## 3 Results and Discussion
### Partial Least Squares Regression  
The PLS regression was used to predict the addition level of CKP powder to the CPF and FxF flour samples based on their RVA pasting profiles. The cross-validation statistics  
Figure 2: Principal component scores (**A**) and loadings (**B**) derived from the RVA analysis of insect blends and pure flours. Different pasting curves are represented as circles on the score plot. The circles are colour-scaled from high (red) to low (blue) for their final viscosity measurements to aid in visual interpretation. Two significant PCs were generated for this model, where the following variance was explained: PC1: 89%, PC2: 10%. R\({}^{2}\) = 99.3% and Q\({}^{2}\) = 99.3%. Figure 2B loading corresponds to PC1 and the blue line corresponds to PC2.  
are reported in Table 2. In the case of the addition of CKP powder to CPF flour, R\({}^{2}\)CV and SECV were 0.95 and 7.7, respectively. For the addition of CKP powder to FxF flour, R\({}^{2}\)CV and SECV were 0.90 and 10.9, respectively. The PLS regression statistics for the mix of the two plant flour samples, FxF and CPF, were R\({}^{2}\)CV = 0.94 and SECV = 5.7. The cross-validation statistics were very similar between the models; however, differences were observed in the loadings. The loadings for predicting the addition level of CKP powder into the CPF and FxF flour blends are shown in Figure 3. The highest loadings were observed around 4.5, 10.5, and 11.5 min for the addition of CKP powder to the CPF flour, while the highest and negative loadings for the blends of FxF flour and CKP powder were observed around 10.8 min. These time points correspond to changes in PV and FV. For the mixtures between FxF and CPF, the highest loadings were observed at 4.2, 7.6, and 9.9 min. These time points correspond to PV, onset of BKD, STB, and the increase towards FV. The PLS regression statistics indicate that the addition of CKP powder to either CPF or FxF blends can be reliably modelled, and loading plots can be used to interpret changes in the key parameters of the RVA pasting curve. Therefore, the application of PLS modelling is highly applicable for monitoring the pasting parameters of flour and meal blends fortified with insect powders. In addition, the PLS models allowed us to identify the addition level of insect flour to other plant protein sources. The results from such models can be used for optimising the ratio of insect powders in animal feed blends that are often heated, cooled, and pumped at processing facilities or via on-farm feeding machines [25]. Furthermore, these key parameters may also be correlated with the digestibility [26] and the glass transition of the samples, which influences shelf stability [27; 28; 29; 30].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **CPF-CKP** & **FxF-CKP** & **FxF-CPF** \\ \hline R\({}^{2}\)CV & 0.95 & 0.90 & 0.94 \\ SECV & 7.7 & 10.9 & 5.7 \\ Bias & 0.09 & 0.82 & 0.12 \\ slope & 0.9 & 0.87 & 0.94 \\ LVs & 4 & 3 & 6 \\ \hline \hline \end{tabular}  
* CPF-CKP: chicken flour and cricket powder; FxF-CKP: flaxseed flour and cricket powder; FxF-CPF: chickpea and flaxseed flour; R\({}^{2}\)CV: coefficient of determination in cross-validation, LVs: latent variables, SECV: coefficient of cross-validation.  
\end{table}
Table 2: Cross-validation statistic for the predicted addition level of cricket flour to chickpea and flax meal flows analysed using the RVA.  
Figure 3: Partial least squares loadings derived from the predicted addition level of cricket powder to chickpea and flax meal flour analysed using the RVA.