Comparing the Performance of Raman and Near-Infrared Imaging in the Prediction of the In Vitro Dissolution Profile of Extended-Release Tablets Based on Artificial Neural Networks.

In this work, the performance of two fast chemical imaging techniques, Raman and near-infrared (NIR) imaging is compared by utilizing these methods to predict the rate of drug release from sustained-release tablets. Sustained release is provided by adding hydroxypropyl methylcellulose (HPMC), as its concentration and particle size determine the dissolution rate of the drug. The chemical images were processed using classical least squares; afterwards, a convolutional neural network was applied to extract information regarding the particle size of HPMC. The chemical images were reduced to an average HPMC concentration and a predicted particle size value; these were used as inputs in an artificial neural network with a single hidden layer to predict the dissolution profile of the tablets. Both NIR and Raman imaging yielded accurate predictions. As the instrumentation of NIR imaging allows faster measurements than Raman imaging, this technique is a better candidate for implementing a real-time technique. The introduction of chemical imaging in the routine quality control of pharmaceutical products would profoundly change quality assurance in the pharmaceutical industry.

Follow-up of solid-state fungal wood pretreatment by a novel near-infrared spectroscopy-based lignin calibration model.

Lignin removal plays a crucial role in the efficient bioconversion of lignocellulose to fermentable sugars. As a delignification process, fungal pretreatment has gained great interest due to its environmental friendliness and low energy consumption. In our previous study, a positive linear correlation between acid-insoluble lignin degradation and the achievable enzymatic saccharification yield has been found, hereby highlighting the importance of the close follow-up of lignin degradation during the solid-state fungal pretreatment process. However, the standard quantification of lignin, which relies on the two-step acid hydrolysis of the biomass, is highly laborious and time-consuming. Vibrational spectroscopy has been proven as a fast and easy alternative; however, it has not been extensively researched on lignocellulose subjected to solid-state fungal pretreatment. Therefore, the present study examined the suitability of near-infrared spectroscopy (NIR) for the rapid and easy assessment of lignin content in poplar wood pretreated with Phanerochaete chrysosporium. Furthermore, the predictive power of the obtained calibration model and the recently published ATR-FTIR spectroscopy-based model were compared for the first time using the same fungus-treated wood data set. PLSR was used to correlate the NIR spectra to the acid-insoluble lignin contents (19.9%-27.1%) of pretreated wood. After normalization and second derivation, a PLSR model with a good coefficient of determination (RCV2 = 0.89) and a low root mean square error (RMSECV = 0.55%) were obtained despite the heterogeneous nature of the fungal solid-state fermentation. The performance of this PLSR model was comparably good to the one obtained by ATR-FTIR (RCV2 = 0.87) while it required more extensive spectral pre-processing. In conclusion, both methods will be highly useful for the high-throughput and user-friendly monitoring of lignin degradation in a solid-state fungal pretreatment-based biorefinery concept.

Rapid lignin quantification for fungal wood pretreatment by ATR-FTIR spectroscopy.

Lignin determination in lignocellulose with the conventional two-step acid hydrolysis method is highly laborious and time-consuming. However, its quantification is crucial to monitor fungal pretreatment of wood, as the increase of acid-insoluble lignin (AIL) degradation linearly correlates with the achievable enzymatic saccharification yield. Therefore, in this study, a new attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy method was developed to track fungal delignification in an easy and rapid manner. Partial least square regression (PLSR) with cross-validation (CV) was applied to correlate the ATR-FTIR spectra with the AIL content (19.9 %-27.1 %). After variable selection and normalization, a PLSR model with a high coefficient of determination (RCV2 = 0.87) and a low root mean square (RMSECV = 0.60 %) were obtained despite the heterogeneous nature of the fungal solid-state fermentation. These results show that ATR-FTIR can reliably predict the AIL content in fungus-treated wood while being a high-throughput method. This novel method can facilitate the transition to the wood-based economy.

Validation of microplastic sample preparation method for freshwater samples.

