2D Principal Component Analysis Research Articles

Comparative Analysis of Phytochemical and Functional Profiles of Arabica Coffee Leaves and Green Beans Across Different Cultivars.

This study analyzed the phytochemical composition and functional properties of leaves and green beans from seven Arabica coffee cultivars. The total phenolic and flavonoid contents were measured using spectrophotometric methods, while caffeine, chlorogenic acid (CGA), and mangiferin levels were quantified via High-Performance Liquid Chromatography (HPLC). Volatile compounds were identified using Gas Chromatography-Mass Spectrometry (GC-MS). Antioxidant activity was assessed using 2,2-Diphenyl-1-Picrylhydrazyl (DPPH) and 2,2'-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS) radical scavenging assays, and anti-inflammatory effects were evaluated by measuring reactive oxygen species (ROS), nitric oxide (NO) levels, and nuclear factor kappa B (NF-κB) activation in lipopolysaccharide (LPS)-stimulated macrophages. The results revealed that coffee leaves had significantly higher levels of total phenols, flavonoids, and CGAs, and exhibited stronger antioxidant activities compared to green beans. Notably, Geisha leaves exhibited the highest concentrations of phenolics and flavonoids, along with potent anti-inflammatory properties. Among green beans, the Marsellesa cultivar exhibited a significant flavonoid content and strong ABTS scavenging and anti-inflammatory effects. GC-MS analysis highlighted distinct volatile compound profiles between leaves and green beans, underscoring the phytochemical diversity among cultivars. Multivariate 3D principal component analysis (PCA) demonstrated clear chemical differentiation between coffee leaves and beans across cultivars, driven by key compounds such as caffeine, CGAs, and pentadecanoic acid. Hierarchical clustering further supported these findings, with dendrograms revealing distinct grouping patterns for leaves and beans, indicating cultivar-specific chemical profiles. These results underscore the significant chemical and functional diversity across Arabica cultivars, positioning coffee leaves as a promising functional alternative to green beans due to their rich phytochemical content and bioactive properties.

The ventricular septal defect in tetralogy of Fallot: A multi-centre morphological and statistical shape analysis study

Abstract Background Ventricular septal defects (VSDs) in Tetralogy of Fallot (TOF) are often thought to be ‘large and unrestricted’ but experience suggests they have a more heterogenous morphology. Understanding the morphological variations and dimensions is the first step in modifying surgical closure or optimising potential device design. Purpose The objective of this study was to perform a comprehensive multi-center morphological analysis of the characteristics of the VSD in TOF. Additionally, we sought to perform a preliminary 2D shape analysis and principal component analysis (PCA) on standardised photos of the VSDs. Through this multi-dimensional approach, we aim to elucidate the morphological variations and structural characteristics of the VSD in TOF as a basis for future 3D shape analysis. Methods Of 495 specimens analysed, 460 were included in the morphological analysis. Standardised protocol was applied to assess the characteristics of the VSD: basic measurements of the height, width, inferior limb, and length of the inferior fibrous rim (if perimembranous) were taken and indexed to ventricular length. The degree of aortic override and presence of aorto-mitral continuity were also documented with double outlet right ventricle (DORV) being defined as more than 50% of aorta supported by the right ventricle. Statistical analysis was performed to assess variations in TOF morphology across different age groups and disease phenotypes. Standardised photographs of 130 VSDs were taken from the right ventricle and traced for PCA, in order to assess shape variations among the VSDs. Results Both the length of the inferior limb of the PM VSD and the length of the fibrous rim increased with ventricular length but the rate of change was lower for the fibrous rim. A chi-square analysis comparing the presence of double outlet right ventricle and the presence of a fibrous continuity between the aortic and mitral valves showed no association between these features (p = 0.89). The results of PCA in the indexed datasets showed that the largest variation was in the size of the VSD and then from the shape of the VSD where two distinct morphologies emerged: a classic V-shaped VSD and wide but shallow variant. Discussion Our study challenges conventional definitions and assumptions regarding the morphological characteristics of the VSD in TOF and suggests that components may change size at differing rates. Our study conclusively shows that DORV and TOF can co-exist and the definition of DORV should be made independently of the presence of aorto-mitral continuity. Contrary to the traditional view of TOF featuring large unrestricted VSDs, our findings suggest wide shallow VSDs are common. These findings challenge paradigms and underscore a necessity for the continuing morphological re-evaluation of TOF utilising large datasets with a diverse phenotypic spread.

