Principal Component Analysis Projection Research Articles

Optimizing lung cancer prediction: leveraging Kernel PCA with dendritic neural models

Lung cancer is considered a cause of increased mortality rate due to delays in diagnostics. There is an urgent need to develop an effective lung cancer prediction model that will help in the early diagnosis of cancer and save patients from unnecessary treatments. The objective of the current paper is to meet the extensiveness measure by using collaborative feature selection and feature extraction methods to enhance the dendritic neural model (DNM) in comparison to traditional machine learning (ML) models with minimum features and boost the accuracy, precision, and sensitivity of lung cancer prediction. Comprehensive experiments on a dataset comprising 1000 lung cancer patients and 23 features obtained from Kaggle. Crucial features are identified, and the proposed method’s effectiveness is evaluated using metrics such as accuracy, precision, F1 score, sensitivity, specificity, and confusion matrix against other ML models. Feature extraction techniques including Principal Component Analysis (PCA), Kernel PCA (K-PCA), and Uniform Manifold Approximation and Projection (UMAP) are employed to optimize model performance. PCA evaluated the DNM accuracy at 96.50%, precision at 96.64% and 97.45% sensitivity. K-PCA explained the DNM accuracy of 98.50%, precision rate of 99.42%, and 98.84% sensitivity and UMAP elaborated the DNM accuracy of 98%, precision of 98.82%, and 98.82% sensitivity. The K-PCA approach showed outstanding performance in enhancing the DNM model. Highlighting the DNM's accurate prediction of lung cancer. These results emphasize the potential of the DNM model to contribute positively to healthcare research by providing better predictive outcomes.

Comparative assessment of projection and clustering method combinations in the analysis of biomedical data

BackgroundClustering on projected data is common in biomedical research analysis. Principal component analysis (PCA) is widely used for projection, focusing on data dispersion (variance), while clustering identifies data concentrations (neighborhood). These are conflicting aims. This study re-evaluates combinations of PCA and other projection methods with common clustering algorithms. MethodsSix projection methods (PCA, ICA, isomap, MDS, t-SNE, UMAP) were combined with five clustering algorithms (k-means, k-medoids, single link, Ward's method, average link). Projections and clusterings were evaluated using a numerical criterion for evaluating clustering performance and a visual criterion based on plotting the projected data on a Voronoi tessellation plane with class-wise coloring. Nine artificial and five real biomedical datasets were analyzed. ResultsNo combination consistently captured prior classifications in projections and clusters. Visual inspection proved essential. PCA was often but not always outperformed or equaled by neighborhood-based methods (UMAP, t-SNE) and manifold learning techniques (isomap). ConclusionsThe results dissaprove PCA as a standard projection method prior to clustering. Therefore, method selection should be data specific as a tailored approach to data projection and clustering in biomedical analysis. To aid this process, we propose a novel visualization technique that combines Voronoi tessellation with color coding.

