Multivariate Principal Component Analysis Research Articles

EVALUATION OF UPLAND COTTON GENOTYPES FOR MORPHOMETRIC, PHOTOSYNTHESIS-RELATED TRAITS AND PARAMETERS RELATED TO ENZYMATIC AND NON-ENZYMATIC ANTIOXIDANT ACCUMULATION

: To develop new crop varieties, the evaluation of existing germplasm is the basic step based on the agro-morphological, physiological, biochemical traits and molecular traits. The current study was designed to evaluate ten cotton genotypes based on key morpho-physiological and biochemical traits. Cotton genotypes were arranged in triplicates using a randomized complete block design (RCBD) with plant-to-plant and bed-to-bed distances of 30 cm and 75 cm, respectively. Data from fully guarded, mature plants was collected. The results obtained through ANOVA revealed the presence of significant variations in the studied plant parameters. The correlation analysis revealed a significant relationship between seed cotton yield and net photosynthetic rate (0.92**), tannins (0.80**), malondialdehyde (0.73*), total oxidant status (-0.86**) and hydrogen peroxide (-0.84**). Multivariate analysis approaches, i.e., principal component, biplot, and cluster analysis, were used to classify and characterize cotton genotypes based on their performance. These analyses revealed that BH-341 and BH-254 were the most productive cotton genotypes. Therefore, these genotypes could be recommended for cultivation in core-cotton areas following extensive multilocation testing.

Volatilomic profile of the tree tomato (Solanum betaceum Cav.) pulp during ripening and senescence using HS–SPME with GC–MS

The volatilomic profile of the tree tomato (Solanum betaceum Cav.) pulp during ripening stages and senescence times was determined by headspace solid-phase microextraction in combination with gas chromatography-mass spectrometry. The HS-SPME-GC-MS analysis allowed the identification of 46 metabolites that produce the metabolic fingerprint of this fruit, the major compounds at all ripening stages and senescence times were ethyl butanoate, methyl butanoate, β-myrcene and eucalyptol. The composition in the different stages was subjected to principal component analysis (PCA), which explained 77.3% of the total variance in the principal components. It allowed to classify the samples by ripening stages and senescence times and it was corroborated with K-means clustering and dendrogram analyses. Through multivariate analyses PCA, partial least squares-discriminant analysis (PLS-DA), and heatmap it was possible to determine volatiles methyl (E)-2-hexenoate, 4-tert-butylcyclohexyl acetate, methyl benzoate, methyl (Z)-3-hexenoate, 3-methylbutyl acetate, β-thujene, cis-3-hexenol, hexanal and trans-2-hexenal as potential markers in the ripening stages, while volatiles propyl butanoate, ethyl octanoate, ethyl acetate, ethyl butanoate, β-myrcene and estragol predominated in the senescence times. HS-SPME-GC-MS analysis of volatiles in combination with the chemometric approach used in this research allowed to make a clear distinction between ripening stages and senescence times in common red variety of tree tomato pulp.

Implementation of White Analytical Chemistry-Assisted Analytical Quality by Design Approach to Green Liquid Chromatographic Method for Concomitant Analysis of Anti-Hypertensive Drugs in Human Plasma.

According to current concepts of white analytical chemistry (WAC), the use of organic solvents those are teratogenic and carcinogenic must be avoided for the protection of the environment and of the analysts. This led to the development and validation of the WAC-assisted green liquid chromatographic technique (reverse-phase high-pressure liquid chromatography (RP-HPLC)) for the simultaneous analysis of anti-hypertensive drugs (azilsartan medoxomil, chlorthalidone and cilnidipine) in human plasma and their fixed-dose combinations. The analytical quality by design approach was used in conjunction with the design of experiments and chemometrics concepts to develop the method. To develop the green RP-HPLC method, critical method variables (CMVs) and critical analytical attributes were identified using the multivariate analytical tools principal component analysis and partial least square regression. Using the Box-Behnken design, the design of experiments was used for CMV optimization and response surface analysis. It was possible to explore the analytical design space for the life cycle management of the RP-HPLC method. The developed method was found to be validated following International Council for Harmonization Q2 (R1) and M10 requirements. Using the red, green and blue paradigm, the existing and proposed chromatographic methods were evaluated for their validation efficacy, greenness profile and cost-effectiveness.

