scholarly journals Importance of Spatial and Spectral Data Reduction in the Detection of Internal Defects in Food Products

2015 ◽  
Vol 69 (4) ◽  
pp. 473-480 ◽  
Author(s):  
Xuechen Zhang ◽  
Christian Nansen ◽  
Nader Aryamanesh ◽  
Guijun Yan ◽  
Farid Boussaid
Keyword(s):  
Spectral Data ◽  
Data Reduction ◽  
Food Products ◽  
Internal Defects

Specplot - An Interactive Data Reduction and Display Program for Spectral Data

1979 ◽  
Vol 23 ◽  
pp. 305-311 ◽  
Author(s):  
Raymond P. Goehner
Keyword(s):  
Spectral Data ◽  
Data Reduction ◽  
Usual Method ◽  
Spectral Lines ◽  
Digital Data ◽  
Strip Chart Recorder ◽  
Automatic Data ◽  
Chart Recorder ◽  
Rapid Pace ◽  
Interactive Data

The automation of analytical equipment is proceeding at a rapid pace, particularly since the introduction of inexpensive microcomputer systems. Most of this equipment has one characteristic in common, that is, they produce digital spectral data. The usual method of recording spectral data has been the strip chart recorder. Strip charts require the hand encoding of position and intensities of the spectral lines. This requires that all of the lines be on scale or that the sample be run several times in order to amplify weaker lines. This problem is eliminated by recording the data digitally. Digital data can then be rapidly plotted on a cathode ray terminal to any desired scale. The user of digital data has access to a great variety of automatic data reduction programs.

scholarly journals Reduction of 3D Spectral Data, Obtained with the 6-meter Telescope

1995 ◽  
Vol 149 ◽  
pp. 308-310
Author(s):  
V. V. Vlasiouk
Keyword(s):  
Spectral Data ◽  
Spectral Methods ◽  
Data Reduction ◽  
Simultaneous Detection ◽  
Personal Computers ◽  
Specialized Software ◽  
Fabry Perot ◽  
Extended Sources ◽  
Integral Field Spectrograph ◽  
Integral Field

Development of 3D spectral methods on the 6-meter telescope prompted the necessity of creating specialized software for data reduction, adapted to personal computers IBM AT/386/486 class. We had to concentrate our efforts on preparing the software for bidimensional spectral data produced by Multi-Pupil Integral Field Spectrograph of the 6-meter telescope (MPFS) and analog of panoramic spectrophotometer with spectral channeling, using scanning Fabry-Perot interferometer (PYTHEAS-6).MPFS - analog of spectrograph TIGER (Courtes et al., 1988), developed for the 6-meter telescope, provides bidimensional spectroscopy of extended sources, allowing the simultaneous detection of 99 individual spectra, produced by a lens raster from 9 by 11 microlenses. The main features of this instrument are described in (Afanasiev et al, 1994).

scholarly journals Spectral data reduction via wavelet decomposition

2002 ◽  
Author(s):  
Sinthop Kaewpijit ◽  
Jacqueline Le Moigne ◽  
Tarek El-Ghazawi
Keyword(s):  
Spectral Data ◽  
Data Reduction ◽  
Wavelet Decomposition

Spectral data reduction

2011 ◽  
Vol 47 ◽  
pp. 73-101
Author(s):  
V. Desnoux
Keyword(s):  
Spectral Data ◽  
Data Reduction

Impact of data reduction on multivariate classification models built on spectral data from bio-samples

2015 ◽  
Vol 30 (5) ◽  
pp. 1117-1127 ◽  
Author(s):  
Anders Larsson ◽  
Henrik Andersson ◽  
Lars Landström
Keyword(s):  
Spectral Data ◽  
Data Reduction ◽  
Image Data ◽  
Classification Models ◽  
Multivariate Classification

Multivariate classification models, both from 1D spectra and 2D image data (also with simulated shifts), were evaluated and compared.

scholarly journals Identification of Internal Defects in Potato Using Spectroscopy and Computational Intelligence Based on Majority Voting Techniques

Foods ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 982
Author(s):  
Kamal Imanian ◽  
Razieh Pourdarbani ◽  
Sajad Sabzi ◽  
Ginés García-Mateos ◽  
Juan Ignacio Arribas ◽  
...  
Keyword(s):  
Spectral Data ◽  
Near Infrared ◽  
Search Algorithm ◽  
Statistical Tests ◽  
Harmony Search ◽  
Imperialist Competitive Algorithm ◽  
Majority Voting ◽  
Cultural Algorithm ◽  
Internal Defects ◽  
Linear Discriminant

Potatoes are one of the most demanded products due to their richness in nutrients. However, the lack of attention to external and, especially, internal defects greatly reduces its marketability and makes it prone to a variety of diseases. The present study aims to identify healthy-looking potatoes but with internal defects. A visible (Vis), near-infrared (NIR), and short-wavelength infrared (SWIR) spectrometer was used to capture spectral data from the samples. Using a hybrid of artificial neural networks (ANN) and the cultural algorithm (CA), the wavelengths of 861, 883, and 998 nm in Vis/NIR region, and 1539, 1858, and 1896 nm in the SWIR region were selected as optimal. Then, the samples were classified into either healthy or defective class using an ensemble method consisting of four classifiers, namely hybrid ANN and imperialist competitive algorithm (ANN-ICA), hybrid ANN and harmony search algorithm (ANN-HS), linear discriminant analysis (LDA), and k-nearest neighbors (KNN), combined with the majority voting (MV) rule. The performance of the classifier was assessed using only the selected wavelengths and using all the spectral data. The total correct classification rates using all the spectral data were 96.3% and 86.1% in SWIR and Vis/NIR ranges, respectively, and using the optimal wavelengths 94.1% and 83.4% in SWIR and Vis/NIR, respectively. The statistical tests revealed that there are no significant differences between these datasets. Interestingly, the best results were obtained using only LDA, achieving 97.7% accuracy for the selected wavelengths in the SWIR spectral range.

Outlier modeling for spectral data reduction

2014 ◽  
Vol 31 (7) ◽  
pp. 1445 ◽  
Author(s):  
Farnaz Agahian ◽  
Brian Funt
Keyword(s):  
Spectral Data ◽  
Data Reduction
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