Pharmaceutical tablet identification methods
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Spectroscopy-Based Pharmaceutical Tablet Identification Methods
Spectroscopy techniques are widely used for identifying pharmaceutical tablets due to their speed, accuracy, and non-destructive nature. Raman spectroscopy, when combined with chemometric methods like Support Vector Machines (SVMs), can accurately identify tablet product families and specific formulations, making it useful for both quality control and counterfeit detection without prior sample information . Near-infrared spectroscopy (NIRS) is another reliable method, offering high-throughput and accurate identification of active pharmaceutical ingredients (APIs) in large-scale tablet production. NIRS methods have been validated and approved for use in regulated environments, and are effective for both routine quality control and counterfeit detection 489. Mid-infrared spectroscopy using quantum cascade lasers (QCLs) and machine learning can also classify tablets by brand with high accuracy, even when tablets contain the same API .
Mass Spectrometry and Real-Time Recognition for Counterfeit Detection
Mass spectrometry (MS), particularly using atmospheric solids analysis probe (ASAP)-MS, provides a fast and user-friendly approach for identifying falsified and substandard tablets. When paired with real-time sample recognition software, this method allows non-experts, such as law enforcement officers, to quickly and accurately distinguish authentic from counterfeit tablets in under two minutes, even when counterfeits contain the correct API but in incorrect amounts or with different excipients .
Machine Learning and Deep Learning in Tablet Identification
Machine learning and deep learning techniques are increasingly applied to pharmaceutical tablet inspection and recognition. These methods analyze various parameters, such as visual features and chemical signatures, to identify tablets in production, packaging, and consumer settings . Deep learning frameworks, such as those using the YOLO object detection algorithm, can accurately detect and identify pills and capsules in images, supporting automation in manufacturing, quality control, and medicine identification systems . Machine learning models, including SVM, principal component analysis (PCA), and artificial neural networks (ANN), have demonstrated high accuracy in classifying tablets by brand and formulation 73.
Image-Based and Visual Identification Systems
For tablets without markings or additives, geometric invariant image matching can be used. This method captures micro-scale surface features of plain tablets and uses image matching algorithms to identify them, providing a robust solution for unmarked tablets . Additionally, software applications like the Information System for Tablets Identification (ISTI) use visual information—such as shape, color, logo, and physical dimensions—to help users identify tablets, especially in cases where packaging is missing. These systems are designed for accessibility by healthcare professionals and law enforcement, and can be accessed via computers or mobile devices .
Handheld and Field-Deployable Identification Tools
Handheld NIR spectrometers have been evaluated for field use, offering fast and accurate detection of counterfeit tablets. Both low-cost and classical NIR devices, when combined with machine learning classifiers like SVM and linear discriminant analysis (LDA), can achieve high identification rates and are suitable for on-site authentication and counterfeit detection . These portable tools are particularly valuable for rapid screening outside laboratory settings.
Conclusion
Pharmaceutical tablet identification methods have advanced significantly, leveraging spectroscopy, mass spectrometry, machine learning, and image analysis. These technologies enable fast, accurate, and non-destructive identification for quality control, counterfeit detection, and field use. The integration of machine learning and user-friendly software further enhances accessibility and reliability, supporting both industry professionals and non-experts in ensuring the authenticity and safety of pharmaceutical products.
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