Principal Component Analysis Test Research Articles

Assessment of metals and metalloids agglutinated to airborne suspended particulate matter in selected plant species during the pre-and post-monsoon in the urban area

The elemental accumulation has emerged as a major environmental concern due to various anthropogenic sources such as vehicles, road dust, and industrial activities, contributing to the agglutination of elements to airborne Suspended Particulate Matter (SPM). SPM-bound elements accumulate on plant surfaces impact air quality and human health due to their noxiousness. Therefore, plants' ability to capture and mitigate air pollutants plays a crucial role in urban areas. This study aimed to investigate the levels and distribution of twenty-six elements, comprised of heavy metals (Cd, Pb, Cr, Cu Zn, Co, Ni, Fe, Mn, Ag, Mo, V, Ga, and Bi), light metals (B, As, Te, and Se), and metalloids (Al, Li, Sr, K, Mg, Na, Ca, and Ba) accumulated on the surface and inside the leaves of dominant plant species during the pre-and post-monsoon at six categorized (commercial, traffic-prone, residential, educational, greenbelt and industrial areas) locations in Delhi, India. In addition, the Metal Accumulation Index (MAI) was determined, and the statistical analysis was conducted using two-way ANOVA, Principal Component Analysis (PCA), and Hierarchical Cluster Analysis (HCA). In the pre-and post-monsoon, two-way ANOVA revealed significant differences (P < 0.05) in metal concentrations. During the pre-monsoon plants exhibited the highest metal accumulation (∼21%) at the Anand Vihar (commercial) in Delhi, with the maximum average concentrations of Cr (118.25 mg/kg), Cu (204.38 mg/kg), Zn (293.27 mg/kg), and Fe (2721.17 mg/kg). Ficus benghalensis L exhibited the maximum 213.73 MAI at the Anand Vihar in the pre-monsoon. Ni and Cr indicated the highest correlation (P < 0.05, r = 0.82) in the PCA test. HCA test revealed similarity (∼87.7%) at ITO (traffic-prone) and Okhla Phase-2 (industrial) in F. religiosa regarding metal concentration patterns. Findings highlighted seasonal elemental pollutants uptake dynamics of plant species and explored species-specific metal accumulation, revealing potential implications of metal-tolerant plants for urban greenbelt.

Establishing Research instrument to measure inclusiveness of adaptive methodologies in engineering education

Engineering Education has been a key to socioeconomic growth of India (developing economy). However, during the last decade there has been less enrollment in engineering institutes especially across rural India which has even led to closure of some institutes. It is conjectured that this problem can be addressed by adaptive educational strategies. Hence it is necessary to understand the efforts taken by engineering institutes in practicing adaptive educational strategies. The objective of this study is to design and test a reliable survey instrument to understand the extent of existence of adaptive education in engineering educational institutes. For the same, a research survey instrument with 8 constructs involving 57 items related to adaptive educational techniques was developed. A pilot survey comprising 61 students at various engineering institutes was carried out and its analysis was done using SPSS software. The reliability test was carried out by checking Cronbach alpha. Research instrument so tested, found reliable since scale reliability for all constructs lies within permissible range. Descriptive Statistics, demographics and related cross tabulation, and correlation between the constructs were established. Principal Component Analysis (PCA) was carried out. It was found from the result that the constructs are independent of demographic variables. Out of 08 constructs, 04 are factorable based on BTS, KMO and PCA tests. The deviation of mean from the top ratings of likert scale indicates the need of adaptive system inclusiveness in almost all educational parameters. The Research instrument can be used for large sample surveys.

Seasonal variation of biochemical parameters and air pollution tolerance index (APTI) of selected plant species in Delhi city, and detailed meta-analysis from Indian metropolitan cities

