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This is a Experimental Study study (also classified as: Quantitative Study).

September 24, 2025Lebensmittelchemie

Evaluation of innovative tools for the rapid and accurate authentication of expensive and highly used food ingredients

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The developed NIR method achieved high accuracy in detecting adulteration in black pepper samples, ranging from 5% to 30%.
Analysis using DART‐QTOF‐MS showed a rapid authentication of black pepper at 5 seconds per sample, indicating significant time efficiency.
Multivariate statistical analysis and machine learning classifiers were utilized to enhance the accuracy of black pepper authentication.
A local web application was created to streamline data processing for statistical assessment, addressing time inefficiencies in non-targeted methods.

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Implication

Evaluation demonstrates high detection rates for adulteration in black pepper using NIR and statistical analysis, highlighting novel applications of machine learning.

Cite This Study

Diederich et al. (2025) studied this question.

synapsesocial.com/papers/68d6d8768b2b6861e4c3e978 https://doi.org/10.1002/lemi.202559082

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Implication

Evaluation demonstrates high detection rates for adulteration in black pepper using NIR and statistical analysis, highlighting novel applications of machine learning.

Evaluation of innovative tools for the rapid and accurate authentication of expensive and highly used food ingredients

Key Points

Discussion

Implication

Cite This Study

Evaluation of innovative tools for the rapid and accurate authentication of expensive and highly used food ingredients

Key Points

Discussion

Implication

Cite This Study

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