A Study of the Effects of Medical Dental Laser and Diamond Drill on Dentin Tissue during Dental Restoration Based on Spectral Imaging and Multivariate Analysis of Synchrotron FTIR Microspectroscopy Data

In our work, the effect of a dental Er:YAG pulsed laser and a diamond cylindrical drill with a turbine handpiece on dentin tissue was studied using spectral imaging. The combination of spectral imaging of FTIR microspectroscopy data and subsequent multivariate analysis (hierarchical cluster analysis (HCA) and principal component analysis (PCA)) was shown to unambiguously detect visually indistinguishable structural changes occurring in the hard dental tissue (dentin) depending on the method used for their pre-processing, and to classify and differentiate the identified features at the submicron level with high spatial resolution. The detectable spectral transformations indicate that the preparation of dental tissue with a dental laser leads to significant changes in the organic components of dentin, which may affect adhesion. The use of a diamond cylindrical drill with a turbine handpiece is characterized by a larger area (depth) of the altered hard tissue than in the case of a dental laser for dental cavity preparation. The observed redistribution of the phase composition of the inorganic component in the tissue is associated with the emergence of additional phases of weak calcium phosphates, and changes in the organic component with transformations in the secondary structure of proteins. Active use of the proposed integrated approach in the future will clarify the areas of its applicability to the analysis of biological tissues and pathologies in them, which will help in the clinical setting to choose the optimal personalized approach for patients.

Lipid Composition Analysis and Characterization of Acyl Sterol Glycosides in Adzuki and Soybean Cultivars by Non-Targeted LC-MS.

Beans, a globally significant economic and nutritional food crop, are rich in polyphenolic chemicals with potential health advantages, providing high protein, fiber, minerals, and vitamins. However, studies on the global profiling of lipids in beans are limited. We applied a non-targeted lipidomic approach based on high-performance liquid chromatography coupled with linear ion trap-Orbitrap mass spectrometry (HPLC/LTQ-Orbitrap-MS) to comprehensively profile and compare the lipids in six distinct bean cultivars, namely, adzuki red beans-adzuki cultivar (ARB-AC), adzuki red beans-Benidainagon cultivar (ARB-BC), adzuki red beans-Erimoshouzu cultivar (ARB-EC), soybean-Fukuyutaka cultivar 2021 (SB-FC21), soybean-Fukuyutaka cultivar 2022 (SB-FC22), and soybean-Oosuzu cultivar (SB-OC). MS/MS analysis defined 144 molecular species from four main lipid groups. Multivariate principal component analysis indicated unique lipid compositions in the cultivars except for ARB-BC and ARB-EC. Evaluation of the concentrations of polyunsaturated fatty acid to saturated fatty acid ratio among all the cultivars showed that SB-FC21 and SB-FC22 had the highest value, suggesting they are the most beneficial for health. Furthermore, lipids such as acyl sterol glycosides were detected and characterized for the first time in these bean cultivars. Hierarchical cluster correlations revealed the predominance of ceramides in ARB-EC, lysophospholipids in SB-FC21, and glycerophospholipids in SB-OC. This study comprehensively investigated lipids and their compositions in beans, indicating their potential utility in the nutritional evaluation of beans as functional foods.

Genome Analysis of Potential Probiotic Levilactobacillus brevis AcCh91 Isolated from Indian Home-Made Fermented Milk Product (Chhurpi).

