Fluorescence and Hyperspectral Sensors for Nondestructive Analysis and Prediction of Biophysical Compounds in the Green and Purple Leaves of Tradescantia Plants.

The application of non-imaging hyperspectral sensors has significantly enhanced the study of leaf optical properties across different plant species. In this study, chlorophyll fluorescence (ChlF) and hyperspectral non-imaging sensors using ultraviolet-visible-near-infrared shortwave infrared (UV-VIS-NIR-SWIR) bands were used to evaluate leaf biophysical parameters. For analyses, principal component analysis (PCA) and partial least squares regression (PLSR) were used to predict eight structural and ultrastructural (biophysical) traits in green and purple Tradescantia leaves. The main results demonstrate that specific hyperspectral vegetation indices (HVIs) markedly improve the precision of partial least squares regression (PLSR) models, enabling reliable and nondestructive evaluations of plant biophysical attributes. PCA revealed unique spectral signatures, with the first principal component accounting for more than 90% of the variation in sensor data. High predictive accuracy was achieved for variables such as the thickness of the adaxial and abaxial hypodermis layers (R2 = 0.94) and total leaf thickness, although challenges remain in predicting parameters such as the thickness of the parenchyma and granum layers within the thylakoid membrane. The effectiveness of integrating ChlF and hyperspectral technologies, along with spectroradiometers and fluorescence sensors, in advancing plant physiological research and improving optical spectroscopy for environmental monitoring and assessment. These methods offer a good strategy for promoting sustainability in future agricultural practices across a broad range of plant species, supporting cell biology and material analyses.

An Integrated Approach: A Hybrid Machine Learning Model for the Classification of Unscheduled Stoppages in a Mining Crushing Line Employing Principal Component Analysis and Artificial Neural Networks.

This article implements a hybrid Machine Learning (ML) model to classify stoppage events in a copper-crushing equipment, more specifically, a conveyor belt. The model combines Artificial Neural Networks (ANNs) and Support Vector Machines (SVMs) with Principal Component Analysis (PCA) to identify the type of stoppage event when they occur in an industrial sector that is significant for the Chilean economy. This research addresses the critical need to optimise maintenance management in the mining industry, highlighting the technological relevance and motivation for using advanced ML techniques. This study focusses on combining and implementing three ML models trained with historical data composed of information from various sensors, real and virtual, as well from maintenance reports that report operational conditions and equipment failure characteristics. The main objective of this study is to improve the efficiency when identifying the nature of a stoppage serving as a basis for the subsequent development of a reliable failure prediction system. The results indicate that this approach significantly increases information reliability, addressing the persistent challenges in data management within the maintenance area. With a classification accuracy of 96.2% and a recall of 96.3%, the model validates and automates the classification of stoppage events, significantly reducing dependency on interdepartmental interactions. This advancement eliminates the need for reliance on external databases, which have previously been prone to errors, missing critical data, or containing outdated information. By implementing this methodology, a robust and reliable foundation is established for developing a failure prediction model, fostering both efficiency and reliability in the maintenance process. The application of ML in this context produces demonstrably positive outcomes in the classification of stoppage events, underscoring its significant impact on industry operations.

Exploration of moisture activated dry granulation for the development of gastroretentive tablets aided by SeDeM diagram.

Moisture activated dry granulation (MADG) is an attractive granulation process. However, only a few works have explored modified drug release achieved by MADG, and to the best of the authors knowledge, none of them have explored gastroretention. The aim of this study was to explore the applicability of MADG process for developing gastroretentive placebo tablets, aided by SeDeM diagram. Floating and swelling capacities have been identified as critical quality attributes (CQAs). After a formulation screening step, the type and concentration of floating matrix formers and of binders were identified as the most relevant critical material attributes (CMAs) to investigate in ten formulations. A multiple linear regression analysis (MLRA) was applied against the factors that were varied to find the design space. An optimized product based on principal component analysis (PCA) results and MLRA was prepared and characterized. The granulate was also assessed by SeDeM. In conclusion, granulates lead to floating tablets with short floating lag time (<2 min), long floating duration (>4 h), and showing good swelling characteristics. The results obtained so far are promising enough to consider MADG as an advantageous granulation method to obtain gastroretentive tablets or even other controlled delivery systems requiring a relatively high content of absorbent materials in their composition.

