Abstract
Surface-enhanced Raman spectroscopy is a powerful analytical technique for the determination of analytes with the advantages of sensitivity, portability, and simplicity, able to provide structural information for the identification of compounds. However, when it comes to the analysis of complex samples, matrix components may interfere with the analyte quantification. To overcome this shortcoming, a number of approaches have been proposed, such as extraction techniques. Among them, the coupling of chromatography with surface-enhanced Raman spectroscopy seems to be promising. It allows combining the advantages of both techniques, i.e., high efficiency of chromatographic separation and high sensitivity of surface enhanced Raman scattering detection, and makes possible simultaneous quantification of multiple analytes. The review summarizes the latest achievements in the combination of these techniques.
1 Introduction
Despite the rapid development of analytical methods, most of them still suffer from insufficient sensitivity and selectivity, high cost, and inapplicability for on-site analysis. Therefore, there exists a need to develop techniques devoid of these shortcomings. One such technique is surface enhanced Raman scattering (SERS), discovered in the 1970s (; ; ), with the advantages of portability, high sensitivity, rapid and powerful non-destructive detection (; ), which is very sensitive to both chemical and biological species. It is a molecular vibration spectroscopy technique, resulting from an inelastic scattering process, able to provide structural information of a molecule (). Two mechanisms of signal enhancement in SERS have been proposed, namely, electromagnetic enhancement and charge-transfer or chemical enhancement (; ; ). Chemical enhancement is generally limited to two orders of magnitude, while electromagnetic–up to 14 orders of magnitude (; ). Historically, these two mechanisms were thought to be quite different (). To date, numerous studies have revealed that these two mechanisms work together; however, their contribution varies (; ). For both mechanisms, the analyte should be first adsorbed on a SERS active substrate followed by substrate irradiation by monochromatic radiation usually from a laser ().
Electromagnetic enhancement is caused by localized surface plasmon resonance (LSPR) of compounds at the nanostructured surface of noble metals, e.g., Ag, Au, or bi/trimetallic noble metal alloys (), and depends on the roughness of the metal surface (). For the enhancement to occur, the molecule and surface of substrate should be in very close vicinity, i.e., less than 10 nm (; ). Chemical enhancement is considered to involve electron transfer between the analyte and nanostructured surface with the formation of a charge transfer complex (; ), which alters the molecule polarization (; ; ). The morphology and size of the substrates play an essential role in the enhancement of electromagnetic field (). Even slight variations of such parameters of colloidal solutions as cluster sizes and shapes produce changes in the enhancement factors by several orders of magnitude. Optimum enhancement can be observed for metallic substrates with the diameters between 10 and 150 nm (). To achieve better performance, structures with different shapes, e.g., nanospheres, nanorods, nanostars, etc., were synthesized (). In practice, a substrate should provide reasonable enhancement, be reproducible, stable, and robust ().
SERS is highly efficient in the analysis of samples with a simple matrix. However, it is not a separation technique (), and when it comes to real sample analysis, matrix components may interfere with the determination of target analytes (; ; ; ), e.g., due to high fluorescence of natural compounds or interactions with irrelevant components present in the samples (; ; ). The most apparent solution of this issue is a preliminary separation of a complex mixture. For this, several approaches can be applied, such as liquid-liquid extraction (), solid-phase extraction (), dispersive liquid-liquid extraction (), cloud-point extraction (), or utilization of filter membranes (; ; ; ). These approaches provide analyte purification from matrix components along with its concentration. However, analyte losses during extraction can occur, and these techniques can be applied for a limited number of compounds. For the simultaneous determination of multiple analytes, chromatography is the most powerful method. This method is suitable for analysis of complex samples and allows the separation of analytes into narrow zones depending on their physicochemical properties. One of the most informative detection methods coupled with chromatography is mass spectrometry, which allows identification of molecules based on their mass-to-charge ratios (m/z) and fragmentation patterns. However, the instruments are expensive, hardly portable, and require highly qualified personnel. At the same time, surface-enhanced Raman spectroscopy, capable of analytes identification based on their fingerprint spectra (), is devoid of these shortcomings. In this regard, hyphenation of Raman spectroscopy with chromatography has been proposed and is widely applied nowadays. The review summarizes the results of studies utilizing chromatographic separation with subsequent SERS detection for the analysis of real samples and discusses future perspectives of the method.
2 Combination of surface-enhanced Raman spectroscopy with chromatography
Chromatography is based on the analyte distribution between two phases, i.e., mobile and stationary, followed by detection of separated analytes applying various physicochemical principles. The chromatographic methods are divided into planar and column. Paper chromatography and thin-layer chromatography (TLC) are related to planar chromatography. In these methods, the separation of analytes is carried out on plane surfaces and is driven by capillary forces. In gas chromatography (GC) and liquid chromatography (LC), the separation is conducted in columns. The schematic of this classification is given in Figure 1. Nowadays, all these methods were combined with surface-enhanced Raman spectroscopy. Among them, the coupling of SERS with planar chromatography is easier to implement; as a result, it has been applied more widely.
FIGURE 1
2.1 Planar chromatography
2.1.1 Thin-layer chromatography
Compared to column chromatography, thin-layer chromatography with SERS detection has the advantages of rapid on-site detection with low cost, decreased sample pretreatment, and ability to simultaneously analyze several samples (). In thin layer chromatography, chromatographic plates with a sorbent layer are utilized as the stationary phase. After spiking the sample at the starting line, the plates are placed in a separation chamber containing the mobile phase for the development. The analytes with the eluent pass through the sorbent by capillary forces and are separated on the plate according to their affinity to the stationary phase. The parameter used for analyte identification is the retention factor (Rf), i.e., the ratio of the distance passed by the analyte to the distance passed by the eluent (solvent front). After compounds separation, the analytes can be detected by means of several methods, for example, densitometry or fluorescence spectroscopy. TLC provides fast and efficient analyte separation, but the detection methods lack sensitivity and selectivity; hence, SERS was proposed as an alternative due to their high compatibility (). The combination of TLC and SERS was first described in 1977 (; ) and nowadays has found numerous applications in various fields from food safety to environmental protection. Other essential advantages of the method include the lack of cross-contamination due to the use of disposable TLC plates, ability to simultaneously analyze several samples, and its portability. As a result, it can be used for on-site analysis of complex samples. Another interesting application of TLC-SERS is on-site quantitative monitoring of chemical reactions. The reaction mixture is spiked on a TLC plate; after the plate development, SERS is applied for detection, which enables to distinguish even incompletely separated compounds according to their characteristic Raman bands without loss of chemicals. In addition, it can be applied to the detection of compounds unstable under ultraviolet radiation and compounds present at low levels (; ; ).
There exist several approaches for TLC-SERS assay. The first one is based on the analyte separation on commercially available TLC plates with conventional stationary phases followed by the addition of metal nanoparticle (NP) colloids on the developed plate for the sensitive SERS detection (Figure 2); these substrates have random morphology (; ). The other is based on the stationary phase modification to obtain a SERS-active surface with subsequent analyte separation and detection (Figure 3) (; ; ); the substrates have deterministic morphology and were termed as engineered substrates ().
