Device-Independent Discrimination of Falsified Amoxicillin Capsules Using Heterogeneous Near-Infrared Spectroscopic Devices for Training and Testing of a Support Vector Machine

Portable NIR spectroscopy: the route to green analytical chemistry

There is a growing interest for cost-effective and nondestructive analytical techniques in both research and application fields. The growing approach by near-infrared spectroscopy (NIRs) pushes to develop handheld devices devoted to be easily applied for in situ determinations. Consequently, portable NIR spectrometers actually result definitively recognized as powerful instruments, able to perform nondestructive, online, or in situ analyses, and useful tools characterized by increasingly smaller size, lower cost, higher robustness, easy-to-use by operator, portable and with ergonomic profile. Chemometrics play a fundamental role to obtain useful and meaningful results from NIR spectra. In this review, portable NIRs applications, published in the period 2019-2022, have been selected to indicate starting references. These publications have been chosen among the many examples of the most recent applications to demonstrate the potential of this analytical approach which, not having the need for extraction processes or any other pre-treatment of the sample under examination, can be considered the "true green analytical chemistry" which allows the analysis where the sample to be characterized is located. In the case of industrial processes or plant or animal samples, it is even possible to follow the variation or evolution of fundamental parameters over time. Publications of specific applications in this field continuously appear in the literature, often in unfamiliar journal or in dedicated special issues. This review aims to give starting references, sometimes not easy to be found.

Algorithm and hyperparameter optimizations for hetero-device classification by near-infrared spectra of falsified and substandard amoxicillin capsules

In this work, we optimized classification algorithms and the hyperparameters for screening falsified and substandard amoxicillin capsules. The distribution of low-quality medical products is a serious problem, especially in low- and middle-income countries. Near-infrared (NIR) spectroscopy has been proposed as the first choice for a screening device. However, preparation of the reference library for the classification training is a highly difficult process. We herein propose a hetero-device classification between training and test devices. In this proposal, Fourier-transform NIR spectrometer and portable wavelength dispersive NIR spectrometer were used as training and test devices, respectively. As the classifier candidates, we examined 13 algorithms and selected 8. We then optimized the hyperparameters for these classifiers by the grid search and cross validation methods. In the final analysis, few classifiers were found to give acceptable prediction results by the hetero-device classification. When using these methods, it is crucial to examine the results by the classification probability, due to the trade-off between sensitivity and specificity. Finally, we suggest that k-nearest neighbors, extra trees, and gradient boosting classifiers are the optimal algorithms with high classification probability for the substandard and falsified amoxicillin capsules.

[Advances in Pharmaceutical Manufacturing Process Management -From Physical Pharmaceutics to Automatic Pharmaceutical Production]

Since entering graduate school 43 years ago, I have been studying physical pharmaceutics with a focus on the effects of environmental factors on pharmaceutical properties of solid oral dosage forms during the manufacturing process. I have reported on changes in the characteristics of pharmaceutical products during manufacturing processes, such as grinding, mixing, granulation, and tableting owing to complicated phenomena based on chemical reactions or the crystalline polymorphic transitions of bulk drugs and excipients. To develop modern pharmaceutical manufacturing processes based on process analysis technology (PAT) as a next generation good manufacturing practice, real-time monitoring was introduced in these processes using a non-destructive analytical method, such as the near-infrared spectroscopy combined with chemometrics. Many case studies related to the mixing, granulation, tableting, and coating processes involving PAT have been reported. In those studies, I focused on clarifying the physical and chemical mechanism through "design space" representation. Additionally, non-destructive analytical methods, including X-ray computed tomography, audible acoustic emission, Raman spectroscopy, terahertz spectroscopy, and infrared thermal imaging analysis were applied as novel candidate analytical methods to the pharmaceutical process to monitor critical quality attributes. To achieve this purpose in various pharmaceutical dosage forms, I have been attempting the assembly of a modern manufacturing process managed through a "design space" paradigm involving in-line monitoring using novel analytical methods, multivariate analyses, and feed-back systems.