Application of two-dimensional fluorescence spectroscopy for the on-line monitoring of teff-based substrate fermentation inoculated with certain probiotic bacteria

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dc.contributor.authorAlemneh, Sendeku Takele
dc.contributor.authorEmire, Shimelis Admassu
dc.contributor.authorJekle, Mario
dc.contributor.authorPaquet-Durand, Olivier
dc.contributor.authorvon Wrochem, Almut
dc.contributor.authorHitzmann, Bernd
dc.date.accessioned2024-10-23T12:25:49Z
dc.date.available2024-10-23T12:25:49Z
dc.date.issued2022de
dc.description.abstractThere is increasing demand for cereal-based probiotic fermented beverages as an alternative to dairy-based products due to their limitations. However, analyzing and monitoring the fermentation process is usually time consuming, costly, and labor intensive. This research therefore aims to apply two-dimensional (2D)-fluorescence spectroscopy coupled with partial least-squares regression (PLSR) and artificial neural networks (ANN) for the on-line quantitative analysis of cell growth and concentrations of lactic acid and glucose during the fermentation of a teff-based substrate. This substrate was inoculated with mixed strains of Lactiplantibacillus plantarum A6 (LPA6) and Lacticaseibacillus rhamnosus GG (LCGG). The fermentation was performed under two different conditions: condition 1 (7 g/100 mL substrate inoculated with 6 log cfu/mL) and condition 2 (4 g/100 mL substrate inoculated with 6 log cfu/mL). For the prediction of LPA6 and LCGG cell growth, the relative root mean square error of prediction (pRMSEP) was measured between 2.5 and 4.5%. The highest pRMSEP (4.5%) was observed for the prediction of LPA6 cell growth under condition 2 using ANN, but the lowest pRMSEP (2.5%) was observed for the prediction of LCGG cell growth under condition 1 with ANN. A slightly more accurate prediction was found with ANN under condition 1. However, under condition 2, a superior prediction was observed with PLSR as compared to ANN. Moreover, for the prediction of lactic acid concentration, the observed values of pRMSEP were 7.6 and 7.7% using PLSR and ANN, respectively. The highest error rates of 13 and 14% were observed for the prediction of glucose concentration using PLSR and ANN, respectively. Most of the predicted values had a coefficient of determination (R2) of more than 0.85. In conclusion, a 2D-fluorescence spectroscopy combined with PLSR and ANN can be used to accurately monitor LPA6 and LCGG cell counts and lactic acid concentration in the fermentation process of a teff-based substrate. The prediction of glucose concentration, however, showed a rather high error rate.en
dc.identifier.swb1801220581
dc.identifier.urihttps://hohpublica.uni-hohenheim.de/handle/123456789/16805
dc.identifier.urihttps://doi.org/10.3390/foods11081171
dc.language.isoengde
dc.rights.licensecc_byde
dc.source2304-8158de
dc.sourceFoods; Vol. 11, No. 8 (2022) 1171de
dc.subjectArtificial neural network
dc.subjectFunctional beverage
dc.subjectPartial least-squares regression
dc.subjectProbiotics
dc.subjectTeff-based substrate
dc.subject2D-fluorescence spectroscopy
dc.subject.ddc660
dc.titleApplication of two-dimensional fluorescence spectroscopy for the on-line monitoring of teff-based substrate fermentation inoculated with certain probiotic bacteriaen
dc.type.diniArticle
dcterms.bibliographicCitationFoods, 11 (2022), 8, 1171. https://doi.org/10.3390/foods11081171. ISSN: 2304-8158
dcterms.bibliographicCitation.issn2304-8158
dcterms.bibliographicCitation.issue8
dcterms.bibliographicCitation.journaltitleFoods
dcterms.bibliographicCitation.volume11
local.export.bibtex@article{Alemneh2022, url = {https://hohpublica.uni-hohenheim.de/handle/123456789/16805}, doi = {10.3390/foods11081171}, author = {Alemneh, Sendeku Takele and Emire, Shimelis Admassu and Jekle, Mario et al.}, title = {Application of Two-Dimensional Fluorescence Spectroscopy for the On-Line Monitoring of Teff-Based Substrate Fermentation Inoculated with Certain Probiotic Bacteria}, journal = {Foods}, year = {2022}, volume = {11}, number = {8}, }
local.export.bibtexAuthorAlemneh, Sendeku Takele and Emire, Shimelis Admassu and Jekle, Mario et al.
local.export.bibtexKeyAlemneh2022
local.export.bibtexType@article

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