AdaptFerm: Bioprocess Monitoring Using FTIR spectroscopy: Insights into Substrate Effects and Domain Adaptation

1. Introduction The AdaptFerm dataset is designed to support the development of a monitoring framework for lactic acid production fermentation using Fourier Transform Infrared (FTIR) spectroscopy. Its primary goal is to facilitate the control strategies for continuous fermentation processes to maximize the lactic acid production. The AdaptFerm encompasses data from two distinct batch fermentation environments: one employing simple sugar (glucose) as the substrate and the other utilizing complex sugars derived from bio-waste. The study focuses on developing accurate predictive models for glucose and lactic acid concentrations, with an emphasis on applying classical machine learning techniques and enhancing domain generalization capabilities. 2. Prediction Model for Different Substrate Environments The chemical composition of substrates are presented in Table 1 [1]. The dataset is utilized to train and test models within the same substrate domain. For instance, data from a single fermentation environment (e.g., glucose substrate) is used for both training and testing phases. The applied machine learning models showed accurate prediction within the same domain [1]. For more details on the methods applied, please refer to the following link: https://doi.org/10.1016/j.heliyon.2024.e38791. In this study, the MIR results correspond to the AdaptFerm dataset. The spectra of the glucose and biowaste hydrolysate fermentation process are presented in Figure 3 and Figure 4. 3. Domain Adaptation The dataset was also used to address the challenge posed by shifts in FTIR data when substrates change. Transitioning from simple sugar (glucose) to complex sugar (bio-waste) causes significant variations in the FTIR spectra, making it difficult for models trained on glucose fermentation data to maintain prediction accuracy in the complex sugar fermentation environment. This results in reduced robustness and performance when applied to out-of-distribution data. To address these challenges, we explore methods that improve the generalization ability and robustness of models in such scenarios without using labels from complex sugar fermentation [2]. It shows the application of machine learning interpretation to find domain invariant features for glucose and lactic acid. For more details on the methods applied, please refer to the following link: https://dx.doi.org/10.2139/ssrn.5012080. The code is available at https://github.com/shl-shawn/ShapFS. 4. Real-World Use Cases 4.1. Regr