## 1 Introduction  
Recently, the European Union approved a regulation allowing the use of insect meal for feed production, especially aquafeed [1]. This regulation included seven approved insects: _Hermetia illucens_ (black soldier fly), _Gryllodes sigillatus_ (tropical house cricket), _Gryllusassimilis_ (Jamaican field cricket), _Acheta domesticus_ (house cricket), _Alphitobius diaperinus_ (small mealworm), _Tenebrio molitor_ (yellow mealworm), and _Musca domestica_ (common housefly) [2]. Although the Black Fly Soldier (BFS), _H. illucens_, is not yet approved for human consumption, its larvae gained a special status in recent years due to its potential to convert organic waste and manure into high-quality, nutrient-rich protein [3]. For this reason, BFS meal has been used in poultry [4; 5], swine [6], and aquaculture [7] industries as feed or for specific manure management (e.g., botanical origin, composition, etc.). In this regard, the search for methodologies to analyze the nutritional compounds of these insects has been steadily increasing. Comprehensive research on the molecular (nutritional) composition of these insects is necessary, using physicochemical, safe, accurate analytical methods that preferably do not destroy the samples, and that comply with food safety and control standards. Currently, it is possible to obtain results on the physicochemical composition through the application of image analysis in scientific and analytical fields. Spectral imaging combines imaging and spectroscopy to capture both spatial and spectral information. By examining the spectral signatures of different regions within an image, researchers can identify and quantify the presence of specific chemical compounds or elements. This technique is particularly useful in areas such as remote sensing, biomedical imaging, and environmental monitoring [1; 3; 7]. The use of images offers a non-destructive and non-contact approach for sample analysis, providing valuable insights into material properties, aiding quality control, and supporting research across various scientific and industrial fields. However, it is essential to implement appropriate image acquisition techniques, as well as calibration and validation methods, to ensure accurate and reliable results [8; 9; 10]. In recent years, many authors have reported the use of near-infrared hyperspectral imaging (NIR-HSI) to assess quality attributes in food and other products [9; 11]. [PERSON] et al. (2019) [8] conducted a review on the use of NIR-HSI as a high-throughput phenotyping tool for root and tuber cultures. Several authors describe the application of this tool for physicochemical analysis, such as the biochemical composition of starch, potatoes, sweet potatoes [12; 13], fungal growth in rice, sugar content, and color of potato tubers [12; 14]. NIR-HSI is a powerful method for the non-destructive evaluation of various quality characteristics of processed and raw foods [11]. These techniques, when integrated with established laboratory quality control methods, such as control charts, can optimize data analysis by enabling more rigorous monitoring and facilitating the identification and correction of deviations. The application of the control line in the respective control chart allows for the early detection of potential errors in the treatment process, ensuring greater reliability of results. The combination of these methodologies enhances the efficiency and accuracy of data processing, contributing to the continuous improvement of laboratory procedures [15; 16]. NIR-HIS allows for capturing images at multiple wavelengths, enabling the identification of unique spectral patterns associated with various molecular components, such as humidity and fat. Based on the available literature [15; 16; 17], the spectral regions associated with moisture and fat parameters have been investigated. For instance, moisture is linked to spectral ranges at 970 nm and 1410 nm [15], while fat is associated with spectral ranges at 928 nm, 1170 nm, and 1190-1390 nm [16]. [PERSON] et al.