Search Results (61)

The aim of this study was to compare the types, textile structures, labels, and fiber compositions of 64 red garments submitted as evidence in selected criminal cases between 2022 and 2024. The research enhanced the current knowledge of the characteristics of red clothing available to consumers and demonstrated the relevance of textile analysis in forensic science. Knitted fabrics were the most commonly used in the garments, followed by woven fabrics, nonwovens, and felts. Fiber identification focused on color and shade, generic classification, morphological structure, and chemical composition, revealing both similarities and distinctions among the samples. In a small percentage of cases, label information was found to be inaccurate. The study also examined the fiber content of threads, patches, logos, prints, and embroidery, underscoring the forensic potential of these often-overlooked elements. The identification of over 300 individual fibers enabled a critical evaluation of the analytical procedures and confirmed their effectiveness in forensic contexts. Full article

This study aims to develop a system combining fluorescence spectroscopy and machine learning through a convolutional neural network (CNN) to identify the origins of various Japanese green teas (Sayama tea, Kakegawa tea, Yame tea, and Chiran tea). Although food origin labeling is important for ensuring consumer quality and safety, ac-curate identification remains a priority for the food industry due to the emergence of problems with false origin labeling. In this study, image data of the fluorescent fingerprints of green teas were collected using fluorescence spectroscopy and analyzed using a CNN model implemented in Python (ver. 3.13.2), TensorFlow (ver. 2.18.0), and Keras (ver. 3.9). The fluorescence of each sample was measured in the range of 250 to 550 nm, highlighting the differences in chemical composition that reflect each region. Using these data, a CNN suitable for image recognition successfully identified the origins of the teas with an average accuracy of 92.83% in 10 trials. For Chiran tea and Yame tea, precision and recall rates of over 95% were achieved, showing clear differences from other regions. In contrast, the classification of Kakegawa and Sayama teas proved challenging due to their similar fluorescence patterns in the 300–350 nm spectral range, corresponding to catechins and polyphenolic compounds. These similarities are presumed to reflect the comparable growing conditions and processing methods characteristic of the two regions. This study shows the potential of this system in food origin identification, suggesting applications in preventing origin fraud and quality control. Future research will aim to extend the system to other regions and foods, enhance data preprocessing to improve accuracy, and develop a versatile identification system. Full article

Raman spectroscopy enables fast, label-free, qualitative, and quantitative observation of the physical and chemical properties of various substances. Here, we present a 785 nm custom-built Raman spectroscopy instrument designed for sensing applications in the 400–1700 cm⁻¹ spectral range. We demonstrate the performance of the instrument by fingerprinting 14 pesticide reference samples with over twenty technical repeats per sample. We present molecular Raman fingerprints of the pesticides comprehensively and distinguish similarities and differences among them using multivariate analysis and machine learning techniques. The same pesticides were additionally investigated using a commercial 532 nm Raman instrument to see the potential variations in peak shifts and intensities. We developed a unique Raman fingerprint library for 14 reference pesticides, which is comprehensively documented in this study for the first time. The comparison shows the importance of selecting an appropriate excitation wavelength based on the target analyte. While 532 nm may be advantageous for certain compounds due to resonance enhancement, 785 nm is generally more effective for reducing fluorescence and achieving clearer Raman spectra. By employing machine learning techniques like the Random Forest Classifier, the study automates the classification of 14 different pesticides, streamlining data interpretation for non-experts. Applying such combined techniques to a wider range of agricultural chemicals, clinical biomarkers, or pollutants could provide an impetus to develop monitoring technologies in food safety, diagnostics, and cross-industry quality control applications. Full article

The formation of agate is related to syngenetic or epigenetic magmatic hydrothermal fluids. Trace elements incorporated during the alteration of host rocks caused by hydrothermal magmatic fluids precipitate during their crystallization, reflecting the characteristics of the host rocks. While prior research has yielded extensive trace element data to differentiate agate types found in volcanic rocks, there remains a need for a more efficient method to identify the host volcanic rock of alluvial agate. In this study, a two-stage Random Forest approach was employed to classify the chemical compositions of agate originating from rhyolite, andesite, and basalt, with the aim of facilitating the determination of the host volcanic rock for unknown alluvial agate samples. A dataset comprising 203 agate compositional analyses, sourced from 16 distinct locations, was compiled and labeled for the purpose of training the Random Forest model. The classification results indicate that the developed models exhibit high accuracy (0.9524) and an F1 score of 0.9512, demonstrating their superior performance and efficiency. The feature importance analysis of these models reveals that U, Sb, and Sr contents are particularly crucial for discriminating between different types of volcanic rocks hosting agate. Furthermore, this study introduces a novel discriminant plot utilizing linear discriminant analysis (LDA) to evaluate the host volcanic rock of agate based on trace element data. Verifying the trace element data of agate samples related to basalt based on actual measurements shows that both the Random Forest (with accuracy of 0.813) and LDA plot underscore the effectiveness of using the trace elements found in alluvial agate for the identification of the host volcanic rock. Full article

