Protein and Peptide Research at GALAB Laboratories

Agriculture activity must be increased

In 2022 approximately 7.9 billion people are living on our planet and the world’s population is still about to grow. According to statistical projections, more than 9 billion people will populate the world in 2050 ^1,2. But why is that problematic?

3 billion people on earth cannot afford a healthy and nutritious diet apart from the fact that 690 million people are starving³. That means that the agriculture activity must be increased 30-40% to provide food for all people on the planet⁴. This approach might not be feasible because the resources are limited⁵. Another way to make the best use of the resources we have is to develop a method to predict the shelf life of food more accurately.

Nowadays the shelf life is determined by microbiological and sensory applications ^6,7 which are not accurate enough to avoid food waste in the first place⁸. Some consumers blindly trust the best-before date and throw away food as soon as it expires, even if it is still edible and meets the required quality standards⁹. With a more accurate prediction method, food waste can be reduced because the consumers will not throw away food which is perfectly fine to eat.

The goal of that particular research project is to develop a workflow which predicts the shelf life of food based on the proteomic profile. Proteomics is one discipline of the so-called omics-cascade¹⁰. The proteome includes all proteins in an organism or food¹¹. It is quite variable and adjusts easily to the external factors¹¹. The differences in the environment can affect the proteome¹¹. Lisa-Carina Class and her colleague at GALAB Laboratories are working on developing an application to investigate the proteomic profile and discover marker peptides that lead to a more accurate prediction of the shelf life of food. To perform that kind of analytics we use high resolution mass spectrometry-technologies and process the outcoming data with deep learning algorithms. In our review paper from 2021 “Diving Deep into Data: A Review of Deep Learning Approaches and Potential Applications in Foodomics” we outline the state of the research in that field¹².

Resources:

1. Scholik, N. Limit. Ein Essay zur Lage der Weltbevölkerung. Teil 1: Grenzen und Herausforderungen. Zeitschrift für Außen- und Sicherheitspolitik 14, 189–212 (2021).
2. Schug, W. Die Entwicklung der Weltbevölkerung und die Globale Nahrungsmittelversorgung. J. fur Verbraucherschutz und Leb. 4, 44–51 (2009).
3. FAO. Food Waste Index Report 2021. Unep (2021).
4. Augustin, M. A. et al. Role of food processing in food and nutrition security. Trends Food Sci. Technol. 56, 115–125 (2016).
5. Corradini, M. G. Shelf Life of Food Products: From Open Labeling to Real-Time Measurements. Annu. Rev. Food Sci. Technol. 9, 251–269 (2018).
6. Vallejo-Cordoba, B. & Nakai, S. Keeping-Quality Assessment of Pasteurized Milk by Multivariate Analysis of Dynamic Headspace Gas Chromatographic Data. 1. Shelf-Life Prediction by Principal Component Regression. J. Agric. Food Chem. 42, 989–993 (1994).
7. Condurso, C. et al. A new approach for the shelf-life definition of minimally processed carrots. Postharvest Biol. Technol. 163, 111138 (2020).
8. Bishop, J. R. & White, C. H. Assessment of Dairy Product Quality and Potential Shelf-Life – A Review. J. Food Prot. 49, 739–753 (1986).
9. Parfitt, J., Barthel, M. & MacNaughton, S. Food waste within food supply chains: Quantification and potential for change to 2050. Philos. Trans. R. Soc. B Biol. Sci. 365, 3065–3081 (2010).
10. Baumann, F. et al. Lebensmittelsicherheit & Verpackung Food Science Dialog der Hochschule für Angewandte Wissenschaft (HAW) Hamburg, 15.-19. September 2014. J. fur Verbraucherschutz und Leb. 9, 391–420 (2014).
11. Pischetsrieder, M. & Baeuerlein, R. Proteome research in food science. Chem. Soc. Rev. 38, 2600–2608 (2009).
12. Class, L., Kuhnen, G. & Rohn, S. Diving Deep into the Data : A Review of Deep Learning Approaches and Potential Applications in Foodomics. 1–18 (2021).