Abstract: The rising awareness of harmful ingredients in consumer products, ranging from food and cosmetics to pharmaceuticals, has necessitated the development of tools for evaluating product safety. This paper explores the design of a Product Ingredient Toxicity Analyzer and Recommender system that utilizes Natural Language Processing (NLP) and machine learning to assess the toxicity of ingredients in various products and suggest safer alternatives. The study addresses key concerns such as database accuracy, data privacy, and ethical implications, highlighting the potential for AI to enhance consumer safety and well-being by ensuring the absence of harmful substances in everyday products The Product Ingredient Toxicity Analyzer and Recommender system aims to empower consumers by providing an automated, user-friendly platform for evaluating the safety of product ingredients. By leveraging Natural Language Processing (NLP) techniques, the system can extract relevant ingredient information from labels or user inputs, while machine learning models assess toxicity levels based on pre-trained datasets and real-time data fetched from reliable sources, such as scientific research papers and regulatory databases. The system incorporates a toxicity assessment engine that classifies ingredients into categories such as Low, Moderate, or High toxicity, accompanied by actionable recommendations. These recommendations inform users about safe usage levels or suggest alternative, safer ingredients. The analysis is performed without requiring extensive technical expertise from the user, ensuring accessibility to a broad audience, including consumers, product developers, and regulatory professionals.
A critical aspect of the system's development involves curating a robust and dynamic database that combines static information (e.g., toxicity scores, historical case studies) with dynamic updates sourced through APIs. The system ensures data privacy by anonymizing user inputs and adhering to global privacy regulations such as GDPR and HIPAA, while ethical considerations guide its recommendations to avoid misinformation or unwarranted alarm.
|
DOI:
10.17148/IJARCCE.2024.131142
