scholarly journals Enhanced near infrared spectral data to improve prediction accuracy in determining quality parameters of intact mango

Data in Brief ◽  
2020 ◽  
Vol 30 ◽  
pp. 105571
Author(s):  
Rita Hayati ◽  
Agus Arip Munawar ◽  
F. Fachruddin
Keyword(s):  
Spectral Data ◽  
Prediction Accuracy ◽  
Near Infrared ◽  
Quality Parameters ◽  
Infrared Spectral

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.

scholarly journals Kernel partial diagnostic robust potential to handle high-dimensional and irregular data space on near infrared spectral data

Heliyon ◽  
2020 ◽  
Vol 6 (1) ◽  
pp. e03176
Author(s):  
Divo Dharma Silalahi ◽  
Habshah Midi ◽  
Jayanthi Arasan ◽  
Mohd Shafie Mustafa ◽  
Jean-Pierre Caliman
Keyword(s):  
Spectral Data ◽  
Near Infrared ◽  
High Dimensional ◽  
Data Space ◽  
Infrared Spectral ◽  
Irregular Data

scholarly journals Fast prediction of quality parameters in whole seeds of oilseed rape (Brassica napus L.)

2011 ◽  
Vol 49 (No. 4) ◽  
pp. 141-145 ◽  
Author(s):  
V. Míka ◽  
P. Tillmann ◽  
R. Koprna ◽  
P. Nerušil ◽  
V. Kučera
Keyword(s):  
Prediction Accuracy ◽  
Near Infrared ◽  
Quality Parameters ◽  
Breeding Cycle ◽  
Brassica Napus L ◽  
Calibration Equation ◽  
Whole Seed ◽  
Fast Prediction ◽  
Short Time ◽  
Non Destructive

A calibration equation for NIRSystems 6500 instrument was derived at VSTE Jevíčko using the measurement of broad collection of Czech samples of winter rape, allowing sufficiently accurate prediction of content of dry matter (DM), crude protein (XP), crude fat (XL), glucosinolates (GSL), oleic and linoleic acids in an extremely short time. The prediction accuracy was verified on a validation file (n = 60). The coefficients of determinance (R2) were 0.83 for XP, 0.71 for XL, and 0.84 for GSL. The prediction accuracy according to the VSTE equation was compared to the prediction accuracy according to the VDLUFA calibration equation (Kassel, FRG) used in EU near infrared spectroscopy network. It was stated that the former was not distinctly worse. Non-destructive NIR-analysis of the whole seed also allows sowing selected seeds in the year of harvest and thus accelerates the breeding cycle.

scholarly journals Classifying Reflectance Targets under Ambient Light Conditions Using Passive Spectral Measurements

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5375
Author(s):  
Ali Hamidisepehr ◽  
Michael P. Sama ◽  
Joseph S. Dvorak ◽  
Ole O. Wendroth ◽  
Michael D. Montross
Keyword(s):  
Spectral Data ◽  
Prediction Accuracy ◽  
Near Infrared ◽  
Machine Learning Algorithms ◽  
Spectral Signature ◽  
Integration Time ◽  
Ambient Light ◽  
Light Conditions ◽  
Light Spectrum ◽  
Compensation Methods

Collecting remotely sensed spectral data under varying ambient light conditions is challenging. The objective of this study was to test the ability to classify grayscale targets observed by portable spectrometers under varying ambient light conditions. Two sets of spectrometers covering ultraviolet (UV), visible (VIS), and near−infrared (NIR) wavelengths were instrumented using an embedded computer. One set was uncalibrated and used to measure the raw intensity of light reflected from a target. The other set was calibrated and used to measure downwelling irradiance. Three ambient−light compensation methods that successively built upon each other were investigated. The default method used a variable integration time that was determined based on a previous measurement to maximize intensity of the spectral signature (M1). The next method divided the spectral signature by the integration time to normalize the spectrum and reveal relative differences in ambient light intensity (M2). The third method divided the normalized spectrum by the ambient light spectrum on a wavelength basis (M3). Spectral data were classified using a two−step process. First, raw spectral data were preprocessed using a partial least squares (PLS) regression method to compress highly correlated wavelengths and to avoid overfitting. Next, an ensemble of machine learning algorithms was trained, validated, and tested to determine the overall classification accuracy of each algorithm. Results showed that simply maximizing sensitivity led to the best prediction accuracy when classifying known targets. Average prediction accuracy across all spectrometers and compensation methods exceeded 93%.

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