Principal Component-Discrimination Model and Its Application

Having researched for many years, seismologists in China presented about 80 earthquake prediction factors which reflected omen information of earthquake. How to concentrate the information that the 80 earthquake prediction factors have and how to choose the main factors to predict earthquakes precisely have become one of the topics in seismology. The model of principal component-discrimination consists of principal component analysis, correlation analysis, weighted method of principal factor coefficients and Mahalanobis distance discrimination analysis. This model combines the method of maximization earthquake prediction factor information with the weighted method of principal factor coefficients and correlation analysis to choose earthquake prediction variables, applying Mahalanobis distance discrimination to establishing earthquake prediction discrimination model. This model was applied to analyzing the earthquake data of Northern China area and obtained good prediction results.

Near-Infrared Spectroscopy Combined with Partial Least Squares Discriminant Analysis Applied to Identification of Liquor Brands

The identification of liquor brands is very important for food safety. Most of the fake liquors are usually made into the products with the same flavor and alcohol content as regular brand, so the identification for the liquor brands with the same flavor and the same alcohol content is essential. However, it is also difficult because the components of such liquor samples are very similar. Near-infrared (NIR) spectroscopy combined with partial least squares discriminant analysis (PLS-DA) was applied to identification of liquor brands with the same flavor and alcohol content. A total of 160 samples of Luzhou Laojiao liquor and 200 samples of non-Luzhou Laojiao liquor with the same flavor and alcohol content were used for identification. Samples of each type were randomly divided into the modeling and validation sets. The modeling samples were further divided into calibration and prediction sets using the Kennard-Stone algorithm to achieve uniformity and representativeness. In the modeling and validation processes based on PLS-DA method, the recognition rates of samples achieved 99.1% and 98.7%, respectively. The results show high prediction performance for the identification of liquor brands, and were obviously better than those obtained from the principal component linear discriminant analysis method. NIR spectroscopy combined with the PLS-DA method provides a quick and effective means of the discriminant analysis of liquor brands, and is also a promising tool for large-scale inspection of liquor food safety.

Moving-window bis-correlation coefficients method for visible and near-infrared spectral discriminant analysis with applications

The moving window bis corelation coefficients(MW BiCC)was proposed and employed for the discriminant analysis of transgenic sugarcane leaves and B-thalassemia with visible and near-infrared(Vis NIR)spectroscopy.The well-performed moving window principal component analysis linear discriminant analysis(MWPCA-LDA)was also conducted for comparison.A total of 306 transgenic(positive)and 150 nont ransgenic(negative)leave samples of sugarcane were collected and divided to calibration,prediction,and validation.The diffuse reflection spectra were corected using Savitzky-Golay(SG)smoothing with first-order derivative(d=1),third-degree polynomial(p=3)and 25 smpothing points(m=25).The selected waveband was 736-1054nm with MW-BiCC,and the positive and negative validation recognition rates(V_REC^(+),VREC^(-))were 100%,98.0%,which achieved the same effect as MWPCA-LDA.Another example,the 93 B-thalassemia(positive)and 148 nonthalassemia(negative)of human hemolytic samples were colloctod.The transmission spectra were corrected using SG smoothing withd=1,p=3 and m=53.Using M W-BiCC,many best wavebands were selected(e.g.,1116-1146,17941848 and 22842342nm).The V_REC^(+)and V_REC^(-)were both 100%,which achieved the same effect as MW-PCA-LDA.Importantly,the BICC only required ca lculating correlation cofficients between the spectrum of prediction sample and the average spectra of two types of calibration samples.Thus,BiCC was very simple in algorithm,and expected to obtain more applications.The results first confirmed the feasibility of distinguishing B-thalassemia and normal control samples by NIR spectroscopy,and provided a promising simple tool for large population thalassemia screening.

On-line Batch Process Monitoring and Diagnosing Based on Fisher Discriminant Analysis

A new on-line batch process monitoring and diagnosing approach based on Fisher discriminant analysis (FDA) was proposed. This method does not need to predict the future observations of variables, so it is more sensitive to fault detection and stronger implement for monitoring. In order to improve the monitoring performance, the variables trajectories of batch process are separated into several blocks. The key to the proposed approach for on-line monitoring is to calculate the distance of block data that project to low-dimension Fisher space between new batch and reference batch. Comparing the distance with the predefine threshold, it can be considered whether the batch process is normal or abnormal. Fault diagnosis is performed based on the weights in fault direction calculated by FDA. The proposed method was applied to the simulation model of fed-batch penicillin fermentation and the results were compared with those obtained using MPCA. The simulation results clearly show that the on-line monitoring method based on FDA is more efficient than the MPCA.

