Multiplicative Scatter Correction during On-line Measurement with Near Infrared Spectroscopy

Maleki, Mohammad Reza; Mouazen, Abdul Mounem; Ramón, Herman; Baerdemaeker, Josse De

doi:10.1016/j.biosystemseng.2006.11.014

Cited by 115 publications

(66 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…However, in reality, the ideally ''standard'' sample does not exist, and hence that MSC would deliver different results even for the same spectrum if the MSC model was calibrated using different datasets (Datt, 1999a;Maleki et al, 2007).…”

Section: Theoretical Background and Hypothesismentioning

confidence: 95%

Estimating leaf chlorophyll of barley at different growth stages using spectral indices to reduce soil background and canopy structure effects

Lenz-Wiedemann

Chen

et al. 2014

ISPRS Journal of Photogrammetry and Remote Sensing

120

View full text Add to dashboard Cite

Section: Theoretical Background and Hypothesismentioning

confidence: 95%

Estimating leaf chlorophyll of barley at different growth stages using spectral indices to reduce soil background and canopy structure effects

Lenz-Wiedemann

Chen

et al. 2014

ISPRS Journal of Photogrammetry and Remote Sensing

120

View full text Add to dashboard Cite

“…The MSC method uses linear regression of the spectral variables vs. the average spectrum and simultaneously corrects for both multiplicative and additive scatter effects (Isaksson and Naes, 1988). This method has attractive conceptual properties and has given many promising results in other studies (Helland et al, 1995, Geladi et al, 1985, Maleki et al, 2007. Details of MSC for spectral data preprocessing can be found elsewhere (Maleki et al, 2007).…”

Section: Spectral Analysismentioning

confidence: 99%

“…This method has attractive conceptual properties and has given many promising results in other studies (Helland et al, 1995, Geladi et al, 1985, Maleki et al, 2007. Details of MSC for spectral data preprocessing can be found elsewhere (Maleki et al, 2007). could be used to choose the best variable subset (Galvao et al, 2008).…”

Section: Spectral Analysismentioning

confidence: 99%

Detection of Waxed Chestnuts using Visible and Near-Infrared Hyper-spectral Imaging

Hou

Zhou

et al. 2016

FSTR

View full text Add to dashboard Cite

This paper presents a study that was performed for rapid and noninvasive detection of waxed chestnuts using hyper-spectral imaging. A visual near-infrared (400 _ 1026 nm) hyper-spectral imaging system was assembled to acquire scattering images from two groups of chestnuts (waxed and non-waxed chestnuts). The spectra of the samples were extracted from the hyper-spectral images using image segmentation process. Then multiplicative scatter correction (MSC) was conducted to preprocess the original spectra. Effective wavelengths were selected to reduce the computational burden of the hyper-spectral data. Using the seven effective wavelengths that were obtained from a successive projections algorithm (SPA), three calibration algorithms were compared: partial least squares regression (PLSR), multiple linear regression (MLR) and linear discriminant analysis (LDA). The best model for discriminating between waxed and non-waxed chestnuts was found to be the MSC-SPA-MLR model.Keywords: hyper-spectral imaging, waxed chestnut, effective wavelength, pear IntroductionThe chestnut (Castanea mollissima Bl.) is an important edible fruit in the northern hemisphere, which has been consumed as extensively as the potato in the past (Ferreira-Cardoso et al., 1999).and has become increasingly important in human nutrition because of its nutrient composition and potential beneficial health effects, for example, in reducing coronary heart disease and cancer rates (Sabaté et al., 2001). Fried chestnut is the most popular preparation method for chestnuts. However, some unscrupulous traders add industrial wax while frying chestnuts to ensure that the chestnuts are brighter and more attractive. Generally, industrial wax is extracted directly from oil, which will also extract polycyclic aromatic hydrocarbons and polycyclic aromatic hydrocarbons which are present in the industrial process, both of which are highly carcinogenic (Bonser et al., 1963). Mixed industrial wax will infiltrate into the pulp in the process of frying chestnuts which seriously endangers human health. Hence, a sorting process is necessary to identify and screen out waxed chestnuts.Traditionally, screening waxed chestnuts is usually dependent on subjective criteria to distinguish between waxed and non-waxed chestnuts, taking into consideration that brighter and more attractive chestnuts are mixed with industrial wax. Although industrial wax will make chestnuts brighter and more attractive, it is not a reliable way to identify waxed chestnuts. Hyper-spectral imaging (also called imaging spectrometry or imaging spectroscopy) is an emerging analytical technology which is receiving growing attention as a rapid, efficient, and nondestructive analytical tool for quantitative determination of the quality and safety of agricultural goods. By combining the advantages of computer vision and spectroscopy technology into one system, hyper-spectral imaging can generate a spatial image of the spectral variation, which provides structural, chemical, physical and functional information abou...

show abstract

“…In this study, MSC, SG smoothing, and MSC-SG were individually used. MSC method is effectively used in spectroscopic applications where light scattering variation and multiplicative noise were present [25,26]. SG smoothing were widely used in removing noise of baseline excursion and smoothing spectra [27,28].…”

Section: Spectral Preprocessingmentioning

confidence: 99%

Integration of Artificial Neural Network Modeling and Hyperspectral Data Preprocessing for Discrimination of Colla Corii Asini Adulteration

Wang

et al. 2018

Journal of Food Quality

View full text Add to dashboard Cite

The study of hyperspectral imaging in tandem with spectral preprocessing and neural network techniques was conducted to realize Colla Corii Asini (CCA, E'jiao) adulteration discrimination. CCA was adulterated with pig skin gelatin (PSG) in the range of 5-95% (w/w) at 5% increments. Three methods were used to pretreat the original spectra, which are multiplicative scatter correction (MSC), Savitzky-Golay (SG) smoothing, and the combination of MSC and SG (MSC-SG). SPA was employed to select the characteristic wavelengths (CWs) to reduce the high dimension. Colour and texture features of CWs were extracted as input of prediction model. Two kinds of artificial neural network (ANN) with three spectral preprocessing methods were applied to establish the prediction models. The prediction model of generalized regression neural network (GRNN) in tandem with the MSC-SG preprocessed method presented satisfactory performance with the correct classification rate value of 92.5%. The results illustrated that the integration of preprocessing methods, hyperspectral imaging features, and ANN modeling had a great potential and feasibility for CCA adulteration discrimination.

show abstract

Multiplicative Scatter Correction during On-line Measurement with Near Infrared Spectroscopy

Cited by 115 publications

References 5 publications

Estimating leaf chlorophyll of barley at different growth stages using spectral indices to reduce soil background and canopy structure effects

Estimating leaf chlorophyll of barley at different growth stages using spectral indices to reduce soil background and canopy structure effects

Detection of Waxed Chestnuts using Visible and Near-Infrared Hyper-spectral Imaging

Integration of Artificial Neural Network Modeling and Hyperspectral Data Preprocessing for Discrimination of Colla Corii Asini Adulteration

Contact Info

Product

Resources

About