The Effect of Multiplicative Scatter Correction (MSC) and Linearity Improvement in NIR Spectroscopy

Isaksson, Tomas; Næs, Tormod

doi:10.1366/0003702884429869

Cited by 513 publications

(236 citation statements)

References 10 publications

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“…The inverse MSC method was the best scatter correction of the FSC samples, whereas the standard MSC method was the best scatter correction of the XDS samples (Isaksson and Naes, 1988). The best mathematical pre-processing were 0,0,1,1 and 2,4,4,1 treatments for FSC and XDS samples, respectively.…”

Section: Resultsmentioning

confidence: 97%

Prediction of fat quality in pig carcasses by near-infrared spectroscopy

Gjerlaug-Enger

Kongsro

Aass

et al. 2011

Animal

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This study was conducted to evaluate the potential of near-infrared (NIR) spectroscopy (NIRS) technology for prediction of the chemical composition (moisture content and fatty acid composition) of fat from fast-growing, lean slaughter pig samples coming from breeding programmes. NIRS method I: a total of 77 samples of intact subcutaneous fat from pigs were analysed with the FOSS FoodScan NIR spectrophotometer (850 to 1050 nm) and then used to predict the moisture content by using partial least squares (PLS) regression methods. The best equation obtained has a coefficient of determination for cross-validation (CV; R 2 cv ) and a root mean square error of a CV (RMSECV) of 0.88 and 1.18%, respectively. The equation was further validated with (n 5 15) providing values of 0.83 and 0.42% for the coefficient of determination for validation (R 2 val ) and root mean square error of prediction (RMSEP), respectively. NIRS method II: in this case, samples of melted subcutaneous fat were analysed in an FOSS XDS NIR rapid content analyser (400 to 2500 nm). Equations based on modified PLS regression methods showed that NIRS technology could predict the fatty acid groups, the main fatty acids and the iodine value accurately with R 2 cv, RMSECV, R 2 val and RMSEP of 0.98, 0.38%, 0.95 and 0.49%, respectively (saturated fatty acids), 0.94, 0.45%, 0.97 and 0.65%, respectively (monounsaturated fatty acids), 0.97, 0.28%, 0.99 and 0.34%, respectively (polyunsaturated fatty acids), 0.76, 0.61%, 0.84 and 0.87%, respectively (palmitic acid, C16:0), 0.75, 0.16%, 0.89 and 0.10%, respectively (palmitoleic acid, C16:1n-7), 0.93, 0.41%, 0.96 and 0.64%, respectively (steric acid, C18:0), 0.90, 0.51%, 0.94 and 0.44%, respectively (oleic acid, C18:1n-9), 0.97, 0.25%, 0.98 and 0.29% (linoleic acid, C18:2n-6), 0.68, 0.09%, 0.57 and 0.16% (a-linolenic acid, C18:3n-3) and 0.97, 0.57, 0.97 and 1.22, respectively (iodine value, calculated). The magnitude of this error showed quite good accuracy using these rapid methods in prediction of the moisture and fatty acid composition of fat from pigs involved in breeding schemes.Keywords: fat moisture, fatty acids composition, near-infrared spectroscopy, gold reflector, repeatability file ImplicationsThis study shows that it is possible to establish simple routine methods for measuring the quality of pig fat, as well as using preparation methods and instruments that are safe, user and environmentally friendly and do not require chemical solvents. The results reveal that several parameters for fat quality can be predicted by near-infrared spectroscopy (NIRS) technology, and a continuation of this study documents the use of NIRS predicted values in the estimation of genetic parameters for breeding purposes. The high heritability obtained in the follow-up study clearly shows the power of NIRS and the high prediction ability of the calibrations developed in this study.

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Section: Resultsmentioning

confidence: 97%

Prediction of fat quality in pig carcasses by near-infrared spectroscopy

Gjerlaug-Enger

Kongsro

Aass

et al. 2011

Animal

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“…Previous work (Geladi et al, 1985;Isaksson & Naes, 1988) has demonstrated that MSC of spectral data results in better linear ®t between NIR data and chemical concentratons, better spectral interpretability and improved prediction results compared with uncorrected data. The multivariate calibration method PLS (partial least squares) regression was used.…”

Section: Discussionmentioning

confidence: 99%

Near infrared spectroscopy—a potentially useful method for rapid determination of fat and protein content in homogenized diets

et al. 2000

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Objective: To investigate the potential of near-infrared re¯ectance (NIR) spectroscopy as a rapid and nondestructive method to determine total fat and protein in mixed, homogenized and freeze-dried human diets. Design: 29 students collected duplicate portions of their diet for four consecutive days. In addition, a detailed food diary was kept. The duplicate portions were analysed for total protein and fat content both by traditional chemical analysis (Kjeldahl and Folch methods) and through the recently developed NIR spectroscopy method. In addition, traditional computerized estimation of nutrient composition was performed. Results: Plotting of the NIR-predicted fat content against the chemically analysed fat content gave a correlation coef®cient of 0.99. Plotting of the NIR-predicted protein content against the Kjeldahl-analysed protein gave a correlation coef®cient of 0.81. Conclusion: NIR-spectroscopy seems to be able to determine fat content in mixed, homogenized diets to a high degree of accuracy. In surveys involving duplicate portion sampling this will save time and money. The prediction accuracy for protein was less convincing, but acceptable depending on the need for accurate individual data.

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“…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.…”

Section: Spectral Analysismentioning

confidence: 99%

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

Hou

Zhou

et al. 2016

FSTR

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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...

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The Effect of Multiplicative Scatter Correction (MSC) and Linearity Improvement in NIR Spectroscopy

Cited by 513 publications

References 10 publications

Prediction of fat quality in pig carcasses by near-infrared spectroscopy

Prediction of fat quality in pig carcasses by near-infrared spectroscopy

Near infrared spectroscopy—a potentially useful method for rapid determination of fat and protein content in homogenized diets

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

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