Multispectral Imaging in Combination with Multivariate Analysis Discriminates Selenite Induced Cataractous Lenses from Healthy Lenses of Sprague-Dawley Rats

Cataracts are the leading cause of blindness worldwide. Current methods for discriminating cataractous lenses from healthy lenses of Sprague-Dawley rats during preclinical studies are based on either histopathological or clinical assessments which are weakened by subjectivity. In this work, both cataractous and healthy lens tissues of Sprague-Dawley rats were studied using multispectral imaging technique in combination with multivariate analysis. Multispectral images were captured in transmission, reflection and scattering modes. In all, five spectral bands were found to be markers for discriminating cataractous lenses from healthy lenses;470 nm and 625 nm discriminated in reflection mode whereas 435 nm, 590 nm and 700 nm discriminated in transmission mode. With Fisher’s Linear discriminant analysis, the midpoints for classifying cataractous from healthy lenses were found to be 14.718 × 10−14 and 3.2374 × 10−14 for the two spectra bands in the reflection mode and the three spectral bands in the transmission mode respectively. Images in scattering mode did not show significant discrimination. These spectral bands in reflection and transmission modes may offer potential diagnostic markers for discriminating cataractous lenses from healthy lenses thereby promising multispectral imaging applications for characterizing cataractous and healthy lenses.

Discrimination of toxic ingredient between raw and processed Pinellia ternata by UPLC/Q-TOF-MS/MS with principal component analysis and T-test

Objective: To investigate the toxicity difference between raw and processed Pinelliae Rhizoma(Banxia in Chinese, BX), the rhizoma of Pinellia ternata, from the view of chemical composition.Methods: Sixteen samples of raw and processed BX were prepared and analyzed by UPLC/Q-TOF-MS/MS.The discrimination(chemical marker) between the two group was investigated by principal component analysis(PCA) and T-test analysis. According to the accurate charge-to-mass ratio, MS/MS fragments, and comparison of corresponding data with the reference or database, the chemical markers were identified preliminarily.Results: Liquiritin, liquiritigenin, and lysophosphatidylcholine(LPC) were identified as the characteristic markers. The reducing of LPC in processed BX was one of the main reasons for detoxification because LPC could induce the inflammatory response;Liquiritin and liquiritigenin showed the anti-inflammatory effect and reduced liver injury, therefore the appearance of them in processed BX was an another reason for detoxification.Conclusion: An approach to explain the mechanisms of reducing the toxicity in medicinal plants by processing was proposed. Moreover, the chemical markers of toxicity could be used to differentiate the raw material from processed herbs for the quality control and safety application in clinical practice.

A new image processing method for discriminating internal layers from radio echo sounding data of ice sheets via a combined robust principal component analysis and total variation approach

Discriminating internal layers by radio echo sounding is important in analyzing the thickness and ice deposits in the Antarctic ice sheet.The signal processing method of synthesis aperture radar(SAR)has been widely used for improving the signal to noise ratio(SNR)and discriminating internal layers by radio echo sounding data of ice sheets.This method is not efficient when we use edge detection operators to obtain accurate information of the layers,especially the ice-bed interface.This paper presents a new image processing method via a combined robust principal component analysis-total variation(RPCA-TV)approach for discriminating internal layers of ice sheets by radio echo sounding data.The RPCA-based method is adopted to project the high-dimensional observations to low-dimensional subspace structure to accelerate the operation of the TV-based method,which is used to discriminate the internal layers.The efficiency of the presented method has been tested on simulation data and the dataset of the Institute of Electronics,Chinese Academy of Sciences,collected during CHINARE 28.The results show that the new method is more efficient than the previous method in discriminating internal layers of ice sheets by radio echo sounding data.

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.

