Construction of a sustainable model to predict the moisture content of porang powder (Amorphophallus oncophyllus) based on pointed-scan visible near-infrared spectroscopy

Amanah, Hanim Zuhrotul and Rahayoe, Sri and Harmayani, Eni and Putra Hernanda, Reza Adhitama and Khoirunnisaa, Khoirunnisaa and Rohmat, Siti Rohmat and Lee, Hoonsoo (2024) Construction of a sustainable model to predict the moisture content of porang powder (Amorphophallus oncophyllus) based on pointed-scan visible near-infrared spectroscopy. Open Agriculture, 9 (1): 20220268. pp. 1-12. ISSN 23919531

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Abstract

The moisture content of porang powder (PP) is an inherent quality parameter. Therefore, several analytical methods, such as oven drying and Karl–Fischer titration, were applied to determine the content. However, these techniques are noted to have various disadvantages, such as being time-consuming, requiring sample preparation, being labor-intensive, and producing chemical waste. This study aims to investigate the potential of visible nearinfrared (Vis-NIR) spectroscopy as a nondestructive and sustainable analytical technology to predict moisture content in PP. In this study, we developed a traditional machine learning algorithm, a partial least squares regression (PLSR), in tandem with two spectral bands, which are Vis-NIR (400–1,000 nm) and NIR (954–1,700 nm). To upgrade the performance of PLSR, we applied seven preprocessing techniques: mean normalization, maximum normalization, range normalization, multiplicative scatter correction, standard normal variate (SNV), and Savitzky–Golay first and second derivatives. We found that PLSR using NIR spectral bands was more effective; the preprocessed mean normalization exhibited the best results with a coefficient of determination ( )Rp 2 of 0.96 and a standard error prediction (SEP) of 0.56 using five latent variables. Furthermore, we also extracted 39 optimum wavelengths using variable importance in projection and achieved better performance (Rp 2 = 0.95, SEP = 0.56%wb, and 5 LVs) via SNV preprocessed NIR spectra.

Item Type: Article
Uncontrolled Keywords: moisture content, machine learning, partial least squares regression, spectroscopy, porang powder
Subjects: S Agriculture > S Agriculture (General)
Divisions: Faculty of Agricultural Technology > Food and Agricultural Product Technology
Depositing User: Diah Ari Damayanti
Date Deposited: 29 Apr 2025 08:44
Last Modified: 29 Apr 2025 08:44
URI: https://ir.lib.ugm.ac.id/id/eprint/16184

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