Search Results (155)

As a high-value product used as food, medicine, or cosmetics, honey is particularly susceptible to adulteration. Therefore, it must be regularly tested at various stages of its life cycle to ensure its quality and authenticity, especially its botanical origin. Sensory quality features play a huge role in creating the quality of products, but also in determining their authenticity. Sensory analysis helps determine the honey’s overall quality based on attributes like color, aroma, taste, and texture. Sensory evaluation of honey can reveal issues like crystallization, off-flavors, or off-odors that might indicate adulteration or spoilage. The aim of our work was therefore sensory quality assessment of 84 honey samples in order to create sensory profiles for the varietal classification of honeys. In order to obtain information on the differences in sensory features and their classification based on the assessment of honey quality descriptors, a discriminant analysis was carried out. Then, an assessment was carried out to check whether the compared varieties differ in terms of the value of the sensory feature parameter assessment. As a result, a statistical tool was constructed (canonical discriminant functions, distinguishing/classifying the varieties of honeys tested). These models will ensure the repeatability of results in the classification of sensory profiles of varietal honeys on the example of Polish honey varieties. The results indicate that the sensory analysis is a good analytical tool to differentiate honey types. The findings of this study can be applied by honey producers, suppliers, and customers to differentiate and determine honey varieties according to their sensorial attributes. Full article

Machine vision/image analysis is used in the sorting and handling of many aquatic species. Pictures of 474 New Zealand Greenshell™ (Perna canaliculus, Gmelin, 1791) whole unopened mussels (215 females and 259 males) from the top and from the side were analyzed to evaluate if visual attributes (size, shape, and color) can be used to differentiate gender. Size (length, width, height, and view area), color, and shape (by elliptic Fourier analysis and by ray length-ray angle analysis) were analyzed and differences by gender tested. Application of Artificial Neural Networks (ANN), Principal Component Analysis (PCA), Canonical Discriminant Analysis (CDA), and Random Forest (RF) to the shape parameters failed to reliably predict gender. Comprehensive morphometric and color characterization of males and females, as well as shape parameters, are presented as a reference for future image-based research. The parasitic crustacean pea crab can change the shape of mussel shells, and elliptic Fourier analysis can quantify this difference. Full article

The Andalusian Black Cattle (Negra Andaluza) represents a genetic lineage linked to the ancient Eurasian aurochs, shaped by domestication events in the Near East and later introgressions from Italian and North African wild cattle. This study investigates the breed’s anthropological and historical origins, geographical distribution, and genetic structure. Key influences include historical use as draft animals, regional breeding preferences, and gene flow via transhumant routes. The genetic analysis reveals that Córdoba is the principal nucleus, accounting for 448 identified ancestors, compared to 252 in Huelva and 193 in Seville. In Córdoba, contributions of nodal ancestors through inbreeding loops reached a maximum of 0.0447, while mean inbreeding ($\overline{\mathrm{F}}$) was 0.000949 and mean coancestry ($\overline{\mathrm{C}}$) was 0.000475, indicating moderate but geographically structured genetic drift. In contrast, areas with better connectivity showed higher heterogeneity and lower inbreeding contributions. Canonical discriminant analysis (CDA) revealed that the first discriminant function (F1) explained 79.72% of the variation among groups, primarily driven by nodal ancestors and inbreeding loops. Despite these signs of inbreeding, historical transhumance has helped preserve overall genetic diversity. These findings offer essential insights for conservation programs aimed at maintaining both the genetic integrity and adaptive potential of this historically and culturally important breed. Full article

Academic performance is a factor of concern and interest in the educational context for the improvement of the educational and economic system of any country. Determining the factors influencing it has been the subject of multiple investigations. This study focused on analysing which dimensions of school resilience could act as determinants of academic performance in a sample of 609 Spanish secondary education students, aged between 11 and 17 years. The School Resilience Scale (SRS) was used as a data collection instrument. The data were analysed using analysis of variance and discriminant analysis based on a canonical function model, which suggested the existence of a direct and significant relationship between academic performance and all dimensions of resilience, with somewhat larger effect sizes for the Internal Resources and Identity–Self-Esteem dimensions, which allowed us to classify students with particularly high levels of performance. The results also show that the school year was significantly associated with academic performance, with the highest percentages of students at the highest level observed in the 2nd and 3rd years. Full article

