Standard Principal Component Analysis Research Articles

A nonlinear principal component analysis to study archeometric data

Statistical techniques, when applied to data obtained by chemical investigations on ancient artworks, are usually expected to recognize groups of objects to classify the archeological finds, to attribute the provenance of items compared with earlier investigated ones, or to determine whether an archaelogical attribution is possible or not. The statistical technique most frequently used in archeometry is the principal component analysis (PCA), because of its simplicity in theory and implementation. However, the application of PCA to archeometric data showed severe limitations because of its linear feature. Indeed, PCA is inadequate to classify data whose behavior describe a curve or a curved subspace of the original data space. As a consequence of it, an amount of information is lost because the multi‐dimensional data space is compressed into a lower‐dimensional subspace including principal components. The aim of this work is then to test a novel statistical technique for archeometry. We propose a nonlinear PCA method to extract maximum chemical information by plotting data on the smallest number of principal components and to answer archeological questions. The higher accuracy and effectiveness of nonlinear PCA approach with respect to standard PCA for the analysis of archeometric data are shown through the study of Apulian red figured pottery (fifth–fourth century BC) coming from some of the most relevant archeological sites of ancient Apulia (Monte Sannace (Gioia del Colle), Egnatia (Fasano), Canosa, Altamura, Conversano, and Arpi(Foggia)). Copyright © 2016 John Wiley & Sons, Ltd.

SU‐F‐R‐41: Regularized PCA Can Model Treatment‐Related Changes in Head and Neck Patients Using Daily CBCTs

Purpose:To use daily cone beam CTs (CBCTs) to develop regularized principal component analysis (PCA) models of anatomical changes in head and neck (H&N) patients, to guide replanning decisions in adaptive radiation therapy (ART).Methods:Known deformations were applied to planning CT (pCT) images of 10 H&N patients to model several different systematic anatomical changes. A Pinnacle plugin was used to interpolate systematic changes over 35 fractions, generating a set of 35 synthetic CTs for each patient. Deformation vector fields (DVFs) were acquired between the pCT and synthetic CTs and random fraction‐to‐fraction changes were superimposed on the DVFs. Standard non‐regularized and regularized patient‐specific PCA models were built using the DVFs. The ability of PCA to extract the known deformations was quantified. PCA models were also generated from clinical CBCTs, for which the deformations and DVFs were not known. It was hypothesized that resulting eigenvectors/eigenfunctions with largest eigenvalues represent the major anatomical deformations during the course of treatment.Results:As demonstrated with quantitative results in the supporting document regularized PCA is more successful than standard PCA at capturing systematic changes early in the treatment. Regularized PCA is able to detect smaller systematic changes against the background of random fraction‐to‐fraction changes. To be successful at guiding ART, regularized PCA should be coupled with models of when anatomical changes occur: early, late or throughout the treatment course.Conclusion:The leading eigenvector/eigenfunction from the both PCA approaches can tentatively be identified as a major systematic change during radiotherapy course when systematic changes are large enough with respect to random fraction‐to‐fraction changes. In all cases the regularized PCA approach appears to be more reliable at capturing systematic changes, enabling dosimetric consequences to be projected once trends are established early in the treatment course.This work is supported in part by a grant from Varian Medical Systems, Palo Alto, CA

Exploring the spectroscopic diversity of Type Ia supernovae with dracula: a machine learning approach

The existence of multiple subclasses of type Ia supernovae (SNeIa) has been the subject of great debate in the last decade. One major challenge inevitably met when trying to infer the existence of one or more subclasses is the time consuming, and subjective, process of subclass definition. In this work, we show how machine learning tools facilitate identification of subtypes of SNeIa through the establishment of a hierarchical group structure in the continuous space of spectral diversity formed by these objects. Using Deep Learning, we were capable of performing such identification in a 4 dimensional feature space (+1 for time evolution), while the standard Principal Component Analysis barely achieves similar results using 15 principal components. This is evidence that the progenitor system and the explosion mechanism can be described by a small number of initial physical parameters. As a proof of concept, we show that our results are in close agreement with a previously suggested classification scheme and that our proposed method can grasp the main spectral features behind the definition of such subtypes. This allows the confirmation of the velocity of lines as a first order effect in the determination of SNIa subtypes, followed by 91bg-like events. Given the expected data deluge in the forthcoming years, our proposed approach is essential to allow a quick and statistically coherent identification of SNeIa subtypes (and outliers). All tools used in this work were made publicly available in the Python package Dimensionality Reduction And Clustering for Unsupervised Learning in Astronomy (DRACULA) and can be found within COINtoolbox (https://github.com/COINtoolbox/DRACULA).