FIGURE 2
FIGURE 3
In the former approach, commercially available TLC plates are mostly used to separate target analytes. Nowadays, there exists a wide range of TLC plates allowing to separate analytes with various physicochemical properties. Even though TLC plates are generally weak Raman scatters (; ), several studies have revealed that they are able to provide pronounced background Raman scattering from the stationary phase (; ; ), which should be considered during optimization of conditions. Among the stationary phases, the most commonly used is silica gel. Although it does not provide significant background to the SERS signal, the interactions between the Si−OH groups of the TLC plate and the analyte can result in hydrogen bonding leading to a shift in the obtained spectrum (; ). What is more, the solvents for the mobile phase should be carefully selected; for example, acetonitrile was reported to adsorb readily to the silver surface and disturb SERS measurements at low analyte concentrations (). To achieve the signal enhancement by SERS detection, valuable metal colloids are introduced to the separated spots of analytes. The spots of analytes can be easily visualized, since most TLC plates contain a fluorescent indicator. To obtain a strong SERS signal, the choice of NPs material is crucial (). For obtaining the best results, nanoparticles preparation procedure, concentration, and spiking volume are optimized (; ). The most used metals for the preparation of colloid solutions are gold and silver due to the simplicity of their preparation by a chemical reduction method, relatively low cost, reasonable stability, and narrow particle size distribution (; ). Even though gold is more inert in comparison to silver, it quite strongly absorbs at around 500 nm. Therefore, silver is more suitable in this wavelength range. At the same time, silver has higher chemical reactivity reducing it lifetime (; ).
Application of conventional TLC plates with subsequent addition of noble metal colloids for SERS detection has been described in various fields, i.e., dietary supplements analysis, food quality control, analysis of environmental samples, or showing the method capability on model solutions (Table 1), for the quantification of antitussive and antiasthmatic drugs (), antidiabetic drugs (; ), lipid-lowering agents (), β-carboline alkaloids (), aflatoxins (), pesticides (; ), substituted aromatic pollutants (), synthetic dyes (; ), etc. Solid samples were mostly prepared for analysis by homogenization followed by their extraction, centrifugation, and spotting supernatant on the starting line of a TLC plate for separation. Liquid samples were diluted with the solvent and centrifuged to reduce matrix effects. In addition to introducing noble metal colloids after TLC separation, immersing TLC plate in silver ion solution was proposed. This approach was applied to the assay of binary mixtures of cresyl violet, bixine, crystal violet, and Cu(II) complex of 4-(2-pyridylazo)resorcinol. Prior to analyte separation, a silica gel 60 TLC plate was immersed in silver nitrate solution. Then, the separated analytes were detected by SERS (). Another implementation of TLC-SERS combination was shown in () for the quantification of 14 citrus flavonoids in various matrices. After their separation on a normal-phase TLC plate, the analytes from spots were extracted in methanol. The supernatant was then analyzed by SERS. A similar approach was applied to the quantification of cotinine and trans-3′-hydroxycotinine, metabolites of nicotine and indicators of smoking status, in urine (). However, non-uniform adsorption of metal colloids on the stationary phase owing to its porosity may occur, and the process of dispensing colloids may disrupt the separated target molecules due to the spot partial solubilization thus affecting accuracy and reproducibility of results (; ; ; ; ). The phenomenon of the formation of a ring-shape spot after the addition of nanoparticles caused by their aggregation was termed as the coffee ring effect (CRE): single CRE if nanoparticles aggregate alone and double CRE if nanoparticles aggregate with the analyte (). The migration of the nanoparticles and analyte due to the CRE can also affect the repeatability of results (). Another important observation is based on the time of SERS signal measurement since Ag or Au colloid spiking to the analyte spot. The nanoparticles were reported to generate hot spots during solvent volatilization, and solvent volatilization-induced analyte concentration occurred. The SERS signal was the strongest when the droplet tended to become dry. However, the reproducibility of the results was poor even if continuous measurements during solvent evaporation were carried out (). The signal enhancement through modification of the TLC plate surface by adding a thin film of water was also noted in (; ). To prevent local drying effects from the laser, a cover glass was used (). The control of nanoparticles colloid solution stability is also quite challenging, since they are prone to spontaneous aggregation, which may also affect reproducibility of results (; ).
TABLE 1
| Matrix | Analyte | Sample preparation | TLC | SERS | LOD | References | ||
|---|---|---|---|---|---|---|---|---|
| Stationary phase | Mobile phase | SERS-active species | Detection | |||||
| Botanical dietary supplements | Gemfibrozil, benproperine phosphate, chlorphenamine maleate | Ultrasound-assisted extraction with methanol | High performance silica gel 60-F254 | Dichloromethane: methanol: water (9 : 1: 0.1, v/v) | DMF and silver colloid (2 : 1, w/w) | 785 nm laser source; ×20 objective; 220 mW excitation power; 15 s | 0.001–0.01 μg/μL | |
| Botanical dietary supplements | Diphenhydramine hydrochloride, benproperine phosphate, chlorphenamine maleate, phenformin hydrochloride | Ultrasound-assisted extraction with methanol | High-performance silica gel 60-F254 (glass back plate) | Dichloromethane: methanol: water (9 : 1: 0.1, v/v) | 50% glycerol silver colloid | 785 nm excitation wavelength; 5 cm−1 resolution; ×20 objective; 200 mW excitation power; 5 s | 0.005–0.01 μg/μL | |
| Botanical dietary supplements | Phenformin hydrochloride, metformin hydrochloride, rosiglitazone maleate, pioglitazone hydrochloride | Ultrasound-assisted extraction with methanol | Silica gel | Dichloromethane: methanol: water (8 : 2: 0.2, v/v) | Silver colloid | 785 nm excitation wavelength; 5 cm−1 resolution; ×20 objective; 100 mW laser power; 5 s | 0.001%–0.2% | |
