## 4 Conclusions  
This study aimed to better understand how biotic (reduced genetic variation) and abiotic factors (temperature, humidity, pupation substrate, and diet) influence the uses of chemical analyses for species identification of forensically important pupae. Considerable chemical differences were documented among _Cochlomytia macellaria_, _Lucilia cuprina_, _Lucilia sericata_, and _Phormia regina_ as a function of species and genetics. Classification accuracies of 89-94% were obtained for the G1 generation, and classification accuracies of 93%-98% were obtained for the G10 generation. The compounds most correlated and \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**From/To** & **Rats** & **HF-Beef** & **LF-Beef** & **Chicken Liver** & **Total** & **\% Correct** \\ \hline Rats & 21 & 2 & 0 & 1 & 24 & 87.5 \\ HF-Beef & 0 & 36 & 17 & 0 & 53 & 67.9 \\ LF-Beef & 0 & 13 & 41 & 0 & 54 & 75.9 \\ Chicken Liver & 0 & 0 & 0 & 53 & 53 & 100 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Leave-one-out confusion matrix for _P. regina_ pupae reared on larval diets. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**From/To** & **C. macellaria** & **L. cuprina** & **L. sericata** & _P. regina_ & **Total** & **\% Correct** \\ \hline Rats & 0 & 3 & 21 & 0 & 24 & 0 \\ HF-Beef & 2 & 50 & 0 & 1 & 53 & 1.9 \\ LF-Beef & 2 & 51 & 0 & 1 & 54 & 1.9 \\
15 \({}^{\circ}\)C & 0 & 8 & 10 & 6 & 24 & 25.0 \\
20 \({}^{\circ}\)C & 4 & 8 & 18 & 23 & 53 & 43.4 \\
30 \({}^{\circ}\)C & 20 & 5 & 12 & 16 & 53 & 30.2 \\
40\% r.h. & 0 & 9 & 21 & 12 & 42 & 28.6 \\
70\% r.h. & 3 & 26 & 19 & 4 & 52 & 7.7 \\
85\% r.h. & 0 & 15 & 25 & 13 & 53 & 24.5 \\ Sand & 2 & 8 & 18 & 26 & 54 & 48.1 \\ Vermiculite & 0 & 10 & 11 & 23 & 44 & 52.3 \\ CONTROL & 0 & 6 & 27 & 20 & 53 & 37.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Leave-one-out confusion matrix for _P. regina_ larvae reared under different abiotic conditions and to which species they assign. The control abiotic conditions were 25 \({}^{\circ}\)C, 55% r.h., chicken liver and sawdust—the same conditions as the G10 generation. Only the abiotic condition in column one changed, other conditions remained as in the control condition. anti-correlated with species were identified. Data from the G10 generations were shown to be a suitable model for G1 pupae, although performance on _P. regina_ suffered, with classification accuracy falling to 75%. However, genetic homogenization was found to have an impact on the observed chemical profiles, which should serve as a cautionary note for future researchers hoping to extract data with real world relevance from colonies at advanced generations. Temperature and pupation substrate were found to have minimal impact on the lipid profiles, whereas small chemical changes were observed with changes in humidity, and significant changes were observed with variations in diet. If comparing the classification accuracies of the different abiotic conditions relative to the control, it is immediately clear that diet plays the most important role in the composition of lipid profiles in developing pupae (Table 8). Therefore, in \"real-world\" scenarios where pupae are recovered from a scene but their diet is unknown, we suspect that using chemical profiles to determine species is not ideal, and thus more research is necessary. Although we used a single classification method here (discriminant analysis), other methods such as (Soft Independent Modeling of Class Analogy (SIMCA), Neural networks or Bayesian classifiers, etc.) could change the classification accuracies presented here. However, the effect of diet is a biochemical process and not one that could be easily corrected for via statistical methods. It is important that additional data be collected to establish a greater understanding of the changes in chemical profiles over time under different conditions to establish a reliable database for species identification purposes.