Exchange-correlation kernel for perturbation dependent auxiliary functions in auxiliary density perturbation theory.

CONTEXT: Analytic exchange-correlation kernel formulations are of the outermost importance for density functional theory (DFT) perturbation calculations. In this paper, the working equation for the exchange-correlation kernel of the generalized gradient approximation (GGA) for perturbation dependent auxiliary functions is derived and discussed in the framework of auxiliary density functional theory (ADFT). The presented new formulation is extended to the unrestricted approach, too. A comprehensive discussion of the implementation of the GGA ADFT kernel, using either the native exchange-correlation functional implementations in deMon2k or the ones from the LibXC library, is given. Calculations with analytic exchange-correlation kernels are compared to their finite difference counterparts. The obtained results are in quantitative agreement. Nevertheless, analytic GGA ADFT kernel implementations show substantial improvement in the computational performance. Similar results are reported for analytic second derivatives of effective core potential (ECP) and model core potential (MCP) matrix elements when compared to their finite difference counterparts in molecular frequency analyses. METHOD: All calculations are performed in the framework of ADFT as implemented in deMon2k. In the ADFT analytic frequency calculations, auxiliary density perturbation theory was used. The underlying two-center exchange-correlation kernel matrix elements are calculated by numerical integration either with analytic or finite difference kernel expressions. Validation calculations are performed with the VWN and PBE functionals employing DFT-optimized DZVP basis sets in conjunction with automatically generated GEN-A2 auxiliary density function sets. In the (Pt3Cu)n cluster benchmark calculations, the RPBE functional was used. For Pt atoms, the quasi-relativistic LANL2DZ effective core potential with the corresponding valence basis set was employed, whereas for Cu atoms, the all-electron DFT-optimized TZVP basis was applied. The auxiliary density was expanded by the automatically generated GEN-A2* auxiliary function set. We run all benchmark calculations in parallel on 24 cores.

Factors influencing fumonisin B1 contamination in maize: insights from two production regions in Costa Rica.

Maize (Zea mays L.) is an important cereal crop worldwide. Contaminated maize kernels pose a significant mycotoxin exposure risk for humans in Latin America. Fumonisins, the most prevalent mycotoxin in maize, typically occur during pre-harvest conditions leading to significant economic losses. Various factors, including weather conditions, may influence this contamination. This study aimed to determine the association between fumonisin B1 (FB1) contamination, prevalence of Fusarium verticillioides, weather conditions and kernel quality in the two primary maize production areas in Costa Rica (Brunca and Chorotega). All maize samples (100%) showed FB1 contamination, with higher concentrations in samples from Brunca region, consistent with the presence of F. verticilliodes. Weather conditions appeared to play an important role in this contamination, since Brunca region had the highest mean temperature and relative humidity after maize silking (R1) and the total monthly rainfall in this region was significantly higher during the last two months of maize cultivation (grain-filling and physiological maturity stages R3 to R6). Interestingly, this study found a negative correlation between grain damage and kernel contamination with FB1 and F. verticillioides. The concentration of mineral nutrients in kernels from both regions was largely similar. Most nutrients in kernels exhibited a negative correlation with FB1, particularly nitrogen. Zinc and phosphorus were the only nutrients in kernels showing a positive correlation with FB1 in samples from the Brunca region. The results highlight elevated levels of FB1 contamination in maize and contribute to a better understanding of pre-harvest factors influencing FB1 contamination in tropical conditions.

Addressing the Concern of Orange-Yellow Fungus Growth on Palm Kernel Cake: Safeguarding Dairy Cattle Diets for Mycotoxin-Producing Fungi.

Palm kernel cake (PKC), a byproduct of palm oil extraction, serves an important role in Ecuador's animal feed industry. The emergence of yellow-orange fungal growth in PKC on some cattle farms in Ecuador sparked concerns within the cattle industry regarding a potential mycotoxin-producing fungus on this substrate. Due to the limited availability of analytical chemistry techniques in Ecuador for mycotoxin detection, we chose to isolate and identify the fungus to determine its association with mycotoxin-producing genera. Through molecular identification via ITS region sequencing, we identified the yellow-orange fungus as the yeast Candida ethanolica. Furthermore, we isolated two other fungi-the yeast Pichia kudriavzevii, and the fungus Geotrichum candidum. Molecular identification confirmed that all three species are not classified as mycotoxin-producing fungi but in contrast, the literature indicates that all three have demonstrated antifungal activity against Aspergillus and Penicillium species, genera associated with mycotoxin production. This suggests their potential use in biocontrol to counter the colonization of harmful fungi. We discuss preventive measures against the fungal invasion of PKC and emphasize the importance of promptly identifying fungi on this substrate. Rapid recognition of mycotoxin-producing and pathogenic genera holds the promise of mitigating cattle intoxication and the dissemination of mycotoxins throughout the food chain.

