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Updated: Feb 5, 2026

Cross-Modal Multivariate Pattern Analysis
Published on: November 9, 2011
Elemental fingerprinting and regional verification of Pakistani basmati and non-basmati rice varieties using ICP-MS
Syed Abdul Wadood1, Hanyi Mei2, Chunlin Li2
1School of Food Science and Pharmaceutical Engineering, Nanjing Normal University, Nanjing, China; State Key Laboratory for Quality and Safety of Agro-products, Institute of Agro-Products Safety and Nutrition, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, China; GreenTown Agricultural Testing Technology, Co., Ltd, Hangzhou, China; Department of Nutrition and Health Promotion, University of Home Economics Lahore, Pakistan.
Abstract:
This study investigates the elemental composition of Basmati and non-Basmati rice cultivars grown across five different regions in Punjab, Pakistan using inductively coupled plasma mass spectrometry (ICP-MS). Distinct cultivar and regional variations in elemental concentrations were observed, with twelve key elements identified as regional classifiers. Basmati rice exhibited higher accumulation of essential macro and trace elements compared to non-Basmati varieties. Supervised classification methods (LDA) accurately separated rice cultivars and successfully classified their geographical origins with high accuracy. Environmental factors, including sunshine, precipitation, and temperature exhibited positive correlations with several elements, highlighting the influence of climate on nutrient distribution in rice grains. The spatial distribution of elements was heavily influenced by geographic location, reflecting distinct geochemical patterns for Basmati and non-Basmati growing zones. This comprehensive elemental fingerprinting approach provides a robust tool for rice cultivar discrimination and regional origin authentication, supporting efforts in food quality control and traceability of premium Pakistani Basmati rice.
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