The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Miguel De La Guardia - One of the best experts on this subject based on the ideXlab platform.
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nutritional parameters of commercially available milk samples by ftir and chemometric techniques
Analytica Chimica Acta, 2004Co-Authors: Fernando A Inon, Salvador Garrigues, Miguel De La GuardiaAbstract:Abstract A chemometric study on the prediction of the main nutritional aspects of milk has been carried out by using fourier transform infrared spectroscopy (FTIR) attenuated total reflectance (ATR) measurements of commercially available milk samples of different types. Whole, semi and skimmed milks, enriched or not with calcium, vitamins or modified by alteration of lipid or sugar composition were considered. After evaluating different strategies for data acquisition and ATR cleaning between samples, hierarchical cluster analysis (HCA) was carried out for classification of samples in order to choose the Calibration Set. The prediction capabilities of partial least squares (PLS) data treatment were evaluated in order to obtain information about total fat, total protein, total carbohydrates (CH), calories and calcium. On using the mean square error of cross-validation and prediction as control variables, a critical evaluation were made about the Calibration Set to be used, the spectral range to be considered and the data treatment (PLS-1 or PLS-2) to be performed. By selecting a Calibration Set of 33 samples the properties of 48 samples were predicted with relative precision of triplicates of 0.062, 0.040 and 0.039% w/v for total fat, protein and carbohydrates, and 0.66 kcal/100 ml for calories, and 2.1 mg of Ca/100 ml. The mean difference ( d x − y ) between predicted and actual values and standard deviation of mean differences ( s x − y ), were of 0.06 (0.38), 0.03 (0.18) and −0.15 (0.41), being s x − y values between brackets, for total fat, proteins and carbohydrates, 0.06 (3.8) kcal/100 ml for calories and −4.5 (9) mg/100 ml for calcium. The sensitivity and selectivity of the methodology developed were evaluated on terms of the net analyte signal. Selectivity factors ranging from 2 to 7.6% have been calculated for the five parameters considered.
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selection of Calibration Set samples in determination of olive oil acidity by partial least squares attenuated total reflectance fourier transform infrared spectroscopy
Analytica Chimica Acta, 2003Co-Authors: Fernando A Inon, Jose M Garrigues, Salvador Garrigues, Antonio Molina, Miguel De La GuardiaAbstract:Abstract A chemometric method has been applied for the determination of the free fatty acid (FFA) concentration in commercial olive oil samples of different types an origins by using Fourier transform infrared spectroscopy (FTIR) attenuated total reflectance (ATR) measurements. Different methods for selecting the training Set, including hierarchical cluster analysis, were applied and compared. The prediction capabilities of partial least squares (PLS) multivariate Calibration methods, net analyte signal (NAS) preprocessing followed by PLS or classical least squares (CLS) regression method of ATR–FTIR data were evaluated. Several aspects, like spectral range to be considered, different preprocessing alternatives (mean centering, multiplicative scattering correction, standard normal variate (SNV)), together with a critical evaluation of the Calibration Set were made on using the mean square error of cross-validation and prediction, as control parameters. Using a Calibration Set of 16 samples the properties of 28 samples were predicted with relative precision of triplicates of 0.017 wt.%. The mean difference between predicted and actual values and the standard deviation of mean differences were −0.001 and 0.037 wt.%, respectively. The limit of detection (LOD), sensitivity and selectivity of the methodology developed were evaluated in terms of the net analyte signal, being found a limit of detection of 0.072 wt.%, a sensitivity value of 0.077 in terms of analytical signal per unit of concentration, being expressed that in wt.%, and a linear relationship ( R 2 =0.9963) between selectivity and FFA concentration (equivalent to 0.24% for a sample containing 1 wt.% of FFA).
Manel Alcala - One of the best experts on this subject based on the ideXlab platform.
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Strategy for design NIR Calibration Sets based on process spectrum and model space: An innovative approach for process analytical technology
Journal of Pharmaceutical and Biomedical Analysis, 2015Co-Authors: Vanessa Cárdenas, M. Cordobés, M Blanco, Manel AlcalaAbstract:The pharmaceutical industry is under stringent regulations on quality control of their products because is critical for both, productive process and consumer safety. According to the framework of "process analytical technology" (PAT), a complete understanding of the process and a stepwise monitoring of manufacturing are required.Near infrared spectroscopy (NIRS) combined with chemometrics have lately performed efficient, useful and robust for pharmaceutical analysis. One crucial step in developing effective NIRS-based methodologies is selecting an appropriate Calibration Set to construct models affording accurate predictions.In this work, we developed Calibration models for a pharmaceutical formulation during its three manufacturing stages: blending, compaction and coating. A novel methodology is proposed for selecting the Calibration Set -"process spectrum"-, into which physical changes in the samples at each stage are algebraically incorporated.Also, we established a "model space" defined by Hotelling's T2 and Q-residuals statistics for outlier identification - inside/outside the defined space - in order to select objectively the factors to be used in Calibration Set construction.The results obtained confirm the efficacy of the proposed methodology for stepwise pharmaceutical quality control, and the relevance of the study as a guideline for the implementation of this easy and fast methodology in the pharma industry.
