Classification of Ciprofloxacin Tablets Using Near-Infrared Spectroscopy and Chemometric Modeling

Area of Science:

• Analytical Chemistry
• Pharmaceutical Sciences
• Chemometrics

Background:

• Counterfeit and mislabeled pharmaceuticals pose significant global public health risks.
• Near-infrared (NIR) spectroscopy offers a rapid, cost-effective method for pharmaceutical screening.
• Chemometric analysis of NIR spectra can detect subtle variations in drug products, even with identical active pharmaceutical ingredients (APIs).

Purpose of the Study:

• To evaluate spectral variability in ciprofloxacin tablets from multiple manufacturers using NIR spectroscopy.
• To apply unsupervised and supervised chemometric methods for classifying pharmaceutical products based on spectral data.
• To develop a predictive model for identifying the origin of ciprofloxacin tablets.

Main Methods:

• Hierarchical cluster analysis (HCA) was used to analyze NIR spectra from over 800 ciprofloxacin tablets.
• Principal component analysis (PCA) was applied for dimensionality reduction.
• Quadratic discriminant analysis (QDA) was employed to build a classification model.

Main Results:

• Hierarchical cluster analysis revealed significant NIR spectral dissimilarities primarily between manufacturers.
• The developed QDA model achieved over 96% accuracy in classifying 907 ciprofloxacin tablets.
• Manufacturing site process variables and excipient differences were identified as sources of spectral variation.

Conclusions:

• NIR spectroscopy combined with chemometrics is a powerful tool for pharmaceutical quality control and counterfeit detection.
• The developed QDA model demonstrates high accuracy in predicting the origin of ciprofloxacin tablets.
• These chemometric approaches can be integrated into comprehensive drug surveillance programs to enhance public safety.