Characterization of Pharmaceutical Tablets Using UV Hyperspectral Imaging as a Rapid In-Line Analysis Tool

Assessment of Directional-Hemispherical Reflectance of Tablets with Cefuroxime during Storage under Elevated Temperature and Ultraviolet Radiation

Environmental conditions can lead to changes in the physical and chemical structures of drug products. In this study, the stability of cefuroxime tablets stored under adverse conditions was evaluated based on total directional-hemispherical reflectance (THR). The THR value was measured before and after the tablets' exposure to stress factors (temperature of 45 °C and UV radiation). Each measurement was performed three times within seven spectral bands at the beginning of the experiment (day 0), and then on days 1, 2, 3, 5, and 7. In addition, hyperspectral profiles (400-1030 nm) were analyzed on days 0 and 7. A significant decrease in THR values in all wavelength ranges was observed when day 7 vs. day 0 were compared, especially for spectral bands of 335-380 nm and 1700-2500 nm (Δ = 0.220, p < 0.001 and Δ = 0.171, p < 0.001, respectively). The hyperspectral analysis confirmed a decrease in the reflectance after the end of stress conditions in the visible light range (400-700 nm) compared to tablets before the experiment. This may indicate that more radiation entered the tablets. In conclusion, the THR of cefuroxime tablets decreases during the exposure to heat and UV radiation, which may result from some physicochemical changes that have occurred during storage.

Detection and prevention of foreign material in food: A review

This review highlights the critical concern foreign material contamination poses across the food processing industry and provides information on methods and implementations to minimize the hazards caused by foreign materials. A foreign material is defined as any non-food, foreign bodies that may cause illness or injury to the consumer and are not typically part of the food. Foreign materials can enter the food processing plant as part of the raw materials such as fruit pits, bones, or contaminants like stones, insects, soil, grit, or pieces of harvesting equipment. Over the past 20 years, foreign materials have been responsible for about one out of ten recalls of foods, with plastic fragments being the most common complaint. The goal of this paper is to further the understanding of the risks foreign materials are to consumers and the tools that could be used to minimize the risk of foreign objects in foods.

Deep ultraviolet high-resolution microscopic hyperspectral imager and its biological tissue detection

Ultraviolet (UV) hyperspectral imaging technology is commonly used in the field of atmospheric remote sensing. In recent years, some in-laboratory research has been carried out for substance detection and identification. In this paper, UV hyperspectral imaging technology is introduced into microscopy to better utilize the obvious absorption characteristics of components, such as proteins and nucleic acids in biological tissues in the ultraviolet band. A deep UV microscopic hyperspectral imager based on the Offner structure with F # 2.5, low spectral keystone and smile is designed and developed. A 0.68 numerical aperture microscope objective is designed. The spectral range of the system is from 200 nm to 430 nm; the spectral resolution is better than 0.5 nm; and the spatial resolution is better than 1.3 µm. The K562 cells can be distinguished by transmission spectrum of nucleus. The UV microscopic hyperspectral image of the unstained mouse liver slices showed similar results to the microscopic image after hematoxylin and eosin staining, which could help to simplify the pathological examination process. Both results show a great performance in spatial and spectral detecting capabilities of our instrument, which has the potential for biomedical research and diagnosis.

Copper concentrate dual-band joint classification using reflectance hyperspectral images in the VIS-NIR and SWIR bands

A study on the classification of copper concentrates relevant to the copper refining industry is performed by means of reflectance hyperspectral images in the visible and near infrared (VIS-NIR) bands (400-1000 nm) and in the short-wave infrared (SWIR) (900-1700 nm) band. A total of 82 copper concentrate samples were press compacted into 13-mm-diameter pellets, and their mineralogical composition was characterized via quantitative evaluation of minerals and scanning electron microscopy. The most representative minerals contained in these pellets are bornite, chalcopyrite, covelline, enargite, and pyrite. Three databases (VIS-NIR, SWIR, and VIS-NIR-SWIR) containing a collection of average reflectance spectra computed from 9×9p i x e l neighborhoods in each pellet hyperspectral image are compiled to train the classification models. The classification models tested in this work are a linear discriminant classifier and two non-linear classifiers, a quadratic discriminant classifier, and a fine K-nearest neighbor classifier (FKNNC). The results obtained show that the joint use of VIS-NIR and SWIR bands allows for the accurate classification of similar copper concentrates that contain only minor differences in their mineralogical composition. Specifically, among the three tested classification models, the FKNNC performs the best in terms of overall classification accuracy, achieving 93.4% accuracy in the test set when only VIS-NIR data are used to construct the classification model, up to 80.5% using only SWIR data, and up to 97.6% using both VIS-NIR and SWIR bands together.