Epicutaneous house dust mite (HDM)-induced skin lesions feature early activation of T helper 2 inflammatory and pruritogenic pathways in HDM-nonsensitised dogs.

Epicutaneously house dust mite-sensitised (HDM-S) healthy dogs are commonly used as canine atopic dermatitis (cAD) models; however, the exact mechanisms of HDM-induced AD immune activation in HDM-S and HDM-nonsensitised (NS) dogs remain unclear. To characterise the inflammatory and pruritogenic transcriptome of acute epicutaneous HDM-induced skin lesions at 6 h and 24 h in HDM-NS and HDM-S dogs; untreated skin at 0 h from each dog served as control. Six HDM-S and six HDM-NS laboratory beagles. Processed expression data from GEO deposited by Schamber etal. (G3 (Bethesda), 2014, 4 and 1787) (GSE58442) were downloaded and analysed using R and the Bioconductor package. Significance analysis was performed with the limma package; genes with false discovery rate <0.05 and fold-change ≤/≥1.5 were considered significantly differentially expressed (DEGs). A 2D principal component analysis revealed no clear separation between HDM-NS and HDM-S dogs at 6 h and 24 h time points. HDM-induced skin lesions in sensitised and nonsensitised dogs at the 24 h time point showed significant upregulation of T helper cell (Th)2 genes (interleukin [IL]-4R, IL-5, IL-13, CCL13 and CCL17), as well as proinflammatory- (LTB, IL-1A and IL-18), Th1- (CXCL10, OASL and MX-1) and Th17-related markers (IL-17B, IL-17F, CCL19 and CCL20). The key Th22-related maker, IL-22, was upregulated only in the HDM-S group at the 24 h time point. Both groups at 24 h featured significant upregulation of several noncytokine pruritogens, such as trypsin, chymase, cathepsin S, periostin and neuromedin B. Taken together, we establish that epicutaneous HDM patch application induces immune changes in HDM-NS dogs with Th2 dominance and activates several itch-promoting pathways.

Improved GAN-based deep learning approach for strain field prediction and failure analysis of precast bridge slab joints

Nonlinear finite element analysis (FEA) is crucial for understanding complex stress-strain behaviors and assessing potential structural failure risks. Building on the recent success of deep learning (DL) methods in physical field predictions, there has been growing interest in forecasting internal force fields of structures beyond the existing data scope. In this study, a data-driven strain field analysis model based on an improved Generative Adversarial Network (GAN) is proposed. To enrich the input data, this novel approach incorporates the Signed Distance Field (SDF) derived from the characteristics of the original Finite Element (FE) models. The generator, based on U-Net, is enhanced with channel attention mechanisms and loss penalty terms, forming the core of the improved GAN. The effectiveness of the proposed method is demonstrated using two parallel datasets comprising 3-Headed bar tension models across two planes. Subsequently, the generated strain field is subjected to feature extraction using the 2D-Principal Component Analysis (2D-PCA) method, followed by structural failure determination through the K-Nearest Neighbor (KNN) algorithm. The results indicate that the proposed improved approach achieves a significant reduction in Root Mean Square Error (RMSE) of 20.71 % and 13.21 % across the two datasets, with the structural failure determination method reaching an F1-score of 0.971. Thus, the intelligent strain field analysis method introduced here effectively predicts strain distributions and promptly identifies failure states, offering valuable insights for further numerical analysis and structural design.