INNV-24. MACHINE LEARNING-BASED SPECTROSCOPIC TISSUE DIFFERENTIATION IN FLUORESCENCE-GUIDED NEUROSURGERY

Abstract Maximal resection of malignant gliomas is hindered by difficulty in distinguishing tumor margins. Fluorescence-guided resection with 5-ALA assists in reaching this goal. Previously, we characterized five fluorophore emissions that accurately represent any spectrum measured from human brain tumor biopsies with a wide-field hyperspectral device. In this study, the effectiveness of these five spectra was explored for different tumor classification tasks in 891 hyperspectral widefield measurements of 184 patients harboring low- (n=30) and high-grade gliomas (n=115), non-glial primary brain tumors (n=19), radiation necrosis (n=2), miscellaneous (n=10) and metastases (n=8), which corresponds to up to 15000 spectra for a given test. The statistical differences in fluorophore abundances between classes were determined and visualized using dimensionality reduction techniques, including principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE). Three machine-learning models were trained to classify tumor type (12 classes), grade (3 classes), and glioma margins (3 classes). Five algorithms were tested with varying hyperparameters for each classification task. We explored whether PCA projection onto five different axes than fluorophore abundances can provide more information for visualization and classification. The abundances of the five a priori fluorophore spectra matched or outperformed the five optimal PCA components for all classification tasks. These five axes capture 96-99% of the variance in the dataset. Using random forests and multilayer perceptrons, the classifiers achieved average test accuracies of 74-82%, 79%, and 81%, respectively. All five fluorophore abundances varied between tumor margin type as well as between grades (p < 0.01). For tissue type, at least four of five fluorophore abundances were found to be significantly different (p < 0.01) between all classes. These results demonstrate the differing contribution of fluorophores in different tissue classes tissue, as well as the five fluorophores value as potential optical biomarkers, opening new opportunities for intraoperative classification systems in fluorescence-guided neurosurgery.

Principal component and linear discriminant analyses for the classification of hominoid primate specimens based on bone shape data.

In this study, we tested the hypothesis that machine learning methods can accurately classify extant primates based on triquetrum shape data. We then used this classification tool to observe the affinities between extant primates and fossil hominoids. We assessed the discrimination accuracy for an unsupervised and supervised learning pipeline, i.e. with principal component analysis (PCA) and linear discriminant analysis (LDA) feature extraction, when tasked with the classification of extant primates. The trained algorithm is used to classify a sample of known fossil hominoids. For the visualization, PCA and uniform manifold approximation and projection (UMAP) are used. The results show that the discriminant function correctly classified the extant specimens with an F1-score of 0.90 for both PCA and LDA. In addition, the classification of fossil hominoids reflects taxonomy and locomotor behaviour reported in literature. This classification based on shape data using PCA and LDA is a powerful tool that can discriminate between the triquetrum shape of extant primates with high accuracy and quantitatively compare fossil and extant morphology. It can be used to support taxonomic differentiation and aid the further interpretation of fossil remains. Further testing is necessary by including other bones and more species and specimens per species extinct primates.

Mother-Fetus Immune Cross-Talk Coordinates "Extrinsic"/"Intrinsic" Embryo Gene Expression Noise and Growth Stability.

Developmental instability (DI) is thought to be inversely related to a capacity of an organism to buffer its development against random genetic and environmental perturbations. DI is represented by a trait’s inter- and intra-individual variabilities. The inter-individual variability (inversely referred to as canalization) indicates the capability of organisms to reproduce a trait from individual to individual. The intra-individual variability reflects an organism’s capability to stabilize a trait internally under the same conditions, and, for symmetric traits, it is expressed as fluctuating asymmetry (FA). When representing a trait as a random variable conditioned on environmental fluctuations, it is clear that, in statistical terms, the DI partitions into “extrinsic” (canalization) and “intrinsic” (FA) components of a trait’s variance/noise. We established a simple statistical framework to dissect both parts of a symmetric trait variance/noise using a PCA (principal component analysis) projection of the left/right measurements on eigenvectors followed by GAMLSS (generalized additive models for location scale and shape) modeling of eigenvalues. The first eigenvalue represents “extrinsic” and the second—“intrinsic” DI components. We applied this framework to investigate the impact of mother–fetus major histocompatibility complex (MHC)-mediated immune cross-talk on gene expression noise and developmental stability. We showed that “intrinsic” gene noise for the entire transcriptional landscape could be estimated from a small subset of randomly selected genes. Using a diagnostic set of genes, we found that allogeneic MHC combinations tended to decrease “extrinsic” and “intrinsic” gene noise in C57BL/6J embryos developing in the surrogate NOD-SCID and BALB/c mothers. The “intrinsic” gene noise was negatively correlated with growth (embryonic mass) and the levels of placental growth factor (PLGF), but not vascular endothelial growth factor (VEGF). However, it was positively associated with phenotypic growth instability and noise in PLGF. In mammals, the mother–fetus MHC interaction plays a significant role in development, contributing to the fitness of the offspring. Our results demonstrate that a positive impact of distant MHC combinations on embryonic growth could be mediated by the reduction of “intrinsic” gene noise followed by the developmental stabilization of growth.

