Ultrasensitive Isomer Discrimination: A Joint
Surface-Enhanced Raman Scattering (SERS)
Spectroscopy and Machine Learning Strategy

Verónica Montes-garcía, Victor F Martín, Manuel Obelleiro-liz, Ignacio

Pérez-juste, Artur Ciesielski, Paolo Samorì

► To cite this version:

Verónica Montes-garcía, Victor F Martín, Manuel Obelleiro-liz, Ignacio Pérez-juste, Artur Ciesielski, et al. Ultrasensitive Isomer Discrimination: A Joint Surface-Enhanced Raman Scattering (SERS) Spectroscopy and Machine Learning Strategy. Advanced Sensor Research, 2025, 4 (3), pp.2400160. 10.1002/adsr.202400160. hal-04986113

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# Ultrasensitive Isomer Discrimination: A Joint Surface-Enhanced Raman Scattering (SERS) Spectroscopy and Machine Learning Strategy

Verónica Montes-García,\* Victor F. Martín, Manuel Obelleiro-Liz, Ignacio Pérez-Juste, Artur Ciesielski, and Paolo Samori\*

Isomer discrimination is of paramount importance across various sectors, including pharmaceuticals, agriculture, and the food industry, owing to their unique physicochemical characteristics. Because of their extremely similar characteristics, traditional analytical methods fail or encounter severe limitations in isomer discrimination. To overcome this grand challenge, a novel sensing strategy is proposed based on surface-enhanced Raman scattering (SERS) substrates (i.e., plasmonic platforms) combined with machine learning algorithms. These plasmonic platforms exhibit exceptional signal uniformity across wide regions and sensitivity, enabling the discrimination of structural isomers (hydroquinone, resorcinol, pyrocatechol), geometric isomers ((Z/E)-stilbene, (Z/E)-resveratrol), and optical isomers (R/S-ibuprofen). Notably, for the analysis of optical isomers, 1-naphthalenethiol is employed as a probe to facilitate specific isomer orientation on the surface of the plasmonic platform through, for the first time, $π-π$ interactions. The integration of machine learning methodologies, such as Partial Least Squares Regression and Artificial Neural Networks, significantly enhances both quantitative analysis and classification accuracy, achieving detection limits as low as $2 \times 10^{-8}$ м. Validation with commercially available ibuprofen samples shows excellent agreement with traditional circular dichroism results, highlighting the method's robustness and precision. The strategy provides a versatile, ultrasensitive, and reliable solution for isomer discrimination, with broad applications in pharmaceuticals, environmental monitoring, and clinical diagnostics.

## 1. Introduction

Isomers are compounds that share the same molecular formula but possess different structural configurations, resulting in distinct physicochemical properties.[1-3] Generally, isomers can be classified into two main types: structural isomers and stereoisomers. Structural isomers, also known as constitutional isomers, vary in the connectivity of their constituent atoms, whereas stereoisomers maintain identical atomic connectivity but differ in their spatial arrangement. The latter category encompasses geometric isomers (Z/E isomers) and optical isomers (which include enantiomers and diastereomers). The distinct properties of isomers render them indispensable across diverse fields, from pharmaceutical industry,[4-6] to agriculture,[7] or the food industry.[8,9] However, these differences can have profound implications for human health, as one isomer may be beneficial while another could be detrimental. For example, the R-isomer of thalidomide was utilized to alleviate morning sickness, while the S-isomer was associated with severe birth defects.[10] Similarly, S-ibuprofen serves as an effective

V. Montes-García, A. Ciesielski, P. Samori
Université de Strasbourg, CNRS
ISIS 8 allée Gaspard Monge, Strasbourg 67000, France
E-mail: montesgarcia@unistra.fr; samori@unistra.fr

V. F. Martín
Signal Theory and Communications Department
King Juan Carlos University
Madrid 28032, Spain

M. Obelleiro-Liz
Departamento Tecnología de los Computadores y de las Comunicaciones
Universidad de Extremadura
Cáceres 10003, Spain

M. Obelleiro-Liz
EM3Works
Spin-off of the University of Vigo and the University of Extremadura
Vigo 36315, Spain

I. Pérez-Juste
Departamento Química Física
Universidade de Vigo
Vigo 36310, Spain

The ORCID identification number(s) for the author(s) of this article can be found under https://doi.org/10.1002/adsr.202400160

© 2025 The Author(s). Advanced Sensor Research published by Wiley-VCH GmbH. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

DOI: 10.1002/adsr.202400160
analgesic, whereas R-ibuprofen is pharmacologically inactive.[11] L-3,4-dihydroxyphenylalanine (L-Dopa) is employed in the treatment of Parkinson's disease, while D-3,4-dihydroxyphenylalanine (D-Dopa) is neurotoxic.[12] Furthermore, D-amphetamine (dextromethamphetamine) is prescribed for attention deficit hyperactivity disorder (ADHD) treatment, while L-amphetamine (levomethamphetamine), commonly found in nasal decongestants, offers limited therapeutic benefits and poses potential toxicity risks.[13] These examples highlight the critical need for a thorough understanding and control of isomeric chemical compounds to ensure their safe and effective application across various industries.

