A Study on “Clean” in Beauty: A Machine LearningApproach to Ingredient Transparency and ConsumerTrust

Year : 2026 | Volume : 03 | Issue : 01 | Page : 1 12
    By

    Heena Tajoddin Shaikh,

  • Kazi Kutubuddin Sayyad Liyakat,

  1. Assistant Professor, E & TC Engg, BMIT, Solapur, Maharashtra, India
  2. Professor, E & TC Engg, BMIT, Solapur, Maharashtra, India

Abstract

The burgeoning “clean beauty” market, while driven by consumer demand for safer and more sustainable products, is plagued by ambiguous definitions and the pervasive challenge of “greenwashing”. This ambiguity hinders informed consumer choices and complicates brand authenticity. This study addresses these complexities by developing a novel machine learning (ML) framework designed to objectively analyze cosmetic ingredient lists, classify products based on their “cleanliness” profile, and identify key ingredient attributes that correlate with consumer perception and scientific safety. Leveraging extensive datasets comprising ingredient databases, scientific literature on toxicology and allergens, and public regulatory guidelines, our approach utilized natural language processing (NLP) for efficient ingredient parsing and feature extraction. Various supervised learning models (e.g., Random Forest, Gradient Boosting) were then trained to predict a multi-faceted “cleanliness score” or categorical classification. The results demonstrate superior accuracy in objectively categorizing products, not only distinguishing between “clean” and “non- clean” formulations but also identifying commonly used “cleanwashing” ingredients. Furthermore, the ML models uncovered latent correlations between ingredient profiles and consumer-perceived “cleanliness” derived from sentiment analysis of product reviews, highlighting a significant alignment potential. This research offers a robust, data-driven approach to demystify clean beauty, empowering consumers with greater transparency, guiding brands toward authentic product development, and informing regulatory efforts to standardize health and environmental claims in the cosmetic industry.

Keywords: Clean Beauty, Cosmetic Ingredient, cleanliness, ingredient attributes, cleanliness score, greenwashing

[This article belongs to Recent Trends in Cosmetics ]

How to cite this article:
Heena Tajoddin Shaikh, Kazi Kutubuddin Sayyad Liyakat. A Study on “Clean” in Beauty: A Machine LearningApproach to Ingredient Transparency and ConsumerTrust. Recent Trends in Cosmetics. 2026; 03(01):1-12.
How to cite this URL:
Heena Tajoddin Shaikh, Kazi Kutubuddin Sayyad Liyakat. A Study on “Clean” in Beauty: A Machine LearningApproach to Ingredient Transparency and ConsumerTrust. Recent Trends in Cosmetics. 2026; 03(01):1-12. Available from: https://journals.stmjournals.com/rtc/article=2026/view=238294


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Regular Issue Subscription Review Article
Volume 03
Issue 01
Received 22/10/2025
Accepted 24/10/2025
Published 20/01/2026
Publication Time 90 Days
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