Liberica coffee is a popular one in the global trade market beside Arabica and Robusta. Moisture content (MC) is one of the most important quality parameters of green coffee beans production and trading. NIR spectroscopy is one of the alternative methods for the rapid determination of chemical compounds in the products non-destructively. The purposes of this study are to determine the best calibration model, data transformation, and data pretreatment for moisture content of Liberica coffee. The ground coffee samples were measured by FT-NIRS in the wavelength of 1000-2500 nm, properly the moisture contents of the same samples were determined by the oven method. The obtained spectrums were transformed into absorbance (Log 1/R) and Kubelka-Munk (K/S) units. Data pretreatment, such as standard normal variance (SNV), second derivative (dg2), and multivariate calibration method such as a partial least square (PLS), were carried out to develop the best calibration model. Good accuracy for the prediction of moisture content of Liberica coffee green bean was obtained from the spectral data pretreated with dg2 and Kubelka-Munk(K/S) data transformation with the statistical evaluation values of r (0.87), RPD (2.05), consistency (99%) and CV (5.76 %)
<p>Liberica is one of coffee species that is becoming popular and increasingly in demand in present days due to its unique characteristics. Caffeine is one of the important coffee quality parameter which determines the coffee flavor, consumer preference and market price. Caffeine content is usually analyzed by chemical method which is destructive, time consuming, expensive and involving a lot of procedures. NIR Spectroscopy is one of the non-destructive techniques to overcome these disadvantages. This study was conducted at the Department of Mechanical and Biosystem Engineering, IPB University for NIR measurement and the Center of Agro-based Industry (BBIA), Bogor for chemical analysis from August to November 2019. The study aimed to determine the best calibration model for the prediction of caffeine content in Liberica coffee green bean powder. In this study, FT-NIRS in the wavelength of 1000-2500 nm was used for NIR measurement and HPLC tool was used for chemical analysis. Kubelka-Munk (K/S) and Absorbance (Log 1/R) were used as data transformation, whereas Standard Normal Variance (SNV) and Second derivative of Savitzky-Golay (dg2) as data pretreatment. In addition, Partial Least Square (PLS) and Multiple Linear Regression (MLR) were applied for multivariate calibration method. The best calibration model for the prediction of caffeine content of Liberica coffee green bean powder was obtained by the spectral data pretreated with second derivative of Savitzky-Golay (dg2) and Kubelka-Munk data transformation using PLS calibration method with the results of r = 0.90, RPD = 2.24, CV = 2.01%.</p>
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.