Agriculture plays a dual role in shaping biodiversity, providing secondary habitats while posing significant threats to ecological systems through habitat fragmentation and land-use intensification. This study aims to assess the relationship between bird species composition and land-use types in Apulia, Italy. Specifically, we investigate how different agricultural and semi-natural landscapes influence avian biodiversity and which agricultural models can have a positive impact on biodiversity. Biodiversity indices were calculated for each bird community observed. The abundance curves showed a geometric series pattern for the AGR communities, indicative of ecosystems at an early stage of ecological succession, and a lognormal distribution for the MIX and NAT communities, typical of mature communities with a more even distribution of species. Analysis of variance showed significant differences in richness and diversity between AGR and NAT sites, but not between NAT and MIX, which had the highest values. Logistic regression estimated the probability of sites belonging to the three ecosystem categories as a function of biodiversity, confirming a strong similarity between NAT and MIX. Finally, linear discriminant analysis confirmed a clear separation from AGR areas, as evidenced by the canonical components. The results highlight the importance of integrating high-diversity landscape elements and appropriate agricultural practices to mitigate biodiversity loss. Even a small increase in the naturalness of agricultural land would be sufficient to convert it from the AGR to the MIX ecosystem category, with significant biodiversity benefits. Full article

Degenerative changes are characterized by the formation of vertebral osteophytes, the hypertrophy of facet joints, and narrowing of the intervertebral space. This study aimed to investigate the concentrations of trace elements (Al, As, Se, Zn, Fe, Mo, Cu) in spinal tissues (intervertebral discs, muscle, and bone) of patients with degenerative lumbar spine disease (DLSD) and their potential associations with the disease. The research involved 13 patients undergoing surgery for symptomatic degenerative spine disease. The trace element concentrations were analyzed using chemical and radiographic assessments, with a statistical analysis performed through a Mann–Whitney U-test, Spearman’s rank correlation test, principal component analysis (PCA), and canonical discriminant analysis (CDA). The results showed significant variations and correlations among the trace elements across different spinal tissues, suggesting their roles in metabolic and oxidative processes and the pathology of spinal degeneration. Full article

This study evaluates the impact of Rhizobium tropici reinoculation and Azospirillum brasilense co-inoculation at different growth stages on the physiological quality of common bean seeds. A randomized block design was used, assessing germination, vigor, electrical conductivity, seedling length, and dry mass. Treatments T7 (co-inoculation R. tropici + A. brasilense at R5) showed the highest germination rates, indicating enhanced seed viability. The accelerated aging test revealed that T7 exhibited greater resistance to stress, presenting greater seedling vigor, whereas T10 and T11 were more susceptible. The electrical conductivity results remained stable across treatments, suggesting that cell membrane integrity was not significantly compromised. Seedling length and dry mass did not present significant variations, reinforcing the idea that early germination and vigor are primary indicators of seed quality. Canonical discriminant analysis and MANOVA confirmed significant treatment differences, highlighting the influence of inoculation strategies on seed physiology. Overall, co-inoculation with Rhizobium tropici and Azospirillum brasilense (particularly in T7) demonstrated potential to improve seed quality at lower cost, offering sustainable alternatives for optimizing agricultural production. Full article

Capsicum annuum L. var. glabriusculum is a semi-domesticated species of economic importance; however, its establishment in commercial plantations has been hampered by the low germination and emergence rates of its seeds. The aim of this study was to evaluate the effect of the fruit ripening stage on seed germination and seedling emergence in C. annuum var. glabriusculum. Seeds were extracted from fruits with six different ripening stages. The evaluated traits were the germination and emergence percentages, germination and emergence rates, and 17 physical traits of the seeds. According to the results, seeds extracted from red, orange, and pinto fruits presented better germination and seedling emergence percentages (85, 86, and 82% and 95, 93, and 94%, respectively). A principal component analysis showed that some differences in the physical traits of the seed were associated with the fruit ripening stages and seed development. A canonical discriminant analysis showed a high correlation between the fruit ripening stages and the physical and physiological characteristics of the seed, allowing the formation of four groups. The fruit ripening stages (pinto, orange, and red) influence the germination of the seeds and the emergence of the seedlings of C. annuum L. var. glabriusculum, so obtaining seeds from physiologically ripe fruits allows for obtaining seeds of better quality. Full article

For the evaluation of food efficacy, in vitro experiments and cell and animal models are heavily relied on, with a need for quick and non-invasive monitoring methods. In this study, the fecal odor of aging mice supplemented with goat whey powder was obtained by an E-nose, and the correlation between odor information and the antioxidant indexes, serum antibody, cytokine, and intestinal bacteria were analyzed, aiming to establish a non-invasive method for monitoring and differentiating the effect of goat whey powder. As a result, the fecal odor differed with intervention groups and intervention time, and most of the sensor responses were significantly correlated with weight gain rate, SOD activity, and MDA content. For serum antibodies, cytokines, IL-2, and IL-6 were negatively correlated with the responses of sensor S7. A strong correlation was found between the E-nose sensor responses and the dominant intestinal bacteria. The E-nose could differentiate aging mice of different intervention times and intervention groups with canonical discriminate analysis (CDA). The effective predictive model was built by multiple linear regression (MLR) and a multilayer perceptron neural network (MLP) for SOD, MDA, and weight gain rate, with R² ranging from 0.1571 to 0.6361. These results indicated that E-nose technology could be used in the tracking of goat whey powder intervention in aging mice. Full article