Plants act as the primary barrier to atmospheric pollution. Thus, while considering the role of plants in restricting air pollutants, one of the environmental tools used to assess tolerant plant species is the Air Pollution Tolerance Index (APTI). We conducted a study in Delhi during the pre-and post-monsoon seasons at polluted (Nehru Nagar) and non-polluted (Pusa Road) sites to assess the site-specific and seasonal variation of biochemical parameters (ascorbic, pH, relative water content, and chlorophyll) and APTI. Two-way ANOVA, Pearson's test, and Principal Component Analysis (PCA) were performed for the statistical analysis. Also, a detailed meta-analysis of biochemical parameter and their influence on APTI based on published literature in metropolitan cities of India has been performed. The study conducted in Delhi at a non-polluted site found higher APTI in the pre-monsoon than post-monsoon season. In addition, the maximum ascorbic acid content of 2.47 mg/g (Azadirachta indica A.Juss) and 1.37 mg/g (Ficus benjamina L) was reported at polluted and non-polluted sites, respectively, during pre-monsoon. The detailed meta-analysis of published literature revealed that plants' chlorophyll and ascorbic acid content were more at polluted sites than at non-polluted sites and substantially impacted APTI calculation. Linear regression showed a significant correlation of ascorbic acid (Mangifera indica L, R2 = 0.9670) and chlorophyll content (Polyalthia longifolia (Sonn.), R2 = 0.958) with APTI. Also, in the PCA test, measured characteristics of different species were strongly correlated, with 72.55% of overall variability. Furthermore, the meta-analysis revealed the average APTI of the plants in the following order Ficus benghalensis L > Polyalthia longifolia > M.indica > A. indica > Delonix regia (Boj. ex Hook) > Ficus religiosa L. A substantial seasonal, site-specific variation and statistically significant differences in the biochemical parameters of plants were observed. These results could be used to classify plant species as air pollution sinks and bioindicators.

Estimating yield in commercial wheat cultivars using the best predictors of powdery mildew and rust diseases.

This four-year research determined the best predictors of black, brown and yellow rusts and powdery mildew development in different wheat cultivars and planting dates across 282 experimental field plots. Parameters estimated by exponential (for black rust and powdery mildew) and Gaussian (for brown and yellow rusts) models, area under disease progress curve (AUDPC), and maximum disease severity were considered as disease progress curve elements. Factor analysis determined the most predictive variables among 19 indicators in order to describe wheat yield. According to principal component analysis (PCA), 11 selected wheat diseases and yield predicators accounted for 60% of total variance in datasets. This PCA test described four principal components involving these selected predictors. Next, multivariate regression model, which developed according to four independent principal components, justified a noticeable part of yield variability over and within growing seasons. Present findings may improve accuracy of future studies to examine seasonal patterns of powdery mildew and rusts, predict wheat yield and develop integrative disease management programs.

Morphological characterization of certain Jasminum sambac genotypes using principal component analysis

Jasminum sambac is one of the most important traditional flower crops of India especially Tamil Nadu. Understanding the genetic variability among the different genotypes of Jasminum sambac helps in its exploitation in breeding programmes. A total of 34 genotypes comprising of traditional landraces collected from all over Tamil Nadu were evaluated for 40 qualitative traits according to the DUS descriptors specified for Jasminum sambac (Kannan M. et al., Plant Variety Journal, 2016). In order to reduce the dimensionality of the data, examine the variation and the relative contribution of the traits for the total variability, Principal Component Analysis (PCA) method was adopted. PCA test in 34 genotypes for all the 40 characters resulted in fifteen Principal Components (PCs) with an eigenvalue more than one accounted for 97.21 per cent of the total variability and revealed that the traits leaf margin undulation, flower bud shape, flower shape, shape of corolla lobe, flower petal tip, leaf blade undulations, flower bud length and root suckers exhibited maximum variation. Agglomerative Hierarchical Clustering and PCA results showed that the genotypes Acc. Js- 11, Acc. Js- 12, Acc. Js- 13, Acc. Js- 14, Acc. Js- 20, Acc. Js- 25, Acc. Js- 27 and Acc. Js- 32 were found to be the most diverse genotypes.

How do machine translators measure up to human literary translators in stylometric tests?

Abstract This study aims to test the classification of literary translations by human and machine translators in a bid to explore stylistic distinctions between the two groups and among machine translators and their evolution over 1 year between 2019 and 2020. For this, the study takes a stylometric approach by employing two statistical methods popularly used in authorship attribution—support vector machine (SVM) and principal component analysis (PCA), which are applied to analyzing three types of features—1-grams, 2-grams, and 3 grams. The results show that over the researched period, the three machine translators investigated (Google, Bing, and Papago) moved in the direction of converging in style while advancing toward human translators. The distance gained by them on human translators, however, was not significant enough to challenge the clear-cut divide between the two groups. The PCA tests additionally revealed some characteristics of the machine translator that might be responsible for the stylistic distinction between them and their human counterparts including their tendency of overusing basic standard language.

