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

Author(s): Juan C. Oviedo-Lopera*, Jhon W. Zartha-Sossa, Diego L. Zapata-Ruiz, Isabela Bohorquez-Naranjo, Karen S. Morales-Arevalo

Journal Name: Recent Patents on Biotechnology

Volume 14 , Issue 3 , 2020

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Graphical Abstract:


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.

Keywords: Solid state fermentation, digital image processing, systematic literature review, S-curve, biomass measurement, micellar growth.

Rodríguez S, Sanromán MÁ. Application of solidstate fermentation to food industry - A review. J Food Eng 2006; 76: 291-302.
Hölker U, Lenz J. Solid-state fermentation-are there any biotechnological advantages? Curr Opin Microbiol 2005; 8(3): 301-6.
[ PMID: 15939353]
Pandey A, Soccol CR, Larroche C. Current developments in solid-state fermentation. Biochem Eng J 2008; 81: 146-61.
Robinson T, Nigam P. Bioreactor design for protein enrichment of agricultural residues by solid state fermentation. Biochem Eng J 2003; 13(2-3): 197-203.
[ ]
Robledo A, Aguilera-Carbó A, Rodriguez R, Martinez JL, Garza Y, Aguilar CN. Ellagic acid production by Aspergillus niger in solid state fermentation of pomegranate residues. J Ind Microbiol Biotechnol 2008; 35(6): 507-13.
[ PMID: 18228068]
Zanutto-Elgui MR, Vieira JCS, Prado DZD, Buzalaf MAR. , Padilha P de-M, de Oliveira DE, et al. Production of milk peptides with antimicrobial and antioxidant properties through fungal proteases. Food Chem 2019; 278: 823-31.
[ PMID: 30583449]
Oviedo JC, Casas AE, Valencia JA. Analysis of biomass measurement in solid-state fermentation using neural networks and a logistic model. Inf Tecnol 2014; 25(4): 141-52.
Abd-Aziz S, Hung GS, Hassan AM, Karim Abdul IM, Samat N. Indirect method for quantification of cell Biomass during solid-state fermentation of palm Kernel Cake based on protein content. Asian J Sci Res 2008; 4(1): 385-93.
Levin Gal. Process and facility for the treatment of livestock waste. US Patent 20180186672A1. 2017.
Couri S, Merces EP, Neves BCV, Senna LF. Image analysis to monitor biomass growth in solid-state fermentation: preliminary results. J Microscopy 2006; 224: 290-7.
Manterola C, Astudillo P, Arias E, Claros N. Revisiones sistemáticas de la literatura. Qué se debe saber acerca de ellas. Cirugia Espanola 2013; 91(3): 149-55.
Robinson RV, Echavez FM, López ME. Una propuesta metodológica para la conducción de revisiones sistemáticas de la literatura en la investigación biomédica. CES Movimiento y Salud 2013; 1(1): 61-73.
Pastrana L. Fundamentos de la fermentación en estado sólido y aplicación a la industria alimentaria. Ciencia y Tecnologia Alimentaria 2009; 1(3): 4-12.
Olofsson K, Bertilsson M, Lidén G. A short review on SSF - An interesting process option for ethanol production from lignocellulosic feedstocks. Biotechnol Biofuels 2008; 1: 1-14.
Saharkhiz S, Mazaheri D, Shojaosadati SA. Evaluation of bioethanol production from carob pods by Zymomonas mobilis and Saccharomyces cerevisiae in solid submerged fermentation. Prep Biochem Biotechnol 2013; 43(5): 415-30.
Aguilar S, Avalos A, Giraldo D, Quintero S, Zartha J, Cortes F. La Curva en S como Herramienta para la Medición de los Ciclos de Vida de Productos. J Technol Management Innov 2012; 7: 1.
González SJ, Ochoa SD, Alzate BA, Hernández R. Vigilancia tecnológica de las curvas en S y ciclo de vida de las tecnologías. Revista Espacios 2017; 38: 36.
Ortiz S, Pedroza Á. ¿Qué es la Gesitón de la innovación y la tecnología? J Technol Manag Innov 2006; 1(2): 64-82.
Z, R. H., & S, J. F. M. (2015). Aplicaciones de prospectiva tecnológica Y curvas En “S” En agroindustria bases para estudios de futuro en facultades de agroindustria jhon wilder zartha sossa, Gina Lía Orozco Mendoza, Arango A , Juan Carlos Palacio P. In: Bases para estudios de futuro en facultades de Agroindustria
