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

Juntae Kim1, Hary Kurniawan, Mohammad Akbar Faqeerzada1, Geonwoo Kim2, Hoonsoo Lee3, Moon S. Kim4, Insuck Baek4, Byoung-Kwan Cho1,5,*
Author Information & Copyright
1Department of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam national university, Daejeon 34134, Korea.
2Department of Bio-Industrial Machinery Engineering, College of Agriculture and Life Science, Gyeongsang National University, Jinju-si 52828, Korea.
3Department of Biosystems Engineering, College of Agriculture, Life & Environment Science, Chungbuk National University, Cheongju 28644, Korea.
4Environmental Microbial and Food Safety Laboratory, Agricultural Research Service, United States Department of Agriculture, Beltsville 20705, United States.
5Department of Smart Agriculture Systems, College of Agricultural and Life Science, Chungnam National University, Daejeon 34134, Korea.
*Corresponding Author: Byoung-Kwan Cho. E-mail:

© Copyright 2023 Korean Society for Food Science of Animal Resources. This is an Open-Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Received: May 17, 2023 ; Revised: Jun 26, 2023 ; Accepted: Jul 02, 2023

Published Online: Jul 12, 2023


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 an SWIR camera. A partial least-squares regression (PLSR) model was developed to predict the proximate content. The SWIR-based hyperspectral camera accurately predicted the proximate composition of black soldier fly larvae from the best preprocessing model; moisture, crude protein, crude fat, crude fiber, and crude ash content were predicted with high accuracy, with R2 values of 0.89 or more, and RMSEP values were within 2%. Among preprocessing methods, mean normalization and max normalization methods were effective in proximate prediction models. Therefore, SWIR-based hyperspectral cameras can be used to create automated quality management systems for black soldier fly larvae.

Keywords: Black soldier fly larvae; Feed insect; Quality monitoring; Chemical image; Hyperspectral image