Electronic nose based on Pd- and Pt-incorporated ZnO nanowires: a case study

Effect of incorporation of sensitizers namely palladium (Pd) and platinum (Pt) on the gas-sensing behaviour of zinc oxide (ZnO) nanowires has been studied. The specificity achieved is further studied and demonstrated for its efficacy towards the simultaneous detection of multiple gases employing the developed sensors in an electronic nose configuration. Incorporation of salt solutions containing the desired sensitizer concentration in the starting reaction mixture of hydrothermal growth has been effectively used to achieve heterostructure ZnO nanowires. Pd and Pt gets incorporated as PdO and metallic Pt, in the host matrix resulting in the formation of random heterojunctions namely p–n junction and Schottky junctions. Consequently, an increase in the work function as studied using Kelvin probe studies is observed. Utilizing statistical implements namely principal component analysis (PCA) and hierarchical cluster analysis (HCA) the discrimination of three gases namely H2, H2S and NO2 has been successfully accomplished. 3D PCA discriminates the three gases successfully with first three components exhibiting a percentage of variance of 42.32, 33.26 and 24.20%, respectively. A reasonable discrimination of H2, H2S and NO2, grouped into three clusters as evident from HCA dendrograms, was achieved using utilizing Ward’s method and Euclidian distance metric approach.

Abstract 3766: Utilizing whole-transcriptome digital spatial profiling for glioblastoma clinical biomarker discovery

Abstract Objective: IDH-wildtype glioblastoma (GBM) is the most aggressive brain tumor in adults. Notable for substantial inter- and intra-tumoral heterogeneity, GBM is highly resistant to standard-of-care treatment. Emerging spatial omics technologies, such as GeoMx Digital Spatial Profiling (DSP), offer novel approaches to interrogate the molecular features of regionally defined cellular ecosystems in cancer. Here, we leverage DSP to study whole-transcriptome gene expression signatures in a spatially defined manner across five IDH-wildtype GBMs. Methods: Formalin-fixed paraffin-embedded tumors were assessed histologically and three spatially distinct areas from each tumor were used to construct a custom “spatial” tissue microarray. From each set of three areas, we identified nine spatially distinct regions of interest (ROIs) for bulk transcriptomic analysis according to two histopathological features: distribution (cellular tumor core or tumor periphery) and vascular proximity (near or far). We evaluated normalized target expression data statistically using linear mixed modeling (LMM) with Benjamini-Hochberg correction. Results: 44 profiled ROIs and 8531 transcriptomic targets were analyzed following quality control trimming. 3D principal component analysis and unsupervised hierarchical clustering analysis showed clustering according to tumor specimen as well as distribution (core versus periphery), thereby substantiating the presence of inter- and intra-tumoral transcriptional heterogeneity. LMM-based differential gene expression analysis identified distinct molecular signatures when comparing core and periphery. Specifically, we observed elevated log2-transformed expression of migration and proliferation markers including LAMC1 (p=0.00297) and FABP7 (p=0.00434) in core ROIs and neurodevelopmental markers including NTRK2 (p=0.00734) and DAAM2 (p=0.00263) in periphery ROIs. Additionally, reactome analysis demonstrated considerable enrichment for the aberrant programmed cell death pathway (R-HSA-9645723, coverage=71.84%) in core ROIs and the MHC class II antigen presentation pathway (R-HSA-2132295, coverage=69.11%) in periphery ROIs. Interestingly, differential gene expression analysis did not reveal any biologically significant targets when subsetting for vascular proximity within cellular tumor core ROIs. However, reactome analysis of this regional subset demonstrated upregulation of various cellular proliferation pathways (G2/M transition, APC/C activity, DNA synthesis, etc.) within vessel-near ROIs when compared with vessel-far ROIs. Conclusions: We preliminarily characterize the molecular heterogeneity of GBM using a spatially guided approach. Ongoing analyses are focused on dataset validation, mixed cell deconvolution, further regional genotyping, and potential biomarkers. Citation Format: Adam Luo, Kelly Casella, Shivang Sharma, Ezra Baraban, Calixto-Hope Lucas, Eugene Shenderov. Utilizing whole-transcriptome digital spatial profiling for glioblastoma clinical biomarker discovery [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3766.

