## 5 Conclusions  
This work presents a new color standardization algorithm, in which the main stages consist of preparing a template based on an initial series of photos, developing a color mapping principle using equalized digital RGB histograms of an ideal pattern and its photo, color conversion of photographs of the examined object involving the use of information about mapping template histograms, and image reconstruction based on the transformed histogram. The algorithm was developed for cases where the examined objects differ little in color and the color of each photographed object is almost uniform. The operation of the proposed procedure was demonstrated and verified in an analytical chemistry task, which involved the colorimetric determination of vitamin C. Photographs of AA solutions taken under controlled conditions did not show much color difference within the concentration range. The research was carried out under daylight and artificial lighting conditions \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Change in Architecture** & **RMSE/\(\mathbf{\mu g}\):mL\({}^{-1}\)** & **R\({}^{2}\) Score** & **Comparison** \\ \hline Batch size = 120 & 2.3535 & 0.9947 & \begin{tabular}{c} Decrease of R\({}^{2}\) score and increase of RMSE in comparison to final model. \\ comparison to final model. \\ during training. \\ \end{tabular} \\ \hline Image size = 16 & 4.0146 & 0.9825 & \begin{tabular}{c} Significant decrease of R\({}^{2}\) score and increase of RMSE in comparison to final model. Lower decline of initial rate in comparison to final version. \\ \end{tabular} \\ \hline Loss function modification & 3.8203 & 0.9856 & \begin{tabular}{c} Decrease of R\({}^{2}\) score and significant increase of RMSE in comparison to final model. Minimized differences between training and validation loss during training. \\ \end{tabular} \\ \hline Optimizer = Adagrad & 3.1625 & 0.9877 & \begin{tabular}{c} Decrease of R\({}^{2}\) score and increase of RMSE in comparison to final model. \\ \end{tabular} \\ \hline Optimizer = SGD & - & - & \
\begin{tabular}{c} There was no change in loss value between the epochs. The model did not learn to predict the concentration of vitamin C solutions. \\ \end{tabular} \\ \hline Initial learning rate = 0.01 & - & - &
\begin{tabular}{c} No significant change in the results was detected. \\ The results achieved did not vary significantly in comparison to final model results. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Evaluation of DL regression models considered in the ablation study for mixed lighting and matched condition of testing, depending on changes in the model. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \begin{tabular}{c} **Artificial** \\ **Lighting,** \\ **Original** \\ \end{tabular} & \begin{tabular}{c} **Artificial** \\ **Lighting,** \\ **Matched** \\ \end{tabular} & \begin{tabular}{c} **Natural** \\ **Lighting,** \\ **Original** \\ \end{tabular} & \begin{tabular}{c} **Natural** \\ **Lighting,** \\ **Matched** \\ \end{tabular} & \
\begin{tabular}{c} **Mixed** \\ **Lighting,** \\ **Original** \\ \end{tabular} &
\begin{tabular}{c} **Mixed** \\ **Lighting,** \\ **Matched** \\ \end{tabular} \\ \hline Slope & 0.9058 & 1.0138 & 0.9538 & 1.0071 & 1.0005 & 1.0112 \\ \hline Intercept & 0.6891 & \(-\)0.3598 & \(-\)0.8347 & 0.2263 & 0.8747 & 0.0203 \\ \hline \(\mathbf{RMSE}/\mathbf{\mu g}\):mL\({}^{-1}\) & **5.8415** & **1.7077** & **3.9504** & **2.5151** & **4.3488** & **2.8211** \\ \hline \(\mathbf{R^{2}}\) **score** & **0.9557** & **0.9962** & **0.9798** & **0.9918** & **0.9755** & **0.9897** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Evaluation of DL regression models considered in the ablation study for six different variations of testing, depending on the lighting conditions and chosen dataset. in order to combine them into a single dataset and obtain a model that allows for the determination of vitamin C regardless of the lighting conditions used when taking the photos.