AI Article Synopsis
- High-priced Basmati rice is often mislabelled to boost profits, risking consumer trust and brand reputation.
- Researchers collected 36 Basmati rice samples from two areas in Punjab, Pakistan, to differentiate between high-quality and inferior products using advanced testing methods.
- Multivariate data analysis techniques successfully clustered samples by their growing regions, and partial least squares-discriminant analysis proved effective in grouping and identifying unknown samples, highlighting the need for improved methods to trace rice origins.
Article Abstract
High-priced Basmati rice is vulnerable to deliberate mislabeling to increase profits. This type of fraud may lower consumers' confidence as inferior products can affect brand reputation. To address this problem, there is a need to devise a method that can efficiently distinguish Basmati rice grown in regions that are famous versus the regions that are not suitable for their production. Therefore, in this investigation, thirty-six samples of Basmati rice were collected from two zones of Punjab province (one known for Basmati rice) of Pakistan which is the major producer of Basmati rice. The elemental composition of rice samples was assessed using inductively coupled plasma-optical emission spectrometry and an organic elemental analyzer, whereas data on δC was acquired using isotopic ratio-mass spectrometry. Regional clustering of samples based on their respective cultivation zones was observed using multivariate data analysis techniques. Partial least squares-discriminant analysis was found to be effective in grouping rice samples from the different locations and identifying unknown samples belonging to these two regions. Further recommendations are presented to develop a better model for tracing the origin of unidentified rice samples.
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| http://dx.doi.org/10.1016/j.foodchem.2024.138549 | DOI Listing |
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