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Natural plant extracts for quality assessment in milk by replacing conventional color reduction methods

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submitted on 2025-10-21, 11:34 and posted on 2025-10-21, 11:36 authored by Mizaj Shabil Sha, Farsana Bin Faisal, Thasneem Khalid Ottakath, Nihal Maliekkal Poovadichalil, Kishor Kumar Sadasivuni
<p dir="ltr">Our study proposes a novel approach using aqueous extracts of plant material<i> Ruellia simplex</i> to screen milk for microbiological quality rapidly. We tested plant extracts for colour changes at various pH levels, with different dyes as the control. The differential pH method was used to assess milk freshness using plant extracts. The <i>Ruellia simplex</i>flower extract was tested with its pH-dependent colour shift (6; purple to 1; dark pink). The <i>Ruellia simplex</i> extract has an anthocyanin content of 0.609 g/ml. The approach demonstrated great linearity (R<sup>2</sup> = 0.978), 100% accuracy, and reproducibility. The limits of detection and quantification were 0.027 and 0.082 g/mL, respectively. This technology has the potential to revolutionise milk quality testing by providing a cost-effective and efficient alternative to traditional dye-based procedures, especially in rural and remote locations. Its application in intelligent food packaging has the potential to alter dairy sector practices internationally by allowing for accessible and real-time quality monitoring.</p><h2>Other Information</h2><p dir="ltr">Published in: Journal of Food Measurement and Characterization<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1007/s11694-025-03226-5" target="_blank">https://dx.doi.org/10.1007/s11694-025-03226-5</a></p>

Funding

Open Access funding provided by the Qatar National Library.

Qatar National Research Fund (ME03-1226-210042), Developing highly productive nutrition framework for poultry via synergetic fusion of natural bioactive supplement, smart health monitoring and machine learning dietary prediction approach.

History

Language

  • English

Publisher

Springer Nature

Publication Year

  • 2025

License statement

This Item is licensed under the Creative Commons Attribution 4.0 International License.

Institution affiliated with

  • Qatar University
  • Center for Advanced Materials - QU
  • College of Engineering - QU

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