Traditional methods for differentiating isomers, such as mass spectrometry (MS),[14] nuclear magnetic resonance (NMR) spectroscopy,[15] infrared (IR) spectroscopy,[16] gas/liquid chromatography,[17,18] and optical activity[19] provide detailed molecular information. However, these methods exhibit several limitations. They often involve lengthy sample preparation, complex data interpretation, and may suffer from insufficient sensitivity and resolution. Additionally, some techniques require a substantial sample quantity to yield accurate results. Optical activity, for instance, is restricted to chiral molecules,[19] and advanced methods like X-ray crystallography are not only expensive but also require sophisticated equipment.[20] Therefore novel analytical methods that allow for the rapid, unequivocal, and ultrasensitive isomer discrimination are highly sought after.

Surface-enhanced Raman scattering (SERS) spectroscopy, an advanced analytical technique, represents a compelling solution to these challenges.[21,22] By enhancing the characteristic inelastic Raman scattering signals of a molecule in proximity to nanostructured metal surfaces (i.e., SERS substrates), SERS can achieve high sensitivity with enhancement factors up to 10,[14] enabling the detection of individual molecules.[23] SERS requires minimal sample preparation, is cost-effective and offers high versatility, making it suitable for a broad range of samples, including biological tissues, cells, and bacteria to environmentally-relevant compounds like heavy metal ions or pollutants.[24-26] Pristine plasmonic nanoparticles (i.e., without any probe) have been effectively employed for the ultrasensitive sensing of structural isomers including, spiropyran/merocyanine,[27] 2-, 3- and 4-aminothiophenols,[28] DNA bases,[29] diarylethene derivatives,[30,31] and 2-, 3-, and 4-chlorobiphenyls.[32] Lee and coworkers used cucurbit[n]urils (CBn, n = 7, 8) to induce the self-assembly of gold nanoparticles (Au NPs) while selectively capturing structural isomers of methylxanthine drug molecules via host-guest chemistry.[33] Wu et al. developed an indirect SERS approach combined with machine learning (ML) algorithms to detect glycan stereoisomers by using Au NPs functionalized with 4-mercaptophenylboronic acid (4MBA) or 1-thianthrenylboronic acid (1TBA) probes as SERS sensors.[34] These sensors produce distinct SERS spectra that are uniquely altered when interacting with the different glycans. The most common SERS approaches for detecting optical isomers rely on the use of chiral molecular imprinting[35] or metallic NPs decorated with one or multiple probes.[36-38] The latter SERS sensors exploit the differences in hydrogen bonding interactions between the isomers and the probes, leading to distinct molecular orientations and, consequently, clear and distinguishable spectroscopic features.[36-39] Ozaki et al. also noted that variations in hydrogen bonding

lead to different charge-transfer (CT) contributions in SERS sensing.[37,38] Notably, Ling et al. showed that the combination of SERS sensing with machine learning algorithms enables rapid and accurate isomer classification accuracy of >97% from potential structural interferences, as well as achieves multiplex quantification of the relative isomer compositions with <3% error rates.[39]

While these examples are promising for SERS-based isomer sensing, there remains a need for the development of more robust and versatile SERS substrates that can offer high sensitivity, uniformity, and adaptability across a wide range of isomeric compounds. Current SERS techniques often encounter challenges such as inconsistent nanoparticle aggregation, limited enhancement factors, or difficulty in distinguishing closely related isomers, especially optical isomers, where only probes relying on differences in hydrogen bonding with the isomers have been explored. Moreover, the integration of advanced data analysis methods, such as machine learning, with SERS is an emerging field that is beginning to unlock new potentials for rapid and accurate isomer discrimination in complex sample matrices.

In this study, we directly address critical challenges in SERS isomer detection by fabricating advanced SERS substrates composed of quasi-spherical Au NPs synthesized via a seeded growth method. By precisely controlling the size and amount of Au NPs through an electrostatic layer-by-layer (LbL) assembly process on a glass substrate, we develop highly uniform and sensitive SERS-active surfaces (i.e., plasmonic platforms) with tunable localized surface plasmon resonance (LSPR) properties. These optimized platforms enable accurate detection and discrimination of various isomers, including structural, geometrical, and optical isomers, showcasing their versatility and reliability. Our plasmonic platforms successfully differentiate closely related structural isomers such as hydroquinone, resorcinol, pyrocatechol, as well as geometric isomers such as (Z/E)-stilbene, based on their unique vibrational signatures. For more complex cases, such as (Z/E)-resveratrol, principal component analysis (PCA) proved essential for accurate classification. Moreover, we further enhance the quantitative analysis and classification of optical isomers like R-ibuprofen and S-ibuprofen by utilizing 1-naphthalenethiol (1-NAT) as a probe molecule that interacts with isomers via π-π interactions, and by integrating machine learning algorithms such as Partial Least Squares Regression and Artificial Neural Networks. This combination achieves remarkable sensitivity, with detection limits as low as 2 × 10⁻⁸ M, and demonstrates high precision when applied to real-world pharmaceutical samples. This study underscores the importance of developing advanced, highly sensitive SERS platforms and integrating data-driven approaches to overcome existing limitations and elevate the capabilities of isomer discrimination to new heights.

