Microalgae provide valuable bio-components with economic and environmental benefits. The monitoring of microalgal production is mostly performed using different sensors and analytical methods that, although very powerful, are limited to qualified users. This study proposes an automated Raman spectroscopy-based sensor for the online monitoring of microalgal production. For this purpose, an in situ system with a sampling station was made of a light-tight optical chamber connected to a Raman probe. Microalgal cultures were routed to this chamber by pipes connected to pumps and valves controlled and programmed by a computer. The developed approach was evaluated on Parachlorella kessleri under different culture conditions at a laboratory and an industrial algal platform. As a result, more than 4000 Raman spectra were generated and analysed by statistical methods. These spectra reflected the physiological state of the cells and demonstrate the ability of the developed sensor to monitor the physiology of microalgal cells and their intracellular molecules of interest in a complex production environment.

Food waste is one of the main problems contributing to climate change, as its piling up in landfills produces the greenhouse gas methane. Food waste occurs at every stage of food production; however, a major source of food waste occurs at businesses that supply food to consumers. Industry 4.0 technologies have shown promise in helping to reduce food waste in food supply chains. However, more innovative technologies, such as Raman spectroscopy, hold great promise in helping to reduce food waste, although this has largely been ignored in the literature. In this context, we propose a portable Raman platform to monitor food quality during transportation. The developed system was tested in conditions mimicking those present in a refrigerated truck by analyzing chicken samples stored at temperatures of 4 °C. Raman spectra were acquired for non-packaged and packaged samples over the duration of 30 days resulting in 6000 spectra. The analysis of Raman spectra revealed that the system was able to detect noticeable changes in chicken quality starting on day six. The main Raman bands contributing to this change are amide I and tyrosine. The proposed system will offer the potential to reduce food losses during transportation by consistently checking the food quality over time.