Epipactis helleborine (L.) Crantz is considered a challenging and phenotypically difficult species to identify due to its wide range of morphological variability. This variability is mainly observed in the perianth parts but also extends to the gynostemium structure, which has so far been considered one of the most useful diagnostic characteristics. As a result, a simple graphic illustrating the structural pattern of gynostemium morphology has appeared in 10 different forms in available European taxonomic keys, which significantly complicates the identification of this species. A total of 122 flowers of E. helleborine were collected from four natural populations in the Lower Silesia region (Poland) between 2017 and 2019 and analysed for gynostemium morphological variation. Geometric morphometric analyses, including Procrustes ANOVA, PCA, and CVA, were used to examine gynostemium shape, with statistical tests assessing variation in size and stigma inclination angle among populations, individual plants (ramets), and years of research. Statistical analysis revealed significant positive correlations between gynostemium width and height, with significant variation in size and angle of stigma inclination, primarily driven by population, while ramet and year of research had a lesser impact. Geometric morphometric analyses indicated significant population-level variation in gynostemium shape, with principal component analysis identifying the ventral view as the most informative for discriminating these differences. The first two principal components explained the major shape variation, and canonical variate analysis confirmed that this view is most important for species identification. Full article

Objectives: This scoping review aimed to provide both researchers and practitioners with an overview of how machine learning (ML) methods are applied to infrared spectroscopy for the diagnosis and prognosis of head and neck precancer and cancer. Methods: A subject headings and keywords search was conducted in MEDLINE, Embase, and Scopus on 14 January 2024, using predefined search algorithms targeting studies that integrated infrared spectroscopy and ML methods in head and neck precancer/cancer research. The results were managed through the COVIDENCE systematic review platform. Results: Fourteen studies met the eligibility criteria, which were defined by IR spectroscopy techniques, ML methodology, and a focus on head and neck precancer/cancer research involving human subjects. The IR spectroscopy techniques used in these studies included Fourier transform infrared (FTIR) spectroscopy and imaging, attenuated total reflection-FTIR, near-infrared spectroscopy, and synchrotron-based infrared microspectroscopy. The investigated human biospecimens included tissues, exfoliated cells, saliva, plasma, and urine samples. ML methods applied in the studies included linear discriminant analysis (LDA), principal component analysis with LDA, partial least squares discriminant analysis, orthogonal partial least squares discriminant analysis, support vector machine, extreme gradient boosting, canonical variate analysis, and deep reinforcement neural network. For oral cancer diagnosis applications, the highest sensitivity and specificity were reported to be 100%, the highest accuracy was reported to be 95–96%, and the highest area under the curve score was reported to be 0.99. For oral precancer prognosis applications, the highest sensitivity and specificity were reported to be 84% and 79%, respectively. Conclusions: This review highlights the promising potential of integrating infrared spectroscopy with ML methods for diagnosing and prognosticating head and neck precancer and cancer. However, the limited sample sizes in existing studies restrict generalizability of the study findings. Future research should prioritize larger datasets and the development of advanced ML models to enhance reliability and robustness of these tools. Full article

Brain–computer interfaces (BCIs) enable people to communicate with others or devices, and improving BCI performance is essential for developing real-life applications. In this study, a steady-state visual evoked potential-based BCI (SSVEP-based BCI) with multi-domain features and multi-task learning is developed. To accurately represent the characteristics of an SSVEP signal, SSVEP signals in the time and frequency domains are selected as multi-domain features. Convolutional neural networks are separately used for time and frequency domain signals to extract the embedding features effectively. An element-wise addition operation and batch normalization are applied to fuse the time- and frequency-domain features. A sequence of convolutional neural networks is then adopted to find discriminative embedding features for classification. Finally, multi-task learning-based neural networks are used to detect the corresponding stimuli correctly. The experimental results showed that the proposed approach outperforms EEGNet, multi-task learning-based neural networks, canonical correlation analysis (CCA), and filter bank CCA (FBCCA). Additionally, the proposed approach is more suitable for developing real-time BCIs than a system where an input’s duration is 4 s. In the future, utilizing multi-task learning to learn the properties of the embedding features extracted from FBCCA can further improve the BCI system performance. Full article