Oviedo-Lopera JC, Urrea-Galeano V, Zuluagahernandez CD, Rodriguez-Ortiz LM. Curvas en S – aplicación en tecnologías de evaporación y secado de frutas. Espacios 2017; 38(51): 9-23.
Arora S, Singh P, Rani R, Ghosh S. Oxygen uptake rate as a tool for on-line estimation of cell biomass and bed temperature in a novel solid-state fermentation bioreactor. Bioproc Biosys E 2018; 41(7): 917-29.
Schoen HR, Peyton BM, Knighton WB. Rapid total volatile organic carbon quantification from microbial fermentation using a platinum catalyst and proton transfer reaction-mass spectrometry. AMB Express 2016; 6(1): 90.
Manan MA, Webb C. Extracted substrate colour as an indicator of fungal growth in solid state fermentation. Malaysian J Microbiol 2016; 12(6): 445-9.
Simeng Z, Sacha G, Isabelle HG, Marie-Noëlle R. A PCR-based method to quantify fungal growth during pretreatment of lignocellulosic biomass. J Microbiol Meth 2015; 115: 67-70.
Krull R, Bley T. Filaments in bioprocesses. New York: Springer 2015.
Lee J, Shin SG, Ahn J, et al. Use of swine wastewater as alternative substrate for mycelial bioconversion of white rot fungi. Appl Biochem Biotechnol 2017; 181(2): 844-59.
Cornet I, Wittner N, Tofani G, Tavernier S. FTIR as an easy and fast analytical approach to follow up microbial growth during fungal pretreatment of poplar wood with Phanerochaete chrysosporium. J Microbiol Methods 2018; 145: 82-6.
Javed S, Meraj M, Mahmood S, et al. Biosynthesis of lovastatin using agro-industrial wastes as carrier substrates. Trop J Pharm Res 2017; 16(2): 263-9.
Anusa N. Hirzun, Umikalsom Md, Ling T, Aruffm A. Comparative study on lipase production using solid state and submerged fermentation systems by several fungal strains and the predicted molecular characteristics. Minerva Biotecnologica 2017; 29(2): 53-61.
Dhillon GS. Agro-industrial wastes as feedstock for enzyme production: apply and exploit the emerging and valuable use options of waste biomass. Massachusetts, United States: Academic Press 2016.
Bobbiesi G. A plant for solid-state fermentation. WO 2004/111181 A1 2004.
Ding Y, Yin Y. Rapid detection of microorganisms indicators for agricultural products. Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural 2013; 44: 182-88.
[ 1298.2013.S1.033]
Garcia J, Barbedo A. Entomology Using digital image processing for counting white fl ies on soybean leaves. J Asia Pacific Entomol 2014; 17(4): 685-94.
Leow LK, Chew L, Chong VC, Dhillon SK. Automated identification of copepods using digital image processing and artificial neural network. BMC Bioinformatics 2015; 16(Suppl. 18): 1-12.
Zhao Y, Zheng DJ, Shen Y. An automatic microalgal cells counting method. Adv Mater Res 2014; 1010-2: 178-81.
Xia M, Wang L, Hong Y. High-throughput screening of high Monascus pigment - producing strain based on digital image processing. J Ind Microbiol Biotechnol 2016; 1-11.
Dias PA, Dunkel T, Fajado DAS, et al. Image processing for identification and quantification of filamentous bacteria in in situ acquired images. BioMed Eng OnLine 2016; 15-64.
Ramirez EE, Cabrera H, Grassi HC, Andrades DJ, Otero I, Rodr D, et al. Digital imaging information technology for biospeckle activity assessment relative to bacteria and parasites. Lasers Med Sci 2017; 32(6): 1-12.
Martinez B, Pazoti M, Pessoa J. Method for identifying guignardia citricarpa. WO2006113979A1 2006.
Tongqiang L, Xiuping W. System for identifying impurities of edible funguses on line. CN102495067A 2011.
Chen H, Duan Y. Method for online in-situ monitoring of solid-state fermentation fungus biomass. CN102392068A 2012.

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Article Details

Year: 2020
Published on: 25 September, 2020
Page: [194 - 202]
Pages: 9
DOI: 10.2174/1872208314666200312094447
Price: $65

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