Silicon supplementation improves the nutritional and sensory characteristics of lentil seeds obtained from drought‐stressed plants. (15th September 2020) - Biju, Sajitha; Fuentes, Sigfredo; Gonzalez Viejo, Claudia; Torrico, Damir D; Inayat, Sumayya; Gupta, Dorin
Abstract: BACKGROUND: Lentil is an important nutritionally rich pulse crop in the world. Despite having a prominent role in human health and nutrition, it is very unfortunate that global lentil production is adversely limited by drought stress, causing a huge decline in yield and productivity. Drought stress can also affect the nutritional profile of seeds. Silicon (Si) is an essential element for plants and a general component of the human diet found mainly in plant‐based foods. This study investigated the effects of Si on nutritional and sensory properties of seeds obtained from lentil plants grown in an Si‐supplied drought‐stressed environment. RESULTS: Significant enhancements in the concentration of nutrients (protein, carbohydrate, dietary fibre, Si) and antioxidants (ascorbate, phenol, flavonoids, total antioxidants) were found in seeds. Significant reductions in antinutrients (trypsin inhibitor, phytic acid, tannin) were also recorded. A novel sensory analysis was implemented in this study to evaluate the unconscious and conscious responses of consumers. Biometrics were integrated with a traditional sensory questionnaire to gather consumers responses. Significant positive correlations ( R = 0.6–1) were observed between sensory responses and nutritional properties of seeds. Seeds from Si‐treated drought‐stressed plants showed higher acceptability scores among consumers. CONCLUSION: The results demonstrated that Si supplementation can improve the nutritional and sensory properties of seeds. This study offers an innovative approach in sensory analysis coupled with biometrics to accurately assess a consumer's preference towards tested samples. In the future, the results of this study will help in making a predictive model for sensory traits and nutritional components in seeds using machine‐learning modelling techniques. © 2020 Society of Chemical Industry