In recent decades, the global use of ashes derived from agro-industrial by-products, such as oil palm kernel shells, which are widely cultivated in Colombia and other tropical regions of the world, has increased. However, the application of these ashes in engineering remains limited due to their heterogeneity and variability. This study utilized scanning electron microscopy (SEM) to assess the influence of calcination temperatures, ranging from 500 °C to 1000 °C, as well as the physical processes of cutting, grinding, and crushing, on the silica content of the studied ashes. Specifically, the sample labeled M18A-c-m-T600°C-t1.5h-tr1h, which was subjected to a calcination temperature of 600 °C and underwent cutting and grinding before calcination, followed by post-calcination crushing, exhibited the highest silica concentration. Complementary techniques such as X-ray fluorescence (XRF) and X-ray diffraction (XRD), were applied to this sample to evaluate its feasibility as an additive or partial replacement for cement in concrete. XRF analysis revealed a composition of 71.24% SiO₂, 9.39% Al₂O₃, and 2.65% Fe₂O₃, thus, meeting the minimum oxide content established by ASTM C 618 for the classification as a pozzolanic material. Furthermore, XRD analysis confirmed that the sample M18A-c-m-T600°C-t1.5h-tr1h is in an amorphous state, which is the only state in which silica can chemically react with calcium hydroxide resulting from the hydration reactions of cement, forming stable cementitious products with strong mechanical properties. Full article

Antifungal substances are essential for managing fungal infections in humans, animals, and plants, and their usage has significantly increased due to the global rise in fungal infections. However, the extensive application of antifungal agents in pharmaceuticals, personal care products, and agriculture has led to their widespread environmental dissemination through various pathways, such as excretion, improper disposal, and agricultural runoff. Despite advances in wastewater treatment, many antifungal compounds persist in the environment, affecting non-target organisms and contributing to resistance development. This study investigates the environmental impact of two novel antifungal agents, VT-1161 and T-2307, recently introduced as alternatives for treating resistant Candida spp. We assessed their ecotoxicity and mutagenicity using multiple bioassays: immobilization of Daphnia magna, growth inhibition of Raphidocelis subcapitata, luminescence inhibition of Aliivibrio fischeri, and mutagenicity on Salmonella typhimurium strain TA100. Results indicate that both VT-1161 and T-2307 exhibit lower toxicity compared to existing antifungal compounds, with effective concentrations (EC₅₀) causing 50% response ranging from 14.34 to 27.92 mg L⁻¹. Furthermore, both agents were classified as less hazardous based on the Globally Harmonized System of Classification and Labeling of Chemicals. Despite these favorable results, further research is needed to understand their environmental behavior, interactions, and potential resistance development among non-target species. Our findings highlight the importance of comprehensive environmental risk assessments to ensure the sustainable use of new antifungal agents. Full article

In a previous study, a novel human corneal-like epithelium model utilizing an immortalized human corneal epithelial cell line (iHCE-NY1) was developed as an alternative to animal models to identify chemicals not classified under the United Nations Globally Harmonized System of Classification and Labeling of Chemicals (GHS) and was evaluated following the criteria of Test Guideline 492 of the Organization for Economic Co-operation and Development (OECD). In the present study, our aim was to establish an eye irritation test protocol using the iHCE-NY1 model to classify liquid chemicals under the GHS ocular hazard categories: no effect, no classification (No Cat.), Category 2 (Cat. 2) reversible effects, and Category 1 (Cat. 1) irreversible eye damage. The protocol involved exposing the iHCE-NY1 model to 31 liquid test chemicals for 5 min, followed by observation at post-incubation periods (PIPs) to assess recovery. Classification was based on cell viability, and histopathological findings on PIP days 7, 14, and 21. The outcomes were compared with an established database of classifications. All Cat. 1 liquid chemicals, 62.5% of No Cat., and 63.2% of Cat. 2 were correctly categorized. This study demonstrates that the iHCE-NY1 model can not only distinguish No Cat. test liquid chemicals but also differentiate between Cat. 2 and Cat. 1 liquid chemicals. Full article