The key aroma compounds and sensory characteristics of commercial Cheddar cheeses

To study the key aroma components and flavor profile differences of Cheddar cheese with different maturity and from different countries, the flavor components of 25 imported commercial Cheddar cheese samples in the China market were determined by gas chromatography-mass spectrometry. The quality and quantity of 40 flavor compounds were analyzed by gas chromatography-olfactometry among 71 aroma compounds determined by gas chromatography-mass spectrometry. Combined with odor activity value calculation, principal component analysis (PCA) was conducted to analyze the relationship among 26 flavor compounds with odor activity values >1 and the maturity of Cheddar cheese. The PCA results showed significant differences between the group of mild Cheddar cheese and the groups of medium Cheddar cheese and mature Cheddar cheese, and no significant differences were observed between medium Cheddar cheese and mature Cheddar cheese. According to the results of PCA and consumers' preference test, representative Cheddar cheese samples with different ripening times were selected for the flavor profile analysis. Partial least squares regression analysis was conducted to obtain the relationship between sensory properties and flavor compounds of different Cheddar cheeses. Based on partial least squares regression analysis, 1-octen-3-one, hexanal, acetic acid, 3-methylindole, and acetoin were positively correlated with milky, sour, and yogurt of mild Cheddar cheese. Dimethyl trisulfide, phenylacetaldehyde, ethyl caproate, octanoic acid, and furaneol and other compounds were positively correlated with fruity, caramel, rancid, and nutty notes of the medium and mature Cheddar cheeses.

Shakespeare and principal components analysis

Abstract The use of principal components analysis (PCA) in literary studies was pioneered by John Burrows. From him it was adopted by the New Oxford Shakespeare project team, who used it to support their controversial attributions of parts of Arden of Faversham to Shakespeare and parts of the Henry VI trilogy to Marlowe. This essay examines a representative sample of the PCA tests done using function words by that team. It observes that, as with their Zeta method, they used the unsound bisector line method of interpretation. By calculating information which they did not disclose, it finds that their decision to discard all but two principal components was not justified by the data they were using, putting into doubt their interpretations of the results. It shows that their test method is vulnerable by design to the making of false attributions and demonstrates this by a new experiment. By a further experiment, it shows that the PCA tests may be telling us that differences between characters are greater than those between authors, at least in the use of function words. It concludes that the PCA tests done by that project cannot be relied on; and, given the already demonstrated unreliability of their Zeta tests, these new findings invalidate all work presented in the influential book Shakespeare, Computers, and the Mystery of Authorship.

Two sample tests for high-dimensional autocovariances

The problem of testing for the equality of autocovariances of two independent high-dimensional time series is studied. Tests based on the suprema or sums of suitable averages across the dimensions are adapted from the available literature. Another test based on principal component analysis (PCA) is introduced and studied in theory. An extension is also considered to the setting of testing for the equality of autocovariances of two populations, having multiple individual high-dimensional series from the two populations. The proposed methodologies are assessed on simulated data, with the performance of the introduced PCA testing being superior overall. An application using fMRI data from individuals experiencing two different emotional states is provided.

An ecological barrier between the Himalayas and the Hengduan Mountains maintains the disjunct distribution of Roscoea

AbstractAimRegional disjunction in mountainous regions is ubiquitous and is closely associated with the evolution of mountain biodiversity. However, the mechanisms of maintaining regional disjunction are often ignored, especially regarding the role of ecological barriers. The aim of this study is to test whether an ecological barrier maintains the regional disjunction of Roscoea between the Himalayas (HIM) and the Hengduan Mountains/Yun‐Gui Plateau (HMY).LocationThe Himalayas and the Hengduan Mountains/Yun‐Gui Plateau, Asia.TaxonMonocot:Zingiberales:Zingiberaceae:Roscoea.MethodsOccurrence data from Roscoea were collected from public databases and field investigations. Environmental niche modelling (ENM) was used to predict the potential distribution of Roscoea during the Last Interglacial (LIG), the Last Glacial Maximum (LGM), the present and the future. An environmental variable difference test, principal component analysis (PCA), discriminant function analysis (DFA) and niche identity test were employed to qualify and quantify the contributions of environmental factors to ecological divergence based on 7 and 26 environmental variables.ResultsEnvironmental niche modelling predicted an ecological gap of Roscoea between the HIM and the HMY from the LIG into the future, excluding the LGM. Statistically significant differences were detected in eight unique environmental variables across the Gap, which was mostly related to low temperatures and high moisture levels. The PCA, DFA and niche identity test suggested larger ecological separations among the Gap, the HIM and the HMY based on 26 environmental variables than based on seven variables.Main conclusionsAll clues point to an ecological barrier that has been maintaining the regional disjunction of Roscoea. This ecological barrier is predicted to be stable under future climate change and may reflect long‐term ecological divergence shaped by the adaptation of Roscoea to divergent habitats. Our study highlights that the effects of specific environmental variables, with possible interactions among multiple ecological factors, being closely related to the ecological barrier.