The global presence of microplastics in the environment is well documented nowadays. Studies already showed the potential risks that microplastic particles might cause to the ecosystem, while potential human health effects are currently under investigation. As one of the main inputs of these crucial researches, the concentration of microplastics in the environment should be measured precisely, confidently and monitored regularly to determine exposure levels of these pollutants. Some study highlights, that the results are usually inconsistent and uncertain, due to different sampling and sample preparation methods and the lack of quality assurance and quality control of these processes. The need for a standardized methodology is an emerging issue, as this would provide the right tools to establish a global monitoring system of microplastics. Validated sample preparation methods of water (especially freshwater) samples for microplastic analysis are rarely described. To fulfil the gap, this study aims to create and validate a special toolset and the related standard operating procedure for enhanced sample preparation. A newly developed equipment, the Small Volume Glass Separator was designed to easily isolate microplastics from freshwater samples and concentrate the treated sample in a small volume, thus reducing the brine solution use and the sample transfer steps. These features enable better prevention of contamination and making sample preparation easy, fast and cost-effective. The Small Volume Glass Separator and the related standard operation procedure was validated on model freshwater and wastewater samples with the use of fluorescently tagged microplastics and environmentally relevant microplastics (fragments, fibres). Recoveries were measured with optical microscopy under UV light and with near-infrared spectroscopy/microscopy. Recovery tests with fluorescently tagged microspheres showed that average recovery with the Small Volume Glass Separator is 12-39% higher than that of a widespread sample preparation method. This procedure was also able to recover on average 64%±29% of all the environmentally relevant particles during the validation process. Results show that size and density have a great influence on potential particle loss. Recovery of smaller particles are less with both methods than that of the larger particles, but Small Volume Glass Separator yielded significantly higher recovery for more dense particles. The results of this study help to better understand particle loss during sample preparation and thus contribute to the establishment of standardised microplastic analysis processes.

Validation of pressurized fractionated filtration microplastic sampling in controlled test environment.

In the field of microplastic (MP) research in the environment, a significant amount of the currently reported results is uncertain because of the inappropriate methods of sampling, detection and quantification of MPs. Fortunately, many research groups are aware of these challenges, but validated methods, which are the prerequisite of standardized measurements, are scarce. Recovery tests are especially rare in the field of MP sampling. The aim of our research was to take a step forward and collect data on cascade filtration recoveries by modeling different turbulance conditions and sampling depth applying environmentally relevant MP concentrations while obtaining large sample volumes. As reference materials, different polymer types (polyethylene - PE; polypropylene - PP; poly[ethylene terephthalate] - PET; poly[vinyl chloride] - PVC; polyamide - PA) and shapes (sphere, fragment, fiber) were used, and for detection near-infrared spectroscopy/microscopy was applied. The developed method provides information not only on system based MP losses, but on sampling efficiency in a model environment as well. Based on the results, the highest recovery rate of all polymers was 31.4% on average, sampled from the water surface during continuous stirring. In these conditions, 92.4% of the PE spheres and 31.9% of the PE fragments were recovered. This indicates, particles reported in environmental monitoring studies might be less than the real environmental concentration. We can conclude, that surface sampling is more efficient than sampling in a deeper layer of the water column. Our research revealed, that the widespread application of microspheres as reference materials might lead to too optimistic recovery values. The application of reference particles (fragments, fibers) with higher environmental relevance shows much lower recovery rates. Our results highlight, that validating the efficiency of the whole sampling process from the environment is more important than measuring only the filtration device's recovery. This study helps us to better understand the relationship and the possible gaps between the reported MP results and the real-life concentrations in the environment.

A novel approach to the characterization of old wheat (Triticum aestivum L.) varieties by complex rheological analysis.

