Brand classification of detergent powder using near-infrared spectroscopy and extreme learning machines

Tan, Chao; Chen, Hui; Lin, Zan

doi:10.1016/j.microc.2020.105691

Cited by 14 publications

(2 citation statements)

References 32 publications

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“…A sound and effective learning algorithm is required for soil prediction and mapping on hyperspectral imagery at various scales. The ELM method with random weights of the hidden neurons and inherent steps has benefits in high training speed and easily ensemble [ 60 ]. However, ELM usually requires more hidden neurons than traditional algorithms to make good predictions, which might result in slow responses of ELM to new data [ 26 ].…”

Section: Discussionmentioning

confidence: 99%

Soil Nutrient Estimation and Mapping in Farmland Based on UAV Imaging Spectrometry

Yang

Bao

et al. 2021

Sensors

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Soil nutrient is one of the most important properties for improving farmland quality and product. Imaging spectrometry has the potential for rapid acquisition and real-time monitoring of soil characteristics. This study aims to explore the preprocessing and modeling methods of hyperspectral images obtained from an unmanned aerial vehicle (UAV) platform for estimating the soil organic matter (SOM) and soil total nitrogen (STN) in farmland. The results showed that: (1) Multiplicative Scattering Correction (MSC) performed better in reducing image scattering noise than Standard Normal Variate (SNV) transformation or spectral derivatives, and it yielded a result with higher correlation and lower signal-to-noise ratio; (2) The proposed feature selection method combining Successive Projections Algorithm (SPA) and Competitive Adaptive Reweighted Sampling algorithm (CARS), could provide selective preference for hyperspectral bands. Exploiting this method, 24 and 22 feature bands were selected for SOM and STN estimation, respectively; (3) The particle swarm optimization (PSO) algorithm was employed to obtain optimized input weights and bias values of the extreme learning machine (ELM) model for more accurate prediction of SOM and STN. The improved PSO-ELM model based on the selected preference bands achieved higher prediction accuracy (R2 of 0.73 and RPD of 1.91 for SOM, R2 of 0.63, and RPD of 1.53 for STN) than support vector machine (SVM), partial least squares regression (PLSR), and the ELM model. This study provides an important guideline for monitoring soil nutrient for precision agriculture with imaging spectrometry.

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Section: Discussionmentioning

confidence: 99%

Soil Nutrient Estimation and Mapping in Farmland Based on UAV Imaging Spectrometry

Yang

Bao

et al. 2021

Sensors

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“…11,12 In order to overcome the above-mentioned problems, multivariate calibration methods have been introduced for NIR spectral quantitative analysis. Principal component regression (PCR), 13,14 partial least squares (PLS), [15][16][17][18] articial neural network (ANN), 19,20 support vector regression (SVR) 21,22 and extreme learning machine (ELM) 23,24 have been successively proposed. However, NIR spectra usually contain thousands of variables and some of them may be irrelevant to target values.…”

Section: Introductionmentioning

confidence: 99%

Discretized butterfly optimization algorithm for variable selection in the rapid determination of cholesterol by near-infrared spectroscopy

Bian,

Zhao,

Liu

et al. 2023

Anal. Methods

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Firefly Interval Selection Combined With Extreme Learning Machine for Spectral Quantification of Complex Samples

Wang,

Zhang,

Mpango

et al. 2024

Journal of Chemometrics

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Firefly algorithm (FA) combined with extreme learning machine (ELM) is developed for spectral interval selection and quantitative analysis of complex samples. The method firstly segments the spectra into a certain number of intervals. Vectors with 1 and 0, which represent the interval selected or not, are used as the inputs of the FA. The RMSEP value predicted by ELM model is used as the fitness function of the FA. The activation function and number of hidden layer nodes of ELM, number of spectral intervals, population number, environmental absorbance, and constant of FA are optimized. The predictive performance of FA‐ELM is compared with full‐spectrum PLS, ELM, genetic algorithm‐ELM (GA‐ELM), and particle swarm optimization‐ELM (PSO‐ELM) by one ultraviolet (UV) spectrum dataset of gasoil and three near‐infrared (NIR) spectral datasets of corn, wheat, and tablet samples. The results show that FA‐ELM has a better performance compared with its competitors in predicting monoaromatics, water, wheat kernel texture, and active pharmaceutical ingredients (APIs) in gasoil, corn, wheat, and tablet samples.

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Brand classification of detergent powder using near-infrared spectroscopy and extreme learning machines

Cited by 14 publications

References 32 publications

Soil Nutrient Estimation and Mapping in Farmland Based on UAV Imaging Spectrometry

Soil Nutrient Estimation and Mapping in Farmland Based on UAV Imaging Spectrometry

Discretized butterfly optimization algorithm for variable selection in the rapid determination of cholesterol by near-infrared spectroscopy

Firefly Interval Selection Combined With Extreme Learning Machine for Spectral Quantification of Complex Samples

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