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Fusion of elemental and molecular fingerprints for accurate classification of kimchi by country of origin

Authors
Kumar, SandeepOh, YujinJung, HyeminHam, Kyung-SikKim, Hyun-JinHan, Song-HeeNam, Sang-HoLee, Yonghoon
Issue Date
Jul-2025
Publisher
Royal Society of Chemistry
Keywords
Binary Alloys; Infrared Spectroscopy; Nearest Neighbor Search; Spectroscopic Analysis; Classification Methods; Economic Values; Geographical Origins; Infrared: Spectroscopy; Key Indicator; Laserinduced Breakdown Spectroscopy (libs); Molecular Fingerprint; Nearest-neighbour; Quality Value; Spectroscopy:spectroscopy; Principal Component Analysis
Citation
Journal of Analytical Atomic Spectrometry, v.40, no.8, pp 2222 - 2231
Pages
10
Indexed
SCIE
SCOPUS
Journal Title
Journal of Analytical Atomic Spectrometry
Volume
40
Number
8
Start Page
2222
End Page
2231
URI
https://scholarworks.gnu.ac.kr/handle/sw.gnu/79563
DOI
10.1039/d5ja00200a
ISSN
0267-9477
1364-5544
Abstract
The geographical origin of commercial kimchi products is a key indicator of their quality, authenticity, and economic value. In this study, we propose a spectroscopic classification method combining laser-induced breakdown spectroscopy (LIBS) and infrared (IR) spectroscopy to differentiate kimchi samples from South Korea and China. LIBS was used to obtain elemental profiles based on the emission intensities of K, Mg, Na, Ca, C, H, and O, while IR spectroscopy captured molecular features. Principal component analysis of IR spectra in the carbohydrate absorption region (1254-1018 cm-1) identified the third principal component (PC3) as the most discriminative. Classification models using k-nearest neighbors (k-NN) were evaluated with leave-one-out cross-validation. Two LIBS-only models-using variable sets (i) K I (766 nm), O I (777 nm), C I (248 nm), and (ii) K I, O I, Mg II (279 nm)-achieved 94.4% accuracy. The IR-only model reached 86.4%. Fusion of LIBS and IR features, with optimized weighting for the IR variable, enhanced model performance. The best result (96.8% accuracy) was achieved by combining LIBS variables K I, O I, and C I with IR PC3. We also introduce a statistical method to predict the optimal weighting factor for fusion, reducing computational complexity by minimizing the number of neighbors in k-NN. This LIBS-IR fusion strategy provides a robust tool for verifying kimchi origin.
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