Machine learning for rapid quantification of trace analyte molecules using SERS and flexible plasmonic paper substrates

Rapid identification and quantitative analysis of malachite green in fish via SERS and 1D convolutional neural network

Rapid and quantitative detection of malachite green (MG) in aquaculture products is very important for safety assurance in food supply. Here, we develop a point-of-care testing (POCT) platform that combines a flexible and transparent surface-enhanced Raman scattering (SERS) substrate with deep learning network for achieving rapid and quantitative detection of MG in fish. The flexible and transparent SERS substrate was prepared by depositing silver (Ag) film on the polydimethylsiloxane (PDMS) film using laser molecular beam epitaxy (LMBE) technique. The wrinkled Ag NPs@PDMS film exhibits high SERS activity, excellent reproducibility and good mechanical stability. Additionally, the fast in situ detection of MG residues onfishscales was achieved by using the wrinkled Ag NPs/PDMS film and a portable Raman spectrometer, with a minimum detectable concentration of 10-6 M. Subsequently, a one-dimensional convolutional neural network (1D CNN) model was constructed for rapid quantification of MG concentration. The results demonstrated that the 1D CNN quantitative analysis model possessed superior predictive performance, with a coefficient of determination (R2) of 0.9947 and a mean squared error (MSE) of 0.0104. The proposed POCT platform, integrating a transparent flexible SERS substrate, a portable Raman spectrometer and a 1D CNN model, provides an efficient strategy for rapid identification and quantitative analysis of MG in fish.

A Wettability Contrast SERS Droplet Assay for Multiplexed Analyte Detection

Droplet assay platforms have emerged as a significant methodology, providing distinct advantages such as sample compartmentalization, high throughput, and minimal analyte consumption. However, inherent complexities, especially in multiplexed detection, remain a challenge. We demonstrate a novel strategy to fabricate a plasmonic droplet assay platform (PDAP) for multiplexed analyte detection, enabling surface-enhanced Raman spectroscopy (SERS). PDAP efficiently splits a microliter droplet into submicroliter to nanoliter droplets under gravity-driven flow by wettability contrast between two distinct regions. The desired hydrophobicity and adhesive contrast between the silicone oil-grafted nonadhesive hydrophilic zone with gold nanoparticles is attained through (3-aminopropyl) triethoxysilane (APTES) functionalization of gold nanoparticles (AuNPs) using a scotch-tape mask. The wettability contrast surface facilitates the splitting of aqueous droplets with various surface tensions (ranging from 39.08 to 72 mN/m) into ultralow volumes of nanoliters. The developed PDAP was used for the multiplexed detection of Rhodamine 6G (Rh6G) and Crystal Violet (CV) dyes. The limit of detection for 120 nL droplet using PDAP was found to be 134 pM and 10.1 nM for Rh6G and CV, respectively. These results align with those from previously reported platforms, highlighting the comparable sensitivity of the developed PDAP. We have also demonstrated the competence of PDAP by testing adulterant spiked milk and obtained very good sensitivity. Thus, PDAP has the potential to be used for the multiplexed screening of food adulterants.

TriFNet: A triple-branch feature fusion network for pH determination by surface-enhanced Raman spectroscopy

Due to the acidic tumor microenvironment caused by metabolic changes in tumor cells, the accurate pH detection of extracellular fluid is helpful for doctors in precise tumor resection. The combination of Raman spectroscopy and deep learning provides a solution for pH detection. However, most existing studies use one-dimensional convolutional neural networks (1D-CNNs) for spectral analysis, which limits the performance due to insufficient feature extraction. In this work, we propose a 2D triple-branch feature fusion network (TriFNet) for accurate pH determination using surface-enhanced Raman spectra (SERS). Specifically, we design a triple-branch network structure by converting Raman spectra into three types of images to extensively extract complex patterns in spectra. In addition, an attention fusion module, which leverages the complementarity among features in both space and channel, is designed to obtain the valuable information, achieving further accurate pH determination. On our Raman spectral dataset containing 14,137 samples, we achieved mean absolute error (MAE) of 0.059, standard deviation of the absolute error (SD) of 0.07, root mean squared error (RMSE) of 0.092, and coefficient of determination (R2) of 0.991 on the test set. Compared with other published methods, the four metrics showed an average improvement of 47%, 39%, 43%, and 6%, respectively. In addition, visualization validates the diagnostic capability of our model to correlate with biomolecular signatures. Meanwhile, our model has robustness to different SERS chips. These results prove the potential of our method to develop an effective technology based on Raman spectroscopy for accurate pH determination to guide surgery.

