## 3 Results and Discussion
In this study,  
Figure 4: Principal component score plot of the black soldier fly larvae samples analyzed using near-infrared reflectance spectroscopy. Panel (**A**) is highlighted by waste stream (children and supermarket, irrespective of sample number), panel (**B**) is highlighted by sample presentation (number of larvae scanned for both waste streams), and panels (**C**,**D**) report the PCA score plot for the specific waste streams. (**C**) Childcare waste. (**D**) Supermarket waste. Blue: 0.5 larvae; Red: 1 larvae; Green: 2 larvae; Sky blue; 3 larvae. the NIR spectra of the samples were not affected by the number of BSFL used during the spectra collection. The PCA loadings were also interpreted and are shown in Figure 5. The highest loadings in PC1 corresponding to the separation between the waste streams were observed around 1193 nm (C-H\({}_{3}\)) and 1422 nm associated with C-H overtones and the first overtone of O-H (water or phenolic compounds), respectively [1; 3; 28; 29; 39]. The highest loadings in PC2 were observed around 1199 nm (C-H\({}_{3}\)) (inverse to the one observed in PC1), around 1447 nm associated with C-H and O-H overtones, and around 1393 nm associated with C-H and C-H\({}_{2}\) combinations, while the highest loadings in PC3 were observed around 1155 nm, 1267 nm (C-H\({}_{3}\) overtones, lipids), 1347 nm (C-H and C-H\({}_{2}\)), and 1496 nm (O-H, water) [1; 3; 28; 29; 39]. The effect of the number of larvae was also evaluated on the accuracy of the LDA classification models used to predict the waste stream (Table 1). The classification results showed that high classification rates (>93%) were obtained regardless of the number of larvae used during the scanning of the sample. The classification rate using half of a larvae sample was 93%, while 100% classification was obtained using 1, 2, and 3 larvae samples. Neither the aluminum back cover nor the number of BSFL samples scanned have a large effect on the NIR spectra, and consequently, they did not affect the classification results obtained by the LDA models, as the accuracy was around 100%. Similar results were reported by other authors when different variables were tested on the accurate classification of foods [9]. However, these authors indicated that the influence of sample characteristics and the type of sensors used will affect the classification results [9]. The results are of importance for the industry as they indicate that a portable instrument can be used to assess or monitor the traceability, as well as to evaluate the composition of the BSFL reared with different types of organic waste streams. Both tasks can be achieved utilizing a minimal number of larvae samples. Overall, the portable NIR instrument used in this study covers the wavelength range between 950 and 1650 nm. This region contains information on the relative proportions of C-H, N-H, and O-H bonds associated with lipids, protein, carbohydrates, and water content, providing enough information to classify the BSFL samples according to the waste stream used to feed them [1; 3; 28; 29; 39]. Sample presentation to an NIR spectrometer (e.g., dry vs. wet; particle size) is one important characteristic that will contribute to explaining the accuracy of the results obtained during the development of either a calibration or classification model. Consequently,  
Figure 5: Principal component loadings of the black soldier fly larvae samples analyzed using near-infrared reflectance spectroscopy and sourced from two waste streams (e.g., supermarket and childcare). proper attention should be dedicated to understanding any properties or issues associated with the sample (e.g., compaction, particle size variation, other sample presentation issues) before the development and implementation of an NIR method. Moreover, the evaluation and identification of these effects prior to the development and during the interpretation of a calibration model will help in better understanding the results as well as in determining the robustness and reliability of the NIR models to be implemented.