| Herbal products (herbal extract, granule, and capsules) | Sildenafil | Vortex- and ultrasound-assisted extraction with methanol | Silica gel 60 F254 | Ethyl acetate: isopropanol: 25% ammonia (45 : 5: 2.6, v/v) | Silver colloid | 633 nm excitation wavelength; ×50 objective; 60 s | 2 ng/spot | |
| Herbal products (capsules, pills, concentrated extract) | Sildenafil | Ultrasound-assisted extraction with methanol | HPTLC Silica gel 60 F254 (aluminum base) | Ethyl acetate: isopropanol: 25% ammonia solution (45 : 5: 1, v/v) | Nano silver colloid | 633 nm excitation wavelength; ×50 objective; 60 s | 1.65 ng/spot | |
| Herbal products | Glibenclamide, metformin | Vortex- and ultrasound-assisted extraction with methanol | Silica gel 60 F254 | n-Butyl acetate: methanol: formic acid (11 : 2.5 : 1.5, v/v) | Silver colloid | 633 nm excitation wavelength | 0.5–1 μg/spot | |
| Herbal dietary supplements | Nicotinic acid, simvastatin, probucol, fenofibrate, bezafibrate, ciprofibrate, atorvastatin calcium, fluvastatin sodium, pravastatin tetramethylbutylamine | Ultrasound-assisted extraction with methanol | Silica gel 60-F254 (aluminum back plate) | Petroleum ether: acetic ether: acetic acid (5.5 : 2.8 : 1, v/v) | Silver colloid | 785 nm excitation wavelength; 5 cm−1 resolution; ×20 objective; 200 mW laser power; 15 s | 0.002%–0.10% | |
| Tea leaves | Dursban, phoxim, parathion, methidathion, isocarbophos | Extraction with ethyl acetate and acetone | Slica 80-GF254 TLC with fluorescent indicator | Petroleum ether: ethyl acetate (9 : 1, v/v) | Silver colloid | 785 nm excitation wavelength; ×20 objective; 24 mW laser power | 0.1 ppm (LOQ) | |
| Seeds of Syrian rue (Peganum harmala) | Harmalol, harmaline, harmane, harmine | Extraction with methanol | Silica gel | Trichloromethane: methanol: 10% NH3 (80 : 20: 1.5, v/v) | Silver colloid and 0.5 M KNO3 aqueous solution | 488 nm excitation wavelength; ×20 objective; 30 s | - | |
| Milk | Melamine | Dilution with methanol | Silica gel 60 | Acetone: chloroform: ammonia (7 : 0.5 : 2, v/v) | Gold nanoparticles | 785 nm excitation wavelength; 2 cm−1 resolution; 30 mW laser power; 5,000 ms | - | |
| Hot pot condiments | Papaverine, noscapine | QuEChERS | - | Methylbenzene: acetone: ethanol: ammonium hydroxide 20 : 20: 5 : 1 (v/v) | Silver colloid | 785 nm excitation wavelength; ×50 objective; 50 mW laser power; 5 s | 0.5–3 mg/kg | |
| Fish | Histamine | Extraction with 10% trichloroacetic acid aqueous solution | Silica gel 60 | Methanol: acetone: ammonia (25%), (3 : 7: 0.5, v/v) | Silver nanoparticles and 2 M NaCl aqueous solution | 633 nm excitation wavelength; 10 s | - | |
| Pork | Metronidazole, tinidazole, ronidazole, nitrobenzimidazole, ornidazole, secnidazole, dimetridazole, 2-methyl-5-nitroimidazole, 2-nitroimidazole, 4-nitroimidazole, ipronidazole, metronidazole-OH, hydroxy dimetridazole and 5-chloro-1-methyl-4-nitroimidazole | Extraction with ethyl acetate and n-hexane | - | Methanol: ether: chloroform | Gold glue | 785 nm excitation wavelength | 0.1–100 mg/L | |
| Cherry | Phosmet, thiabendazole, triazophos | Ultrasound-assisted extraction with methanol and dichloromethane mixture (1:1, v/v) | Silica gel 60-F254 | Dichloromethane: petroleum benzin: isopropanol (80 : 20: 3, v/v) | Silver nanoparticles and polyurethane mixture | 785 nm excitation wavelength; 3 mW laser power; ×50 objective | 0.02–0.8 μg/mL | |
| Peanuts | Aflatoxin B1, B2, G1, G2 | Extraction with methanol | Silica gel 60-F254 | Acetone: chloroform (9 : 95, v/v) | Gold colloid | 785 nm excitation wavelength; 2 cm−1 resolution; 30 mW laser power; 10 s | 6.0⋅10−7–1.1⋅10–5 M | |
| Human blood plasma | Apomorphine hydrochloride | EDTA addition | Silica | - | Silver colloid | 785 nm excitation wavelength; ×10 and ×50 objective | 10–4 M | |
| Wastewater | Aniline, benzidine, pyrocatechol, chlorobenzene (p-toluidine, p-nitroaniline, and lentine – library identification)) | - | Silica 60-F254 | n-Hexane: ethyl acetate (3 : 1, v/v) | Silver colloid and 20 mM NaCl mixture | 785 nm excitation wavelength; 5 cm−1 resolution; 40 mW laser power; 50 s | 0.008–0.2 ppm | |
| Food contact materials (paper cups, polypropylene food containers, and polyethylene glycol terephthalate bottles) | Benzidine, 4-aminobiphenyl | Extraction with water, acetic acid, and ethanol | H254TLC | Petroleum ether: ethyl acetate (75 : 25, v/v) | Concentrated gold nanoparticle doped metal-organic framework (AuNPs/MIL-101(Cr)) | 785 nm excitation wavelength; 10 s | 0.21–0.23 μg/L | |
| - | Rhodamine 6G, 4-aminothiolphenol, malachite green isothiocyanate | - | TLC C-18 silica gel | Ethanol | Silver nanocomposite | 488 nm excitation wavelength; ×10 objective; 1.0 mW laser power; 4 s | 1.47⋅10−7–2.74⋅10–6 M | |
| - | Purine, benzoic acid, 1-nitropyrene | - | Silica gel 60 HPTLC | - | Silver colloidal spheres | 514.5 nm excitation wavelength; 10 mW laser power | - | |
| - | Abietic acid, gibberellic acid, kaurenoic acid | - | Si60 F254s RAMAN | - | Silver colloid | 514.5 nm excitation wavelength; ×40 objective | 1.039 ng (gibberellic acid) | |
TLC-SERS using conventional TLC plates.
Abbreviations: LOD, limit of detection; LOQ, limit of quantification.
The latter strategy consists in the development of TLC plates with a built-in SERS structure (Table 2). This allows to avoid uneven distribution of metal nanoparticles on the TLC surface and obtain desirable SERS signal. The method was applied for the quantification of polyaromatic hydrocarbons (), pesticides (; ), antibiotics (), synthetic dyes (), and other compounds. In most cases, sample preparation was simple and fast, which is an apparent advantage of TLC. However, sophisticated equipment is required to prepare such substrates resulting in their high cost (; ). Silver nanorods have been described as efficient substrates for TLC-SERS experiments. At the same time, their efficiency for the separation of different compounds is to be studied (). Also, metal-organic frameworks (MOF) decorated with metal nanoparticles were proposed as SERS substrates due to their high ability to adsorb compounds owing to porous structure and ultra-high surface area. These structures are also not prone to external interference and undesirable aggregation, but the attempts to provide the metallic structures with stable NPs location and orientation failed. The strength of the SERS signal was found to be inversely dependent on the distance from the noble metal surface, making the thickness of the MOF a key element for optimization during the development of SERS substrates (). Alternatively, the fabrication of chips has been described () for simple and fast SERS detection of polycyclic aromatic hydrocarbons in cooking oil.
