Hempseed (Cannabis sativa L.) bulk mass modeling based on engineering properties

Abstract

AbstractHempseed (Cannabis sativa L.) owing to its excellent nutritional and pharmaceutical potential has resurrected the industrial and scientific community to exploit their processing and utilize for new food products optimization. The design and development of storage, postharvest processing and quality analysis of seeds depend upon the correlation properties with their physical parameters. In this study, the mass of hempseed is predicted as a function of its linear dimensional property, projected area and bulk seed density using linear and nonlinear mathematical models. Various engineering properties of different hempseed grades were investigated at a moisture content of 6.49% (wet basis). The effect of size and density‐based grading was also studied on mass modeling and was compared with ungraded hempseed. Results revealed that the mass models developed using graded seeds had more appropriate results (higher R2) than the ungraded ones. Models based on minor dimension (R2 0.939) and third projected area (R2 0.914) within a 5% level of significance were recommended from an economic standpoint to predict hempseed mass with maximum accuracy. Therefore, bulk mass modeling of hempseed based on engineering properties is strongly recommended for the design and development of grading mechanisms.Practical ApplicationThe growing demands for plant‐based protein and bioactive phytochemicals have directed the agriculture‐based industries towards non‐traditional food sources like hempseed. However, its richer protein and a blend of sensitive bioactive components demand a critical storage and processing unit. Mass is one of the important physical aspects needed for the design of various storage and processing units. Determining the mass of small grains that are almost unique in size and structure is a challenging task. In recent years, mass modeling based on physical dimension has been increasingly used for various fruits like kinnow mandarin, pomegranate, and lime fruits; therefore, we decided to test the applicability and accuracy of the same technique, along with examining the effect of grading, for mass modeling of hempseed for the first time. Various hempseed processing operations including dehulling, require crucial information about the bulk seed behavior and their correlation with their engineering properties. The study results might be useful to formulate an automatic grading mechanism for hempseed grading based on the mutual effect of mass and size. In this study, in addition to the introduction of a mass modeling system for hempseed, some grade‐specific engineering properties of hempseed have been determined, which are important for the design of equipment for harvesting, transporting, storing, cleaning, packing and processing.