Aharix app: An Intelligent Mobile Application for Grocery Barcode Scanning and Health-Aware Shopping

Abstract

Health consciousness and dietary awareness have become increasingly important in today’s fast-paced lifestyle [1]. Aharix is an Android-based application designed to assist users in making informed food choices by integrating personalized health data with product nutritional analysis [2]. The system allows users to input and store their health information, creating a personalized health filter based on critical parameters such as sugar, carbohydrates, and fat levels. Using the Open Food Facts API, Aharix scans the barcode of packaged food items to retrieve real-time nutritional details [3]. It then compares these values against the user’s predefined health criteria and alerts them if any component exceeds recommended limits, promoting healthier consumption habits [4]. Additionally, the application provides a convenient e-commerce feature, enabling users to add selected items to a cart, complete payments securely through Razorpay’s UPI gateway, and generate digital bills [5]. Built using Kotlin and Jetpack Compose, Aharix ensures a smooth, modern, and responsive user experience [6][7]. This project demonstrates how mobile and data-driven technologies can be synergized to promote personalized health management and conscious food selection.

Introduction

The rise of diet-related non-communicable diseases (NCDs), such as obesity, diabetes, and hypertension, has highlighted the limitations of conventional nutritional labeling, which is often too complex for consumers to interpret. Current mobile solutions are either informational or commercial, lacking integration for personalized dietary guidance, real-time verification, and seamless transactions.

Problem:
Consumers with dietary restrictions face cognitive overload when shopping, needing to manually inspect labels and switch between platforms to verify and purchase safe products.

Solution – Aharix:
Aharix is an Android-based app built with Kotlin and Jetpack Compose that acts as a personalized dietary assistant:

• Automated Health Filter: Users input medical profiles; the app provides real-time alerts if a scanned product conflicts with their health goals.

• Barcode Scanning & Open Food Facts API: Instantly reveals nutritional composition.

• Integrated "Scan-to-Pay": Secure UPI payments via Razorpay and instant bill generation.

• Data Management: Firebase stores user health profiles, while local session management ensures fast performance.

Significance:

1. Bridges nutritional info gap using simplified Green/Red alerts for health risks.

2. Supports chronic disease management by enforcing dietary restrictions at point-of-purchase.

3. Enhances shopping efficiency with a unified scanning, filtering, and payment workflow.

4. Promotes data-driven health awareness through itemized digital bills for tracking purchasing habits.

Future Enhancements:

• AI-based recognition for non-barcoded items using computer vision.

• Predictive health modeling from purchase history.

• Seller-side analytics and USI-based seamless payments.

• BLE-enabled wearable barcode scanners for faster, hands-free shopping.

Conclusion

The development of Aharix demonstrates a successful paradigm shift in how mobile technology can be leveraged to mitigate the rising tide of diet-related non-communicable diseases in India. By integrating the Open Food Facts API, Firebase Cloud Firestore, and the Razorpay payment gateway into a unified Jetpack Compose environment, this research has successfully addressed the \"Information Asymmetry\" prevalent in modern retail. The system effectively transforms a standard smartphone from a passive communication device into a proactive medical screening tool and a secure financial terminal. The core innovation of the Automated Health Filter solves a critical cognitive challenge for health-compromised individuals, allowing for precision dietetics at the point of purchase. Rather than overwhelming users with technical nutritional tables, Aharix provides actionable, personalized intelligence that aligns with the FSSAI\'s \"Eat Right India\" mission. Furthermore, the integration of a full-stack e-commerce workflow — from barcode recognition to UPI-based digital billing — optimizes the retail cycle, proving that health consciousness and consumer convenience are not mutually exclusive. Ultimately, Aharix establishes a benchmark for the next generation of m-Health applications. It proves that when open-source data is synergized with modern Android development practices, it can empower consumers to bypass misleading marketing and make choices that foster long-term behavioural change. As India moves toward a more digitized and health-aware econour, Aharix provides a scalable, data-driven blueprint for a healthier, more transparent future in food consumption.

References

[1] FSSAI (Food Safety and Standards Authority of India). (2025). The Eat Right India Handbook: A guide to implementing food safety and nutrition initiatives. Government of India. https://www.fssai.gov.in/book-details.php?bkid=357 [2] GS1 India. (2024). GS1 Sunrise 2027: Transitioning from 1D to 2D barcodes for retail and supply chain transparency. GS1 Global. https://www.gs1.org/standards/sunrise-2027 [3] Jha, P., & Banu, S. (2025). Artificial intelligence-enabled QR codes in nutrition labelling: A conceptual paper on personalized dietary management. Current Research in Food and Nutrition Journal, 13(3). https://www.foodandnutritionjournal.org/volume13number3/ [4] Open Food Facts. (2025). Open Food Facts API Documentation: Collaborative, free and open database of food products from the entire world. https://wiki.openfoodfacts.org/API [5] PubMed Central. (2025). Food labelling in India: A scoping review of consumer engagement, comprehension, and purchase behaviour. National Library of Medicine. https://pmc.ncbi.nlm.nih.gov/articles/PMC12608109/ [6] PwC India. (2025). PwC’s Voice of the Consumer 2025: India perspective — Evolving consumer preferences towards health, transparency, and digital wellness. PriceWaterhouseCoopers. https://www.pwc.in/industries/retail-and-consumer/pwcs-voice-of-the-consumer-2025.html [7] Razorpay Software Private Limited. (2024). Standard integration for Android: Processing UPI and card payments via Razorpay SDK. Razorpay Documentation. https://razorpay.com/docs/payments/payment-gateway/android-integration/standard/ [8] Sharma, S., et al. (2025). Awareness, perception, and use of Front-of-Pack Nutrition Labels (FOPNLs) among parents of school children: A mixed-method study in South Delhi. International Journal of Nutrition, 12(2), 32-45. [9] Technavio. (2025). India Grocery Market Analysis, Size, and Forecast 2025-2029: Impact of m-Commerce and health consciousness. Market Research Reports. https://www.technavio.com/report/grocery-market-in-india-industry-analysis [10] Android Developers. (2025). Jetpack Compose documentation: Modern toolkit for building native UI. Google. https://developer.android.com/develop/ui/compose/documentation [11] Firebase. (2025). Cloud Firestore documentation: Store and sync data at global scale. Google. https://firebase.google.com/docs/firestore [12] Kotlin Documentation. (2025). Kotlin for Android: The preferred language for modern Android app development. JetBrains. https://kotlinlang.org/docs/android-overview.html

Copyright

Copyright © 2026 Sarang Salunkhe, Ishaan Jog, Shivram Parkhi, Vinayak Rakshe, Mrs. Sonal Jogdand. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.