Geospatial analysis and prevalence of Schistosoma mansoni and soil-transmitted helminth infections in an endemic area in Eastern Brazilian Amazon.

OBJECTIVES: This study evaluated the occurrence of Schistosoma mansoni and soil-transmitted helminths in an endemic area in the Eastern Brazilian Amazon, analysing prevalence and spatial distribution. METHODS: The study was conducted in four localities of Primavera Municipality, in Pará state. Data was obtained from the Decit 40/2012 project and the participants were divided into five age range categories for evaluation: children, adolescents, young adults, adults and elderly individuals. For the diagnostic tests, Kato-Katz slides were prepared to detect S. mansoni and soil-transmitted helminths eggs. The spatial distribution map and the Kernel Density Estimation were performed to assess the presence and location of infections. RESULTS: Stool samples revealed the presence of hookworms, S. mansoni, Ascaris lumbricoides and Trichuris trichiura eggs. Mono-, bi- and poly-parasitic infections were observed, with a significant prevalence of hookworm monoparasitism. CONCLUSIONS: The high frequency of children infected with soil-transmitted helminths confirms their significance as an ongoing public health problem in the poorest municipalities of Brazil. The Geographic Information System plays a crucial role in environmental surveillance and in the control of epidemics and endemic diseases, enabling accurate assessment and informed decision-making for their control.

A distance-based kernel for classification via Support Vector Machines.

Support Vector Machines (SVMs) are a type of supervised machine learning algorithm widely used for classification tasks. In contrast to traditional methods that split the data into separate training and testing sets, here we propose an innovative approach where subsets of the original data are randomly selected to train the model multiple times. This iterative training process aims to identify a representative data subset, leading to improved inferences about the population. Additionally, we introduce a novel distance-based kernel specifically designed for binary-type features based on a similarity matrix that efficiently handles both binary and multi-class classification problems. Computational experiments on publicly available datasets of varying sizes demonstrate that our proposed method significantly outperforms existing approaches in terms of classification accuracy. Furthermore, the distance-based kernel achieves superior performance compared to other well-known kernels from the literature and those used in previous studies on the same datasets. These findings validate the effectiveness of our proposed classification method and distance-based kernel for SVMs. By leveraging random subset selection and a unique kernel design, we achieve notable improvements in classification accuracy. These results have significant implications for diverse classification problems in Machine Learning and data analysis.

The Characterization of Pastures by Grazing Cycle and Evaluation of Supplementation with Agro-Industry Co-Products on the Performance of Buffaloes in the Humid Tropics.

The objective was to characterize the pastures by grazing cycle, as well as to evaluate the performance of buffaloes in intensive rotational grazing in a silvopastoral system in the eastern Amazon supplemented with agro-industry co-products in order to characterize the grazing cycles, the composition of the fractions, and the carcass yield. Fifteen non-castrated, crossbred water buffaloes (Murrah × Mediterranean) were used. All animals used in the study were clinically healthy and weighed approximately 458 kg. The animals were grazed in a single group, and supplementation (1% of live weight-LW/day) was divided into three treatments: control (control-conventional ingredients); Cocos nucifera coconut cake (Cocos nucifera) (coconut cake-70%); and palm kernel cake (Guinean elaeis) (palm kernel cake-70% palm kernel cake). The chemical composition of the forage is different in each part of the plant, with higher protein values in the leaves (11.40%) and higher acid detergent fiber (ADF) values in the stems (50.03%). Among the ingredients of the supplement, corn has the highest percentage of indigestible protein (35.57%), most of the protein in palm kernel cake is B3 (49.11%), and in Coco, B2 (51.52%). Mombasa grass has a higher percentage of B3 and B2 proteins; the indigestible fraction is lower in the leaves (17.16%). The leaf/stem ratio also varied between grazing cycles, being better in the second cycle (2.11%) and with an overall average of 1.71. Supplement consumption varied between cycles and was higher in the control treatment, with an overall mean of 4.74. There was no difference in daily weight gain and carcass yield, with an average of 1 kg/day and 49.23%, respectively. Therefore, it can be concluded that including supplements based on by-products from the coconut and palm oil agro-industries promotes performance and carcass yields compatible with conventional supplements. Their use reduces production costs, optimizes the utilization of forage mass, enhances the sustainability of the production chain, and, therefore, is recommended.

