Dataset Information

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

ABSTRACT: Methanol, naturally present in small quantities in the distillation of alcoholic beverages, can lead to serious health problems. When it exceeds a certain concentration, it causes blindness, organ failure, and even death if not recognized in time. Analytical techniques such as chromatography are used to detect dangerous concentrations of methanol, which are very accurate but also expensive, cumbersome, and time-consuming. Therefore, a gas sensor that is inexpensive and portable and capable of distinguishing methanol from ethanol would be very useful. Here, we present a resistive gas sensor, based on tin oxide nanowires, that works in a thermal gradient. By combining responses at various temperatures and using machine learning algorithms (PCA, SVM, LDA), the device can distinguish methanol from ethanol in a wide range of concentrations (1-100 ppm) in both dry air and under different humidity conditions (25-75% RH). The proposed sensor, which is small and inexpensive, demonstrates the ability to distinguish methanol from ethanol at different concentrations and could be developed both to detect the adulteration of alcoholic beverages and to quickly recognize methanol poisoning.

SUBMITTER: Tonezzer M

PROVIDER: S-EPMC9329758 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature

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

Tonezzer Matteo M Bazzanella Nicola N Gasperi Flavia F Biasioli Franco F

Sensors (Basel, Switzerland) 20220725 15

Methanol, naturally present in small quantities in the distillation of alcoholic beverages, can lead to serious health problems. When it exceeds a certain concentration, it causes blindness, organ failure, and even death if not recognized in time. Analytical techniques such as chromatography are used to detect dangerous concentrations of methanol, which are very accurate but also expensive, cumbersome, and time-consuming. Therefore, a gas sensor that is inexpensive and portable and capable of di ...[more]

PMID: 35898057

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Project description:To elucidate the effect of the polyphenols contained in alcoholic beverages on the metabolic stress induced by ethanol consumption, four groups of mice were fed for five weeks on Lieber's diet with or without ethanol, with ethanol plus ellagic acid, and with ethanol plus trans-resveratrol. Alcoholic fatty liver was observed in the group fed the ethanol diet but not in those fed the ethanol plus polyphenol diets. Liver transcriptome analysis revealed that the addition of the polyphenols suppressed the expression of the genes related to cell stress that were up-regulated by ethanol alone. Conversely, the polyphenols up-regulated the genes involved in bile acid synthesis, unsaturated fatty acid elongation, and tetrahydrofolate synthesis that were down-regulated by ethanol alone. Because parts of these genes were known to be regulated by the constitutive androstane receptor (CAR), we performed the same experiment in the CAR-deficient mice. As a result, fatty liver was observed not only in the ethanol group but also with the ethanol plus polyphenol groups. In addition, there was no segregation of the gene expression profiles among these groups. These results provide a molecular basis for the prevention of alcohol-induced stress by the polyphenols in alcoholic beverages. Five-week-old C3H/HeN female mice (CLEA, Japan) were acclimated to the maintenance condition (25Â°C, 8:00-20:00 day / 20:00-8:00 night cycle and 35~40 % humidity), fed a CE-2 diet (CLEA, Japan), and given water ad libitum for one week. Each group of mice (n=4 for wild type mice analysis and n=3 for CAR decficient mice analysis) was fed Lieber's isocaloric diet (Oriental yeast, Japan) containing water, containing ethanol, containing ethanol and ellagic acid (Fluka Biochemika, Switzerland), or containing ethanol and trans-resveratrol (Sigma, USA) (Supplementary Table 1) for one week at 10:00 ad libitum. Then, the mice were fed each diet at 12 g / day for four weeks (Supplementary Fig. 1A). The approximate intake of each polyphenol was 50 mg / kg body weight / day. At 10:00 of the final day of the experimental period, the animals were anesthetized by diethyl ether, sacrificed by cervial fracture, and the heart blood, and the liver were collected.

Project description:ImportanceChildhood lead poisoning causes irreversible neurobehavioral deficits, but current practice is secondary prevention.ObjectiveTo validate a machine learning (random forest) prediction model of elevated blood lead levels (EBLLs) by comparison with a parsimonious logistic regression.Design, setting, and participantsThis prognostic study for temporal validation of multivariable prediction models used data from the Women, Infants, and Children (WIC) program of the Chicago Department of Public Health. Participants included a development cohort of children born from January 1, 2007, to December 31, 2012, and a validation WIC cohort born from January 1 to December 31, 2013. Blood lead levels were measured until December 31, 2018. Data were analyzed from January 1 to October 31, 2019.ExposuresBlood lead level test results; lead investigation findings; housing characteristics, permits, and violations; and demographic variables.Main outcomes and measuresIncident EBLL (≥6 μg/dL). Models were assessed using the area under the receiver operating characteristic curve (AUC) and confusion matrix metrics (positive predictive value, sensitivity, and specificity) at various thresholds.ResultsAmong 6812 children in the WIC validation cohort, 3451 (50.7%) were female, 3057 (44.9%) were Hispanic, 2804 (41.2%) were non-Hispanic Black, 458 (6.7%) were non-Hispanic White, and 442 (6.5%) were Asian (mean [SD] age, 5.5 [0.3] years). The median year of housing construction was 1919 (interquartile range, 1903-1948). Random forest AUC was 0.69 compared with 0.64 for logistic regression (difference, 0.05; 95% CI, 0.02-0.08). When predicting the 5% of children at highest risk to have EBLLs, random forest and logistic regression models had positive predictive values of 15.5% and 7.8%, respectively (difference, 7.7%; 95% CI, 3.7%-11.3%), sensitivity of 16.2% and 8.1%, respectively (difference, 8.1%; 95% CI, 3.9%-11.7%), and specificity of 95.5% and 95.1% (difference, 0.4%; 95% CI, 0.0%-0.7%).Conclusions and relevanceThe machine learning model outperformed regression in predicting childhood lead poisoning, especially in identifying children at highest risk. Such a model could be used to target the allocation of lead poisoning prevention resources to these children.

Dataset Information

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

Publications

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

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