Authentication of apples (Malus × domestica borkh.) according to geographical origin, variety and production method using 1H NMR spectroscopy and random forest

Wenck, Soeren; Bachmann, René; Barmbold, Sarah-Marie; Horns, Anna Lena; Paasch, Nele; Seifert, Stephan

doi:10.1016/j.foodcont.2024.110817

Food Control

2025

DOI: 10.1016/j.foodcont.2024.110817

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Authentication of apples (Malus × domestica borkh.) according to geographical origin, variety and production method using 1H NMR spectroscopy and random forest

Soeren Wenck,

René Bachmann,

Sarah-Marie Barmbold

et al.

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2025

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Cited by 4 publications

References 52 publications

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Detection technologies, and machine learning in food: Recent advances and future trends

He,

Huang,

Wang

2024

Food Bioscience

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Detection technologies, and machine learning in food: Recent advances and future trends

He,

Huang,

Wang

2024

Food Bioscience

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Synergistic approaches of AI and NMR in enhancing food component analysis: A comprehensive review

Cao,

Li,

Ding

et al. 2025

Trends in Food Science & Technology

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Food Authentication Goes Green: Method Optimization for Origin Discrimination of Apples Using Apple Juice and ICP-MS

Müller,

Oest,

Scheffler

et al. 2024

Foods

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Apples are among the most important fruits worldwide and the most consumed fruit in Germany. Due to higher energy and personnel costs, domestic apples are more expensive and thus offer an incentive for mixing with foreign goods. Moreover, imported apples have a higher carbon footprint, which is an obstacle regarding sales in times of climate change. Not only the transport of the goods but also the analysis influences the carbon footprint. Inductively coupled plasma mass spectrometry (ICP-MS) is a powerful tool for origin discrimination. In this study, 85 apple juice samples were analyzed, whereby sample preparation for ICP-MS was optimized by eliminating the freeze-drying step and thereby reducing CO2 emissions. The CO2 emission was lowered by around 97%. The optimized method was applied to 272 apple juice samples from seven countries to create models for origin determination. The differentiation of European and non-European apples provided an accuracy of 90.9% ± 2.4%. German samples can be differentiated from other countries with an accuracy of 83.2% ± 1.4%. The regional differentiation of German samples (north vs. south) achieved an accuracy of 92.3% ± 5.4%. The results show that the optimized ICP-MS method, in which freeze-drying is not required is well suited for determining the origin of apples from apple juice.

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Authentication of apples (Malus × domestica borkh.) according to geographical origin, variety and production method using 1H NMR spectroscopy and random forest

Cited by 4 publications

References 52 publications

Detection technologies, and machine learning in food: Recent advances and future trends

Detection technologies, and machine learning in food: Recent advances and future trends

Synergistic approaches of AI and NMR in enhancing food component analysis: A comprehensive review

Food Authentication Goes Green: Method Optimization for Origin Discrimination of Apples Using Apple Juice and ICP-MS

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