Improvement of the production and quality of Cordyceps javanica conidia for the control of Diaphorina citri adults.

This study aimed to evaluate the use of palm kernel meal (PKM) in the traditional solid-state fermentation system to improve the production and quality of Cordyceps javanica conidia. The impact of PKM was determined by measuring conidia yield, viability, hydrophobicity, shelf life, and conidia pathogenicity against Diaphorina citri adults. By supplementing rice grains with 5% palm kernel meal increased the conidial yield by up to 40%, without compromising conidia viability and hydrophobicity. In addition, conidia caused higher levels of mortality by mycosis against D. citri adults (90%), relative to conidia harvested from rice (52%). The conidia recovered from rice/palm kernel meal mixtures also retained viability greater than 90% after storage for 10 months at 4 °C, while the conidia produced on rice reached 80%. Thus, conidia produced in the presence of palm kernel meal can be consumed immediately or in the medium term. Some process advantages of the palm kernel meal as co-substrate in the traditional production system of C. javanica are also mentioned. These results are attractive for improving the mycoinsecticide production process, with excellent cost-benefit and minimal changes in infrastructure and process.

TreeKDE: clustering multivariate data based on decision tree and using one-dimensional kernel density estimation.

In this paper, we present an algorithm for clustering multidimensional data, which we named TreeKDE. It is based on a tree structure decision associated with the optimization of the one-dimensional kernel density estimator function constructed from the orthogonal projections of the data on the coordinate axes. Among the main features of the proposed algorithm, we highlight the automatic determination of the number of clusters and their insertion in a rectangular region. Comparative numerical experiments are presented to illustrate the performance of the proposed algorithm and the results indicate that the TreeKDE is efficient and competitive when compared to other algorithms from the literature. Features such as simplicity and efficiency make the proposed algorithm an attractive and promising research field, which can be used as a basis for its improvement, and also for the development of new clustering algorithms based on the association between decision tree and kernel density estimator.

Production of a potential multistrain probiotic in co-culture conditions using agro-industrial by-products-based medium for fish nutrition.

BACKGROUND: Probiotics are viable microorganisms that when administered in adequate amounts confer health benefits to the host. In fish, probiotic administration has improved growth, and immunological parameters. For this reason, it is necessary production of probiotic bacteria, however, commercial culture mediums used for probiotic growth are expensive, so the design of a "low" cost culture medium is necessary. Therefore, this research aimed to produce a potential multistrain probiotic preparation composed of L. lactis A12 and Priestia species isolated from Nile tilapia (Oreochromis niloticus) gut using an agro-industrial by-products-based culture medium. RESULTS: A Box-Behnken design with three factors (whey, molasses, and yeast extract concentration) was used. As the main results, a high concentration of three components enhanced the viability of L. lactis A12, however, viable cell counts of Priestia species were achieved at low molasses concentrations. The Optimal conditions were 1.00% w/v whey, 0.50% w/v molasses, and 1.50% w/v yeast extract. L. lactis A12 and Priestia species viable counts were 9.43 and 6.89 Log10 CFU/mL, respectively. L. lactis A12 concentration was higher (p < 0.05) in the proposed medium compared to commercial broth. CONCLUSIONS: It was possible to produce L. lactis A12 and Priestia species in co-culture conditions. Whey and molasses were suitable components to produce the multistrain preparation. The cost of the proposed culture medium was 77.54% cheaper than the commercial medium. The proposed culture medium could be an alternative to commercial mediums for the production of this multistrain probiotic.

Patrones de actividad de mamíferos alrededor de la línea transportadora de gas natural de Camisea en la Reserva Comunal Machiguenga, Cusco, Perú

En el presente trabajo se estudia la actividad horaria de los mamíferos que habitan el área circundante a la línea transportadora de gas de Camisea que atraviesa la Reserva Comunal Machiguenga. Desde febrero del 2020 hasta enero del 2021, se realizó un registro fotográfico mediante cámaras trampa dispuestas a lo largo de la tubería de gas. Los patrones de actividad se estimaron mediante la función de densidad de Kernel. Durante el periodo de estudio, se registraron 25 especies de mamíferos. Se encontró que Dasyprocta kalinowskii y Eira barbara presentan un patrón de actividad diurno; mientras que Cuniculus paca, Tapirus terrestris, Dasypus spp. y Mazama spp. presentan un patrón predominantemente nocturno. Se sugiere que los patrones de actividad observados estarían influenciados por varios factores como la exclusión competitiva entre D. kalinowskii y C. paca, disponibilidad estacional del alimento para T. terrestris, variación de temperatura y precipitación para Dasypus spp., restricciones filogenéticas en Mazama spp., y segregación temporal con otros carnívoros para E. barbara. Se destaca la importancia de la colaboración entre las empresas del rubro energético, las comunidades nativas y las organizaciones gubernamentales.

