# Advancing Visible Spectroscopy through Integrated Machine Learning and Image Processing Techniques
## 4 Results and Discussions
\begin{tabular}{c} **Training** \\ **Data [48]** \\ \end{tabular}  & **Polynomial Regression** \\ \hline
31 & 584 & 583.80 & 112 & 528 & 528.59 & 193 & 489 & 489.22 \\
32 & 584 & 583.50 & 113 & 526 & 527.59 & 194 & 489 & 489.10 \\
33 & 583 & 583.20 & 114 & 525 & 526.60 & 195 & 488 & 488.97 \\
34 & 583 & 582.91 & 115 & 523 & 525.62 & 196 & 488 & 48 However, for copper (Cu), there is a larger discrepancy between the predicted wavelength (572.38412 nm) and the original value (578.2 nm), resulting in an error percentage of 1%. While this error is relatively small, it suggests that there may be some limitations or challenges in accurately predicting the spectral characteristics of copper vapour using the current methodology. The results for mercury (Hg) indicate a more significant error, with the predicted wavelength (566.5212 nm) deviating considerably from the original value (546.074 nm). The error percentage in this case is 3.7%, indicating that the methodology may struggle to accurately predict the dominant wavelength of mercury vapour, potentially due to the complex spectral characteristics of mercury emissions. Lastly, for helium (He), the predicted wavelength (587.97620 nm) is very close to the original value of 587.562 nm, resulting in a relatively low error percentage of 0.07%. This indicates that the methodology performs well in predicting the dominant wavelength of helium vapour with a high level of accuracy. Overall, the results, as shown in Table 1 and Figure 5, demonstrate the effectiveness of the proposed methodology in predicting the dominant wavelengths of vapour sources, with generally low error percentages across different elements. While some discrepancies exist, particularly for elements like copper and mercury, the methodology shows promise for a wide range of applications in visible spectroscopy. Further refinement and optimisation may be necessary to improve accuracy, particularly for elements with complex spectral characteristics. This approach enhances the versatility and applicability of spectroscopic analysis. By leveraging image data, researchers can analyse vapour sources in real-world scenarios, capturing variations in lighting conditions, camera settings, and other environmental factors. This capability is particularly valuable for field applications where traditional spectroscopic instruments may be impractical or inaccessible. Furthermore, the ability to accurately predict dominant wavelengths based on hue values extracted from vapour images holds immense potential for various scientific and industrial applications. For instance, in environmental monitoring, this methodology could be employed to identify and quantify pollutants or trace gases based on their spectral signatures. In material science, it could aid in the characterisation of materials based on their optical properties. Overall, the integration of image processing and ML techniques in spectroscopic analysis represents a significant advancement with far-reaching implications. By leveraging visual data to enhance spectral analysis, researchers can unlock new insights into the composition, behaviour, and properties of vapour sources, paving the way for solutions in diverse fields.