 |
International Journal for Research Trends and Innovation
International Peer Reviewed & Refereed Journals, Open Access Journal
ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.14 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI)
|
Issue: April 2026
Volume 11 | Issue 4
Review Result and Publication of Paper within : 2-3 days
Click Here For more DetailsFor Authors
Forms / Download
Published Issue Details
Editorial Board
Other IMP Links
Facts & Figure
Impact Factor : 8.14
Issue per Year : 12
Volume Published : 11
Issue Published : 119
Article Submitted : 23417
Article Published : 9054
Total Authors : 24024
Total Reviewer : 831
Total Countries : 162
Indexing Partner
Licence
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
|
Published Paper Details
|
|
| Paper Title: | AI-Based Packed Food Safety and Ingredient Analysis System |
| Authors Name: | Haris KanthaKumar K M , Dhanalakshmi K |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJRTI_210213
|
| Published Paper Id: | IJRTI2603138 |
| Published In: | Volume 11 Issue 3, March-2026 |
| DOI: | https://doi.org/10.56975/ijrti.v11i3.210213 |
| Abstract: | In recent years, the rapid increase in the consumption of packaged and processed food products has created a growing need for accessible and practical tools that help consumers understand what they eat and how it affects their health. Although nutritional labels and ingredient lists are available on most packaged foods, they are often difficult for the general public to interpret. Technical terminology, small print, and the lack of personalized context make it challenging for individuals to determine whether a product is safe or suitable for their dietary needs, allergies, or health conditions. As a result, consumers frequently rely on guesswork when making food choices, which may lead to unintended health risks over time. This project addresses this gap by presenting a consumer-oriented food safety analysis system designed to assist users in evaluating packaged food products in a simple, understandable, and personalized manner. The system focuses on ingredient-level analysis and aims to support real-time decision-making by transforming complex product information into meaningful safety insights. By emphasizing clarity, personalization, and interpretability, the proposed solution attempts to bridge the divide between technical food data and everyday consumer awareness. The developed system operates through a barcode-driven workflow, where a product is identified using its barcode and linked to a structured database containing ingredient composition and related information. Once a product is identified, its ingredients are analyzed using a deterministic reasoning approach that evaluates potential risks, allergens, and dietary incompatibilities. User preferences such as allergies, dietary choices, and specific health considerations are incorporated into the analysis to ensure that the results are relevant to individual needs rather than generic recommendations. This allows the system to provide suitability classifications and safety insights that are both personalized and understandable. A key aspect of the project is the use of a rule-based health and allergen evaluation engine supported by a curated ingredient knowledge base. This approach was selected to ensure consistency, transparency, and explainability in the system’s decisions. During the development process, several data-driven and deep learning–based methods were explored to assess their potential for automated risk prediction. However, the limited availability of reliable and well-labeled food safety datasets posed challenges for model generalization and safe deployment. Given the consumer-facing nature of the application and the importance of predictable outcomes, the deterministic rule-based framework was finalized as the primary analytical mechanism. In addition to analytical evaluation, the system incorporates an explanation layer that communicates the reasoning behind safety decisions in a clear and user-friendly manner. The application translates ingredient-level analysis into structured safety scores, suitability classifications, and simplified explanations that help users understand the implications of their food choices. This emphasis on interpretability ensures that the system supports awareness and informed decision-making rather than acting as a black-box recommendation tool. The final implementation is structured as a mobile-oriented application workflow integrating barcode scanning, ingredient analysis, personalized safety assessment, and explainable reporting into a unified platform. The design prioritizes reliability, ease of use, and real-time responsiveness so that users can evaluate food products during everyday purchasing or consumption scenarios. By combining structured datasets, deterministic reasoning, and user preference modeling, the system demonstrates how practical engineering solutions can support consumer health awareness without relying solely on complex or data-intensive AI models. Overall, this work highlights the importance of building human-centered intelligent systems that prioritize transparency, stability, and usability in health-related applications. The proposed framework shows that deterministic reasoning, when supported by well-structured knowledge and personalization mechanisms, can deliver meaningful and trustworthy food safety insights. At the same time, the architecture remains open for future enhancement through the integration of advanced machine learning and AI-driven approaches as larger and more reliable datasets become available. The system therefore serves as both a functional consumer assistance tool and a foundation for continued research in personalized food safety analysis and intelligent dietary support systems. |
| Keywords: | Food Safety, Ingredient Analysis, Mobile Application, Rule-Based Systems, Explainable AI, Consumer Health, Personalized Recommendation Systems, Decision Support. |
| Cite Article: | "AI-Based Packed Food Safety and Ingredient Analysis System", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2456-3315, Vol.11, Issue 3, page no.b310-b315, March-2026, Available :http://www.ijrti.org/papers/IJRTI2603138.pdf |
| Downloads: | 00097 |
| ISSN: |
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
| Publication Details: |
Published Paper ID: IJRTI2603138
Registration ID:210213
Published In: Volume 11 Issue 3, March-2026
DOI (Digital Object Identifier): https://doi.org/10.56975/ijrti.v11i3.210213
Page No: b310-b315 Country: Tenkasi, Tamil Nadu, India Research Area: Other Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2603138 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2603138 |
| Share Article: | |
|
Click Here to Download This Article |
|
| Article Preview | |
|
|
|
Major Indexing from www.ijrti.org
| Google Scholar | ResearcherID Thomson Reuters | Mendeley : reference manager | Academia.edu |
| arXiv.org : cornell university library | Research Gate | CiteSeerX | DOAJ : Directory of Open Access Journals |
| DRJI | Index Copernicus International | Scribd | DocStoc |
ISSN Details
 |
 |
|
ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
DOI (A digital object identifier)
| Providing A digital object identifier by DOI.ONE How to Get DOI? |
Conference
Open Access License Policy
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Important Details
Join RMS/Earn 300
WhatsApp
Click Here
Click Here
Indexing Partner |
|||
| Copyright © 2026 - All Rights Reserved - IJRTI | |||