Oral potentially malignant disorders (OPMDs) are precursors to over 80% of oral cancers. Hematoxylin and eosin (H&E) staining, followed by pathologist interpretation of tissue and cellular morphology, is the current gold standard for diagnosis. However, this method is qualitative, can result in errors during the multi-step diagnostic process, and results may have significant inter-observer variability. Chemical imaging (CI) offers a promising alternative, wherein label-free imaging is used to record both the morphology and the composition of tissue and artificial intelligence (AI) is used to objectively assign histologic information. Here, we employ quantum cascade laser (QCL)-based discrete frequency infrared (DFIR) chemical imaging to record data from oral tissues. In this proof-of-concept study, we focused on achieving tissue segmentation into three classes (connective tissue, dysplastic epithelium, and normal epithelium) using a convolutional neural network (CNN) applied to three bands of label-free DFIR data with paired darkfield visible imaging. Using pathologist-annotated H&E images as the ground truth, we demonstrate results that are 94.5% accurate with the ground truth using combined information from IR and darkfield microscopy in a deep learning framework. This chemical-imaging-based workflow for OPMD classification has the potential to enhance the efficiency and accuracy of clinical oral precancer diagnosis. Full article

Decades of drug development research have explored a vast chemical space for highly active compounds. The exponential growth of virtual libraries enables easy access to billions of synthesizable molecules. Computational modeling, particularly molecular docking, utilizes physics-based calculations to prioritize molecules for synthesis and testing. Nevertheless, the molecular docking process often yields docking poses with favorable scores that prove to be inaccurate with experimental testing. To address these issues, several approaches using machine learning (ML) have been proposed to filter incorrect poses based on the crystal structures. However, most of the methods are limited by the availability of structure data. Here, we propose a new pose classification approach, PECAN2 (Pose Classification with 3D Atomic Network 2), without the need for crystal structures, based on a 3D atomic neural network with Point Cloud Network (PCN). The new approach uses the correlation between docking scores and experimental data to assign labels, instead of relying on the crystal structures. We validate the proposed classifier on multiple datasets including human mu, delta, and kappa opioid receptors and SARS-CoV-2 Mpro. Our results demonstrate that leveraging the correlation between docking scores and experimental data alone enhances molecular docking performance by filtering out false positives and false negatives. Full article

In the European Union, Occupational Safety and Health legislation generally refers to European Regulation (CE) n. 1272/2008 to define and classify carcinogens of concern for occupational risk assessment and exposure assessment. In Europe, the current reference is Directive (UE) 2022/431, regarding carcinogen, mutagen, and reprotoxic agent (CMR) exposure. However, at the worldwide level, different classification approaches are used to establish carcinogenicity of substances and it is often difficult to compare the classifications of carcinogenicity (CoCs) proposed by different international bodies. This study aims to investigate a list of carcinogens of concern in occupational settings based on the CLP (Classification Labelling Packaging) CoC and to create a tool that allows a rapid translation–comparison of some international CoCs with the reference one. CoCs proposed by various sources were consulted and used to apply a translation method, to favor an alignment of different CoCs according to a reference. Results outlined that, considering diverse sources, CoCs can result in different classifications of the same chemicals. Overall, this may have implications for the hazard assessment process, which is the base of risk assessment. The proposed tool is expected to help risk assessors in the occupational field when it is needed to have a comparison with different CoC systems. Full article

During the growing season, olives progress through nine different phenological stages, starting with bud development and ending with senescence. During their lifespan, olives undergo changes in their external color and chemical properties. To tackle these properties, we used hyperspectral imaging during the growing season of the olives. The objective of this study was to develop a lightweight model capable of identifying olives in the hyperspectral images using their spectral information. To achieve this goal, we utilized the hyperspectral imaging of olives while they were still on the tree and conducted this process throughout the entire growing season directly in the field without artificial light sources. The images were taken on-site every week from 9:00 to 11:00 a.m. UTC to avoid light saturation and glitters. The data were analyzed using training and testing classifiers, including Decision Tree, Logistic Regression, Random Forest, and Support Vector Machine on labeled datasets. The Logistic Regression model showed the best balance between classification success rate, size, and inference time, achieving a 98% F1-score with less than 1 KB in parameters. A reduction in size was achieved by analyzing the wavelengths that were critical in the decision making, reducing the dimensionality of the hypercube. So, with this novel model, olives in a hyperspectral image can be identified during the season, providing data to enhance a farmer’s decision-making process through further automatic applications. Full article