TABLE 2
| Matrix | Analyte | Sample preparation | TLC conditions | SERS conditions | LOD | References | ||
|---|---|---|---|---|---|---|---|---|
| Stationary phase | Mobile phase | SERS-active species | Detection | |||||
| Edible oil | Pyrene | - | Diatomite | Chloroform: n-hexane (1 : 6, v/v) | Gold colloid | 785 nm excitation wavelength; 30 mW excitation power; 2 s | 10–4 ppb | |
| Chili sauce | Sudan I | Dilution with acetone | Diatomaceous earth plates for TLC | Cyclohexane: ethyl acetate (6 : 1, v/v) | Gold nanoparticles | 785 nm excitation wavelength; ×50 objective | 0.5 ng/spot | |
| Chili oil | - | |||||||
| Orange juice and kale leaves | Carbendazim | - | Diatomite TLC chips fabricated by spin coating | Chloroform: methanol (9 : 1, v/v) | Silver nanoparticles | 785 nm excitation wavelength; 5 cm−1 resolution; 2 s | 2 ppm | |
| Tuna | Histamine | Ultrasound-assisted extraction with 10% trichloroacetic acid | Diatomaceous earth TLC-SERS plates fabricated by spin coating on glass slides | Ethanol: ammonia (3 : 1, v/v) | Gold nanoparticles | 785 nm excitation wavelength; 30 mW excitation power; 2 cm−1 resolution; 5,000 ms | 10 ppm | |
| Beef | Ofloxacin | Centrifugation | Diatomite TLC plate | Chloroform: methanol: ammonia water (20 : 10: 3, v/v) | Silver colloid | 785 nm excitation wavelength; 2 s | - | |
| - | Histamine, melamine, malachite green, rhodamine 6G, and sudan I | - | Diatomite biosilica channel arrays on a glass plate with integrated high density silver nanoparticles | Methanol: ethyl acetate: ammonia (6 : 2: 2, v/v) | 10 mW excitation power; 1 s | - | ||
| Milk | Melamine | Protein precipitation | Si nanowire arrays functionalized with silver nano-dendrites | Acetic acid | - | 785 nm excitation wavelength; 200 mW excitation power; 1 s | 2.5 ppm | |
| - | Methyl orange, cresol red, trans-1,2-bis(4-pyridyl)ethylene, methylene violet 2B | - | Ag nanorods UTLC plate | Methanol: acetonitrile | 785 nm excitation wavelength; 30 mW excitation power; 1–10 s | 10−6–10−5 M | ||
| Melamine, rhodamine 6G | Methanol | |||||||
| - | Rhodamine 6G, crystal violet, and 1,2-di (4-pyridyl)ethylene | - | TLC plate with a built-in Ag nanoparticles layer; silica gel separation layer | Methanol: water (80 : 20) | 633 nm excitation wavelength | - | ||
| Skim milk | Melamine | Dilution with deionized water | Methanol | |||||
| Dyed wool | Synthetic dyes (Diamond Green, Methyl violet, Crystal violet, Rhodamine B, Rhodamine 6G, Eosin A, Martius yellow, Cochineal red A, Fast red AV, Indigo carmine, Methylen blue) | Extraction with methanol | A thin layer of AgI deposited on a gold coated glass slide (AgI@Au) | Ethanol: ammonia (9 : 1, v/v) | - | 785 nm excitation wavelength; 10 mW excitation power; ×20 objective; 3 s | - | |
| Chili oil | Rhodamine B and sudan I | Extraction with methanol | GF254 TLC plate modified with Au@Ag NCs | Cyclohexane: ethyl acetate (6 : 1, v/v) | 785 nm excitation wavelength; 80 mW excitation power; 5 s | 1 ppm | ||
| Soil aqueous solution | Thiram, triadimefon, benzimidazole, thiamethoxam | - | Self-assembly of Ag nanoparticles on TLC plate to preparate TLC-Ag SERS substrate | Hexane: methanol water: ethyl acetate (1 : 2: 4 : 2, v/v) | - | 785 nm excitation wavelength; 1 cm-1 resolution; 30 mW excitation power; 5 s | - | |
| - | Rhodamine B, sudan III, malachite green, congo red | - | TLC fabric with a Cot@Au NPs@β-CD built-in SERS structure | Dichloromethane: methanol (3 : 1, v/v) | 785 nm excitation wavelength; 0.5% excitation power; 10 s | - | ||
| Bellamya aeruginosa | Hypertoxic natural toxin (microcystin-leucine-arginine) | - | ZIF-67/Ag NPs/Au NWs substrate implanted TLC plate | n-Hexane: ethyl acetate: acetonitrile: glacial acetic acid (1 : 3: 9 : 0.1, v/v) | 532 nm excitation wavelength; 1 mW excitation power; 10 s | 2.27⋅10–9 mM | ||
| Paprika powder | Sudan I | Ultrasound-assisted extraction with acetonitrile | Glass plate modified by MIPs | Hexane: chloroform (1 : 1, v/v) | Gold colloid | 785 nm excitation wavelength; 25 mW excitation power; ×50 objective | 1 ppm | |
TLC-SERS using modified TLC plates.
To improve the results of quantification by TLC-SERS, machine learning algorithms were also applied, i.e., principal component analysis (PCA)-back propagation neural network () and PCA with machine learning analysis based on support vector regression (SVR) (), which resulted in elimination of interferences from complex samples.
2.1.2 Paper chromatography
Paper chromatography is a type of planar chromatography utilizing a cellulose paper as the stationary phase. Despite its simplicity, low cost, and portability, the separation process is often slower, less accurate, and spots tend to diffuse higher in comparison to TLC (). Therefore, it is less commonly used than TLC. The combination of paper chromatography with SERS detection was applied for the quantification of dyes, i.e., crystal violet, malachite green, and basic fuchsin (). A conventional chromatographic paper was utilized to separate the analytes, and the developed chromatograms were sprayed with silver colloidal hydrosols prior to SERS detection. Another approach consisted in the paper strip modification for the quantification of rhodamine-6G. For this, a paper strip was fabricated with Au nanodendrite on nickel foam structure (). Also, an inkjet-printed SERS substrate was fabricated by using paper and polymer membranes for sample cleanup and analyte separation with SERS detection. This approach was applied for the determination of melamine ().
2.2 Column chromatography
2.2.1 Liquid chromatography
In the analysis of complex samples containing compounds with similar structures or polarities, efficiency of TLC separation can be insufficient. Column chromatography, especially, high-performance liquid chromatography, has a significantly higher potential for the resolution of complex samples. Despite this, unique identification of compound peaks is still challenging [; ). Coupling of HPLC with mass spectrometry significantly improved the capabilities of both methods and allowed to overcome this limitation. However, these instruments are expensive and require highly qualified personnel. SERS can be a suitable alternative allowing sensitive detection of analytes, because chromatographic separation of complex samples allows producing individual SERS signals (). The combination of HPLC and SERS was first described in 1988 for the determination of pararosaniline hydrochloride, an organic dye (; ). In this method, an effluent from the chromatograph was mixed with Ag sol in a post-column mixing coil followed by SERS detection. Since that time, several studies utilizing HPLC-SERS detection have been reported, which can be divided into at-line and on-line modes. In the at-line approach, after the separation of analytes in the analytical column, the eluate fractions are collected for SERS analysis (; ; ). This approach is easier to implement, and SERS conditions can be optimized for each fraction independently (); however, the analysis time is increased in comparison to on-line approach. In contrast, the on-line approach requires the mixing unit of the eluate with nanoparticles solution and its passing through a Raman flow cell (; ; ), but it has a disadvantage that a continued supply of nanoparticles is required (). Alternatively, a capillary with SERS-active substrates can be connected to the chromatograph for on-line analysis (; ). However, such approach can be prone to memory effects, i.e., a subsequent analyte signal is disturbed by a previous compound residue (; ). Mobile phase composition can also cause Raman spectral interferences and affect SERS activity of the sol (). In this way, acetonitrile was noted to increase a background spectrum, therefore it is often replaced by methanol to increase sensitivity due to the reduced background signal (; ).
For the sensitive determination of purine bases, both at-line and on-line approaches were applied. The former approach was implemented for the quantification of main purine and pyrimidine bases (adenine, guanine, thymine, and cytosine) and two of their most common degradation products (xanthine and hypoxanthine) using a novel SERS substrate based on ZnS/CdSe silver quantum dots for detection (). In the latter approach, eluate containing four separated purine bases (adenine, guanine, hypoxanthine, and xanthine) was mixed with Ag sol and passed through a Raman flow cell (). HPLC-SERS was also applied to the quantification of a folate antagonist methotrexate and its metabolites (), amoxicillin in milk (), pesticides (; ), illicit drugs (; ), etc. Also, the potential of on-line LC-SERS was demonstrated for untargeted tumor metabolomics. The method was applied to the determination of metabolites from cell lysate samples of tumors and showed results comparable with LC-MS (). SERS favorably compares to other detection methods due to the ability to identify certain class members of compounds owing to characteristic band patterns ().
