Abstract:
Himalayan Pink Salt, which originates in the Khewra Salt Mine of the Punjabregion of Pakistan, has great cultural and economic significance, attracting over250,000 visitors every year. It is globally marketed as a premium or spa product,very few studies have addressed the possibilities of the application of artificialintelligence to the assessment and grading of various grades of salt. Present saltprocessing techniques have been wanting in the ability to discern between gradesof edible and non-edible salts. Non-dispensable salt can have a pink or crystallinesensory appearance; it may contain heavy metals, insoluble materials, ormicrobiological pollutions that do not become apparent by mere observation. Wehypothesize that a supervised learning system that pairs CNN-based featureextraction with those of Random Forest and SVM classifiers can accuratelypredict the grades of Himalayan Pink Salt. However, lack of raw image datasetsof salts sourced directly from mines limits the validity of the model, highlightingthe importance of orderly data collection to prove the assumption above. Theproposed system derives patterns of adulteration and picks out distinct featuresthat allow precise classification of the edible and non-edible grades, thusproviding pre- screening of the fields, quick sorting process, and minimizedhuman inspections. The envisaged model is trained on adulteration profiles andconstructs new features to enable the appropriate classification of non-edible andedible grades, thereby enabling preliminary assessment of fields, fast-trackingsorting operations, and reducing the reliance on manual analyses. By integratingcultural heritage with high- performance computational methods, the crossdisciplinary research enhances the practice of the safety of trading, qualitycontrol, and demonstration of the promise of authentication systems of theobjects of foods by artificial intelligence
Page(s):
107-107
DOI:
DOI not available
Published:
Journal: 4th International Conference of Sciences “Revamped Scientific Outlook of 21st Century, 2025” , November 12,2025, Volume: 1, Issue: 1, Year: 2025
Keywords:
machine learning
,
Quality Assurance
,
food safety
,
Artificial Intelligence
,
Image classification
,
food authentication
,
Himalayan pink salt