Wavelet Analysis of Near Infrared Spectral Data in the Application of Denoising

2011 ◽  
Vol 48-49 ◽  
pp. 1358-1362
Author(s):  
Xiao Mei Lin ◽  
Juan Wang ◽  
Qing Hua Yao
Keyword(s):  
Wavelet Analysis ◽  
Infrared Spectrum ◽  
Spectral Data ◽  
Signal Analysis ◽  
Near Infrared ◽  
Fourier Method ◽  
Signal Denoising ◽  
Data Pretreatment ◽  
Infrared Spectral ◽  
Near Infrared Spectrum

Spectrum signal may contain many peaks or mutations and noise also is not smooth white noise, to this kind of signal analysis, must do signal pretreatment, remove part of signal and extract useful part of signal.Based on the data of blood glucose near-infrared spectrum as the research object to explore the application of wavelet transform in the near infrared spectrum signal denoising, and through the simulation results show that using wavelet analysis of near infrared spectral data pretreatment than the traditional Fourier method can be higher precision of prediction.

scholarly journals THE DEVELOPMENT OF PORTABLE DETECTOR FOR APPLE' S SOLUBLE SOLIDS CONTENT BASED ON VISIBLE AND NEAR INFRARED SPECTRUM

2020 ◽  
pp. 277-288
Author(s):  
Fa Peng ◽  
ShuangXi Liu ◽  
Hao Jiang ◽  
XueMei Liu ◽  
JunLin Mu ◽  
...  
Keyword(s):  
Infrared Spectrum ◽  
Prediction Model ◽  
Spectral Data ◽  
Near Infrared ◽  
Mobile App ◽  
Partial Least Square ◽  
Soluble Solids ◽  
Soluble Solids Content ◽  
Solids Content ◽  
Near Infrared Spectrum

In order to detect the soluble solids content of apples quickly and accurately, a portable apple soluble solids content detector based on USB2000 + micro spectrometer was developed. The instrument can communicate with computer terminal and mobile app through network port, Bluetooth and other ways, which can realize the rapid acquisition of apple spectral information. Firstly, the visible / near-infrared spectrum data and soluble solids content information of 160 apple samples were collected; secondly, the spectral data preprocessing methods were compared, and the results showed that the prediction model of sugar content based on partial least square (PLS) method after average smoothing preprocessing was accurate. The correlation coefficient (RP) and root mean square error (RMSEP) of the prediction model were 0.902 and 0.589 ° Brix, respectively. Finally, on the basis of average smoothing preprocessing, competitive adaptive reweighted sampling (CARS) and successive projections algorithm (SPA) were used to optimize the wavelength of spectral data, and PLS model was constructed based on the selected 17 characteristic wavelengths, which can increase the accuracy of soluble solids content prediction model, increase the RP to 0.912, and reduce RMSEP to 0.511 ° Brix. The portable visible / near infrared spectrum soluble solids prediction model based on the instrument and method has high accuracy, and the detector can quickly and accurately measure the soluble solids content of apple.

Apple near Infrared Spectral Preprocessed Based on Wavelet Analysis

2014 ◽  
Vol 926-930 ◽  
pp. 1775-1778 ◽  
Author(s):  
Min Li ◽  
Qiang Jiang ◽  
Xiao Ying Zhang ◽  
Li Qun Chen
Keyword(s):  
Wavelet Analysis ◽  
Data Compression ◽  
Model Prediction ◽  
Near Infrared ◽  
Noise Removal ◽  
Analysis Method ◽  
Wavelet Method ◽  
Scattering Correction ◽  
Data Pretreatment ◽  
Infrared Spectral

The original near infrared spectral (NIR) inevitably includes noise signals and usually has a large amount of data. So de-noising and compression are the main tasks of spectral data pretreatment, in order to improve the accuracy of model prediction and modeling speed. Traditional spectral pretreatment methods have their own limitations in these two aspects. In this paper, the wavelet analysis method was applied to preprocess apple NIR data. Compared with Savitzky-Golay smoothing method and multiple scattering correction , the wavelet method has its own superiority in noise removal and data compression.

scholarly journals Nanopatterned metallic transparent electrodes for the near-infrared spectrum

