Quantitative determination of pharmaceutical drug formulations by near-infrared spectroscopic imaging

A technique based on infrared spectroscopy for determining sulfanilamide levels sustainably: Progress and comparisons of greenness and whiteness using ComplexGAPI, AGREE, and RGB

The study aimed to determine the potential of the infrared (IR) spectrophotometric technique for measuring the content of sulphanilamide with the sulfonamide group. The study aimed to obtain the IR spectra of sulfanilamide and use the -SO2 band at 1114.37 for the quantitative assay, determining its area under the curve (AUC). The study gives an alternative approach to existing analytical techniques that require vast amounts of organic solvents, which are costly and can be toxic, thus impacting the environment and increasing the analysis cost. The study evaluated the method's whiteness and greenness by utilizing the Complex green analytical procedure index, analytical GREEness calculator and Red Green Blue algorithm tool. The linierity was found to be 5 to 30 µg/ml. The present study has developed an infrared (IR) spectroscopic method that employs a straightforward sample preparation technique in methanol. The IR spectroscopic method's linearity range was determined to be 5-30 µg/ml. The p-value was 0.001 at 95 % confidence level assuring better recovery. This method is evaluated according to the Q2R1 ICH guideline. It is applicable to routine quality control analysis without pre-extraction using green IR spectroscopy. In conclusion, the study demonstrated that IR spectrophotometric techniques can quantify sulfanilamide while reducing the use of organic solvents, contributing to the green-and-white analytical chemistry approach. The developed methods are reliable, accurate, and cost-effective and have the potential to be implemented in routine analysis of sulfanilamide.

Evaluation of laser direct infrared imaging for rapid analysis of pharmaceutical tablets

Vibrational spectroscopic chemical imaging is an important tool in the pharmaceutical industry for characterising the spatial distribution of components within final drug products. The applicability of these techniques is currently limited by the long data acquisition times required to obtain high-definition chemical images of a sample surface. Advancements in quantum cascade laser (QCL) technology have provided an exciting new opportunity for infrared (IR) imaging. Instead of collecting a full IR spectrum at each point, it is possible to focus on distinct spectral bands to reduce imaging data collection time. This study explores a laser direct infrared (LDIR) chemical imaging approach that couples QCL technology with rapid scanning optics to provide high-definition chemical images at an order of magnitude faster than traditional imaging techniques. The capabilities of LDIR chemical imaging were evaluated for pharmaceutical formulations and compared with other established spectroscopic chemical imaging techniques including Raman, near-infrared (NIR) and scanning electron microscopy-energy dispersive X-ray (SEM-EDX) spectroscopy with regards to data acquisition time and image quality. The study showed that LDIR imaging provided high-definition component distribution maps comparable to Raman and SEM-EDX at orders of magnitude faster in terms of time. The ability to obtain high-definition chemical images of the whole tablet surface in relatively fast time frames indicates LDIR imaging could be a promising tool in the pharmaceutical industry to rapidly characterise the size and distribution of components within tablets and could help enhance drug product manufacturing understanding.

Rapid Determination of Nutritional Parameters of Pasta/Sauce Blends by Handheld Near-Infrared Spectroscopy

Nowadays, near infrared (NIR) spectroscopy has experienced a rapid progress in miniaturization (instruments < 100 g are presently available), and the price for handheld systems has reached the < $500 level for high lot sizes. Thus, the stage is set for NIR spectroscopy to become the technique of choice for food and beverage testing, not only in industry but also as a consumer application. However, contrary to the (in our opinion) exaggerated claims of some direct-to-consumer companies regarding the performance of their "food scanners" with "cloud evaluation of big data", the present publication will demonstrate realistic analytical data derived from the development of partial least squares (PLS) calibration models for six different nutritional parameters (energy, protein, fat, carbohydrates, sugar, and fiber) based on the NIR spectra of a broad range of different pasta/sauce blends recorded with a handheld instrument. The prediction performance of the PLS calibration models for the individual parameters was double-checked by cross-validation (CV) and test-set validation. The results obtained suggest that in the near future consumers will be able to predict the nutritional parameters of their meals by using handheld NIR spectroscopy under every-day life conditions.

Application of a handheld NIR spectrometer in prediction of drug content in inkjet printed orodispersible formulations containing prednisolone and levothyroxine

Quality control tools to assess the quality of printable orodispersible formulations are yet to be defined. Four different orodispersible dosage forms containing two poorly soluble drugs, levothyroxine and prednisolone, were produced on two different edible substrates by piezoelectric inkjet printing. Square shaped units of 4cm² were printed in different resolutions to achieve an escalating drug dose by highly accurate and uniform displacement of droplets in picoliter range from the printhead onto the substrates. In addition, the stability of drug inks in a course of 24h as well as the mechanical properties and disintegration behavior of the printed units were examined. A compact handheld near-infrared (NIR) spectral device in the range of 1550-1950nm was used for quantitative estimation of the drug amount in printed formulations. The spectral data was treated with mean centering, Savitzky-Golay filtering and a third derivative approach. Principal component analysis (PCA) and orthogonal partial least squares (OPLS) regression were applied to build predictive models for quality control of the printed dosage forms. The accurate tuning of the dose in each formulation was confirmed by UV spectrophotometry for prednisolone (0.43-1.95mg with R²=0.999) and high performance liquid chromatography for levothyroxine (0.15-0.86mg with R²=0.997). It was verified that the models were capable of clustering and predicting the drug dose in the formulations with both Q² and R²Y values between 0.94-0.99.

