## 1 Introduction  
Fermentations are, in general, inherently complex biological processes for the production of valuable compounds, characterized by interactions among various intrinsic and extrinsic variables. Intrinsic factors, such as metabolism, genetic variability, and cellular responses, interact dynamically with extrinsic variables, such as nutrient availability, pH, and oxygen levels, influencing the dynamics and output of the process. These variables are often interdependent and can vary over time, making fermentation processes unpredictable and challenging to control. This complexity impacts the consistency and efficiency of consecutive process batches, leading to variability in yield and quality. As a result, there is a demand for advanced strategies to enable the real-time monitoring, modeling, and consequent optimization of processes. Such improvements are essential to enhance reproducibility and increase productivity, ultimately leading to cost efficiency, which is essential for scaling these bioprocesses to the industrial scale. The large volumes of data that can be generated during fermentation bioprocesses, from online monitoring and offline sampling, present a still underexplored opportunity to develop data-driven models, such as those based on machine learning (ML), which in turn could be used to understand the process better and improve its predictability, contributing to a higher process efficiency. Fermentation bioprocesses have historically been used for food production and conservation and to improve food's taste and texture [1]. In recent years, precision fermentation has attracted significant interest as a sustainable method for producing high-value ingredients, such as proteins, vitamins, and other bioactive compounds, for the alternative protein industry [2]. Beyond food, fermentation technologies have been widely developed for the production of biofuels, industrial chemicals, and pharmaceutical products [3]. Among these various fermentation products, biosurfactants have emerged as a promising class of biomolecules due to their unique physicochemical properties and broad industrial applications, including the cosmetic, cleaning, and pharmaceutical industries [4]. Surfactants are molecules that reduce the surface or interfacial tension between liquids, a liquid and a gas, or a liquid and a solid and are used extensively across various industries. Synthetic surfactants, mostly produced by the petrochemical industry, are commonly used, but presently, several concerns regarding their toxicity are emerging, as they affect the microbial world, soil, plants, and aquatic life [5]. With environmental issues becoming a pressing concern for both the public and industry, there is an urgent need to identify greener, more sustainable alternatives to petrochemical surfactants. Biosurfactants offer an appealing solution, as they are naturally derived and produced as secondary metabolites by bacteria or fungi, making their production more environmentally friendly and sustainable [6]. Mannosylerythritol lipids (MELs), a type of glycolipid compounds, are among the most interesting biosurfactants. MELs are surface-active agents with hydrophobic and hydrophilic moieties that allow them to reduce surface and interfacial tension [7]. The hydrophobic moieties that compose these molecules consist of a mannose and an erythri-tol, specifically 4-O-\(\beta\)-D-mannopyranosyl-meso-erythritol, and the hydrophilic moiety is composed of fatty acids and acetyl groups [8]. Their exceptional surface-active properties, versatile biochemical functions, non-toxicity, biodegradability, and environmental compatibility, among other attributes, make MELs an interesting potential option for various applications [8]. Despite the advantage of using MELs or other biosurfactants instead of the current less environmentally friendly alternatives, their production remains economically challenging [9].