Characterization of fast-growing foams in bottling processes by endoscopic imaging and convolutional neural networks

Regardless of whether the occurrence of foams in industrial processes is desirable or not, the knowledge about the characteristics of their formation and morphology is crucial. This study addresses the measuring of characteristics in foam and the trailing bubbly liquid that result from air bubble entrainment by a plunging jet in the environment of industry-like bottling process es of non-carbonated beverages. Typically encountered during the bottling of fruit juices, this process configuration is characterized by very fast filling speeds with high dynamic system parameter changes. Especially in multiphase systems with a sensitive disperse phase like gas bubbles, the task of its measurement turns out to be difficult. The aim of the study is to develop and employ an image processing capability in real geometries under realistic industrial conditions, e.g. as opposed to a narrow measurement chamber. Therefore, a typically sized test bottle was only slightly modified to adapt an endoscopic measurement technique and to acquire image data in a minimally invasive way. Two convolutional neural networks (CNNs) were employed to analyze irregular non-overlapping bubbles and circular overlapping bubbles. CNNs provide a robust object recognition for varying image qualities and therefore can cover a broad range of process conditions at the cost of a time-consuming training process. The obtained single bubble and population measurements allow approximation, correlation and interpretation of the bubble size and shape distributions within the foam and in the bubbly liquid. The classification of the measured foam morphologies and the influence of operating conditions are presented. The applicability to the described test case as an industrial multiphase process reveals high potential for a huge field of operations for particle size and shape measurement by the introduced method

Komplexe SANS SAXS Datenauswertung, Simulation und Interpretation mit einem Bezug zur statistischen Inferenz

Die vorliegende Arbeit gibt einen grundlegenden Diskurs zu unterschiedlichen Methoden zur Auswertung von Kleinwinkelstreudaten (SAS Daten), insbesondere hier zum ersten Mal in einem präzise statistisch definierten Rahmen. Die Ermittlung von Strukturinformationen via Anpassungen von physikalischen und Freiform-Modellen wird in zwei unterschiedlichen statistischen Inferenz-Ansätzen diskutiert, einem Bayes'schen und einem frequentistischen Ansatz. Somit basiert eine Auswertung und Interpretation der SAS Daten auf gut fundierten Theorien, die zudem aufzeigen wie optimale Schlussfolgerungen zu ziehen sind. Die Diskussion zeigt, dass es wichtig ist genug Informationen über das streuende System (a priori Strukturinformation und Information in den Streudaten) zur Verfügung zu haben, um zugrundeliegende Strukturen zu ermitteln. Beispielhaft wird eine simultane Anpassung eines physikalischen Modells an Kontrastvariationsdaten eines Inter-Polyelektrolyt-Komplexes diskutiert. Darüber hinaus werden die statistischen Inferenz-Ansätze auf die Indirekte Fourier Transformation (IFT) angewendet, um objektiv eine Freiform-Lösung zu erhalten, und moderne Methoden des Maschinellen Lernens (RVM, SVR, LASSO) werden benutzt, um eine stabilere Lösung zu ermitteln. Ein neues, selbst entwickeltes Programm (SASET) wird präsentiert, das es erlaubt effizient umfangreiche und gekoppelte 1-dimensionale Datensätze/-serien auszuwerten; somit ermöglicht das Programm viele Informationen in den Auswertungsprozess einfließen zu lassen. Auch können 2-dimensionale Datensätze/-serien effizient ausgewertet werden. Außerdem wird dargelegt wie Strukturinformationen aus 2-dimensionalen anisotropen Streudaten gewonnen werden können. Die Bildung von Vesikeln wird durch die Differentialgleichung von Smoluchowski simuliert. Experimentelle SAS Daten eines Vesikel-bildenden Systems werden simultan angepasst, somit viel Information verwendet, um wenige Parameter zuverlässig zu ermitteln. Streuintensitäten von komplex, hierarchisch strukturierten kolloidalen Systemen werden mittels Monte Carlo (MC) Simulationen analysiert. Die MC Simulationen zeigen welche strukturellen Informationen in SAS Daten enthalten sind.This work presents an in-depth discussion of different methods used for Small-Angle Scattering (SAS) data analysis, and especially for the first time within a precisely defined statistical framework. Inferring structural information from SAS data via physical model fitting and free-form model fitting is discussed within two different statistical inference approaches, namely Bayesian and frequentist statistics, which put the analysis and interpretation of SAS data on well founded theories, hence showing how to draw optimal inferences. The discussion shows the importance of having enough of scattering system information (a priori knowledge about the system and information contained in the experimental SAS data) available in order to infer structural information. As an example, simultaneous physical model fitting is performed on contrast variation data of an InterPolyElectrolyte Complex (IPEC) system. Moreover, statistical inference is applied to the Indirect Fourier Transform (IFT) in order to get objectively a free-form solution, and additionally, modern machine learning methods (RVM, SVR, LASSO) are employed to determine a more robust solution. A new and homemade program, called SASET, is presented that easily allows to efficiently evaluate comprehensive and coupled 1-dimensional SAS data sets/series, hence allowing to include a lot of information in the evaluation process. 2-dimensional data sets/series can also be evaluated efficiently. Moreover, a discussion about inferring structural information from anisotropic 2-dimensional scattering data is given. Vesicle formation is simulated by the von Smoluchowski differential equation. Experimental SAS data of a vesicle building system are simultaneously fitted by adjusting parameters of the kernel of the differential equation, and therefore a lot of information is used to determine reliably a few parameters. The scattering intensity of complex, hierarchically structured colloidal systems is analyzed by Monte Carlo (MC) simulations. The MC simulations show which structural information are contained in the SAS data

Real-time monitoring of the budding index in Saccharomyces cerevisiae batch cultivations with in situ microscopy

Abstract Background The morphology of yeast cells changes during budding, depending on the growth rate and cultivation conditions. A photo-optical microscope was adapted and used to observe such morphological changes of individual cells directly in the cell suspension. In order to obtain statistically representative samples of the population without the influence of sampling, in situ microscopy (ISM) was applied in the different phases of a Saccharomyces cerevisiae batch cultivation. The real-time measurement was performed by coupling a photo-optical probe to an automated image analysis based on a neural network approach. Results Automatic cell recognition and classification of budding and non-budding cells was conducted successfully. Deviations between automated and manual counting were considerably low. A differentiation of growth activity across all process stages of a batch cultivation in complex media became feasible. An increased homogeneity among the population during the growth phase was well observable. At growth retardation, the portion of smaller cells increased due to a reduced bud formation. The maturation state of the cells was monitored by determining the budding index as a ratio between the number of cells, which were detected with buds and the total number of cells. A linear correlation between the budding index as monitored with ISM and the growth rate was found. Conclusion It is shown that ISM is a meaningful analytical tool, as the budding index can provide valuable information about the growth activity of a yeast cell, e.g. in seed breeding or during any other cultivation process. The determination of the single-cell size and shape distributions provided information on the morphological heterogeneity among the populations. The ability to track changes in cell morphology directly on line enables new perspectives for monitoring and control, both in process development and on a production scale

MOESM1 of Real-time monitoring of the budding index in Saccharomyces cerevisiae batch cultivations with in situ microscopy

Additional file 1. Tables and Figures