Application of Non-Destructive Raman Spectroscopy and Process Analytical Technology (PAT) for Real-Time Trace Contaminant Profiling in Botanical Active Pharmaceutical Ingredients
Abstract
Background
Botanical active pharmaceutical ingredients (APIs) and botanical drug substances require quality strategies capable of controlling variability and managing contamination risks using a “totality of the evidence” approach that includes botanical raw material control and chemical testing such as spectroscopic and/or chromatographic methods. [1] Regulatory guidance explicitly expects tests for residual pesticides and adventitious toxins (e.g., aflatoxins), as well as controls addressing foreign materials and adulterants, which motivates rapid screening approaches that can be deployed across the supply chain and manufacturing lifecycle. [1]
Objective
This conceptual proof-of-concept and data-synthesis study evaluates how non-destructive Raman spectroscopy (including SERS-enhanced variants) can be integrated into a Process Analytical Technology (PAT) framework for real-time or near-real-time trace contaminant profiling in botanical APIs, with emphasis on feasibility, analytical performance, and deployment constraints supported by published evidence. [2, 3]
Methods
We synthesized evidence showing:
- Raman’s chemical-structure sensitivity and minimal sample preparation needs; [2, 4]
- SERS enhancement and representative trace pesticide demonstrations (including ppm-to-sub-ppb regimes); [5–8]
- Chemometric strategies for adulterant authentication and quantitative prediction; [9–11]
- PAT-aligned process monitoring examples and known barriers to industrial translation. [3]
Results
Across compiled studies, Raman and chemometrics discriminated adulterated essential oils when visual inspection was insufficient, with PCA providing spectral separation between pure and adulterated samples. [9] Quantitative Raman modeling (PLSR) achieved high levels of prediction accuracy in concentration-prediction tasks, supporting the plausibility of calibration-based quantitation in complex formulations. [10]
For trace contaminants, SERS studies reported detection down to 1 ppm on fruit surfaces for selected pesticides and, in other work, measured LODs spanning 0.001–10 ppm across 21 pesticides using colloidal gold nanoparticles. [6, 7] Handheld SERS with QuEChERS acetate extraction detected multiple pesticides below an EU MRL of 10 ppb in basmati rice for selected analytes, with extraction completed in less than 15 min, illustrating a pragmatic “screen-first” workflow. [8]
For PAT use, Raman’s rapid, non-destructive, noninvasive measurements and ability to be deployed from laboratory to production lines support inline/online monitoring. However, evidence also emphasizes that most PAT research remains lab-scale, and that Raman process models can have relatively high LODs that miss low-concentration targets in extraction monitoring settings. [2, 3]
Conclusions
Evidence supports a feasible Raman/SERS-enabled PAT concept for botanical API contaminant risk management: deploy portable Raman for incoming-material authentication and adulterant screening; use SERS modules for targeted pesticide screening; and integrate Raman-based multivariate models into PAT control loops where process conditions allow stable calibration transfer and adequate detection capability. [3, 12]
The primary limitations are sensitivity for ultra-trace targets in heterogeneous botanical matrices, fluorescence and weak Raman signals, and validation/model-transfer requirements needed for regulatory acceptance of reduced or skip testing approaches. [3, 4, 13]
Keywords
- Raman spectroscopy
- SERS
- Process analytical technology
- Botanical API
- Pesticide residues
- Adulterant detection
- Chemometrics
- Real-time monitoring
Introduction
Botanical drug substances and botanical APIs are regulated under quality paradigms that emphasize therapeutic consistency supported by a “totality of the evidence” approach, including botanical raw material control and chemical quality control testing that may use spectroscopic and/or chromatographic methods. [1] Within this paradigm, contamination and adulteration risks are explicitly named as quality concerns requiring test strategies, including tests for residual pesticides (including parent pesticides and major toxic metabolites) and adventitious toxins such as aflatoxins, as well as controls addressing foreign materials and adulterants. [1]
In parallel, European specifications guidance for herbal substances and preparations defines specifications as the tests, procedures, and acceptance criteria used to assure quality at release and during shelf-life, and identifies groups of contaminants that should be addressed as appropriate, including heavy metals/elemental impurities, residues of pesticides and fumigants, mycotoxins (aflatoxins, ochratoxin A), and microbial contamination. [13, 14] EMA guidance also indicates that periodic/skip testing of contaminant residues may be acceptable when justified through risk assessment and batch data, establishing a clear regulatory incentive for faster screening and process understanding tools that can justify risk-based control strategies without compromising safety. [13]
