Detection of Volatile Organic Compounds (VOCs) from the breath is becoming a viable route for the early detection of diseases non-invasively. This paper presents a sensor array with three metal oxide electrodes that can use machine learning methods to identify four distinct VOCs in a mixture. The metal oxide sensor array was subjected to various VOC concentrations, including ethanol, acetone, toluene and chloroform. The dataset obtained from individual gases and their mixtures were analyzed using multiple machine learning algorithms, such as Random Forest (RF), K-Nearest Neighbor (KNN), Decision Tree, Linear Regression, Logistic Regression, Naive Bayes, Linear Discriminant Analysis, Artificial Neural Network, and Support Vector Machine. KNN and RF have shown more than 99% accuracy in classifying different varying chemicals in the gas mixtures. In regression analysis, KNN has delivered the best results with R2 value of more than 0.99 and LOD of 0.012, 0.015, 0.014 and 0.025 PPM for predicting the concentrations of varying chemicals Acetone, Toluene, Ethanol, and Chloroform, respectively in complex mixtures. Therefore, it is demonstrated that the array utilizing the provided algorithms can classify and predict the concentrations of the four gases simultaneously for disease diagnosis and treatment monitoring.

翻译：从呼吸中检测挥发性有机化合物（VOCs）正成为非侵入式早期疾病诊断的可行途径。本文提出一种由三个金属氧化物电极组成的传感器阵列，可结合机器学习方法识别混合物中的四种不同VOCs。该金属氧化物传感器阵列暴露于不同浓度的VOCs，包括乙醇、丙酮、甲苯和氯仿。利用随机森林（RF）、K近邻（KNN）、决策树、线性回归、逻辑回归、朴素贝叶斯、线性判别分析、人工神经网络和支持向量机等多种机器学习算法，分析了单个气体及其混合物数据集。KNN和RF在分类气体混合物中不同化学物质时，准确率超过99%。在回归分析中，KNN取得了最佳结果，对复杂混合物中丙酮、甲苯、乙醇和氯仿浓度预测的R²值均超过0.99，检测限（LOD）分别为0.012、0.015、0.014和0.025 PPM。因此，该阵列结合所提算法可同时分类并预测四种气体的浓度，为疾病诊断与治疗监测提供支持。