Effect of Experimental Design on the Prediction Performance of Calibration Models Based on Near-Infrared Spectroscopy for Pharmaceutical Applications

2012 ◽  
Vol 66 (12) ◽  
pp. 1442-1453 ◽  
Author(s):  
Robert W. Bondi ◽  
Benoît Igne ◽  
James K. Drennen ◽  
Carl A. Anderson
Keyword(s):  
Infrared Spectroscopy ◽  
Experimental Design ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Prediction Performance ◽  
Pharmaceutical Applications ◽  
Calibration Models

Linear transformations of multivariate calibration models in near infrared spectroscopy: A comparative study

2017 ◽  
Vol 25 (4) ◽  
pp. 223-230 ◽  
Author(s):  
Joseph Dubrovkin
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Multivariate Calibration ◽  
Cetane Number ◽  
Total Protein Content ◽  
Prediction Errors ◽  
Calibration Data ◽  
Linear Transformations ◽  
Calibration Models

It was shown that linear transformations are suitable for use in multivariate calibration in near infrared spectroscopy as data compression tools. Partial Least Squares calibration models were built using spectral data transformed by expansion in the series of classical orthogonal polynomials, Fourier and wavelet harmonics. These models allowed effective prediction of the cetane number of diesel fuels, Brix and pol parameters of syrup in sugar production and fat and total protein content in milk. Depending on the compression ratio, prediction errors were no larger than 30% of corresponding errors obtained by the use of the non-transformed models. Although selection of the most suitable transformation depends on the calibration data and on the cross-validation method, in many cases Fourier transform gave satisfactory results.

scholarly journals Determination of glomalin in agriculture and forest soils by near-infrared spectroscopy

2017 ◽  
Vol 63 (No. 5) ◽  
pp. 226-230 ◽  
Author(s):  
Zbíral Jiří ◽  
Čižmár David ◽  
Malý Stanislav ◽  
Obdržálková Elena
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Forest Soils ◽  
Near Infrared ◽  
Arable Land ◽  
Calibration Model ◽  
Sustainable Land Management ◽  
Chemical Methods ◽  
Calibration Models

Determining and characterizing soil organic matter (SOM) cheaply and reliably can help to support decisions concerning sustainable land management and climate policy. Glomalin was recommended as one of possible indicators of SOM quality. Extracting glomalin from and determining it in soils using classical chemical methods is too complicated and therefore near-infrared spectroscopy (NIRS) was studied as a method of choice for the determination of glomalin. Representative sets of 84 different soil samples from arable land and grasslands and 75 forest soils were used to develop NIRS calibration models. The parameters of the NIRS calibration model (R = 0.90 for soils from arable land and grasslands and R = 0.94 for forest soils) proved that glomalin can be determined in air-dried soils by NIRS with adequate trueness and precision simultaneously with determination of nitrogen and oxidizable carbon.

scholarly journals Determination Residues of Penicillin G and Cloxacillin in Raw Cow Milk Using Fourier Transform Near Infrared Spectroscopy

2009 ◽  
Vol 78 (4) ◽  
pp. 685-690 ◽  
Author(s):  
Michaela Dračková ◽  
Pavlína Navrátilová ◽  
Luboš Hadra ◽  
Lenka Vorlová ◽  
Lenka Hudcová
Keyword(s):  
Fourier Transform ◽  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Raw Milk ◽  
Partial Least Square ◽  
Penicillin G ◽  
Standard Errors ◽  
Calibration And Validation ◽  
Calibration Models

The objective of this study was to study the use of Fourier transform near infrared spectroscopy (FTNIR) combined with the partial least square (PLS) method for determining the residues of penicillin and cloxacillin in raw milk. The spectra were measured in the reflectance mode with transflectance cell in the spectral range of 10,000 – 4,000 cm-1 with 100 scans. Calibration models were developed. They were assessed statistically based on correlation coefficients (R) and standard errors of calibration (SEC). For penicillin, the following values were established: R = 0.951 and SEC = 0.004. For cloxacillin, they were R = 0.871 and SEC = 0.007. These calibration models were verified later with cross-validation. Better results were obtained in the calibration and validation models that were developed on milk samples coming from one farm. Using FT-NIR, the maximum residue limit (MRL) of cloxacillin in milk can be determined. However, standard errors of calibration and validation for penicillin G exceed the fixed MRL. FT-NIR spectroscopy is not a suitable method for accurate determination of these substances in raw milk. Variability in milk composition has a major influence on detection of substances present at very low concentrations.

Estimation of Viscosity of Biodiesel-Diesel Blends with near Infrared Spectroscopy

2012 ◽  
Vol 608-609 ◽  
pp. 324-327
Author(s):  
Wei Bo Zhang ◽  
Ming Ming Wu
Keyword(s):  
Infrared Spectroscopy ◽  
Least Squares ◽  
Partial Least Squares ◽  
Near Infrared Spectroscopy ◽  
Kinematic Viscosity ◽  
Near Infrared ◽  
Diesel Oil ◽  
Processing Methods ◽  
Calibration Models ◽  
Using Data

Biodiesel is one of the most important substitutes for diesel oil. This work reports the use of near Infrared Spectroscopy (NIR) to estimate the kinematic viscosity value of biodiesel-diesel blends. Partial least squares models were developed using data of different spectra regions and different pre-processing methods were employed for developing the calibration models. The results indicate that NIR can be used in biodiesel-diesel blends properties detecting.

Analysis of Energy Traits of Populus spp. Clones by Near-Infrared Spectroscopy

2007 ◽  
Vol 1 (1) ◽  
pp. 155-162 ◽  
Author(s):  
Melchor C. Maranan ◽  
Marie-Pierre G. Laborie
Keyword(s):  
Infrared Spectroscopy ◽  
Specific Gravity ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Hybrid Poplar ◽  
Calorific Value ◽  
Validation Dataset ◽  
Hybrid Poplars ◽  
Lower Accuracy ◽  
Calibration Models

The application of near-infrared spectroscopy (NIRS) and multivariate analysis for determining the calorific value and specific gravity of Populus spp. clones was assessed. Projection to latent structure (PLS) models of calorific value and specific gravity were developed from NIR original spectra and also from the first and second spectral derivatives. The best calibration models were built from the NIR second spectral derivative with good calibration statistics for both calorific value (r = 0 97, RMSEC = 0 05 kJ/g) and specific gravity (r = 0 98; RMSEC = 0 005). The calibration models from the NIR first spectral derivative were also good for specific gravity (r = 0 92, RMSEC = 0 010) and moderate for calorific value (r = 0 82; RMSEC = 0 11 kJ/g). When evaluated on a validation dataset, the models from the NIR first spectral derivative performed best for both specific gravity (r = 0 84; RMSEP = 0 021) and calorific value (r = 0 81; RMSEP = 0 13 kJ/g). In both cases, the standard errors of prediction (SEP) obtained from the NIRS calibration models were less than twice those of the corresponding laboratory measurement. The NIRS models were therefore useful for quickly determining calorific value and specific gravity of hybrid poplars but with a lower accuracy than the corresponding laboratory measurements. The study also helped delineate parentage as a factor of choice for manipulating wood specific gravity and thus biomass yield in hybrid poplars. On the other hand, calorific value was uniform within the population evaluated, indicating that little improvement in calorific value can be expected from selecting for it in hybrid poplar programs.

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