Noreldeen Hamada A A, He Shao-Bin, Huang Kai-Yuan, Zhu Chen-Ting, Zhou Qing-Lin, Peng Hua-Ping, Deng Hao-Hua, Chen Wei
Fujian Key Laboratory of Drug Target Discovery and Structural and Functional Research, School of Pharmacy, Fujian Medical University, Fuzhou, 350004, China.
National Institute of Oceanography and Fisheries, NIOF, Cairo, Egypt.
Anal Bioanal Chem. 2022 Dec;414(29-30):8365-8378. doi: 10.1007/s00216-022-04372-1. Epub 2022 Oct 25.
Different acquisition data approaches have been used to fetch the fluorescence spectra. However, the comparison between them is rare. Also, the extendability of a sensor array, which can work with heavy metal ions and other types of analytes, is scarce. In this study, we used first- and second-order fluorescent data generated by 6-Aza-2-thiothymine-gold nanocluster (ATT-AuNCs) as a single probe along with machine learning to distinguish between a group of heavy metal ions. Moreover, the dimensionality reduction was carried out for the different acquisition data approaches. In our case, the accuracy of different machine learning algorithms using first-order data outperforms the second-order data before and after the dimensionality reduction. For proving the extendibility of this approach, four anions were used as an example. As expected, the same finding has been found. Furthermore, random forest (RF) showed more stable and accurate results than other models. Also, linear discriminant analysis (LDA) gave acceptable accuracy in the analysis of the high-dimensionality data. Accordingly, using LDA in high-dimensionality data (the first- and second-order data) analysis was highlighted for discrimination between the selected heavy metal ions in different concentrations and in different molar ratios, as well as in real samples. Also, the same method was applied for the anion's discrimination, and LDA gave an excellent separation ability. Moreover, LDA was able to differentiate between all the selected analytes with excellent separation ability. Additionally, the quantitative detection was considered using a wide concentration range of Cd, and the LOD was 60.40 nM. Therefore, we believe that our approach opens new avenues for linking analytical chemistry, especially sensor array chemistry, with machine learning.
已采用不同的采集数据方法来获取荧光光谱。然而，它们之间的比较却很少见。此外，能够与重金属离子和其他类型分析物配合使用的传感器阵列的可扩展性也很缺乏。在本研究中，我们将6-氮杂-2-硫代胸腺嘧啶-金纳米簇（ATT-AuNCs）产生的一阶和二阶荧光数据作为单一探针，并结合机器学习来区分一组重金属离子。此外，还对不同的采集数据方法进行了降维处理。在我们的案例中，使用一阶数据的不同机器学习算法在降维前后的准确性均优于二阶数据。为了证明该方法的可扩展性，以四种阴离子为例进行了研究。不出所料，得到了相同的结果。此外，随机森林（RF）显示出比其他模型更稳定、更准确的结果。而且，线性判别分析（LDA）在高维数据分析中给出了可接受的准确性。因此，在高维数据（一阶和二阶数据）分析中使用LDA来区分不同浓度、不同摩尔比的选定重金属离子以及实际样品中的重金属离子受到了关注。同样的方法也应用于阴离子的鉴别，LDA表现出出色的分离能力。此外，LDA能够以出色的分离能力区分所有选定的分析物。此外，还考虑了使用宽浓度范围的镉进行定量检测，检测限为60.40 nM。因此，我们相信我们的方法为将分析化学，尤其是传感器阵列化学与机器学习联系起来开辟了新途径。