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strategies for constructing the Calibration Set for a near infrared spectroscopic quantitation method
Talanta, 2004Co-Authors: M Blanco, M A Romero, Manel AlcalaAbstract:Abstract Three strategies for the construction of Calibration Sets have been tried, with the objective to develop and to validate a NIR quantitation method. The first two approaches consist of the use of two types of samples, named: samples of laboratory obtained by mixing the ingredients that compose the drug, and doped samples obtained by under- and over-dosed production samples. In order to improve the prediction results, production samples have been added to each Calibration model. The ensuing models were validated with a view to determine their fitness for purpose. However, spectral differences between the laboratory samples and doped samples resulted in spurious predictions in quantifying samples of one type using the model developed from samples of the other. Such differences were studied in depth and a third procedure has been proposed, based on a Calibration model constructed with an unique type of sample (laboratory sample) for later to correct it with a few doped samples. This corrected model has a good predictive ability on production samples.
M Blanco - One of the best experts on this subject based on the ideXlab platform.
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Strategy for design NIR Calibration Sets based on process spectrum and model space: An innovative approach for process analytical technology
Journal of Pharmaceutical and Biomedical Analysis, 2015Co-Authors: Vanessa Cárdenas, M. Cordobés, M Blanco, Manel AlcalaAbstract:The pharmaceutical industry is under stringent regulations on quality control of their products because is critical for both, productive process and consumer safety. According to the framework of "process analytical technology" (PAT), a complete understanding of the process and a stepwise monitoring of manufacturing are required.Near infrared spectroscopy (NIRS) combined with chemometrics have lately performed efficient, useful and robust for pharmaceutical analysis. One crucial step in developing effective NIRS-based methodologies is selecting an appropriate Calibration Set to construct models affording accurate predictions.In this work, we developed Calibration models for a pharmaceutical formulation during its three manufacturing stages: blending, compaction and coating. A novel methodology is proposed for selecting the Calibration Set -"process spectrum"-, into which physical changes in the samples at each stage are algebraically incorporated.Also, we established a "model space" defined by Hotelling's T2 and Q-residuals statistics for outlier identification - inside/outside the defined space - in order to select objectively the factors to be used in Calibration Set construction.The results obtained confirm the efficacy of the proposed methodology for stepwise pharmaceutical quality control, and the relevance of the study as a guideline for the implementation of this easy and fast methodology in the pharma industry.
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strategies for constructing the Calibration Set for a near infrared spectroscopic quantitation method
Talanta, 2004Co-Authors: M Blanco, M A Romero, Manel AlcalaAbstract:Abstract Three strategies for the construction of Calibration Sets have been tried, with the objective to develop and to validate a NIR quantitation method. The first two approaches consist of the use of two types of samples, named: samples of laboratory obtained by mixing the ingredients that compose the drug, and doped samples obtained by under- and over-dosed production samples. In order to improve the prediction results, production samples have been added to each Calibration model. The ensuing models were validated with a view to determine their fitness for purpose. However, spectral differences between the laboratory samples and doped samples resulted in spurious predictions in quantifying samples of one type using the model developed from samples of the other. Such differences were studied in depth and a third procedure has been proposed, based on a Calibration model constructed with an unique type of sample (laboratory sample) for later to correct it with a few doped samples. This corrected model has a good predictive ability on production samples.
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strategies for constructing the Calibration Set in the determination of active principles in pharmaceuticals by near infrared diffuse reflectance spectrometry
Analyst, 1997Co-Authors: M Blanco, J Coello, H Iturriaga, S Maspoch, C De La PezuelaAbstract:The active principle in the blended phase of a commercially available pharmaceutical preparation was determined using near infrared diffuse reflectance spectrometry in combination with a fibre optic probe and multivariate Calibration by partial least-squares regression. Two different ways of preparing laboratory samples spanning an appropriate concentration range for constructing the Calibration Set were compared. One of the procedures involves preparing synthetic samples by weighing and the other using under- and overdosed production samples. Although the results provided by the two strategies were not significantly different, the second was judged more effective because it has a less marked effect on those physical properties of the samples that affect the IR spectrum. The prediction errors obtained (less than 1%) indicate the suitability of the proposed sample preparation procedure, which is faster than the usual method of choice and provides comparable results.
