Time-resolved spectral-image laser-induced breakdown spectroscopy for precise qualitative and quantitative analysis of milk powder quality by fully excavating the matrix information

Zhang, Deng; Nie, Junfei; Niu, Xuechen; Chen, Feng; Hu, Zhenlin; Wen, Xuelin; Li, Yuqiong; Guo, Lianbo

doi:10.1016/j.foodchem.2022.132763

Cited by 12 publications

(2 citation statements)

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“…It has been praised as the “future super star” in analytical chemistry with the advantages of multi-element analysis, fast response, remote detection and no or simple preparation [ 22 ]. In recent years, LIBS has been gradually applied to the field of food detection, including monitoring calcium content in comminuted poultry meat [ 23 ], adulteration detection of milk powder [ 24 , 25 ], and classification of red wine based on its protected designation of origin (PDO) [ 26 ]. However, few LIBS works were reported on the quality detection of brown rice products.…”

Section: Introductionmentioning

confidence: 99%

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Time-Resolved Laser-Induced Breakdown Spectroscopy for Accurate Qualitative and Quantitative Analysis of Brown Rice Flour Adulteration

Shi

Deng

et al. 2022

Foods

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To solve the adulteration problem of brown rice flour in the commodity market, a novel, accurate, and stable detection method based on time-resolved laser-induced breakdown spectroscopy (TR-LIBS) is proposed. Qualitative and quantitative analysis was used to detect five adulterants and seven different adulterant ratios in brown rice flour. Being able to excavate more information from plasma by obtaining time-resolved spectra, TR-LIBS has a stronger performance, which has been further verified by experiments. For the qualitative analysis of adulterants, the traditional machine learning models based on TR-LIBS, linear discriminant analysis (LDA), naïve Bayes (NB) and support vector machine (SVM) have significantly better classification accuracy than those based on traditional LIBS, increasing by 3–11%. The deep learning classification model based on TR-LIBS also achieved the same results, with an accuracy increase of more than 8%. For the quantitative analysis of the adulteration ratio, compared with traditional LIBS, the quantitative model based on TR-LIBS reduces the limit of detection (LOD) of five adulterants from about 8–51% to 4–19%, which effectively improves the quantitative detection performance. Moreover, t-SNE visualization proved that there were more obvious boundaries between different types of samples based on TR-LIBS. These results demonstrate the great prospect of TR-LIBS in the identification of brown rice flour adulteration.

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Section: Introductionmentioning

confidence: 99%

“…In this work, a new simplified time-resolved LIBS (TR-LIBS) was developed for accurate qualitative and quantitative analysis of brown rice flour adulteration based on [ 25 ]. TR-LIBS technology does not require sample digestion.…”

Section: Introductionmentioning

confidence: 99%