Feasibility Of Principal Component Analysis Research Articles

Regulation of tapioca starch 3D printability by yeast protein: Rheological, textural evaluation, and machine learning prediction

This article investigated the effects of yeast protein (YP) on gel rheology, texture, and 3D printability of tapioca starch and the feasibility of Principal component analysis (PCA) and support vector machine (SVM) algorithms for classification and prediction of printability. The results indicated that increasing YP content enhanced the viscosity, storage and loss moduli, and hardness, thereby improving extrudability and supportability of 3D printing. The addition of 15% YP exhibited the best 3D printing performance, but excessively high YP addition hindered ink extrusion. PCA analysis based on rheological and texture indices categorized the ink's 3D printing performance into four classes: poor support and low printing accuracy; good support but low printing accuracy; good support and high printing accuracy; and non-smooth extrusion. Furthermore, SVM algorithm used texture data to predict printability classification, with the highest prediction accuracy (91.67%) achieved at polynomial kernel among four different kernel functions. These results confirm that YP can serve as a potential ink for 3D printing and underscore SVM's efficacy in predicting ink's 3D printing performance.

Feasibility of Principal Component Analysis for Multi-Class Earthquake Prediction Machine Learning Model Utilizing Geomagnetic Field Data

Geomagnetic field data have been found to contain earthquake (EQ) precursory signals; however, analyzing this high-resolution, imbalanced data presents challenges when implementing machine learning (ML). This study explored feasibility of principal component analyses (PCA) for reducing the dimensionality of global geomagnetic field data to improve the accuracy of EQ predictive models. Multi-class ML models capable of predicting EQ intensity in terms of the Mercalli Intensity Scale were developed. Ensemble and Support Vector Machine (SVM) models, known for their robustness and capabilities in handling complex relationships, were trained, while a Synthetic Minority Oversampling Technique (SMOTE) was employed to address the imbalanced EQ data. Both models were trained on PCA-extracted features from the balanced dataset, resulting in reasonable model performance. The ensemble model outperformed the SVM model in various aspects, including accuracy (77.50% vs. 75.88%), specificity (96.79% vs. 96.55%), F1-score (77.05% vs. 76.16%), and Matthew Correlation Coefficient (73.88% vs. 73.11%). These findings suggest the potential of a PCA-based ML model for more reliable EQ prediction.

Spectral Correction and Dimensionality Reduction of Hyperspectral Images for Plant Water Stress Assessment

Hyperspectral Imaging (HSI) is one of the emerging techniques used in plant phenotyping as it carries abundant information and is non-invasive to plants. However, factors like illumination effect and high-dimensional spectral features need to be solved to attain higher accuracy of plant trait analysis. This research explored and analysed spectral normalisation and dimensionality reduction methods. The focus of this paper is twofold; the first objective was to explore the Standard Normal Variate (SNV), Least Absolute Deviations (L1) and Least Squares (L2) normalisation for spectral correction. The second objective was to explore the feasibility of Principal Component Analysis (PCA) and Analysis of Variance Fisher’s Test (ANOVA F-test) for spectral dimensionality reduction in spectral discriminative modelling. The analysis techniques were validated with HSI data of maise plants for early detection of water deficit stress response. Results showed that SNV performed the best among the three normalisation methods. Besides, ANOVA F-test outperformed PCA for the band selection method as it improved the trait assessment on the water deficit response of maise plants.

Comparative analysis of volatile profiles in two grafted pine nuts by headspace-SPME/GC–MS and electronic nose as responses to different roasting conditions

As an attempt to fulfill the massive demand for pine nuts, two grafted trees were cultivated: grafted Pinus koraiensis on the same scions (PK) and grafted Pinus koraiensis on Pinus sylvestris rootstocks (PKS) trees. Both PK and PKS are acknowledged as important economic trees in the northeastern area of China. This study aimed to compare the volatile compounds and aroma profiles in PK and PKS by Headspace Solid Phase Microextraction (HS-SPME) coupled with Gas Chromatography-Mass Spectrometry (GC–MS) and Electronic nose (E-nose) as responses to different roasting conditions. The results showed that a total of 286 volatile compounds were identified in the PK and PKS samples, which some of them were considered to contribute to the desirable aroma of samples. Abundance of terpenes and aromatic hydrocarbons, such as D-limonene and toluene, were respectively present in both raw PK and PKS. The increasing temperature and duration of roasting significantly decreased terpenes and aromatic hydrocarbons content, while more alkanes/alkenes, acids, and ketones were generated in the medium temperature condition. The late phase of roasting was dominated by aldehydes, furans, furfurals, pyrazines, and pyrroles, for which PKS showed a higher content than PK. The aroma profiles detected by E-nose showed that the influence of roasting time was less at high temperatures than those at low and medium temperatures. This study also highlighted the feasibility of principal component analysis (PCA) combined with HS-SPME/GC–MS and E-nose to discriminate the samples.

Principal Component Analysis in Projection and Image Domains—Another Form of Spectral Imaging in Photon-Counting CT

We explore the feasibility of principal component analysis (PCA) as a form of spectral imaging in photon-counting CT. Using the data acquired by a prototype system and simulated by computer, we investigate the feasibility of spectral imaging in photon-counting CT via PCA for feature extraction and study the impacts made by data standardization and de-noising on its performance. The PCA in the projection domain maintains the data consistence that is essential for tomographic image reconstruction and performs virtually the same as that in the image domain. The first three primary components account for more than 99.99% covariance of the data. Within anticipation, the contrast-to-noise ratio (CNR) between the target and background in the first principal component image can be larger than that in the image generated from the data acquired in each energy bin. More importantly, the CNR in the first principal component image may be larger than that in the image formed by the summed data acquired in all energy bins (i.e., the conventional polychromatic CT image). In addition, de-noising can not only reduce the noise in images but also improve the effectiveness/efficiency of PCA in feature extraction. The PCA in either projection or image domain provides another form of spectral imaging in photon-counting CT that fits the essential requirements on spectral imaging in true color. The verification of PCA's feasibility in CT as a form spectral imaging and observation of its potential superiority in CNR over conventional polychromatic CT are meaningful in theory and practice.

