Enhancing and monitoring spore production in Clostridium butyricum using pH‐based regulation strategy and a robust soft sensor based on back‐propagation neural networks

Abstract

Clostridium butyricum (C. butyricum) is a probiotic that forms anaerobic spores and plays a crucial role in regulating gut microbiota. However, the total viable cell count and spore yield of C. butyricum in industrial production are comparatively low. To this end, we investigated the metabolic characteristics of the strain and proposed three distinct pH regulation strategies for enhancing spore production. In addition, precise measurement of fermentation parameters such as substrate concentration, total viable cell count, and spore concentration is crucial for successful industrial probiotics production. Nevertheless, online measurement of these intricate parameters in the fermentation of C. butyricum poses a considerable challenge owing to the complex, nonlinear, multivariate, and strongly coupled characteristics of the production process. Therefore, we analyzed the capacitance and conductivity acquired from a viable cell sensor as the core parameters for the fermentation process. Subsequently, a robust soft sensor was developed using a seven‐input back‐propagation neural network model with input variables of fermentation time, capacitance, conductivity, pH, initial total sugar concentration, ammonium ion concentration, and calcium ion concentration. The model enables the online monitoring of total viable biomass count, substrate concentrations, and spore yield, and can be extended to similar fermentation processes with pH changes as a characteristic feature.This article is protected by copyright. All rights reserved.

Data synchronization techniques and their impact on the prediction performance of automated recalibrated soft sensors in bioprocesses

“…The methanol addition was also supplemented with 12 mL L −1 PTM4 solution. Methanol concentration (4.5 g L −1 ), pH (5), pressure (500 mbar), temperature (30 °C), and dissolved oxygen (40%) were controlled. The FM22 medium contained the following [23]: (NH 4 ) 2 SO 4 , 5 g L −1 ; CaSO 4 2H 2 O, 1 g L −1 ; K 2 SO 4 , 14.3 g L −1 ; KH 2 PO 4 , 42.9 g L −1 ; MgSO 4 7H 2 O, 11.7 g L −1 ; and glycerol, 40 g L −1 .…”

“…Soft sensor models for predicting biomass, substrate concentration, and spore yield were also successfully developed for a bioprocess with Clostridium butyricum. The input variables used were fermentation time, capacitance, conductivity, pH, initial total sugar concentration, as well as ammonium and calcium concentration [5]. Further examples of the successful application of soft sensors in bioprocesses can be found in several review articles [6][7][8][9].…”