AIP Advances ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 045005
Author(s):  
Aliaksandr Hubarevich ◽  
Mikita Marus ◽  
Yauhen Mukha ◽  
Aliaksandr Smirnov ◽  
Xiao Wei Sun
Keyword(s):  
Infrared Spectrum ◽  
Near Infrared ◽  
Transparent Electrodes ◽  
Near Infrared Spectrum

An electrically driven whispering gallery polariton microlaser

Nanoscale ◽  
2021 ◽  
Vol 13 (10) ◽  
pp. 5448-5459
Author(s):  
Mingming Jiang ◽  
Peng Wan ◽  
Kai Tang ◽  
Maosheng Liu ◽  
Caixia Kan
Keyword(s):  
Infrared Spectrum ◽  
Near Infrared ◽  
Whispering Gallery ◽  
Near Infrared Spectrum

An electrically driven whispering gallery polariton microlaser composed of a ZnO:Ga microwire and a p-GaAs template was fabricated. Its working characteristics of polariton lasing in the near-infrared spectrum were demonstrated.

scholarly journals Biomechanical, biochemical, and near infrared spectral data of bovine knee ligaments and patellar tendon

Data in Brief ◽  
2021 ◽  
Vol 36 ◽  
pp. 106976
Author(s):  
Aapo Ristaniemi ◽  
Jari Torniainen ◽  
Tommi Paakkonen ◽  
Lauri Stenroth ◽  
Mikko A.J. Finnilä ◽  
...  
Keyword(s):  
Spectral Data ◽  
Patellar Tendon ◽  
Near Infrared ◽  
Knee Ligaments ◽  
Infrared Spectral

scholarly journals Theoretical Simulation of Near-Infrared Spectrum of Piperine: Insight into Band Origins and the Features of Regression Models

2021 ◽  
pp. 000370282110279
Author(s):  
Justyna Grabska ◽  
Krzysztof B. Beć ◽  
Sophia Mayr ◽  
Christian W. Huck
Keyword(s):  
Infrared Spectrum ◽  
Regression Models ◽  
Near Infrared ◽  
Computing Time ◽  
Black Pepper ◽  
Partial Least Square ◽  
Least Square ◽  
Complex Nature ◽  
Chemical Information ◽  
Near Infrared Spectrum

We investigated the near-infrared spectrum of piperine using quantum mechanical calculations. We evaluated two efficient approaches, DVPT2//PM6 and DVPT2//ONIOM [PM6:B3LYP/6-311++G(2df, 2pd)] that yielded a simulated spectrum with varying accuracy versus computing time factor. We performed vibrational assignments and unveiled complex nature of the near-infrared spectrum of piperine, resulting from a high level of band convolution. The most meaningful contribution to the near-infrared absorption of piperine results from binary combination bands. With the available detailed near-infrared assignment of piperine, we interpreted the properties of partial least square regression models constructed in our earlier study to describe the piperine content in black pepper samples. Two models were compared with spectral data sets obtained with a benchtop and a miniaturized spectrometer. The two spectrometers implement distinct technology which leads to a profound instrumental difference and discrepancy in the predictive performance when analyzing piperine content. We concluded that the sensitivity of the two instruments to certain types of piperine vibrations is different and that the benchtop spectrometer unveiled higher selectivity. Such difference in obtaining chemical information from a sample can be one of the reasons why the benchtop spectrometer performs better in analyzing the piperine content of black pepper. This evidenced direct correspondence between the features critical for applied near-infrared spectroscopic routine and the underlying vibrational properties of the analyzed constituent in a complex sample.

Functional Classification of Feed Items in Pampa Grassland, Based on Their Near-Infrared Spectrum

2020 ◽  
Vol 73 (3) ◽  
pp. 358-367
Author(s):  
Júlio Cezar Rebés Azambuja Filho ◽  
Paulo Cesar de Faccio Carvalho ◽  
Olivier Jean François Bonnet ◽  
Denis Bastianelli ◽  
Magali Jouven
Keyword(s):  
Infrared Spectrum ◽  
Near Infrared ◽  
Functional Classification ◽  
Near Infrared Spectrum
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