The present study investigates the hourly activity patterns of mammals inhabiting the area surrounding the Camisea gas pipeline that crosses the Machiguenga Communal Reserve. From February 2020 to January 2021, a photographic record was conducted using camera traps placed along the gas pipeline. Activity patterns were estimated using Kernel density functions. During the study period, 25 mammal species were recorded. It was found that Dasyprocta kalinowskii and Eira barbara exhibit a diurnal activity pattern, whereas Cuniculus paca, Tapirus terrestris, Dasypus spp., and Mazama spp. display predominantly nocturnal behavior. It is suggested that observed activity patterns could be influenced by various factors such as competitive exclusion between D. kalinowskii and C. paca, seasonal food availability for T. terrestris, temperature and precipitation variations for Dasypus spp., phylogenetic constraints in Mazama spp., and temporal segregation with other carnivores for E. barbara. The significance of collaboration between energy industry companies, native communities, and governmental organizations is emphasized.

Affective Neural Responses Sonified through Labeled Correlation Alignment.

Sound synthesis refers to the creation of original acoustic signals with broad applications in artistic innovation, such as music creation for games and videos. Nonetheless, machine learning architectures face numerous challenges when learning musical structures from arbitrary corpora. This issue involves adapting patterns borrowed from other contexts to a concrete composition objective. Using Labeled Correlation Alignment (LCA), we propose an approach to sonify neural responses to affective music-listening data, identifying the brain features that are most congruent with the simultaneously extracted auditory features. For dealing with inter/intra-subject variability, a combination of Phase Locking Value and Gaussian Functional Connectivity is employed. The proposed two-step LCA approach embraces a separate coupling stage of input features to a set of emotion label sets using Centered Kernel Alignment. This step is followed by canonical correlation analysis to select multimodal representations with higher relationships. LCA enables physiological explanation by adding a backward transformation to estimate the matching contribution of each extracted brain neural feature set. Correlation estimates and partition quality represent performance measures. The evaluation uses a Vector Quantized Variational AutoEncoder to create an acoustic envelope from the tested Affective Music-Listening database. Validation results demonstrate the ability of the developed LCA approach to generate low-level music based on neural activity elicited by emotions while maintaining the ability to distinguish between the acoustic outputs.

Characterizing social and cognitive EEG-ERP through multiple kernel learning.

EEG-ERP social-cognitive studies with healthy populations commonly fail to provide significant evidence due to low-quality data and the inherent similarity between groups. We propose a multiple kernel learning-based approach to enhance classification accuracy while keeping the traceability of the features (frequency bands or regions of interest) as a linear combination of kernels. These weights determine the relevance of each source of information, which is crucial for specialists. As a case study, we classify healthy ex-combatants of the Colombian armed conflict and civilians through a cognitive valence recognition task. Although previous works have shown accuracies below 80% with these groups, our proposal achieved an F1 score of 98%, revealing the most relevant bands and brain regions, which are the base for socio-cognitive trainings. With this methodology, we aim to contribute to standardizing EEG analyses and enhancing their statistics.

A machine learning-based model for a dose point kernel calculation.

PURPOSE: Absorbed dose calculation by kernel convolution requires the prior determination of dose point kernels (DPK). This study reports on the design, implementation, and test of a multi-target regressor approach to generate the DPKs for monoenergetic sources and a model to obtain DPKs for beta emitters. METHODS: DPK for monoenergetic electron sources were calculated using the FLUKA Monte Carlo (MC) code for many materials of clinical interest and initial energies ranging from 10 to 3000 keV. Regressor Chains (RC) with three different coefficients regularization/shrinkage models were used as base regressors. Electron monoenergetic scaled DPKs (sDPKs) were used to assess the corresponding sDPKs for beta emitters typically used in nuclear medicine, which were compared against reference published data. Finally, the beta emitters sDPK were applied to a patient-specific case calculating the Voxel Dose Kernel (VDK) for a hepatic radioembolization treatment with [Formula: see text]Y. RESULTS: The three trained machine learning models demonstrated a promising capacity to predict the sDPK for both monoenergetic emissions and beta emitters of clinical interest attaining differences lower than [Formula: see text] in the mean average percentage error (MAPE) as compared with previous studies. Furthermore, differences lower than [Formula: see text] were obtained for the absorbed dose in patient-specific dosimetry comparing against full stochastic MC calculations. CONCLUSION: An ML model was developed to assess dosimetry calculations in nuclear medicine. The implemented approach has shown the capacity to accurately predict the sDPK for monoenergetic beta sources in a wide range of energy in different materials. The ML model to calculate the sDPK for beta-emitting radionuclides allowed to obtain VDK useful to achieve reliable patient-specific absorbed dose distributions required short computation times.