Consumption of naturally fermented milk (NFM) products is the dietary culture in India. The mountainous people of Arunachal Pradesh in India prepare the assorted artisanal home-made NFM products from cow and yak milk. Previously, we isolated and identified 76 strains of lactic acid bacteria (LAB) from NFM products of Arunachal Pradesh, viz. mar, chhurpi and churkam. We hypothesized that some of these LAB strains may possess probiotic potentials; hence, we investigated the probiotic potentials of these strains. On the basis of in vitro and genetic screening for probiotic attributes including haemolytic ability, 20 LAB strains were selected out of 76 strains, for further analysis. Using in silico analysis, viz. multivariate heatmap and PCA (principal component analysis) biplot, Levilactobacillus brevis AcCh91 was selected as the most promising probiotic strain, which was further characterized by the whole-genome analysis. Lev. brevis AcCh91 showed the highest survival rate of 93.38% in low pH and 86.68 ± 2.69% in low bile and the highest hydrophobicity average of 86.34 ± 5.53%. This strain also showed auto-aggregation and co-aggregation with antimicrobial properties against the pathogens, showed ability to produce beta-galactosidase and cholesterol reduction property and, most importantly, produced GABA, an important psychobiotic element. Genomic analysis of Lev. brevis AcCh91 showed the presence of genes corresponding to GABA, vitamins, amino acids, cholesterol reduction, immunomodulation, bioactive peptides and antioxidant activity. The absence of antimicrobial-resistant genes and virulence factors was observed. Hence, genome analysis supports the probiotic potentials of Lev. brevis AcCh91, which may be further investigated to understand its health-promoting properties.

Application of Amelioran Compost of Empty Palm Plus Fruit Bunches to Soybean Plants in Ultisols through Multivariate Tests and Principal Component Analysis

The decline in national soy output results from a decline in planted area. Optimizing ultisol land is one of the efforts undertaken to increase national soybean production. This study seeks to examine the effect of composting empty palm oil bunches on the growth and yield of soybeans using multivariate tests and principal component analysis during the compost's incubation period. The application of compost and empty palm fruit clusters was able to chelate Al-DD and increase the soil's available P and K. There is a positive Pearson correlation between pH, Ca, K, and Mg, as well as a positive Pearson correlation between all observed growth and development parameters of soybean plants. Pearson's correlation demonstrates the relationship between the characteristics of plant height, number of leaves, number of branches, number of pods, age of blossoming, age of harvest, and productivity. Positive correlations are observed for each parameter observed.

Factors Affecting the Use and Understanding of Nutrition Labeling on Food Labels among Nepalese Youths

Nutrition labeling, crucial for informing consumers about food content, faces significant gaps in Nepal's regulatory framework. These gaps impact how the nutritional information is understood and used by consumers, contributing to challenges like obesity and chronic diseases in the country. This study aims to uncover the factors affecting the use and understanding of nutrition labeling on food labels among Nepalese youths. Conducted through a cross-sectional approach, this research involved administering a structured questionnaire to 225 participants in June 2023, targeting regular consumers of packaged foods. Data analysis, employing both descriptive and multivariate principal component analysis using SPSS 25, revealed that 24% of respondents always read nutrition labeling on food labels in packed food items. The Principal Component Analysis identified four key factors affecting the use and understanding of nutrition labeling: "perception of the nutritional information", "motivations in dietary decisions", "awareness" and "health priority interest in nutritional attributes". These factors collectively explained over 54% of the variance in the data, as validated by the scree plot and Kaiser Criterion. The study underscores the necessity of initiatives aimed at enhancing the accessibility, clarity, and appeal of nutritional labeling on food labels in packaged food products. Such initiatives are pivotal for policymakers, educators, and marketers striving to encourage healthier dietary practices among Nepalese youths.

Darjeeling tea profiling using 1H NMR and multivariate analysis

The current study emphasizes the metabolic fingerprints of tea’s considerable dependency on the plucking season. The taste, color, and aroma represent the quality of tea in general, and chemical constituent concentration levels in tea are greatly impacted by seasonal changes, such as temperature, humidity, precipitation, sun exposure and rainfall. 1H NMR spectral datasets of tea, in conjunction with multivariate techniques, can be used to distinguish differences in tea metabolic signatures influenced by seasonal changes. In the present study, the multivariate techniques PCA (Principal Component Analysis) and LDA (Linear Discriminant Analysis) are coupled to classify 'India’s premium Darjeeling tea based on plucking seasons. A total of 164 NMR spectra of Darjeeling tea samples from the first, second and autumn flushes are taken into consideration. The PCA followed by LDA has demonstrated a clear classification by minimizing variability within the samples of clusters as well as maximizing variability between the clusters with a good F1 score (0.95), indicating that there are significant differences in the metabolic fingerprints between the tea flushes and developed classification module helps in authenticating Darjeeling tea geographical origin.

