Machine learning analysis of colorimetric sensor arrays for amine classification

Abstract

This research explores the application of machine learning for the classification of amines using colorimetric sensor arrays. Colorimetric sensor arrays are chemical sensors that detect substances through color changes, offering simplicity and ease of use for rapid on-site analysis. The identification of amines plays a crucial role in various fields due to their significant impact on health, safety, environmental quality, and industrial efficiency. In particular, the fish industry relies heavily on the identification of amines to ensure product quality and safety. Amines are often associated with spoilage and degradation in fish products. In this study, the identification of the presence or absence of amines in colorimetric sensor array images was performed using machine learning. The sensor array consists of several metal-based and acid-base indicators. Colorimetric sensor array images in strip format were utilized. The images, of varying quality, were in JPEG/JPG and PNG formats. The RGB values were extracted from 76 images of 10-hole strips and 38 for each class: Amine, and Not Amine. The dataset was split randomly into two subsets: 75% for the training set and 25% for the validation set. A separate dataset was used to test the model. This study explores various machine learning models, including Knearest neighbors (KNN), decision tree, and support vector machine (SVM). The KNN model achieved promising results with an accuracy of 94.74% on both training and validation datasets, an average precision of 95%, an average recall of 95%, and an average F1 score of 95% demonstrating effective classification capabilities across both training and validation sets. The confusion matrix method was employed to evaluate the model's performance. Hyperparameter tuning was conducted to optimize model performance and to avoid overfitting and underfitting, techniques such as selecting the optimal number of neighbors, and choosing the appropriate distance metric were employed. A user-friendly web prototype was developed to demonstrate the practical application of the model. After building the prototype, the model was tested on previously unseen data using a web prototype. The model achieved 90% accuracy on this new data. Future work aims to expand the dataset and include additional compounds to enhance the model's robustness and utility. This study highlights the potential of machine learning in advancing the detection and classification of chemical substances, contributing to various fields requiring precise amine identification. This makes it possible for people in different areas to use technology and understand the results without having to be experts.