## 2. Results and Discussion

### 2.1. Fabrication and Optimization of the Plasmonic Platforms

Quasi-spherical Au NPs are first synthesized by a seeded-growth method (see Experimental Section for details).[40] Briefly, chloroauric acid (HAuCl₄) is added to a boiling aqueous solution of trisodium citrate to form Au NP seeds. After cooling down
<figure><img src="image_2.png" /><figcaption>**Scheme 1.** a) Schematic representation of the plasmonic platforms fabrication by LbL deposition approach, b) schematic representation of the SERS/machine learning sensing approach. Created with BioRender.com.</figcaption></figure>

to 90 °C, additional HAuCl₄ and trisodium citrate are added to promote the growth of the Au NP seeds. After each step, the resulting Au NPs serve as seeds for the subsequent growth stages. The stepwise growth process can be repeated multiple times to achieve various NP sizes. In order to optimize the sensing capabilities of the plasmonic platforms, five different Au NP sizes are selected; therefore the growth steps are repeated 2, 5, 8, 9, or 10 times.

Figure S1 (Supporting Information) displays the optical properties of the resulting colloidal Au NPs, highlighting the typical LSPR band of spherical NPs. Notably, the LSPR band maxima exhibit a red shift as the number of growth steps increases, indicating a corresponding increase in nanoparticle size. This is confirmed by scanning transmission electron microscopy (STEM) characterization (Figure S2, Supporting Information). As can be seen in Figure S3 (Supporting Information), the average size of the Au NPs determined from STEM images amounts to 18.5 ± 2.2 nm (2 growth steps), 32.0 ± 5.1 nm (5 growth steps), 55.0 ± 7.9 nm (8 growth steps), 70.2 ± 7.1 nm (9 growth steps) and 80.9 ± 11.2 nm (10 growth steps). Representative X-ray photoelectron spectroscopy (XPS) analysis of 55.0 nm Au NPs reveals that the produced nanoparticles are minimally oxidized (Figure S4, Supporting Information). The high-resolution spectrum of the Au4f core level is characterized by three pairs of peaks due to Au4f<sub>7/2</sub> and Au4f<sub>5/2</sub> spin-orbit coupling.[41] The dominant pair can be ascribed to elemental Au(0) (binding energies of 83.8 and 87.5 eV, respectively). The other pairs are related to the only two stable gold oxide states, Au(I) (binding energies of 85.0 and 88.7 eV) and Au(III) (binding energies of 86.0 and 89.9 eV). On the basis of relative peak areas, their respective atomic percent-

ages are estimated as 92.8% for Au(0), 4.6% for Au(I), and 2.6% for Au(III).

The fabrication of the plasmonic platforms involves the electrostatic LbL assembly of negatively charged Au NPs and the positively charged polyelectrolyte poly(diallyldimethylammonium chloride) (PDDA) (see the experimental section for details) (**Scheme 1a**). Briefly, glass substrates are modified with PDDA by immersing them in an aqueous PDDA solution (1 mg mL⁻¹, 0.5 mM NaCl). Subsequently, citrate-stabilized Au NPs are electrostatically attached to the PDDA-modified glass by immersing the substrates in an aqueous dispersion of 1.0 mM Au NPs, referred to as one deposition. Au NPs and PDDA deposition steps are alternated to produce controlled nanoparticle assemblies, with a total of three Au NP depositions performed.

Visible-near-infrared (NIR) spectroscopy (Figure 1a; Figure S5, Supporting Information) and scanning electron microscopy (SEM) (Figure 1b–d; Figures S6–S10, Supporting Information) are employed to monitor the LbL assembly of Au NPs. Figure 1a and Figure S5 (Supporting Information) display the optical properties of the plasmonic platforms in air obtained after one (black spectra), two (red spectra), and three (blue spectra) depositions of Au NPs with sizes of 18.5 nm (Figure S5a, Supporting Information), 32.0 nm (Figure S5b, Supporting Information), 55.0 nm (Figure 1a), 70.2 nm (Figure S5c, Supporting Information) and 80.9 nm (Figure S5d, Supporting Information). In all cases, the optical responses exhibit a main LSPR band centered ≈520–540 nm, attributed to individual Au NPs, along with a broad band in the NIR, indicating the presence of Au NP assemblies (i.e., plasmon coupling band). The first observation is that for the initial deposition of Au NPs, the LSPR band centered ≈520–540 nm,
<figure><img src="image_3.png" /></figure>

<figure><img src="image_4.png" /><figcaption>**Figure 1.** Physical characterization of the plasmonic platforms composed of 55.0 nm Au NPs. a) Extinction spectra recorded in the air after one (black spectra), two (red spectra), and three (red spectra) depositions, b–d) SEM images after b) one, c) two, and d) three depositions.</figcaption></figure>

is blue-shifted compared to the colloidal LSPR band (Figure S1b, Supporting Information). This initial shift can be ascribed to the individual Au NPs, where the blue shift occurs due to the lower average refractive index of the surrounding medium (air versus water) after their deposition on the glass substrate. Interestingly, for a single deposition of 18.5 nm Au NPs, the plasmon coupling band is dominant, suggesting a significant amount of Au aggregates, which is corroborated by the SEM images (Figure S6a,b, Supporting Information). As the Au NP size increases to 32.0 nm, the intensities of the two LSPR bands become equal, and for larger sizes, the individual LSPR band becomes dominant. This indicates that with increasing Au NP size, fewer Au NPs exhibit strong coupling after a single deposition. This observation is consistent with the fact that larger Au NPs experience higher electrostatic repulsion, reducing their tendency to form aggregates. Notably, the amount of gold deposited remains nearly constant, as indicated by comparable extinction values in all five cases. After two and three Au NP depositions, the extinction increases in all cases, along with the prominence of the plasmon coupling band, as confirmed by the SEM analysis (Figure 1b–d; Figures S6–S10, Supporting Information) and in agreement with similar systems.[42] The redshift observed in the individual LSPR band with increasing depositions can be attributed to several factors. First, the density of Au NP on the substrate increases with each additional deposition, as evidenced by SEM characterization (Figure 1b–d; Figures S6–S10, Supporting Information), altering the local dielectric environment and resulting in a higher effective refractive index around the particles. This change in the