The automated and precise classification of lung and colon cancer from histopathological photos continues to pose a significant challenge in medical diagnosis, as current computer-aided diagnosis (CAD) systems are frequently constrained by their dependence on singular deep learning architectures, elevated computational complexity, and their ineffectiveness in utilising multiscale features. To this end, the present research introduces a CAD system that integrates several lightweight convolutional neural networks (CNNs) with dual-layer feature extraction and feature selection to overcome the aforementioned constraints. Initially, it extracts deep attributes from two separate layers (pooling and fully connected) of three pre-trained CNNs (MobileNet, ResNet-18, and EfficientNetB0). Second, the system uses the benefits of canonical correlation analysis for dimensionality reduction in pooling layer attributes to reduce complexity. In addition, it integrates the dual-layer features to encapsulate both high- and low-level representations. Finally, to benefit from multiple deep network architectures while reducing classification complexity, the proposed CAD merges dual deep layer variables of the three CNNs and then applies the analysis of variance (ANOVA) and Chi-Squared for the selection of the most discriminative features from the integrated CNN architectures. The CAD is assessed on the LC25000 dataset leveraging eight distinct classifiers, encompassing various Support Vector Machine (SVM) variants, Decision Trees, Linear Discriminant Analysis, and k-nearest neighbours. The experimental results exhibited outstanding performance, attaining 99.8% classification accuracy with cubic SVM classifiers employing merely 50 ANOVA-selected features, exceeding the performance of individual CNNs while markedly diminishing computational complexity. The framework’s capacity to sustain exceptional accuracy with a limited feature set renders it especially advantageous for clinical applications where diagnostic precision and efficiency are critical. These findings confirm the efficacy of the multi-CNN, multi-layer methodology in enhancing cancer classification precision while mitigating the computational constraints of current systems. Full article

Polyploidy is a powerful mechanism driving genetic, physiological, and phenotypic changes among cytotypes of the same species across both large and small geographic scales. These changes can significantly shape population structure and increase the evolutionary and adaptation potential of cytotypes. Alyssum moellendorfianum, an edaphic steno-endemic species with a narrow distribution in the Balkan Peninsula, serves as an intriguing case study. We conducted a comprehensive analysis of genetic diversity and population structure across the species’ range, employing an array of genetic techniques (nuclear microsatellites, amplified fragment length polymorphisms, and plastid DNA sequences), flow cytometry (FCM), morphometry, and pollen analysis. The study reveals two genetic lineages: spatially distributed diploid and tetraploid cytotypes. Clear divergence between diploids and tetraploids was shown by AFLP, while plastid DNA sequences confirmed private haplotypes in each of the studied populations. Higher genetic diversity and allelic richness following the north-south pattern were documented in tetraploids compared to diploids, as indicated by nuclear microsatellites. Morphometric analysis via principal component analysis (PCA) and canonical discriminant analysis (CDA) did not reveal any divergence between diploid and tetraploid cytotypes. Nonetheless, a distinction in pollen size was clearly observed. The results suggest an autopolyploid origin of tetraploids from diploid ancestors. Despite the population fragmentation in a very small geographic range, these populations harbour high genetic diversity, which would allow them to remain stable if natural processes remain undisturbed. Full article

The general increase in awareness of environmental pollutants and typical sources reflects the application of sustainability and development goals. Energy-Dispersive X-Ray Fluorescence spectroscopy analysis has been used to analyse sand samples collected from five different beaches located on the east and north-eastern coasts of Malta and Gozo during two summers and two winters. Samples were collected along linear transects perpendicular to the shoreline at three different depths. Chemometrics were used to discriminate between four latent variables, including season, location, depth, and distance from shoreline. The highest concentrations were attributed to Fe₂O₃, Al₂O₃, SrO, and SnO₂. Principal Components Analysis and Factor Analysis classified distributions of Fe₂O₃, CoO, As₂O₃, MnO, SrO, SeO₂, and CaCO₃ under Principal Component 1. However, since no loading value dominance was observed, such distributions most likely represent a combination of lithogenic and anthropogenic natures. Discrimination using Stepwise Linear Canonical Discriminant Analysis (SLC-DA) and Partial Least Squares Discriminant Analysis (PLS-DA) using Leave-One-Out-Cross-Validation with Variance Importance Plots proved highly effective in classifying data by location, followed by seasonal variability. It follows that concentrations are not affected by depth and distance from shoreline variability, proving that accumulation and anthropogenic effects from land are not concentrated in specific zones but are spatially spread out along the bays and do not increase with depth. Full article