Clustering of End Stage Renal Disease Patients by Dimensionality Reduction Algorithms According to Lymphocyte Senescence Markers.

End stage renal disease (ESRD) engenders detrimental effects in the Immune system, manifested as quantitative alterations of lymphocyte subpopulations, akin, albeit not identical to those observed during the ageing process. We performed dimensionality reduction of an extended lymphocyte phenotype panel of senescent and exhaustion related markers in ESRD patients and controls with Principal Component Analysis (PCA) and Uniform Manifold Approximation and Projection (UMAP). The plane defined by the first two principal components of PCA showed two fuzzy clusters, for patients and controls, respectively, with loadings of non-senescent markers pointing towards the controls’ centroid. Naive lymphocytes were reduced in ESRD patients compared to controls (CD4+CD45RA+CCR7+ 200(150-328) vs. 426(260-585cells/μl respectively, P = 0.001, CD19+IgD+CD27- 54(26-85) vs. 130(83-262)cells/μl respectively, P < 0.001). PCA projections of the multidimensional ESRD immune phenotype suggested a more senescent phenotype in hemodialysis compared to hemodiafiltration treated patients. Lastly, clustering based on UMAP revealed three distinct patient groups, exhibiting gradual changes for naive, senescent, and exhausted lymphocyte markers. Machine learning algorithms can distinguish ESRD patients from controls, based on their immune-phenotypes and also, unveil distinct immunological groups within patients’ cohort, determined possibly by dialysis prescription.

MO004: Hidden Patterns of T Cell and B Cell Phenotypes seen Through Machine Learning Algorithms in End-Stage Renal Disease

Abstract BACKGROUND AND AIMS End-stage renal disease (ESRD) is essentially a chronic inflammatory state and, consequently, it engenders detrimental effects on the immune system. Due to the specific uremic environment, multiple phenotypic lymphocyte alterations are described, similar but not identical to ageing. We aimed to evaluate these changes through simple unsupervised dimensionality reduction algorithms in order to reveal unique phenotypical lymphocyte patterns in ESRD patients. METHOD A wide panel of senescent and exhaustion-related lymphocyte markers, including CD45RA, CCR7, CD31, CD28, CD57, and PD1 on T cells and CD27 and IgD on B cells, was assessed by flow cytometry in 30 ESRD patients and 20 healthy controls of similar age, sex and ethnicity. The resulting immunological multidimensional phenotype was projected at lower dimensions using two algorithms: principal component analysis (PCA) and Uniform Manifold Approximation and Projection (UMAP). RESULTS The plane defined by the two first eigenvectors of PCA showed two clusters, patients and controls, with PCA variable loadings of non-senescent markers pointing towards the controls' centroid. Indeed, ‘naïve’ lymphocytes were reduced in ESRD patients compared with controls [CD4+CD45RA+CCR7+200(150–328) versus 426(260–585) cells/μL, respectively; P = .001] and [CD19+IgD+CD27–54(26–85) versus 130(83–262) cells/μL, respectively; P &amp;lt; .0001]. Also, PCA projections of the multidimensional ESRD immune phenotype suggested a more senescent phenotype in haemodialysis compared with haemodiafiltration treated patients. Finally, clustering based on UMAP projections revealed three distinct patients groups (UMAP 1–3), exhibiting gradual changes for naive, senescent and exhausted lymphocyte markers. Out of these, the UMAP 1 group was characterized by a predominance of CD8 senescent T cell subsets, in contrast, to groups UMAP 2 and 3, which showed a gradual decrease of all T cell subsets, in comparison to healthy controls. These groups were found to differ in the type of dialyzer used, with polysulfone or derivatives alone more often prescribed in UMAP 2–3 patients (13 out of 17) compared with 4 out of 13 in UMAP 1, P = .012. CONCLUSION Simple machine learning algorithms may help to unravel hidden lymphocyte markers and define patterns characteristic of ESRD. Moreover, significant connections of immune changes with dialysis methods and dialyzers were revealed and described.