Prediction of Honeydew Contaminations on Cotton Samples by In-Line UV Hyperspectral Imaging

UV hyperspectral imaging (225 nm-410 nm) was used to identify and quantify the honeydew content of real cotton samples. Honeydew contamination causes losses of millions of dollars annually. This study presents the implementation and application of UV hyperspectral imaging as a non-destructive, high-resolution, and fast imaging modality. For this novel approach, a reference sample set, which consists of sugar and protein solutions that were adapted to honeydew, was set-up. In total, 21 samples with different amounts of added sugars/proteins were measured to calculate multivariate models at each pixel of a hyperspectral image to predict and classify the amount of sugar and honeydew. The principal component analysis models (PCA) enabled a general differentiation between different concentrations of sugar and honeydew. A partial least squares regression (PLS-R) model was built based on the cotton samples soaked in different sugar and protein concentrations. The result showed a reliable performance with R²_cv = 0.80 and low RMSECV = 0.01 g for the validation. The PLS-R reference model was able to predict the honeydew content laterally resolved in grams on real cotton samples for each pixel with light, strong, and very strong honeydew contaminations. Therefore, inline UV hyperspectral imaging combined with chemometric models can be an effective tool in the future for the quality control of industrial processing of cotton fibers.

The Use of Novel, Rapid Analytical Tools in the Assessment of the Stability of Tablets—A Pilot Analysis of Expired and Unexpired Tablets Containing Nifuroxazide

In the analysis of finished pharmaceutical products, numerous innovative analytical techniques are often used, i.e., Raman spectroscopy, scanning electron microscopy, computer microtomography, directional hemispherical reflectance, and hyperspectral analyses. These techniques allow for the identification of changes in solid phases. Many advantages over other techniques can be attributed to these techniques, e.g., they are rapid, non-destructive, and comprehensive. They allow for the identification of changes occurring in solid phases. However, the above-mentioned methods are still not standard procedures in pharmaceutical research. The present study aimed to assess the possible usefulness of total directional hemispherical reflectance (THR), hyperspectral imaging, and computer microtomography to evaluate the stability of tablets containing nifuroxazide during storage. In the study, expired and unexpired coating tablets containing nifuroxazide (n = 10 each) were analyzed. In addition, four unexpired tablets were stored at 40°C over 3 months (stressed tablets). Reflectance was determined with seven wavelength bands from 335 nm to 2500 nm using an SOC-410 Directional Hemispherical Reflectometer (Surface Optics Corporation, San Diego, CA, USA). A Specim IQ hyperspectral camera (Spectral Imaging Ltd., Oulu, Finland) was used with a wavelength range of 400–1030 nm. Tablets were also scanned using X-ray microtomography (Phoenix vǀtomeǀx, GE Sensing & Inspection Technologies GmbH, Wunstorf, Germany). The results indicated that total reflectance was lower in expired tablets than in unexpired tablets in all spectral bands, except for 700–1100 nm and 1700–2500 nm. In turn, the stressed tablets showed higher THR values than expired tablets in all spectral bands, except for 1000–1700 nm. In addition, hyperspectral analysis of the homogeneity of the tablets, as well as X-ray microtomographic analysis of tablet density and coating thickness, indicated that these parameters differed significantly between the analyzed tablets.

