Image processing for identification and quantification of filamentous bacteria in in situ acquired images

Current and future technologies for monitoring cultured meat: A review

The high population growth rate, massive animal food consumption, fast economic progress, and limited food resources could lead to a food crisis in the future. There is a huge requirement for dietary proteins including cultured meat is being progressed to fulfill the need for meat-derived proteins in the diet. However, production of cultured meat requires monitoring numerous bioprocess parameters. This review presents a comprehensive overview of various widely adopted techniques (optical, spectroscopic, electrochemical, capacitive, FETs, resistive, microscopy, and ultrasound) for monitoring physical, chemical, and biological parameters that can improve the bioprocess control in cultured meat. The methods, operating principle, merits/demerits, and the main open challenges are reviewed with the aim to support the readers in advancing knowledge on novel sensing systems for cultured meat applications.

TUM-ParticleTyper 2: automated quantitative analysis of (microplastic) particles and fibers down to 1 μ m by Raman microspectroscopy

Accurate quantification of small microplastics in environmental and food samples is a prerequisite for studying their potential hazard. Knowledge of numbers, size distributions and polymer type for particles and fibers is particularly relevant in this respect. Raman microspectroscopy can identify particles down to 1 μ m in diameter. Here, a fully automated procedure for quantifying microplastics across the entire defined size range is presented as the core of the new software TUM-ParticleTyper 2. This software implements the theoretical approaches of random window sampling and on-the-fly confidence interval estimation during ongoing measurements. It also includes improvements to image processing and fiber recognition (when compared to the previous software TUM-ParticleTyper for analysis of particles/fibers> 10  μ m), and a new approach to adaptive de-agglomeration. Repeated measurements of internally produced secondary reference microplastics were evaluated to assess the precision of the whole procedure.

Towards Automated Classification of Zooplankton Using Combination of Laser Spectral Techniques and Advanced Chemometrics

Zooplankton identification has been the subject of many studies. They are mainly based on the analysis of photographs (computer vision). However, spectroscopic techniques can be a good alternative due to the valuable additional information that they provide. We tested the performance of several chemometric techniques (principal component analysis (PCA), non-negative matrix factorisation (NMF), and common dimensions and specific weights analysis (CCSWA of ComDim)) for the unsupervised classification of zooplankton species based on their spectra. The spectra were obtained using laser-induced breakdown spectroscopy (LIBS) and Raman spectroscopy. It was convenient to assess the discriminative power in terms of silhouette metrics (Sil). The LIBS data were substantially more useful for the task than the Raman spectra, although the best results were achieved for the combined LIBS + Raman dataset (best Sil = 0.67). Although NMF (Sil = 0.63) and ComDim (Sil = 0.39) gave interesting information in the loadings, PCA was generally enough for the discrimination based on the score graphs. The distinguishing between Calanoida and Euphausiacea crustaceans and Limacina helicina sea snails has proved possible, probably because of their different mineral compositions. Conversely, arrow worms (Parasagitta elegans) usually fell into the same class with Calanoida despite the differences in their Raman spectra.

Label-free detection and enumeration of Giardia cysts in agitated suspensions using in situ microscopy

Laboratory procedures performed in water treatment studies frequently require the characterization of (oo)cyst suspensions. Standard methods commonly used are laborious, expensive and time-consuming, besides requiring well-trained personnel to prepare samples with fluorescent staining and perform analysis under fluorescence microscopy. In this study, an easy cost-effective in situ microscope was assessed to acquire images of Giardia cysts directly from agitated suspensions without using any chemical labels or sample preparation steps. An image analysis algorithm analyzes the acquired images, and automatically enumerates and provides morphological information of cysts within 10 min. The proposed system was evaluated at different cyst concentrations, achieving a limit of detection of ~30 cysts/mL. The proposed system overcomes cost, time and labor demands by standard methods and has the potential to be an alternative technique for the characterization of Giardia cyst suspensions in resource-limited facilities, since it is independent of experts and free of consumables.

