Algae are cultivated commercially to produce biomass feedstock for the manufacturing of biofuels, nutritional supplements, pharmaceuticals, bioplastics, and other high-value products. As with terrestrial monocultures, large-scale algal cultivation systems are vulnerable to contamination, which can result in rapid, catastrophic loss of valuable biomass. Traditional monitoring methods lack the sampling resolution needed for early detection. Volatile organic compounds (VOCs), emitted as metabolic byproducts, offer a real-time, non-invasive signal of culture health. In this study, a low-cost chemical ionization mass spectrometer was used to monitor VOC emissions from an intentionally infected algae culture. Time series data were then modeled using a set of numerical momentum indicators, technical analysis tools typically used for stock tracking in economic markets, to flag early perturbations in VOC intensity related to changing culture health. Results show this approach was able to flag culture contamination within 28 h of infection, outperforming traditional detection methods by more than 60 h. In addition, the employment of multiple simultaneous momentum indicators was determined to reduce the incidences of false positives, suggesting that VOC monitoring of algal cultures could be semi-automated without constant manual supervision. Finally, we apply a financial loss model to a simulated full-scale agricultural growth pond and estimate that early detection via CIMS can lead to approximately 2. 25 million in recovered profit. • A low-cost chemical ionization mass spectrometer (CIMS) monitors algal culture VOCs in real time. • Time series momentum indicators detect contamination 64 h earlier than standard methods. • Combining SCA, MACD, and SO indicators eliminates false alarms in VOC monitoring. • CIMS-based detection could recover 4–7 million in profit at full pond scale. • Demonstrates scalable, automated early warning for algal crop contamination
Rico et al. (Sun,) studied this question.
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