Browsing by Author "Ravindu, M. A. Y."

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Development of rapid detection strip for amines from other organic functional groups
(Faculty of Science, University of Kelaniya Sri Lanka, 2024) Ravindu, M. A. Y.; Maheshani, Y. K. D. C.; Kumarika, B. M. T.; Wanniarachchi, D. D. C. de S.
Identifying organic compounds in a laboratory requires a lot of chemicals and hence, the process is expensive. To address the challenges of controlling costs and reducing chemical waste, an investigation into the integration of chemistry with computer science techniques has been initiated. This approach emphasizes the significance and innovative aspects of the research. The research focuses on predicting Organic Compounds using both color strips and machine learning methods. A disposable strip was designed with ten separate holes, each serving as a colorimetric indicator. The first hole does not contain any chemical, from the second hole FeCl3, Chromic Acid, CuCl2, FCP, Methyl Orange, Phenol Red, Bromophenol Blue, Thymol Blue, Bromocresol Green were in holes respectively. These sensor indicators react with Functional group, causing distinctive color changes. RGB values from colorimetric strips were extracted as the dataset using ImageJ, an image analysis software, which analyzed photos of the sensor strip to obtain the RGB values for each hole. Two methods were used to classify compounds. Initially, the dataset containing RGB values of every compound was subjected to Principal Component Analysis (PCA) to evaluate the sensor array's intrinsic capacity for distinguishing between distinct categories of organic compounds. Second, specific chemicals were categorized using their RGB profiles because of the development of machine learning algorithms. It was shown that alcohol, ester, aldehyde, ketone, carboxylic acid could not be effectively separated using a single-color value (red, green, or blue) using PCA. But in the green value PCA plots, amines frequently formed unique clusters that allowed for their independent identification. Using PCA-derived green values, the K-Nearest Neighbors (KNN) model proved to be the most effective among all models for classifying chemicals as amines or non-amines, with an accuracy of 94%, recall of 95%, and precision of 95%. The KNN model achieved 99% training accuracy by adding additional amine and non-amine chemicals (27 and 26 respectively) to the training dataset. This study demonstrates the potential of RGB data for chemical identification, particularly for amines, suggesting that the colorimetric sensor array can be used as an identification strip for amine compounds in environmental samples and for educational purposes. Clustering mixtures like Carboxylic Acid-Aldehyde, Alcohol-Ketone into different categories was shown to be a substantial issue. Mixtures' color patterns frequently matched the dominating component's (amine in an amine-alcohol-ester combination, for example). This shows that in complex samples, clear categorization is made difficult by solvent effects or inter-component interactions.
Machine learning analysis of colorimetric sensor arrays for amine classification
(Faculty of Science, University of Kelaniya Sri Lanka, 2024) Maheshani, Y. K. D. C.; Ravindu, M. A. Y.; Wanniarachchi, D. D. C.; Kumarika, B. M. T.
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.