Machine Learning-based USD/PKR Exchange Rate Forecasting Using Sentiment Analysis of Twitter Data

This study proposes an approach based on machine learning to forecast currency exchange rates by applying sentiment analysis to messages on Twitter(called tweets).A dataset of the exchange rates between the United States Dollar(USD)and the Pakistani Rupee(PKR)was formed by collecting information from a forex website as well as a collection of tweets from the business community in Pakistan containing finance-related words.The dataset was collected in raw form,and was subjected to natural language processing by way of data preprocessing.Response variable labeling was then applied to the standardized dataset,where the response variables were divided into two classes:“1”indicated an increase in the exchange rate and“−1”indicated a decrease in it.To better represent the dataset,we used linear discriminant analysis and principal component analysis to visualize the data in three-dimensional vector space.Clusters that were obtained using a sampling approach were then used for data optimization.Five machine learning classifiers—the simple logistic classifier,the random forest,bagging,naïve Bayes,and the support vector machine—were applied to the optimized dataset.The results show that the simple logistic classifier yielded the highest accuracy of 82.14%for the USD and the PKR exchange rates forecasting.

Prioritization of Sub-Watersheds in a Large Semi-Arid Drainage Basin (Southern Jordan) Using Morphometric Analysis, GIS, and Multivariate Statistics

GIS-based morphometric analysis was employed to prioritize the W. Mujib-Wala watershed southern Jordan. Seventy six fourth-order sub-watersheds were prioritized using morphometric analysis of ten linear and shape parameters. Each sub-watershed is prioritized by designated ranks based on the calculated compound parameter (Cp). The total score for each sub-basin is assigned as per erosion threat. The 76 sub-basins were grouped into four categories of priority: very high (12 sub-basins, 15.8% of the total), high (32 sub-watersheds, 42.1% of the total), moderate (25 sub-watersheds, 32.9% of the total), and low (7 sub-watersheds, 9.2% of the total). Sub-watersheds categorized as very high and high are subjected to high erosion risk, thus creating an urgent need for applying soil and water conservation measures. The relative diversity in land use practices and land cover, including variation in slope and soil types, are considered in proposing suitable conservation structures for sub-watersheds connected to each priority class. The adaptation of soil conservation measures priority-wise will reduce the erosivity effect on soil loss;while increasing infiltration rates;and water availability in soil profile. Principal component analysis (PCA) reduces the basic parameters and erosion risk parameters to three components, explaining 88% of the variance. The relationships of these components to the basic and erosion risk parameters were evaluated, and then the degree of inter-correlation among the morphometric parameters was explored. The verification of priority classes obtained through morphometric analysis was tested using Discriminant Analysis (DA). The results show a complete separation existing between the identified priority classes. Thus, soil erosion risk and geomorphic conditions are found entirely different from one class to another. The present results are intended to help decision makers to plan for efficient soil and water conservation measures to achieve future agricultural sustainability in the rainfed highlands of Jordan.

Image Analysis in Microbiology: A Review

This review is focused on using computer image analysis as a means of objective and quantitative characterizing optical images of the macroscopic (e.g. microbial colonies) and the microscopic (e.g. single cell) objects in the microbiological research. This is the way of making many visual inspection assays more objective and less time and labor consuming. Also, it can provide new visually inaccessible information on relation between some optical parameters and various biological features of the microbial cul-tures. Of special interest is application of image analysis in fluorescence microscopy as it opens new ways of using fluorescence based methodology for single microbial cell studies. Examples of using image analysis in the studies of both the macroscopic and the microscopic microbiological objects obtained by various imaging techniques are presented and discussed.

Functional Data Analysis of Spectroscopic Data with Application to Classification of Colon Polyps

In this study, two functional logistic regression models with functional principal component basis (FPCA) and functional partial least squares basis (FPLS) have been developed to distinguish precancerous adenomatous polyps from hyperplastic polyps for the purpose of classification and interpretation. The classification performances of the two functional models have been compared with two widely used multivariate methods, principal component discriminant analysis (PCDA) and partial least squares discriminant analysis (PLSDA). The results indicated that classification abilities of FPCA and FPLS models outperformed those of the PCDA and PLSDA models by using a small number of functional basis components. With substantial reduction in model complexity and improvement of classification accuracy, it is particularly helpful for interpretation of the complex spectral features related to precancerous colon polyps.

