# Advancing Visible Spectroscopy through Integrated Machine Learning and Image Processing Techniques
## 3 Methodology
The experiment encompassed the examination of sodium vapour, neon lamp, copper vapour laser, and helium vapour, where their respective hue values were utilised to deduce their dominant wavelengths, as shown in Figure 2. This methodological fusion empowered the system to adeptly scrutinise diverse vapour sources, harnessing visual data input to furnish valuable spectral insights. The significance of this phase lies in its ability to bridge the gap between visual information and spectral data through a systematic and data-driven approach. By acquiring images of different vapours along with their known dominant wavelengths, researchers can establish a robust dataset for training ML algorithms. This dataset serves as a foundation for teaching the ML model to recognise and associate specific hue values extracted from the vapour images with their corresponding dominant wavelengths. Moreover, the development of an algorithm to process these images and extract hue values adds a layer of sophistication to the analysis process. This algorithm plays a pivotal role in preprocessing the visual data and converting it into a format suitable for ML analysis. By calculating the hue values from the images, the algorithm provides quantitative inputs that can be directly fed into the ML model. The integrated approach of combining image processing with ML techniques offers several significant advantages. It enables the ML algorithm to learn complex patterns and relationships between hue values and dominant wavelengths. Through iterative training on a diverse dataset of vapour images, the ML model can refine its predictions and improve its accuracy over time. Figure 2: Different vapour light discharge test cases: (**i**) sodium [49] (**ii**) neon [50] (**iii**) copper [51] (**iv**) helium [52] (**v**) mercury [53]. Combining polynomial regression with machine learning (ML) algorithms represents a powerful synergy that enhances the methodology for visible spectroscopy in several ways. Firstly, polynomial regression provides a mathematical framework to model the relationship between hue values and dominant wavelengths. By fitting a polynomial function to the dataset, it captures the underlying patterns and trends in the spectral data. This allows for more accurate predictions of dominant wavelengths based on hue values extracted from images. The use of polynomial regression ensures that the model can effectively handle nonlinear relationships and complex spectral variations, which are common in visible spectroscopy. On the other hand, ML algorithms bring additional capabilities to the methodology. These algorithms have the ability to learn from data, recognise patterns, and make predictions without being explicitly programmed. By integrating ML techniques, the methodology gains the advantages of adaptability and scalability. ML algorithms can iteratively refine their predictions based on new data, leading to continuous improvement in accuracy and performance over time. Moreover, ML algorithms can handle complex datasets with high dimensionality, making them well-suited for analysing diverse vapour sources with varying spectral characteristics. Unlike traditional spectroscopic methods that may be limited to specific types of samples or conditions, ML algorithms can generalise patterns across different vapour sources. This flexibility enables the methodology to accommodate a wide range of applications and scenarios, from industrial process monitoring to environmental sensing. Furthermore, the combination of polynomial regression with ML algorithms offers a robust framework for spectroscopic analysis. Polynomial regression provides a solid mathematical foundation, while ML algorithms add a layer of intelligence and adaptability. This dual approach ensures that the methodology is both accurate and versatile, capable of handling the complexities inherent in visible spectroscopy. Overall, through integrating polynomial regression with ML algorithms, the methodology for visible spectroscopy becomes more than the sum of its parts.