Investigating a non-destructive alternative for a preliminary evaluation of fungal growth in solid state fermentations

Automation and artificial intelligence in filamentous fungi-based bioprocesses: A review

By utilizing their powerful metabolic versatility, filamentous fungi can be utilized in bioprocesses aimed at achieving circular economy. With the current digital transformation within the biomanufacturing sector, the interest of automating fungi-based systems has intensified. The purpose of this paper was therefore to review the potentials connected to the use of automation and artificial intelligence in fungi-based systems. Automation is characterized by the substitution of manual tasks with mechanized tools. Artificial intelligence is, on the other hand, a domain within computer science that aims at designing tools and machines with the capacity to execute functions that would usually require human aptitude. Process flexibility, enhanced data reliability and increased productivity are some of the benefits of integrating automation and artificial intelligence in fungi-based bioprocesses. One of the existing gaps that requires further investigation is the use of such data-based technologies in the production of food from fungi.

A Temporal Evolution Perspective of Lipase Production by Yarrowia lipolytica in Solid-State Fermentation

Lipases are enzymes that, in aqueous or non-aqueous media, act on water-insoluble substrates, mainly catalyzing reactions on carboxyl ester bonds, such as hydrolysis, aminolysis, and (trans)esterification. Yarrowia lipolytica is a non-conventional yeast known for secreting lipases and other bioproducts; therefore, it is of great interest in various industrial fields. The production of lipases can be carried on solid-state fermentation (SSF) that utilizes solid substrates in the absence, or near absence, of free water and presents minimal problems with microbial contamination due to the low water contents in the medium. Moreover, SSF offers high volumetric productivity, targets concentrated compounds, high substrate concentration tolerance, and has less wastewater generation. In this sense, the present work provides a temporal evolution perspective regarding the main aspects of lipase production in SSF by Y. lipolytica, focusing on the most relevant aspects and presenting the potential of such an approach.

Potential Role of Sequential Solid-State and Submerged-Liquid Fermentations in a Circular Bioeconomy

An efficient processing of organic solid residues will be pivotal in the development of the circular bioeconomy. Due to their composition, such residues comprise a great biochemical conversion potential through fermentations. Generally, the carbohydrates and proteins present in the organic wastes cannot be directly metabolized by microorganisms. Thus, before fermentation, enzymes are used in a hydrolysis step to release digestible sugars and nitrogen. Although enzymes can be efficiently produced from organic solid residues in solid-state fermentations (SsF), challenges in the development and scale-up of SsF technologies, especially bioreactors, have hindered a wider application of such systems. Therefore, most of the commercial enzymes are produced in submerged-liquid fermentations (SmF) from expensive simple sugars. Instead of independently evaluating SsF and SmF, the review covers the option of combining them in a sequential process in which, enzymes are firstly produced in SsF and then used for hydrolysis, yielding a suitable medium for SmF. The article reviews experimental work that has demonstrated the feasibility of the process and underlines the benefits that such combination has. Finally, a discussion is included which highlights that, unlike typically perceived, SsF should not be considered a counterpart of SmF but, in contrast, the main advantages of each type of fermentation are accentuated in a synergistic sequential SsF-SmF.

Water activity and biomass estimation using digital image processing in solid-state fermentation

Water activity in biotechnological applications plays a vital role. Estimation and precise control of water activity will enhance the product. The objective here is to use digital image processing technique in MATLAB for the quantification of the biomass and water condensate on a horizontal surface. A model has been developed with the data produced experimentally in this study in abiotic conditions. Also, a comparative study has been given for the estimation of the water condensate produced. It was observed that at a particular threshold value the error rate is less than 30%. The result of experimental study was good, and it showed a linear relation with proposed model. Also, the similar experimental estimation was done in biotic conditions, and results are showing good agreement between experimental observation and the model developed. Further, this method could be used in solid state fermentation process for water activity estimation and low-cost biomass estimation.

New method for rapid identification and quantification of fungal biomass using ergosterol autofluorescence

This research reports on the development of a method to identify and quantify fungal biomass based on ergosterol autofluorescence using excitation-emission matrix (EEM) measurements. In the first stage of this work, several ergosterol extraction methods were evaluated by APCI-MS, where the ultrasound-assisted procedure showed the best results. Following an experimental design, various quantities of the dried mycelium of the fungus Schizophyllum commune were mixed with the starchy solid residue (BBR) from the babassu (Orbignya sp.) oil industry, and these samples were subjected to several ergosterol extraction methods. The EEM spectral data of the samples were subjected to Principal Component Analysis (PCA), which showed the possibility to qualitatively evaluate the presence of ergosterol in the samples by ergosterol autofluorescence without the addition of any reagent. In order to assess the feasibility of quantifying fungal biomass using ergosterol autofluorescence, the EEM spectral data and known amounts of fungal biomass were modeled using partial least squares (PLS) regression and a procedure of backward selection of predictors (AutoPLS) was applied to select the Excitation-Emission wavelength pairs that provide the lowest prediction error. The results revealed that the amount of fungal biomass in samples containing interfering substances (BBR) can be accurately predicted with R²CV = 0.939, R²P = 0.936, RPDcv = 4.07, RPDp = 4.06, RMSECV = 0.0731 and RMSEP = 0.0797. In order to obtain an easy-to-understand equation that expresses the relationship between fungal biomass and fluorescence intensity, multiple linear regression (MLR) was applied to the VIP variables selected by the AutoPLS method. The MLR model selected only 2 variables and showed a very good performance, with R²CV = 0.862, R²P = 0.809, RPDcv = 2.18, RPDp = 2.35, RMSECV = 0.137 and RMSEP = 0.138. This study demonstrated that ergosterol autofluorescence can be successfully used to quantify fungal biomass even when mixed with agroindustrial residues, in this case BBR.