Raman spectroscopy is a candidate for such strategies because Raman scattering provides chemically specific “fingerprint” spectra, and Raman methods are commonly framed as rapid, non-destructive, and noninvasive with simple sample preparation, which are operational properties aligned with real-time decision-making during manufacturing and supply-chain control. [2, 4]
Reviews of pharmaceutical Raman applications describe a deployment range that extends from laboratory use to docks and production lines, implying that Raman can be considered not only as an off-line identification tool but also as a potential in-process analytical sensor in a PAT context. [2] PAT is explicitly defined as using a series of tools and means to realize real-time analysis and feedback control during industrial production to ensure a controllable production process and optimal product quality, and vibrational spectroscopy techniques are described as enabling online, real-time, and rapid detection of internal quality attributes of herbs during processing. [3]
However, trace contaminant profiling in botanicals is analytically demanding, and the literature indicates major translation challenges: most PAT research has been conducted on lab-scale equipment where experimental conditions are easier to control, and Raman-based process models may have relatively high LODs that fail to detect low-concentration targets in simulated extraction monitoring tasks. [3] These constraints motivate a design-oriented question for botanical APIs: how can Raman (and SERS-enhanced Raman) be deployed within a PAT framework such that it provides rapid, non-destructive screening and, where feasible, quantitative predictions that are robust to matrix and process variability, while remaining compatible with risk-based regulatory expectations for contaminant control and method validation? [2, 3, 13]
Accordingly, the research question addressed here is: Can published Raman and SERS performance evidence support a practical PAT architecture for near-real-time trace contaminant profiling in botanical APIs that complements or triages classical confirmatory assays? [3, 6, 8] The working hypothesis is that Raman-based non-destructive fingerprinting will be most effective as a tiered PAT system: (i) Raman + chemometrics for rapid authentication/adulteration screening; (ii) targeted SERS modules for trace pesticide detection in relevant matrices; and (iii) process Raman monitoring for internal quality attributes where sensitivity is adequate, with risk-based skip-testing justified by data and batch history rather than by sensor deployment alone. [3, 6, 9, 13]
Quantitative Prediction and Calibration-Based Inference
For quantitative prediction and calibration-based inference, a Raman study of methyl eugenol formulations adulterated with xylene reported that PCA was useful for differentiating Raman spectral datasets of different concentrations. Additionally, a PLSR model was able to predict the concentration of an unknown sample with reliability, demonstrating that the combination of Raman spectroscopy and PLSR could achieve high predictive performance. This underscores its potential utility in developing quantitative models for known-risk adulterants in botanical APIs when reference materials are available [10].
Identity Confirmation in Finished Products
A barcode-based Raman method has proven effective for confirming the identity of APIs in finished products. The technique works by comparing the percentage of nonzero overlap between expected API and finished drug product barcodes, where spectra are transformed to emphasize Raman peaks [11]. Utilizing this approach, 18 approved finished drug products and nine simulated counterfeits were identified with 100% accuracy. This supports the feasibility of using Raman-based “fingerprint overlap” logic for robust identity verification in formulated products, provided appropriate transformation and decision rules are applied [11].
Raman Analysis for Botanical 'Look-Alike' Risks
Raman spectral signature approaches have been deployed to distinguish genuine samples from adulterated ones in botanical contexts. For instance, analysis of Phansomba/Phellinus samples revealed distinct separation between genuine and adulterated specimens. Key Raman bands (487, 528, 786, 892, 915, and 1436 cm) characteristic of Phellinus (especially Ph. merrillii) were identified, suggesting the potential for building databases of signature ranges for inspection workflows in other herbal drugs [21].
