Modified algorithm of principal component analysis for face recognition

In principal component analysis （PCA） algorithms for face recognition, to reduce the influence of the eigenvectors which relate to the changes of the illumination on abstract features, a modified PCA （MPCA） algorithm is proposed. The method is based on the idea of reducing the influence of the eigenvectors associated with the large eigenvalues by normalizing the feature vector element by its corresponding standard deviation. The Yale face database and Yale face database B are used to verify the method. The simulation results show that, for front face and even under the condition of limited variation in the facial poses, the proposed method results in better performance than the conventional PCA and linear discriminant analysis （LDA） approaches, and the computational cost remains the same as that of the PCA, and much less than that of the LDA.

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 resuits 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.

基于KPCA与KLPP及Wilks统计量的留兰香三维荧光数据特征提取与鉴别分析

为实现留兰香产地的快速鉴别,该研究提出了一种核主成分分析(kernel principal component analysis,KPCA)与核局部保持投影(kernel locality preserving projections,KLPP)及WilksΛ统计量序贯融合的特征波长提取策略,在此基础上鉴别5个产地的留兰香。首先,在采集5个产地300个留兰香样本的三维荧光数据后,运用三角形内插值法去除原始光谱中的瑞利散射和拉曼散射,并运用SG(Savitzky-Golay)对数据进行平滑预处理。然后,对预处理后的荧光光谱数据分别利用KPCA、KPCA+KLPP、KPCA+WilksΛ统计量、 KPCA+KLPP+WilksΛ统计量4种方法提取特征激发波长和特征发射波长。接着,按特征激发波长从小到大顺序将其对应的特征发射波长光谱值首尾相连转换成行向量;4种方法从300个样本中各得到1个300行的特征波长光谱值矩阵。再者,运用Fisher判别分析(fisher discriminant analysis,FDA)对特征波长光谱值矩阵进行数据可分性融合,生成可分性FD(fisher discriminant)变量。选取前4个累计判别能力达到99%的FD变量作为鉴别模型的输入向量。最后,用支持向量机(support vector machine,SVM)算法分析4个FD变量,分别得到对应于4种特征提取波长方法的FDA+SVM鉴别结果,其正确率分别为92.00%、96.00%、94.67%、100%。结果表明,所提出的KPCA+KLPP+WilksΛ统计量序贯融合的特征波长提取策略能够有效减少三维荧光光谱数据的冗余,并能表征原始荧光数据的信息特征,实现了5种留兰香产地的正确鉴别。该研究可为后续利用三维荧光光谱开展留兰香重要组分量化分析提供一定的基础。

基于氨基酸模式和判别分析的畜禽肉源性成分鉴别方法研究

为建立畜禽肉中动物源性成分快速有效的鉴别方法,对鸡肉、鸭肉、鹅肉、猪肉、牛肉、羊肉6种动物肉样绝干基础样品中16种氨基酸含量进行测定,建立各种动物样品的氨基酸简洁模式,并通过主成分分析和判别分析对不同样品16种氨基酸含量进行综合评价和分类。结果表明,6种动物样品均具有独特的氨基酸模式。通过主成分分析将16个氨基酸指标简化为3个主成分,其累积方差贡献率为90.575%。判别分析显示鸡肉、鸭肉、鹅肉、猪肉、牛肉、羊肉6种动物样品分别集中在不同的区域,彼此之间差异明显,很容易辨别。基于氨基酸模式和判别分析均可进行畜禽肉源性成分鉴别。

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。

经典名方旋覆代赭汤乙酸乙酯部位指纹图谱及化学模式识别研究

目的:建立旋覆代赭汤乙酸乙酯部位UPLC指纹图谱,结合化学模式识别分析不同产地饮片对旋覆代赭汤质量的影响。方法:采用Waters UPLC BEH C_(18)(2.1 mm×150 mm,1.7μm)色谱柱,流动相为乙腈-甲醇-0.4%甲酸水溶液,体积流量0.1 mL·min^(-1),梯度洗脱,检测波长350 nm;建立13批不同产地饮片提取的旋覆代赭汤样品指纹图谱,进行相似度评价,并结合聚类分析(CA)、主成分分析(PCA)和正交偏最小二乘法-判别分析(OPLS-DA),对旋覆代赭汤指纹图谱数据进行分析。结果:13批旋覆代赭汤指纹图谱确定22个共有特征峰,指认出12个多酚类成分,其中11个多酚类成分来自于旋覆花,其相似度均在0.9以上;通过CA发现,13批样品可按旋覆花产地分为三类;PCA与CA结果基本一致,并提取出4个主成分;OPLS-DA筛选出影响分类的差异性质量标志物,其中已指认出的成分有异绿原酸、异槲皮苷、异鼠李素-3-O-葡萄糖苷、异绿原酸B、1,5-二咖啡酰奎宁酸、绿原酸、槲皮万寿菊苷、槲皮素。结论:通过指纹图谱结合化学模式识别技术的分析策略,可快速有效地筛选不同批次旋覆代赭汤中多酚类成分中的差异质量标志物,为后续旋覆代赭汤的药效物质基础研究和质量评价提供参考。