scholarly journals Analysis of the Oil Content of Rapeseed Using Artificial Neural Networks Based on Near Infrared Spectral Data

2014 ◽  
Vol 2014 ◽  
pp. 1-5 ◽  
Author(s):  
Dazuo Yang ◽  
Hao Li ◽  
Chenchen Cao ◽  
Fudi Chen ◽  
Yibing Zhou ◽  
...  
Keyword(s):  
Neural Networks ◽  
Spectral Data ◽  
Oil Content ◽  
Near Infrared ◽  
Nonlinear Method ◽  
Practical Applications ◽  
Infrared Spectral ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Analytical Approaches

The oil content of rapeseed is a crucial property in practical applications. In this paper, instead of traditional analytical approaches, an artificial neural network (ANN) method was used to analyze the oil content of 29 rapeseed samples based on near infrared spectral data with different wavelengths. Results show that multilayer feed-forward neural networks with 8 nodes (MLFN-8) are the most suitable and reasonable mathematical model to use, with an RMS error of 0.59. This study indicates that using a nonlinear method is a quick and easy approach to analyze the rapeseed oil’s content based on near infrared spectral data.

Application of an Artificial Neural Network to the Identification of Amino Acids from near Infrared Spectral Data

1993 ◽  
Vol 1 (4) ◽  
pp. 199-208 ◽  
Author(s):  
Tetsuo Sato
Keyword(s):  
Neural Network ◽  
Amino Acids ◽  
Artificial Neural Network ◽  
Spectral Data ◽  
Near Infrared ◽  
Principal Component ◽  
Mathematical Treatment ◽  
Second Derivative ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

An artificial neural network (ANN) was trained to identify a group of amino acids from near infrared (NIR) spectral data. The input, the hidden and the output layers were composed of 701 (for raw spectral data, fixed) or 324 (for second derivative of the spectral data, fixed) units, 1 to 100 (changeable) units and 20 (fixed) units, respectively. Using the raw spectral data, the ANN did not converge to a suitable error level. However, when the second derivative spectra were used, whether original or standardised spectra, the error reduced to a suitable level, because this mathematical treatment made their differences in NIR spectra clearer. The ANN was trained for non-pretreated amino acids and then applied to the other prediction sets. When standardised spectra were used, the ANN could almost correctly identify the amino acids not only for non-pretreated amino acids but also for ground samples or samples from different batches. The results obtained by principal component analysis (PCA) were also compared with those by the ANN.

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

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.

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