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.

Machine Learning Techniques for Improving Nanosensors in Agroenvironmental Applications

“…A gas sensor composed of SnO 2 nanowires was utilised by Tonezzer et al [163] for the determination of the methanol contamination of alcoholic beverages. Data from responses of the sensor at various temperatures (5 temperatures: 180, 210, 240, 270 and 300 °C) were used and reduced to two-dimensional data using PCA.…”

Applications of Nanomaterials in Food Industry: A Review

“…Fluorescence sensor to detect saxitoxin toxin in shellfish Sun et al [29] Rhodamine B/UiO-66-N 3 H 2 S detection via reaction-based ratiometric fluorescent nanosensor Gao et al [30] SnO 2 nanowires Gas sensor to distinguish methanol from ethanol in alcoholic beverages Tonezzer et al [31] Carboxylated multi-walled carbon nanotubes (c-MWCNT)-modified screen-printed electrode-based bionanosensor Detecting the time of ripening of tomato with respect to its malic acid concentration Dalal et al [32] Glassy carbon electrode (GCE) modified with calixarene and gold nanoparticles…”

Smart Phone Coupled with Machine Learning for Identifying Multiple Antibiotics in Food

“…比色分析和荧光分析作为一种快速、灵敏、廉价的检测方法广受关注。这两类光学分析方法中 光学探针的设计是关键。利用单一探针获取多维信息，发展多目标物质的检测方法有利于提升效率、 实现仪器小型化。近年来，智能手机凭借其出色的光学镜头、丰富的色彩分析软件、易于操作和便 携性、可提供现场测试和方便共享分析结果等优势日益受到关注。在大量数据解析方面，人工智能 中的机器学习方法可指导计算机自动学习输入数据的数据结构和内在规律，有效地从多维数据中提 取隐藏信息，实现准确的分类鉴别。在分析化学领域中，机器学习常用于信号和图像处理，通过统 计分析对数据进行解析，挖掘隐含信息从而实现对分析对象的分类等功能 [2,3] 。然而目前分析化学实 验很少涉及有关机器学习技术及利用智能手机作为数据获取终端实现多目标物识别的实验。 食品中抗生素残留是食品安全中亟待解决的问题。四环素类抗生素(TCs)对革兰氏阳性菌和革兰 氏阴性菌均有广泛的抗菌活性。由于其治疗效果好、成本低、毒性低等优点被广泛用于治疗动物和 人类感染 [4,5] 。此外，四环素类抗生素通常作为促进动物生长的饲料添加剂用于畜牧行业中 [6] [7] ，与Eu 3+ 结合后发射Eu 3+ 特征荧 光。同时，TCs可置换EBT/Eu 3+ 配合物中的EBT，引起体系颜色变化。利用智能手机和荧光仪分别获 取颜色和荧光双通道信息，借助机器学习中线性判别分析(LDA)，实现了对食品中四种TCs的识别。 本实验包含光学探针设计-测试数据获取-测试数据解析的全流程，涵盖了荧光探针设计、智能手机 比色、人工智能、分析平台搭建、线性判别分析方法等多个知识点，不仅可培养学生对海量分析量 测数据的解析能力，还可以培养学生从单纯的"数据提供者"向"问题解决者"的转变。同时，本 实验所涉及的智能手机颜色识别、机器学习技术及食品安全等特色可极大提高学生学习兴趣和积极性。 [8] 。 1.1.3 线性判别分析法的实现 线性判别分析(LDA)是一种监督学习的降维技术，其主要原理为"投影后类内方差最小，类间 方差最大"。即将同类型高维数据投影后，将同类型的高维数据聚类到低维空间，使相同类别之间 相距较近，而不同类别之间相距较远的一种分析方法 [9] 。本实验通过应用OriginPro自带的LDA功能 进行线性判别分析。…”