Multivariate spatiotemporal functional principal component analysis for modeling hospitalization and mortality rates in the dialysis population.

Dialysis patients experience frequent hospitalizations and a higher mortality rate compared to other Medicare populations, in whom hospitalizations are a major contributor to morbidity, mortality, and healthcare costs. Patients also typically remain on dialysis for the duration of their lives or until kidney transplantation. Hence, there is growing interest in studying the spatiotemporal trends in the correlated outcomes of hospitalization and mortality among dialysis patients as a function of time starting from transition to dialysis across the United States Utilizing national data from the United States Renal Data System (USRDS), we propose a novel multivariate spatiotemporal functional principal component analysis model to study the joint spatiotemporal patterns of hospitalization and mortality rates among dialysis patients. The proposal is based on a multivariate Karhunen-Loéve expansion that describes leading directions of variation across time and induces spatial correlations among region-specific scores. An efficient estimation procedure is proposed using only univariate principal components decompositions and a Markov Chain Monte Carlo framework for targeting the spatial correlations. The finite sample performance of the proposed method is studied through simulations. Novel applications to the USRDS data highlight hot spots across the United States with higher hospitalization and/or mortality rates and time periods of elevated risk.

Physico-Chemical Properties of Brazilian Eastern Amazon Honey Assessed by Multivariate Principal Components Analysis

Honey from the Apis mellifera bee (Hymenoptera, Apidae) is one of the most consumed beehive products in the world, mainly due to its nutritional and therapeutic value. In poorer regions, its commercialization can be an essential source of income for families and communities. However, the quality of honey can change both during harvesting and processing, as well as by climatic conditionsand the type of vegetation. Therefore, implementing good production practices, with quality control in collection and processing, can add value to this product, promoting the development of the region in which it is produced. Thus, the objectives of this work were to characterize the honey of Africanized honeybees produced in the Eastern Amazon region in the cities of Nova Olinda do Maranhão(NO), Santa Luzia do Paruá (SL) and verify its correlation with a commercial honey sample (CH) – from São Luiz-MA. The physicochemical properties (color, moisture, solids insoluble in water, minerals, and ash, hydroxymethylfurfural, acidity, anddiastatic activity) were analyzed according to the methods of CODEX Alimentarius and the Association of Official Analytical Chemists, and these data were evaluated by the Principal Component Analysis (PCA). The results showed that commercial honey presented higher levels of insoluble solids, HMF, minerals, and ash than samples from NO and SL. Variables that differentiated NO and SL honey were HMF, moisture, and diastase index. Although the samples have different characteristics, the data comply with Brazilian legislation, which can strengthen the development of apiculture in the region.

Longitudinal Morphometric Changes in the Corticospinal Tract Shape After Hemorrhagic Stroke.

Deep intracerebral hemorrhage (ICH) exerts a direct force on corticospinal tracts (CST) causing shape deformation. Using serial MRI, Generalized Procrustes Analysis (GPA), and Principal Components Analysis (PCA), we temporally evaluated the change in CST shape. Thirty-five deep ICH patients with ipsilesional-CST deformation were serially imaged on a 3T-MRI with a median imaging time of day-2 and 84 of onset. Anatomical and diffusion tensor images (DTI) were acquired. Using DTI color-coded maps, 15 landmarks were drawn on each CST and the centroids were computed in 3 dimensions. The contralesional-CST landmarks were used as a reference. The GPA outlined the shape coordinates and we superimposed the ipsilesional-CST shape at the two-time points. A multivariate PCA was applied to identify eigenvectors associated with the highest percentile of change. The first three principal components representing CST deformation along the left-right (PC1), anterior-posterior (PC2), and superior-inferior (PC3) respectively were responsible for 57.9% of shape variance. The PC1 (36.1%, p < 0.0001) and PC3 (9.58%, p < 0.01) showed a significant deformation between the two-time points. Compared to the contralesional-CST, the ipsilesional PC scores were significantly (p < 0.0001) different only at the first-timepoint. A significant positive association between the ipsilesional-CST deformation and hematoma volume was observed. We present a novel method to quantify CST deformation caused by ICH. Deformation most often occurs in left-right axis (PC1) and superior-inferior (PC3) directions. As compared to the reference, the significant temporal difference at the first time point suggests CST restoration over time.

