Micro- and Nanoengineered Devices for Rapid Chemotaxonomic Profiling of Medicinal Plants
Abstract
:1. Introduction
2. Target Metabolites and Analytical Needs
2.1. Overview of Major Phytochemicals in Medicinal Plants
2.2. Challenges in Traditional Analytical Techniques
3. Microfluidics and Lab-on-a-Chip Platforms
3.1. Introduction to Microfluidic Systems
3.2. Fabrication Techniques for Lab-on-a-Chip Devices
3.3. Integrating Detection Methods in Lab-on-a-Chip Systems
Detection Technique | Advantages | Limitations | Real-World Case Study | Examples of Plant Metabolites Detected |
---|---|---|---|---|
Fluorescence detection | High sensitivity for low-abundance metabolites. Non-invasive, rapid, and real-time detection. Easily integrated with microfluidic systems. | Requires fluorescent tagging or natural fluorescence. May not be applicable to all plant metabolites. | Fluorescence-based lab-on-a-chip devices are employed to detect and quantify flavonoids in plant species, such as Citrus and Ginkgo biloba, aiding in the identifying secondary metabolites in medicinal plants [68]. | Flavonoids, phenolic acids, and anthocyanins |
Absorbance detection | Simple, cost-effective, and widely used. Applicable to UV or visible light-absorbing compounds. | Lower sensitivity than that of fluorescence. May require extensive sample preparation for complex mixtures. | Commonly used to analyze phenolics and flavonoids, especially in agricultural and food safety applications. For example, used for polyphenol analysis in tea and grape samples [69]. | Phenolic compounds, flavonoids, and tannins |
Electrochemical detection | High selectivity and sensitivity for low concentrations. Suitable for real-time monitoring. Highly specific for certain compounds (such as alkaloids and terpenoids). | Requires specialized electrodes and systems. Limited to metabolites that can undergo redox reactions. Potential interference from other electroactive substances. | Electrochemical sensors integrated into lab-on-a-chip devices have been employed to detect alkaloids in Cinchona bark (for quinine) and terpenoids in aromatic plants such as lavender and peppermint. These sensors are particularly useful in herbal medicine research and conservation [70,71] | Alkaloids, terpenoids, cinchonine, and quinine |
SPR | Provides real-time detection without labeling. Sensitive to changes in refractive index near the sensor surface. Non-destructive to samples. | Sensitive to surface conditions and requires highly specialized equipment. | Used in lab-on-a-chip devices to detect polyphenols and flavonoids by measuring refractive index changes at the sensor surface, often used for profiling complex plant mixtures [72]. | Polyphenols, flavonoids, and antioxidants |
CE | High resolution, fast, and effective for separating various metabolites. Can be combined with detection methods (UV, fluorescence, and electrochemical). | Requires more complex sample preparation and sophisticated equipment. May not be suitable for large-scale screening. | Used for separating and quantifying carotenoids and fatty acids in various plant extracts, especially in food quality control and metabolite profiling [73,74]. | Carotenoids, fatty acids, and lipids |
4. Nano-Enabled Optical and Electrochemical Sensors
4.1. Introducing Nano-Enabled Sensors
4.2. Surface-Enhanced Raman Spectroscopy for Plant Metabolites
4.3. Field-Effect Transistor FET-Based Sensors for Phytochemical Detection
5. Integration with Artificial Intelligence and Data Processing
5.1. Role of Machine Learning in Chemotaxonomy
5.2. Data Fusion Techniques: Integrating Metabolomics and Genomics
5.3. Real-Time Data Processing for Field-Based Applications
6. Applications and Case Studies
6.1. Applications in Herbal Medicine
6.2. Medicinal Plant Authentication
6.3. Biodiversity and Conservation Studies
7. Challenges and Future Outlook
7.1. Challenges in Current Micro- and Nanoengineered Devices
7.2. Innovations in Nanoengineering for Field-Based Chemotaxonomy
7.3. Regulatory and Standardization Issues
8. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Sensor Type | Nanomaterials Used | Principle | Specific Applications | References |
---|---|---|---|---|