Nitroaromatic explosives' detection and quantification using an attention-based transformer on surface-enhanced Raman spectroscopy maps

Rapidly and accurately detecting and quantifying the concentrations of nitroaromatic explosives is critical for public health and security. Among existing approaches, explosives' detection with Surface-Enhanced Raman Spectroscopy (SERS) has received considerable attention due to its high sensitivity. Typically, a preprocessed single spectrum that is the average of the entire or a selected subset of a SERS map is used to train various machine learning models for detection and quantification. Designing an appropriate averaging and preprocessing procedure for SERS maps across different concentrations is time-consuming and computationally costly, and the averaging of spectra may lead to the loss of crucial spectral information. We propose an attention-based vision transformer neural network for nitroaromatic explosives' detection and quantification that takes raw SERS maps as the input without any preprocessing. We produce two novel SERS datasets, 2,4-dinitrophenols (DNP) and picric acid (PA), and one benchmark SERS dataset, 4-nitrobenzenethiol (4-NBT), which have repeated measurements down to concentrations of 1 nM to illustrate the detection limit. We experimentally show that our approach outperforms or is on par with the existing methods in terms of detection and concentration prediction accuracy. With the produced attention maps, we can further identify the regions with a higher signal-to-noise ratio in the SERS maps. Based on our findings, the molecule of interest detection and concentration prediction using raw SERS maps is a promising alternative to existing approaches.

Deep learning approach to overcome signal fluctuations in SERS for efficient On-Site trace explosives detection

Surface-enhanced Raman spectroscopy (SERS) is an improved Raman spectroscopy technique to identify the analyte under study uniquely. At the laboratory scale, SERS has realised a huge potential to detect trace analytes with promising applications across multiple disciplines. However, onsite detection with SERS is still limited, given the unwanted glitches of signal reliability and blinking. SERS has inherent signal fluctuations due to multiple factors such as analyte adsorption, inhomogeneous distribution of hotspots, molecule orientation etc. making it a stochastic process. Given these signal fluctuations, validating a signal as a representation of the analyte often relies on an expert's knowledge. Here we present a neural network-aided SERS model (NNAS) without expert interference to efficiently identify reliable SERS spectra of trace explosives (tetryl and picric acid) and a dye molecule (crystal violet). The model uses the signal-to-noise ratio approach to label the spectra as representative (RS) and non-representative (NRS), eliminating the reliability of the expert. Further, experimental conditions were systematically varied to simulate general variations in SERS instrumentation, and a deep-learning model was trained. The model has been validated with a validation set followed by out-of-sample testing with an accuracy of 98% for all the analytes. We believe this model can efficiently bridge the gap between laboratory and on-site detection using SERS.

Recent Trends in SERS-Based Plasmonic Sensors for Disease Diagnostics, Biomolecules Detection, and Machine Learning Techniques

Surface-enhanced Raman spectroscopy/scattering (SERS) has evolved into a popular tool for applications in biology and medicine owing to its ease-of-use, non-destructive, and label-free approach. Advances in plasmonics and instrumentation have enabled the realization of SERS's full potential for the trace detection of biomolecules, disease diagnostics, and monitoring. We provide a brief review on the recent developments in the SERS technique for biosensing applications, with a particular focus on machine learning techniques used for the same. Initially, the article discusses the need for plasmonic sensors in biology and the advantage of SERS over existing techniques. In the later sections, the applications are organized as SERS-based biosensing for disease diagnosis focusing on cancer identification and respiratory diseases, including the recent SARS-CoV-2 detection. We then discuss progress in sensing microorganisms, such as bacteria, with a particular focus on plasmonic sensors for detecting biohazardous materials in view of homeland security. At the end of the article, we focus on machine learning techniques for the (a) identification, (b) classification, and (c) quantification in SERS for biology applications. The review covers the work from 2010 onwards, and the language is simplified to suit the needs of the interdisciplinary audience.

