## 3 Results and Discussion
### SIMCA Validation  
Validation of the SIMCA model consisted of HMI collected from 19 microorganisms, and resulted in 3222 of 3421 bacterial cells correctly labeled as _Salmonella_ or non-_Salmonella_ and are shown in Table 3.  
The SIMCA prediction model had an accuracy of 95.4%, sensitivity of 0.97, and specificity of 0.92. The five _Salmonella_ serotypes used for validation are serotypes that commonly appear in foodborne disease outbreaks, especially SE and ST. Fairly consistent unsupervised prediction accuracies were obtained for all five serotypes, ranging between 94.6% (SH) and 98.0% (SE) accuracy. The PCA projections of the score plots calculated from the validation set are shown overlaying the _Salmonella_ calibration score plot. Figure 6A shows a visual representation of the SE scores projected onto the _Salmonella_ model, with most points projected inside the SIMCA boundaries of the second and third PC, while Figure 6B projectsthe validation set of _Staphylococcus aureus_ (Sa) scores and the SIMCA calibration boundaries, with most Sa cells projected just outside of the model. \begin{table}
\begin{tabular}{c c c c c} \hline \multicolumn{5}{c}{_Salmonella_} \\ \hline
**Microorganism** & **Cells** & **Yes** & **No** & **Accuracy (\%)** \\ \hline _Campylobacter coli_ & 27 & 6 & 21 & 77.8 \\ _Campylobacter fetus_ & 26 & 3 & 23 & 88.5 \\ _Campylobacter jejuni_ & 65 & 9 & 56 & 86.2 \\ _Enterobacter cloae_ & 142 & 4 & 138 & 97.2 \\ _Enterococcus faecalis_ & 157 & 1 & 156 & 99.4 \\ _Escherichia coli_ & 767 & 9 & 758 & 98.8 \\ _Klebsiella oxytocin_ & 82 & 1 & 81 & 98.8 \\ _Listeria innocua_ & 79 & 9 & 70 & 88.6 \\ _Listeria monocytogenes_ & 116 & 1 & 115 & 99.1 \\ _Macrococcus cascolyticus_ & 24 & 0 & 24 & 100 \\ _Pearthobacillus polymorphya_ & 66 & 6 & 60 & 90.9 \\ _Pseudomonas putida_ & 151 & 55 & 96 & 63.6 \\ _Staphylococcus aureus_ & 212 & 10 & 202 & 95.3 \\ _Staphylococcus simulans_ & 190 & 5 & 185 & 97.4 \\ _Salmonella Enteritidis_ & 350 & 343 & 7 & 98.0 \\ _Salmonella_ Heidelberg & 149 & 141 & 8 & 94.6 \\ _Salmonella Infants_ & 284 & 277 & 7 & 97.5 \\ _Salmonella Kentucky_ & 239 & 233 & 6 & 97.5 \\ _Salmonella Typhimurium_ & 295 & 283 & 12 & 95.9 \\ Total & 3421 & 1277 & 1985 & 95.4 \\ \hline \end{tabular}
\end{table} 
Table 3: SIMCA results for a one-class _Salmonella_ prediction model obtained from hyperspectral microscope images of bacteria. Figure 5: Soft-independent modelling of class analogy (SIMCA) calibration diagnostics: (**A**) PC scores 1 and 2, (**B**) loadings for PCs 1–4, (**C**) principal component analysis residuals, and (**D**) scree plot of explained variance (%). Of the 14 non-_Salmonella_ serotypes from the validation dataset, there was a larger range of prediction accuracy, varying from 63.6 to 100%. _Pseudomonas putida_ (Ppu) showed the lowest accuracy, with 63.6% classified as non-_Salmonella_ bacteria, while 36.4% were misclassified as _Salmonella_ cells. Of the three Ppu HMI repetitions, one HMI had a significantly higher misclassification rate at 49%. The single cell mean spectra of this HMI were not marked as outliers and were removed from the dataset; this could suggest that the MD outlier detection threshold should be lowered. _Salmonella_ and _E. coli_ (Ec) are both similar in composition and taxonomy, which is why a larger number of Ec (767 cells) were selected to validate the _Salmonella_ SIMCA prediction model. Previously, [PERSON] and [PERSON] [18] showed that the spectral patterns of _Salmonella_ and Ec were more similar than comparing _Salmonella_ to Sa or Li, with _Salmonella_ and Sa being the most dissimilar. The prediction model resulted in a lower type II error rate, of 0.030, than a type I error rate, of 0.076. This was preferable in regard to a single class model for food safety application, reducing the potential of a false negative sample being made available to consumers. Standard microbial analysis methods for food items such as PCR or the use of nutrient enriched growth media are well established, but come with disadvantages.