A computer aided detection framework for mammographic images using fisher linear discriminant and nearest neighbor classifier

Today, mammography is the best method for early detection of breast cancer. Radiologists failed to detect evident cancerous signs in approximately 20% of false negative mammograms. False negatives have been identified as the inability of the radiologist to detect the abnormalities due to several reasons such as poor image quality, image noise, or eye fatigue. This paper presents a framework for a computer aided detection system that integrates Principal Component Analysis (PCA), Fisher Linear Discriminant (FLD), and Nearest Neighbor Classifier (KNN) algorithms for the detection of abnormalities in mammograms. Using normal and abnormal mammograms from the MIAS database, the integrated algorithm achieved 93.06% classification accuracy. Also in this paper, we present an analysis of the integrated algorithm’s parameters and suggest selection criteria.

Functional Analysis of Chemometric Data

The objective of this paper is to present a review of different calibration and classification methods for functional data in the context of chemometric applications. In chemometric, it is usual to measure certain parameters in terms of a set of spectrometric curves that are observed in a finite set of points (functional data). Although the predictor variable is clearly functional, this problem is usually solved by using multivariate calibration techniques that consider it as a finite set of variables associated with the observed points (wavelengths or times). But these explicative variables are highly correlated and it is therefore more informative to reconstruct first the true functional form of the predictor curves. Although it has been published in several articles related to the implementation of functional data analysis techniques in chemometric, their power to solve real problems is not yet well known. Because of this the extension of multivariate calibration techniques (linear regression, principal component regression and partial least squares) and classification methods (linear discriminant analysis and logistic regression) to the functional domain and some relevant chemometric applications are reviewed in this paper.

红芪搓条前后主要次级代谢产物变化规律研究

目的 考察红芪搓条前后主要次级代谢产物的变化规律。方法 UPLC-MS/MS法测定芒柄花素、芒柄花苷、毛蕊异黄酮、毛蕊异黄酮苷、美迪紫檀素、染料木素、木犀草素、甘草素、异甘草素、香草酸、阿魏酸、γ-氨基丁酸、腺苷、甜菜碱的含量,聚类分析、主成分分析、正交偏最小二乘判别分析进行化学模式识别以寻找差异性成分。结果 搓条后,芒柄花素、毛蕊异黄酮、甘草素、γ-氨基丁酸含量升高,芒柄花苷、毛蕊异黄酮苷、香草酸含量降低。搓条、未搓条药材聚为2类,毛蕊异黄酮苷、芒柄花素、γ-氨基丁酸、香草酸、毛蕊异黄酮、芒柄花苷为差异性成分。结论 本实验阐明红芪搓条前后化学成分差异,可为其他药材搓条机制研究提供参考。

基于电学参数的贺兰山东麓赤霞珠葡萄酒子产区判别

本研究以宁夏贺兰山东麓5个子产区(银川、青铜峡、红寺堡、石嘴山和农垦产区)自然发酵的赤霞珠干红葡萄酒为研究对象,测定其基本理化指标和电学特性,分析不同产区葡萄酒间电学特性的差异,筛选出区分不同产区葡萄酒的特征频率和有效电学参数,初步探索基于电学特性识别宁夏贺兰山东麓不同子产区葡萄酒的能力,以期为简捷快速有效识别产区葡萄酒提供新方法。结果表明,宁夏贺兰山东麓5个子产区的葡萄酒理化指标间存在显著性差异,农垦产区的葡萄酒可滴定酸含量最高,而还原糖含量最低;银川产区的葡萄酒挥发酸含量最低,石嘴山产区的葡萄酒酒精度最高。通过相关性分析、方差分析和多重比较,筛选出电压2 V下,区分不同产区葡萄酒的电学特性特征频率为0.1 kHz,有效电学参数为Z、L_(p)、X、C_(p)和Q。主成分分析和判别分析均显示,利用葡萄酒电学参数能够明显区分贺兰山东麓5个子产区,采用Fisher-判别分析建立的预测模型,其回代检测和交叉验证正确率均为100%。因此,利用葡萄酒电学特性识别产区具有可行性。

基于矿物元素结合化学计量学方法实现烟叶溯源

[目的]通过测定烟叶矿物元素的含量结合化学计量学方法建立关于烟叶溯源的特征指纹图谱,并对方法的准确性和可靠性进行验证。[方法]采用电感耦合等离子发射光谱仪结合微波湿法消解分析种植于5个不同地区土壤的25个烟叶样品中矿物元素的含量,对所得到的元素含量进行单因素方差分析、主成分分析和典型判别分析。[结果]种植于不同地区土壤的烟叶中矿物元素含量存在明显差异。利用主成分分析得到的前2个主成分能够充分反映原始数据信息,并且利用对烟叶中矿物元素含量的分析,实现了种植于不同地域土壤中烟叶的产地溯源。结合Fisher判别分析进行验证并用“留一法”进行交叉检验,得到了理想的验证结果,其正确判别率均达到了100%。[结论]基于烟叶中矿物元素含量的特异性差异可以对烟叶进行正确的溯源分析。