A longitudinal study of the influence of air pollutants on children: a robust multivariate approach.

This paper aims to evaluate the statistical association between exposure to air pollution and forced expiratory volume in the first second (FEV1) in both asthmatic and non-asthmatic children and teenagers, in which the response variable FEV1 was repeatedly measured on a monthly basis, characterizing a longitudinal experiment. Due to the nature of the data, an robust linear mixed model (RLMM), combined with a robust principal component analysis (RPCA), is proposed to handle the multicollinearity among the covariates and the impact of extreme observations (high levels of air contaminants) on the estimates. The Huber and Tukey loss functions are considered to obtain robust estimators of the parameters in the linear mixed model (LMM). A finite sample size investigation is conducted under the scenario where the covariates follow linear time series models with and without additive outliers (AO). The impact of the time-correlation and the outliers on the estimates of the fixed effect parameters in the LMM is investigated. In the real data analysis, the robust model strategy evidenced that RPCA exhibits three principal component (PC), mainly related to relative humidity (Hmd), particulate matter with a diameter smaller than 10 µm (PM10) and particulate matter with a diameter smaller than 2.5 µm (PM2.5).

Evaluation of the Antioxidant Activity and Phenolic Composition of Different Monofloral and Polyfloral Brazilian Honey Extracts.

This study aimed to evaluate the chemical composition and antioxidant activity of phenolic extracts from monofloral and polyfloral honey samples obtained from different Brazilian regions. The chemical composition (total content of phenolic compounds and flavonoids) of extracts were measured by using colorimetric assays and analyzed by high performance liquid chromatographic (HPLC-DAD). The antioxidant activity was evaluated by chemical and biochemical assays (reducing power assay, 1,1-diphenyl-2-picrylhydrazyl (DPPH⋅) and 2,2-azino-bis(3-ethylbenzothiazoline-6-sulphonic) acid (ABTS⋅+) scavenger assays. It was also investigated the ability of extracts in attenuate lipid peroxidation induced by Fe2+ in phospholipids. The obtained results clearly demonstrated that the botanical origin and geographical region of honey collection influenced the chemical composition and antioxidant activity of extracts. Furthermore, the samples were constituted by phenolic acids and flavonoids, which p-coumaric acid was predominant among the compounds identified. All samples were able to scavenge free radicals and inhibit lipid peroxidation, and good correlations were obtained between the flavonoid content and honey color. In conclusion, the obtained extracts were constituted by antioxidant compounds, which reinforce the usage of honey in human diets, and demonstrated that the region of honey collection strong influenced in the chemical composition and, consequently, its biological effect.

Near-infrared spectroscopy and multivariate analysis as effective, fast, and cost-effective methods to discriminate Candida auris from Candida haemulonii.

Candida auris and Candida haemulonii are two emerging opportunistic pathogens that have caused an increase in clinical cases in the recent years worldwide. The differentiation of some Candida species is highly laborious, difficult, costly, and time-consuming depending on the similarity between the species. Thus, this study aimed to develop a new, faster, and less expensive methodology for differentiating between C. auris and C. haemulonii based on near-infrared (NIR) spectroscopy and multivariate analysis. C. auris CBS10913 and C. haemulonii CH02 were separated in 15 plates per species, and three isolated colonies of each plate were selected for Fourier transform near-infrared (FT-NIR) analysis, totaling 90 spectra. Subsequently, principal component analysis (PCA) and variable selection algorithms, including the successive projections algorithm (SPA) and genetic algorithm (GA) coupled with linear discriminant analysis (LDA), were employed to discern distinctive patterns among the samples. The use of PCA, SPA, and GA algorithms associated with LDA achieved 100% sensitivity and specificity for the discriminations. The SPA-LDA and GA-LDA algorithms were essential in selecting the variables (infrared wavelengths) of most importance for the models, which could be attributed to binding of cell wall structures such as polysaccharides, peptides, proteins, or molecules resulting from yeasts' metabolism. These results show the high potential of combined FT-NIR and multivariate analysis techniques for the classification of Candida-like fungi, which can contribute to faster and more effective diagnosis and treatment of patients affected by these microorganisms.

Hyperspectral and Chlorophyll Fluorescence Analyses of Comparative Leaf Surfaces Reveal Cellular Influences on Leaf Optical Properties in Tradescantia Plants.

