High-accuracy and fast determination of chromium content in rice leaves based on collinear dual-pulse laser-induced breakdown spectroscopy and chemometric methods. (15th October 2019)

• Record Type: Journal Article
• Authors: Peng, Jiyu; He, Yong; Jiang, Jiandong; Zhao, Zhangfeng; Zhou, Fei; Liu, Fei
• Is Part Of: Food chemistry. Volume 295(2019)
• Page Start: 327
• Page End: 333
• Publication Date: 2019-10-15
• Subjects: Chromium content -- Dual pulse laser-induced breakdown spectroscopy -- Multivariate analysis -- Rice -- Univariate analysis; Food -- Analysis -- Periodicals; Food -- Composition -- Periodicals; 664
• DOI: 10.1016/j.foodchem.2019.05.119
• Languages: English
• ISSNs: 0308-8146

Abstract

Highlights: Inter-pulse delay time and energy ratio in DPLIBS were investigated and optimized. Regression coefficient in PLS model was successfully used to select feature variables. Support vector machine model based on feature variables performed best. Chromium content in rice leaves was successfully predicted by LIBS. Abstract: Dual-pulse laser-induced breakdown spectroscopy (DPLIBS) and chemometric methods were used to predict chromium content in rice leaves, along with the purpose for increasing the detection sensitivity and accuracy. The influence of important parameters in DPLIBS were investigated and optimized. Then, partial least square (PLS) was used to establish chromium content prediction models, and the value of regression coefficient based on PLS was applied to determine feature variables. In addition, multivariate and univariate analysis were used to verify the modeling performance of selected feature variables. The results indicated that support vector machine model based on feature variables achieved the best performance, with correlation coefficient of 0.9946, root mean square error of 4.85 mg/kg and residual predictive deviation of 9.70 in prediction set. The proposed method provides a high-accuracy and fast approach for chromium content prediction in rice leaves, which could potentially be used for toxic and nutrient elements detection in food.