refractive index shifts the LSPR band to longer wavelengths, as the resonance wavelength is highly sensitive to the surrounding medium. Second, the closer proximity of the particles with increasing depositions enhances plasmonic coupling, which can also contribute to the red shift in the individual band. Additionally, the coupling band at higher wavelengths increases in intensity with increasing depositions, reflecting stronger collective plasmonic interactions between neighboring particles in the assembly. Notably, larger Au NPs exhibit a more pronounced red shift to the NIR region in the plasmon coupling band with an increasing number of depositions. This shift is anticipated to significantly influence the SERS sensing performance of the plasmonic platforms. Additionally, the highest extinction is observed for the 55.0 nm Au NPs (Figure 1, blue spectrum), while larger Au NPs show lower maximum extinction values. This trend is likely due to the strong electrostatic repulsion forces among the larger Au NPs. However, continuing the alternate deposition of PDDA and Au NPs could potentially achieve higher extinction values

To assess the SERS performance of the plasmonic platforms, we examine both the SERS efficiency (i.e., overall SERS intensity using the same experimental conditions) and the signal uniformity along the platforms using 1-naphtalenethiol (1-NAT) as the model molecule. The choice of 1-NAT relies on its strong affinity for gold surfaces and well-characterized Raman spectrum, making it ideal for evaluating SERS efficiency. The different plasmonic platforms are immersed in a solution of $10^{-5}$ M 1-NAT for 1 h, rinsed with water, and then air-dried before the SERS characterization (see experimental section for details). **Figure 2a**
<figure><img src="image_5.png" /><figcaption>**Figure 2.** a) Representative SERS spectrum of 1-NAT, b) SERS mapping obtained at 1375 cm⁻¹ with the optimized plasmonic platform (55.0 nm Au NPs, 2 depositions). Scale bar represents 10 µm, c) average intensities of the ring stretching mode (1375 cm⁻¹) as a function of Au NPs size and depositions with a 785 nm excitation laser line. All SERS measurements are performed with a 100× objective, 1 second of acquisition, and a maximum power of 0.25 kW cm⁻², d) simulated average SERS enhancement as a function of Au NPs size and depositions (see the Experimental Section for details).</figcaption></figure>

shows a representative SERS spectrum of 1-NAT where the dif-
ferent signals can be assigned to ring deformation (790 cm⁻¹),
ring breathing (818, 967 cm⁻¹), C-H bend (1022, 1062, 1137,
1196 cm⁻¹), and ring stretching (1332, 1375, 1452, 1503, 1559,
1586 cm⁻¹).[⁴³] We conduct SERS mappings over large areas (40
× 40 µm² with a 2 µm step size, yielding 441 spectra) by selecting
the ring stretching mode at 1375 cm⁻¹ to evaluate both the unifor-
mity and SERS efficiency of various plasmonic platforms using a
785 nm excitation laser line. A 785 nm excitation laser is selected
because it aligns with the plasmon coupling band. As illustrated
in Figure 2b and Figures S11–S15 (Supporting Information), the
resulting SERS mappings exhibit high uniformity (≥≈66.6% of
the maximum intensity) and consistent intensity across the en-
tire area, regardless of the Au NP size or the number of Au NP de-
positions. This observation further confirms that the disordered
assemblies of Au NPs are capable of generating robust and repro-
ducible SERS signals, even in the presence of uncovered regions
within the substrate. The strong and uniform SERS intensity can
be attributed to the high density of hot spots in the covered re-
gions and the effective averaging effect of the laser excitation spot
(1.13 µm),[⁴⁴] which is significantly larger than the uncovered ar-
eas (below 500 nm). These findings align with previous studies
on disordered Au NP assemblies, reinforcing the reliability of
this approach for SERS-based applications.[²⁵,⁴²,⁴⁵,⁴⁶] The unifor-
mity of the SERS mappings is notably high for specific plasmonic
platforms. Substrates with two or three depositions of 55 nm

