Non-Destructive Moisture Content Prediction Model for Corn Starch Based on Near-Infrared Spectroscopy and Chemometrics

Stella Maria Dyah Cahyarani; Dhevika Aji Nugraha; Reza Adhitama Putra Hernanda; Hoonsoo Lee; Hanim Zuhrotul Amanah

doi:10.29303/jrpb.v14i1.1225

Non-Destructive Moisture Content Prediction Model for Corn Starch Based on Near-Infrared Spectroscopy and Chemometrics

Authors

Stella Maria Dyah Cahyarani , Dhevika Aji Nugraha , Reza Adhitama Putra Hernanda , Hoonsoo Lee , Hanim Zuhrotul Amanah

DOI:

10.29303/jrpb.v14i1.1225

Published:

2026-03-26

Issue:

Vol. 14 No. 1 (2026): Jurnal Ilmiah Rekayasa Pertanian dan Biosistem

Keywords:

corn starch, moisture content, near-infrared spectroscopy, machine learning, deep learning

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Cahyarani, S. M. D., Aji Nugraha, D., Adhitama Putra Hernanda, R., Lee, H., & Zuhrotul Amanah, H. (2026). Non-Destructive Moisture Content Prediction Model for Corn Starch Based on Near-Infrared Spectroscopy and Chemometrics. Jurnal Ilmiah Rekayasa Pertanian Dan Biosistem, 14(1), 1–16. https://doi.org/10.29303/jrpb.v14i1.1225

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Abstract

Moisture content is a critical quality attribute of corn starch that affects shelf life, functional performance, and commercial value. This study developed and externally validated a rapid and non-destructive method to quantify corn starch moisture using near-infrared (NIR) spectroscopy and chemometric/machine-learning regression. Commercial corn starch was conditioned at approximately 76% relative humidity (saturated NaCl) for 20 days to generate moisture variability, and spectra were acquired using a SpectraStar XT-R instrument (900-2200 nm). Three spectral pre-processing strategies (MSC, SNV, and Savitzky-Golay first derivative) were evaluated prior to model development. A total of 951 samples were split by stratified sampling into calibration (70%, n = 666) and independent prediction (30%, n = 285) sets. Three models were compared: partial least squares regression (PLSR), support vector regression optimized by particle swarm optimization (SVR-PSO), and a one-dimensional convolutional neural network (1D-CNN). The best performance was achieved by PLSR with SNV (R²_p = 0.929, RMSE_p = 0.274%, RPD = 3.755), while SVR-PSO with MSC showed comparable accuracy (R²_p = 0.929, RMSE_p = 0.273%, RPD = 3.762). The 1D-CNN yielded lower predictive performance (best R²_p = 0.841). Overall, NIR spectroscopy combined with optimized pre-processing and conventional regression models provides an accurate alternative to gravimetric drying for quality control of corn starch.

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Non-Destructive Moisture Content Prediction Model for Corn Starch Based on Near-Infrared Spectroscopy and Chemometrics

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