Nanosensor Based on Thermal Gradient and Machine Learning for the Detection of Methanol Adulteration in Alcoholic Beverages and Methanol Poisoning

Controlling strategies of methanol generation in fermented fruit wine: Pathways, advances, and applications

Methanol is widely existed in fermented fruit wines (FFWs), and the concentration is excessive at times due to inappropriate fermentation conditions. Methanol is neurotoxic, and its metabolites of formaldehyde and formic acid can cause organic lesions and central respiratory system disorders. FFWs with unspecified methanol limits are often produced with reference to grape wine standards (250/400 mg/L). To clarify the causes of methanol production in FFWs and minimize the methanol content, this study summarizes the current process methods commonly applied for methanol reduction in FFWs and proposes novel potential controlling strategies from the perspective of raw materials (pectin, pectinase, and yeast), which are mainly the low esterification modification and removal of pectin, passivation of the pectinase activity, and the gene editing of yeast to target the secretion of pectinases and modulation of the glycine metabolic pathway. The modified raw materials combined with optimized fermentation processes will hopefully be able to improve the current situation of high methanol content in FFWs. Methanol detection technologies have been outlined and combined with machine learning that will potentially guide the production of low-methanol FFWs and the setting of methanol limits for specific FFW.

A 30-Year Review on Nanocomposites: Comprehensive Bibliometric Insights into Microstructural, Electrical, and Mechanical Properties Assisted by Artificial Intelligence

From 1990 to 2024, this study presents a groundbreaking bibliometric and sentiment analysis of nanocomposite literature, distinguishing itself from existing reviews through its unique computational methodology. Developed by our research group, this novel approach systematically investigates the evolution of nanocomposites, focusing on microstructural characterization, electrical properties, and mechanical behaviors. By deploying advanced Boolean search strategies within the Scopus database, we achieve a meticulous extraction and in-depth exploration of thematic content, a methodological advancement in the field. Our analysis uniquely identifies critical trends and insights concerning nanocomposite microstructure, electrical attributes, and mechanical performance. The paper goes beyond traditional textual analytics and bibliometric evaluation, offering new interpretations of data and highlighting significant collaborative efforts and influential studies within the nanocomposite domain. Our findings uncover the evolution of research language, thematic shifts, and global contributions, providing a distinct and comprehensive view of the dynamic evolution of nanocomposite research. A critical component of this study is the "State-of-the-Art and Gaps Extracted from Results and Discussions" section, which delves into the latest advancements in nanocomposite research. This section details various nanocomposite types and their properties and introduces novel interpretations of their applications, especially in nanocomposite films. By tracing historical progress and identifying emerging trends, this analysis emphasizes the significance of collaboration and influential studies in molding the field. Moreover, the "Literature Review Guided by Artificial Intelligence" section showcases an innovative AI-guided approach to nanocomposite research, a first in this domain. Focusing on articles from 2023, selected based on citation frequency, this method offers a new perspective on the interplay between nanocomposites and their electrical properties. It highlights the composition, structure, and functionality of various systems, integrating recent findings for a comprehensive overview of current knowledge. The sentiment analysis, with an average score of 0.638771, reflects a positive trend in academic discourse and an increasing recognition of the potential of nanocomposites. Our bibliometric analysis, another methodological novelty, maps the intellectual domain, emphasizing pivotal research themes and the influence of crosslinking time on nanocomposite attributes. While acknowledging its limitations, this study exemplifies the indispensable role of our innovative computational tools in synthesizing and understanding the extensive body of nanocomposite literature. This work not only elucidates prevailing trends but also contributes a unique perspective and novel insights, enhancing our understanding of the nanocomposite research field.

Awareness raising and dealing with methanol poisoning based on effective strategies

Intoxication with methanol most commonly occurs as a consequence of ingesting, inhaling, or coming into contact with formulations that include methanol as a base. Clinical manifestations of methanol poisoning include suppression of the central nervous system, gastrointestinal symptoms, and decompensated metabolic acidosis, which is associated with impaired vision and either early or late blindness within 0.5-4 h after ingestion. After ingestion, methanol concentrations in the blood that are greater than 50 mg/dl should raise some concern. Ingested methanol is typically digested by alcohol dehydrogenase (ADH), and it is subsequently redistributed to the body's water to attain a volume distribution that is about equivalent to 0.77 L/kg. Moreover, it is removed from the body as its natural, unchanged parent molecules. Due to the fact that methanol poisoning is relatively uncommon but frequently involves a large number of victims at the same time, this type of incident occupies a special position in the field of clinical toxicology. The beginning of the COVID-19 pandemic has resulted in an increase in erroneous assumptions regarding the preventative capability of methanol in comparison to viral infection. More than 1000 Iranians fell ill, and more than 300 of them passed away in March of this year after they consumed methanol in the expectation that it would protect them from a new coronavirus. The Atlanta epidemic, which involved 323 individuals and resulted in the deaths of 41, is one example of mass poisoning. Another example is the Kristiansand outbreak, which involved 70 people and resulted in the deaths of three. In 2003, the AAPCC received reports of more than one thousand pediatric exposures. Since methanol poisoning is associated with high mortality rates, it is vital that the condition be addressed seriously and managed as quickly as feasible. The objective of this review was to raise awareness about the mechanism and metabolism of methanol toxicity, the introduction of therapeutic interventions such as gastrointestinal decontamination and methanol metabolism inhibition, the correction of metabolic disturbances, and the establishment of novel diagnostic/screening nanoparticle-based strategies for methanol poisoning such as the discovery of ADH inhibitors as well as the detection of the adulteration of alcoholic drinks by nanoparticles in order to prevent methanol poisoning. In conclusion, increasing warnings and knowledge about clinical manifestations, medical interventions, and novel strategies for methanol poisoning probably results in a decrease in the death load.

A Smart Alcoholmeter Sensor Based on Deep Learning Visual Perception

Process automation, in general, enables the enhancement of productivity, product quality, and consistency alongside other production metrics. Liquor production on an industrial scale also follows the automation trend. However, small and medium producers lag with equipment modernization due to the high costs of industrial equipment. One of the important sensors in automation equipment for distilleries is the alcohol concentration sensor used for fraction separation, process automation, and supervision. This paper proposes a novel low-cost approach to alcohol concentration sensing by employing deep learning on the visual perception of traditional alcoholmeter. For purposes of the training model, dataset acquisition apparatus is developed and a large dataset of labeled images of alcoholmeter readings is acquired. The problem of reading alcohol concentration from an alcoholometer image is treated as a regression and classification problem. Performances of both regression and classification models were investigated with Resnet18 as an architecture of choice. Both models achieved satisfying performance metrics demonstrating the feasibility of the proposed approaches. The proposed system implemented on Raspberry Pi with a camera can be integrated into new distillation equipment. Additionally, it can be used for retrofitting existing equipment due to its non-invasive nature of reading. The scope of use can be further expanded to the reading of other types of analog instruments simply by retraining the model.