PAT: From Western solid dosage forms to Chinese materia medica preparations using NIR-CI

Near-infrared chemical imaging (NIR-CI) is an emerging technology that combines traditional near-infrared spectroscopy with chemical imaging. Therefore, NIR-CI can extract spectral information from pharmaceutical products and simultaneously visualize the spatial distribution of chemical components. The rapid and non-destructive features of NIR-CI make it an attractive process analytical technology (PAT) for identifying and monitoring critical control parameters during the pharmaceutical manufacturing process. This review mainly focuses on the pharmaceutical applications of NIR-CI in each unit operation during the manufacturing processes, from the Western solid dosage forms to the Chinese materia medica preparations. Finally, future applications of chemical imaging in the pharmaceutical industry are discussed.

Identification of four Sedum plant medicines by fourier transform infrared spectra

Background:

Sedum sarmentosum bunge (SSB)., S. lineare Thunb. (SLT), S. erythrostictum migo. (SEM), and S. aizoon L. (SAL) were four widely used Chinese traditional drugs or ethnic drugs, which were easy to be confused with each other.

Objective:

This study aimed at developing a rapid and accurate method to identify the four Sedum plant medicines with very similar appearances and close relationships.

Materials and Methods:

The herbal medicines employed here were SSB, SLT, SEM, and SAL collected in different places and seasons. Through comparing the infrared (IR) spectra of their 70% ethanol extracts, the results showed that the IR spectra of the four plant medicines possessed not only some common characteristics but also certain notable distinctions, such as shapes, numbers, positions, intensity, and ratios of the absorbing peaks.

Results:

By fourier transform infrared (FT-IR) spectroscopy, the four medicines could be effectively differed, their habitats could be judged preliminarily, and the genetic relationships of the original plants of the four medicines could also be estimated to some extent.

Conclusion:

The application of FT-IR spectroscopy in crude medicine authentication and quality evaluation deserved to be further emphasized.

Raman mapping of pharmaceuticals

Raman spectroscopy may be implemented through a microscope to provide fine scale axial and lateral chemical maps. The molecular structure of many drugs makes Raman spectroscopy particularly well suited to the investigation of pharmaceutical systems. Chemometric methods currently used to assess bulk Raman spectroscopic data are typically applied to Raman mapping data from pharmaceuticals; few reports exist where the spatial information inherent to a mapped dataset is used for the calculation of chemical maps. Both univariate and multivariate methods have been applied to Raman mapping data to determine the distribution of active pharmaceutical ingredients (APIs) in tablets, solid dispersions for increased solubility and controlled release devices. The ability to axially (depth) profile using Raman mapping has been used in studies of API penetration through membranes, cellular uptake of drug delivery liposomes, and initial API distribution and subsequent elution from coatings of medical devices. New instrumental developments will increase the efficiency of Raman mapping and lead to greater utilisation of Raman mapping for analyses of pharmaceutical systems.

Machine vision detection of bonemeal in animal feed samples

There is growing public concern about contaminants in food and feed products, and reflection-based machine vision systems can be used to develop automated quality control systems. An important risk factor in animal feed products is the presence of prohibited ruminant-derived bonemeal that may contain the BSE (Bovine Spongiform Encephalopathy) prion. Animal feed products are highly complex in composition and texture (i.e., vegetable products, mineral supplements, fish and chicken meal), and current contaminant detection systems rely heavily on labor-intensive microscopy. In this study, we developed a training data set comprising 3.65 million hyperspectral profiles of which 1.15 million were from bonemeal samples, 2.31 million from twelve other feed materials, and 0.19 million denoting light green background (bottom of Petri dishes holding feed materials). Hyperspectral profiles in 150 spectral bands between 419 and 892 nm were analyzed. The classification approach was based on a sequence of linear discriminant analyses (LDA) to gradually improve the classification accuracy of hyperspectral profiles (reduce level of false positives), which had been classified as bonemeal in previous LDAs. That is, all hyperspectral profiles classified as bonemeal in an initial LDA (31% of these were false positives) were used as input data in a second LDA with new discriminant functions. Hyperspectral profiles classified as bonemeal in LDA2 (false positives were equivalent to 16%) were used as input data in a third LDA. This approach was repeated twelve times, in which at each step hyperspectral profiles were eliminated if they were classified as feed material (not bonemeal). Four independent feed materials were experimentally contaminated with 0-25% (by weight) bonemeal and used for validation. The analysis presented here provides support for development of an automated machine vision to detect bonemeal contamination around the 1% (by weight) level and therefore constitutes an important initial screening tool in comprehensive, rapid, and practically feasible quality control of feed materials.