Advanced soft computing techniques for predicting soil compression coefficient in engineering project: a comparative study

Heterogeneous nature of soil consists of various chemical and physical attributes that make the prediction of soil parameters very tedious and challenging. Moreover, it becomes more difficult when we have more number of variables. This study investigates the feasibility of principal component analysis as dimensionality reduction technique to select the input variables in terms of principal components (PCs), which helps in reducing the complexity and multicollinearity problem. The soil attributes, namely depth of the sample, sand percentage, silt percentage, clay percentage, moisture content, dry density, wet density, void ratio, liquid limit, plastic limit, liquid index, and plastic index, have been employed as influencing factors to estimate the coefficient of compression of soil. Furthermore, the extracted variance-based PCs were used as predictor to build the minimax probability machine regression (MPMR), multivariate adaptive regression splines (MARS), and genetic programming regression (GPR). The predictive accuracy of the models has been assessed via five statistical fitness parameters. In the training phase, the PCA-MARS model has shown good outcomes in terms of fitness measurement parameters (RMSE= 0.004, r = 0.981 and NSE = 0.963). During testing phase, PCA-MARS has outperformed (RMSE= 0.006, r = 0.963 and NSE = 0.912) followed by PCA-GPR and PCA-MPMR. The finding of this research concludes that PCA-based MARS model can be used as new and reliable data-driven approach for estimation of soil parameters. Furthermore, this new tool can help to save the time and capital spent on estimation of different parameter of soil.

Hand Gesture Recognition Using Principal Component Analysis

Nowadays actions are increasingly being handled in electronic ways, instead of physical interaction. From earlier times biometrics is used in the authentication of a person. It recognizes a person by using a human trait associated with it like eyes (by calculating the distance between the eyes) and using hand gestures, fingerprint detection, face detection etc. Advantages of using these traits for identification are that they uniquely identify a person and cannot be forgotten or lost. These are unique features of a human being which are being used widely to make the human life simpler. Hand gesture recognition system is a powerful tool that supports efficient interaction between the user and the computer. The main moto of hand gesture recognition research is to create a system which can recognise specific hand gestures and use them to convey useful information for device control. This paper presents an experimental study over the feasibility of principal component analysis in hand gesture recognition system. PCA is a powerful tool for analyzing data. The primary goal of PCA is dimensionality reduction. Frames are extracted from the Sheffield KInect Gesture (SKIG) dataset. The implementation is done by creating a training set and then training the recognizer. It uses Eigen space by processing the eigenvalues and eigenvectors of the images in training set. Euclidean distance with the threshold value is used as similarity metric to recognize the gestures. The experimental results show that PCA is feasible to be used for hand gesture recognition system.

Developing an Instrument for Assessment of Academic Management in Engineering Courses

Higher education in Brazil been expanding in recent years, although, one of the major problems encountered have been the evasion and failure rates in general courses and specifically in engineering undergraduate courses. In this context, educational evaluation, with the goal of diagnosing needs for improvement, can contribute to the quality of higher education, regarding some aspects such as teaching methodology, human interrelationships in the teaching profession and academic management, among others. This study is based on the analysis of the development and subsequent implementation of an assessment tool that would enable the analysis of academic and administrative expertise of academic management. The instrument consists of a list of 15 statements related to aspects of higher education management, where the target audience of the research are the teachers of 6 engineering courses at Federal University of Ceara (UFC), indicating their degree of agreement with the statements. For the analysis, a multivariate statistical tool called Principal Component Analysis (PCA) was used. The results show the feasibility of PCA to analyze aspects of academic management, by explaining relevant information extracted from the latent variables of the data, which allowed us to identify a group of clusters related to management of higher education in engineering within the UFC. The study points to the use of this information in the future related to the contents of external assessments through more robust and detailed mathematical analysis tools.

Analysis of Cheese Small Molecules by UPLC–QToF-MS and Multivariate Statistical Methods Using Several Extraction Procedures

Lyophylized (L) and nonlyophylized (NL) samples of two cheeses from different batches were extracted with either methanol:water (8:2, v/v) or isopropanol:water (7:3, v/v), and the extracts were analyzed by ultraperformance liquid chromatography coupled with electrospray ionization/quadruple-time-of-flight mass spectrometry (UPLC–ESI–QToF-MS) in both positive and negative modes. Chromatographic and mass spectrometric data were further compared for different samples by paired t test and principal component analysis (PCA). Mass spectra at different elution times were manually checked and major peaks tentatively identified according to their m/z values by search in available databases. A number of gangliosides and polyglycosilated ceramides were found to be the most abundant components. PCA showed samples could be clustered according to extraction procedure and cheese, and paired t test comparison also showed significant (p < 0.05) differences between L and NL samples extracted with methanol:water. L and NL samples could only be distinguished in PCA when data from the whole chromatographic range were used. Results are discussed based on extraction solvent polarity and feasibility of PCA for comparison of cheese chemical composition after untargeted UPLC–MS analysis.