Dual-Modal Colorimetric and Surface-Enhanced Raman Scattering (SERS)-Based Lateral Flow Immunoassay for Ultrasensitive Detection of SARS-CoV-2 Using a Plasmonic Gold Nanocrown.

The 2019 coronavirus disease (COVID-19) outbreak created an unprecedented need for rapid, sensitive, and cost-effective point-of-care diagnostic tests to prevent and mitigate the spread of the SARS-CoV-2 virus. Herein, we demonstrated an advanced lateral flow immunoassay (LFIA) platform with dual-functional [colorimetric and surface-enhanced Raman scattering (SERS)] detection of the spike 1 (S1) protein of SARS-CoV-2. The nanosensor was integrated with a specially designed core-gap-shell morphology consisting of a gold shell decorated with external nanospheres, a structure referred to as gold nanocrown (GNC), labeled with a Raman reporter molecule 1,3,3,1',3',3'-hexamethyl-2,2'-indotricarbocyanine iodide (HITC) to produce a strong colorimetric signal as well as an enhanced SERS signal. Among the different plasmonics-active GNC nanostructures, the GNC-2 morphology, which has a shell decorated with an optimum number and size of nanospheres, produces an intense dark-blue colorimetric signal and ultrahigh SERS signal. The limit of detection (LOD) of the S1 protein via colorimetric detection LFIA was determined to be 91.24 pg/mL. On the other hand, the LOD for the SERS LFIA method was more than three orders of magnitude lower at 57.21 fg/mL. Furthermore, we analyzed the performance of the GNC-2 nanosensor for directly analyzing the S1 protein spiked in saliva samples without any sample pretreatment and achieving the LOD as low as 39.65 fg/mL using SERS-based plasmonics-enhanced LFIA, indicating ultrahigh detection sensitivity. Overall, our GNC nanosensor showed excellent sensitivity, reproducibility, and rapid detection of the SARS-CoV-2 S1 protein, demonstrating excellent potential as a promising point-of-care platform for the early detection of respiratory virus infections.

Multiplex SERS detection of polycyclic aromatic hydrocarbon (PAH) pollutants in water samples using gold nanostars and machine learning analysis.

Polycyclic aromatic hydrocarbons (PAHs) have attracted a lot of environmental concern because of their carcinogenic and mutagenic properties, and the fact they can easily contaminate natural resources such as drinking water and river water. This study presents a simple and sensitive point-of-care SERS detection of PAHs combined with machine learning algorithms to predict the PAH content more precisely and accurately in real-life samples such as drinking water and river water. We first synthesized multibranched sharp-spiked surfactant-free gold nanostars (GNSs) that can generate strong surface-enhanced Raman scattering (SERS) signals, which were further coated with cetyltrimethylammonium bromide (CTAB) for long-term stability of the GNSs as well as to trap PAHs. We utilized CTAB-capped GNSs for solution-based 'mix and detect' SERS sensing of various PAHs including pyrene (PY), nitro-pyrene (NP), anthracene (ANT), benzo[a]pyrene (BAP), and triphenylene (TP) spiked in drinking water and river water using a portable Raman module. Very low limits of detection (LOD) were achieved in the nanomolar range for the PAHs investigated. More importantly, the detected SERS signal was reproducible for over 90 days after synthesis. Furthermore, we analyzed the SERS data using artificial intelligence (AI) with machine learning algorithms based on the convolutional neural network (CNN) model in order to discriminate the PAHs in samples more precisely and accurately. Using a CNN classification model, we achieved a high prediction accuracy of 90% in the nanomolar detection range and an f1 score (harmonic mean of precision and recall) of 94%, and using a CNN regression model, achieved an RMSEconc = 1.07 × 10-1 µM. Overall, our SERS platform can be effectively and efficiently used for the accurate detection of PAHs in real-life samples, thus opening up a new, sensitive, selective, and practical approach for point-of-need SERS diagnosis of small molecules in complex practical environments.

Machine learning using convolutional neural networks for SERS analysis of biomarkers in medical diagnostics.

Surface-enhanced Raman spectroscopy (SERS) has wide diagnostic applications because of narrow spectral features that allow multiplexed analysis. Machine learning (ML) has been used for non-dye-labeled SERS spectra but has not been applied to SERS dye-labeled materials with known spectral shapes. Here, we compare the performances of spectral decomposition, support vector regression, random forest regression, partial least squares regression, and convolutional neural network (CNN) for SERS "spectral unmixing" from a multiplexed mixture of 7 SERS-active "nanorattles" loaded with different dyes for mRNA biomarker detection. We showed that CNN most accurately determined relative contributions of each distinct dye-loaded nanorattle. CNN and comparative models were then used to analyze SERS spectra from a singleplexed, point-of-care assay detecting an mRNA biomarker for head and neck cancer in 20 samples. The CNN, trained on simulated multiplexed data, determined the correct dye contributions from the singleplex assay with RMSElabel = 6.42 × 10-2. These results demonstrate the potential of CNN-based ML to advance SERS-based diagnostics.