Identifying the presence of bacteria on digital images by using asymmetric distribution with k-means clustering algorithm

This paper is mainly aimed at the decomposition of image quality assessment study by using Three Parameter Logistic Mixture Model and k-means clustering (TPLMM-k). This method is mainly used for the analysis of various images which were related to several real time applications and for medical disease detection and diagnosis with the help of the digital images which were generated by digital microscopic camera. Several algorithms and distribution models had been developed and proposed for the segmentation of the images. Among several methods developed and proposed, the Gaussian Mixture Model (GMM) was one of the highly used models. One can say that almost the GMM was playing the key role in most of the image segmentation research works so far noticed in the literature. The main drawback with the distribution model was that this GMM model will be best fitted with a kind of data in the dataset. To overcome this problem, the TPLMM-k algorithm is proposed. The image decomposition process used in the proposed algorithm had been analyzed and its performance was analyzed with the help of various performance metrics like the Variance of Information (VOI), Global Consistency Error (GCE) and Probabilistic Rand Index (PRI). According to the results, it is shown that the proposed algorithm achieves the better performance when compared with the previous results of the previous techniques. In addition, the decomposition of the images had been improved in the proposed algorithm.

Morphometric quantification of a pseudohyphae forming Saccharomyces cerevisiae strain using in situ microscopy and image analysis

Yeast morphology and counting are highly important in fermentation as they are often associated with productivity and can be influenced by process conditions. At present, time-consuming and offline methods are utilized for routine analysis of yeast morphology and cell counting using a haemocytometer. In this study, we demonstrate the application of an in situ microscope to obtain a fast stream of pseudohyphae images from agitated sample suspensions of a Saccharomyces cerevisiae strain, whose morphology in cell clusters is frequently found in the bioethanol fermentation industry. The large statistics of microscopic images allow for online determination of the principal morphological characteristics of the pseudohyphae, including the number of constituent cells, cell-size, number of branches, and length of branches. The distributions of these feature values are calculated online, constituting morphometric monitoring of the pseudohyphae population. By providing representative data, the proposed system can improve the effectiveness of morphological characterization, which in turn can help to improve the understanding and control of bioprocesses in which pseudohyphal-like morphologies are found.

Systematic Review and Study of S Curves for Biomass Quantification in Solid-state Fermentation (SSF) and Digital Image Processing (DIP) Applied to Biomass Measurement in Food Processes

BACKGROUND

There are several methods for the quantification of biomass in SSF, such as glucosamine measurement, ergosterol content, protein concentration, change in dry weight or evolution of CO2 production. However, all have drawbacks when obtaining accurate data on the progress of the SSF due to the dispersion in cell growth on the solid substrate, and the difficulty encountered in separating the biomass. Studying the disadvantages associated with the process of biomass quantification in SSF, the monitoring of the growth of biomass by a technique known as digital image processing (DIP), consists of obtaining information on the production of different compounds during fermentation, using colorimetric methods based on the pixels that are obtained from photographs.

OBJECTIVE

The purpose of this study was to know about the state of the technology and the advantages of DIP.

METHODS

The methodology employed four phases; the first describes the search equations for the SSF and the DIP. A search for patents related to SSF and DIP carried out in the Free Patents Online and Patent inspiration databases. Then there is the selection of the most relevant articles in each of the technologies. As a third step, modifications for obtaining the best adjustments were also carried out. Finally, the analysis of the results was done and the inflection years were determined by means of six mathematical models widely studied.

RESULTS

For these models, the inflection years were 2018 and 2019 for both the SSF and the DIP. Additionally, the main methods for the measurement of biomass in SSF were found, and are also indicated in the review, as DIP measurement processes have already been carried out using the same technology.

CONCLUSION

In addition, the DIP has shown satisfactory results and could be an interesting alternative for biomass measurement in SSF, due to its ease and versatility.

DSeg: A Dynamic Image Segmentation Program to Extract Backbone Patterns for Filamentous Bacteria and Hyphae Structures

Analysis of numerous filamentous structures in an image is often limited by the ability of algorithms to accurately segment complex structures or structures within a dense population. It is even more problematic if these structures continuously grow when recording a time-series of images. To overcome these issues we present DSeg; an image analysis program designed to process time-series image data, as well as single images, to segment filamentous structures. The program includes a robust binary level-set algorithm modified to use size constraints, edge intensity, and past information. We verify our algorithms using synthetic data, differential interference contrast images of filamentous prokaryotes, and transmission electron microscopy images of bacterial adhesion fimbriae. DSeg includes automatic segmentation, tools for analysis, and drift correction, and outputs statistical data such as persistence length, growth rate, and growth direction. The program is available at Sourceforge.