K-means Clustering of Tweet Emotions: A 2D PCA Visualization Approach

With the growing prominence of social media as a platform for expressing opinions and emotions, understanding the emotional undercurrents in large volumes of text data has become increasingly crucial. Tweets, often reflecting public sentiment, contain a rich tapestry of emotions that can be harnessed for diverse applications ranging from market analysis to mental health monitoring. The dataset comprises 40,000 tweet records, each tagged with one of thirteen distinct emotions, making it a challenging task to perform multiclass emotion classification due to the sheer volume of data and the nuanced spectrum of emotional expressions. Traditional classification models often struggle with such high-dimensional, multi-category data, leading to the need for a more sophisticated approach that ensures both accuracy and computational efficiency. To address the complexity of multiclass emotion classification, we propose a novel approach that combines text preprocessing, advanced feature extraction using TF-IDF, and dimensionality reduction via 2D Principal Component Analysis (PCA). We then apply K-means clustering to the reduced feature set to identify inherent groupings within the emotional content of the tweets. This method not only reduces computational demands but also logically consolidates the emotions into fewer categories, potentially enhancing the performance of subsequent classification models. The implementation of our method yielded distinct clusters that suggest a logical grouping of the emotions within the tweets. The 2D PCA visualization revealed clear separations among clusters, indicating that our approach successfully captured meaningful patterns in the dataset. The ability to effectively cluster complex emotional data opens the door to creating more nuanced and efficient multiclass classification models. By reducing the number of categories and focusing on clustered groups, we can streamline the classification process and enhance the interpretability of results. This has significant implications for real-world applications, including targeted marketing campaigns, public policy development, and mental health assessment from social media content.

CompCorona: A web application for comparative transcriptome analyses of coronaviruses reveals SARS-CoV-2-specific host response.

Understanding the mechanism of host transcriptomic response to infection by the SARS-CoV-2 virus is crucial, especially for patients suffering from long-term effects of COVID-19, such as long COVID or pericarditis inflammation, potentially linked to side effects of the SARS-CoV-2 spike proteins. We conducted comprehensive transcriptome and enrichment analyses on lung and peripheral blood mononuclear cells (PBMCs) infected with SARS-CoV-2, as well as on SARS-CoV and MERS-CoV, to uncover shared pathways and elucidate their common disease progression and viral replication mechanisms. We developed CompCorona, the first interactive online tool for visualizing gene response variance among the family Coronaviridae through 2D and 3D principal component analysis (PCA) and exploring systems biology variance using pathway plots. We also made preprocessed datasets of lungs and PBMCs infected by SARS-CoV-2, SARS-CoV, and MERS-CoV publicly available through CompCorona. One remarkable finding from the lung and PBMC datasets for infections by SARS-CoV-2, but not infections by other coronaviruses (CoVs), was the significant downregulation of the angiogenin (ANG) and vascular endothelial growth factor A (VEGFA) genes, both directly involved in epithelial and vascular endothelial cell dysfunction. Suppression of the TNF signaling pathway was also observed in cells infected by SARS-CoV-2, along with simultaneous activation of complement and coagulation cascades and pertussis pathways. The ribosome pathway was found to be universally suppressed across all three viruses. The CompCorona online tool enabled the comparative analysis of 9 preprocessed host transcriptome datasets of cells infected by CoVs, revealing the specific host response differences in cases of SARS-CoV-2 infection. This included identifying markers of epithelial dysfunction via interactive 2D and 3D PCA, Venn diagrams, and pathway plots. Our findings suggest that infection by SARS-CoV-2 might induce pulmonary epithelial dysfunction, a phenomenon not observed in cells infected by other CoVs. The publicly available CompCorona tool, along with the preprocessed datasets of cells infected by various CoVs, constitutes a valuable resource for further research into CoV-associated syndromes.

Direct observation of the effects of chemical fixation in MNT-1 cells: A SE-ADM and Raman study.

Aldehydes fixation was accidentally discovered in the early 20th century and soon became a widely adopted practice in the histological field, due to an excellent staining enhancement in tissues imaging. However, the fixation process itself entails cell proteins denaturation and crosslinking. The possible presence of artifacts, that depends on the specific system under observation, must therefore be considered to avoid data misinterpretation. This contribution takes advantage of scanning electron assisted-dielectric microscopy (SE-ADM) and Raman 2D imaging to reveal the possible presence and the nature of artifacts in unstained, and paraformldehyde, PFA, fixed MNT-1 cells. The high resolution of the innovative SE-ADM technique allowed the identification of globular protein clusters in the cell cytoplasm, formed after protein denaturation and crosslinking. Concurrently, SE-ADM images showed a preferential melanosome adsorption on the cluster's outer surface. The micron-sized aggregates were discernible in Raman 2D images, as the melanosomes signal, extracted through 2D principal component analysis, unequivocally mapped their location and distribution within the cells, appearing randomly distributed in the cytoplasm. Protein clusters were not observed in living MNT-1 cells. In this case, mature melanosomes accumulate preferentially at the cell periphery and are more closely packed than in fixed cells. Our results show that, although PFA does not affect the melanin structure, it disrupts melanosome distribution within the cells. Proteins secondary structure, conversely, is partially lost, as shown by the Raman signals related to α-helix, β-sheets, and specific amino acids that significantly decrease after the PFA treatment.

