A One‐Class Autoencoder Neural Network Approach for Rapid Raman‐Based Pharmaceutical Quality Screening

ABSTRACT Raman spectroscopy is widely regarded as a rapid and nondestructive technique for pharmaceutical quality control. However, the development of robust identification models remains challenging due to instrument variability, batch‐to‐batch differences, and the scarcity of negative training data. In this study, we propose an autoencoder‐based neural network approach for the identification and screening of pharmaceutical products using their Raman spectra, trained exclusively on “positive” (authentic) spectra. By implementing difference‐of‐Gaussians filtering and generating synthetic spectra via PCA‐based augmentation (baseline + noise), the model learns to reconstruct valid spectra with high fidelity, enabling the detection of anomalies through elevated reconstruction errors. We validate this method using an injectable diclofenac formulation, incorporating spectra acquired from multiple instruments and varying acquisition parameters. The resulting model exhibits high sensitivity to compositional changes and impurities while maintaining specificity against a diverse set of unrelated spectra. This approach eliminates the need for extensive “negative” datasets, as the autoencoder effectively captures the essential spectral characteristics of the authentic drug. Beyond diclofenac, this methodology is adaptable to a wide range of pharmaceuticals, providing a scalable solution for routine screening and the detection of falsified or substandard medicines. By integrating advanced data augmentation with nonlinear dimensionality reduction, this technique surpasses traditional chemometric methods, offering more reliable, versatile, and rapid analyses. These findings pave the way for the broader implementation of Raman‐based quality assessment in industrial and field applications, ultimately enhancing drug safety and efficacy.