Au NPs demonstrate the highest uniformity, exceeding 83% of
the maximum intensity. Similarly, substrates with three deposi-
tions of 18.5 nm and 32.0 nm Au NPs also achieve uniformity
above 83%, however, these smaller nanoparticles exhibit lower
overall SERS intensity due to their reduced plasmonic enhance-
ment, which limits their efficiency in generating strong electro-
magnetic fields. In contrast, substrates with only one deposition
across all Au NP sizes or two depositions of smaller Au NPs (18.5
and 32.0 nm) exhibit the lowest uniformity (above 66.6%), pri-
marily due to lower surface coverage and reduced interparticle
coupling. Additionally, while two or three depositions of larger
Au NPs (70.2 and 80.9 nm) show improved intensity, their uni-
formity is lower compared to 55 nm Au NPs, likely due to slight
non-uniformities caused by particle aggregation and hot-spot de-
activation. The average SERS intensity derived from the mapping
analysis is presented in Figure 2c, from which two key conclu-
sions can be drawn. First, there is a threshold size that influences
SERS efficiency. The smallest Au NPs (18.5 nm) show the low-
est SERS efficiency, which is expected due to their minimal scat-
tering contribution in the extinction spectra.[⁴⁷] SERS efficiency
increases upon using 32.0 nm Au NPs, and from sizes exceed-
ing 55.0 nm it reaches a plateau, becoming nearly independent
of Au NP size up to 80.9 nm. Second, the average SERS signal
markedly increases between the first and second Au NP deposi-
tions, regardless of NP size. However, the third deposition yields
varying results depending on NP size: for the smallest NPs (18.5
and 32.0 nm), SERS efficiency continues to rise; for 55.0 nm NPs, it remains similar to the second deposition; and for the largest NPs (70.2 and 80.9 nm), SERS efficiency actually decreases compared to two depositions. This deactivation behavior is consistent with previous reports involving self-assembled Au nanostars on polystyrene beads[48] or the LbL assembly between 60 nm Au NPs and a pillar[5]arene macrocycle.[42] In such cases, increasing the NP amount leads to a peak in SERS efficiency, followed by a decline, likely due to strong plasmon coupling between NPs with varying orientations and geometries, resulting in hot-spot deactivation. Among the tested plasmonic platforms, we identify two depositions of 55 nm Au NPs as the optimal condition, offering a combination of near-maximum intensity and exceptional uniformity exceeding 83% across the substrate. This configuration achieves an ideal balance between intensity and uniformity, ensuring both high sensitivity and reproducibility for SERS-based sensing. This carefully optimized platform highlights the robustness of our approach and demonstrates its adaptability to practical applications.

To further confirm the hypothesis of our experimental results, we conduct electromagnetic simulations of the SERS intensities, obtained as the product of the near electric-field enhancements generated under normal irradiation with light at 785 nm wavelength (corresponding to the laser excitation) and the inelastically emitted Raman signal, respectively (Figure 2d; Figures S16–S19, Supporting Information). The results are then weighted by a Gaussian 2D profile with a standard deviation of 0.1 wavelengths to incorporate the effects of lens diffraction. To better represent the distribution of analyte molecules, the SERS enhancement is determined on closed surfaces surrounding each nanoparticle, assuming a nanoparticle-to-molecule separation of 0.5 nm and a surrounding medium with a permittivity of $ε = 1$ (air). The Raman enhancement is expressed as $|E/E_{inc}|^4$, where $E_{inc}$ represents the incident laser field and E denotes the resulting near field. This procedure is applied to CAD models developed from the SEM images for the three largest NPs (55.0, 70.2, and 80.9 nm) and the three different depositions. As shown in Figure 2d, the simulated SERS enhancements exhibit a trend consistent with the experimental SERS intensities observed in Figure 2c.

## 2.2. SERS Sensing of Isomers

To evaluate the sensing capabilities of the optimized plasmonic platforms, various isomers are selected, starting with three structural isomers, namely hydroquinone, resorcinol, and pyrocatechol, each of significant biological importance. Hydroquinone is commonly used in dermatology as a skin-lightening agent to treat hyperpigmentation disorders such as melasma, freckles, and age spots by inhibiting the enzyme tyrosinase, which is responsible for melanin production.[49,50] Resorcinol, known for its antiseptic, keratolytic, and anti-inflammatory properties, is typically applied in the treatment of skin conditions like acne, eczema, and psoriasis.[51] Pyrocatechol, on the other hand, plays a critical role as an intermediate in the biosynthesis of neurotransmitters such as dopamine, epinephrine, and norepinephrine, which are essential for mood regulation, cognition, and stress response.[52]

For all three isomers, the plasmonic platforms are incubated for 1 h in the analyte solutions at varying concentrations, fol-

lowed by drying prior to measurements (see Experimental Section for further details) (Scheme 1b). **Figure 3a** displays the characteristic SERS spectra of hydroquinone (black spectrum), resorcinol (red spectrum), pyrocatechol (blue spectrum), and their mixture (orange spectrum), with all analytes at a concentration of 100 µM. Simulated Raman spectra and vibrational band assignments can be seen in Figures S20–S22 and Table S1 (Supporting Information). Each structural isomer exhibits at least one unique peak that identifies it distinctly. Even in the mixture of the three isomers (orange spectrum), these characteristic peaks remain clearly identifiable (see colored asterisks). Most structural isomers are easily distinguishable by SERS spectroscopy, without the need for any probe, because their unique molecular arrangements produce distinct vibrational signatures. SERS amplifies these differences, allowing for precise identification of each isomer based on their specific spectral features. A notable feature of SERS sensing is its superior sensitivity compared to traditional analytical methods. To assess this, we determine the limit of detection for the three structural isomers. As shown in Figure S23 (Supporting Information), the lowest detectable concentration for all three isomers amounts to 1 µM. Over the concentration range of 1–100 µM, the relationship between isomer concentration and SERS intensity could be expressed using the empirical formula $I = b \cdot C + a$, where I represents the SERS intensity, C is the molar concentration of the isomer, b is the sensitivity (slope), and a is the intercept (Figure S23b,d,f, Supporting Information). Notably, the sensitivity of the plasmonic platforms for hydroquinone (219.4 M⁻¹), resorcinol (173.6 M⁻¹), and pyrocatechol (298.4 M⁻¹) is relatively similar. Importantly, when we calculate the concentrations of hydroquinone, resorcinol, and pyrocatechol in their mixture using the individual linear regression models obtained for each compound, the concentrations are found to be ≈100 µM.