Effect of Silver Diamine Fluoride Treatment on Microbial Profiles of Plaque Biofilms from Root/Cervical Caries Lesions

Purpose: To assess the effect of silver diamine fluoride (SDF) on microbial profiles present in plaque from root/cervical carious lesions, and its association with caries lesion arrest. Materials and Methods: Twenty patients with at least one soft cavitated root/cervical carious lesion were included. One lesion/patient was randomly selected and treated with 38% SDF. Supragingival plaque samples were harvested at preintervention and 1 month postintervention. Using an MiSeq platform, 16S rDNA sequencing of the V3-V4 regions was used to determine bacterial profiles. Clinical evaluation of lesion hardness was used to evaluate arrest. t tests, principal component analysis (PCA), multidimensional scaling (MDS), and generalized linear models (GLMs) tests were used for statistical comparisons. Results: From a total of 40 plaque samples, 468 probe targets were observed. Although 60% of lesions became hard postintervention, PCA and MDS tests showed no distinct pre- and postintervention groups. In addition, pre- and postintervention differences in diversity (Shannon index) of microbial profiles between patients with and without lesion arrest were not statistically different. A likelihood ratio test for pre- versus postintervention differences within patients, i.e., adjusting for differences between patients using negative binomial GLMs, showed 17 bacterial taxa with significant differences (FDR <0.05). Conclusion: Although 60% of lesions hardened after SDF treatment, this was not directly due to either overall statistically significant differences in microbial profiles or differences in microbial diversity. Nevertheless, there was a trend with some acid-producing species in that their relative abundance was reduced postintervention. The negative binomial GLMs showed 17 bacterial taxa that were significantly different after SDF treatment.

Reducing Computational Time of Principal Component Analysis with Chinese Remainder Theorem

It is of paramount importance to establish an identity of citizenry to curb criminalities. Principal Component Analysis (PCA) which is one of the foremost methods for feature extraction and feature selection is adopted for identification and authentication of people. The computational time used by PCA is too much and Chinese Remainder Theorem was employed to reduce its computational time. TOAM database was setup which contained 120 facial images of 40 persons frontal faces with 3 images of each individual. 80 images were used for training while 40 were used for testing. Training time and testing time were used as performance metrics to determine the effect of CRT on PCA in terms of computational time. The experimenal results indicated an average training time of 13.5128 seconds and average testing time of 1.5475 second for PCA while PCA-CRT average training time is 13.2387 seconds and average testing time of 1.5185 seconds. Column chart was used to show the graphical relationship between PCA and PCA-CRT Training time and testing time. The research revealed that CRT reduce PCA computational time.

Process analytical technology in continuous manufacturing of a commercial pharmaceutical product

The implementation of process analytical technology and continuous manufacturing at an FDA approved commercial manufacturing site is described. In this direct compaction process the blends produced were monitored with a Near Infrared (NIR) spectroscopic calibration model developed with partial least squares (PLS) regression. The authors understand that this is the first study where the continuous manufacturing (CM) equipment was used as a gravimetric reference method for the calibration model. A principal component analysis (PCA) model was also developed to identify the powder blend, and determine whether it was similar to the calibration blends. An air diagnostic test was developed to assure that powder was present within the interface when the NIR spectra were obtained. The air diagnostic test as well the PCA and PLS calibration model were integrated into an industrial software platform that collects the real time NIR spectra and applies the calibration models. The PCA test successfully detected an equipment malfunction. Variographic analysis was also performed to estimate the sampling analytical errors that affect the results from the NIR spectroscopic method during commercial production. The system was used to monitor and control a 28 h continuous manufacturing run, where the average drug concentration determined by the NIR method was 101.17% of label claim with a standard deviation of 2.17%, based on 12,633 spectra collected. The average drug concentration for the tablets produced from these blends was 100.86% of label claim with a standard deviation of 0.4%, for 500 tablets analyzed by Fourier Transform Near Infrared (FT-NIR) transmission spectroscopy. The excellent agreement between the mean drug concentration values in the blends and tablets produced provides further evidence of the suitability of the validation strategy that was followed.