The differential effects of cellular and ultrastructural characteristics on the optical properties of adaxial and abaxial leaf surfaces in the genus Tradescantia highlight the intricate relationships between cellular arrangement and pigment distribution in the plant cells. We examined hyperspectral and chlorophyll a fluorescence (ChlF) kinetics using spectroradiometers and optical and electron microscopy techniques. The leaves were analysed for their spectral properties and cellular makeup. The biochemical compounds were measured and correlated with the biophysical and ultrastructural features. The main findings showed that the top and bottom leaf surfaces had different amounts and patterns of pigments, especially anthocyanins, flavonoids, total phenolics, chlorophyll-carotenoids, and cell and organelle structures, as revealed by the hyperspectral vegetation index (HVI). These differences were further elucidated by the correlation coefficients, which influence the optical signatures of the leaves. Additionally, ChlF kinetics varied between leaf surfaces, correlating with VIS-NIR-SWIR bands through distinct cellular structures and pigment concentrations in the hypodermis cells. We confirmed that the unique optical properties of each leaf surface arise not only from pigmentation but also from complex cellular arrangements and structural adaptations. Some of the factors that affect how leaves reflect light are the arrangement of chloroplasts, thylakoid membranes, vacuoles, and the relative size of the cells themselves. These findings improve our knowledge of the biophysical and biochemical reasons for leaf optical diversity, and indicate possible implications for photosynthetic efficiency and stress adaptation under different environmental conditions in the mesophyll cells of Tradescantia plants.

Genetic diversity analysis and core germplasm bank construction in cold resistant germplasm of rubber trees (Hevea brasiliensis).

The rubber tree, Hevea brasiliensis (Willd. ex Adr. de Juss.) Muell. Arg., is the sole plant worldwide utilized for the commercial production of natural rubber. Following years of breeding, there exists a wide array of germplasm differentiation in rubber trees. The exploration of diversity and population structure within rubber tree germplasm resources, alongside the establishment of core germplasm resources, is instrumental in elucidating the genetic background and facilitating the effective utilization and management of these resources. By employing SNP molecular marker technology, 195 rubber tree resources were amplified, their genetic diversity analyzed, and a fingerprint map was subsequently constructed. Through this process, the cold-resistant core germplasm of rubber trees was identified. The results revealed that the PIC, He, and pi values ranged from 0.0905 to 0.3750, 0.095 to 0.5000, and 0.0953 to 0.5013, respectively. Both group structure analysis and cluster analysis delineated the accessions into two groups, signifying a simple group structure. A core germplasm bank was established with a sampling ratio of 10%, comprising 21 accessions divided into two populations. Population G1 consists of 20 accessions, while population G2 comprises 1 accession. The research findings have led to the creation of a molecular database that is anticipated to contribute to the management and subsequent breeding applications of rubber tree accessions.

Multivariate Analysis of Solubility Parameters for Drug-Polymer Miscibility Assessment in Preparing Raloxifene Hydrochloride Amorphous Solid Dispersions.

The success of obtaining solid dispersions for solubility improvement invariably depends on the miscibility of the drug and polymeric carriers. This study aimed to categorize and select polymeric carriers via the classical group contribution method using the multivariate analysis of the calculated solubility parameter of RX-HCl. The total, partial, and derivate parameters for RX-HCl were calculated. The data were compared with the results of excipients (N = 36), and a hierarchical clustering analysis was further performed. Solid dispersions of selected polymers in different drug loads were produced using solvent casting and characterized via X-ray diffraction, infrared spectroscopy and scanning electron microscopy. RX-HCl presented a Hansen solubility parameter (HSP) of 23.52 MPa1/2. The exploratory analysis of HSP and relative energy difference (RED) elicited a classification for miscible (n = 11), partially miscible (n = 15), and immiscible (n = 10) combinations. The experimental validation followed by a principal component regression exhibited a significant correlation between the crystallinity reduction and calculated parameters, whereas the spectroscopic evaluation highlighted the hydrogen-bonding contribution towards amorphization. The systematic approach presented a high discrimination ability, contributing to optimal excipient selection for the obtention of solid solutions of RX-HCl.

Quali-quantitative water behaviour in an intensive swine production catchment in the Atlantic Forest biome, Southern Brazil.