Optical sensors | Gold nanoparticles, silver nanoparticles, and quantum dots | They operate by amplifying light absorption or fluorescence with nanomaterials to enhance the signal. | Used in SERS to detect trace plant metabolites such as terpenoids and flavonoids. | [81,82] |
Fluorescence sensors | Quantum dots, gold nanoparticles | Nanomaterials enhance fluorescence, enabling the detection of low metabolite concentrations. | Used to detect metabolites such as polyphenols and alkaloids in medicinal plants. | [83] |
Electrochemical sensors | Carbon nanotubes and gold nanowires | These sensors detect electrical changes (current/voltage) caused by redox reactions when metabolites interact with the sensor surface. | Used for monitoring plant metabolites such as amino acids, vitamins, and neurotransmitters based on their electrochemical properties. | [84] |
Electrochemical biosensors | Metallic nanoparticles and carbon nanomaterials | Integrate electrochemical sensors with biosensors to detect small biomolecules through surface interactions. | Applied to detect metabolites such as ATP, lactate, and glutamate, aiding in plant stress response and metabolite profiling. | [85] |
Optical detection (SERS) | Gold nanoparticles and silver nanoparticles | SERS amplifies Raman scattering, facilitating the detection of plant metabolites at low concentrations. | Enhances the detection of secondary metabolites such as phenolics, terpenoids, and alkaloids, often used in chemotaxonomy for medicinal plant identification. | [86] |
Nano-electrochemical sensors | Carbon nanotubes and silver nanoparticles | Detects changes in electrical properties induced by metabolite interactions with electrodes. | Provide rapid, on-site detection of plant metabolites with high sensitivity, such as polyphenols in medicinal plants. | [87] |
Quantum dot sensors | Quantum dots and nanostructured carbon | Utilizes photoluminescence properties of quantum dots to detect specific plant metabolites. | Profiles secondary metabolites in plants, especially for identifying medicinal plant varieties. | [88] |
Nanobiocompatible sensors | Chitosan nanoparticles and gold nanoparticles | Combines nanomaterials with biological molecules to improve selectivity and sensitivity. | Used for metabolite detection and profiling secondary metabolites in response to environmental stressors. | [89] |
Biocomposite sensors | Silver nanoparticles and graphene oxide | Leverages the unique properties of carbon-based materials and silver nanoparticles to enhance metabolite detection. | Used to detect bioactive compounds, particularly for agricultural biotechnology and stress tolerance. | [90] |
Multi-platform sensors | Graphene and silver nanoparticles | Combines various nanomaterials to create highly sensitive, multi-platform detection methods. | Applied in food safety for pathogen detection and in plant metabolite analysis. | [91] |
Machine Learning Algorithm | Application in Chemotaxonomy | Strengths | Specific Use Cases | References |
---|---|---|---|---|
NN | Pattern recognition in large-scale plant metabolite datasets. | Capable of modeling complex datasets and recognizing nonlinear relationships. | Applied in plant classification based on their metabolic profiles, such as distinguishing species with overlapping chemical signatures. | [102] |
SVM | Classification of plant species using biochemical data. | Well-suited for high-dimensional datasets and classification tasks. | Utilized in species classification, such as sweet oranges or Miscanthus, based on secondary metabolite profiles. | [103] |
DT | Classifying plants based on metabolic markers. | Transparent and interpretable decision-making process. | Employed for identifying plant species, particularly effective for novel or rare species using chemical profile data. | [104] |
RF | Improving classification accuracy in large and noisy datasets. | Ensemble approach that mitigates overfitting and improves model generalizability. | Classify and cluster plant species based on multifaceted metabolite data and environmental factors. | [105] |