However, limitations exist. In a screening of 50 herbal food supplements with sexual enhancement claims, Raman spectroscopy detected nine adulterated samples (four with sildenafil and five with tadalafil). Yet, it failed to provide conclusive results regarding tadalafil adulteration in two samples, indicating the need for confirmatory methods or enhanced spectral interpretation strategies for certain cases [22].
4.2 Pesticide Residues by SERS
Published evidence highlights that SERS is a rapid, non-destructive technique capable of detecting trace-level pesticides (ppm or ppb) in alignment with botanical contaminant control standards [1, 6, 19]. One study demonstrated SERS's ability to detect pesticides on fruit surfaces at levels as low as 1 ppm, correlating well with regulatory pesticide residue limits for apples [6].
Quantitative SERS studies have shown strong calibration performance. For example, a study reported coefficients of determination (R²) of 0.99 for omethoate and 0.98 for chlorpyrifos, with limits of detection (LODs) of 1.63 mg·cm and 2.64 mg·cm, respectively. This underscores the feasibility of calibration models utilizing characteristic SERS peak intensities for residue quantitation [17]. In this study, analyte-specific Raman peaks (413 cm for omethoate, 346 cm for chlorpyrifos) were used for concentration mapping through calibration models [17].
Colloidal gold nanoparticle SERS has further enhanced Raman scattering from 21 different pesticides. Detection limits ranged from 0.001 to 10 ppm, with simultaneous identification of phosmet and thiram achieved on apple skin using PCA and SERS [7].
For leafy vegetable matrices, calibration curves for pesticide residues of phosmet, thiabendazole, and acetamiprid exhibited strong linear correlation coefficients, achieving recoveries between 94.67% and 112.89%. Recovery-based validations reported relative standard deviations between 3.87% and 8.56%. The entire testing process, including sampling, spectrum analysis, and quantitative prediction, was completed in under five minutes, a marked improvement over traditional chromatographic methods [16].
In a botanical matrix context, SERS demonstrated potential in detecting deltamethrin in Corydalis. The primary characteristic peak was identified at 999 cm, with increments in modeling yielding a detection limit as low as 0.186 mg/L for direct observation at the 999 cm peak. The use of a PLS model also achieved good predictive performance metrics [23].
Handheld SERS devices, paired with QuEChERS acetate extraction, demonstrated the capability to detect multiple pesticide residues in basmati rice within 15 minutes. Pesticides such as CBM, THI, and TRI were detected below the EU maximum residue limit (MRL) of 10 ppb. However, the detection limit for ACE remained capped at 800 ppb, highlighting potential variability in analyte sensitivity within a multi-residue workflow [8].
Dynamic SERS approaches have enhanced sensitivity in sessile-drop contexts, allowing detection of paraquat, thiabendazole, tricyclazole, and isocarbophos down to ppm and ppb levels. This approach exploits a metastable nanoparticle state during volatilization to maintain discriminability in spiked vegetable extracts. Linear relationships between characteristic peak intensities and concentration levels further validate this method [18].
4.3 Mycotoxin and Microbial-Marker Profiling
Regulatory standards mandate mycotoxin and microbiological quality testing for herbal substances, focusing particularly on aflatoxins and ochratoxin A [13, 24]. For example, USP monographs specify a maximum limit of NMT 5 ppb for aflatoxin B1 and NMT 20 ppb for the sum of aflatoxins B1, B2, G1, and G2 [19]. These limits define the sensitivity that screening and confirmatory methods must achieve.
Due to the primary emphasis on Raman/SERS pesticide detection and adulteration applications, this technology is best positioned as a complementary screening tool within a broader contaminant control strategy. This aligns with regulatory guidelines suggesting that quality control be supported by chemical tests such as spectroscopy or chromatography, while also incorporating emerging technologies [1, 13].