The sensing capabilities of the plasmonic platforms are subsequently evaluated using geometrical isomers, specifically Z/E isomers. For these types of isomers, two distinct scenarios emerge. On one hand, certain Z/E isomers, such as (Z)-stilbene and (E)-stilbene, can be easily distinguished by the naked eye. Figure 3b illustrates the characteristic SERS spectra of (Z)-stilbene (spectrum in black), (E)-stilbene (spectrum in blue), and their mixture (spectrum in orange), with all analytes at a concentration of 100 µM. Experimental and simulated Raman spectra together with the assignments of vibrational bands are presented in Figures S24–S26 and Table S2 (Supporting Information). Analogously to structural isomers, these geometrical isomers show at least one unique peak that allows for their distinct identification. Moreover, even in the mixture of the two isomers (orange spectrum), these characteristic peaks remain clearly identifiable, as indicated by the colored asterisks.

On the other hand, there are other geometrical isomers, such as (Z)-resveratrol and (E)-resveratrol, whose characteristic Raman features are not easily distinguished by the naked eye (Figure 3c). These geometrical isomers are selected for their distinct biological activities. (E)-resveratrol possesses antioxidant, anti-inflammatory, and anti-cancer properties,[53] while (Z)-resveratrol exhibits significant antiviral activity.[54] Experimental and simulated Raman spectra together with the assignments of vibrational bands are presented in Figures S27–S29 and Table S3 (Supporting Information). To effectively distinguish
<figure><img src="image_6.png" /><figcaption>**Figure 3.** Representative SERS spectra of a) individual structural isomers and their mixture, and b,c) individual geometrical isomers and their mixtures, d) 3D PCA score plot of (Z)-resveratrol, (E)-resveratrol and their mixture. In all cases, the analyte concentration is 100 µM.</figcaption></figure>

between (Z)-resveratrol, (E)-resveratrol, and their mixtures, we employed principal component analysis (Figure 3d; Figures S30, S31, Supporting Information). Despite the high similarity of these compounds, as evidenced by the fact that the first principal component (PC1) accounts for over 95% of the explained variance (Figure S31, Supporting Information), the PCA approach successfully leveraged more subtle variations captured by PC2, PC3, and PC4 scores. As shown in Figure 3d, the PCA score plot clearly clusters these three compounds into distinct groups. This distinct clustering demonstrates the effectiveness of our analytical strategy in identifying and separating structural differences among similar geometrical isomers. Besides, Figure S32 (Supporting Information) shows the X-loadings for each component in the PCA model. These loadings highlight the spectral features that most influence the variance captured by each component, providing insights into the key shifts responsible for distinguishing between the different samples. To evaluate the sensitivity of the plasmonic platforms, we determine the limit of detection for the two geometrical isomers of resveratrol. As shown in Figure S33 (Supporting Information), the lowest detectable concentration are 0.1 µM for (Z)-resveratrol and an impressive 0.01 µM for (E)-resveratrol. Over the concentration range from 0.01 to 100 µM, the relationship between isomer concentration and SERS intensity are best described by a logarithmic empirical model: $log I = 0.293 log C \pm 4.62$ for (Z)-resveratrol and $log I = 0.35 log C \pm 4.57$ for (E)-resveratrol (Figure S33b,d, Supporting Information).

Finally, the sensing capabilities of the plasmonic platforms are assessed using optical isomers, namely S- and R-ibuprofen, due to their significant relevance in the pharmaceutical industry. S-ibuprofen is an effective pain reliever, while R-ibuprofen is pharmacologically inactive. These types of isomers present a unique challenge in detection because they possess identical chemical structures, differing only in their spatial configuration. As can be seen in their Raman spectra (Figure S34, Supporting Information) and their SERS spectra (Figure 4a), no major differences are visible between the two enantiomers. Simulated Raman spectra and vibrational band assignments are provided in Figures S35, S36 and Table S4 (Supporting Information). PCA analysis is then performed on both enantiomers and their mixture (Figure 4b; Figure S37, Supporting Information). However, due to the lack of significant structural differences between S- and R-ibuprofen, clear clusters could not be formed, indicating that PCA analysis alone is insufficient to differentiate the enantiomers.

To address this challenge, our strategy involves the use of a probe molecule specifically designed to induce a specific orientation of the analytes on the plasmonic surface in a way that enhances the distinction between the enantiomers. While most existing probes rely on hydrogen bonding between the probe and the analyte,[³⁶⁻³⁹] it is important to explore alternative probes that employ non-covalent interactions to improve selectivity as well as sensitivity in the detection process. In this study, we select 1-NAT as the probe for several key reasons: 1) its thiol group has a strong
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<figure><img src="image_8.png" /></figure>

<figure><img src="image_9.png" /></figure>

<figure><img src="image_10.png" /></figure>

<figure><img src="image_11.png" /></figure>

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affinity for Au NPs, ensuring stable attachment to the plasmonic platform; 2) it produces a distinct Raman spectrum, facilitating clear identification of the probe; and 3) it engages in π-π interactions with a range of analytes, including ibuprofen isomers, adding additional layer of selectivity in isomer detection.

