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Calibration Set (calibration + set)
Selected AbstractsComparison of fatty acid profiles and mid-infrared spectral data for classification of olive oilsEUROPEAN JOURNAL OF LIPID SCIENCE AND TECHNOLOGY, Issue 2 2010Gozde Gurdeniz Abstract The composition of olive oils may vary depending on environmental and technological factors. Fatty acid profiles and Fourier-transform infrared (FT-IR) spectroscopy data in combination with chemometric methods were used to classify extra-virgin olive oils according to geographical origin and harvest year. Oils were obtained from 30,different areas of northern and southern parts of the Aegean Region of Turkey for two consecutive harvest years. Fatty acid composition data analyzed with principal component analysis was more successful in distinguishing northern olive oil samples from southern samples compared to spectral data. Both methods have the ability to differentiate olive oil samples with respect to harvest year. Partial least squares (PLS) analysis was also applied to detect a correlation between fatty acid profile and spectral data. Correlation coefficients (R2) of a calibration set for stearic, oleic, linoleic, arachidic and linolenic acids were determined as 0.83, 0.97, 0.97, 0.83 and 0.69, respectively. Fatty acid profiles were very effective in classification of oils with respect to geographic origin and harvest year. On the other hand, FT-IR spectra in combination with PLS could be a useful and rapid tool for the determination of some of the fatty acids of olive oils. [source] Authentication of Green Asparagus Varieties by Near-Infrared Reflectance SpectroscopyJOURNAL OF FOOD SCIENCE, Issue 2 2001D.P. Perez ABSTRACT: Near-infrared reflectance spectroscopy (NIRS) was used for the authentication of 2 green asparagus varieties (Taxara and UC-157), grown in Huetor-Tajar (Granada, Spain) protected by the Quality Specific Appellation "Espárragos de Huétor-Tájar". To develop the prediction model, the method chosen was modified partial least square (MPLS) regression. Two sample sets (N = 219 and N2 = 145 samples, respectively) were used to obtain the calibration equations. The standard error of cross-validation (SECV) and the r2 value were 0.082 and 0.97, respectively, for the 1st calibration set and 0.077 and 0.97 for the 2nd calibration set. The 2nd chemometric model obtained was tested with independent validation sample set (N3 = 74 samples), and the resulting values for standard error of prediction (SEP) and for r2 were 0.07 and 0.96, respectively. These results prove that NIRS is an accurate technology for identification and authentication of asparagus varieties and easily implemented in industry. [source] Monitoring the film coating unit operation and predicting drug dissolution using terahertz pulsed imagingJOURNAL OF PHARMACEUTICAL SCIENCES, Issue 12 2009Louise Ho Abstract Understanding the coating unit operation is imperative to improve product quality and reduce output risks for coated solid dosage forms. Three batches of sustained-release tablets coated with the same process parameters (pan speed, spray rate, etc.) were subjected to terahertz pulsed imaging (TPI) analysis followed by dissolution testing. Mean dissolution times (MDT) from conventional dissolution testing were correlated with terahertz waveforms, which yielded a multivariate, partial least squares regression (PLS) model with an R2 of 0.92 for the calibration set and 0.91 for the validation set. This two-component, PLS model was built from batch I that was coated in the same environmental conditions (air temperature, humidity, etc.) to that of batch II but at different environmental conditions from batch III. The MDTs of batch II was predicted in a nondestructive manner with the developed PLS model and the accuracy of the predicted values were subsequently validated with conventional dissolution testing and found to be in good agreement. The terahertz PLS model was also shown to be sensitive to changes in the coating conditions, successfully identifying the larger coating variability in batch III. In this study, we demonstrated that TPI in conjunction with PLS analysis could be employed to assist with film coating process understanding and provide predictions on drug dissolution. © 2009 Wiley-Liss, Inc. and the American Pharmacists Association J Pharm Sci 98:4866,4876, 2009 [source] Comparison of Artificial Neural Networks with Partial Least Squares Regression for Simultaneous Determinations by ICP-AESCHINESE JOURNAL OF CHEMISTRY, Issue 11 2007Mohamad KHAYATZADEH MAHANI Abstract Simultaneous determination of several elements (U, Ta, Mn, Zr and W) with inductively coupled plasma atomic emission spectrometry (ICP-AES) in the presence of spectral interference was performed using chemometrics methods. True comparison between artificial neural network (ANN) and partial least squares regression (PLS) for simultaneous determination in different degrees of overlap was investigated. The emission spectra were recorded at uranium analytical line (263.553 nm) with a 0.06 nm spectral window by ICP-AES. Principal component analysis was applied to data and scores on 5 dominant principal components were subjected to ANN. A 5-5-5 (input, hidden and output neurons) network was used with linear transfer function after both hidden and output layers. The PLS model was trained with five latent variables and 20 samples in calibration set. The relative errors of predictions (REP) in test set were 3.75% and 3.56% for ANN and PLS respectively. [source] | ||||||||||