PCA | Dimensionality reduction for large chemotaxonomic datasets. | Simplifies complex data while preserving key variance. | Applied to simplify plant metabolite profile analysis and clustering of plant species. | [106] |
KNN | Plant classification based on metabolite similarities. | Simple and effective for small to medium-sized datasets. | Applied in species classification by comparing chemical profiles and visual attributes. | [107] |
LR | Binary classification for identifying specific plant traits. | Suitable for probability-based classification in binary scenarios. | Identifies specific plant diseases or traits based on metabolite data. | [108] |
CNN | Image-based plant species and disease identification. | Well-suited for image recognition tasks and extracting spatial features from plant images. | Applied in real-time identification of plant disease and species from leaf images. | [109] |
Capsule networks | Image-based classification, particularly for plant diseases. | Effectively captures spatial hierarchies and addresses CNN limitations. | Enhances accuracy and efficiency in plant disease classification and reduces computational overhead. | [110] |
FPA | Optimizing plant phenotypic data and classification tasks. | Solves complex optimization problems through nature-inspired strategies. | Applied to optimize plant classification models and environmental data analysis. | [111] |
AIS | Adaptive identification and optimization of plant data. | Mimics immune system for anomaly detection and pattern recognition. | Enhances species classification accuracy in complex environments through adaptive algorithms. | [112] |
RF | Classification using complex plant traits and metabolite profiles. | Robust ensemble learning that mitigates overfitting. | Applied for classifying and clustering plant species from metabolic and environmental datasets. | [113] |
No. | Title | Year | Medicinal Plant | Technology/Technique | Outcome |
---|---|---|---|---|---|
1 | Pandey and Ambwani [127] | 2022 | Ginseng | Nanoparticle-based sensors | Detected adulteration in ginseng products by identifying non-authentic species, supporting quality control |
2 | Hezekiah [128] | 2021 | Artemisia annua | Nano-fingerprinting techniques | Developed a nano-enabled framework to prevent misidentification of Artemisia annua |
3 | Munir et al. [129] | 2020 | Artemisia annua | Chemotaxonomic profiling and nanotechnology | Accurately distinguished Artemisia annua from similar species |
4 | Geetha, Sudha, and Praveena [126] | 2023 | Ginseng | Nano-biosensors | Enhanced accuracy in identifying Panax species, minimizing the risk of species substitution |
5 | Thiruvengadam, et al. [130] | 2024 | Various herbs | Nano-based biosensors and molecular analysis | Enabled detection of adulterated and counterfeit herbal products, ensuring consumer safety |
6 | Kumar [131] | 2023 | Various herbs | Nanotechnology for plant species identification | Demonstrated the efficacy of nanotechnology for rapid and accurate plant species identification |
7 | Singh and Yadav [132] | 2024 | Various herbs | Nano-based spectrometry and chemical analysis | Identified counterfeit herbs and quantified adulterants with high precision |
8 | Gasmi et al. [133] | 2023 | Ginseng | Nanoengineered delivery systems | Advanced understanding of the therapeutic properties of Ginseng through nanoengineered systems |
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Ali, S.; Amin, A.; Akhtar, M.S.; Zaman, W. Micro- and Nanoengineered Devices for Rapid Chemotaxonomic Profiling of Medicinal Plants. Nanomaterials 2025, 15, 899. https://doi.org/10.3390/nano15120899
Ali S, Amin A, Akhtar MS, Zaman W. Micro- and Nanoengineered Devices for Rapid Chemotaxonomic Profiling of Medicinal Plants. Nanomaterials. 2025; 15(12):899. https://doi.org/10.3390/nano15120899
Chicago/Turabian StyleAli, Sajid, Adnan Amin, Muhammad Saeed Akhtar, and Wajid Zaman. 2025. "Micro- and Nanoengineered Devices for Rapid Chemotaxonomic Profiling of Medicinal Plants" Nanomaterials 15, no. 12: 899. https://doi.org/10.3390/nano15120899
APA StyleAli, S., Amin, A., Akhtar, M. S., & Zaman, W. (2025). Micro- and Nanoengineered Devices for Rapid Chemotaxonomic Profiling of Medicinal Plants. Nanomaterials, 15(12), 899. https://doi.org/10.3390/nano15120899