Detection of polyphenols-to-amino acids ratio in Wuyi Rock Tea <i>via</i> CARS-PLSR processing of near infrared spectroscopy. [PDF]
RSC AdvXu Z, Jiang X, Chen Q, Cai P.
europepmc +1 more source
Hyperspectral Inversion of Soil Organic Carbon in Daylily Cultivation Areas of Yunzhou District. [PDF]
Sensors (Basel)Yao Z, Ran X, Yang C, Li P, Bi R.
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Tumour marker analysis using a machine learning assisted vibrational spectroscopy approach
Fatayer R, Sammut SJ, Senthil Murugan G.
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Application of PLSR in rapid detection of glucose in sheep serum
Optik, 2020Abstract This paper proposes for the first time to establish grid search-support vector regression (GS-SVR), random forest (RF) and partial least squares regression (PLSR) models based on the measured Raman spectral data of sheep serum to find a method that can quickly detect glucose (GLU) concentration.
Xiaoyi Lv, Huijie Han, Cheng Chen
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Quantification of fructan concentration in grasses using NIR spectroscopy and PLSR
Field Crops Research, 2011Abstract Near-infrared reflectance (NIR) spectroscopy combined with chemometrics was used to quantify fructan concentration in samples from seven grass species. Savitzky–Golay first derivative with filter width 7 and polynomial order 2 with mean centering was applied as a spectral pre-treatment method to remove unimportant baseline signals.
René Gislum
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Forecasting the transport energy demand based on PLSR method in China
Energy, 2009Abstract Transportation sector accounts for a major share of energy consumption in China, especially the petroleum products, which experienced rapid increases in energy demand. The purpose of this study is to forecast transport energy demand for 2010, 2015 and 2020 based on partial least square regression (PLSR) method under two scenarios.
Yadong Ning, Hailin Mu
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Research of DBN PLSR algorithm Based on Sparse Constraint
2021 3rd International Conference on Pattern Recognition and Intelligent Systems, 2021DBN is a generative model based on unsupervised learning, with strong computing and information processing capabilities. But at the same time, there are some drawbacks: the model is constructed through intensive expression, which leads to relatively low computing performance of the network.
Mengxi Liu, Yingliang Li
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CWT-PLSR for quantitative analysis of Raman spectrum
2012 IEEE International Conference on Bioinformatics and Biomedicine, 2012Quantitative analysis of Raman spectra using Surface Enhanced Raman scattering (SERS) nanoparticles has shown the potential and promising trend of development in vivo molecular imaging. Partial Least Square Regression (PLSR) methods are the state-of-the-art methods.
Shuo Li 0009 +3 more
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Determination of tetracycline hydrochloride by terahertz spectroscopy with PLSR model
Food Chemistry, 2015Antibiotic residues in agricultural and food products are of great concern to legislatures and consumers. Reliable techniques for rapid and sensitive detection of these residues are necessary to ensure food safety. In this study, tetracycline hydrochloride (TC-HCl) in powder and solution form was detected and quantified using terahertz (THz ...
Jianyuan, Qin, Lijuan, Xie, Yibin, Ying
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A deep belief network with PLSR for nonlinear system modeling
Neural Networks, 2018Nonlinear system modeling plays an important role in practical engineering, and deep learning-based deep belief network (DBN) is now popular in nonlinear system modeling and identification because of the strong learning ability. However, the existing weights optimization for DBN is based on gradient, which always leads to a local optimum and a poor ...
Junfei Qiao 0001 +3 more
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