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 (BSFL) 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 BSFL, 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 BSFL from the best preprocessing model; moisture, crude protein, crude fat,
crude fiber, and crude ash content were predicted with high accuracy, with
R
2
values of 0.89 or more, and root mean square error of
prediction 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 BSFL.