Realization of qualitative to semi-quantitative trace detection via SERS-ICA based on internal standard method

Li，Xiaoming1; Hu，Jiaqi1,2; Zhang，De3; Zhang，Xiubin1; Wang，Zhetao1; Wang，Yufeng4; Chen，Qiang5; Liang，Pei1

2024-05-01

10.1016/j.talanta.2024.125650

Talanta   影响因子和分区

0039-9140

Surface-enhanced Raman spectroscopy (SERS) can quickly identify molecular fingerprints and has been widely used in the field of rapid detection. However, the non-uniformity inherent in SERS substrate signals, coupled with the finite nature of the detection object, significantly hampers the advancement of SERS. Nowadays, the existing mature immunochromatographic assay (ICA) method is usually combined with SERS technology to address the defects of SERS detection. Nevertheless, the porous structure of the strip will also affect the signal uniformity during detection. Obviously, a method using SERS-ICA is needed to effectively solve signal fluctuations, improve detection accuracy, and has certain versatility. This paper introduces an internal standard method combining deep learning to predict and process Raman data. Based on the signal fluctuation of single-antigen SERS-ICA test strip, the double-antigen SERS-ICA test strip was constructed. The full spectrum Raman data of double-antigen SERS-ICA test strip was normalized by the sum of two characteristic peaks of internal standard molecules, and then processed by deep learning algorithm. The Relative Standard Deviation (RSD) of Raman data of bisphenol A was compared before and after internal standard normalization of double-antigen SERS-ICA test strip. The RSD processed by this method was increased by 3.8 times. After normalization, the prediction accuracy of Root Mean Square Error (RMSE) is improved by 2.66 times, and the prediction accuracy of R-square (R) is increased from 0.961 to 0.994. The results showed that RMSE and R were used to comprehensively predict the collected data of double-antigen SERS-ICA test strip, which could effectively improve the prediction accuracy. The internal standard algorithm can effectively solve the challenges of uneven hot spots and poor signal reproducibility on the test strip to a certain extent, so as to improve the semi-quantitative accuracy.

Deep learning Immunochromatographic assay Internal standard Surface-enhanced Raman spectroscopy Trace detection

SCI ; EI

英语

其他

CHEMISTRY

2-s2.0-85183205775

Scopus

1.College of Optical and Electronic Technology,China Jiliang University,Hangzhou,310018,China
2.EEE Department,Southern University of Science and Technology,Shenzhen,518055,China
3.National Key Laboratory for Germplasm Innovation and Utilization for Fruit and Vegetable Horticultural Crops,College of Horticulture & Forestry Sciences,Huazhong Agricultural University,Wuhan,430070,China
4.MOE Joint International Research Laboratory of Animal Health and Food Safety,College of Veterinary Medicine,Nanjing Agricultural University,Nanjing,210095,China
5.College of Metrology and Measurement Engineering,China Jiliang University,Hangzhou,310018,China

Li，Xiaoming,Hu，Jiaqi,Zhang，De,et al. Realization of qualitative to semi-quantitative trace detection via SERS-ICA based on internal standard method[J]. Talanta,2024,271.

Li，Xiaoming.,Hu，Jiaqi.,Zhang，De.,Zhang，Xiubin.,Wang，Zhetao.,...&Liang，Pei.(2024).Realization of qualitative to semi-quantitative trace detection via SERS-ICA based on internal standard method.Talanta,271.

Li，Xiaoming,et al."Realization of qualitative to semi-quantitative trace detection via SERS-ICA based on internal standard method".Talanta 271(2024).