Patients with multiple sclerosis (MS) often take multiple drugs at the same time to modify the course of disease, alleviate neurological symptoms and manage co-existing conditions. A major consequence for a patient taking different medications is a higher risk of treatment failure and side effects. This is because a drug may alter the pharmacokinetic and/or pharmacodynamic properties of another drug, which is referred to as drug-drug interaction (DDI). We aimed to predict interactions of drugs that are used by patients with MS based on a deep neural network (DNN) using structural information as input. We further aimed to identify potential drug-food interactions (DFIs), which can affect drug efficacy and patient safety as well. We used DeepDDI, a multi-label classification model of specific DDI types, to predict changes in pharmacological effects and/or the risk of adverse drug events when two or more drugs are taken together. The original model with ~34 million trainable parameters was updated using >1 million DDIs recorded in the DrugBank database. Structure data of food components were obtained from the FooDB database. The medication plans of patients with MS (n = 627) were then searched for pairwise interactions between drug and food compounds. The updated DeepDDI model achieved accuracies of 92.2% and 92.1% on the validation and testing sets, respectively. The patients with MS used 312 different small molecule drugs as prescription or over-the-counter medications. In the medication plans, we identified 3748 DDIs in DrugBank and 13,365 DDIs using DeepDDI. At least one DDI was found for most patients (n = 509 or 81.2% based on the DNN model). The predictions revealed that many patients would be at increased risk of bleeding and bradycardic complications due to a potential DDI if they were to start a disease-modifying therapy with cladribine (n = 242 or 38.6%) and fingolimod (n = 279 or 44.5%), respectively. We also obtained numerous potential interactions for Bruton’s tyrosine kinase inhibitors that are in clinical development for MS, such as evobrutinib (n = 434 DDIs). Food sources most often related to DFIs were corn (n = 5456 DFIs) and cow’s milk (n = 4243 DFIs). We demonstrate that deep learning techniques can exploit chemical structure similarity to accurately predict DDIs and DFIs in patients with MS. Our study specifies drug pairs that potentially interact, suggests mechanisms causing adverse drug effects, informs about whether interacting drugs can be replaced with alternative drugs to avoid critical DDIs and provides dietary recommendations for MS patients who are taking certain drugs. Full article

This review provides a comprehensive analysis of the production, classification, and quality control of cachaça, a traditional Brazilian sugarcane spirit with significant cultural importance. It explores the fermentation and distillation of sugarcane juice, the ageing process in wooden containers, and the regulatory aspects of cachaça labelling. It emphasises the role of quality control in maintaining the spirit’s integrity, focusing on monitoring copper levels in distillation stills. Ethyl carbamate (EC), a potential carcinogen found in cachaça, is investigated, with the study illuminating factors influencing its formation and prevalence and the importance of its vigilant monitoring for ensuring safety and quality. It also underscores the control of multiple parameters in producing high-quality cachaça, including raw material selection, yeast strains, acidity, and contaminants. Further, the impact of ageing, wood cask type, and yeast strains on cachaça quality is examined, along with potential uses of vinasse, a cachaça by-product, in yeast cell biomass production and fertigation. A deeper understanding of the (bio)chemical and microbiological reactions involved in cachaça production is essential to facilitate quality control and standardisation of sensory descriptors, promoting global acceptance of cachaça. Continued research will address safety concerns, improve quality, and support the long-term sustainability and success of the cachaça industry. Full article

In this study, we introduce semi-supervised machine learning models designed to predict molecular properties. Our model employs a two-stage approach, involving pre-training and fine-tuning. Particularly, our model leverages a substantial amount of labeled and unlabeled data consisting of SMILES strings, a text representation system for molecules. During the pre-training stage, our model capitalizes on the Masked Language Model, which is widely used in natural language processing, for learning molecular chemical space representations. During the fine-tuning stage, our model is trained on a smaller labeled dataset to tackle specific downstream tasks, such as classification or regression. Preliminary results indicate that our model demonstrates comparable performance to state-of-the-art models on the chosen downstream tasks from MoleculeNet. Additionally, to reduce the computational overhead, we propose a new approach taking advantage of 3D compound structures for calculating the attention score used in the end-to-end transformer model to predict anti-malaria drug candidates. The results show that using the proposed attention score, our end-to-end model is able to have comparable performance with pre-trained models. Full article

In the era of the first Industrial Revolution, many buildings were built with red bricks, and the heritage buildings built at that time are more than 100 years old. In these old heritage buildings, damage is bound to occur due to chemical and physical effects. Technologies such as automatic damage detection can effectively manage damage, but they can be affected by other categories present in heritage buildings. Therefore, this paper proposes a CNN algorithm that can automatically detect cracks and damage that occur in heritage buildings, as well as multi-label classification, such as doors, windows, arches, artwork, brick walls, stonewalls, and vents. A total of 2400 thermal infrared images are collected for 8 categories and automatic classification was performed using the CNN algorithm. The average precision and average sensitivity for the eight categories of heritage buildings are 97.72% and 97.43%, respectively. This paper defines the causes of misclassification as the following two causes: misclassification by multiple objects and misclassification by the perception of the CNN algorithm. Full article