A Review of Pharmaceutical Robot based on Hyperspectral Technology

The quality and safety of medicinal products are related to patients’ lives and health. Therefore, quality inspection takes a key role in the pharmaceutical industry. Most of the previous solutions are based on machine vision, however, their performance is limited by the RGB sensor. The pharmaceutical visual inspection robot combined with hyperspectral imaging technology is becoming a new trend in the high-end medical quality inspection process since the hyperspectral data can provide spectral information with spatial knowledge. Yet, there is no comprehensive review about hyperspectral imaging-based medicinal products inspection. This paper focuses on the pivotal pharmaceutical applications, including counterfeit drugs detection, active component analysis of tables, and quality testing of herbal medicines and other medical materials. We discuss the technology and hardware of Raman spectroscopy and hyperspectral imaging, firstly. Furthermore, we review these technologies in pharmaceutical scenarios. Finally, the development tendency and prospect of hyperspectral imaging technology-based robots in the field of pharmaceutical quality inspection is summarized.

Challenges and Opportunities of Implementing Data Fusion in Process Analytical Technology—A Review

The release of the FDA’s guidance on Process Analytical Technology has motivated and supported the pharmaceutical industry to deliver consistent quality medicine by acquiring a deeper understanding of the product performance and process interplay. The technical opportunities to reach this high-level control have considerably evolved since 2004 due to the development of advanced analytical sensors and chemometric tools. However, their transfer to the highly regulated pharmaceutical sector has been limited. To this respect, data fusion strategies have been extensively applied in different sectors, such as food or chemical, to provide a more robust performance of the analytical platforms. This survey evaluates the challenges and opportunities of implementing data fusion within the PAT concept by identifying transfer opportunities from other sectors. Special attention is given to the data types available from pharmaceutical manufacturing and their compatibility with data fusion strategies. Furthermore, the integration into Pharma 4.0 is discussed.

Graphene Oxide-Doped Polymer Inclusion Membrane for Remediation of Pharmaceutical Contaminant of Emerging Concerns: Ibuprofen

The application of polymer inclusion membranes (PIMs) for the aquatic remediation of several heavy metals, dyes, and nutrients has been extensively studied. However, its application in treating organic compounds such as Ibuprofen, an emerging pharmaceutical contaminant that poses potential environmental problems, has not been explored satisfactorily. Therefore, graphene oxide (GO) doped PIMs were fabricated, characterized, and applied to extract aqueous Ibuprofen at varied pH conditions. The doped PIMs were synthesized using a low concentration of Aliquat 336 as carrier and 0, 0.15, 0.45, and 0.75% GO as nanoparticles in polyvinyl chloride (PVC) base polymer without adding any plasticizer. The synthesized PIM was characterized by SEM, FTIR, physical, and chemical stability. The GO doped PIM was well plasticized and had an optimal Ibuprofen extraction efficiency of about 84% at pH of 10 and 0.75% GO concentration. Furthermore, the GO doped PIM's chemical stability indicates better stability in acidic solution than in the alkaline solution. This study demonstrates that the graphene oxide-doped PIM significantly enhanced the extraction of Ibuprofen at a low concentration. However, further research is required to improve its stability and efficiency for the remediation of the ubiquitous Ibuprofen in the aquatic environment.

UV Hyperspectral Imaging as Process Analytical Tool for the Characterization of Oxide Layers and Copper States on Direct Bonded Copper

Hyperspectral imaging and reflectance spectroscopy in the range from 200–380 nm were used to rapidly detect and characterize copper oxidation states and their layer thicknesses on direct bonded copper in a non-destructive way. Single-point UV reflectance spectroscopy, as a well-established method, was utilized to compare the quality of the hyperspectral imaging results. For the laterally resolved measurements of the copper surfaces an UV hyperspectral imaging setup based on a pushbroom imager was used. Six different types of direct bonded copper were studied. Each type had a different oxide layer thickness and was analyzed by depth profiling using X-ray photoelectron spectroscopy. In total, 28 samples were measured to develop multivariate models to characterize and predict the oxide layer thicknesses. The principal component analysis models (PCA) enabled a general differentiation between the sample types on the first two PCs with 100.0% and 96% explained variance for UV spectroscopy and hyperspectral imaging, respectively. Partial least squares regression (PLS-R) models showed reliable performance with R²_c = 0.94 and 0.94 and RMSEC = 1.64 nm and 1.76 nm, respectively. The developed in-line prototype system combined with multivariate data modeling shows high potential for further development of this technique towards real large-scale processes.