Zoltan Kovacs

Mathematical Modeling of Diafiltration

The main objective of this study is to provide a general mathematical model in a compact form for batch diafiltration techniques. The presented mathematical framework gives a rich representation of diafiltration processes due to the employment of concentration-dependent solute rejections. It unifies the existing models for constant-volume dilution mode, variable-volume dilution mode, and concentration mode operations. The use of such a mathematical framework allows the optimization of the overall diafiltration process. The provided methodology is particularly applicable for decision makers to choose an appropriate diafiltration technique for the given separation design problem.

Multicomponent blood lipid analysis by means of near infrared spectroscopy, in geese

This study provides accurate near infrared (NIR) spectroscopic models on some laboratory determined clinicochemical parameters (i.e. total lipid (5.57±1.95 g/l), triglyceride (2.59±1.36 mmol/l), total cholesterol (3.81±0.68 mmol/l), high density lipoprotein (HDL) cholesterol (2.45±0.58 mmol/l)) of blood serum samples of fattened geese. To increase the performance of multivariate chemometrics, samples significantly deviating from the regression models implying laboratory error were excluded from the final calibration datasets. Reference data of excluded samples having outlier spectra in principal component analysis were not marked as false. Samples deviating from the regression models but having non outlier spectra in PCA were identified as having false reference constituent values. Based on the NIR selection methods, 5% of the reference measurement data were rated as doubtful. The achieved models reached R(2) of 0.864, 0.966, 0.850, 0.793, and RMSE of 0.639 g/l, 0.232 mmol/l, 0.210 mmol/l, 0.241 mmol/l for total lipid, triglyceride, total cholesterol and HDL cholesterol, respectively, during independent validation. Classical analytical techniques focus on single constituents and often require chemicals, time-consuming measurements, and experienced technicians. NIR technique provides a quick, cost effective, non-hazardous alternative method for analysis of several constituents based on one single spectrum of each sample, and it also offers the possibility for looking at the laboratory reference data critically. Evaluation of reference data to identify and exclude falsely analyzed samples can provide warning feedback to the reference laboratory, especially in the case of analyses where laboratory methods are not perfectly suited to the subjected material and there is an increased chance of laboratory error.

Biochemical activities of lactose-derived prebiotics — a review

In the dairy industry different types of prebiotics, such galacto-oligosaccharide, lactulose, lactosucrose, tagatose, lactitol, lactobiono- and glucono-δ-lactone are synthesized through different chemical and biochemical reactions (hydrolysis, transgalactosylation, isomerization, fructosyl-transfer, reduction, and oxidation) as well as microbial fermentation processes using raw whey or isolated lactose as feedstock. Lactose-derived prebiotics have several functional and nutritional values. The biochemical activities of lactose-based prebiotics are expressed in the presence of probiotics (lactic acid bacteria, yeasts, Bacillus spp.). Galacto-oligosaccharide and lactosucrose reduce the risk of bowel disorder (diarrhea), inflammatory bowel disease (ulcerative colitis and crohn’s disease), and colon cancer. Galacto-oligosaccharide helps colonic absorption of minerals (iron, magnesium and calcium) and prevents osteoporosis. Lactulose, galacto-oligosaccharide, and lactitol promote laxative activity. Furthermore...

Essentials of Aquaphotomics and Its Chemometrics Approaches

Aquaphotomics is a novel scientific discipline involving the study of water and aqueous systems. Using light-water interaction, it aims to extract information about the structure of water, composed of many different water molecular conformations using their absorbance bands. In aquaphotomics analysis, specific water structures (presented as water absorbance patterns) are related to their resulting functions in the aqueous systems studied, thereby building an aquaphotome—a database of water absorbance bands and patterns correlating specific water structures to their specific functions. Light-water interaction spectroscopic methods produce complex multidimensional spectral data, which require data processing and analysis to extract hidden information about the structure of water presented by its absorbance bands. The process of extracting information from water spectra in aquaphotomics requires a field–specific approach. It starts with an appropriate experimental design and execution to ensure high-quality spectral signals, followed by a multitude of spectral analysis, preprocessing and chemometrics methods to remove unwanted influences and extract water absorbance spectral pattern related to the perturbation of interest through the identification of activated water absorbance bands found among the common, consistently repeating and highly influential variables in all analytical models. The objective of this paper is to introduce the field of aquaphotomics and describe aquaphotomics multivariate analysis methodology developed during the last decade. Through a worked-out example of analysis of potassium chloride solutions supported by similar approaches from the existing aquaphotomics literature, the provided instruction should give enough information about aquaphotomics analysis i.e. to design and perform the experiment and data analysis as well as to represent water absorbance spectral pattern using various forms of aquagrams—specifically designed aquaphotomics graphs. The explained methodology is derived from analysis of near infrared spectral data of aqueous systems and will offer a useful and new tool for extracting data from informationally rich water spectra in any region. It is the hope of the authors that with this new tool at the disposal of scientists and chemometricians, pharmaceutical and biomedical spectroscopy will substantially progress beyond its state-of-the-art applications.

Artificial Neural Network-Assisted Spectrophotometric Method for Monitoring Fructo-oligosaccharides Production

AbstractShort-chain fructo-oligosaccharides (FOS) are considered as low-calorie carbohydrates with prebiotic function. They can be produced from sucrose by fructosyltransferase activity, resulting in a mixture of saccharides with different chain lengths. Current practice for carbohydrate analysis involves the use of time-costly and off-line chromatographic procedures. This study is dedicated to the development of an artificial neural network (ANN) model for predicting carbohydrate composition from the direct measurement of UV spectra. A total of 182 samples were generated by operating an enzyme membrane reactor (EMR) under both optimal and suboptimal settings. The concentration data determined by HPLC and corresponding absorbance readings were used to train a two-layer feedforward neural network. The optimized model was then validated by using new observations that were not involved in the training. The model explained 98, 97, and 88% of the variation in the composition of the new observations regarding the main components sucrose, kestose, and glucose with a mean squared error of prediction of 6.59, 3.40, and 2.81, respectively. The results indicate that the proposed UV-ANN method has a great potential to be used for the real-time monitoring of the bioconversion. Graphical Abstractᅟ