BACKGROUND: Lines of the internationally recognized old Hungarian Bánkúti 1201 variety are important genetic resources for breeding programmes. Their protein composition and gluten dependent technological traits have been comprehensively studied, however, little information is available about their carbohydrate dependent viscous properties. The aim of this work was to obtain comprehensive rheological characterization of all sublines of Bánkúti 1201 maintained at Martonvásár and to investigate their variability if the carbohydrate dependent viscous behaviour was also included in the analyses. RESULTS: The majority of the lines reflected the famously good mixing quality of Bánkúti, however, much higher diversity of pasting behaviour was detected. Cluster analysis of the Mixolab data was performed resulting in four sample groups. Since several lines of similar mixing properties had significantly different pasting characteristics, it was assumed that classification was mainly based on the viscous properties. From each cluster two to three representative samples were selected for wider examination using conventional testing methods. These results also supported the higher variability of pasting behaviour of the lines, which can be critical for end product quality. The members of the second cluster can be highlighted due to their waxy wheat like behaviour. CONCLUSIONS: Possible reasons for the great variability of pasting behaviour could be the compositional and structural differences of starch and other carbohydrates (e.g. arabinoxylans). Complex rheological characterization and study of molecular background can provide information about important traits from the point of view of technology and product development, which are unknown in the case of old wheat varieties and landraces. © 2020 Society of Chemical Industry.

Attenuated total reflection fourier transform infrared spectroscopy based methods for identification of chromatography media formulations used in downstream processes.

Chromatographic media play a crucial role in the downstream processing of biotechnology products. The physical and chemical properties of these processing aids are mostly monitored by expensive and time-consuming preparative tests, but spectroscopic techniques may also be used to measure chromatographic media samples. In this study, chromatographic media formulations used in downstream processes were investigated using attenuated total reflection Fourier transform infrared (ATR FT-IR) spectroscopy. Samples were measured both in original suspension form and after drying to examine the possibilities of a potential spectroscopic method without sample preparation. Principal component analysis (PCA) was employed to identify the spectral differences among the formulations with distinct support matrices and functional groups and soft independent modeling of class analogy (SIMCA) was performed to creating classification models for identification of chromatography media. To increase the number of samples in the SIMCA, simulated spectra were generated based on the experimental spectra. PCA models indicated that spectra of samples in original suspension form and after drying contained similar information about the chemical properties of chromatographic media samples. Moreover, during the classification of spectra based on SIMCA, both measurement techniques gave high sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) results. These results show that ATR FT-IR could be applied as a simple alternative method for monitoring the chromatography media samples. This technique is also feasible without sample preparation. Thereby the multi-hours drying steps may be omitted, the measurements can be performed in a few minutes, and the potential effects of sample preparations can be eliminated.

On-line glucose monitoring by near infrared spectroscopy during the scale up steps of mammalian cell cultivation process development.

NIR spectroscopy is a non-destructive tool for in-situ, on-line bioprocess monitoring. One of its most frequent applications is the determination of metabolites during cultivation, especially glucose. Previous studies have usually investigated the applicability of Near Infrared (NIR) spectroscopy at one bioreactor scale but the effect of scale up was not explored. In this study, the complete scale up from shake flask (1 L) through 20 L, 100 L and 1000 L up to 5000 L bioreactor volume level was monitored with on-line NIR spectroscopy. The differences between runs and scales were examined using principal component analysis. The bioreactor runs were relatively similar regardless of scales but the shake flasks differed strongly from bioreactor runs. The glucose concentration throughout five 5000 L scale bioreactor runs were predicted by partial least squares regression models that were based on pre-processed spectra of bioreactor runs and combinations of them. The model that produced the lowest error of prediction (4.18 mM on a 29 mM concentration range) for all five runs in the prediction set was based on the combination of 20 L and 100 L data. This result demonstrated the capabilities and the limitations of an NIR system for glucose monitoring in mammalian cell cultivations.

Comparison of multivariate data analysis techniques to improve glucose concentration prediction in mammalian cell cultivations by Raman spectroscopy.