How to reduce dimension with PCA and random projections?

In our "big data" age, the size and complexity of data is steadily increasing. Methods for dimension reduction are ever more popular and useful. Two distinct types of dimension reduction are "data-oblivious" methods such as random projections and sketching, and "data-aware" methods such as principal component analysis (PCA). Both have their strengths, such as speed for random projections, and data-adaptivity for PCA. In this work, we study how to combine them to get the best of both. We study "sketch and solve" methods that take a random projection (or sketch) first, and compute PCA after. We compute the performance of several popular sketching methods (random iid projections, random sampling, subsampled Hadamard transform, CountSketch, etc) in a general "signal-plus-noise" (or spiked) data model. Compared to well-known works, our results (1) give asymptotically exact results, and (2) apply when the signal components are only slightly above the noise, but the projection dimension is non-negligible. We also study stronger signals allowing more general covariance structures. We find that (a) signal strength decreases under projection in a delicate way depending on the structure of the data and the sketching method, (b) orthogonal projections are slightly more accurate, (c) randomization does not hurt too much, due to concentration of measure, (d) CountSketch can be somewhat improved by a normalization method. Our results have implications for statistical learning and data analysis. We also illustrate that the results are highly accurate in simulations and in analyzing empirical data.

Genotypes of informative loci from 1000 Genomes data allude evolution and mixing of human populations

Principal Component Analysis (PCA) projects high-dimensional genotype data into a few components that discern populations. Ancestry Informative Markers (AIMs) are a small subset of SNPs capable of distinguishing populations. We integrate these two approaches by proposing an algorithm to identify necessary informative loci whose removal from the data deteriorates the PCA structure. Unlike classical AIMs, necessary informative loci densely cover the genome, hence can illuminate the evolution and mixing history of populations. We conduct a comprehensive analysis to the genotype data of the 1000 Genomes Project using necessary informative loci. Projections along the top seven principal components demarcate populations at distinct geographic levels. Millions of necessary informative loci along each PC are identified. Population identities along each PC are approximately determined by weighted sums of minor (or major) alleles over the informative loci. Variations of allele frequencies are aligned with the history and direction of population evolution. The population distribution of projections along the top three PCs is recapitulated by a simple demographic model based on several waves of founder population separation and mixing. Informative loci possess locational concentration in the genome and functional enrichment. Genes at two hot spots encompassing dense PC 7 informative loci exhibit differential expressions among European populations. The mosaic of local ancestry in the genome of a mixed descendant from multiple populations can be inferred from partial PCA projections of informative loci. Finally, informative loci derived from the 1000 Genomes data well predict the projections of an independent genotype data of South Asians. These results demonstrate the utility and relevance of informative loci to investigate human evolution.

Factor analysis of ancient population genomic samples

The recent years have seen a growing number of studies investigating evolutionary questions using ancient DNA. To address these questions, one of the most frequently-used method is principal component analysis (PCA). When PCA is applied to temporal samples, the sample dates are, however, ignored during analysis, leading to imperfect representations of samples in PC plots. Here, we present a factor analysis (FA) method in which individual scores are corrected for the effect of allele frequency drift over time. We obtained exact solutions for the estimates of corrected factors, and we provided a fast algorithm for their computation. Using computer simulations and ancient European samples, we compared geometric representations obtained from FA with PCA and with ancestry estimation programs. In admixture analyses, FA estimates agreed with tree-based statistics, and they were more accurate than those obtained from PCA projections and from ancestry estimation programs. A great advantage of FA over existing approaches is to improve descriptive analyses of ancient DNA samples without requiring inclusion of outgroup or present-day samples.

