Influence of Fermentation Conditions (Temperature and Time) on the Physicochemical Properties and Bacteria Microbiota of Amasi

Mpho Maleke,Oluwafemi Ayodeji Adebo,Kedibone Masenya,Wesley Doorsamy,Martins Ajibade Adefisoye,Adrian Mark Abrahams

doi:10.3390/fermentation8020057

Mpho Maleke, Oluwafemi Ayodeji Adebo + Show 4 more

Open Access

https://doi.org/10.3390/fermentation8020057

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Journal: Fermentation	Publication Date: Jan 28, 2022
Citations: 7	License type: CC BY 4.0

Affiliation: University of Johannesburg, University of Cape Town

Abstract

The aim of this present study was to optimize the fermentation conditions (time and temperature) of amasi (a Southern African fermented dairy product) using response surface methodology (RSM), and to determine the physicochemical properties, as well as the microbial composition, using next generation sequencing. Fermentation time and temperature were optimized to produce different amasi samples and different parameters, including pH, total soluble solids (TSS), total titratable acids (TTA), and consistency. All the variables studied were found to show significant (p ≤ 0.05) changes with increasing fermentation time and temperature. Numerical optimization was used to obtain the optimal fermentation conditions for amasi; based on RSM, it was 32 °C for 140 h, while with k-means clustering, it was 25 °C for 120 h. Under both conditions for the optimal samples, the pH reduced from 6.64 to 3.99, TTA increased from 0.02 to 0.11 (% lactic acid), TSS decreased from 9.47 to 6.67 °Brix, and the consistency decreased from 23 to 15.23 cm/min. Most of the identified bacteria were linked to lactic acid bacteria, with the family Lactobacillaceae being the most predominant in amasi, while in raw milk, Prevotellaceae was the most abundant. The fermentation conditions (time and temperature) had a significant influence on the parameters investigated in this study. Results of this study could provide information for the commercialization of quality amasi.

Highlights

Traditional fermented milk (TFM) products are widely consumed in Southern Africa and play an important role in people’s nutrition [1]
Considering the dearth of information on the influence of fermentation time and temper‐ ature on the composition of amasi, the current study aims at optimizing fermentation con‐ ditions of amasi using a multi‐response numerical optimization and subsequent use of an unsupervised machine learning (ML) technique (k‐means clustering) for validating the response surface methodol‐ ogy (RSM) generated models
The unsupervised learning technique yields optimal regions or solution subspaces which are useful, due to the variability of experimental conditions, in contrast to being solely dependent on points estimated from the RSM

Summary

Introduction

Traditional fermented milk (TFM) products are widely consumed in Southern Africa and play an important role in people’s nutrition [1]. Most of the TFMs are usually domestically produced and mostly for home consumption These TFM products with comparable or identical production processes and characteristics may be known by other names throughout the Southern Africa region, including madila from Botswana [3], mafi from Lesotho, mabisi from Zambia and Namibia [4], and amasi from South Africa and Zimbabwe [5]. According to Gadaga et al [7], amasi is made by spontaneously (naturally) ferment‐ ing raw milk for 1–3 days at room temperature During this process, lactose in milk is converted to lactic acid by activities of the fermenting microbes present in the milk [8]

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