4.4 Heavy Metal and Inorganic Contaminant Inference
The EMA requires testing for heavy metals and other elemental impurities in herbal medicinal products unless otherwise justified, framing a regulatory expectation for trace contaminant profiling in botanical APIs [13, 24].
In the current Raman/SERS evidence base, these contaminants are addressed indirectly through improved control of raw-material identity, faster adulteration screening, and prioritization of confirmatory testing for high-risk samples. However, Raman methods are not currently positioned as standalone methods for elemental impurity quantitation without additional validation or complementary technologies [1, 13, 21].
4.5 In-Line and On-Line Raman PAT for Botanical Processing
The Process Analytical Technology (PAT) framework utilizes real-time analysis to optimize product quality and process control. Raman spectroscopy is described as well-suited for this purpose, offering rapid, non-invasive analysis compatible with in-process manufacturing conditions [3].
One example of Raman-PAT is the use of an RS-CARS-PLS model for monitoring extraction processes in Wenxin granule manufacturing. While the model demonstrated effective process monitoring, its sensitivity for low-concentration analytes, such as saccharides, was limited—highlighting the need for SERS or complementary techniques for detecting trace-level contaminants [3].
Industrial deployment poses additional challenges, as most PAT research occurs in lab-controlled environments. Robustness and control of variability need to be addressed for successful scale-up and live implementation [3].
4.6 Comparative Analytical Performance
Conventional Raman spectroscopy provides rapid, non-destructive chemical fingerprints without requiring sample pre-treatment. In contrast, SERS enhances sensitivity to detect trace-level contaminants, achieving detection limits from 1 ppm to as low as 0.001 ppm for certain pesticides depending on the method and matrix [4, 5, 6, 7]. For example, SERS coupled with calibration demonstrated pesticide detection in leafy vegetables with correlation coefficients up to 0.98291 and overall workflow completion in just five minutes [16].
For authentication applications, PCA has been useful in differentiating subtle spectral variations in essential oils, and barcode-based Raman techniques showed 100% accuracy in identifying counterfeit and authentic finished products [9–11].
4.7 Portable and Handheld Instrumentation for Raw-Material Screening
Portable Raman instruments are positioned as time-efficient, non-destructive tools capable of rapidly analyzing herbal materials without the need for complex preparation. They are also applicable for monitoring health and safety compliance in herbal products, offering a valuable tool for both in-factory and post-market screening [12].
Regulatory guidelines from the FDA highlight emerging methods like morphology-directed Raman spectroscopy (MDRS) as useful for tasks such as particle size distribution characterization when supported by rigorous validation. While not botanical API-specific, these methods demonstrate the capability of Raman to supplement traditional analytical techniques [25, 26].
Discussion
The synthesized evidence supports Raman and SERS as valuable tools for non-destructive, rapid screening and real-time monitoring within PAT environments. These technologies can be effectively integrated into contaminant control and quality assurance workflows for botanical APIs [2, 3, 5].
5.1 Strengths of Raman and PAT vs Classical Destructive Methods
Raman spectroscopy is advantageous for its speed, non-destructive properties, and minimal sample preparation requirements. SERS extends this utility, enabling trace-level detection through enhancement mechanisms, which has been demonstrated to detect pesticides down to ppb levels with rapid total workflow times, making it ideal for initial screening and triage of samples for confirmatory testing [2, 4, 5, 16].
5.2 Limitations
Key limitations include sensitivity challenges in baseline Raman methods, especially for low-concentration analytes without SERS enhancement. Industrial use of Raman-based PAT also requires overcoming challenges of variability and robust scale-up. Additionally, some reliance on chemometric models, such as PCA and PLS, introduces complexity and potential uncertainty depending on matrix variability and model training [3, 9, 22, 23].
