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Comprehensive examination and comparison of machine learning techniques for the quantitative determination of adulterants in honey using Fourier infrared spectroscopy with attenuated total reflectance accessory

Research article (Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy, 2022) · cited 15× · AI/ML
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Comprehensive examination and comparison of machine learning techniques for the quantitative determination of adulterants in honey using Fourier infrared spectroscopy with attenuated total reflectance accessory

Summary

Comprehensive examination and comparison of machine learning techniques for the quantitative determination of adulterants in honey using Fourier infrared spectroscopy with attenuated total reflectance accessory is a scholarly article[1].

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  • Comprehensive examination and comparison of machine learning techniques for the quantitative determination of adulterants in honey using Fourier infrared spectroscopy with attenuated total reflectance accessory's instance of is recorded as scholarly article[2].

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Direct Wikidata claims

  1. [2] ↑ . wikidata.org.

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  1. [1] ↑ . Wikidata. wikidata.org.

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APA 4ort.xyz Knowledge Graph. (2026). Comprehensive examination and comparison of machine learning techniques for the quantitative determination of adulterants in honey using Fourier infrared spectroscopy with attenuated total reflectance accessory. Retrieved May 24, 2026, from https://4ort.xyz/entity/comprehensive-examination-and-comparison-of-machine-learning-techniques-for-the-quantitative-determination-of-adulterant
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BibTeX @misc{4ortxyz_comprehensive-examination-and-comparison-of-machine-learning-techniques-for-the-quantitative-determination-of-adulterant_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{Comprehensive examination and comparison of machine learning techniques for the quantitative determination of adulterants in honey using Fourier infrared spectroscopy with attenuated total reflectance accessory}}, year = {2026}, url = {https://4ort.xyz/entity/comprehensive-examination-and-comparison-of-machine-learning-techniques-for-the-quantitative-determination-of-adulterant}, note = {Accessed: 2026-05-24}}
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