FRI103 Developmental Programming: Sex-specific Differences In Lipid Metabolism Of Newborn Lambs Exposed To Prenatal Testosterone Excess

Abstract Disclosure: N. Saadat: None. J.N. Ciarelli: None. B. Pallas: None. V. Padmanabhan: None. A.K. Vyas: None. Gestational hyperandrogenism such as that seen in Polycystic Ovary Syndrome (PCOS), leads to adverse offspring health outcomes in both humans and animals. Prenatal T excess (T) in a sheep model of PCOS phenotype induces peripheral insulin resistance, dyslipidemia, adipocyte defects, liver steatosis, pathological cardiac remodeling and hypertension in the female offspring. Whether these adverse cardiometabolic outcomes evidenced in the female offspring also extends to male offspring needs to be addressed. We hypothesized that prenatal T excess will induce sex-specific disruptions in lipid metabolism of newborn lambs thereby contributing to the aberrant cardiometabolic phenotype. Shotgun Lipidomics was performed on the plasma samples collected at 48 hours after birth from prenatal T treated (100mg T-propionate in corn oil from days 30 to 90 of gestation intramuscularly; Term: 147days) and control (C, vehicle only) lambs (N=26, Female C=4, T=7 and Male C=5, T=10). The data was analyzed by multivariate dimensionality reduction modeling followed by functional enrichment and pathway analyses. Unsupervised 3D principal component analysis and supervised models showed separation between C and T groupings in female and male lambs. Most lipids responsible for this separation were from glycerophospholipids and glycerolipids classes in both sexes. Prenatal T excess led to an increase in cholesterol levels only in female lambs as opposed to a decrease in levels of carnitine species (CAR) CAR 16:1 and CAR 14:1 and phosphatidylcholine species (PC) PC 39:4, PC 39:5 and PC 38:5 in male lambs. To investigate the impact of prenatal T excess on lipid metabolism pathways and active reactions, 738 lipid species were mapped, of which 147 were found to be active in reactions. The active reactions involving conversion of ether linked diglycerides (DG) to ether linked phosphatidylethanolamines (PE, O-DG→O-PE, z-score 3.367, p &amp;lt;0.05) and ether linked PC (O-DG →O-PC, z-score 2.299, p &amp;lt; 0.05) were increased in female lambs. In contrast, the conversion of PE to PC to phosphatidic acids (PA) was suppressed (PE→PC→PA, z-score 2.133, p &amp;lt;0.05) in prenatal T-treated male lambs pointing towards the suppression of Phosphatidylethanolamine N-methyltransferase (PEMT) phosphatidylcholine synthesis pathway. Higher cholesterol levels in T female lambs could contribute toward the adverse cardiometabolic phenotype evidenced earlier in this model, whereas suppressed PEMT pathway could lead to endoplasmic reticulum stress, defects in membrane permeability and lipid transport that may lead to liver steatosis in T male lambs. These novel findings of sex-specific effects of gestational exposure to T excess on lipid metabolism of newborn lamb, a precocial model of translational relevance, may have sex-specific functional outcomes in cardiometabolic systems. Presentation: Friday, June 16, 2023

An Improved Image Compression Algorithm Using 2D DWT and PCA with Canonical Huffman Encoding.