Assessing activated sludge morphology and oxygen transfer performance using image analysis

The morphology of the microbial communities can have dramatic impacts on not only the treatment performance, but also the energy use performance of an activated sludge process. In this research, we developed and calibrated an image analysis technique to determine key morphological parameters such as the floc diameter and the specific filament length (SFL) and discovered that the SFL has significant impacts on sludge floc size, the specific extracellular polymeric substances production, the settleability, mixed liquor viscosity, and oxygen transfer efficiency. When the SFL increased from 2.5 × 10⁹ μm g^-1 to 6.0 × 10¹⁰ μm g^-1, the apparent viscosity normalized by the mixed liquor suspended solids concentration increased by 67%, and the oxygen transfer efficiency decreased by 29%. A long solids retention time (SRT) of 40 day reduced SFL, improved sludge settling performance, and improved oxygen transfer efficiency as compared to shorter SRTs of 10 and 20 day. The findings underscore the need to assess microbial morphology when quantifying the treatment performance and energy performance of activated sludge processes.

Image segmentation of activated sludge phase contrast images using phase stretch transform

Activated sludge (AS) is a biological treatment process that is employed in wastewater treatment plants. Filamentous bacteria in AS plays an important role in the settling ability of the sludge. Proper settling of the sludge is essential for normal functionality of the wastewater plants, where filamentous bulking is always a persistent problem preventing sludge from settling. The performance of AS plants is conventionally monitored by physico-chemical procedures. An alternative way of monitoring the AS in wastewater treatment process is to use image processing and analysis. Good performance of the image segmentation algorithms is important to quantify flocs and filaments in AS. In this article, an algorithm is proposed to perform segmentation of filaments in the phase contrast images using phase stretch transform. Different values of strength (S) and warp (W) are tested to obtain optimum segmentation results and decrease the halo and shade-off artefacts encountered in phase contrast microscopy. The performance of the algorithm is assessed using DICE coefficient, accuracy, false positive rate (FPR), false negative rate (FNR) and Rand index (RI). Sixty-one gold approximations of ground truth images were manually prepared to assess the segmentation results. Thirty-two of them were acquired at 10× magnification and 29 of them were acquired at 20× magnification. The proposed algorithm exhibits better segmentation performance with an average DICE coefficient equal to 52.25%, accuracy 99.74%, FNR 41.8% and FPR 0.14% and RI 99.49%, based on 61 images.

SParticle, an algorithm for the analysis of filamentous microorganisms in submerged cultures

Streptomycetes are filamentous bacteria that produce a plethora of bioactive natural products and industrial enzymes. Their mycelial lifestyle typically results in high heterogeneity in bioreactors, with morphologies ranging from fragments and open mycelial mats to dense pellets. There is a strong correlation between morphology and production in submerged cultures, with small and open mycelia favouring enzyme production, while most antibiotics are produced mainly in pellets. Here we describe SParticle, a Streptomyces Particle analysis method that combines whole slide imaging with automated image analysis to characterize the morphology of submerged grown Streptomyces cultures. SParticle allows the analysis of over a thousand particles per hour, offering a high throughput method for the imaging and statistical analysis of mycelial morphologies. The software is available as a plugin for the open source software ImageJ and allows users to create custom filters for other microbes. Therefore, SParticle is a widely applicable tool for the analysis of filamentous microorganisms in submerged cultures.

A Changeable Lab-on-a-Chip Detector for Marine Nonindigenous Microorganisms in Ship's Ballast Water

The spread and invasion of many nonindigenous species in the ship's ballast water around the world has been a hazard and threat to ecology, economy, and human health. The rapid and accurate detection of marine invasive species in ship's ballast water is essential. This article is aimed at analysing ballast water quality by means of a changeable microfluidic chip detector thus comply with the D-2 standard of ship's ballast water management and sediment convention. The detection system was designed through the integration of microfluidic chip technology, the impedance pulse sensing and LED light induced chlorophyll fluorescence (LED-LICF) detection. This system can measure the number, size, shape, and volume of targeted microorganisms, and it can also determine the chlorophyll fluorescence intensity, which is an important factor in analysing the activity of phytoplankton. The targeted samples were Chlorella volutis, Dunaliella salina, Platymonas subcordiformis, Chrysophytes, Escherichia coli, and Enterococci. The whole detection or operation can be accomplished through online detection in a few minutes with using micron volume of the sample solution. The valid data outputs are simultaneously displayed in terms of both impedance pulse amplitudes and fluorescent intensity signals. The detection system is designed for multi-sizes real time detection through changing the microchannel sizes on the microfluidic chip. Because it can successfully detect the label-free microorganisms, the system can be applicable to in-situ detections with some modifications to the system.