Environmental Analysis of GOES-R Proving Ground Convection-Initiation Forecasting Algorithms

AbstractEnhanced temporal and spatial resolution of the Geostationary Operational Environmental Satellite–R Series (GOES-R) will allow for the use of cloud-top-cooling-based convection-initiation (CI) forecasting algorithms. Two such algorithms have been created on the current generation of GOES: the University of Wisconsin cloud-top-cooling algorithm (UWCTC) and the University of Alabama in Huntsville’s satellite convection analysis and tracking algorithm (SATCAST). Preliminary analyses of algorithm products have led to speculation over preconvective environmental influences on algorithm performance. An objective validation approach is developed to separate algorithm products into positive and false indications. Seventeen preconvective environmental variables are examined for the positive and false indications to improve algorithm output. The total dataset consists of two time periods in the late convective season of 2012 and the early convective season of 2013. Data are examined for environmental relationships using principal component analysis (PCA) and quadratic discriminant analysis (QDA). Data fusion by QDA is tested for SATCAST and UWCTC on five separate case-study days to determine whether application of environmental variables improves satellite-based CI forecasting. PCA and significance testing revealed that positive indications favored environments with greater vertically integrated instability (CAPE), less stability (CIN), and more low-level convergence. QDA improved both algorithms on all five case studies using significantly different variables. This study provides an examination of environmental influences on the performance of GOES-R Proving Ground CI forecasting algorithms and shows that integration of QDA in the cloud-top-cooling-based algorithms using environmental variables will ultimately generate a more skillful product.

Comparison of Outlier Detection Methods in Crossover Design Bioequivalence Studies

The significance of bioequivalence (BE) studies is rising due to large scale production and utilization of generic products all over the world. The correct identification of outlying data in BE studies is substantial for deciding two products either bioequivalence or bioinequivalent. For the detection of outliers in BE studies with the crossover designs different methods have been suggested in the literature. In the present work, we compared three outlier detection tests; (i) the Likelihood distance (LD) test (ii) the estimated distance (ED) test and the principal component analysis (PCA) test. In this work, the PCA test has been first time compared with the LD and ED test. For the purpose of comparison, we used two-way and three-way BE crossover data sets on linear and logarithmic scales. During the course of work it was found interesting and note-worthy that the performances of the ED and PCA tests in the sense of outlier detection are better than the LD test and this performance persists even for the log-transformed data. The results of our simulation study also indicated that the performance of the ED test for outliers’ identification is better than the other two tests.

Evaluation of PCA and Gamma test techniques on ANN operation for weekly solid waste prediction

Artificial neural networks (ANNs) are suitable for modeling solid waste generation. In the present study, four training functions, including resilient backpropagation (RP), scale conjugate gradient (SCG), one step secant (OSS), and Levenberg–Marquardt (LM) algorithms have been used. The main goal of this research is to develop an ANN model with a simple structure and ample accuracy. In the first step, an appropriate ANN model with 13 input variables is developed using the afore-mentioned algorithms to optimize the network parameters for weekly solid waste prediction in Mashhad, Iran. Subsequently, principal component analysis (PCA) and Gamma test (GT) techniques are used to reduce the number of input variables. Finally, comparison amongst the operation of ANN, PCA-ANN, and GT-ANN models is made. Findings indicated that the PCA-ANN and GT-ANN models have more effective results than the ANN model. These two models decrease the number of input variables from 13 to 7 and 5, respectively.

Characterization of volatile organic compound emission sources in Fort Saskatchewan, Alberta using principal component analysis