The first step involves exposing the plasmonic platforms, pre-
functionalized with 1-NAT, to solutions containing R-ibuprofen,
S-ibuprofen, or their mixture and recording their SERS spec-
tra. As can be seen in Figure 4c, although the differences be-
tween the spectra are not clearly visible, when PCA is applied
(Figure 4d; Figures S38-S40, Supporting Information), distinct
clusters form for each enantiomer and the mixture, enabling
clear differentiation between them. This demonstrates that the
use of 1-NAT as a probe significantly enhances the capability of
the plasmonic platform to differentiate optical isomers, by deter-
mining the specific orientation of ibuprofen enantiomers on the
surface through non-covalent interactions with 1-NAT.

To gain further insight into the intermolecular interactions
between the ibuprofen enantiomers and 1-naphthalenethiol,
geometrical optimizations of various parallel orientations of
both compounds are performed using DFT calculations (see
Figure S41, Supporting Information). The optimized complexes
show different π–π displaced stacking interactions between the
central phenyl ring of ibuprofen and the fused rings of 1-
naphthalenethiol, with distances ≈3.3–3.5 Å. According to the
computed interaction energies, the formation of these π–π com-
plexes stabilizes the system by ≈7.9 kcal mol⁻¹, with minor differ-
ences depending on the relative orientation of the carboxyl and
thiol groups of both compounds (see Table S5, Supporting Infor-
mation). The results of the energy decomposition analysis, per-
formed using the EDA method,[55] suggest that induction is the
main factor stabilizing these complexes. This contribution is as-
sociated with the electrostatic interaction between the induced
multipoles and the electric field created by both molecules. It
should be noted that the same analysis for the benzene dimer, a
typical model for π–π displaced stacked interactions, shows that
the π–π interaction is weaker (E<sub>int</sub> = −3.6 kcal mol<sup>−1</sup>), and the rel-
ative contribution of dispersion to the stabilization of the dimer
is considerably larger (see Table S5, Supporting Information).
While our analysis focuses on π–π interactions, we acknowledge
that additional forces, such as hydrophobic interactions or van
der Waals forces, could contribute to the overall interaction. The
current results, however, strongly support the predominance of
π–π interactions as the key mechanism.

Additionally, the simulated Raman spectra for the complexes
involving both R/S enantiomers and 1-naphthalenethiol are very
similar, and both profiles agree closely with the experimental data
(Figure S42, Supporting Information). However, a detailed anal-
ysis of individual wavenumbers in the region between 400 and
1500 cm⁻¹ reveals significant differences of ≈10–30 cm⁻¹ for
some signals associated with vibrational modes involving ibupro-
fen (Table S6, Supporting Information). These differences are
due to the distinct geometrical arrangements of the R/S enan-

tiomers relative to 1-naphthalenethiol. Although the computed
spectra are visually indistinguishable, the differences could be
related to the ability of the plasmonic platform to distinguish be-
tween the enantiomers when PCA is applied.

The next step to evaluate the sensing capabilities of the
plasmonic platforms involves quantifying the concentrations of
the analytes using the 1-NAT-modified plasmonic platform. To
achieve this, the platforms are exposed to various concentrations
of R-ibuprofen and S-ibuprofen, ranging from 1 to 100 µM and
their SERS spectra are recorded (Figures S43 and S44, Support-
ing Information). Two machine learning regression techniques
are applied: Partial Least Squares (PLS) Regression (Figure 4e,f)
and Artificial Neural Networks (ANN) (Figure S45, Supporting
Information). The PLS and ANN models are trained using the
Raman spectra obtained from the different concentrations of the
analytes, with the goal of establishing a quantitative relationship
between the Raman signal and the analyte concentration (see ex-
perimental section for details). In both cases, the regression mod-
els exhibit excellent performance (R² ≈0.9999), indicating a high
degree of accuracy and precision in predicting the analyte con-
centrations. The Root Mean Squared Error of Prediction (RM-
SEP), which is directly related to the limit of detection (LOD), is
≈2 × 10⁻⁸ M for both R- and S-ibuprofen (see Table S7, Support-
ing Information). This low RMSEP indicates that the platform
is highly sensitive, capable of detecting ibuprofen isomers at ex-
tremely low concentrations.

Finally, we apply the PLS and ANN models to analyze a
real-world sample, specifically commercial ibuprofen. It is well
known that commercial ibuprofen is sold as a racemic mix-
ture, containing both R- and S-ibuprofen. Given the pharma-
cological inactivity of R-ibuprofen, its concentration is typically
of lesser concern in commercial formulations, and some stud-
ies have reported that R-ibuprofen is metabolized into active S-
ibuprofen following ingestion.[56] However, the exact ratio of each
isomer in commercial ibuprofen is not typically disclosed by the
manufacturers. To investigate the concentration ratio between
the isomers in the commercial product, we first employ a well-
established method, circular dichroism (CD) (Figure S46a, Su-
porting Information).[57] Circular dichroism is often used to de-
termine the concentration of chiral molecules, though it is known
to be less sensitive than SERS spectroscopy combined with ma-
chine learning approaches. In this case, a concentration of 1 mM
is employed to determine the ratio of R-ibuprofen to S-ibuprofen.
We first perform a linear calibration using a total ibuprofen con-
centration of 1 mM, varying the ratios of R- and S-ibuprofen
(Table S8, Supporting Information) to establish reliable calibra-
tion curves (Figure S46b,c, Supporting Information). This cali-
bration is then applied to the real-world sample (Figure S46d,
Supporting Information), where the same total concentration of
1 mM is employed by accurately weighing the powdered pill. The
precise concentrations of each enantiomer in the real-world sam-
ple are found to be 0.54 mM for R-ibuprofen and 0.43 mM for S-
ibuprofen, corresponding to an enantioenrichment of ≈55.82%