Olfactory Atlases with an Array of Porphyrinoids Coated ZnO Nanoparticle

Olfactory Atlases with an Array of Porphyrinoids Coated ZnO Nanoparticle

Sensing Matrix Design for Compressive Spectral Imaging via Binary Principal Component Analysis.

Compressive spectral imaging (CSI) is a framework that captures coded-and-multiplexed low-dimensional projections of spectral data-cubes. In general, the sensing process in many CSI architectures is described using binary matrices, so-called sensing/projection matrices, whose elements can be either random or designed. However, some characteristics of the spectral data, such as the ℓ2-norm or the second moment statistics, can be lost when this dimensionality reduction is performed. Similarly, principal component analysis (PCA) is a data dimensionality reduction technique that minimizes the least-squared error between the spectral data and its low-dimensional projection, but preserving its structure or variance. Thus, PCA can be used to guide the CSI acquisition process by designing the binary sensing matrix. Nonetheless, PCA requires to know the spectral image a-priori, and also, its associated projection matrix is not binary, as required by CSI optical architectures. Therefore, in this paper, an algorithm to design CSI sensing matrices by exploiting the structure-preserving property of the PCA projection is proposed. First, a set of compressive measurements obtained with random sensing matrices is used to rapidly estimate the covariance matrix associated with the spectral data. Then, a new sensing matrix is designed by solving a non-convex optimization problem that finds a set of binary vectors that approximate the principal components of the covariance matrix, thus maximizing the explanation of the data variance. Experimental results show an improvement of up to 3 dB in image reconstruction quality, in terms of the peak signal to noise ratio (PSNR), when the binary PCA-based sensing matrices are used and compared with conventional random sensing matrices and state-of-art designed matrices based on PCA.

UHPLC-HRMS metabonomics to study effect of sulfur-fumigated Ophiopogonis Radix on endogenous metabolites in rats

The project was launched to analyze the effects of sulfur-fumigated Ophiopogonis Radix on endogenous metabolites in rats by metabonomics. The preparation method of sulfur-fumigated Ophiopogonis Radix in laboratory was established. Then the blood samples of SD rats in blank group,Ophiopogonis Radix extract group and sulfur-fumigated Ophiopogonis Radix extract group were investigated by UHPLC-Q-Exactive. The differential metabolites were screened and identified by PCA(principal component analysis),OPLSDA(orthogonal partial least squares discriminant analysis) and variable importance projection(VIP),and the metabolic pathways were analyzed. Finally,a total of 15 potential biomarkers were identified. Compared with the samples of Ophiopogonis Radix extract group,sulfur-fumigated Ophiopogonis Radix mainly affected the biosynthesis and metabolism of amino acids in normal rats. Its mechanism may be related to the biosynthesis of phenylalanine,tyrosine,tryptophan and aminoacyl-tRNA as well as the metabolism of phenylalanine and tryptophan. Based on UHPLC-HRMS metabonomics,this paper discussed the effects of sulfur-fumigated Ophiopogonis Radix on endogenous metabolites in rats,which provided an idea for the metabolic study of other sulfur-fumigated traditional Chinese medicines.

Determination of the bruise degree for cherry using Vis-NIR reflection spectroscopy coupled with multivariate analysis.

Determination and classification of the bruise degree for cherry can improve consumer satisfaction with cherry quality and enhance the industry’s competiveness and profitability. In this study, visible and near infrared (Vis-NIR) reflection spectroscopy was used for identifying bruise degree of cherry in 350–2500 nm. Sampling spectral data were extracted from normal, slight and severe bruise samples. Principal component analysis (PCA) was implemented to determine the first few principal components (PCs) for cluster analysis among samples. Optimal wavelengths were selected by loadings of PCs from PCA and successive projection algorithm (SPA) method, respectively. Afterwards, these optimal wavelengths were empolyed to establish the classification models as inputs of least square-support vector machine (LS-SVM). Better performance for qualitative discrimination of the bruise degree for cherry was emerged in LS-SVM model based on five optimal wavelengths (603, 633, 679, 1083, and 1803 nm) selected directly by SPA, which showed acceptable results with the classification accuracy of 93.3%. Confusion matrix illustrated misclassification generally occurred in normal and slight bruise samples. Furthermore, the latent relation between spectral property of cherries in varying bruise degree and its firmness and soluble solids content (SSC) was analyzed. The result showed both colour, firmness and SSC were consistent with the Vis-NIR reflectance of cherries. Overall, this study revealed that Vis-NIR reflection spectroscopy integrated with multivariate analysis can be used as a rapid, intact method to determine the bruise degree of cherry, laying a foundation for cherry sorting and postharvest quality control.