Volatile organic compounds (VOCs) were measured at two sites in a highly industrialized zone in western Canada from September 2004 to March 2006. Principal component analysis (PCA) with varimax rotation was performed on 30 VOCs to identify the pollution sources. Aliphatics, aromatics, and halogenated aliphatics were studied. The two monitoring sites were 11 km apart, with site 1 closer to the city of Fort Saskatchewan and site 2 predominantly down wind from the industrial sources. PCA results provided the basis for interpreting the relationship between the ambient 24-h integrated VOC samples and the emission sources in the region. Challenges existed in interpreting the PCA results in such a highly industrialized region; however a unique feature to this study was the fact that the region was home to the only 1,2-dichloroethane emitting facility in Canada. Other specific industry related VOCs in the region were vinyl chloride, styrene and HCFC-22. Making use of these specific VOCs in the PCA allowed for easy identification of an industrial contribution. For factors that were not easily distinguishable, further PCA tests were conducted using carbon monoxide concentrations, wind direction data and seasonal splitting of the samples. The analysis found that five factors accounted for 82% of the variance at site 1 and five factors accounted for 81% of the variance at site 2. The factor accounting for the highest variability (∼40%) at the two sites was the most difficult to interpret, but showed contributions from both industry and vehicle related emissions. Specific industrial sources were identified using 1,2-dichloroethane as a chemical tracer or by corroborating wind speed with known industry VOC emissions. Both sites had two factors identified as specific industry sources and these factors totaled to over 20% of the variance. Long range transport of stable halogenated compounds accounted for greater than 10% of the variance, and seasonal effects accounted for 5% of the variance.

A GC-TOF-MS study of the stability of serum and urine metabolomes during the UK Biobank sample collection and preparation protocols

The stability of mammalian serum and urine in large metabolomic investigations is essential for accurate, valid and reproducible studies. The stability of mammalian serum and urine, either processed immediately by freezing at -80 degrees C or stored at 4 degrees C for 24 h before being frozen, was compared in a pilot metabolomic study of samples from 40 separate healthy volunteers. Metabolic profiling with GC-TOF-MS was performed for serum and urine samples collected from 40 volunteers and stored at -80 degrees C or 4 degrees C for 24 h before being frozen at -80 degrees C. Subsequent Wilcoxon rank sum test and Principal Components Analysis (PCA) methods were used to assess whether differences in the metabolomes were detected between samples stored at 4 degrees C for 0 or 24 h. More than 700 unique metabolite peaks were detected, with over 200 metabolite peaks detected in any one sample. PCA and Wilcoxon rank sum tests of serum and urine data showed as a general observation that the variance associated with the replicate analysis per sample (analytical variance) was of the same magnitude as the variance observed between samples stored at 4 degrees C for 0 or 24 h. From a functional point of view the metabolomic composition of the majority of samples did not change in a statistically significant manner when stored under two different conditions. Based on this small pilot study, the UK Biobank sampling, transport and fractionation protocols are considered suitable to provide samples, which can produce scientifically robust and valid data in metabolomic studies.

Ecology of the Major Periphytic Diatom Communities from the Mesta River, Bulgaria

AbstractEpilithon, epiphyton, epipelon, epipsammon and plocon diatom samples and water chemistry samples were collected bimonthly from 11 stations along the Mesta River, Bulgaria between December 1989 and April 1991. Principal component analysis (PCA), correlation, and dominance analysis were employed for describing the seasonal dynamics of diatom assemblages and estimating the correlation between diatom distribution and the physico‐chemical parameters. All periphytic communities were compared by PCA, MANOVA and Fisher's LSD multiple comparison test. Ionic strength and eutrophication were the major abiotic factors affecting diatom distribution in the epilithon, epiphyton, epipelon and epipsammon. Current velocity had an important influence on the diatom assemblages from the epilithon and epiphyton. Epilithon showed the lowest correlation with most of the environmental factors and had significantly lower species diversity than epiphyton, epipelon and epipsammon. Multivariate analysis of the diatom data suggested that in all benthic habitats of the Mesta River seasonal replacement of diatom assemblages was controlled by the water chemistry rather than substratum, current velocity or discharge. The oligotrophic, mesotrophic and eutrophic diatom communities in the Mesta River comprised early‐, mid‐ and late‐successional colonizers respectively. Multiple comparisons of all periphytic communities showed that habitat specificity was positively correlated with current velocity.

Comparison of Canonical Variate and Principal Component Analyses of Wine Descriptive Analysis Data

ABSTRACTCabernet Sauvignon wines from four regions and Chardonnay wines from three vintages were evaluated by descriptive analysis. The sensory ratings were evaluated by principal component analysis (PCA) and by canonical variate analysis (CVA) using wines (CVA‐Wine) and using regions or vintages (CVA‐Group) as classification variables. PCA and CVA‐Wine analyses provides similar results for both data sets. Whereas, the CVA‐Group analyses demonstrated significant differences among regions or vintages, the variable configuration differed from the other two methods, reflecting the differences among groups rather than among the wines overall. To understand the structure of the data, CVA‐Wine and PCA tests were superior to CVA‐Group.