Application of Two-Dimensional Fluorescence Spectroscopy for the On-Line Monitoring of Teff-Based Substrate Fermentation Inoculated with Certain Probiotic Bacteria

Abstract

There 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.

Online Monitoring of the Growth of Probiotic Bacteria and Metabolites in the Fermentation of a Teff Substrate Using Model-Based Calibration of 2D Fluorescence Spectra

“…It is necessary to control the fermentation process to determine important state variables in order to produce a satisfactory food product. However, monitoring of the fermentation process is usually challenging when using classical approaches, which are time consuming, costly, and labor intensive [1,2].…”

“…Clearly, a wide range of spectrum data are obtained during a fluorescence spectroscopy measurement, which therefore requires techniques for the extraction of important information. For the evaluation of spectrum data, chemometric methods such as principal component regression and partial least squares regression can be applied [2,7].…”

Effect of refrigerated storage on some physicochemical characteristics of a teff‐based fermented beverage and the viability of the fermenting Lactiplantibacillus plantarum and Lacticaseibacillus rhamnosus used

“…However, drawbacks associated with dairy products such as lactose intolerance and allergies related to milk protein have limited the consumption of dairy‐based products. Moreover, for medical reasons some consumers prefer to eat vegan‐based foods (Alemneh, Emire, Jekle, et al, 2022). Thus, plant‐based food products could be a good alternative.…”