2.2.2 Gas chromatography
The coupling of gas chromatography with SERS detection is less common, mostly due to the advantages in mass spectrometric (MS) detection (), where the direct coupling of a GC with a mass spectrometer is easy to implement. Moreover, for GC-MS method, there exist mass spectral libraries, allowing the identification of compounds, which hinders the benefits of SERS. As a results, even though the combination of GC and SERS in principle is possible (), the general acceptance is low ().
Several attempts were made to combine GC and SERS, e.g., trapping GC eluate either in liquid silver sol (“flow-cell” method) or on TLC plates coated with silver colloidal solution (“mobile-phase elimination” method) followed by SERS quantification of pyridine (). Alternatively, an off-line approach was proposed for the quantification of caffeine and p-nitrothiophenol by condensing the analytes on a liquid-nitrogen-cooled silver surface that had been formed on the surface of a ZnSe plate by physical vapor deposition ().
3 Future perspectives
Even though a huge progress in coupling chromatographic separation with SERS detection has been achieved, there still exist opportunities and challenges in the application of these techniques, especially in substrate stability and reproducibility of results. Noble metal nanoparticles are widely used as SERS substrates; however, the sensitivity of detection is largely dependent on nano-substrate properties. To achieve more accurate and sensitive detection, the shape and formation of the nanoparticles of noble metals should be improved further (). To improve sensitivity and reproducibility of results, the development of SERS-active surfaces seems to be perspective. For obtaining higher accuracy, there is a great potential of applying machine-learning techniques, especially in the case of coeluting compounds or interferences from matrix components. Also, the application of noble metals, e.g., gold, silver, as SERS substrates hinders biocompatibility and poses other issues (). Another important aspect aimed at improving biocompatibility is reusability of the developed SERS-active surfaces. Since highly sensitive and accurate quantification of analytes in diverse complex matrices is required, there is a need to the reveal the potential of the SERS combined techniques on a wider range of target analytes and matrices. Preferably, the developed techniques should be simple, convenient, and inexpensive.
Statements
Author contributions
ED: Conceptualization, Methodology, Writing–original draft. OK: Conceptualization, Methodology, Writing–review and editing. SL: Formal Analysis, Writing–review and editing. OS: Writing–review and editing. IV: Conceptualization, Methodology, Project administration, Writing–review and editing.
Funding
The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This research was funded by a grant from the Ministry of Science and Higher Education of the Russian Federation, grant number 075-15-2024-646.
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Generative AI statement
The author(s) declare that no Generative AI was used in the creation of this manuscript.
Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
References
1
AlvesI. M.MeloN. O.MarinhoP. A.AlmeidaM. R. (2020). Liquid–liquid extraction-assisted SERS-based detection of clonazepam in spiked drinks. Vib. Spectrosc.110, 103112. 10.1016/j.vibspec.2020.103112
2
AyK. O.DikmenG.KoyuncuO. (2024). Application of high sensitive silver nanocubes SERS substrate for detection of metribuzin. J. Mol. Struct.12975, 136869. 10.1016/j.molstruc.2023.136869
3
CabalinL. M.RuperezA.LasernaJ. J. (1993). Surface-enhanced Raman spectrometry for detection in liquid chromatography using a windowless flow cell. Talanta40 (11), 1741–1747. 10.1016/0039-9140(93)80092-6
4
CaiG.GeK.OuyangX.HuY.LiG. (2020). Thin-layer chromatography combined with surface-enhanced Raman scattering for rapid detection of benzidine and 4-aminobiphenyl in migration from food contact materials based on gold nanoparticle doped metal-organic framework. J. Sep. Sci.43 (14), 2834–2841. 10.1002/jssc.202000145
5
CañamaresM. V.ReaganD. A.LombardiJ. R.LeonaM. (2014). TLC-SERS of mauve, the first synthetic dye. J. Raman Spectrosc.45, 1147–1152. 10.1002/jrs.4508
6
Carrillo-CarriónC.ArmentaS.SimonetB. M.ValcárcelM.LendlB. (2011). Determination of pyrimidine and purine bases by reversed-phase capillary liquid chromatography with at-line surface-enhanced Raman spectroscopic detection employing a novel SERS substrate based on ZnS/CdSe silver-quantum dots. Anal. Chem.83 (24), 9391–9398. 10.1021/ac201821q
7
Carrillo-CarriónC.SimonetB. M.ValcárcelM.LendlB. (2012). Determination of pesticides by capillary chromatography and SERS detection using a novel silver-quantum dots “sponge” nanocomposite. J. Chromatogr. A1225, 55–61. 10.1016/j.chroma.2011.12.002
8
ChenD.HanX.JinW.ZhangB. (2015a). Metal nanoparticle catalyzed cyclobutane cleavage reaction. RSC Adv.5, 100722–100724. 10.1039/C5RA21225A
9
ChenJ.AbellJ.HuangY. W.ZhaoY. (2012). On-chip ultra-thin layer chromatography and surface enhanced Raman spectroscopy. Lab. Chip12 (17), 3096–3102. 10.1039/c2lc40221a
10
ChenJ.HuangY. W.ZhaoY. (2015b). Detection of polycyclic aromatic hydrocarbons from cooking oil using ultra-thin layer chromatography and surface enhanced Raman spectroscopy. J. Mater. Chem. B3 (9), 1898–1906. 10.1039/c4tb01632g
11
CimpoiuC.CasoniD.HosuA.MiclausV.HodisanT.DamianG. (2005). Separation and identification of eight hydrophilic vitamins using a new TLC method and Raman spectroscopy. J. Liq. Chromatogr. Relat. Technol.28 (16), 2551–2559. 10.1080/10826070500189737
12
CowcherD. P.JarvisR.GoodacreR. (2014). Quantitative online liquid chromatography-surface-enhanced Raman scattering of purine bases. Anal. Chem.86 (19), 9977–9984. 10.1021/ac5029159
13
DaiH.HeL. (2023). Mirror-Stamping: a new TLC-SERS method for color authentication. Talanta Open7, 100237. 10.1016/j.talo.2023.100237
14
Duy VuT.Thang NguyenD.Yen Thi NguyenH.Hoang DoH.Duc PhamT.Thanh LeS.et al (2024). A facile paper-based chromatography coupled Au nanodendrite on nickel foam for application in separation and SERS measurement. Spectrochim. Acta A Mol. Biomol. Spectrosc.313, 124137. 10.1016/j.saa.2024.124137
