Rapid detection of total and ammonium nitrogen in pit mud by hyperspectral imaging combined with PSO-LSSVM

Hu, Xinjun; Lei, Yu; Tian, Jianping; Ma, Xiao-Yan; Wang, Jianzhi; Huang, Haoping; Chen, Manjiao; Luo, Huibo; Huang, Dan

doi:10.1016/j.infrared.2024.105298

Infrared Physics & Technology

2024

DOI: 10.1016/j.infrared.2024.105298

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Rapid detection of total and ammonium nitrogen in pit mud by hyperspectral imaging combined with PSO-LSSVM

Xinjun Hu,

Yu Lei,

Jianping Tian

et al.

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2024

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Quantitative analysis of wool and cashmere fiber mixtures using NIR spectroscopy

Chen,

Men,

et al. 2024

AUTEX Research Journal

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The quantitative determination of wool and cashmere mixed fiber is an indispensable quality control link in the textile industry, crucial for improving international trade status, ensuring product quality, and safeguarding consumer rights. Therefore, the goal of this study is to develop a reliable method for estimating fiber contents in wool–cashmere blends based on near-infrared (NIR) spectroscopy. A total of 210 mixed samples of 21 different proportions of cashmere and wool are prepared in the experiment, and data are collected in the NIR spectral band of 1,000–2,500 nm. Convolution Savitzky–Golay (S–G) combined with the second-order derivative is then used for spectral preprocessing. The variable iterative space shrinkage approach (VISSA) optimizes the characteristic wavelengths, and 339 wavelength points are selected. The prediction model of the least squares support vector machine (LSSVM) is established by particle swarm optimization (PSO), fast positioning, and analysis of key information related to the target in complex spectral data. Finally, the training set and the prediction set are divided according to the ratio of 8 : 2. Experiments show that in terms of modeling and prediction, the PSO-LSSVM model based on the wavelength selected by VISSA has a prediction determination coefficient R-squared of 0.9821, a prediction root mean square error of 1.1263, and an mean absolute error of 0.6527. The hybrid modeling method of VISSA, PSO, and LSSVM based on NIR spectroscopy (VISSA–PSO–LSSVM) can provide a more accurate and stable method for the non-destructive detection of cashmere and wool blended fiber content.

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Quantitative analysis of wool and cashmere fiber mixtures using NIR spectroscopy

Chen,

Men,

et al. 2024

AUTEX Research Journal

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show abstract

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Rapid detection of total and ammonium nitrogen in pit mud by hyperspectral imaging combined with PSO-LSSVM

Cited by 1 publication

References 21 publications

Quantitative analysis of wool and cashmere fiber mixtures using NIR spectroscopy

Quantitative analysis of wool and cashmere fiber mixtures using NIR spectroscopy

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