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

IF 3.1 2区 化学 Q2 CHEMISTRY, ANALYTICAL
Sandeep Kumar, Yujin Oh, Hyemin Jung, Kyung-Sik Ham, Hyun-Jin Kim, Song-Hee Han, Sang-Ho Nam and Yonghoon Lee
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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.

Abstract Image

元素指纹和分子指纹融合用于泡菜原产国准确分类†
商品泡菜产品的产地是其质量、真实性和经济价值的关键指标。在这项研究中,我们提出了一种结合激光诱导击穿光谱(LIBS)和红外光谱(IR)的光谱分类方法来区分韩国和中国的泡菜样品。LIBS基于K、Mg、Na、Ca、C、H和O的发射强度获得元素谱图,IR光谱捕获分子特征。对碳水化合物吸收区(1254 ~ 1018 cm−1)的红外光谱进行主成分分析,发现第三主成分(PC3)的判别性最强。使用k近邻(k-NN)的分类模型通过留一交叉验证进行评估。两个仅使用libs的模型-使用变量集(i) K i (766 nm), O i (777 nm), C i (248 nm)和(ii) K i, O i, Mg ii (279 nm) -实现了94.4%的精度。仅ir模型达到86.4%。融合LIBS和IR特征,优化IR变量权重,增强模型性能。将LIBS变量K I、O I和C I与IR PC3结合可获得最佳结果(准确率为96.8%)。我们还引入了一种统计方法来预测融合的最佳加权因子,通过最小化k-NN中的邻居数量来降低计算复杂度。这种LIBS-IR融合策略为验证泡菜的来源提供了强大的工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
6.20
自引率
26.50%
发文量
228
审稿时长
1.7 months
期刊介绍: Innovative research on the fundamental theory and application of spectrometric techniques.
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