Of late, image compression has become crucial due to the rising need for faster encoding and decoding. To achieve this objective, the present study proposes the use of canonical Huffman coding (CHC) as an entropy coder, which entails a lower decoding time compared to binary Huffman codes. For image compression, discrete wavelet transform (DWT) and CHC with principal component analysis (PCA) were combined. The lossy method was introduced by using PCA, followed by DWT and CHC to enhance compression efficiency. By using DWT and CHC instead of PCA alone, the reconstructed images have a better peak signal-to-noise ratio (PSNR). In this study, we also developed a hybrid compression model combining the advantages of DWT, CHC and PCA. With the increasing use of image data, better image compression techniques are necessary for the efficient use of storage space. The proposed technique achieved up to 60% compression while maintaining high visual quality. This method also outperformed the currently available techniques in terms of both PSNR (in dB) and bit-per-pixel (bpp) scores. This approach was tested on various color images, including Peppers 512 × 512 × 3 and Couple 256 × 256 × 3, showing improvements by 17 dB and 22 dB, respectively, while reducing the bpp by 0.56 and 0.10, respectively. For grayscale images as well, i.e., Lena 512 × 512 and Boat 256 × 256, the proposed method showed improvements by 5 dB and 8 dB, respectively, with a decrease of 0.02 bpp in both cases.

Estimating the vertical direction in a photogrammetric 3D model, with application to visualization

The process of building a Virtual Reality (VR) environment from images involves several steps: choose experimental conditions (scene, camera, trajectory, weather), take the images, reconstruct a textured 3D model thanks to a photogrammetry software, and import the 3D model into a game engine. This paper focuses on a postprocessing technique for the photogrammetry step, mostly for outdoor environments that cannot be reconstructed using an unmanned aerial vehicle. As visualization applications (including VR) need a 3D model with a known vertical direction, a method is introduced to compute it. The method is based on 3D principal component analysis and a 2D Hough transform. In the experiments, we first reconstruct both man-made and natural immersive environments using a helmet-held 360 camera, then we import the 3D models in good coordinate systems (i.e. with a vertical axis and a plausible scale) into Unity, and finally we use VR headsets to explore the scenes like a pedestrian. We also experiment on scanner data and show that our method is competitive with previous work.

Single-cell scattering and auto-fluorescence-based fast antibiotic susceptibility testing for gram-negative and gram-positive bacteria.

In this study, we assess the scattering of light and auto-fluorescence from single bacterial cells to address the challenge of fast (<2 h), label-free phenotypic antimicrobial susceptibility testing (AST). Label-free flow cytometry is used for monitoring both the respiration-related auto-fluorescence in two different fluorescence channels corresponding to FAD and NADH, and the morphological and structural information contained in the light scattered by individual bacteria during incubation with or without antibiotic. Large multi-parameter data are analyzed using dimensionality reduction methods, based either on a combination of 2D binning and Principal Component Analysis, or with a one-class Support Vector Machine approach, with the objective to predict the Susceptible or Resistant phenotype of the strain. For the first time, both Escherichia coli (Gram-negative) and Staphylococcus epidermidis (Gram-positive) isolates were tested with a label-free approach, and, in the presence of two groups of bactericidal antibiotic molecules, aminoglycosides and beta-lactams. Our results support the feasibility of label-free AST in less than 2 h and suggest that single cell auto-fluorescence adds value to the Susceptible/Resistant phenotyping over single-cell scattering alone, in particular for the mecA+ Staphylococcus (i.e., resistant) strains treated with oxacillin.

Anthropometric accommodation percentage comparison for univariate and multivariate representative Mexican hand models.

Adopting awkward postures at work has a great impact on productivity and work-related musculoskeletal disorders. Considering anthropometric data in the design of products and workplaces can diminish this impact. The traditional univariate-percentile-approach is one of the most implemented in the anthropometric analysis, even though it has proved limitations in comparison with multivariate-approaches. To develop univariate and multivariate hand models considering four anthropometric dimensions, and to theoretically compare the univariate and multivariate accommodation percentages. Univariate percentile models corresponding to the database of real subject nearest-neighbors to the 5th and 95th percentiles were obtained for the male and female population. Two multivariate approaches were implemented on the central 90% of both populations: 2D principal component analysis and archetypal analysis. The accommodation percentage for each family of models was obtained based on the population that simultaneously fit all the anthropometric dimensions. The goodness-of-fit and McNemar's tests were performed to statistically analyze the accommodation percentages. Eight human hand models were obtained via Principal Component Analysis while two, three, four, and eight Archetypal Analysis models (male-population) and two, three, six, and eight Archetypal Analysis models (female-population) were selected after a root-sum-of-squares analysis for k = 1, ... ,10 archetypes. The results showed that the Principal Component Analysis models obtained a higher accommodation level, followed by the Archetypal Analysis and percentile models (male population). In the case of the female population, models obtained by multivariate-Archetypal Analysis (k = 8) obtained a higher accommodation percentage.