15
FangF.QiY.LuF.YangL. (2016). Highly sensitive on-site detection of drugs adulterated in botanical dietary supplements using thin layer chromatography combined with dynamic surface enhanced Raman spectroscopy. Talanta146, 351–357. 10.1016/j.talanta.2015.08.067
16
FangG.HasiW.LinX.HanS. (2024). Automated identification of pesticide mixtures via machine learning analysis of TLC-SERS spectra. J. Hazard. Mater.474, 134814. 10.1016/j.jhazmat.2024.134814
17
FateixaS.RaposoM.NogueiraH. I. S.TrindadeT. (2018). A general strategy to prepare SERS active filter membranes for extraction and detection of pesticides in water. Talanta182, 558–566. 10.1016/j.talanta.2018.02.014
18
FerrettiA.FlorisE.CampanellaB.DeganoI.LegnaioliS. (2024). “Welcome to the jungle”: TLC-SERS to wade through real complex mixtures of synthetic dyes. Microchem. J.206, 111439. 10.1016/j.microc.2024.111439
19
FreemanR. D.HammakerR. M.MeloanC. E.FateleyW. G. (1988). A detector for liquid chromatography and flow injection analysis using surface-enhanced Raman spectroscopy. Appl. Spectrosc.42 (3), 456–460. 10.1366/0003702884427997
20
FreyeC. E.CraneN. A.KirchnerT. B.SepaniakM. J. (2013). Surface enhanced Raman scattering imaging of developed thin-layer chromatography plates. Anal. Chem.85 (8), 3991–3998. 10.1021/ac303710q
21
FukunagaY.OgawaR.HommaA.OkadaT. (2023). Thin layer chromatography-freeze surface-enhanced Raman spectroscopy: a powerful tool for monitoring synthetic reactions. Chem. Eur. J.29, e202300829. 10.1002/chem.202300829
22
GaoF.HuY.ChenD.Li-ChanE. C. Y.GrantE.LuX. (2015). Determination of Sudan I in paprika powder by molecularly imprinted polymers-thin layer chromatography-surface enhanced Raman spectroscopic biosensor. Talanta143, 344–352. 10.1016/j.talanta.2015.05.003
23
GoodacreR.GrahamD.FauldsK. (2018). Recent developments in quantitative SERS: moving towards absolute quantification. Trends Anal. Chem.102, 359–368. 10.1016/j.trac.2018.03.005
24
HanC.WangQ.YaoY.ZhangQ.HuangJ.ZhangH.et al (2023). Thin layer chromatography coupled with surface enhanced Raman scattering for rapid separation and on-site detection of multi-components. J. Chromatogr. A1706, 464217. 10.1016/j.chroma.2023.464217
25
HeapsD. A.GriffithsP. R. (2005). Off-line direct deposition gas chromatography/surface-enhanced Raman scattering and the ramifications for on-line measurements. Appl. Spectrosc.59 (11), 1305–1309. 10.1366/000370205774783160
26
HenzelU. B. (1977). Journal of chromatography library. Editors Zlatkis,A.KaiserR. E. (Amsterdam: Elsevier), 9. chapter 8.
27
HermanK.MircescuN. E.SzaboL. L.LeopoldF.ChişV.LeopoldN. (2013). In situ silver spot preparation and on-plate surface-enhanced Raman scattering detection in thin layer chromatography separation. J. Appl. Spectrosc.80, 311–314. 10.1007/s10812-013-9765-9
28
HorváthE.KátayG.TyihákE.KristófJ.RédeyÁ. (2000). Critical evaluation of experimental conditions influencing the surface-enhanced Raman spectroscopic (SERS) detection of substances separated by layer liquid chromatographic techniques. Chromatographia51, S297–S301. 10.1007/BF02492821
29
HouX.SivashanmuganK.ZhaoY.ZhangB.WangA. X. (2022). Multiplex sensing of complex mixtures by machine vision analysis of TLC-SERS images. Sens. Actuators B Chem.357, 131355. 10.1016/j.snb.2021.131355
30
HuX.FangG.HanA.LiuJ.WangS. (2017). Rapid detection of Pericarpium papaveris in hot pot condiments using thin-layer chromatography and surface enhanced Raman spectroscopy combined with a support vector machine. Anal. Methods.9, 2177–2182. 10.1039/C7AY00151G
31
HuangR.HanS.LiX. S. (2013). Detection of tobacco-related biomarkers in urine samples by surface-enhanced Raman spectroscopy coupled with thin-layer chromatography. Anal. Bioanal. Chem.405 (21), 6815–6822. 10.1007/s00216-013-7107-7
32
IstvánK.KereszturyG.SzépA. (2003). Normal Raman and surface enhanced Raman spectroscopic experiments with thin layer chromatography spots of essential amino acids using different laser excitation sources. Spectrochim. Acta A Mol. Biomol. Spectrosc.59 (8), 1709–1723. 10.1016/s1386-1425(02)00408-0
33
JeanmaireD. L.Van DuyneR. P. (1977). Surface Raman spectroelectrochemistry: Part I. Heterocyclic, aromatic, and aliphatic amines adsorbed on the anodized silver electrode. J. Electroanal. Chem.84 (1), 1–20. 10.1016/s0022-0728(77)80224-6
34
JensenL.AikensC. M.SchatzG. C. (2008). Electronic structure methods for studying surface-enhanced Raman scattering. Chem. Soc. Rev.37 (5), 1061–1073. 10.1039/b706023h
35
JiangJ.LiuM.XuD.JiangT.ZhangJ. (2024). Quantitative detection of microcystin-LR in Bellamya aeruginosa by thin-layer chromatography coupled with surface-enhanced Raman spectroscopy based on in-situ ZIF-67/Ag NPs/Au NWs composite substrate. Food Chem.452, 139481. 10.1016/j.foodchem.2024.139481
36
KangY.WuT.ChenW.LiL.DuY. (2019). A novel metastable state nanoparticle-enhanced Raman spectroscopy coupled with thin layer chromatography for determination of multiple pesticides. Food Chem.270, 494–501. 10.1016/j.foodchem.2018.07.070
37
KhrushchevA.Yu.AkmaevE. R.KisI. V.GulyaevaA.Yu.BondarenkoV. O. (2022). Combination of HPLC and SERS detection applied to the analysis of the trace content of amoxicillin in milk. Vib. Spectrosc.123, 103473. 10.1016/j.vibspec.2022.103473
38
KoglinE. (1988). Combining surface enhanced Raman scattering (SERS) and high-performance thin-layer chromatography (HPTLC). J. Mol. Struct.173, 369–376. 10.1016/0022-2860(88)80068-1
39
KongX.SquireK.ChongX.WangA. X. (2017). Ultra-sensitive lab-on-a-chip detection of Sudan I in food using plasmonics-enhanced diatomaceous thin film. Food control.79, 258–265. 10.1016/j.foodcont.2017.04.007
40
LeeB. S.LinD. Z.HuangC. H.YenT. J. (2018). A high‐performance multifunctional substrate of ultrathin‐layer chromatography (UTLC) and surface‐enhanced Raman scattering (SERS) for rapid biochemical mixture screening. J. Raman Spectrosc.49, 1920–1927. 10.1002/jrs.5477
41
