# Advancing Visible Spectroscopy through Integrated Machine Learning and Image Processing Techniques
## 3 Methodology  
Visible spectroscopy plays a crucial role in various scientific and industrial applications, facilitating the analysis of materials based on their spectral characteristics. In recent years, the integration of image processing techniques and machine learning algorithms has emerged as a promising approach to enhance the accuracy and efficiency of spectroscopic methods. This research aims to leverage such advancements by developing a methodology for visible spectroscopy using polynomial regression and machine learning algorithms. The TAS-990 Atomic Absorption Spectrophotometer was used by the researchers for the spectroscopy of different vapours and stands out as one of the most precise spectrometers available, renowned for its exceptional accuracy in providing data on elemental emissions (See Table 1 for its features and specifications). Artificial intelligence (AI) encompasses a broad spectrum of technologies and techniques aimed at enabling machines to mimic human cognitive functions, learn from data, and make autonomous decisions. Within the realm of AI, machine learning is a pivotal subfield that empowers systems to improve their performance on tasks through experience. One notable technique within machine learning is polynomial regression, which is a type of regression analysis where the relationship between the independent variable (or variables) and the dependent variable is modelled as an nth-degree polynomial function. Polynomial regression extends linear regression to capture more complex relationships between variables, making it particularly useful when the data do not follow a strictly linear pattern. By fitting a polynomial curve to the data points, polynomial regression enables AI systems to uncover intricate patterns and trends, thus enhancing their predictive capabilities. In the context of AI, polynomial regression finds applications in various domains, including finance, economics, engineering, and the sciences. For instance, in finance, polynomial regression can be employed to model stock price movements, considering factors beyond linear trends. Similarly, in climate science, it can aid in analysing the nonlinear relationships between atmospheric variables. Moreover, polynomial regression serves as a fundamental building block for more advanced AI techniques, such as neural networks. Neural networks often leverage polynomial regression as an activation function within their layers, allowing them to learn and represent nonlinear relationships in data. In summary, polynomial regression plays a crucial role in the AI landscape by enabling systems to model and understand nonlinear phenomena, thereby advancing their capabilities in prediction, decision-making, and pattern recognition. The research begins by acquiring a dataset containing hue versus dominant wavelength values. In [PERSON] and [PERSON]'s [PERSON] and [PERSON] (2018) study, the generation of the dataset, hue values, and corresponding wavelengths involves a multi-step process. Initially, 500 simulated satellite remote sensing reflectance (Rrs) spectra are utilised, and corrected for \begin{table}
\begin{tabular}{c c} \hline \hline
**Feature** & **Specification** \\ \hline Technique & Atomic Absorption Spectroscopy (AAS) \\ Wavelength Range & 190 nm to 900 nm \\ Spectral Bandwidth & 0.1, 0.2, 0.4, 1.0, 2.0 nm (selectable) \\ Wavelength Accuracy & \(\pm\)0.25 nm \\ Wavelength Repeatability & \(\pm\)0.15 nm \\ Baseline Drift & \(\leq\)0.005 Abs/30 min \\ Background Correction & Deuterium lamp background correction \\ Atomisation Methods & Flame Atomiser, Graphite Furnace Atomiser \\ Heating temperature range (Graphite Furnace) & Room temperature\(-\)2650 \({}^{\circ}\)C \\ Lamp Type & Hollow Cathode Lamp (HCL) \\ Number of Lamp Positions & 8 (automatic turret) \\ Sample Types & Liquids, Solids \\ Precision (RSD) & \(\leq\)3\% \\ Software Compatibility & Windows Platform \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Specifications of the spectrometer. atmospheric influences and other optical properties.