## 3 Results and Discussion
### Quantification of Adulterants Using SORS and PLSR  
Having established that these models were excellent for data collected from olive oil contained in vials, we then proceeded to analyse the data from SORS, which had been collected through-container mode where the mixtures of oils were contained within a commercially available olive oil bottle. In this case we set the instrument to SORS mode (so called \"Thick, coloured or opaque\" mode) where two Raman spectra were taken at different layers (at the surface of the bottle and from within the bottle) and then the spectrum from the surface layer was subtracted from the Raman spectrum collected from within the bottle containing the oils.  
As previously stated, we first tested the volume of oil that was needed to obtain satisfactory results. As the sunflower oil and EVOO were in abundant supply, we used mixtures of these two oils and prepared these in maximum volumes of 90 and 28 mL, as detailed in Table S1. The raw SORS spectra looked very similar for both volumes (Figure 2). As SORS requires spectral subtraction to provide SORS spectra from different layers, we obtained the raw output files from the Resolve and used SNV for normalisation of the spectra prior to PLSR results for the test sets compared. The results of 1000 PLSR models from bootstrapping are shown in Table 2 where the predictions of % sunflower oil in 28 mL was actually better that 90 mL. We therefore proceeded to use 28 mL for all through-container analyses.  
The PLSR results for the SORS approach are shown visually in Figures S8D-S10D and the figures of merit for linearity, error and LOD are provided in Table 2. We can see that the predictions are best for quantifying sunflower oil, followed by pomace OO, with refined OO being the worst. If we focus on LOD, this is 2.24% compared to 10.64% and 13.60%, respectively. Similar trends were also observed in terms of the linearity (\(Q^{2}\) = 1.00, 0.97, 0.84) and error (RMSE = 1.37, 4.91, 10.15) in the test sets, which confirmed that due to its chemical similarity, refined OO was the most difficult to predict.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**785 nm** & **\# PLS Factors** & **\(\mathbf{R^{2}}\)** & **\(\mathbf{Q^{2}}\)** & **RMSEC** & **RMSEP** & **LOD (\%)** \\ \hline EVOO \& Sunflower O & 6 & 0.99 & 0.99 & 3.64 & 4.23 & 2.92 \\ EVOO \& Pomace OO & 3 & 0.95 & 0.89 & 7.21 & 10.20 & 19.94 \\ EVOO \& Refined OO & 4 & 0.90 & 0.93 & 9.67 & 8.00 & 12.02 \\
**1064 nm** & & & & & & \\ EVOO \& Sunflower O & 2 & 0.98 & 0.99 & 4.07 & 3.71 & 6.19 \\ EVOO \& Pomace OO & 4 & 0.97 & 0.95 & 5.32 & 6.50 & 11.50 \\ EVOO \& Refined OO & 4 & 0.97 & 0.92 & 5.27 & 8.13 & 11.29 \\
**830 nm** & & & & & & \\ EVOO \& Sunflower O & 5 & 1.00 & 1.00 & 1.93 & 1.98 & 3.97 \\ EVOO \& Pomace OO * & 3 & 0.98 & 0.92 & 4.30 & 5.34 & 10.39 \\ EVOO \& Refined OO * & 6 & 0.93 & 0.96 & 4.84 & 6.35 & 11.39 \\ \hline \end{tabular} The spectral range for all analyses was 700–1800 cm\({}^{-1}\). Standard normal variate (SNV) was used to pre-process these data. # PLS factors represents the number of latent variables used in each PLSR model. \(R^{2}\) and \(Q^{2}\) represent the goodness of the model’s fit to the dataset for the 1000 training sets and the 1000 test sets, respectively. RMSEC and RMSEP denote root mean squared error results pertaining to the training sets and test sets, respectively. LOD is the limit of detection calculated from the PLSR models. * For these results, the processing was detrimental to the model, therefore the analysis was done directly on the raw data.  
\end{table}
Table 1: PLSR results for the bootstrap models of the adulterated oils in EVOO with data collected in vial mode.