In-situ Raman spectroscopy is frequently applied to monitor and even control the glucose concentration of monoclonal antibody producing mammalian cell cultivations. Previous studies used the PLSR algorithm only, however other multivariate algorithms were applied successfully for different protein production processes. In this study, four mammalian cell cultivation runs were followed with Raman spectroscopy and the spectra were analysed quantitatively and qualitatively as well. The PCA analysis showed that one of the most dominant factors in the Raman spectra were the concentration of glucose, which strongly correlated with the score values of the eighth principal component. This observation further substantiated that Raman spectroscopy is an excellent tool for bioprocess monitoring and induced the test of the Multivariate Linear Regression (MLR), Principal Component Regression (PCR) and Partial Least Squares Regression (PLSR) algorithms, using the results of the PCA as one of the variable selection techniques, to determine the glucose concentration during cultivation. However, the novel variable selection technique of PCA correlation enhanced only the model accuracy when it was applied with MLR and only model robustness was increased when it was used with PCR and PLSR because the relatively strong Raman signal of glucose concentration seemed to be enough to build an accurate model on. Therefore, PLSR, the most advanced algorithm of the three, delivered the lowest 2.21 mM RMSEP but it was demonstrated that in certain cases PCR could also produce satisfactorily results.

On-line prediction of the glucose concentration of CHO cell cultivations by NIR and Raman spectroscopy: Comparative scalability test with a shake flask model system.

In this study, near-infrared (NIR) and Raman spectroscopy were compared in parallel to predict the glucose concentration of Chinese hamster ovary cell cultivations. A shake flask model system was used to quickly generate spectra similar to bioreactor cultivations therefore accelerating the development of a working model prior to actual cultivations. Automated variable selection and several pre-processing methods were tested iteratively during model development using spectra from six shake flask cultivations. The target was to achieve the lowest error of prediction for the glucose concentration in two independent shake flasks. The best model was then used to test the scalability of the two techniques by predicting spectra of a 10l and a 100l scale bioreactor cultivation. The NIR spectroscopy based model could follow the trend of the glucose concentration but it was not sufficiently accurate for bioreactor monitoring. On the other hand, the Raman spectroscopy based model predicted the concentration of glucose in both cultivation scales sufficiently accurately with an error around 4mM (0.72g/l), that is satisfactory for the on-line bioreactor monitoring purposes of the biopharma industry. Therefore, the shake flask model system was proven to be suitable for scalable spectroscopic model development.

Synthesis of gluten-forming polypeptides. 1. Biosynthesis of gliadins and glutenin subunits.

Five winter wheat cultivars--GK Othalom (HMW-GS composition 2*, 7+8, 5+10), Ukrainka (1, 7+8, 5+10), Palotás (2*, 7+9, 5+10), Ködmön (2*, 7+8, 5+10), and Csongrád (2*, 7+9, 2+12)--grown in Hungary and harvested in the year 2005 were studied. The biosynthesis of gluten-forming polypeptides was followed starting at the 12th day after anthesis to the 53rd. Fresh kernel weight, moisture, and dry matter content of fresh kernels and gliadin and glutenin contents were determined. Gliadin components, total amounts of HMW and LMW polypeptides, and individual HMW polypeptides were determined using a RP-HPLC technique. Although considerable quantitative differences were observed concerning the content of total protein, gliadin, glutenin, and individual gluten-forming polypeptides, the character of accumulation of protein components--determined on the basis protein mass/kernel--was the same for the all of the cultivars studied and could be presented by a sigmoid curve. Small quantities of the gliadin and glutenin monomers may be detected in early stages of kernel development, but the bulk of these proteins is synthesized in later stages of development. It is generally suggested by specialists that the formation and accumulation of glutenin polymers starts later than the synthesis of monomers. Experimental data presented in this paper confirm this suggestion and show that in the first phase of protein synthesis the monomers are in "free" form; polymeric glutenin is detected only later. HMW glutenin subunits are synthesized synchronously, and quantitatively the polypeptides coded by chromosomes D and B dominate.