A KPCA-based parameterization model for composite materials representation

It is supposed that there exist means to generate material microstructure representations by varying processing parameters numerically [1] or experimentally [2], the goal of this work is to establish a parameterized geometrical description model from learning the set of given snapshot instances. In this work, we propose a parameterization model for the representation of the Representative Volume Elements (RVE) of composite materials based on Kernel Principal Component Analysis (KPCA) method. The set of synthetic RVE snapshots governed by a series of control parameters is firstly mapped into a high-dimensional feature space. Then, Principal Component Analysis (PCA) is performed, together with the establishment of approximated response surfaces of the retained PCA projection coefficients. We showcase the performance of KPCA-based surrogate by applying it for an optimal design of the effective mechanical properties of a two-phase composite material.

Grading bunch tightness for grape by multiperspective imaging approach coupled with multivariate classification methods

AbstractBunch's tightness is a significant trait in determining tablegrape and wine quality. But standard determination of tightness needs the trained technicians and complex manual measurements. This study is to validate the feasibility of multiperspective imaging analysis combined with multivariate modeling methods to grade the grape bunch compactness in a rapid, automatic, and objective approach. The detection system was developed by multiperspective projection imaging structure, where mono‐camera was used to simultaneously capture the whole surface of grape bunch by two mirrors' reflections. Through series of image processes, 23 features that described bunch regions, contours, the cluster density, and so forth, were digitalized and normalized for linear projections by principal component analysis (PCA) and partial least square (PLS), respectively. Then the projected latent components were obtained and were taken as input of the developed classifiers, to qualitatively assess the bunch's tightness. Results indicated these components compressed more information from PLS than PCA projection, and PLS‐support vector machine classifier outperformed best with 88.33% accuracy rate. It was concluded that the multiperspective simultaneously imaging approach coupled with image process and pattern recognition technology could be applied to evaluate the external tightness of grape bunch. Moreover, it has the potential to be a good reference for rapid grade of berry cluster.Practical applicationsThe proven performance of machine vision coupled with pattern recognition could be implemented in automatic measurement system to rapidly and objectively classify the tightness of postharvest grape bunch or other berry‐clustered fruit. This work could be a useful application for the specific consumptions to distinguish the tight from the loose on account of that the tightness plays a significant role in determining table grape and wine quality.

Generalized biplots for stress-based multidimensionally scaled projections

Dimension reduction and visualization are staples of data analytics. Methods such as Principal Component Analysis (PCA) and Multidimensional Scaling (MDS) provide low dimensional (LD) projections of high dimensional (HD) data while preserving an HD relationship between observations. Traditional biplots assign meaning to the LD space of a PCA projection by displaying LD axes for the attributes. These axes, however, are specific to the linear projection used in PCA. Stress-based MDS (s-MDS) projections, which allow for arbitrary stress and dissimilarity functions, require special care when labeling the LD space. An iterative scheme is developed to plot an LD axis for each attribute based on the user-specified stress and dissimilarity metrics. The resulting plot, which contains both the LD projection of observations and attributes, is referred to as the Generalized s-MDS Biplot. The details of the Generalized s-MDS Biplot methodology, its relationship with PCA-derived biplots, and an application to a real dataset are provided.