Seminal infrared-based chemometric analysis of oxygen-bearing structural groups in vitrain samples from ten successive coal seams (Sydney Coalfield, Canada)

Reported are seminal results of a systematic application of infrared spectrometry to a succession of coal seams in a Carboniferous basin from Sydney Coalfield, Nova Scotia, Canada. Fifty-four lithotypic vitrain samples from ten successive coal seams were analyzed by solid-state (potassium-bromide pellet method) Fourier transform infrared spectroscopy and evaluated using principal-component analysis to explore chemical fingerprints of vitrain structural-group contents and distributions. At this level of sampling, the 3D principal-component analysis and ANOVA tests identified chemical structural-group changes related to relatively low contents of aliphatic and oxygen-bearing compounds. These probably confirm, for data represented by “younger” seams, the moderate thermal histories (0.75–0.90 Ro %) from which minimal diagenetic alterations are inferred in the preservation of fossils in the roof rocks of the coal seams. It is concluded that, throughout the lithostratigraphic column, the continual vertical changes in vitrain structural-group composition are related to some changes in the species diversity of the associated macroflora that filled niches in the peat-producing lower flood plains.

Microwaved Vermicast Physicochemical Properties and Active Microbial Groups Impact on Photosynthetic Activity, Growth and Yield of Kale

Microwave technology has wide applications, including extraction of active compounds in biomass and compost for agricultural use. A study was carried out to determine the effects of microwave power level from 0 (control) to 1000 W on the properties and active microbial groups in vermicast, and how it may impact the photosynthesis, plant growth, and yield of kale (Brassica oleracea var. sabellica) ‘Red Russian’. Heat accumulation in the vermicast increased rapidly to a peak of 86 °C at 400 W before declining to 68 °C at 1000 W. Vermicast water loss increased exponentially up to 800 W before declining. The C:N ratio of the vermicast was reduced at ≥600 W while the pH remained the same. In a 2D-principal component analysis biplot, vermicast treated at 600, 800 and 1000 W were associated with Gram-positive (G+), GGram-negative (G−), G + G− bacteria, protozoa, and fungi groups while the 0, 200, and 400 W treated vermicast were associated with eukaryotes. However, the trend for total microbial mass was 200 W = 400 W &gt; 0 W &gt; 600 W = 800 W = 1000 W. Kale leaf anthocyanin, chlorophylls, and carotenoids were significantly (p = 0.001) increased by the 400 W or 600 W treatment compared to the other treatments. Stomatal conductance, transpiration, and photosynthesis rates were increased by the 400 W followed by the 600 W. As a result, yield of kale grown in the 400 W microwaved vermicast was the highest. Future studies will explain the functions of specific microbial populations and elemental composition in microwaved vermicast.

Principal component analysis of comprehensive three-dimensional gas chromatography time-of-flight mass spectrometry data

Comprehensive three-dimensional (3D) gas chromatography (GC3) coupled to time-of-flight-mass spectrometry (GC3-TOFMS) is an intriguing extension of the well-established comprehensive two-dimensional gas chromatography (GC×GC) technique. Although impressive gains have been made in the instrumentation realm, the utility of non-targeted chemometric analysis of GC3-TOFMS data has yet to be explored. Herein, we present the first application of principal component analysis (PCA) to a GC3-TOFMS dataset of jet fuel samples. Five replicates each of four jet fuels (JP8, J1800A, JP4, and JP7) were collected by GC3-TOFMS with commercial thermal modulation from the first-dimension column (1D) to the second-dimension column (2D), and dynamic pressure gradient modulation (DPGM) from the 2D column to the third-dimension column (3D), thus providing full mass transfer (100% duty cycle both modulation stages). A novel re-registration technique is introduced in which a user-selected series of vacant 3D modulations are removed to correct 2D shifting and effectively “center” the data. This shifting has consistently been observed in the 2D versus 1D view of GC3-TOFMS chromatograms, likely due to the slowed flow on 2D in DPGM and/or temperature programming effects. The 3D PCA loadings of the re-registered data revealed subtle chemical differences between the fuels which would not be as easily elucidated using PCA of GC×GC data. Finally, PCA of the two most chemically similar fuels (J1800A and JP7) revealed additional chemical differences which were drowned out in the initial multi-fuel PCA model, highlighting the potential advantage of “pairwise” PCA for multi-class GC3-TOFMS datasets.