Petroleum Geochemistry of the Central and Eastern Onshore Niger Delta Basin: Oil-Oil Correlation Using Biomarkers and Multi-Variate Statistics

Petroleum geochemistry of oils in several depobelts of the world has been well documented based on biomarkers. However, there is paucity of data regarding the use of multi-variate statistics in oil-oil correlation study, particularly in the Sub-Saharan African region. The purpose of the study was to gain an insight into the geochemical differences and identify their cause(s) through the application of bulk properties, biomarker indices and multi-variate statistics. Correlation of oils from eight wellheads of three stratigraphic units within the Central and Eastern onshore Niger Delta Basin fields (AG and AS) were undertaken by gas chromatography-flame ionization detector. Utilization of bulk properties, biomarker indices and cross plot parameters somewhat separated the mature AG1-4, AS3 and AS4 oils deposited in oxic/suboxicshaly environment from AS2 oil deposited in anoxic fluvio deltaic environment. In-reservoir alteration and migration differences were the major factors responsible for the observed slight geochemical variability in the studied oils. Nevertheless, multi-variate principal component analysis indicated that the Central and Eastern onshore Niger Delta Basin oils were principally generated from similar organic matter sources. The implication here was that the geochemical variations in the studied oils were not significant, a conclusion corroborated by Pearson correlation model data.

Alteration of metabolic profile during the progression of Alzheimer’s disease

AbstractBackgroundAlzheimer’s disease (AD) is a progressive neurodegenerative disorder that threatens the population health of older adults. However, the mechanisms of how altered metabolism involves AD pathology are poorly understood. This study intends to provide novel insights into the role of metabolic alteration in the pathology and identify potential metabolic biomarkers for AD diagnosisMethodThe Gas chromatography‐mass spectrometry (GC‐MS) was used to measure the concentrations of serum metabolites in a cohort of subjects with AD (n = 88) and cognitively normal control (CN) (n = 85). The patients were classified as very mild (n = 25), mild (n = 27), moderate (n = 25), and severe (n = 11). Serum metabolic profiles were analyzed by multivariate (principal component analysis) and univariate approaches (t‐tests, false discovery, receiver operating characteristic curves, and correlation analysis). The artificial neural network model (ANN) was applied to identify potential biomarkers of AD. Biofunctional enrichment analysis was performed using the Kyoto Encyclopedia of Genes and Genomes.ResultThe results revealed considerable separation between the AD and CN patients. Edetic acid, (S)‐3,4‐Dihydroxybutyric acid, and arachidonic acid were identified as potential AD diagnostic biomarkers using an ANN model. And the classification model could predict the risk for AD with high accuracy (AUC = 0.93). The most significant changes in the metabolite’s composition occur during the very mild to the mild stage. The metabolic enrichment analysis revealed that carbohydrate metabolism deficiency and disturbance of amino acids, fatty acids, and lipids metabolism are involved in AD progression. Especially, pathway analysis highlighted that l−glutamate participated in four crucial nervous system pathways (including GABAergic synapse, glutamatergic synapse, retrograde endocannabinoid signaling and synaptic vesicle cycle).ConclusionSerum metabolomic analysis demonstrated that the metabolic alterations had already arisen at the very mild stages of dementia. Disrupt energy metabolism may be the core of the vicious cycle in AD, resulting in a wide range of metabolic disorders, including abnormal glucose metabolism, TCA cycle impairments, amino acids, fatty acids, and lipids dysregulation. Furthermore, a metabolic diagnostic model of AD was constructed using ANN, which could robustly differentiate AD patients from CN. Our study identified new markers for AD diagnosis and highlighted the roles of metabolic changes on the progression of AD.

Investigating the Urinary Metabolome in the First Year of Life and Its Association with Later Diagnosis of Autism Spectrum Disorder or Non-Typical Neurodevelopment in the MARBLES Study.