LeeY.-H.DaiS.YoungJ. P. (1998). Silver-doped sol–gel films as the substrate for surface-enhanced Raman scattering. J. Raman Spectrosc.28, 635–639. 10.1002/(SICI)1097-4555(199708)28:8<635::AID-JRS152>3.0.CO;2-0
42
LiD.QuL.ZhaiW.XueJ.FosseyJ. S.LongY. (2011). Facile on-site detection of substituted aromatic pollutants in water using thin layer chromatography combined with surface-enhanced Raman spectroscopy. Environ. Sci. Technol.45 (9), 4046–4052. 10.1021/es104155r
43
LiY.ZhaoC.LuC.ZhouS.TianG.HeL.et al (2021). Simultaneous determination of 14 bioactive citrus flavonoids using thin-layer chromatography combined with surface enhanced Raman spectroscopy. Food Chem.338, 128115. 10.1016/j.foodchem.2020.128115
44
LuX.MaY.JiangS.WangZ.YuQ.JiC.et al (2024). Quantitative monitoring ofloxacin in beef by TLC-SERS combined with machine learning analysis. Spectrochim. Acta A Mol. Biomol. Spectrosc.308, 123790. 10.1016/j.saa.2023.123790
45
LucottiA.TommasiniM.CasellaM.MorgantiA.GramaticaF.ZerbiG. (2012). TLC–surface enhanced Raman scattering of apomorphine in human plasma. Vib. Spectrosc.62, 286–291. 10.1016/j.vibspec.2012.07.009
46
McNayG.EustaceD.SmithW. E.FauldsK.GrahamD. (2011). Surface-enhanced Raman scattering (SERS) and surface-enhanced resonance Raman scattering (SERRS): a review of applications. Appl. Spectrosc.5 (8), 825–837. 10.1366/11-06365
47
MinhD. T. C.NhuN. T. Q.ThiL. A.VuL. V.LanD. T. N.AnhN. T. K.et al (2024). HPTLC sequentially coupled to UV and SERS: a cost-effective tool for confirmative identification and quantitation of sildenafil in falsified herbal products. J. Pharm. Biomed. Anal.251, 116392. 10.1016/j.jpba.2024.116392
48
MinhD. T. C.ThiL. A.HuyenN. T. T.Van VuL.AnhN. T. K.HaP. T. T. (2019). Detection of sildenafil adulterated in herbal products using thin layer chromatography combined with surface enhanced Raman spectroscopy: “Double coffee-ring effect” based enhancement. J. Pharm. Biomed. Anal.174, 340–347. 10.1016/j.jpba.2019.05.043
49
MinhD. T. C.TramL. T. B.PhongN. H.HuongH. T. L.VuL. V.ThiL. A.et al (2023). Single versus double coffee-ring effect patterns in thin-layer chromatography coupled with surface-enhanced Raman spectroscopic analysis of anti-diabetic drugs adulterated in herbal products. Molecules28 (14), 5492. 10.3390/molecules28145492
50
MuruganE.SanthoshkumarS.GovindarajuS.PalanichamyM. (2021). Silver nanoparticles decorated g-C3N4: an efficient SERS substrate for monitoring catalytic reduction and selective Hg2+ ions detection. Spectrochim. Acta A Mol. Biomol. Spectrosc.246, 119036. 10.1016/j.saa.2020.119036
51
NguyenA.SchultzZ. D. (2016). Quantitative online sheath-flow surface enhanced Raman spectroscopy detection for liquid chromatography. Analyst141, 3630–3635. 10.1039/c6an00155f
52
OrinakA.TalianI.EfremovE. V.ArieseF.OrinakovaR. (2008). Diterpenoic acids analysis using a coupled TLC-surface-enhanced Raman spectroscopy system. Chromatographia67 (5–6), 501–319. 10.1365/s10337-008-0566-x
53
PozziF.ShibayamaN.LeonaM.LombardiJ. R. (2012). TLC-SERS study of Syrian rue (Peganum harmala) and its main alkaloid constituents. J. Raman Spectrosc.44 (1), 102–107. 10.1002/jrs.4140
54
QuL.WangN.XuH.WangW.LiuY.KuoL.et al (2017). Gold nanoparticles and g-C3N4-intercalated graphene oxide membrane for recyclable surface enhanced Raman scattering. Adv. Funct. Mater.27, 1701714. 10.1002/adfm.201701714
55
QuL. L.JiaQ.LiuC.WangW.DuanL.YangG.et al (2018). Thin layer chromatography combined with surface-enhanced Raman spectroscopy for rapid sensing aflatoxins. J. Chromatogr. A1579, 115–120. 10.1016/j.chroma.2018.10.024
56
RaduA. I.KuellmerM.GieseB.HuebnerU.WeberK.Cialla-MayD.et al (2016). Surface-enhanced Raman spectroscopy (SERS) in food analytics: detection of vitamins B2 and B12 in cereals. Talanta160, 289–297. 10.1016/j.talanta.2016.07.027
57
RothE.KieferW. (1994). Surface-enhanced Raman spectroscopy as a detection method in gas chromatography. Appl. Spectrosc.48 (10), 1193–1195. 10.1366/0003702944027444
58
SackmannM.MaternyA. (2006). Surface enhanced Raman scattering (SERS) – a quantitative analytical tool?J. Raman Spectrosc.37, 305–310. 10.1002/jrs.1443
59
SägmüllerB.SchwarzeB.BrehmG.TrachtaG.SchneiderS. (2003). Identification of illicit drugs by a combination of liquid chromatography and surface-enhanced Raman scattering spectroscopy. J. Mol. Struct.661 (662), 279–290. 10.1016/S0022-2860(03)00507-6
60
SanthoshkumarS.WeiS. W.KuoC. C.MadhuM.KumarA.TsengS. K.et al (2025). Engineering of phase composition in molybdenum disulfide nanoflowers to amplify SERS activity: sensitivity enhancement through dispersive liquid-liquid microextraction integration. Sens. Actuators B Chem.422, 136577. 10.1016/j.snb.2024.136577
61
SciuttoG.PratiS.BonaciniI.LittiL.MeneghettiM.MazzeoR. (2017). A new integrated TLC/MU-ATR/SERS advanced approach for the identification of trace amounts of dyes in mixtures. Anal. Chim. Acta.991, 104–112. 10.1016/j.aca.2017.08.020
62
SeifarR. M.DijkstraR. J.BrinkmanU. A. Th.GooijerC. (1999). At-line coupling of surface-enhanced resonance Raman spectroscopy and reversed-phase ion-pair chromatography. Anal. Commun.36, 273–276. 10.1039/A903254A
63
ShaX.HanS.FangG.LiN.LinD.HasiW. (2022). A novel suitable TLC-SERS assembly strategy for detection of Rhodamine B and Sudan I in chili oil. Food control.138, 109040. 10.1016/j.foodcont.2022.109040
64
ShenZ.FanQ.YuQ.WangR.WangH.KongX. (2021). Facile detection of carbendazim in food using TLC-SERS on diatomite thin layer chromatography. Spectrochim. Acta A Mol. Biomol. Spectrosc.247, 119037. 10.1016/j.saa.2020.119037
65
ShengR.NiF.CottonT. M. (1991). Determination of purine bases by reversed-phase high-performance liquid chromatography using real-time surface-enhanced Raman spectroscopy. Anal. Chem.63, 437–442. 10.1021/ac00005a010
66
ShiS.YuH.YangF.YaoW.XieY. (2022). Simultaneous determination of 14 nitroimidazoles using thin-layer chromatography combined with surface-enhanced Raman spectroscopy (TLC-SERS). Food Biosci.48, 101755. 10.1016/j.fbio.2022.101755
67
SubaihiA.TrivediD. K.HollywoodK. A.BluettJ.XuY.MuhamadaliH.et al (2017). Quantitative online liquid chromatography-surface-enhanced Raman scattering (LC-SERS) of methotrexate and its major metabolites. Anal. Chem.89 (12), 6702–6709. 10.1021/acs.analchem.7b00916