Regulatory Guidance and Raman-Based Screening Tools
Regulatory guidance supports a quality approach for botanicals based on totality of evidence, including botanical raw material control and chemical quality control tests using spectroscopic and/or chromatographic methods. This provides a conceptual pathway for Raman-based screening tools to be integrated into overall control strategies rather than treated as standalone replacements for all classical assays. [1]
FDA guidance explicitly calls for tests for residual pesticides and adventitious toxins such as aflatoxins, as well as foreign materials and adulterants. This aligns with Raman/SERS capabilities in pesticide screening and adulterant detection, reinforcing the need for contaminant-class coverage in a comprehensive control program. [1]
FDA also states that applicants should evaluate current and emerging technologies and develop orthogonal analytical methods to provide adequate identification and quantification. This can be interpreted as supportive of Raman/SERS deployment as part of an orthogonal method set paired with confirmatory methods such as LC–MS or other assays for definitive quantitation, especially where SERS performance depends on sample pretreatment control for accurate quantifiability relative to LC–MS. [1, 27] Supporting this view, a study comparing SERS and LC–MS for an unexpected herbicide in a complicated matrix reported that SERS exhibited high sensitivity and higher detection efficiency for ultra-trace target detection, while LC–MS provided more accurate quantifiability facilitated by well-controlled sample pretreatment. This motivates a tiered architecture: SERS for rapid sensitive detection and LC–MS for confirmatory quantitation. [27]
In the EU, EMA specifications guidance defines specifications and identifies contaminant groups that should be addressed (including heavy metals, pesticide residues, mycotoxins, microbial contamination). It permits periodic/skip testing where justified by risk assessment and batch data, implying that Raman/PAT data streams could contribute supporting evidence for risk-based testing strategies if they are validated and shown to detect relevant deviations in a timely manner. [13, 14]
5.4 Risk-Based Deployment Strategy and Lifecycle Management
USP guidance indicates that the extent of testing may be determined using a risk-based approach that considers the likelihood of contamination. This supports a strategy where Raman/SERS screening intensity and confirmatory testing are allocated based on risk factors such as source, geography, batch history, and prior screening data. [19] EMA similarly indicates that periodic/skip testing may be acceptable where justified, and that justification should consider plant material, cultivation/production conditions, neighboring-farm contamination, geographical origin, and be supported by risk assessment and batch data, reinforcing the need for data-rich monitoring systems rather than ad hoc testing reductions. [13]
Within this risk-based context, Raman-based PAT can be positioned as a generator of rapid, repeatable fingerprints and screening outcomes that support trend monitoring and rapid identification of abnormal batches, while confirmatory assays are reserved for batches flagged by screening or for periodic verification of screening system performance and calibration stability. [2, 13] The barcode-based API identity method and handheld essential-oil adulteration detection illustrate how robust decision rules (barcode overlap, intense diagnostic bands) can simplify screening decisions in some contexts, while PCA-based discrimination indicates where multivariate models are required to maintain sensitivity to subtle adulteration patterns. [9, 11, 20]
Lifecycle management for Raman methods is also implied by FDA observations on MDRS submissions: missing validation data on reproducibility and accuracy is a deficiency, emphasizing that Raman-based PAT methods must be developed with validation and performance documentation as central deliverables for regulatory interactions. [25]
5.5 Outlook
The evidence suggests multiple technical directions to increase feasibility of Raman-based PAT for trace contaminants. First, increased technique variety (Fourier transform Raman, resonance Raman, confocal Raman, and SERS) is described as feasible for enhancing Raman signals and evolving instruments and sample processing, supporting a strategy of selecting technique variants according to matrix and sensitivity needs rather than relying on a single Raman configuration across all botanical processes. [4]
Second, SERS selectivity can be enhanced by functionalizing nanostructures with receptor molecules such as aptamers, indicating a pathway toward targeted trace contaminant assays embedded into PAT modules where interference is a dominant risk. [5]