Developmental disabilities are often associated with alterations in metabolism. However, it remains unknown how early these metabolic issues may arise. This study included a subset of children from the Markers of Autism Risks in Babies-Learning Early Signs (MARBLES) prospective cohort study. In this analysis, 109 urine samples collected at 3, 6, and/or 12 months of age from 70 children with a family history of ASD who went on to develop autism spectrum disorder (ASD n = 17), non-typical development (Non-TD n = 11), or typical development (TD n = 42) were investigated by nuclear magnetic resonance (NMR) spectroscopy to measure urinary metabolites. Multivariate principal component analysis and a generalized estimating equation were performed with the objective of exploring the associations between urinary metabolite levels in the first year of life and later adverse neurodevelopment. We found that children who were later diagnosed with ASD tended to have decreased urinary dimethylamine, guanidoacetate, hippurate, and serine, while children who were later diagnosed with Non-TD tended to have elevated urinary ethanolamine and hypoxanthine but lower methionine and homovanillate. Children later diagnosed with ASD or Non-TD both tended to have decreased urinary 3-aminoisobutyrate. Our results suggest subtle alterations in one-carbon metabolism, gut-microbial co-metabolism, and neurotransmitter precursors observed in the first year of life may be associated with later adverse neurodevelopment.

Statistical Learning for Nonlinear Dynamical Systems with Applications to Aircraft-UAV Collisions

ABSTRACT This article investigates a physics-informed statistical approach capable of (i) learning nonlinear system dynamics by using data generated from a nonlinear system as well as the underlying governing physics, and (ii) predicting system dynamics with reasonable accuracy and a computational speed much faster than numerical methods. The proposed approach obtains the reduced-order model from the full-order governing equations. A function-to-function regression, based on multivariate Functional Principal Component Analysis, establishes the mapping between external forcing and system dynamics, while a multivariate Gaussian Process is used to capture the relationship between parameters and external forcing. In the application, the proposed approach is applied to predict aircraft nose skin deformation after Unmanned Aerial Vehicle (UAV) collisions at different impact attitudes (i.e., pitch, yaw and roll degrees). We show that the proposed physics-informed statistical model can achieve a 12% out-of-sample mean relative error, and is more than 103 times faster than Finite Element Analysis (FEA). Computer code and sample data are available on GitHub.

Liming and micronutrient application improves soil properties and productivity of the groundnut‐rapeseed cropping system in an acidic Inceptisol of India's eastern Himalayas

AbstractAluminum toxicity, nutrient imbalance, and reduced microbial activity are the most limiting factors for optimum agronomic productivity in acid soils. A field study was conducted to investigate the impact of micronutrient and liming on soil properties and productivity of groundnut‐rapeseed system in an acidic Inceptisol. The impact of six micronutrient (control or no micronutrient, Zn at 5 kg ha−1, B at 1 kg ha−1, Mo at 0.5 kg ha−1, Zn + Mo, and Zn + B + Mo) and two soil amendment (furrow liming at 500 kg ha−1 and no lime addition) treatments were first tested in groundnut and theirs residual effects were assessed on soil properties of succeeding rapeseed crop. Results indicated that liming significantly improved soil pH, soil organic carbon (SOC), available N, P and K, and diethylene triamine penta acetate (DTPA)‐extractable micronutrients, soil microbial biomass C and dehydrogenase activity as compared to no‐lime. After harvesting groundnut, Mo + Zn + B treated plots maintained the highest SOC content, whereas sole B and Mo treatment had the highest SOC content after harvesting rapeseed. Further, Mo + Zn + B treated plots showed higher contents of soil DTPA‐extractable micronutrients as compared to others. The highest groundnut equivalent yield (GEY) of the cropping system (pooled data of 2 years) was obtained with Mo + Zn + B (2.12 Mg ha−1) but remained at par with Zn + Mo (2.02 Mg ha−1). Multivariate principal component analysis indicated that available P was the most prominent soil nutrient with a strong effect on GEY. The results indicated that integrated application of Zn + B + Mo along with liming improves soil properties and agronomic productivity of groundnut‐rapeseed cropping system.

