Accuracy and stability improvement in detecting Wuchang rice adulteration by piece-wise multiplicative scatter correction in the hyperspectral imaging system

2018 ◽  
Vol 10 (26) ◽  
pp. 3224-3231 ◽  
Author(s):  
Yunxin Yu ◽  
Hanyue Yu ◽  
Lianbo Guo ◽  
Jun Li ◽  
Yanwu Chu ◽  
...  
Keyword(s):  
Hyperspectral Imaging ◽  
Imaging System ◽  
Scatter Correction ◽  
Processing Methods ◽  
Multiplicative Scatter Correction ◽  
System P ◽  
Accuracy And Stability

The adulterated Wuchang rice were detected using hyperspectral imaging system with the optimal spectral pre-processing methods.

Mapping of fatty acids composition in shelled almonds analysed in bulk using a Hyperspectral Imaging system

LWT ◽  
2021 ◽  
Vol 138 ◽  
pp. 110678
Author(s):  
Irina Torres ◽  
Dolores Pérez-Marín ◽  
Miguel Vega-Castellote ◽  
María-Teresa Sánchez
Keyword(s):  
Fatty Acids ◽  
Hyperspectral Imaging ◽  
Imaging System ◽  
Fatty Acids Composition

scholarly journals Nondestructive and rapid determination of lignocellulose components of biofuel pellet using online hyperspectral imaging system

2018 ◽  
Vol 11 (1) ◽  
Author(s):  
Xuping Feng ◽  
Chenliang Yu ◽  
Xiaodan Liu ◽  
Yunfeng Chen ◽  
Hong Zhen ◽  
...  
Keyword(s):  
Hyperspectral Imaging ◽  
Imaging System ◽  
Rapid Determination

Design of a Cooperative Chemical Agent (CA) Detection Algorithm with the Hyperspectral Imaging System

2021 ◽  
Author(s):  
Hyeong-Geun Yu ◽  
Whimin Kim ◽  
Dong-Jo Park ◽  
Dong Eui Chang ◽  
Hyunwoo Nam
Keyword(s):  
Hyperspectral Imaging ◽  
Imaging System ◽  
Detection Algorithm ◽  
Chemical Agent

Remediation-related monitoring of heavy metal concentration of contaminated Technosol using hyperspectral measurements

2021 ◽  
Author(s):  
Friederike Kaestner ◽  
Magdalena Sut-Lohmann ◽  
Thomas Raab ◽  
Hannes Feilhauer ◽  
Sabine Chabrillat
Keyword(s):  
Heavy Metal ◽  
Random Forest ◽  
Metal Concentration ◽  
Strong Dependence ◽  
Scatter Correction ◽  
Heavy Metal Concentration ◽  
Standard Normal Variate ◽  
Multiplicative Scatter Correction ◽  
Second Derivatives ◽  
Standard Normal

<p>Across Europe there are 2.5 million potentially contaminated sites, approximately one third have already been identified and around 15% have been sanitized. Phytoremediation is a well-established technique to tackle this problem and to rehabilitate soil. However, remediation methods, such as biological treatments with microorganisms or phytoremediation with trees, are still relatively time consuming. A fast monitoring of changes in heavy metal content over time in contaminated soils with hyperspectral spectroscopy is one of the first key factors to improve and control existing bioremediation methods.</p><p>At former sewage farms near Ragow (Brandenburg, Germany), 110 soil samples with different contamination levels were taken at a depth between 15-20 cm. These samples were prepared for hyperspectral measurements using the HySpex system under laboratory conditions, combing a VNIR (400-1000 nm) and a SWIR (1000-2500 nm) line-scan detector. Different spectral pre-processing methods, including continuum removal, first and second derivatives, standard normal variate, normalisation and multiplicative scatter correction, with two established estimation models such as Partial Least Squares Regression (PLSR) and Random Forest Regression (RFR), were applied to predict the heavy metal concentration (Ba, Ni, Cr, Cu) of this specific Technosol. The coefficient of determination (R2) shows for Ba and Ni values between 0.50 (RMSE: 9%) and 0.61 (RMSE: 6%) for the PLSR and between 0.84 (RMSE: 0.03%) and 0.91 (RMSE: 0.02%) for the RFR model. The results for Cu and Cr show values between 0.57 (RMSE: 17.9%) and 0.69 (RMSE: 15%) for the PLSR and 0.86 (0.12%) and 0.93 (0.01%) for the RFR model. The pre-processing method, which improve the robustness and performance of both models best, is multiplicative scatter correction followed by the standard normal variate for the first and second derivatives. Random Forest in a first approach seems to deliver better modeling performances. Still, the pronounced differences between PLSR and RFR fits indicate a strong dependence of the results on the respective modelling technique. This effect is subject to further investigation and will be addressed in the upcoming analysis steps.</p>

