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All Journal Jurnal Ilmiah Rekayasa Pertanian dan Biosistem Jurnal Teknik Pertanian Lampung (Journal of Agricultural Engineering)
Reza Adhitama Putra Hernanda
Chungbuk National University
Agriculture, Biological Sciences & Forestry Engineering

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Rapid Detection of Dragon Fruit Peel Powder Adulteration by Vis-NIR and SW-NIR Spectroscopy with PLSR Model Nadya Hafidzatun Nisa; Rudiati Evi Masithoh; Muhammad Fahri Reza Pahlawan; Hanim Zuhrotul Amanah; Reza Adhitama Putra Hernanda
Jurnal Teknik Pertanian Lampung (Journal of Agricultural Engineering) Vol. 15 No. 3 (2026): June 2026
Publisher : The University of Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jtepl.v15i3.991-1006

Abstract

An important factor in choosing a food product is its quality and safety. Meanwhile, visual aspects are a benchmark for product acceptance. Dragon fruit peel powder (DFP) has excellent potential as a natural food coloring. This study aims to detect adulteration in dragon fruit peel powder using two spectroscopy techniques: Visible-Near Infrared (Vis-NIR) and Shortwave-Near Infrared (SW-NIR) spectroscopy. The adulterants include purple sweet potato flour (PP), erythrosine dye powder (ER), and remazol textile dye powder (TX) with varying concentrations of 0%, 0.5%, 1%, 5%, 10%, 20%, 30%, 40%, 50%, and 100%. Partial least squares regression (PLSR) with ten spectral preprocessing methods was used to analyze data and assess model performance. The results show that combining of spectroscopy with the PLSR model significantly improves accuracy, achieving R²P values above 0.92 for all adulterants. These findings highlight Vis-NIR and SW-NIR spectroscopy combined with PLSR modeling, as rapid, non-destructive tools. Vis-NIR spectroscopy proved superior to SW-NIR spectroscopy in detecting food colorant adulteration because of its sensitivity to color pigments.
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
Jurnal Ilmiah Rekayasa Pertanian dan Biosistem Vol 14 No 1 (2026): Jurnal Ilmiah Rekayasa Pertanian dan Biosistem
Publisher : Fakultas Teknologi Pangan & Agroindustri (Fatepa) Universitas Mataram dan Perhimpunan Teknik Pertanian (PERTETA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jrpb.v14i1.1225

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 (R2p = 0.929, RMSEp = 0.274%, RPD = 3.755), while SVR-PSO with MSC showed comparable accuracy (R2p = 0.929, RMSEp = 0.273%, RPD = 3.762). The 1D-CNN yielded lower predictive performance (best R2p = 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.
Co-Authors Dhevika Aji Nugraha Hanim Zuhrotul Amanah Hoonsoo Lee Muhammad Fahri Reza Pahlawan Nadya Hafidzatun Nisa Rudiati Evi Masithoh Stella Maria Dyah Cahyarani