Low-cost and flexible paper-based plasmonic nanostructure for a highly sensitive SERS substrate

The application of a noble-metal-based plasmon-enhanced substrate to detect low-concentration analytes has attracted extensive attention. Most of the substrates used in recently reported researches are based on two-dimensional structures. Hence, we prepared a higher efficiency Raman activity substrate with a filter paper structure, which not only provides more plasmonic "hot spots," but also facilitates analyte extraction and detection due to the flexibility of the paper. The preparation of the plasmonic paper substrate adopted centrifugation to deposit the alloy nanoparticles onto the paper base. The optimal particle deposition condition was found by adjusting the centrifugal force and centrifugation time. Then, the surface-enhanced Raman spectroscopy (SERS) performance of the substrate was enhanced by altering the plasmon resonance peak on the surface of the nanoparticles. The enhancement factor of this paper-based substrate was 1.55×10⁷, with high detection uniformity (10^-6 M, rhodamine 6G) and a low detection limit (10^-11 M, rhodamine 6G). Then, we applied the SERS substrate to pesticide detection; the detection limit of the thiram reached 10^-6 M. As a result, the simple and cost-effective paper-based SERS substrate obtained in this way has high detection performance for pesticides and can be used for rapid detection in the field, which is beneficial to food safety and environmental safety.

Wafer-scale Silver Nanodendrites with Homogeneous Distribution of Gold Nanoparticles for Biomolecules Detection

We report the fabrication and demonstrate the superior performance of robust, cost-effective, and biocompatible hierarchical Au nanoparticles (AuNPs) decorated Ag nanodendrites (AgNDs) on a Silicon platform for the trace-level detection of antibiotics (penicillin, kanamycin, and ampicillin) and DNA bases (adenine, cytosine). The hot-spot density dependence studies were explored by varying the AuNPs deposition time. These substrates’ potential and versatility were explored further through the detection of crystal violet, ammonium nitrate, and thiram. The calculated limits of detection for CV, adenine, cytosine, penicillin G, kanamycin, ampicillin, AN, and thiram were 348 pM, 2, 28, 2, 56, 4, 5, and 2 nM, respectively. The analytical enhancement factors were estimated to be ∼10⁷ for CV, ∼10⁶ for the biomolecules, ∼10⁶ for the explosive molecule, and ∼10⁶ for thiram. Furthermore, the stability of these substrates at different time intervals is being reported here with surface-enhanced Raman spectroscopy/scattering (SERS) data obtained over 120 days.

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Wafer-scale surface-enhanced Raman spectroscopy/scattering (SERS) substrate of Ag nanodendrites decorated with Au nanoparticles prepared

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Trace level detection of antibiotics achieved

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Versatility of these substrates demonstrated by detecting explosive, dye molecules

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Typical enhancement factors achieved were 10⁵–10⁷

Picosecond Laser-Ablated Nanoparticles Loaded Filter Paper for SERS-Based Trace Detection of Thiram, 1,3,5-Trinitroperhydro-1,3,5-triazine (RDX), and Nile Blue

Recently, filter paper (FP)-based surface-enhanced Raman scattering (SERS) substrates have stimulated significant attention owing to their promising advantages such as being low-cost, easy to handle, and practically suitable for real-field applications in comparison to the solid-based substrates. Herein, a simple and versatile approach of laser-ablation in liquid for the fabrication of silver (Ag)-gold (Au) alloy nanoparticles (NPs). Next, the optimization of flexible base substrate (sandpaper, printing paper, and FP) and the FP the soaking time (5−60 min) was studied. Further, the optimized FP with 30 min-soaked SERS sensors were exploited to detect minuscule concentrations of pesticide (thiram-50 nM), dye (Nile blue-5 nM), and an explosive (RDX-1,3,5-Trinitroperhydro-1,3,5-triazine-100 nM) molecule. Interestingly, a prominent SERS effect was observed from the Au NPs exhibiting satisfactory reproducibility in the SERS signals over ~1 cm2 area for all of the molecules inspected with enhancement factors of ~105 and relative standard deviation values of <15%. Furthermore, traces of pesticide residues on the surface of a banana and RDX on the glass slide were swabbed with the optimized FP substrate and successfully recorded the SERS spectra using a portable Raman spectrometer. This signifies the great potential application of such low-cost, flexible substrates in the future real-life fields.