Tagging the oligomer-to-polymer crossover on EG and PEGs by infrared and Raman spectroscopies and by wavelet cross-correlation spectral analysis

Infrared and Raman spectra on pure ethylene glycol (EG) and on low molecular weight polyethylene glycols (PEGs) with increasing chain length have been collected and analysed in a wide spectral range in order to determine the oligomer-to-polymer regime crossover. Previous spectroscopic crossover determinations reported in literature dealt only with Raman investigation of the Disordered Longitudinal Acoustic Mode (D-LAM). The IR and Raman contribution features, when analysed in different spectral ranges, show a dramatic change as a function of the polymer chain length, signaling an oligomer-polymer crossover for a polymerization degree of m=13. An innovative Wavelet Transform (WT) approach allows to confirm the above reported physical picture. In particular WT, differently from standard Principal Component Analysis (PCA) allows a multiscale analysis which permits to quantify the spectral shape modifications of different spectral regions of the collected IR and Raman spectra. It is shown that the wavelet cross-correlation parameters evaluated between each couple of the registered data, from EG to PEG 1000, change as a function of the polymer chain length, confirming that a plateau value is reached for m=13.

Low-rank plus sparse decomposition for exoplanet detection in direct-imaging ADI sequences

Data processing constitutes a critical component of high-contrast exoplanet imaging. Its role is almost as important as the choice of a coronagraph or a wavefront control system, and it is intertwined with the chosen observing strategy. Among the data processing techniques for angular differential imaging (ADI), the most recent is the family of principal component analysis (PCA) based algorithms. PCA serves, in this case, as a subspace projection technique for constructing a reference point spread function (PSF) that can be subtracted from the science data for boosting the detectability of potential companions present in the data. Unfortunately, when building this reference PSF from the science data itself, PCA comes with certain limitations such as the sensitivity of the lower dimensional orthogonal subspace to non-Gaussian noise. Inspired by recent advances in machine learning algorithms such as robust PCA, we aim to propose a localized subspace projection technique that surpasses current PCA-based post-processing algorithms in terms of the detectability of companions at near real-time speed, a quality that will be useful for future direct imaging surveys. We used randomized low-rank approximation methods recently proposed in the machine learning literature, coupled with entry-wise thresholding to decompose an ADI image sequence locally into low-rank, sparse, and Gaussian noise components (LLSG). This local three-term decomposition separates the starlight and the associated speckle noise from the planetary signal, which mostly remains in the sparse term. We tested the performance of our new algorithm on a long ADI sequence obtained on beta Pictoris with VLT/NACO. Compared to a standard PCA approach, LLSG decomposition reaches a higher signal-to-noise ratio and has an overall better performance in the receiver operating characteristic space. (abridged).

Hydrogeochemical analysis of multiple aquifers in a coal mine based on nonlinear PCA and GIS

Hydrogeochemical characteristic analysis of multiple categorical aquifers, including the delineation of hydrogeochemical spatial distributions, can provide a basic understanding of the groundwater flow system necessary for the prevention and control of groundwater damage associated with coal mines. Nonlinear principal component analysis (PCA) was used to analyse the groundwater chemical characteristics of water samples from five aquifers in Gubei Coal Mine, Anhui Province, China. In particular, Ca2+, Mg2+, Na+ + K+, HCO3−, Cl−, SO42−, and pH were analysed. To analyse the spatial distribution of groundwater chemistry in this Permian fractured aquifer, interpolation of principal component 1 (PC1) scores obtained from the nonlinear PCA was performed using the inverse distance weighting (IDW) method. Additionally, concentrations of the hydrogeochemical variables were plotted against depth. The relationships between water samples, variables, and water samples with variables were simultaneously analysed by nonlinear PCA. The results show: (1) nonlinear PCA explains more variability than standard PCA in the first two principal components (PCs); (2) the Cenozoic top aquifer has low concentrations of Na+ + K+ and Cl−; the Cenozoic middle aquifer and Cenozoic bottom aquifer are characterised by high concentrations of Ca2+ and Mg2+ and low concentrations of HCO3−; the shallow groundwater of the Permian fractured aquifer is recharged by the Cenozoic bottom aquifer, while other parts have high concentrations of HCO3− and low concentrations of Ca2+ and Mg2+. The Taiyuan limestone aquifer has similar groundwater chemistry to that of the Cenozoic bottom aquifer, and thus it may be recharged by the Cenozoic bottom aquifer in outcropping Taiyuan Formation strata; (3) the wide south-western and north-western areas of the Permian fractured aquifer may be recharged by the Cenozoic bottom aquifer; and (4) concentrations of Ca2+ and Mg2+ in the Permian fractured aquifer are negatively correlated with sampling depth, while the concentrations of Na+ + K+ and HCO3− are positively correlated with sampling depth, which verifies that the shallow groundwater of this Permian fractured aquifer is recharged by the Cenozoic bottom aquifer. The findings demonstrate that combining nonlinear PCA with GIS can be a powerful approach for conducting spatial nonlinear analysis of hydrogeochemical characteristics.