Application of Lipidomics in Psychiatry: Plasma-Based Potential Biomarkers in Schizophrenia and Bipolar Disorder

In this study, we obtained a lipidomic profile of plasma samples from drug-naïve patients with schizophrenia (SZ) and bipolar disorder (BD) in comparison to healthy controls. The sample cohort consisted of 30 BD and 30 SZ patients and 30 control individuals. An untargeted lipidomics strategy using liquid chromatography coupled with high-resolution mass spectrometry was employed to obtain the lipid profiles. Data were preprocessed, then univariate (t-test) and multivariate (principal component analysis and orthogonal partial least squares discriminant analysis) statistical tools were applied to select differential lipids, which were putatively identified. Afterward, multivariate receiver operating characteristic tests were performed, and metabolic pathway networks were constructed, considering the differential lipids. Our results demonstrate alterations in distinct lipid pathways, especially in glycerophospholipids, sphingolipids and glycerolipids, between SZ and BD patients. The results obtained in this study may serve as a basis for differential diagnosis, which is crucial for effective treatment and improving the quality of life of patients with psychotic disorders.

Investigating lactation curve characteristics of dairy cows

The main goal of this study was to analysis of the main characteristics (latent variables) of lactation curve in dairy cows using multivariate Principal Component Analysis. This work used primary database from the milk production of Holstein cows (n = 238 heads) in the PJSC “Pedigree farm ‘Stepnoy’ Kamianka-Dniprovska Raion of Zaporizhzhia Oblast over a 4-yr period (2014-2017). Recording is done with an interval of 30 days for 10 test-days (TD1-TD10), i.e., TD1 is milk production recorded on milking day 30th, TD2 is day 60th, TD3 is day 90th, etc., and 305-day milk yield records (Y305) were used also. High significant correlations were found between daily milk yields for certain test-days. The Principal Component Analysis performed on the variance-correlation matrix of TD1-TD10 records are able to explain about 90.33 % of the total variance. The first principal component (PC1) explained 66.32 % of the total variance and was highly-positively correlated with TD2-TD10 records. Thus, PC1 were defined as “total milk production”. The second principal component (PC2) explained 19.06 % of the total variance and was highly-positively correlated with TD1-TD2 records and highly-negatively correlated with TD9-TD10 records. Thus, PC2 were defined as “lactation curve persistency”. Finally, the third principal component (PC3) explained 4.95 % of the total variance and was highly-positively correlated with TD1 and TD10 records and highly-negatively correlated with TD4-TD5 records. Thus, PC3 were defined as “lactation curve type”. The use of a multivariate method (namely, the PCA) for the analysis of lactation curve characteristics based on monthly test-day records gave very close results of the analysis of milk productivity in different groups of domestic animals (cattle, goats and sheep). In all cases, the first principal component (PC1) described the absolute level of milk productivity during lactation, and the second principal component (PC2) described the persistency of the lactation curve. Significant influence on the PC1-PC3 factor scores was revealed to the greatest extent for such non-genetic factors as age of cow (in lactations), year and month of calving. Of the genetic factors, the greatest influence on the shape of the lactation curve was not so much the differences between the bull lines (Bell, Valiant, Elevation, Starbuck and Chief), but differences between individual bulls within some lines.

Influence of pre‐gelatinization in conjunction with guar gum addition on texture functionality, bioactive profile, in vitro nutrient digestibility, morphology and secondary structure of protein of quality protein maize pasta

SummaryThe current investigation aimed to develop quality protein maize (QPM) specialty pasta (partial: 50% QPM and full replacement of semolina: 100% QPM) and study the influence of pre‐gelatinization in conjunction with guar gum addition (1.5% or 3%) on bio‐techno‐functional properties of pasta. Scanning electron microscopy depicted the encapsulation of swollen starch within a protective sheet developed as a result of gum addition and starch gelatinisation. The Fourier transform infrared spectra denoted a shift from crystalline towards amorphous state in starch as a consequence of partial gelatinisation. The secondary structure of protein signified a drop in the alpha‐helix structure and thereby enhanced the in vitro protein digestibility of the pregelatinised samples. Multivariate principal component analysis was utilised to validate the differences between the parameters. The overall study inferred that the QPM owned an excellent potentiality for the development of nutrient‐dense pasta by employing the functionality of gums and pre‐gelatinisation treatment.