An embedded system based on DSP platform and PCA-SVM algorithms for rapid beef meat freshness prediction and identification

Statistical and artificial intelligence learning algorithms such as Principal Component Analysis (PCA) and Support Vector Machine (SVM) are widely used in many meat quality assessment applications to classify and predict the freshness of beef meat. This paper presents the implementation of the PCA and SVM algorithms on an embedded system based on a Digital Signal Processor (DSP). A dataset of eighty-one hue, saturation, and intensity (HSI) beef meat images was used. The PCA is used as a projection and prediction model where the SVM is used for the classification and identification of the beef meat. Results obtained from PCA projection model on a desktop system using Matlab software show the projection of three groups which represent the degree of beef meat freshness during the days of cold storage. A perfect prediction of the new unknown samples was obtained by PCA prediction model. A successful rate 100% of classification and identification was obtained by SVM. The PCA and SVM methods were implemented on the C6678 multi-core DSP. The implementation results of these algorithms were similar as those obtained by Matlab software. The processing time of the algorithms measured on the embedded system was lower compared to the desktop system. The embedded platforms based on DSP as portable tools can be used to predict or identify the sample beef meat freshness anywhere and in real-time.

Variation of morphological descriptors for the evaluation of tomato germplasm and their stability across different growing conditions

• Tomato standardized descriptors are essential for germplasm management. • Little information exists on stability of tomato descriptors across environments. • Wide ranges of variation were found for 12 tomato varieties in three environments. • Genotype × environment interaction and heritabilities of descriptors were very variable. • PCA projections of a same accession from different environments clustered together. Germplasm and breeding materials are usually characterized using morphological and agronomic descriptors, which should have a high heritability. Despite the widespread use of tomato ( Solanum lycopersicum ) standardized descriptors, little information exists on environmental effects on descriptor values and their heritability. We have evaluated 12 tomato accessions from seven cultivar groups in three different environments (open-field conventional, open-field organic, and greenhouse) and characterized them with 36 descriptors. A wide range of variation was found for most descriptors, demonstrating their utility for describing tomato materials and their diversity and relationships. The analysis of descriptors variation reveals that while for some descriptors with a simple genetic control the accession effect accounts for 100% of the variation, for others like yield per plant only 10.83% of the variation observed is due to the accession effect. Although significant differences were found among environments for most descriptors, including a much higher yield in the open-field conventional environment than in the two others, the environmental effect was low for most traits. However, the genotype × environment effect generally had an important contribution to the structure of variation for many descriptors, and for three traits it had the highest contribution to the percentage of the sum of squares. As a result of the variation structure, the heritability values are high (>0.7) for only 10 descriptors, while for five is low (<0.3). Principal components analysis (PCA) reveals that projections in the PCA graph of a same accession grown in different environments plot together in the same area of the PCA graph. Although cultivar groups are generally clearly separated in the PCA graph, accessions from the same cultivar group in some cases are intermixed. These results have important implications for detecting tomato duplicates and establishing core collections, as well as for analyzing germplasm and breeding results, when using data sets containing data of accessions grown in different environments.

Data-driven based Fault Diagnosis using Principal Component Analysis

Modern industrial systems are growing day by day and unlikely their complexity is also increasing. On the other hand, the design and operations have become a key focus of the researchers in order to improve the production system. To cope up with these chellenges, the data-driven technique like principal component analysis (PCA) is famous to assist the working systems. A data in bulk quanitity from the sensor measurements are often available in such industrial systems. Considering the modern industrial systems and their economic benifits, the fault diagnostic techniqes have been deeply studied. For example, the techniques that consider the process data as the key element. In this paper, the faults have been detected with the data-driven approach using PCA. In particular, the faults have been detected by using T^2 and Q statistics. In this process, PCA projects large data into smaller dimensions. Additionally it also preserves all the important information of process. In order to understand the impact of the technique, Tennessee Eastman chemical plant is considerd for the performance evaluation.