Agriculture is an essential economic activity in Brazil. However, it is also the main source of water quality degradation. Monitoring catchments with agricultural land use is a way to generate information on a scale to identify causes and sources of water quality degradation. This work used monitoring data derived from hydrology and the quality of surface and underground water in an intensive agricultural catchment in the Atlantic Forest biome. The Fortaleza River catchment is located in the western part of Santa Catarina state in southern Brazil and has 62 km2 of drainage area. Hydrological and water quality monitoring was conducted for 7 years at two fluviometric stations, three lysimeters, one meteorological station, and one piezometer. Data on precipitation, temperature, water flow, surface runoff, drainage, and water quality were used. Statistical analyses were also developed. Precipitation between 2013 and 2019 presented a homogeneous distribution in monthly and annual data, with January and July the months with the highest and lowest values, respectively. Statistical difference in the average and Q95 flows was found in upstream and downstream fluviometric sections. In terms of quality, statistical differences were identified for ammonium, nitrate, and potassium concentrations, which had higher concentrations in lysimeter runoff, indicating direct influence of agricultural activity on water quality. Principal component analysis (PCA) indicated that (i) surface water presented a positive relationship in Component 1 for the magnesium-calcium, sulphate-chloride, and acetate-bromide groups and a negative relationship for phosphate-nitrate; (ii) in lysimeters, the positive relationship occurred for Component 2 for the phosphate-chloride and sulphate-nitrate groups and was negative for ammonium-lithium and calcium-potassium-magnesium; and (iii) in piezometer, positive relationships were found for chloride-sodium and phosphate-nitrite pairs, while negative relationships were found for calcium-magnesium.

Performance measurement of Brazilian federal university hospitals: an overview of the public health care services through principal component analysis.

PURPOSE: This paper aimed to contextualize the process of public hospital providing services, based on the measurement of the performance of Federal University Hospitals (HUFs) of Brazil, using the technique of multivariate statistics of principal component analysis. DESIGN/METHODOLOGY/APPROACH: This research presented a descriptive and quantitative character, as well as exploratory purpose and followed the inductive logic, being empirically structured in two stages, that is, the application of principal component analysis (PCA) in four healthcare performance dimensions; subsequently, the full reapplication of principal component analysis in the most highly correlated variables, in module, with the first three main components (PC1, PC2 and PC3). FINDINGS: From the principal component analysis, considering mainly component I, with twice the explanatory power of the second (PC2) and third components (PC3), it was possible to evidence the efficient or inefficient behavior of the HUFs evaluated through the production of medical residency, by specialty area. Finally, it was observed that the formation of two groups composed of seven and eight hospitals, that is, Groups II and IV shows that these groups reflect similarities with respect to the scores and importance of the variables for both hospitals' groups. RESEARCH LIMITATIONS/IMPLICATIONS: Among the main limitations it was observed that there was incomplete data for some HUFs, which made it impossible to search for information to explain and better contextualize certain aspects. More specifically, a limited number of hospitals with complete information were dealt with for 60% of SIMEC/REHUF performance indicators. PRACTICAL IMPLICATIONS: The use of PCA multivariate technique was of great contribution to the contextualization of the performance and productivity of homogeneous and autonomous units represented by the hospitals. It was possible to generate a large quantity of information in order to contribute with assumptions to complement the decision-making processes in these organizations. SOCIAL IMPLICATIONS: Development of public policies with emphasis on hospitals linked to teaching centers represented by university hospitals. This also involved the projection of improvements in the reach of the efficiency of the services of assistance to the public health, from the qualified formation of professionals, both to academy, as to clinical practice. ORIGINALITY/VALUE: The originality of this paper for the scenarios of the Brazilian public health sector and academic area involved the application of a consolidated performance analysis technique, that is, PCA, obtaining a rich work in relation to the extensive exploitation of techniques to support decision-making processes. In addition, the sequence and the way in which the content, formed by object of study and techniques, has been organized, generates a particular scenario for the measurement of performance in hospital organizations.

Application of a microfluidic electronic tongue based on impedance spectroscopy for coconut water analysis.