ATR-FTIR Spectroscopy Combined with Chemometric Methods for the Classification of Polyethylene Residues Containing Different Contaminants

Low density polyethylene (LDPE) and high density polyethylene (HDPE) are the principal plastics present in solid plastic waste and are found out as the main components of microplastics in marine and terrestrial environments. Currently, efforts have been made to develop new and effective methods to ensure the identification and separation of plastics in waste, ensuring the necessary purity to obtain quality and economically competitive recycled products. In this contribution, we investigated the usage of Fourier-Transform Infrared Spectroscopy in Attenuated Total Reflection mode (ATR-FTIR) combined with Principal Component Analysis (PCA), Linear Partial Least Squares Regression by Intervals (iPLS-R) and Competitive Adaptive Weighted Sampling (CARS/PLS-R) as chemometric methods to classify and determine the compositional fraction of the pristine and recycled mixtures of HDPE and LDPE from plastic waste in São Paulo, Brazil. Here, the 3D PCA plots were not effective in classifying the different polyethylenes and their polymer blends using the three main Principal Components (PC), but 2D PCA diagrams using PC1 and PC3 presented promising performance for this purpose. The iPLS-R presents the best predictive ability than CARS/PLS-R to determine the LDPE content in HDPE/LDPE recycled blends. However, the presence of different contaminants (in 5 wt%), such as silicon dioxide (SiO2), calcium carbonate (CaCO3), recycled polypropylene (PP), and recycled poly(ethylene terephthalate) (PET), reduces the potential usage of the iPLS-R models as identification tools for LDPE and HDPE sorting in industrial recycling processes.

Radio-frequency and optical techniques for evaluating anchovy freshness

The freshness of anchovies ( Engraulis Encrasicolus ) was estimated by radio-frequency and optical techniques in order to propose non-destructive and objective methods for rapid screening. Measurements were conducted until 7 days at 0 °C, storing the fish under ice. Image analysis, dielectric spectroscopy and hyperspectral images were conducted on fish eyes whilst mechanical properties were measured on the fish body. Evaluation of images by RGB scale highlighted differences due to ageing. Maximum force decreased as a function of degradation process time. Cluster analysis on dielectric spectra and 3D-principal component analysis on hyperspectral images of fish eyes revealed a good ability to characterise ageing modification demonstrating suitable methods for the development of a non-destructive, and rapid system for evaluation of fish freshness. • Anchovies freshness was measured by dielectric spectroscopy & hyperspectral imaging. • Dehydration of anchovy eyes was exploited for ageing estimation. • Multivariate approaches allowed to group the samples according to storage days. • Both techniques are rapid and contactless being promising for practical application.

Research on Microvideo Character Perception and Recognition Based on Target Detection Technology

With the rapid development of the Internet short-video platform, microvideo is increasingly attracting users because of its small traffic and time period. However, the content of microvideo on the Internet is complex, with illegal information. This research is an improved algorithm based on target detection technology, aiming at the characteristics of difficult character recognition in microvideo. The algorithm combines Gabor wavelet transform algorithm and 2D principal component analysis (PCA) algorithm and makes full use of video perception technology to realize character recognition in microvideo. Through MATLAB simulation analysis, it can be seen that the accuracy of the optimization algorithm proposed in this paper is 86.34% and the recognition time is 34.28s. Compared with the traditional PCA algorithm and artificial neural network (ANN) algorithm, the optimization algorithm proposed in this paper has better recognition rate and recognition efficiency. Received: 28 October 2021 | Revised: 15 December 2021 | Accepted: 21 December 2021 Conflicts of Interest The author declares that he has no conflicts of interest to this work.