<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>The primary objective of this research was to utilise near-infrared reflectance spectroscopy as a swift, non-destructive method for identifying chlorogenic acid in whole coffee beans. Additionally, this investigation explored the efficacy of different spectral improvement techniques alongside partial least square regression to construct predictive models. NIR spectral data was gleaned from whole coffee beans spanning a wavelength range of 1000–2500 nm, while the chlorogenic acid content was ascertained via high-performance liquid chromatography procedures. Our findings revealed that the highest coefficient of determination reached for chlorogenic acid was 0.97, and the root mean square error for calibration was 0.31% when using the multiplicative scatter correction method. Furthermore, upon testing the model using an external validation dataset, a determination coefficient of 0.91 and a ratio error to range index of 11.56 with a root mean square prediction error at 0.51% was attained. From these results, it can be inferred that the near-infrared technology, coupled with an effective spectral enhancement process, can facilitate quick, non-invasive determination of chlorogenic acid in whole coffee beans.</p>
</abstract>

The primary objective of this research was to utilise near-infrared reflectance spectroscopy as a swift, non-destructive method for identifying chlorogenic acid in whole coffee beans. Additionally, this investigation explored the efficacy of different spectral improvement techniques alongside partial least square regression to construct predictive models. NIR spectral data was gleaned from whole coffee beans spanning a wavelength range of 1000–2500 nm, while the chlorogenic acid content was ascertained via high-performance liquid chromatography procedures. Our findings revealed that the highest coefficient of determination reached for chlorogenic acid was 0.97, and the root mean square error for calibration was 0.31% when using the multiplicative scatter correction method. Furthermore, upon testing the model using an external validation dataset, a determination coefficient of 0.91 and a ratio error to range index of 11.56 with a root mean square prediction error at 0.51% was attained. From these results, it can be inferred that the near-infrared technology, coupled with an effective spectral enhancement process, can facilitate quick, non-invasive determination of chlorogenic acid in whole coffee beans.

Near Infrared Technology Coupled with Different Spectra Correction Approaches for Fast and Non-Destructive Prediction of Chlorogenic Acid on Intact Coffee Beans

Syiah Kuala University, Faculty of Agriculture

Padjadjaran University, Faculty of Agriculture

Andalas University, Faculty of Agricultural Technology

Andalas University, Faculty of Animal Husbandry

Acta Technologica Agriculturae

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

The primary objective of this research was to utilise near-infrared reflectance spectroscopy as a swift, non-destructive method for identifying chlorogenic...

Near Infrared Technology Coupled with Different Spectra Correction Approaches for Fast and Non-Destructive Prediction of Chlorogenic Acid on Intact Coffee Beans

Online veröffentlicht: 24. Feb. 2024

Seitenbereich: 23 - 29

DOI: https://doi.org/10.2478/ata-2024-0004

Schlüsselwörter
NIRS, spectrum, quality, agriculture, chemical properties

© 2024 Agus Arip Munawar et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Near Infrared Technology Coupled with Different Spectra Correction Approaches for Fast and Non-Destructive Prediction of Chlorogenic Acid on Intact Coffee Beans

Online veröffentlicht: 24. Feb. 2024

Seitenbereich: 23 - 29

DOI: https://doi.org/10.2478/ata-2024-0004

SchlüsselwörterNIRS, spectrum, quality, agriculture, chemical properties

© 2024 Agus Arip Munawar et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Schlüsselwörter
NIRS, spectrum, quality, agriculture, chemical properties