Milk adulteration identification using hyperspectral imaging and machine learning

Muhammad Aqeel; Ahmed Sohaib; Muhammad Iqbal; Syed Sajid Ullah

doi:10.3168/jds.2024-25635

November 8, 2024Journal of Dairy ScienceOpen Access

Milk adulteration identification using hyperspectral imaging and machine learning

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Key Points

To develop and evaluate a multiclass machine learning pipeline coupled with hyperspectral imaging for the automated detection and classification of milk adulterants.
Designed a multiclass classification framework incorporating hyperspectral imaging data to identify milk adulterant behavior.
Evaluated model performance against existing state-of-the-art methods using validation accuracy, Cohen's kappa, and Matthews correlation coefficient metrics.
Achieved a validation accuracy of 100% in classifying milk adulteration.
Outperformed numerous state-of-the-art approaches based on score, kappa, and Matthews correlation coefficient performance metrics.

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Implication

Experimental study demonstrates 100% validation accuracy in identifying milk adulterants using hyperspectral imaging and machine learning, highlighting a reliable pipeline for food quality control.

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Cite This Study

Aqeel et al. (2024) studied this question.

synapsesocial.com/papers/6a038157252f82193957d50f https://doi.org/10.3168/jds.2024-25635

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Milk adulteration identification using hyperspectral imaging and machine learning

Key Points

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Implication

Abstract

Discussion

Cite This Study

Milk adulteration identification using hyperspectral imaging and machine learning

Key Points

Ask about this paper

Implication

Abstract

Discussion

Cite This Study

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