Ultra-low dual detection of tetrahydrocannabinol and cannabidiol in saliva based on electrochemical sensing and machine learning: overcoming cross-interferences and saliva-to-saliva variations†

IF 3.5 Q2 CHEMISTRY, ANALYTICAL
Greter A. Ortega, Herlys Viltres, Hoda Mozaffari, Syed Rahin Ahmed, Seshasai Srinivasan and Amin Reza Rajabzadeh
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引用次数: 0

Abstract

A novel alternative to cope with saliva-to-saliva variations and cross-interference while sensing delta-9-tetrahydrocannabinol (THC) and cannabidiol (CBD) is reported here using two voltammetric sensors coupled with machine learning. The screen-printed electrodes modified with the same analyte molecules (m-Z-THC and m-Z-CBD) were employed for sensing ultra-low concentrations of THC and CBD in the 0 to 5 ng mL−1 range in real human saliva samples. Simultaneous detection of THC and CBD was carried out using m-Z-THC or m-Z-CBD to study the performance of each modified sensor. Also, CBD and THC have the same molecular structure; there is only a slight difference in how the atoms are arranged, and therefore both molecules will have similar electrochemical performance. Consequently, CBD can be a potential interference while detecting THC and THC can be an interference during CBD detection using electrochemical sensors. Therefore, machine learning was introduced to analyze the sensor analytical responses to overcome such issues. The data processing results provide suitable accuracies of 100% for training in the case of both sensors and 92 and 83% for m-Z-THC and m-Z-CBD, respectively, for dataset testing THC and CBD in saliva samples. Additionally, the saliva samples containing CBD and THC as cross-interference were accurately identified and classified.

Abstract Image

Abstract Image

基于电化学传感和机器学习的唾液中四氢大麻酚和大麻二酚超低双检测:克服交叉干扰和唾液之间的差异
本文报告了一种新颖的替代方法,即使用两种伏安法传感器并结合机器学习,在感测δ-9-四氢大麻酚(THC)和大麻二酚(CBD)时,可以应对唾液与唾液之间的差异和交叉干扰。用相同的分析分子(m-Z-THC 和 m-Z-CBD)修饰的丝网印刷电极可用于检测真实人体唾液样本中 0 至 5 纳克 mL-1 范围内的超低浓度四氢大麻酚和大麻二酚。使用 m-Z-THC 或 m-Z-CBD 同时检测了 THC 和 CBD,以研究每种改良传感器的性能。此外,CBD 和 THC 具有相同的分子结构;原子排列方式仅有细微差别,因此这两种分子具有相似的电化学性能。因此,在使用电化学传感器检测四氢大麻酚时,CBD 可能会产生潜在干扰,而在检测 CBD 时,四氢大麻酚也可能会产生干扰。因此,为了克服这些问题,我们引入了机器学习来分析传感器的分析响应。数据处理结果表明,在唾液样本中检测 THC 和 CBD 的数据集中,两种传感器的训练准确率均为 100%,m-Z-THC 和 m-Z-CBD 的准确率分别为 92% 和 83%。此外,含有 CBD 和 THC 作为交叉干扰的唾液样本也被准确识别和分类。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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CiteScore
2.30
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