# Advancing Visible Spectroscopy through Integrated Machine Learning and Image Processing Techniques
###### Abstract  
This research introduces an approach to visible spectroscopy leveraging image processing techniques and machine learning (ML) algorithms. The methodology involves calculating the hue value of an image and deriving the corresponding dominant wavelength. Initially, a six-degree polynomial regression supervised machine learning model is trained to establish a relationship between the hue values and dominant wavelengths. Subsequently, the ML model is employed to analyse the visible wavelengths emitted by various sources, including sodium vapour, neon lamps, mercury vapour, copper vapour lasers, and helium vapour. The performance of the proposed method is evaluated through error analysis, revealing remarkably low error percentages of 0.04%, 0.01%, 3.7%, 1%, and 0.07% for sodium vapour, neon lamp, copper vapour laser, and helium vapour, respectively. This approach offers a promising avenue for accurate and efficient visible spectroscopy, with potential applications in diverse fields such as material science, environmental monitoring, and biomedical research. This research presents a visible spectroscopy method harnessing image processing and machine learning algorithms. By calculating hue values and identifying dominant wavelengths, the approach demonstrates consistently low error rates across diverse light sources. visible spectroscopy; machine learning; image processing techniques +
Footnote †: journal: Medical Engineering, Parala Maharaja Engineering College, Berhampur 761003, India; [EMAIL_ADDRESS]  
Footnote †: journal: Medical Engineering, Parala Maharaja Engineering College, Berhampur 761003, India; patra. of the polynomial equation that best fits the training data, thereby enabling it to make predictions on unseen data. This model is adept at capturing nonlinear patterns in the data, though caution must be exercised to prevent overfitting, potentially requiring regularisation techniques for improved generalisation. Polynomial regression serves as a mathematical framework for modelling the relationship between hue values extracted from images and the corresponding dominant wavelengths. By fitting polynomial functions to the dataset, the methodology captures complex spectral patterns and enables the accurate prediction of dominant wavelengths. Polynomial regression is often preferred in scenarios where the relationship between the independent and dependent variables is non-linear. Unlike simple linear regression, which assumes a linear relationship between the variables, polynomial regression allows for more flexible modelling through fitting a polynomial function to the data. This flexibility enables polynomial regression to capture complex patterns and variations in the data that cannot be adequately represented by a straight line. One of the key advantages of polynomial regression is its ability to model curvature in the data. By introducing polynomial terms of a higher degree, the model can better capture the curvature and non-linearity present in many real-world datasets. This makes polynomial regression a valuable tool for predicting outcomes in fields such as finance, economics, and engineering, where relationships between variables may be more intricate and nuanced. Additionally, polynomial regression can provide improved accuracy compared to linear regression when the underlying relationship between the variables is non-linear. By allowing the model to flexibly adapt to the data, polynomial regression can yield more accurate predictions and better capture the true underlying patterns in the data. Furthermore, the utilisation of image processing techniques enables the extraction of hue values from images of vapour sources, thereby providing quantitative inputs for ML algorithms. This integrated approach facilitates the automated analysis of spectral data, eliminating the need for manual interpretation and enhancing the efficiency of spectroscopic analysis.