scholarly journals Prediction of Douglas-Fir Lumber Properties: Comparison between a Benchtop Near-Infrared Spectrometer and Hyperspectral Imaging System

2018 ◽  
Vol 8 (12) ◽  
pp. 2602 ◽  
Author(s):  
Laurence Schimleck ◽  
Joseph Dahlen ◽  
Seung-Chul Yoon ◽  
Kurt Lawrence ◽  
Paul Jones
Keyword(s):  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Imaging System ◽  
Nir Spectroscopy ◽  
Physical And Mechanical Properties ◽  
Douglas Fir ◽  
Modulus Of Rupture ◽  
Visual Grade ◽  
Fit Statistics ◽  
Infrared Spectrometer

Near-infrared (NIR) spectroscopy and NIR hyperspectral imaging (NIR-HSI) were compared for the rapid estimation of physical and mechanical properties of No. 2 visual grade 2 × 4 (38.1 mm by 88.9 mm) Douglas-fir structural lumber. In total, 390 lumber samples were acquired from four mills in North America and destructively tested through bending. From each piece of lumber, a 25-mm length block was cut to collect diffuse reflectance NIR spectra and hyperspectral images. Calibrations for the specific gravity (SG) of both the lumber (SGlumber) and 25-mm block (SGblock) and the lumber modulus of elasticity (MOE) and modulus of rupture (MOR) were created using partial least squares (PLS) regression and their performance checked with a prediction set. The strongest calibrations were based on NIR spectra; however, the NIR-HSI data provided stronger predictions for all properties. In terms of fit statistics, SGblock gave the best results, followed by SGlumber, MOE, and MOR. The NIR-HSI SGlumber, MOE, and MOR calibrations were used to predict these properties for each pixel across the transverse surface of the scanned samples, allowing SG, MOE, and MOR variation within and among rings to be observed.

Pork Quality Classification Using a Hyperspectral Imaging System and Neural Network

2007 ◽  
Vol 3 (1) ◽  
Author(s):  
Qiao Jun ◽  
Michael Ngadi ◽  
Ning Wang ◽  
Aynur Gunenc ◽  
Mariana Monroy ◽  
...  
Keyword(s):  
Neural Network ◽  
Hyperspectral Imaging ◽  
Imaging System ◽  
Objective Assessment ◽  
Principal Component ◽  
Pork Quality ◽  
Pca Analysis ◽  
Error Frequency ◽  
Quality Classification ◽  
Average Reflectance

Pork quality is usually determined subjectively as PSE, PFN, RFN, RSE and DFD based on color, texture and exudation of the meat. In this study, a hyperspectral-imaging-based technique was developed to achieve rapid, accurate and objective assessment of pork quality. The principal component analysis (PCA) and stepwise operation methods were used to select feature waveband from the entire spectral wavelengths (430 to 980 nm). Then the feature waveband images were extracted at the selected feature wavebands from raw hyperspectral images, and the average reflectance (R) was calculated within the whole loin-eye area. Artificial neural network was used to classify these groups. Results showed that PCA analysis had a better performance than that of stepwise operation for feature waveband images selection. The 1st derivative data gave a better result than that of mean reflectance spectra data. The best classified result was 87.5% correction. The error frequency showed that RSE samples were easier to classify. The PFN and PSE samples were difficult to separate from each other.

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