<caption>**Figure 4.** a) Representative SERS spectra of individual optical isomers and their mixture, and b) 3D PCA score plot of R-ibuprofen, S-ibuprofen, and their mixture. c) Representative SERS spectra and d) three-dimensional PCA score plot of 1-NAT after incubation with the individual optical isomers and their mixture. In all cases, the analyte concentration is 100 µM. Predictions of the e) S-ibuprofen and f) R-ibuprofen concentrations were made using the PLS model trained with the SERS spectra of solutions with analyte concentrations between 1 and 100 µM.</caption>
<figure><img src="image_13.png" /></figure>

R-ibuprofen and 44.18% S-ibuprofen (Table S9, Supporting Information). To further validate the accuracy and sensitivity of our approach, SERS spectroscopy combined with machine learning is applied to analyze a blind sample of commercial ibuprofen at a significantly lower concentration of 100 µM, one order of magnitude lower than the concentration required for CD analysis, demonstrating the enhanced sensitivity of the SERS-based approach. The PLS and ANN regression models are employed to determine the concentration ratios of R- and S-ibuprofen in this blind sample. The training process utilized Raman spectral data where Raman intensity values are “predictors,” and known concentrations of R- and S-ibuprofen, including solutions of individual enantiomers (Figure S43, Supporting Information) and their mixtures (Figure S44, Supporting Information), are used as response variables to enhance the robustness of the model (see Experimental Section for details). The concentrations of each enantiomer are determined to be 54 µM for R-ibuprofen and 43 µM for S-ibuprofen, corresponding to 55.82% R-ibuprofen and 44.18% S-ibuprofen. This close agreement highlights the robustness and accuracy of the SERS/ML approach. Furthermore, the enhanced sensitivity of SERS combined with machine learning underscores its practical utility for the quantitative analysis of racemic mixtures, particularly in real-world pharmaceutical samples where accurate enantiomeric quantification is critical.

### 3. Conclusion

In summary, we have developed and optimized robust plasmonic platforms for the rapid, ultrasensitive, and precise discrimination of various isomeric compounds using SERS spectroscopy in combination with machine learning algorithms (Figure S48, Supporting Information). Among the five different Au NP sizes and deposition conditions studied, the optimal SERS performance, characterized by both efficiency and uniformity, was achieved with Au NPs of at least 55 nm and two deposition cycles. Increasing the NP size beyond 55 nm or performing additional depositions did not enhance performance, highlighting the optimal configuration for maximum SERS activity.

The optimized plasmonic platforms effectively distinguished structural isomers (hydroquinone, resorcinol, pyrocatechol) and geometric isomers ((Z/E)-stilbene) using their unique vibrational fingerprints. For more complex geometric isomers like (Z/E)-resveratrol, the combination of SERS with PCA enabled accurate classification. By integrating 1-NAT as a probe molecule to orient the enantiomers on the surface of the plasmonic platform through π-π interactions and employing machine learning algorithms such as PLS Regression and ANN, we successfully differentiated and quantified the optical isomers of ibuprofen (R/S), achieving detection limits as low as $2 \times 10^{-8}$ M for both isomers. The effectiveness of this SERS/machine learning approach was validated using a real-world sample of commercial ibuprofen, which is known to be a racemic mixture of R- and S-ibuprofen. The results obtained closely aligned with those from traditional CD analysis, further demonstrating the robustness and accuracy of the developed method.

Overall, our work offers a comprehensive and versatile solution for isomer discrimination, addressing critical challenges in the sensitivity, uniformity, and versatility of SERS substrates. The successful integration of machine learning algorithms with

SERS not only enhances analytical performance but also paves the way for real-time and high-throughput applications across diverse industries, including pharmaceuticals, environmental monitoring, and clinical diagnostics. Future studies may explore the expansion of this platform to a broader range of isomeric compounds and further refinement of machine learning models to enhance classification capabilities in even more complex sample matrices.

### Supporting Information

Supporting Information is available from the Wiley Online Library or from the author.

### Acknowledgements

The authors acknowledge the financial support from the European Union through the HORIZON-EIC-2023-PATHFINDEROPEN-01-01 project ADAPTATION (GA-101129661) as well as the ANR through the Interdisciplinary Thematic Institute SysChem via the IdEx Unistra (ANR-10-INDEX-0002) within the program Investissement d'Avenir, the Foundation Jean-Marie Lehn and the Institut Universitaire de France (IUF).

### Conflict of Interest

The authors declare no conflict of interest.

### Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

### Keywords

gold nanoparticles, isomers, machine learning, plasmonic platforms, sensing, SERS, spectroscopy

Received: October 20, 2024

Revised: December 31, 2024

Published online: January 21, 2025

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