The food industry has grown with the demands for new products and their authentication, which has not been accompanied by the area of analysis and quality control, thus requiring novel process analytical technologies for food processes. An electronic tongue (e-tongue) is a multisensor system that can characterize complex liquids in a fast and simple way. Here, we tested the efficacy of an impedimetric microfluidic e-tongue setup - comprised by four interdigitated electrodes (IDE) on a printed circuit board (PCB), with four pairs of digits each, being one bare sensor and three coated with different ultrathin nanostructured films with different electrical properties - in the analysis of fresh and industrialized coconut water. Principal Component Analysis (PCA) was applied to observe sample differences, and Partial Least Squares Regression (PLSR) was used to predict sample physicochemical parameters. Linear Discriminant Analysis (LDA) and Partial Least Square - Discriminant Analysis (PLS-DA) were compared to classify samples based on data from the e-tongue device. Results indicate the potential application of the microfluidic e-tongue in the identification of coconut water composition and determination of physicochemical attributes, allowing for classification of samples according to soluble solid content (SSC) and total titratable acidity (TTA) with over 90% accuracy. It was also demonstrated that the microfluidic setup has potential application in the food industry for quality assessment of complex liquid samples.

Assessment of the adaptive capacity of Morada Nova ewes with different coat coloration.

Coat color is a factor affecting heat tolerance in tropical ruminant and a particular coat color can determine which is more resilient to environmental changes. The aim of this study was to measure the level of adaptation of Morada Nova sheep with different coat color by using an Adaptability Index (AI). Adult ewes were used, including two different coat colors of Morada Nova sheep (red and white) with mean of body weight of 28.02 ± 5.70 kg and 31.47 ± 3.41 kg, respectively. Physiology parameters, hematology, electrolytes, acid-base status, mineral, renal functions, metabolites, enzymes, and proteins were measured. AI was designed using a multivariate approach (principal component analysis) to "weigh" the influence of each variable in the animal responses. The variables more important for adaptive aspects of Red Morada Nova were: haematology, electrolytes and acid-base status. The hemoglobin (HG), mean corpuscular hemoglobin (MCH), mean corpuscular hemoglobin concentration (MCHC), sodium (Na+), oxygen pressure (PO2), glucose (GLU) and albumin (ALB) were significantly higher in Red Morada Nova sheep and hydrogen carbonate (HCO3), base excess (BE), total carbon dioxide concentration (TCO2) and URE were significantly higher in the white phenotype. The variables more important for adaptive aspects of White Morada Nova sheep were: (K+), total protein (TP), PO2, HG, cholesterol (CHO), rectal temperature (RT) and glucose (GLU). Both phenotypes showed a high adaptation level, however, a higher value was generated for the Red Morada Nova sheep (81.97). This study concludes that both phenotypes of the Morada Nova sheep breed are well adapted to the climatic condition of the Brazilian tropical region using different adaptive mechanisms.

New records of soft ticks (Acari: Argasidae) from caves in Brazil, with a morphological study of Ornithodoros fonsecai and an analysis of the taxonomic status of Antricola inexpectata.

In this study, we report soft ticks from bat-inhabiting caves in different areas of Brazil. From 2010 to 2019, we collected 807 tick specimens from nine caves located in four Brazilian states among two biomes. Ticks were morphologically identified as Antricola guglielmonei (282 specimens), Ornithodoros cavernicolous (260 specimens), and Ornithodoros fonsecai (265 specimens). Whereas A. guglielmonei was collected on bat guano in hot caves, O. cavernicolous and O. fonsecai were collected in cracks and crevices on the walls of cold caves, sometimes in the same chamber. Morphological identifications were corroborated by molecular and phylogenetic analyses inferred from tick mitochondrial 16S rRNA gene partial sequences. The sequences of A. guglielmonei, O. cavernicolous and O. fonsecai collected in this study clustered with conspecific GenBank sequences from different localities of Brazil. Remarkably, a clade containing 12 sequences of O. fonsecai was clearly bifurcated, denoting a degree of genetic divergence (up to 5 %) of specimens from Cerrado/Atlantic Forest biomes with the specimens from the Caatinga biome. To further evaluate this divergence, we performed morphometric analysis of the larval stage of different O. fonsencai populations by principal component analysis, which indicated that the larvae from Caatinga populations were generally smaller than the larvae from other biomes. Some of the present A. guglielmonei specimens were collected from the type locality of Antricola inexpectata. Comparisons of these specimens with the type specimens of A. inexpectata and A. guglielmonei indicated that they could not be separated by their external morphology. Hence, we are relegating A. inexpectata to a synonym of A. guglielmonei. This proposal is corroborated by our phylogenetic analysis.

