Understanding the factors affecting faba bean quality
Pulse Research Program
Dr. Ning Wang
Research scientist/program manager
Pulse Research
ning.wang@grainscanada.gc.ca
Team members
Research scientist/program manager
- Dr. Ning Wang
Chemists
- Dora Fenn
Technicians
- Lisa Maximiuk
- Monica Cabral
The Pulse Research Program investigates the physical and chemical properties of pulses to better understand how grading factors, processing methods, environmental conditions, and genetics affect their quality and end use functionality. We also develop and evaluate new methods for quantifying the functional characteristics of pulses. To support the marketability of Canadian pulses, we conduct an analysis of the pulse and food-type soybean samples submitted to the Harvest Sample Program each year and take part in cargo monitoring.
Increasing use of faba beans
Faba bean production in Canada has increased in recent years due to the rising demand for plant-based proteins. Faba beans are a rich source of proteins, carbohydrates, vitamins and minerals, and can be used as whole or split beans in food products such as soups, stews and pastes. They can also be processed into flours and protein concentrates/isolates that can be used in baked goods, pasta, meat products and meat replacements. There is limited information available, however, on the quality and functionality of Canadian faba beans. Identifying varieties that can grow in different environments and have characteristics that make them valuable to the food industry is important for supporting their use in traditional and new ways.
We conducted a study to determine how variety, growing location and year affected the quality and functionality of Canadian faba beans. Location represents predictable environmental variation, such as the type of soil, and year represents unpredictable environmental variation, such as weather conditions. Data was collected for three different varieties (Fabelle, Malik and Snowbird) grown at three different locations in Saskatchewan in 2019 and 2020. The component of variance, which is the relative contribution of each experimental factor to the overall variability of the data, was calculated for several quality and functional parameters. Understanding data variability helps us to identify which factors are major contributors to the quality parameters.
Factors affecting faba bean quality
We found that variety, growing environment (location and/or year) and their interactions had a significant effect on the protein, starch and total dietary fiber (TDF) content of faba beans. As shown in Figure 1, the largest component of variance in protein content was growing location (45.7%), followed by location x year interaction (16.6%), variety (14.4%) and year (14.2%). This indicates that growing location had the greatest effect on protein content. Variety (39.8%) and growing location (30.7%) accounted for most of the variability in starch content while variety (36.2%) and location x year interaction (19.9%) accounted for most of the variability in TDF content. Protein content ranged from 25.8% to 32.3%, starch content from 38.7% to 45.2% and TDF content from 15.7% to 18.1% on a dry matter basis. We also observed a negative correlation between protein and starch content in faba beans.
Factors affecting faba bean functionality
The ability of pulse flours and protein concentrates/isolates to absorb and retain water or oil affects the texture, mouthfeel and flavor of food, and can reduce loss for products such as meat substitutes. We found that most of the variability in the water holding capacity (WHC) of faba beans was attributed to growing year (30.8%), location x year interaction (26.1%) and location (16.7%) (Figure 2). Variability in the oil absorption capacity (OAC) was largely affected by variety x location interaction (39.8%) and variety (32.4%). WHC and OAC were both positively correlated with TDF, whereas WHC was negatively correlated with protein content.
Foaming capacity (FC) is a measure of the increase in volume achieved by mixing, and foaming stability (FS) measures the change in volume of a foam over a defined period of time. These two properties can affect the performance of pulse flours and protein concentrates/isolates in foods such as mousses, sweets and whipped desserts. We found that variability in FC was mostly accounted for by variety x location x year interaction (27.3%), variety x year interaction (25.0%) and variety x location interaction (18.0%) (Figure 2). Variety accounted for 67.8% variation in FS (Figure 2). It was observed that FS was correlated negatively with starch content.
Knowledge gained from this study can help breeders and growers improve the quality of faba beans and supports the increased use and marketability of Canadian faba beans.
Graph data
| Error | V x L x Y | L x Y | V x Y | V x L | Year (Y) | Location (L) | Variety (V) | |
|---|---|---|---|---|---|---|---|---|
| Protein | 1 | 2.9 | 16.6 | 4.6 | 0.6 | 14.2 | 45.7 | 14.4 |
| Starch | 4.9 | 9.9 | 11.1 | 2.9 | 0.6 | 0.2 | 30.7 | 39.8 |
| TDF | 16.4 | 8.9 | 19.9 | 2.2 | 8.6 | 1.5 | 6.3 | 36.2 |
Graph data
| Error | V x L x Y | L x Y | V x Y | V x L | Year (Y) | Location (L) | Variety (V) | |
|---|---|---|---|---|---|---|---|---|
| WHC | 9.2 | 0.5 | 26.1 | 0.3 | 7.2 | 30.8 | 16.7 | 9.2 |
| OAC | 11.4 | 4.4 | 0.1 | 1.9 | 39.8 | 7.5 | 2.5 | 32.4 |
| FC | 13.9 | 27.3 | 2.2 | 25.0 | 18.0 | 1.1 | 11.2 | 1.3 |
| FS | 7.0 | 5.3 | 3.7 | 2.6 | 6.9 | 3.4 | 3.2 | 67.8 |
Recent publications
- Laing, E., Stone, A. K., Shi, D., Pickard, M., Wang, N. and Nickerson, M.T. 2023. Effect of infrared heating on the functional properties of yellow pea and lentil flours. Cereal Chem. 100(3): 601-613. https://doi.org/10.1002/cche.10662
- Laing, E., Stone, A. K., Shi, D., Pickard, M., House, J. D., Wang, N. and Nickerson, M.T. 2023. Effect of infrared heating on the nutritional properties of yellow pea and lentil flours. Cereal Chem. 100(3):614-627. https://doi.org/10.1002/cche.10653