Multivariate spatial analysis of lake sediment geochemical data; Melville Peninsula, Nunavut, Canada

A multivariate spatial analysis was conducted on a suite of glacial till geochemical data collected over the Melville Peninsula, Nunavut, Canada. Previous studies demonstrated through the application of multivariate statistical techniques that the composition of the lake sediment geochemistry reflects the underlying geology in northern Canada. In this study, the application of minimum/maximum autocorrelation factor analysis (MAF) to glacial till geochemistry has extended the knowledge and description of the underlying geology through the recognition of spatially correlated factors that represent distinct lithologic features and glacial transport processes.Maps of posterior probabilities for the underlying lithologies were estimated based on the MA factors and compared with those from standard principal component analysis. The use of MAF provides a measured improvement in predictive mapping irrespective of the choice of lag separation.

Constrained Statistical Modelling of Knee Flexion From Multi-Pose Magnetic Resonance Imaging.

Reconstruction of the anterior cruciate ligament (ACL) through arthroscopy is one of the most common procedures in orthopaedics. It requires accurate alignment and drilling of the tibial and femoral tunnels through which the ligament graft is attached. Although commercial computer-assisted navigation systems exist to guide the placement of these tunnels, most of them are limited to a fixed pose without due consideration of dynamic factors involved in different knee flexion angles. This paper presents a new model for intraoperative guidance of arthroscopic ACL reconstruction with reduced error particularly in the ligament attachment area. The method uses 3D preoperative data at different flexion angles to build a subject-specific statistical model of knee pose. To circumvent the problem of limited training samples and ensure physically meaningful pose instantiation, homogeneous transformations between different poses and local-deformation finite element modelling are used to enlarge the training set. Subsequently, an anatomical geodesic flexion analysis is performed to extract the subject-specific flexion characteristics. The advantages of the method were also tested by detailed comparison to standard Principal Component Analysis (PCA), nonlinear PCA without training set enlargement, and other state-of-the-art articulated joint modelling methods. The method yielded sub-millimetre accuracy, demonstrating its potential clinical value.

Multilevel principal component analysis (mPCA) in shape analysis: A feasibility study in medical and dental imaging

Background and objectiveMethods used in image processing should reflect any multilevel structures inherent in the image dataset or they run the risk of functioning inadequately. We wish to test the feasibility of multilevel principal components analysis (PCA) to build active shape models (ASMs) for cases relevant to medical and dental imaging. MethodsMultilevel PCA was used to carry out model fitting to sets of landmark points and it was compared to the results of “standard” (single-level) PCA. Proof of principle was tested by applying mPCA to model basic peri-oral expressions (happy, neutral, sad) approximated to the junction between the mouth/lips. Monte Carlo simulations were used to create this data which allowed exploration of practical implementation issues such as the number of landmark points, number of images, and number of groups (i.e., “expressions” for this example). To further test the robustness of the method, mPCA was subsequently applied to a dental imaging dataset utilising landmark points (placed by different clinicians) along the boundary of mandibular cortical bone in panoramic radiographs of the face. ResultsChanges of expression that varied between groups were modelled correctly at one level of the model and changes in lip width that varied within groups at another for the Monte Carlo dataset. Extreme cases in the test dataset were modelled adequately by mPCA but not by standard PCA. Similarly, variations in the shape of the cortical bone were modelled by one level of mPCA and variations between the experts at another for the panoramic radiographs dataset. Results for mPCA were found to be comparable to those of standard PCA for point-to-point errors via miss-one-out testing for this dataset. These errors reduce with increasing number of eigenvectors/values retained, as expected. ConclusionsWe have shown that mPCA can be used in shape models for dental and medical image processing. mPCA was found to provide more control and flexibility when compared to standard “single-level” PCA. Specifically, mPCA is preferable to “standard” PCA when multiple levels occur naturally in the dataset.

