Solution could reduce contamination losses in ethanol production

Technology created at the Research Center for Greenhouse Gas Innovation helps identify contaminating microorganisms, enabling plants to combat them more quickly and effectively.

Agência FAPESP* – The Research Center for Greenhouse Gas Innovation (RCGI) is finalizing a research project that proposes an innovative solution for identifying fermentation contaminants. This solution has the potential to reduce efficiency losses in ethanol production. The technology can be applied to various industries.

The project, coordinated by Professor Carlos Alberto Labate from the Luiz de Queiroz College of Agriculture at the University of São Paulo (ESALQ-USP), is based on the mass spectrometry technique and aims to develop a new methodology for detecting contaminating bacteria in the production of ethanol from sugarcane. To this end, the researchers are using matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF), a device widely used in the health sector for microbiological diagnoses.

According to Labate, “in hospital environments, MALDI-TOF quickly identifies the microorganism responsible for the patient’s infection, enabling medical staff to act quickly and effectively. We’re expanding this concept to industry, developing methods that enable MALDI-TOF to identify microorganisms present in industrial environments with similar speed and precision.”

The new methodology could significantly reduce the time needed to identify contaminants compared to current methods, allowing plants to respond more quickly and accurately to combat contamination while optimizing the use of antimicrobials and inputs. “Microbial contamination is one of the main causes of reduced yields and productivity. Its effective control is fundamental to ensuring industrial efficiency,” adds the ESALQ-USP professor.

AI and automation

One of the project’s major innovations is integrating artificial intelligence (AI) into the analysis process. Currently, MALDI-TOF works on identifying isolated microorganisms. The researchers are developing models that will enable the identification of multiple microorganisms in a single analysis, thereby reducing the complexity, time, and cost of the technique.

“This is the first step in developing automated control systems. In the future, AI could not only detect the contaminant, but also suggest the most effective corrective measures. This would bring even greater efficiency and reduce response times at the plants,” comments Labate.

In addition to benefiting ethanol plants, the technology developed by the RCGI has the potential to be applied in other industrial sectors. For example, the production of food, beer, and meat faces challenges related to microbial contamination. The same technology can be adapted to control these processes, ensuring greater safety in contamination control and production efficiency.

The RCGI is an Applied Research Center (ARC) established by FAPESP and Shell at the Engineering School (POLI-USP). The project has the support of Shell Brasil and Raízen.

* With information from the RCGI.