## 4 Conclusions  
Hyperspectral imaging is a powerful tool for the characterization of a broad range of materials, namely food and feed components. The objective of this work was threefold: (i) develop a generic HSI processing tool in python, agnostic in terms of hypercube dimensions; (ii) develop a procedure that combines analytical assays and HSI acquisition, in or-der to establish a correlation between the two sources of data; and (iii) apply the procedure to real components: a common food component (wheat flour) and a feed component (BSF larvae meal). From an operational perspective, the study demonstrates that it is possible to obtain a quantitative and qualitative correlation between analytical results and HSI methodologies for food or feed product component characterization, specifically regarding moisture and total fat parameters, even when only the 400-1000 nm spectral range is considered. However, to fully exploit the characterization capabilities of the HSI methodology, it is essential to acquire data in the NIR above 1000 nm and up to 2500 nm. This extended range would allow for the detection of critical spectral features, particularly those associated with protein content. Future work will address this spectral range extension and the continuous upgrade of the software to include different HSI processing tools. In the future, the interactions between different feed components and the specific applications of hyperspectral technology in real-time quality control can be further explored to enhance their translational value. This topic has been growing in recent years and will play a crucial role in establishing important correlations for the health and food industries. The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app15073822/s1](https://www.mdpi.com/article/10.3390/app15073822/s1)]([https://www.mdpi.com/article/10.3390/app15073822/s1](https://www.mdpi.com/article/10.3390/app15073822/s1)), Figure S1. Band 80 (\(\lambda\) = 631.15 nm) and definition of the ROI (170 \(\times\) 100 pixels): WR (cyan), and CT (orange). The circles were used to align the ROI; Figure S2. Mean spectrum of each tile: WR (cyan) and CT (orange). The bands correspond to the 17000 spectra obtained from the entire ROI of each tile; Figure S3. Mean spectra derived from the ROI, for each tile: (a) WR and CI band (cyan); (b) CT and CI band (orange). For each mean spectrum the 99% CI is presented; Figure S4. Mean spectra derived from the ROI, for each tile, for heating times 4, 6, 8, 12 min: (a) WR and CI band (cyan); (b) CT and CI band (orange). For each mean spectrum the 99% CI is presented; Figure S5. Mean spectra derived from the ROI, for each tile, for heating times 16, 20, 24, 28 min: (a) WR and CI band (cyan); (b) CT and CI band (orange). For each mean spectrum the 99% CI is presented (dark line); Figure S6. Variation of the 99% CI: (a) CI for each wavelength for all heating times for the WR mean spectrum (blue) and the CT mean spectrum (orange); (b) Maximum and minimum CI across the wavelengths for each heating time for the WR (blue) and the CT (orange). Figure 8: Comparison of processed spectra of BSF larvae meal and wheat flour: mean spectra of each sample derived from the ROI (**b**); corresponding spectra processed with SNV and SGd2 filtering (**a**). Vertical dashed lines indicate the positions of the relevant absorption features. The dashed lines show the linear data fit. The heating time shows no significant influence on the CI of the mean spectra of either tile; Figure S7. Pearson correlation coefficients for each pair of spectra obtained with different heating times: (a) WR spectra; (b) CT spectra; Figure S8. Spearman rank correlation coefficients for each pair of spectra obtained with different heating times: (a) WR spectra; (b) CT spectra. Conceptualization, F.M.O.d.S. and L.G.F. ; methodology, F.M.O.d.S. ; software, N.S.A.P. ; validation, R.F., D.M., M.J.C. and R.S.I. ; formal analysis, F.M.O.d.S. and N.S.A.P. ; investigation, F.M.O.d.S., L.G.F. and N.S.A.P.