A Study of High-Frequency Noise for Microplastics Classification Using Raman Spectroscopy and Machine Learning.

Given the growing urge for plastic management and regulation in the world, recent studies have investigated the problem of plastic material identification for correct classification and disposal. Recent works have shown the potential of machine learning techniques for successful microplastics classification using Raman signals. Classification techniques from the machine learning area allow the identification of the type of microplastic from optical signals based on Raman spectroscopy. In this paper, we investigate the impact of high-frequency noise on the performance of related classification tasks. It is well-known that classification based on Raman is highly dependent on peak visibility, but it is also known that signal smoothing is a common step in the pre-processing of the measured signals. This raises a potential trade-off between high-frequency noise and peak preservation that depends on user-defined parameters. The results obtained in this work suggest that a linear discriminant analysis model cannot generalize properly in the presence of noisy signals, whereas an error-correcting output codes model is better suited to account for inherent noise. Moreover, principal components analysis (PCA) can become a must-do step for robust classification models, given its simplicity and natural smoothing capabilities. Our study on the high-frequency noise, the possible trade-off between pre-processing the high-frequency noise and the peak visibility, and the use of PCA as a noise reduction technique in addition to its dimensionality reduction functionality are the fundamental aspects of this work.

Physiological evaluation of yeast strains under anaerobic conditions using glucose, fructose, or sucrose as the carbon source.

The aim of this study was to evaluate the physiology of 13 yeast strains by assessing their kinetic parameters under anaerobic conditions. They included Saccharomyces cerevisiae CAT-1 and 12 isolated yeasts from different regions in Brazil. The study aimed to enhance understanding of the metabolism of these strains for more effective applications. Measurements included quantification of sugars, ethanol, glycerol, and organic acids. Various kinetic parameters were analyzed, such as specific substrate utilization rate (qS), maximum specific growth rate (µmax), doubling time, biomass yield, product yield, maximum cell concentration, ethanol productivity (PEth), biomass productivity, and CO2 concentration. S. cerevisiae CAT-1 exhibited the highest values in glucose for µmax (0.35 h-1), qS (3.06 h-1), and PEth (0.69 gEth L-1 h-1). Candida parapsilosis Recol 37 did not fully consume the substrate. In fructose, S. cerevisiae CAT-1 stood out with higher values for µmax (0.25 h-1), qS (2.24 h-1), and PEth (0.60 gEth L-1 h-1). Meyerozyma guilliermondii Recol 09 and C. parapsilosis Recol 37 had prolonged fermentation times and residual substrate. In sucrose, only S. cerevisiae CAT-1, S. cerevisiae BB9, and Pichia kudriavzevii Recol 39 consumed all the substrate, displaying higher PEth (0.72, 0.51, and 0.44 gEth L-1 h-1, respectively) compared to other carbon sources.

Detection of Sialic Acid to Differentiate Cervical Cancer Cell Lines Using a Sambucus nigra Lectin Biosensor.

Pap smear screening is a widespread technique used to detect premalignant lesions of cervical cancer (CC); however, it lacks sensitivity, leading to identifying biomarkers that improve early diagnosis sensitivity. A characteristic of cancer is the aberrant sialylation that involves the abnormal expression of α2,6 sialic acid, a specific carbohydrate linked to glycoproteins and glycolipids on the cell surface, which has been reported in premalignant CC lesions. This work aimed to develop a method to differentiate CC cell lines and primary fibroblasts using a novel lectin-based biosensor to detect α2,6 sialic acid based on attenuated total reflectance Fourier transform infrared spectroscopy (ATR-FTIR) and chemometric. The biosensor was developed by conjugating gold nanoparticles (AuNPs) with 5 µg of Sambucus nigra (SNA) lectin as the biorecognition element. Sialic acid detection was associated with the signal amplification in the 1500-1350 cm-1 region observed by the surface-enhanced infrared absorption spectroscopy (SEIRA) effect from ATR-FTIR results. This region was further analyzed for the clustering of samples by applying principal component analysis (PCA) and confidence ellipses at a 95% interval. This work demonstrates the feasibility of employing SNA biosensors to discriminate between tumoral and non-tumoral cells, that have the potential for the early detection of premalignant lesions of CC.

A Clustering Study of Dietary Patterns and Physical Activity among Workers of the Uruguayan State Electrical Company.