An Alternative Formulation of PCA for Process Monitoring Using Distance Correlation

Scale-invariant principal component analysis (PCA) is prevalent in process monitoring because of its simplicity and efficiency. However, a number of limitations are associated with this technique because of underlying assumptions. This article attempts to relax these limitations by introducing three key elements. First, a semiparametric Gaussian transformation is proposed to make the process data follow a multivariate Gaussian distribution, such that the standard PCA can be directly applied to explain the majority of the process data variance. The Gaussian transformation function preserves both important statistical information and the correlation structures of the process data. Second, eigenvectors spanning the feature space are extracted using the Spearman correlation coefficient and the distance correlation coefficient. This feature space is able to retain nonlinear and nonmonotonic correlation structures of the process data. Finally, this technique is computationally more efficient than KPCA, KICA, and improved KICA by avoiding expensive kernel mapping. Semiparametric PCA is tested on two industrial case studies and exhibits satisfactory performance.

Optimization of drying through analytical modeling: clays as bonding agents in refractory materials

The objective of this study was to investigate the clay drying as a unit operation in the refractory materials processing. Two clays that varied in chemical and mineralogical compositions were experimentally tested in a laboratory dryer. The results obtained on the green samples prior to drying indicated that clays have adequate plasticity and refractoriness for application in shaped refractories. The operating parameters of the dryer were regulated: temperature ranged from 40 to 60°C, humidity increased in the interval 30–70%; and the airstream rate was 1.3m/s. The correlation analysis between operating parameters and calculated and/or measured drying outputs was conducted for better comprehension of the clay׳s role as a refractory binder. Subsequently, a mathematical optimization of the drying regime was conveyed. The effect of the variables (operation parameters) on the drying parameters (critical moisture, equilibrium moisture, dryness degree, etc.) was compared and evaluated. The response surface method, standard score analysis, cluster method, and principal component analysis were used as a means of the drying regime optimization. Assessment of the drying regime impact on the dried samples quality highlighted optimal result for both clay types: SS=0.95, temperature 50°C, and humidity 40%. Multiple comparison analyses pointed out that optimized combination of the drying operation parameters decreases the quantity of conducted tests. Furthermore, optimal combination of drying parameters reduces negative effects of clay binder inherent properties on the resulting product which in return improves energetic and economic sustainability of refractories production.

A Distributed Framework for Dimensionality Reduction and Denoising

With the recent upsurge in the amount of data transmission and use in networks, a lot of emphasis is placed on designing efficient methods to extract useful information by reducing the size of large data sets. A major approach in data dimensionality reduction relies on the estimation of the principal data covariance eigenspace. In this paper a distributed algorithmic framework is put forth for finding the principal eigenspace of spatially scattered sensor data. Toward this end, the standard principal component analysis framework is reformulated as a separable constrained minimization problem which is solved by utilizing coordinate descent techniques combined with the alternating direction method of multipliers. Computationally simple local updating recursions are obtained that involve only single-hop intersensor communications. The proposed distributed algorithm is shown to be robust even in the presence of inter-sensor communication noise and converge to the principal eigenspace when inter-sensor links are ideal. The proposed distributed framework is used in a data denoising setting, while extensive numerical tests using both synthetic and real data demonstrate a fast convergence rate, better steady-state performance of the novel algorithm over existing alternatives and communication noise resilience.

Unsupervised Deep Feature Extraction for Remote Sensing Image Classification

This paper introduces the use of single layer and deep convolutional networks for remote sensing data analysis. Direct application to multi- and hyper-spectral imagery of supervised (shallow or deep) convolutional networks is very challenging given the high input data dimensionality and the relatively small amount of available labeled data. Therefore, we propose the use of greedy layer-wise unsupervised pre-training coupled with a highly efficient algorithm for unsupervised learning of sparse features. The algorithm is rooted on sparse representations and enforces both population and lifetime sparsity of the extracted features, simultaneously. We successfully illustrate the expressive power of the extracted representations in several scenarios: classification of aerial scenes, as well as land-use classification in very high resolution (VHR), or land-cover classification from multi- and hyper-spectral images. The proposed algorithm clearly outperforms standard Principal Component Analysis (PCA) and its kernel counterpart (kPCA), as well as current state-of-the-art algorithms of aerial classification, while being extremely computationally efficient at learning representations of data. Results show that single layer convolutional networks can extract powerful discriminative features only when the receptive field accounts for neighboring pixels, and are preferred when the classification requires high resolution and detailed results. However, deep architectures significantly outperform single layers variants, capturing increasing levels of abstraction and complexity throughout the feature hierarchy.

