Prediction of the rhodinol content in Java citronella oil using NIR spectroscopy in the initial stage developing a spectral smart sensor system - Case report

The rhodinol content is an essential component in determining the citronella oil qualities. This study aimed to develop a model calibrated to predict the rhodinol content in citronella oil using near-infrared (NIR) spectroscopy. This research is the initial stage in developing a spectral smart senso...

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Bibliographic Details
Main Authors: Dedi Wahyudi, Erliza Noor, Dwi Setyaningsih, Taufik Djatna, Irmansyah Irmansyah
Format: Article
Language:English
Published: Czech Academy of Agricultural Sciences 2022-04-01
Series:Research in Agricultural Engineering
Subjects:
Online Access:https://rae.agriculturejournals.cz/artkey/rae-202204-0007_prediction-of-the-rhodinol-content-in-java-citronella-oil-using-nir-spectroscopy-in-the-initial-stage-developin.php
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Summary:The rhodinol content is an essential component in determining the citronella oil qualities. This study aimed to develop a model calibrated to predict the rhodinol content in citronella oil using near-infrared (NIR) spectroscopy. This research is the initial stage in developing a spectral smart sensor system that predicts the rhodinol content of citronella oil in the distillation and fractionating process. Citronella oil samples were scanned by NIRFlex liquid N-500 with a wavelength of 1 000-2 500 nm having an absorbance value (log 1/T). The accuracy of the prediction was achieved using the partial least square (PLS) model. Based on the NIR spectrum at a peak of around 1 620 nm, the rhodinol content in the citronella oil was estimated. The finest model to predict the rhodinol content was y = 0.9874x + 15.6439 with a standard error of the calibration set (SEC) = 2.78%, a standard error of the prediction set (SEP) = 2.88%, a ratio of the performance to the deviation (RPD) = 9.23, a coefficient of variation (CV) = 16.81%, and the correlation coefficient (r) = 0.99. The NIR and PLS models are possible to use for the initial stage in developing a spectral smart sensor system to determine the rhodinol content of citronella oils.
ISSN:1212-9151
1805-9376