Salvador Garrigues - One of the best experts on this subject based on the ideXlab platform.
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Evaluation of infrared spectroscopy as a screening tool for serum analysis: Impact of the nature of samples included in the Calibration Set
Microchemical Journal, 2013Co-Authors: David Perez-guaita, Salvador Garrigues, Josep F. Ventura-gayete, C. Pérez-rambla, M. Sancho-andreu, M. GuardiaAbstract:Abstract The application of attenuated total reflectance Fourier transform infrared (ATR-FT-IR) spectroscopy to the determination of clinical parameters in serum using partial least squares (PLS) has been evaluated as a point-of-care diagnostic tool. In this study the effect of using an increased size of the Calibration Set and the influence of the origin of samples and their interyear variation on the prediction capability of the method were considered. PLS-ATR-FT-IR provides a green, fast and cheap point-of-care tool for the determination of total protein. Albumin, glucose, urea, HDL, LDL and total cholesterol were predicted with relative errors between 15 and 32%. The analytical prediction capability of models built from an increased number of samples, from 100 till 750, was evaluated with independent sample Sets. The evolution of the relative root mean square standard error of prediction (RRMSEP) as a function of the number of samples employed for Calibration was different among different analytes, being the prediction capability strongly dependent of the concentration level of each analyte in the sample. Two Sets of 750 each were built in two successive years, including samples from different origins (primary care, pre-dialysis and hospital). Multivariate analysis of variance (MANOVA) and principal component analysis (PCA) evidenced the strong influence of the aforementioned factors on the sample spectra.
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nutritional parameters of commercially available milk samples by ftir and chemometric techniques
Analytica Chimica Acta, 2004Co-Authors: Fernando A Inon, Salvador Garrigues, Miguel De La GuardiaAbstract:Abstract A chemometric study on the prediction of the main nutritional aspects of milk has been carried out by using fourier transform infrared spectroscopy (FTIR) attenuated total reflectance (ATR) measurements of commercially available milk samples of different types. Whole, semi and skimmed milks, enriched or not with calcium, vitamins or modified by alteration of lipid or sugar composition were considered. After evaluating different strategies for data acquisition and ATR cleaning between samples, hierarchical cluster analysis (HCA) was carried out for classification of samples in order to choose the Calibration Set. The prediction capabilities of partial least squares (PLS) data treatment were evaluated in order to obtain information about total fat, total protein, total carbohydrates (CH), calories and calcium. On using the mean square error of cross-validation and prediction as control variables, a critical evaluation were made about the Calibration Set to be used, the spectral range to be considered and the data treatment (PLS-1 or PLS-2) to be performed. By selecting a Calibration Set of 33 samples the properties of 48 samples were predicted with relative precision of triplicates of 0.062, 0.040 and 0.039% w/v for total fat, protein and carbohydrates, and 0.66 kcal/100 ml for calories, and 2.1 mg of Ca/100 ml. The mean difference ( d x − y ) between predicted and actual values and standard deviation of mean differences ( s x − y ), were of 0.06 (0.38), 0.03 (0.18) and −0.15 (0.41), being s x − y values between brackets, for total fat, proteins and carbohydrates, 0.06 (3.8) kcal/100 ml for calories and −4.5 (9) mg/100 ml for calcium. The sensitivity and selectivity of the methodology developed were evaluated on terms of the net analyte signal. Selectivity factors ranging from 2 to 7.6% have been calculated for the five parameters considered.
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selection of Calibration Set samples in determination of olive oil acidity by partial least squares attenuated total reflectance fourier transform infrared spectroscopy
Analytica Chimica Acta, 2003Co-Authors: Fernando A Inon, Jose M Garrigues, Salvador Garrigues, Antonio Molina, Miguel De La GuardiaAbstract:Abstract A chemometric method has been applied for the determination of the free fatty acid (FFA) concentration in commercial olive oil samples of different types an origins by using Fourier transform infrared spectroscopy (FTIR) attenuated total reflectance (ATR) measurements. Different methods for selecting the training Set, including hierarchical cluster analysis, were applied and compared. The prediction capabilities of partial least squares (PLS) multivariate Calibration methods, net analyte signal (NAS) preprocessing followed by PLS or classical least squares (CLS) regression method of ATR–FTIR data were evaluated. Several aspects, like spectral range to be considered, different preprocessing alternatives (mean centering, multiplicative scattering correction, standard normal variate (SNV)), together with a critical evaluation of the Calibration Set were made on using the mean square error of cross-validation and prediction, as control parameters. Using a Calibration Set of 16 samples the properties of 28 samples were predicted with relative precision of triplicates of 0.017 wt.%. The mean difference between predicted and actual values and the standard deviation of mean differences were −0.001 and 0.037 wt.%, respectively. The limit of detection (LOD), sensitivity and selectivity of the methodology developed were evaluated in terms of the net analyte signal, being found a limit of detection of 0.072 wt.%, a sensitivity value of 0.077 in terms of analytical signal per unit of concentration, being expressed that in wt.%, and a linear relationship ( R 2 =0.9963) between selectivity and FFA concentration (equivalent to 0.24% for a sample containing 1 wt.% of FFA).
