms were simultaneously evaluated; namely an 11-point moving average smoothing, mean normalization, and Savitzky–Golay first derivative with 11 points and second-order polynomial fitting (ordo 2), in order to improve the original spectral data. Chemometrics methods, including exploratory analysis of PCA and SIMCA classification method, was used to classify the honey samples. A clear separation of the six different Indonesian honeys, based on botanical, entomological, and geographical origins, was obtained using PCA calculated from pre-processed spectra from 250–400 nm. The SIMCA classification method provided satisfactory results in classifying honey samples according to their botanical, entomological, and geographical origins and achieved 100% accuracy, sensitivity, and specificity. Several wavelengths were identified (266, 270, 280, 290, 300, 335, and 360 nm) as the most sensitive for discriminating between the different Indonesian honey samples.     

原位实时近红外光谱研究核壳乳液聚合过程

将苯乙烯(St)和丙烯酸丁酯(BA)单体以不同的聚合方式制备核壳乳液和共聚乳液, 并采用近红外光谱技术实现了对乳液反应过程的原位实时监测, 通过对近红外光谱的谱带归属和主成分分析, 为近红外光谱技术判别乳液聚合过程提供了科学依据, 也为判断反向核壳乳液核壳翻转的拐点提出了一种新的方法. 采用簇类独立软模式法(SIMCA)建立了定性判别模型, 得到了很好的判别结果, 为进一步研究近红外光谱技术用于核壳乳液聚合过程奠定了基础.     

Effect of scaling on class modeling with the simca method

The first step in multivariate analysis is almost always the scaling of the variables. The pattern recognition technique SIMCA provides the possibility of scaling the variables over all the objects of the training set (classical scaling), or only over the objects belonging to the same group (separate scaling). The former method of scaling is the more used. The effect of separate scaling on the classification of objects with SIMCA is investigated for a data set consisting of the percentage distribution of fatty acids in olive oils originating from two neighbouring regions in Italy. It is shown that separate scaling has a beneficial effect on the classification.     

Carbon nuclear magnetic resonance spectroscopic fingerprinting of commercial gasoline: Pattern-recognition analyses for screening quality control purposes

In this work, the combination of carbon nuclear magnetic resonance (¹³C NMR) fingerprinting with pattern-recognition analyses provides an original and alternative approach to screening commercial gasoline quality. Soft Independent Modelling of Class Analogy (SIMCA) was performed on spectroscopic fingerprints to classify representative commercial gasoline samples, which were selected by Hierarchical Cluster Analyses (HCA) over several months in retails services of gas stations, into previously quality-defined classes. Following optimized ¹³C NMR-SIMCA algorithm, sensitivity values were obtained in the training set (99.0%), with leave-one-out cross-validation, and external prediction set (92.0%). Governmental laboratories could employ this method as a rapid screening analysis to discourage adulteration practices.