Quantitative analysis of chromium in pork by PSO-SVM chemometrics based on laser induced breakdown spectroscopy

Chen, Tianbing; Zhang, Linying; Huang, Lin; Liu, Muhua; Chen, Jinyin; Yao, Mingyin

doi:10.1039/c8ja00392k

Cited by 18 publications

(7 citation statements)

References 27 publications

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“…Suitable values of c and g are helpful to improve the accuracy of an SVM classification model. The purpose to the particle swarm optimization (PSO) algorithm is to find these suitable values using the training samples.The PSO algorithm has the characteristics of fast convergence, good solution quality, and good robustness for multidimensional spatial functions or dynamic target problems [ 37 ]. In the PSO algorithm, the position of each particle contains a pair of SVM parameters, c and g , and the purpose of the procedure for optimization is to find the most suitable position that maximizes the classification accuracy of the training samples.…”

Section: Methodsmentioning

confidence: 99%

Classification of Tea Quality Levels Using Near-Infrared Spectroscopy Based on CLPSO-SVM

Ding

Yan

et al. 2022

Foods

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In this paper, we propose a method for classifying tea quality levels based on near-infrared spectroscopy. Firstly, the absorbance spectra of Huangshan Maofeng tea samples were obtained in a wavenumber range of 10,000~4000 cm−1 using near-infrared spectroscopy. The spectral data were then converted to transmittance and smoothed using the Savitzky–Golay (SG) algorithm. The denoised transmittance spectra were dimensionally reduced using principal component analysis (PCA). The characteristic variables obtained using PCA were used as the input variables and the tea level was used as the output to establish a support vector machine (SVM) classification model. The penalty factor c and the kernel function parameter g in the SVM model were optimized using particle swarm optimization (PSO) and comprehensive-learning particle swarm optimization (CLPSO) algorithms. The final experimental results show that the CLPSO-SVM method had the best classification performance, and the classification accuracy reached 99.17%.

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Section: Methodsmentioning

confidence: 99%

Classification of Tea Quality Levels Using Near-Infrared Spectroscopy Based on CLPSO-SVM

Ding

Yan

et al. 2022

Foods

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“…After cutting, the slices of beef were soaked into Cd and Cr gradient solutions for 12 h, respectively, to ensure homogeneous Cd and Cr distribution inside the beef slices. This method of soaking meat into standard element solutions has been reported in many LIBS works. , …”

Section: Methodsmentioning

confidence: 99%

“…This method of soaking meat into standard element solutions has been reported in many LIBS works. 17,18 Synthesis and Characterization of the NE-Ag Needle. The preparation of the NE-Ag needle sensor included three steps: 19 (1) The Ag needles were put into ethanol, acetone, and water, respectively, for 10 min of ultrasonication to eliminate surface contaminants.…”

Section: ■ Experimental Sectionmentioning

confidence: 99%

Insertable, Scabbarded, and Nanoetched Silver Needle Sensor for Hazardous Element Depth Profiling by Laser-Induced Breakdown Spectroscopy

et al. 2022

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Sensing of hazardous metals is urgent in many areas (e.g., water pollution and meat products) as heavy metals threaten people’s health. Laser-induced breakdown spectroscopy (LIBS), as a rapid, in situ, and multielemental analytical technique, has been widely utilized in rapid hazardous heavy metal sensing. However, loose and water-containing samples (e.g., meat, plant, and soil) are hard to analyze by LIBS directly, and heavy metal depth profiling for bulk samples remains suspenseful. Here, inspired by the Needle, the sword of Arya Stark in Game of Thrones, we propose an insertable, scabbarded, and nanoetched silver (NE-Ag) needle sensor for rapid hazardous element sensing and depth profiling. The NE-Ag needle sensor features a micro–nanostructure surface for inserting into the bulk sample and absorbing hazardous analytes. For accurate elemental depth profiling, we design a stainless-steel scabbard to wrap and protect the NE-Ag needle from pollution (unexpected contaminant absorption) during the needle insertion and extraction process. The results for cadmium (Cd) show that the relative standard deviation equals to 6.7% and the limit of detection reaches 0.8 mg/L (ppm). Furthermore, the correlations (Pearson correlation coefficient) for Cd and chromium (Cr) depth profiling results are no less than 0.96. Furthermore, the total testing time could be less than 1 h. All in all, the insertable and scabbarded NE-Ag needle senor has high potential in rapid hazardous heavy metal depth profiling in different industries.

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“…Chen et al 86 proposed particle swarm optimization-support vector Machine (PSO-SVM) combined with LIBS for the quantitative prediction of heavy metal chromium (Cr) in pork. The R 2 values of the calibration and validation sets were higher than 0.99.…”

Section: Quantitative Analysismentioning

confidence: 99%

A tutorial review on methods of agricultural product sample pretreatment and target analysis by laser-induced breakdown spectroscopy

Yang

Wang

et al. 2022

J. Anal. At. Spectrom.

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Quantitative analysis of chromium in pork by PSO-SVM chemometrics based on laser induced breakdown spectroscopy

Abstract: The PSO-SVM method shown here, for the analysis of LIBS spectral data, provides much better fitting results and prediction accuracy than siPLS and MLP-ANN models for the quantitative prediction of heavy metal content in pork.

Cited by 18 publications

References 27 publications

Classification of Tea Quality Levels Using Near-Infrared Spectroscopy Based on CLPSO-SVM

Classification of Tea Quality Levels Using Near-Infrared Spectroscopy Based on CLPSO-SVM

Insertable, Scabbarded, and Nanoetched Silver Needle Sensor for Hazardous Element Depth Profiling by Laser-Induced Breakdown Spectroscopy

A tutorial review on methods of agricultural product sample pretreatment and target analysis by laser-induced breakdown spectroscopy

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