Recent studies have shown that certain nutrients, specific food groups, or general dietary patterns (DPs) can promote health and prevent noncommunicable chronic diseases (NCCDs). Both developed and developing countries experience a high prevalence of NCCDs due to poor lifestyle habits, DPs, and low physical activity levels. This study aims to examine the dietary, physical activity, sociodemographic, and lifestyle patterns of Uruguayan State Electrical Company workers (the IN-UTE study). A total of 2194 workers participated in the study, providing information about their sociodemographics, lifestyles, and dietary habits through different questionnaires. To identify DPs from 16 food groups, principal component analysis (PCA) was performed. A hierarchical cluster algorithm was used to combine food groups and sociodemographic/lifestyle variables. Four DPs were extracted from the data; the first DP was related to the intake of energy-dense foods, the second DP to the characteristics of the job, the third DP to a Mediterranean-style diet, and the fourth DP to age and body mass index. In addition, cluster analysis involving a larger number of lifestyle variables produced similar results to the PCA. Lifestyle and sociodemographic factors, including night work, working outside, and moderate and intense PA, were significantly correlated with the dietary clusters, suggesting that working conditions, socioeconomic status, and PA may play an important role in determining DPs to some extent. Accordingly, these findings should be used to design lifestyle interventions to reverse the appearance of unhealthy DPs in the UTE population.

Influence of maturation and roasting on the quality and chemical composition of new conilon coffee cultivar by chemometrics.

Coffee is one of the most consumed beverages worldwide. Espírito Santo is the largest Brazilian producer of conilon coffee, and invested in the creation of new cultivars, such as "Conquista ES8152", launched in 2019. Therefore, the present study aimed to evaluate the effects of maturation and roasting on the chemical and sensorial composition of the new conilon coffee cultivar "Conquista ES8152". The coffee was harvested containing 3 different percentages of ripe fruits: 60%, 80%, and 100%, and roasted at 3 different degrees of roasting: light, medium, and dark, to evaluate the moisture and ash content, yield of soluble extract, volatile compound profile, chlorogenic acid and caffeine content, and sensory profile. "Conquista ES8152" coffee has a moisture content between 1.38 and 2.62%; ash between 4.34 and 4.72%; and yield between 30.7 and 35.8%. Sensory scores ranged between 75 and 80 and the majority of volatile compounds belong to the pyrazine, phenol, furan, and pyrrole groups. The content of total chlorogenic acids was drastically reduced by roasting, with values between 2.40 and 9.33%, with 3-caffeoylquinic acid being the majority. Caffeine was not influenced by either maturation or roasting, with values between 2.16 and 2.41%. The volatile compounds furfural, 5-methylfurfural, and 2-ethyl-5-methylpyrazine were positively correlated with the evaluated sensory attributes and 5-methylfurfural was the only one significantly correlated with all attributes. Ethylpyrazine, furfuryl acetate, 1-furfurylpyrrole, 4-ethyl-2-methoxyphenol, and difurfuryl ether were negatively correlated. The stripping did not affect the quality and composition of this new cultivar, however, the roasting caused changes in both the chemical and sensorial profiles, appropriately indicated by the principal component analysis.

Prediction of Body Weight by Using PCA-Supported Gradient Boosting and Random Forest Algorithms in Water Buffaloes (Bubalus bubalis) Reared in South-Eastern Mexico.

This study aims to use advanced machine learning techniques supported by Principal Component Analysis (PCA) to estimate body weight (BW) in buffalos raised in southeastern Mexico and compare their performance. The first stage of the current study consists of body measurements and the process of determining the most informative variables using PCA, a dimension reduction method. This process reduces the data size by eliminating the complex structure of the model and provides a faster and more effective learning process. As a second stage, two separate prediction models were developed with Gradient Boosting and Random Forest algorithms, using the principal components obtained from the data set reduced by PCA. The performances of both models were compared using R2, RMSE and MAE metrics, and showed that the Gradient Boosting model achieved a better prediction performance with a higher R2 value and lower error rates than the Random Forest model. In conclusion, PCA-supported modeling applications can provide more reliable results, and the Gradient Boosting algorithm is superior to Random Forest in this context. The current study demonstrates the potential use of machine learning approaches in estimating body weight in water buffalos, and will support sustainable animal husbandry by contributing to decision making processes in the field of animal science.