Lean histogram of oriented gradients features for effective eye detection

Reliable object detection is very important in computer vision and robotics applications. The histogram of oriented gradients (HOG) is established as one of the most popular hand-crafted features, which along with support vector machine (SVM) classification provides excellent performance for object recognition. We investigate dimensionality deduction on HOG features in combination with SVM classifiers to obtain efficient feature representation and improved classification performance. In addition to lean HOG features, we explore descriptors resulting from dimensionality reduction on histograms of binary descriptors. We consider three-dimensionality reduction techniques: standard principal component analysis, random projections, a computationally efficient linear mapping that is data independent, and locality preserving projections (LPP), which learns the manifold structure of the data. Our methods focus on the application of eye detection and were tested on an eye database created using the BioID and FERET face databases. Our results indicate that manifold learning is beneficial to classification utilizing HOG features. To demonstrate the broader usefulness of lean HOG features for object class recognition, we evaluated our system’s classification performance on the CalTech-101 dataset with favorable outcomes.

Liquidity commonality in order-driven trading: evidence from the Athens Stock Exchange

ABSTRACTWe examine the presence of liquidity commonality in the order-driven Athens Stock Exchange (ASE). Unlike the majority of liquidity commonality studies that focus on the bid–ask spread, our analysis extends deeper in the Limit Order Book, providing insight on the price impact of both small and large trades. We utilize a 6-month FTSE/ATHEX-20 intraday data set to estimate the liquidity factor model of Chordia et al. (2000). To this end, we conduct single-equation analysis as well as panel data analysis with the use of two-way clustered errors, correcting for simultaneous firm and time correlations. Moreover, we apply standard principal component analysis on stock liquidities to extract the marketwide liquidity component. We find that liquidity commonality is low at the bid–ask spread, whereas it increases deeper in the book; consequently, large traders face liquidity risks associated with both individual stock and marketwide illiquidity. Moreover, our empirical evidence hints that liquidity commonality is asynchronous, suggesting that the ASE trading process includes various levels of information speed. Our analysis contributes to the understanding of liquidity commonality in order-driven trading, especially in emerging markets like the ASE where trading activity is limited and information speed is low.

Effects of sunflower meal quality on the technical parameters of the pelleting process and pellet quality

The impact of sunflower meal quality (i.e. crude protein content and crude fibre content) on the technical parameters of the pelleting process and the physical properties of produced pellets was investigated. Five model mixtures were prepared for pelleting, with different ratios of corn, sunflower meal (SFM) and soybean meal (SBM). Three grades of sunflower meal were used in the experiments with crude protein contents of approximately 37%, 40%, and 43%. Within each of the mixtures, granulation of material and retention time in the steam conditioner were varied. In order to describe the effects of the test variables on the observed responses response surface methodology, standard score analysis and principal component analysis (PCA) were used. The increase in the protein content and the decrease in the crude fibre content of sunflower meals caused an increase in the pelleting temperature, specific energy consumption, pellet hardness, and pellet durability. In addition, an increase in retention time increased specific energy consumption of pellet press, and produced fines. In terms of pellet durability values, a longer retention time was more beneficial for SFM mixtures than for SBM mixture. The type of mixture was found to be the most influential variable for second order polynomial model calculation. Standard score analysis showed that the optimum values for energy consumption, quantity of the fines, and pellet durability indices were obtained for the mixture with 40% protein SFM, with no retention after conditioning and with the finest granulation of the components (0.933). PCA showed that the first two principal components (91.10% of the total variability) enabled a neat separation of the five mixtures.

