The present study compared whole grain teff flours grown in Ethiopia (ETF) and South Africa (STF) for their chemical composition (proximate, mineral, and amino acid) and pasting, thermal, and functional properties. The proximate composition of flours was determined using methods of the European Commission Regulation (152/2009). Atomic Spectrometer, ion-exchange chromatography, Rapid Visco-Analyzer (RVA), and Differential Scanning Calorimetry (DSC) were used to measure minerals, amino acids, pasting, and thermal properties, respectively. The inter-relationships of the measured attributes were determined using Pearson correlation and principal component analysis (PCA). Significant (p < .05) differences in most of the evaluated properties were found between ETF and STF. However, several significant (p < .01) correlations were observed among the measured attributes of flours as revealed by Pearson correlation and PCA. Significantly (p < .05) higher contents of moisture (11.2 g/100 g flour), protein (13.3 g/100 g flour), and zinc (34.6 mg/kg) were found in STF, whereas significantly (p < .05) higher contents of calcium (1306 mg/kg) and iron (363 mg/kg) were perceived in ETF. The ETF was observed to have higher foam stability (71 mL/100 mL initial foam), water absorption capacity (1.1 g/g), oil absorption capacity (0.9 g/g), and swelling power (6.4 g/g) as compared to STF. Observations from thermal and pasting properties revealed that onset, peak and end temperatures, trough, final and set back viscosities, including peak time (6.3 min), and pasting temperature (90 °C) were found to be higher in the case of STF. In conclusion, findings of this study would contribute to the selection of teff for the development of healthier food products to meet the current consumer preferences .

Consumers are demanding healthier foods, and the increasing drawbacks associated with dairy-based products have driven efforts to find plant-based probiotic alternatives. Consequently, this study aimed to evaluate the suitability of a teff-based substrate for delivering the potential probiotics, Lactobacillus rhamnosus GG (LGG) and Lactobacillus plantarum A6 (LA6) with a view to developing probiotic functional beverages. Single-strain and mixed-strain fermentations were performed without any pH control. In single-strain fermentation, LA6 grew to 8.157–8.349 log cfu/mL. Titratable acidity (TA) and pH were measured between 0.513–1.360 g/L and 4.25–3.91, respectively. The explored optimum variables were fermentation time (15 h) and inoculum (6 log cfu/mL). As a result of fermentation, maltose and glucose decreased, but lactic and acetic acids increased. In mixed-strain fermentation, LGG and LA6 were able to grow to 8.247 and 8.416 log cfu/mL, respectively. The pH, TA, lactic, and acetic acids varied between 6.31–3.92, 0.329–1.501 g/L, 0–1672 mg/L, and 20–231.5 mg/L, respectively. In both fermentations, microbial growth reached the stationary phase close to a pH of 4.21–4.82 while sugars were not consumed completely. Less than 5% ethanol was detected, which indicated a non-alcoholic beverage. A combination of the two evaluated lactobacilli strains reduced fermentation time. In conclusion, a substrate made of whole grain teff flour without any supplement could be used as a substrate to produce functional probiotic beverages.

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.

The demand for probiotic bacteria-fermented food products is increasing; however, the monitoring of the fermentation process is still challenging when using conventional approaches. A classical approach requires a large amount of offline data to calibrate a chemometric model using fluorescence spectra. Fluorescence spectra provide a wide range of online information during the process of cultivation, but they require a large amount of offline data (which involves laborious work) for the calibration procedure when using a classical approach. In this study, an alternative model-based calibration approach was used to predict biomass (the growth of Lactiplantibacillus plantarum A6 (LPA6) and Lacticaseibacillus rhamnosus GG (LCGG)), glucose, and lactic acid during the fermentation process of a teff-based substrate inoculated with mixed strains of LPA6 and LCGG. A classical approach was also applied and compared to the model-based calibration approach. In the model-based calibration approach, two-dimensional (2D) fluorescence spectra and offline substituted simulated data were used to generate a chemometric model. The optimum microbial specific growth rate and chemometric model parameters were obtained simultaneously using a particle swarm optimization algorithm. The prediction errors for biomass, glucose, and lactic acid concentrations were measured between 6.1 and 10.5%; the minimum error value was related to the prediction of biomass and the maximum one was related to the prediction of glucose using the model-based calibration approach. The model-based calibration approach and the classical approach showed similar results. In conclusion, the findings showed that a model-based calibration approach could be used to monitor the process state variables (i.e., biomass, glucose, and lactic acid) online in the fermentation process of a teff-based substrate inoculated with mixed strains of LPA6 and LCGG. However, glucose prediction showed a high error value.

This study aims to evaluate a teff-based substrate (hereinafter substrate) for its suitability to carry probiotics Lacticaseibacillus rhamnosus GG (LCGG) and Lactiplantibacillus plantarum A6 (LPA6). In addition, two-dimensional (2D) fluorescence spectroscopy was applied to monitor the fermentation process by analyzing its spectral data using partial least-squares regression (PLSR) and an artificial neural network (ANN). The fermentation process parameters time and inoculum were optimized to 15 h and 6 log cfu/mL, respectively. During a fermentation run using the optimized parameters, cell counts of LPA6 and LCGG were increased from 6 to 8.42 and 8.11 log cfu/mL, respectively. Values of pH, titratable acidity (TA), lactic acid, and acetic acid were measured in the ranges of 6.13–3.92, 0.37–1.5 g/L, 0–1.7 g/L, and 0.04–0.23 g/L, respectively. Glucose was progressively consumed throughout the fermentation process. For the prediction of cell counts of LPA6 and LCGG, relative root mean square error of predictions (pRMSEP) were measured between 0.25 and 0.37%. In addition, for lactic acid prediction, pRMSEP values of 7.6 and 7.7% were obtained. The findings of this research showed that cell counts of LPA6 and LCGG and content of lactic acid could be predicted accurately by 2D fluorescence spectroscopy coupled with PLSR and ANN. Moreover, whole teff flour alone could serve as a substrate to develop a probiotic-rich beverage.