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基于 LIBS 的元素分析与近红外分子指纹图谱的协同组合,增强对豆瓣酱地理来源的区分。

Cooperative combination of LIBS-based elemental analysis and near-infrared molecular fingerprinting for enhanced discrimination of geographical origin of soybean paste.

机构信息

Department of Chemistry and Research Institute for Convergence of Basic Science, Hanyang University, Seoul 04763, Republic of Korea.

Plasma Spectroscopy Analysis Center, Mokpo National University, Jeonnam 58554, Republic of Korea.

出版信息

Food Chem. 2023 Jan 15;399:133956. doi: 10.1016/j.foodchem.2022.133956. Epub 2022 Aug 17.

Abstract

Laser-induced breakdown spectroscopy (LIBS) and near-infrared (NIR) spectroscopy were combined to enhance discrimination of soybean paste samples according to geographical origin. Since element and organic component compositions of soybean pastes depend on soybean cultivation areas and fermentation conditions, utilization of two complementary spectroscopic signatures would be synergetic for the discrimination. When the areas of C (A) and Ca (A) peaks in the LIBS spectra were used as the inputs for linear discriminant analysis, the accuracy was 95.4%. The accuracy became 92.1%, when the principal component (PC) scores obtained by principal component analysis of the NIR spectra were employed. To enhance NIR discrimination, two-trace two-dimensional (2T2D) correlation analysis was adopted to recognize minute spectral differences. With using the 1st/2nd PC scores of 2T2D slice spectra, accuracy increased to 95.0%. When the ratios of A/A and the 2nd PC scores of the samples were combined together, the accuracy improved to 99.6%.

摘要

激光诱导击穿光谱(LIBS)和近红外(NIR)光谱相结合,以增强根据地理来源对豆瓣酱样品的区分。由于豆瓣酱的元素和有机成分组成取决于大豆种植区和发酵条件,因此利用两种互补的光谱特征进行区分将具有协同作用。当 LIBS 光谱中 C(A)和 Ca(A)峰的面积用作线性判别分析的输入时,准确率为 95.4%。当使用近红外光谱主成分分析得到的主成分(PC)得分时,准确率为 92.1%。为了增强近红外区分,采用二维相关分析(2T2D)来识别微小的光谱差异。使用 2T2D 切片光谱的 1st/2nd PC 得分,准确率提高到 95.0%。当将 A/A 的比值和样本的第 2 个 PC 得分结合起来时,准确率提高到 99.6%。

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