Nutrient intake, digestibility, performance, carcass traits and sensory analysis of meat from lambs fed with co-products of Amazon oilseeds.

Introduction: The increase in availability and nutritional composition of oilseed co-products has made it essential to study the use of this biomass. Methods: The objective of this work was to investigate the effects of including oilseed cakes on intake and digestibility, performance, carcass characteristics and meat sensory in feedlot lambs. Twenty-four crossbred Dorper × Santa Inês lambs, with initial body weight of 30 ± 1.3 kg, male, castrated, aged 4-5 months, were distributed in a completely randomized experimental design with four treatments (diets) and six replications (animals), confined in individual stalls for 70 days. Results: The inclusion of tucuma cake (Tuc) reduced dry matter intake (p < 0.01) and diets with cupuassu cake (Cup) and palm kernel cake (Palm) reduced dry matter digestibility (p < 0.05). The Tuc diet also provided the lowest final body weight (p = 0.02); lower average daily gain (p = 0.03); lower feed efficiency (p = 0.03) and lower carcass weight (p < 0.01). However, diets did not influence carcass yield (%), fat thickness (mm) and loin eye area (cm2; p > 0.05). Meat from lambs on the control diet was rated as less fibrous and more tender (p < 0.05). Conclusion: The inclusion of tucuma cake does not influence digestibility, but reduces intake, performance and influences carcass characteristics and meat texture. Diets with cupuassu cake or palmiste cake reduced digestibility, however, intake, performance and carcass characteristics were similar to the control diet.

Conditional Functional Graphical Models.

Graphical modeling of multivariate functional data is becoming increasingly important in a wide variety of applications. The changes of graph structure can often be attributed to external variables, such as the diagnosis status or time, the latter of which gives rise to the problem of dynamic graphical modeling. Most existing methods focus on estimating the graph by aggregating samples, but largely ignore the subject-level heterogeneity due to the external variables. In this article, we introduce a conditional graphical model for multivariate random functions, where we treat the external variables as conditioning set, and allow the graph structure to vary with the external variables. Our method is built on two new linear operators, the conditional precision operator and the conditional partial correlation operator, which extend the precision matrix and the partial correlation matrix to both the conditional and functional settings. We show that their nonzero elements can be used to characterize the conditional graphs, and develop the corresponding estimators. We establish the uniform convergence of the proposed estimators and the consistency of the estimated graph, while allowing the graph size to grow with the sample size, and accommodating both completely and partially observed data. We demonstrate the efficacy of the method through both simulations and a study of brain functional connectivity network.

Some further classes of pseudo-differential operators in the p-adic context and their applications.

The purpose of this article is to study new non-Archimedean pseudo-differential operators whose symbols are determined from the behavior of two functions defined on the p-adic numbers. Thanks to the characteristics of our symbols, we can find connections between these operators and new types of non-homogeneous differential equations, Feller semigroups, contraction semigroups and strong Markov processes.

KCS-FCnet: Kernel Cross-Spectral Functional Connectivity Network for EEG-Based Motor Imagery Classification.

This paper uses EEG data to introduce an approach for classifying right and left-hand classes in Motor Imagery (MI) tasks. The Kernel Cross-Spectral Functional Connectivity Network (KCS-FCnet) method addresses these limitations by providing richer spatial-temporal-spectral feature maps, a simpler architecture, and a more interpretable approach for EEG-driven MI discrimination. In particular, KCS-FCnet uses a single 1D-convolutional-based neural network to extract temporal-frequency features from raw EEG data and a cross-spectral Gaussian kernel connectivity layer to model channel functional relationships. As a result, the functional connectivity feature map reduces the number of parameters, improving interpretability by extracting meaningful patterns related to MI tasks. These patterns can be adapted to the subject's unique characteristics. The validation results prove that introducing KCS-FCnet shallow architecture is a promising approach for EEG-based MI classification with the potential for real-world use in brain-computer interface systems.

