Current projections estimate that in 2050 about 10 billion people will inhabit the earth and food production will need to increase by more than 60%. Food security will therefore represent a matter of global concern not easily tackled with current agriculture practices and curbed by the increasing scarcity of natural resources and climate change. Disrupting technologies are urgently needed to improve the efficiency of the food production system and to reduce the negative externalities of agriculture (soil erosion, desertification, air pollution, water and soil contamination, biodiversity loss, etc.). Among the most innovative technologies, the production of microbial protein (MP) in controlled and intensive systems called “bioreactors” is receiving increasing attention from research and industry. MP has low arable land requirements, does not directly compete with crop-based food commodities, and uses fertilizers with an almost 100% efficiency. This review considers the potential and limitations of four MP sources currently tested at pilot level or sold as food or feed ingredients: hydrogen oxidizing bacteria (HOB), methanotrophs, fungi, and microalgae (cyanobacteria). The environmental impacts (energy, land, water use, and GHG emissions) of these MP sources are compared with those of plant, animal, insect, and cultured meat-based proteins. Prices are reported to address whether MP may compete with traditional protein sources. Microalgae cultivation under artificial light is discussed as a strategy to ensure independence from weather conditions, continuous operation over the year, as well as high-quality biomass. The main challenges to the spreading of MP use are discussed.

Near-infrared spectroscopy (NIRS) provides a high-throughput phenotyping technique to assist breeding for improved faba bean seed quality. We combined chemical analysis of protein, oil content (and composition) with NIRS through chemometrics, employing Partial Least Squares (PLS), Elastic Net (EN), Memory-based Learning (MBL), and Bayes B (BB) as prediction models. Protein was the most reliably predicted trait (R² = 0.96–0.98) across field trials, followed by oil (R² = 0.82–0.86) and oleic acid (R² = 0.31–0.68). Samples for training the models were selected using K-means clustering. The optimal statistical approach for prediction was compound-specific: PLS for protein (Root Mean Squared Error - RMSE = 0.46), BB for oil (RMSE = 0.067), and EN for oleic acid content (RMSE = 2.83). Reduced training set simulations revealed different effects on prediction accuracy depending on the model and compound. Several NIR regions were pinpointed as highly informative for the compounds, using the shrinkage and variable selection capabilities of EN and BB.

Faba bean (Vicia faba L.) is a valuable ingredient in plant-based foods such as meat and dairy analogues. However, its typical taste and aroma are considered off-flavours in these food applications, representing a bottleneck during processing. Breeding is needed to develop varieties with minimal off-flavours and high protein content. The genetic regulation of these traits is underexplored. To dissect their genetic architecture, we performed a genome-wide association study (GWAS). A total of 245 faba bean accessions (the CGN population) were genotyped using the 90K-SPET targeted assay. These accessions were phenotyped in 2021 and 2022 in the Netherlands for protein, oil, fatty acids, lipid-derived products, phenolic acids, flavonoids, and tannins. The CGN population showed large phenotypic variation and moderate-to-high narrow-sense heritability for most traits. The growing environment significantly affected all traits, with trait-specific genotype-by-year (GxY) interactions. Condensed tannins and fatty acids were the most stable across the two years and had the highest heritability estimates (h² > 0.6). GWAS identified a total of 148 single nucleotide polymorphisms (SNPs) loci in 2021 and 167 in 2022. Key candidate regulators included genes involved in lipid biosynthesis (ATS2, KAS, LPP), amino acid transport (CAT4) for protein storage, zero tannins locus-1 (zt-1), and regulators of the phenylpropanoid pathway, such as a shikimate kinase gene and transcription factors bHLH137-like and MYB. These results pave the way for validation studies and biotechnological applications to improve the quality of faba bean-based foods.