Proximate content monitoring of black soldier fly larval (Hermetia illucens) dry matter for feed material using short-wave infrared hyperspectral imaging

Authors: KIM, JUNTAE - Chungnam National University; KURNIAWAN, HARY - Chungnam National University; FAQEERZADA, MOHAMMAD - Chungnam National University; KIM, GEONWOO - Gyeongsang National University; LEE, HOONSOO - Chungbuk National University; Kim, Moon; Baek, Insuck; CHO, BYOUNG-KWAN - Chungnam National University

Submitted to: Food Science of Animal Resources
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 7/2/2023
Publication Date: 7/2/2023
Citation: Kim, J., Kurniawan, H., Faqeerzada, M.A., Kim, G., Lee, H., Kim, M.S., Baek, I., Cho, B. 2023. Proximate content monitoring of black soldier fly larval (Hermetia illucens) dry matter for feed material using short-wave infrared hyperspectral imaging. Food Science of Animal Resources. 43(6):1150-1169. https://doi.org/10.5851/kosfa.2023.e33.
DOI: https://doi.org/10.5851/kosfa.2023.e33

Interpretive Summary: Insects have a rich protein content and are popularly suggested as a new alternative food source for the future. Feed insects can serve as a substitutes for traditional feed ingredients, providing a potential alternative for both grain- and animal-based feeds, such as soybean, corn, and fishmeal. fishmeal. However, in the production of feed insects, the small size of insects and the large quantities required for processing make quality control difficult. Failure to manage quality can result in unpleasant odors and mold, which can threaten the quality of the final product. Therefore, there is a need for selection technology that can quickly and accurately evaluate the nutrient content of feed insects. In this study, an algorithm was developed using a short-wave infrared (SWIR) hyperspectral imaging (HSI) system to assess the general components (moisture, crude protein, crude fat, crude fiber, and ash) of dried black soldier fly larvae and to develop an optimized model for sorting machines to use. The high accuracy of the algorithm in predicting the component contents of dried black soldier fly larvae, demonstrated the feasible possibility of using SWIR-based HSI for quality monitoring of feed insects based on the developed algorithm. This research benefits feed manufacturers seeking to produce safe and nutritious feed materials for animal consumption, demonstrating a rapid inspection method that can help advance the technologies needed for automated mass production and processing of feed insects in the future.

Technical Abstract: Edible insects are gaining popularity as a potential future food source because of their high protein content and efficient use of space. Black soldier fly larvae are noteworthy because they can be used as feed for various animals including reptiles, dogs, fish, chickens, and pigs. However, if the edible insect industry is to advance, we should use automation to reduce labor and increase production. Consequently, there is a growing demand for sensing technologies that can automate the evaluation of insect quality. This study used short-wave infrared (SWIR) hyperspectral imaging to predict the proximate composition of dried black soldier fly larvae, including moisture, crude protein, crude fat, crude fiber, and crude ash content. The larvae were dried at various temperatures and times, and images were captured using a SWIR camera. A partial least-squares regression (PLSR) model was developed to predict the proximate content. Several preprocessing models were used with data from the SWIR-based hyperspectral camera and the best model was found to predict moisture, crude protein, crude fat, crude fiber, and crude ash content with high accuracy, demonstrating R2 values of 0.89 or more, and RMSEP values within 2%. Therefore, SWIR-based hyperspectral cameras can be used to create automated quality management systems for black soldier fly larvae.