Ultrasound-Assisted Extraction of Mango (Mangifera indica) Kernel Starch: Chemical, Techno-Functional, and Pasting Properties.

(1) Background: Starch is the main component of mango (Mangifera indica) kernel, making it an alternative to obtain an ingredient from a non-conventional source with potential application in food and other industrial applications; however, reports on the use of new extraction techniques for this material are scarce. The main objective of this research was to evaluate the effect of ultrasound-assisted extraction (UAE) on the yield, chemical, techno-functional, rheological, and pasting properties of starch isolated from a non-conventional source such as a mango kernel. (2) Methods: Different power sonication conditions (120, 300, and 480 W) and sonication time (10, 20, and 30 min) were evaluated along with a control treatment (extracted by the wet milling method). (3) Results: Ultrasound-assisted extraction increases starch yield, with the highest values (54%) at 480 W and 20 min. A significant increase in the amylose content, water-holding capacity, oil-holding capacity, solubility, and swelling power of ultrasonically extracted starches was observed. Similarly, mango kernel starch (MKS) exhibited interesting antioxidant properties. The sol-gel transition temperature and pasting parameters, such as the breakdown viscosity (BD) and the setback viscosity (SB), decreased with ultrasound application; (4) Conclusion: indicating that ultrasound caused changes in physical, chemical, techno-functional, rheological, and pasting properties, depending on the power and time of sonication, so it can be used as an alternative starch extraction and modification technique, for example, for potential application in thermally processed food products such as baked goods, canned foods, and frozen foods.

Bioactive Compounds in Extracts from the Agro-Industrial Waste of Mango.

Mango by-products are important sources of bioactive compounds generated by agro-industrial process. During mango processing, 35-60% of the fruit is discarded, in many cases without treatment, generating environmental problems and economic losses. These wastes are constituted by peels and seeds (tegument and kernel). The aim of this review was to describe the extraction, identification, and quantification of bioactive compounds, as well as their potential applications, published in the last ten years. The main bioactive compounds in mango by-products are polyphenols and carotenoids, among others. Polyphenols are known for their high antioxidant and antimicrobial activities. Carotenoids show provitamin A and antioxidant activity. Among the mango by-products, the kernel has been studied more than tegument and peels because of the proportion and composition. The kernel represents 45-85% of the seed. The main bioactive components reported for the kernel are gallic, caffeic, cinnamic, tannic, and chlorogenic acids; methyl and ethyl gallates; mangiferin, rutin, hesperidin, and gallotannins; and penta-O-galloyl-glucoside and rhamnetin-3-[6-2-butenoil-hexoside]. Meanwhile, gallic acid, ferulic acid, and catechin are reported for mango peel. Although most of the reports are at the laboratory level, they include potential applications in the fields of food, active packaging, oil and fat, and pharmaceutics. At the market level, two trends will stimulate the industrial production of bioactive compounds from mango by-products: the increasing demand for industrialized fruit products (that will increase the by-products) and the increase in the consumption of bioactive ingredients.

Porosity determination of Vienna sausages through digital images analysis.

BACKGROUND: This research aimed to determine the porosity and particle size distribution in canned Vienna-type sausages using digital image analysis (DIA) on photographs captured with a digital camera and applying a Monte Carlo simulation. The methodology determined morphometric parameters (area and Feret diameter) by DIA of transverse and longitudinal sections of canned sausages. Those images were previously contrast enhanced, color threshold adjusted, and binarized. Subsequently, the estimation of the pore volume was carried out from the inverse Gaussian distributions of Feret diameter and area, as well as the porosity, using Monte Carlo simulation. RESULTS: The pores had an average Feret diameter of 0.335 mm and an average area of 0.085 mm2 . The highest estimated bivariate kernel density was presented for the smallest pores (around 0.02 mm2 in area and 0.25 mm in diameter). Simulation average values of pore volume, assumed as a cylinder, and porosity were 1.455 mm3 and 0.737 respectively. The average porosity value was consistent with the value experimentally estimated by the indirect method, in concordance with the definition of porosity, which was 0.715, presenting a mean relative percentage error of 3.08% concerning the estimated experimental value as well. CONCLUSION: This research presents interesting perspectives for the quantitative analysis of the microstructure of food and biological materials through a novel, low-cost, reliable, and fast proposal. Moreover, this is the first study to report the porosity determination in canned sausages by DIA. © 2022 Society of Chemical Industry.