Using Machine Learning Methods to Predict the ß-Poly (L-Malic Acid) Production by Different Substrates Addition and Secondary Indexes in Strain Aureobasidium melanogenum

ß-poly (L-malic acid) (PMLA) is a polyester ligated by malate subunits. It has a wide prospective application as an anti-cancer drug carrier, and its malate subunits have a great application in the food industry. The strain Aureoabsidium melanogenum could produce a high amount of PMLA during fermentation, and different substrates addition could influence the production. In this study, we directly added potassium acetate, corn steep liquor, MgSO4, MnSO4, vitamin B1, vitamin B2, and nicotinamide as the fermentation substrate to the basic fermentation medium based on a generated random matrix that represented the added value. The PMLA production and four secondary indexes, pH, biomass, osmotic pressure, and viscosity were measured after 144 h fermentation. Finally, a total of 212 samples were collected as the dataset, by which the machine learning methods were deployed to predict the PMLA production by different substrates’ concentrations and the secondary indexes. The results indicated that PMLA production was negatively correlated with corn steep liquor and betaine and positively correlated with potassium acetate. The PMLA production could be predicted using all different substrates’ concentrations with a Mean Absolute Error (MAE) of 4.164 g/L and with an MAE of 6.556 g/L by different secondary indexes. Finally, the convolutional neural network (CNN) was applied to predict the PMLA production by fermentation medium images, in which the collected images were categorized into three groups, 0–20 g/L, 21–40 g/L, and >41 g/L, based on the PMLA production. The CNN model could predict the production with high accuracy. The methods and results presented in this study provided new insight into evaluating different substrates concentration on PMLA production and demonstrating the possibility of using the convolutional neural network model in the PMLA fermentation industry.