Tailored multivariate analysis for modulated enhanced diffraction

Modulated enhanced diffraction (MED) is a technique allowing the dynamic structural characterization of crystalline materials subjected to an external stimulus, which is particularly suited forin situandoperandostructural investigations at synchrotron sources. Contributions from the (active) part of the crystal system that varies synchronously with the stimulus can be extracted by an offline analysis, which can only be applied in the case of periodic stimuli and linear system responses. In this paper a new decomposition approach based on multivariate analysis is proposed. The standard principal component analysis (PCA) is adapted to treat MED data: specific figures of merit based on their scores and loadings are found, and the directions of the principal components obtained by PCA are modified to maximize such figures of merit. As a result, a general method to decompose MED data, called optimum constrained components rotation (OCCR), is developed, which produces very precise results on simulated data, even in the case of nonperiodic stimuli and/or nonlinear responses. The multivariate analysis approach is able to supply in one shot both the diffraction pattern related to the active atoms (through the OCCR loadings) and the time dependence of the system response (through the OCCR scores). When applied to real data, OCCR was able to supply only the latter information, as the former was hindered by changes in abundances of different crystal phases, which occurred besides structural variations in the specific case considered. To develop a decomposition procedure able to cope with this combined effect represents the next challenge in MED analysis.

Compression distance can discriminate animals by genetic profile, build relationship matrices and estimate breeding values

BackgroundGenetic relatedness is currently estimated by a combination of traditional pedigree-based approaches (i.e. numerator relationship matrices, NRM) and, given the recent availability of molecular information, using marker genotypes (via genomic relationship matrices, GRM). To date, GRM are computed by genome-wide pair-wise SNP (single nucleotide polymorphism) correlations.ResultsWe describe a new estimate of genetic relatedness using the concept of normalised compression distance (NCD) that is borrowed from Information Theory. Analogous to GRM, the resultant compression relationship matrix (CRM) exploits numerical patterns in genome-wide allele order and proportion, which are known to vary systematically with relatedness. We explored properties of the CRM in two industry cattle datasets by analysing the genetic basis of yearling weight, a phenotype of moderate heritability. In both Brahman (Bos indicus) and Tropical Composite (Bos taurus by Bos indicus) populations, the clustering inferred by NCD was comparable to that based on SNP correlations using standard principal component analysis approaches. One of the versions of the CRM modestly increased the amount of explained genetic variance, slightly reduced the ‘missing heritability’ and tended to improve the prediction accuracy of breeding values in both populations when compared to both NRM and GRM. Finally, a sliding window-based application of the compression approach on these populations identified genomic regions influenced by introgression of taurine haplotypes.ConclusionsFor these two bovine populations, CRM reduced the missing heritability and increased the amount of explained genetic variation for a moderately heritable complex trait. Given that NCD can sensitively discriminate closely related individuals, we foresee CRM having possible value for estimating breeding values in highly inbred populations.Electronic supplementary materialThe online version of this article (doi:10.1186/s12711-015-0158-9) contains supplementary material, which is available to authorized users.

Multispectral and panchromatic images fusion using the Markov-random-field-based FCM

This article proposes a multispectral (MS) and panchromatic (PAN) images fusion approach exploiting local spatial information by using fuzzy c-means clustering algorithm based on the Markov random field (MRFFCM). The standard principal component analysis (PCA) technique is first employed to transform the MS images into principal component spaces to extract the first principal component (PC1). Then, we decompose the PAN image using the à trous wavelet transform to get the high frequency detailed information and the approximation of the PAN image. In the process, the local relationship is employed through MRFFCM between the two to produce a fused PC1 by choosing the saliency and significant coefficients. The fused MS image is generated after the detailed information has been incorporated with the fused PC1 and finally the inverse PCA is implemented. Experimental results demonstrate that the proposed approach improves the quality of fused images both qualitatively and quantitatively.

Comparison of five Lonicera flowers by simultaneous determination of multi-components with single reference standard method and principal component analysis

The flowers of Lonicera japonica Thunb. were extensively used to treat many diseases. As the demands for L. japonica increased, some related Lonicera plants were often confused or misused. Caffeoylquinic acids were always regarded as chemical markers in the quality control of L. japonica, but they could be found in all Lonicera species. Thus, a simple and reliable method for the evaluation of different Lonicera flowers is necessary to be established. In this work a method based on single standard to determine multi-components (SSDMC) combined with principal component analysis (PCA) for control and distinguish of Lonicera species flowers have been developed. Six components including three caffeoylquinic acids and three iridoid glycosides were assayed simultaneously using chlorogenic acid as the reference standard. The credibility and feasibility of the SSDMC method were carefully validated and the results demonstrated that there were no remarkable differences compared with external standard method. Finally, a total of fifty-one batches covering five Lonicera species were analyzed and PCA was successfully applied to distinguish the Lonicera species. This strategy simplifies the processes in the quality control of multiple-componential herbal medicine which effectively adapted for improving the quality control of those herbs belonging to closely related species.