Audio recordings are a significant component of the world's modern cultural history and are retained for future generations in libraries, archives, and museums. The vast majority of tapes contain polyester-urethane as the magnetic particle binder, the degradation of which threatens the playability and integrity of these often unique recordings. Magnetic tapes with stored historical data are degrading and need to be identified prior to digitization and/or preservation. We demonstrate the successful differentiation of playable and nonplayable quarter-inch audio tapes, allowing the minimally invasive triage of tape collections. Without such a method, recordings are put at risk during playback, which is the current method for identifying degraded tapes. A total of 133 quarter-inch audio tapes were analyzed by attenuated total reflectance Fourier transform-infrared spectroscopy (ATR FT-IR). Classification of IR spectra in regards to tape playability was accomplished using principal component analysis (PCA) followed by quadratic discriminant analysis (QDA) and K-means cluster analysis. The first principal component suggests intensities at the following wavenumbers to be representative of nonplayable tapes: 1730 cm(-1), 1700 cm(-1), 1255 cm(-1), and 1140 cm(-1). QDA and cluster analysis both successfully identified 93.78% of nonplayable tapes in the calibration set and 92.31% of nonplayable tapes in the test set. This application of IR spectra assessed with multivariate statistical analysis offers a path to greatly improve efficiency of audio tape preservation. This rapid, minimally invasive technique shows potential to replace the manual playback test, a potentially destructive technique, ultimately allowing the safe preservation of culturally valuable content.

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Exposure to unknown, mislabeled, and counterfeit pharmaceutical products is a worldwide problem that presents a serious risk to public health. Near-infrared (NIR) spectroscopy can serve as a useful tool for screening pharmaceuticals in a rapid and cost-effective manner to ensure that drug products are safe and effective. By applying chemometric techniques to NIR spectra from finished products in tablet form, minor spectral differences are discoverable, even in instances where the tablets being evaluated contain the same active pharmaceutical ingredients (APIs). Differences in NIR spectra can occur as a result of various factors including the types and quantities of pharmaceutical excipients used to generate the product and associated manufacturing site process variables. In this study, variability in the NIR spectra of intact tablets with the same API was evaluated using an unsupervised chemometric technique in the form of hierarchical cluster analysis (HCA) on a data set consisting of NIR spectra from more than 800 ciprofloxacin tablets from six manufacturers. Results obtained from HCA and squared Euclidean distance measurements indicate the largest dissimilarities in NIR spectra occur between manufacturers. Based on these findings, a quadratic discriminant analysis (QDA) model was built following dimensionality reduction by principal component analysis for the purpose of predicting the origin of ciprofloxacin tablets. Using QDA, we were able to correctly classify a collection of 907 tablets with greater than 96% accuracy. Chemometric models such as the one developed here could ultimately be employed as part of a large, diversified drug surveillance program.

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