68
SzaboN. J.WinefordnerJ. D. (1997). Evaluation of two commercially available TLC materials as SER substrates. Appl. Spectrosc.51 (7), 965–975. 10.1366/0003702971941629
69
TakeiH.SaitoJ.KatoK.ViekerH.BeyerA.GölzhäuserA. (2015). TLC-SERS plates with a built-in SERS layer consisting of cap-shaped noble metal nanoparticles intended for environmental monitoring and food safety assurance. J. Nanomater.2015, 316189. 10.1155/2015/316189
70
TanA.ZhaoY.SivashanmuganK.SquireK.WangA. X. (2019). Quantitative TLC-SERS detection of histamine in seafood with support vector machine analysis. Food control.103, 111–118. 10.1016/j.foodcont.2019.03.032
71
TrachtaG.SchwarzeB.SägmüllerB.BrehmG.SchneiderS. (2004). Combination of high-performance liquid chromatography and SERS detection applied to the analysis of drugs in human blood and urine. J. Mol. Struct.693 (1–3), 175–185. 10.1016/j.molstruc.2004.02.034
72
TranC. D. (1984). In situ identification of paper chromatogram spots by surface enhanced Raman scattering. J. Chromatogr. A292 (2), 432–438. 10.1016/S0021-9673(01)83624-4
73
WangP.SunY.LiX.ShanJ.XuY.LiG. (2021). One-step chemical reaction triggered surface enhanced Raman scattering signal conversion strategy for highly sensitive detection of nitrite. Vib. Spectrosc.113, 103221. 10.1016/j.vibspec.2021.103221
74
WangS.YuQ.GuoJ.YuanC.KongX. (2022). On-site detection of pyrene from mixture with ppb level sensitivity by plasmonic TLC-DSERS. Spectrochim. Acta A Mol. Biomol. Spectrosc.280, 121547. 10.1016/j.saa.2022.121547
75
WangW.XuM.GuoQ.YuanY.GuaR.YaoJ. (2015). Rapid separation and on-line detection by coupling high performance liquid chromatography with surface-enhanced Raman spectroscopy. RSC Adv.5, 47640–47646. 10.1039/C5RA05562H
76
WhiteR. L. (1985). Analysis of thin-layer chromatographic adsorbates by Fourier transform infrared photoacoustic spectroscopy. Anal. Chem.57, 1819–1822. 10.1021/ac00286a008
77
WiggintonK. R.VikeslandP. J. (2010). Gold-coated polycarbonate membrane filter for pathogen concentration and SERS-based detection. Analyst.135, 1320–1326. 10.1039/B919270K
78
WilsonI. D. (2000). “Chromatography. Paper chromatography,” in Encyclopedia of separation science. Editor WilsonI. D. (Academic Press), 397–404.
79
XiaoL.WangC.DaiC.LittlepageL. E.LiJ.SchultzZ. D. (2020). Untargeted tumor metabolomics with liquid chromatography-surface-enhanced Raman spectroscopy. Angew. Chem. Int. Ed. Engl.59 (9), 3439–3443. 10.1002/anie.201912387
80
XieZ.WangY.ChenY.XuX.JinZ.DingY.et al (2017). Tuneable surface enhanced Raman spectroscopy hyphenated to chemically derivatized thin-layer chromatography plates for screening histamine in fish. Food Chem.230, 547–552. 10.1016/j.foodchem.2017.03.081
81
YaoC.ChengF.WangC.WangY.GuoX.GongZ.et al (2013). Separation, identification and fast determination of organophosphate pesticide methidathion in tea leaves by thin layer chromatography–surface-enhanced Raman scattering. Anal. Methods.5, 5560–5564. 10.1039/C3AY41152D
82
YaoH.DongX.XiongH.LiuJ.ZhouJ.YeY. (2022). Functional cotton fabric-based TLC-SERS matrix for rapid and sensitive detection of mixed dyes. Spectrochim. Acta A Mol. Biomol. Spectrosc.280, 121464. 10.1016/j.saa.2022.121464
83
YuW. W.WhiteI. M. (2012). A simple filter-based approach to surface enhanced Raman spectroscopy for trace chemical detection. Analyst137 (5), 1168–1173. 10.1039/c2an15947c
84
YuW. W.WhiteI. M. (2013). Chromatographic separation and detection of target analytes from complex samples using inkjet printed SERS substrates. Analyst138 (13), 3679–3686. 10.1039/c3an00673e
85
ZhangY.ZhaoS.HeL.ZhengJ. (2017). Surface-enhanced Raman spectroscopy (SERS) combined techniques for high-performance detection and characterization. Trends Anal. Chem.90, 1–13. 10.1016/j.trac.2017.02.006
86
ZhangZ.LiuR.LiuJ. (2021). Simultaneously preconcentration of malachite green and construction of SERS substrate in water based on cloud point extraction. Microchem. J.169, 106572. 10.1016/j.microc.2021.106572
87
ZhangZ. M.LiuJ. F.LiuR.SunJ. F.WeiG. H. (2014). Thin layer chromatography coupled with surface-enhanced Raman scattering as a facile method for on-site quantitative monitoring of chemical reactions. Anal. Chem.86 (15), 7286–7292. 10.1021/ac5017387
88
ZhaoY.TanA.SquireK.SivashanmuganK.WangA. X. (2019). Quaternion-based parallel feature extraction: extending the horizon of quantitative analysis using TLC-SERS sensing. Sens. Actuators B Chem.299, 126902. 10.1016/j.snb.2019.126902
89
ZhuQ.CaoY.CaoY.ChaiY.LuF. (2014). Rapid on-site TLC-SERS detection of four antidiabetes drugs used as adulterants in botanical dietary supplements. Anal. Bioanal. Chem.406 (7), 1877–1884. 10.1007/s00216-013-7605-7
90
ZhuQ.CaoY.LiD.FangF.LuF.YuanY. (2019). A fast response TLC-SERS substrate for on-site detection of hydrophilic and hydrophobic adulterants in botanical dietary supplements. New J. Chem.43, 13873–13880. 10.1039/C9NJ02489A
91
ZhuQ.ChenM.HanL.YuanY.LuF. (2017). High efficiency screening of nine lipid-lowering adulterants in herbal dietary supplements using thin layer chromatography coupled with surface enhanced Raman spectroscopy. Anal. Methods.9, 1595–1602. 10.1039/c6ay03441a
Summary
Keywords
surface-enhanced Raman spectroscopy, chromatography, TLC-SERS, HPLC-SERS, method development
Citation
Dmitrieva EV, Kapitanova OO, Lv S, Sinyashin OG and Veselova IA (2025) Coupling of chromatography with surface-enhanced Raman spectroscopy: trends and prospects. Front. Chem. 13:1548364. doi: 10.3389/fchem.2025.1548364
Received
19 December 2024
Accepted
04 February 2025
Published
26 February 2025
Volume
13 - 2025
Edited by
Serban Moldoveanu, Independent Researcher, Greensboro, NC, United States
Reviewed by
Santhoshkumar Sampath Kumar, National Sun Yat-sen University, Taiwan
Shima Salehinia, University of the Balearic Islands, Spain
Updates
Copyright
© 2025 Dmitrieva, Kapitanova, Lv, Sinyashin and Veselova.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Ekaterina V. Dmitrieva, dmitrieva@smbu.edu.cn
Disclaimer
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.