Fernando A Inon - One of the best experts on this subject based on the ideXlab platform.
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nutritional parameters of commercially available milk samples by ftir and chemometric techniques
Analytica Chimica Acta, 2004Co-Authors: Fernando A Inon, Salvador Garrigues, Miguel De La GuardiaAbstract:Abstract A chemometric study on the prediction of the main nutritional aspects of milk has been carried out by using fourier transform infrared spectroscopy (FTIR) attenuated total reflectance (ATR) measurements of commercially available milk samples of different types. Whole, semi and skimmed milks, enriched or not with calcium, vitamins or modified by alteration of lipid or sugar composition were considered. After evaluating different strategies for data acquisition and ATR cleaning between samples, hierarchical cluster analysis (HCA) was carried out for classification of samples in order to choose the Calibration Set. The prediction capabilities of partial least squares (PLS) data treatment were evaluated in order to obtain information about total fat, total protein, total carbohydrates (CH), calories and calcium. On using the mean square error of cross-validation and prediction as control variables, a critical evaluation were made about the Calibration Set to be used, the spectral range to be considered and the data treatment (PLS-1 or PLS-2) to be performed. By selecting a Calibration Set of 33 samples the properties of 48 samples were predicted with relative precision of triplicates of 0.062, 0.040 and 0.039% w/v for total fat, protein and carbohydrates, and 0.66 kcal/100 ml for calories, and 2.1 mg of Ca/100 ml. The mean difference ( d x − y ) between predicted and actual values and standard deviation of mean differences ( s x − y ), were of 0.06 (0.38), 0.03 (0.18) and −0.15 (0.41), being s x − y values between brackets, for total fat, proteins and carbohydrates, 0.06 (3.8) kcal/100 ml for calories and −4.5 (9) mg/100 ml for calcium. The sensitivity and selectivity of the methodology developed were evaluated on terms of the net analyte signal. Selectivity factors ranging from 2 to 7.6% have been calculated for the five parameters considered.
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selection of Calibration Set samples in determination of olive oil acidity by partial least squares attenuated total reflectance fourier transform infrared spectroscopy
Analytica Chimica Acta, 2003Co-Authors: Fernando A Inon, Jose M Garrigues, Salvador Garrigues, Antonio Molina, Miguel De La GuardiaAbstract:Abstract A chemometric method has been applied for the determination of the free fatty acid (FFA) concentration in commercial olive oil samples of different types an origins by using Fourier transform infrared spectroscopy (FTIR) attenuated total reflectance (ATR) measurements. Different methods for selecting the training Set, including hierarchical cluster analysis, were applied and compared. The prediction capabilities of partial least squares (PLS) multivariate Calibration methods, net analyte signal (NAS) preprocessing followed by PLS or classical least squares (CLS) regression method of ATR–FTIR data were evaluated. Several aspects, like spectral range to be considered, different preprocessing alternatives (mean centering, multiplicative scattering correction, standard normal variate (SNV)), together with a critical evaluation of the Calibration Set were made on using the mean square error of cross-validation and prediction, as control parameters. Using a Calibration Set of 16 samples the properties of 28 samples were predicted with relative precision of triplicates of 0.017 wt.%. The mean difference between predicted and actual values and the standard deviation of mean differences were −0.001 and 0.037 wt.%, respectively. The limit of detection (LOD), sensitivity and selectivity of the methodology developed were evaluated in terms of the net analyte signal, being found a limit of detection of 0.072 wt.%, a sensitivity value of 0.077 in terms of analytical signal per unit of concentration, being expressed that in wt.%, and a linear relationship ( R 2 =0.9963) between selectivity and FFA concentration (equivalent to 0.24% for a sample containing 1 wt.% of FFA).