Third, imaging-based SERS approaches are described as allowing real-time monitoring and detection of contamination localization on plant tissue surfaces or inside, suggesting that future botanical API workflows could incorporate spatially resolved contamination mapping for high-risk materials or for investigations of contamination pathways. [5] Finally, practical deployment potential is supported by conclusions that SERS could be further implemented in fast and on-site detection tools for food safety and environment monitoring, and by evidence that portable Raman instruments can be used to monitor the health and safety compliance of herbal products in the consumer market, underscoring a continuum from field screening to manufacturing PAT systems. [12, 27]
6. Conclusions
This conceptual, evidence-synthesis study indicates that Raman spectroscopy is well aligned with PAT objectives because it is rapid, non-destructive, noninvasive, and simple in sample preparation. Raman applications are described as spanning laboratory to production lines, supporting a lifecycle view of Raman-based measurement from incoming raw-material screening to in-process monitoring. [2]
PAT is explicitly defined as enabling real-time analysis and feedback control to ensure controllable production processes and optimal quality. Vibrational spectroscopy is described as enabling online real-time rapid detection of herb internal quality during processing, providing a conceptual basis for Raman sensor placement in botanical manufacturing. [3]
For trace contaminants, SERS provides the strongest evidence base for sensitivity, with enhancement potentially reaching ultra-trace detection limits on noble metals, and with multiple pesticide studies demonstrating ppm-to-ppb and even low-nanomolar detection regimes with quantitation metrics and rapid workflows (e.g., 5 min total test time; <15 min extraction). [5, 8, 16, 18] Chemometrics is essential for many authenticity and quantitation tasks, as visual inspection can be insufficient for adulteration detection, while PCA and PLSR have demonstrated discrimination and quantitative prediction performance. [9, 10]
The primary limitations for real-time trace contaminant profiling in botanical APIs are sensitivity constraints in non-enhanced Raman PAT process models (illustrated by relatively high LODs in extraction monitoring) and robustness/validation challenges for scaling PAT from lab to production, alongside matrix-driven uncertainty in some adulterant screening cases. [3, 22] Consequently, the most defensible operational recommendation supported by the evidence is a tiered PAT architecture:
- Portable Raman + chemometrics for rapid authentication/adulteration screening.
- Targeted SERS assays for high-risk pesticide residues.
- Confirmatory orthogonal methods where quantifiability and regulatory decision-making require higher assurance, consistent with regulatory expectations for orthogonal methods and risk-based justification for skip testing. [1, 5, 12, 13, 27]
Funding
No external funding. [1]
Conflicts of Interest
The authors declare no conflicts of interest. [1]
Data Availability Statement
All data used in this conceptual study are derived from the cited published sources and regulatory documents synthesized herein. [1, 14]
Figure 1
Figure 1. Conceptual PAT workflow for botanical API contaminant risk management integrating non-destructive Raman and SERS: incoming botanical raw material screening using rapid, non-destructive Raman fingerprinting at receiving/dock points; chemometric authentication/adulteration checks (e.g., PCA-based discrimination; barcode overlap identity confirmation) for identity assurance; targeted SERS modules for trace pesticide screening and rapid quantitative prediction (ppm-to-ppb sensitivity with short measurement times); in-process Raman monitoring at manufacturing unit operations framed under PAT as real-time analysis and feedback control; and risk-based periodic verification/skip-testing decisions supported by batch history and formal risk assessments consistent with EMA/USP guidance. [2, 3, 6, 9, 11, 13, 16, 19]
Table 2
| Contaminant/Adulteration Class | Raman/SERS Configuration | PAT Integration Points |
|---|---|---|
| Heavy metals | Non-destructive Raman screening | Raw material screening |
| Pesticide residues | Targeted SERS modules | Trace screening |
| Mycotoxins | Chemometric discrimination | Authentication checks |
Table 3
| Regulatory/Compendial Anchor | Raman-Based PAT Alignment |
|---|---|
| USP Guidance | Validated screening, risk-based testing strategies |
| EMA Specifications | Compliance with contaminant groups, periodic testing justification |
| FDA Recommendations | Supports orthogonal methods, lifecycle management |
