Influence of data pre-processing on the quantitative determination of the ash content and lipids in roasted coffee by near infrared spectroscopy

Pizarro, Consuelo; Esteban‐Díez, I.; Nistal, Antonio-José; González-Sáiz, J.M.

doi:10.1016/j.aca.2003.11.008

Cited by 96 publications

(60 citation statements)

References 18 publications

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Section: Nir Spectroscopymentioning

confidence: 99%

“…These variations result in dissimilar packing densities, intensity differences, light scattering effects, path length variations, and ultimately baseline shifts in the spectra (20). Instrumental effects such as random noise, changes in lamp intensity, and detector response may also cause variations within spectral groups and can adversely affect the robustness and reliability of the multivariate calibration model to be developed (5,20). In an attempt to eliminate, reduce, or standardize the effects of these variations on our multivariate model, we applied three different preprocessing methods, namely multiplicative scatter correction, standard normal variate, and Savitzky-Golay second derivative transformation with third-order polynomial using 7 filter points, to study the influence each pretreatment method on the robustness of our regression model.…”

Section: Nir Spectroscopymentioning

confidence: 99%

“…MSC gives an estimation of the relation of the scatter of each sample with respect to the scatter of a mean spectrum which is calculated from all the spectra in a defined data set by a least squares regression. Using this mean spectrum, the same least squares regression is performed on every spectrum to minimize the variations due to amplified or additive effects (20). SNV, on the other hand, is a row-oriented correction method which The length of the bar shows the order of effect of the factors on EE while the negative sign shows an inverse relationship between the factor and EE Fig.…”

Section: Nir Spectroscopymentioning

confidence: 99%

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Evaluation of Anticancer Drug-Loaded Nanoparticle Characteristics by Nondestructive Methodologies

et al. 2012

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Abstract. The purpose of this study was to utilize near-infrared (NIR) spectroscopy and near-infrared chemical imaging (NIR-CI) as non-invasive techniques to evaluate the drug loading in letrozole-loaded PLGA nanoparticle formulations prepared by the emulsification-solvent evaporation method. A Plackett-Burman design was applied to evaluate the main effects of amount of drug (X 1 ), amount of polymer (X 2 ), stirring rate (X 3 ), emulsifier concentration (X 4 ), organic to aqueous phase volume ratio (X 5 ), type of organic solvent (X 6 ), and homogenization time (X 7 ) on drug entrapment efficiency. The influence of three different spectral pretreatment methods (multiplicative scatter correction, standard normal variate, and Savitzky-Golay second derivative transformation with third-order polynomial) and two different regression methods (PLS regression and principal component regression (PCR)) on model prediction ability were compared. PLS of spectra that were pretreated with Savitzky-Golay second derivative transformation provided better model prediction than PCR as it revealed better linear correlation (correlation coefficient of 0.991) for both calibration and prediction models. Relatively low values of root mean square errors of calibration (RMSEC=0.748) and prediction (RMSEP=0.786) and low standard errors of calibration (SEC=0.758) and prediction (SEP=0.589) suggested good predictability for estimation of the loading of letrozole in PLGA nanoparticles. NIR-CI analysis also revealed mutual homogenous distribution of both polymer and drug and was capable of clearly distinguishing the 12 formulations both quantitatively and qualitatively. In conclusion, NIR and NIR-CI could be potentially used to characterize anticancer drug-loaded nanoparticulate matrix.

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Section: Nir Spectroscopymentioning

confidence: 99%

Section: Nir Spectroscopymentioning

confidence: 99%

Section: Nir Spectroscopymentioning

confidence: 99%

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Evaluation of Anticancer Drug-Loaded Nanoparticle Characteristics by Nondestructive Methodologies

et al. 2012

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“…Calibration methods were developed using the partial least squares regression (PLS) available from MATLAB software (Math-Works Inc.; using the Toolbox_PLS). In order to improve the efficiency of the methods of analysis, data were centered in the middle (Pizarro, E.-Diez, Nistal, & Gonzalez-Saiz, 2004) before the calibration phase, producing regression with the best forecast of new model samples.…”

Section: Pls Analysismentioning

confidence: 99%

Rapid detection of whey in milk powder samples by spectrophotometric and multivariate calibration

Carvalho

Coimbra

et al. 2015

Food Chemistry

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“…Therefore, the input data are often preprocessed before multivariate calibration. Commonly used preprocessing methods contain derivatives, multiplicative signal correction (MSC), standard normal variate (SNV) transformation, fourier transform, and wavelet transform (Barnes et al 1989;Luypaert et al 2007;Pizarro et al 2004). All these methods are based on spectral matrix (X), which is difficult to exclude the irrelevant information related to response (Y).…”

Section: Introductionmentioning

confidence: 99%

Identification of Geographical Origin of Olive Oil Using Visible and Near-Infrared Spectroscopy Technique Combined with Chemometrics

Lin

Chen

2009

Food Bioprocess Technol

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A novel multivariate calibration method was developed to identify the geographical origin of olive oils using visible and near-infrared spectroscopy (Vis/NIRS) on the wavelength between 325 and 1,075 nm. Direct orthogonal signal correction (DOSC) preprocessing method was performed to reduce the influence of light scattering, background noise, and baseline shift during experiment. An optimization method of genetic algorithms (GAs) was used to select informative variables from the full spectrum, and 37 informative variables were selected for partial least squares (PLS) regression analysis. The prediction results indicated that the developed DOSC-GA-PLS model can be successfully employed to predict geographical origin of olive oils. Moreover, the use of GA simplified and improved the predictive ability of the model. The prediction statistical parameters were correlation coefficient (R 2 P ) of 0.987, relative deviation was 0.093, and the recognition ratio was 97%. It was concluded that Vis/NIRS combined with DOSC-GA-PLS method can be successfully used to determine the geographical origin of olive oils accurately and quickly.

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Influence of data pre-processing on the quantitative determination of the ash content and lipids in roasted coffee by near infrared spectroscopy

Cited by 96 publications

References 18 publications

Evaluation of Anticancer Drug-Loaded Nanoparticle Characteristics by Nondestructive Methodologies

Evaluation of Anticancer Drug-Loaded Nanoparticle Characteristics by Nondestructive Methodologies

Rapid detection of whey in milk powder samples by spectrophotometric and multivariate calibration

Identification of Geographical Origin of Olive Oil Using Visible and Near-Infrared Spectroscopy Technique Combined with Chemometrics

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