不同产地太白贝母中11种核苷与碱基类成分分析及产地差异研究

目的对10批采自重庆、云南、陕西等5个省(市)的太白贝母样品中11种核苷和碱基类成分的含量进行测定,采用化学计量学分析法比较太白贝母中的核苷和碱基类成分的含量差异,对其质量进行综合评价,为规范化种植和产地优选提供参考。方法水超声提取太白贝母中的核苷和碱基类成分,采用高效液相色谱-二极管阵列检测器(HPLC-DAD)法测定样品中各成分含量,并采用主成分分析(Principal component analysis,PCA)、层次聚类分析(Hierarchical cluster analysis,HCA)对产地进行划分,偏最小二乘判别分析(Partial least squares discriminant analysis,PLS-DA)确定太白贝母中差异性的指标成分,比较指标性成分在不同产地样品间的含量差异。结果11种核苷和碱基类成分在不同产地太白贝母中存在显著差异;主成分分析和层次聚类分析可将样品聚为4类;PLS-DA鉴定出5个指标性成分,分别为尿嘧啶、胞嘧啶、尿苷、肌苷、腺苷,以重庆、湖北产地样品所含核苷和碱基成分相对较高,质量相对较优。结论该方法操作简单、重复性好、准确可靠,筛选出了鉴定不同产地太白贝母中的特征性核苷和碱基类成分,可用于初步阐明不同产地样品的差异性,并能够较好地反映太白贝母的品质,为太白贝母药材采购产地选择和质量控制提供参考。

基于红外光谱PCA-LDA统计分析的麻纤维鉴别研究

亚麻、汉麻与苎麻纤维的成分组成和物化性质高度相似,三者间的分类鉴别是纺织品检验检测领域的难点。本文对不同种类麻纤维的傅里叶变换衰减全反射红外光谱(ATR-FTIR)作主成分分析(PCA)和线性判别分析(LDA),创建麻纤维分类判别模型以鉴别三种易混麻纤维。选取亚麻、汉麻和苎麻纤维各60组作为样品集进行脱胶清洗处理并采集ATR-FTIR光谱。光谱归一化后对800~2000 cm-1波长的光谱作主成分分析,分析结果显示:随着主成分个数增加,主成分分数依据麻纤维类别逐渐显现聚类趋势,同时前12个主成分对归一化红外光谱数据的累计贡献率超过99.5%。以训练集前12主成分数为自变量,以麻纤维种类为因变量,通过线性判别分析构建了分类判别模型(典型判别函数和分类函数)。模型验证结果显示:典型判别函数可使前12个主成分分数矩阵根据麻纤维样品类型形成良好的聚类,分类函数对训练集和测试集中所有纤维样品的分类准确率达到100%。此外,PCA-LDA分类判别模型留一交叉验证的分类准确率仍能达到99.6%。结果表明,不同类别麻纤维的ATR-FTIR光谱存在差异,基于麻纤维ATR-FTIR光谱的PCA-LDA统计分析可实现亚麻、汉麻和苎麻三种易混麻纤维的快速无损鉴别。

气相色谱-离子迁移谱结合化学计量学分析对新会陈皮的鉴别

采用气相色谱-离子迁移谱(gas chromatography-ion mobility spectrometry,GC-IMS)技术对包括新会陈皮在内的10个产地陈皮的风味成分进行测定,运用主成分分析和偏最小二乘判别分析(partial least squares discriminant analysis,PLS-DA)方法对GC-IMS检出的75种风味成分进行分析,以建立新会陈皮的鉴别方法。结果表明,该方法可将新会陈皮与其他陈皮区分开,实现对新会陈皮的有效鉴别。同时,分析变量投影重要性可进一步筛选出20种对有效区分新会陈皮和其他产地陈皮发挥关键作用的特征标志物。本研究通过引入GC-IMS技术和PLSDA方法实现了新会陈皮与其他产地陈皮的准确鉴别,可为新会陈皮的国家地理标志产品保护和产地溯源提供新的技术参考。