Calibration Set Of Samples Research Articles

Compilation of a Near-Infrared Library for Construction of Quantitative Models of Oral Dosage Forms for Amoxicillin and Potassium Clavulanate.

The accuracy of quantitative models for near-infrared (NIR) spectroscopy is dependent upon calibration samples with concentration variations. Conventional sample-collection methods have shortcomings (especially time-consumption), which creates a “bottleneck” in the application of NIR models for Process Analytical Technology (PAT) control. We undertook a study to solve the problem of sample collection for construction of NIR quantitative models. Amoxicillin and potassium clavulanate oral dosage forms (ODFs) were used as examples. The aim of this study was to find an approach to construct NIR quantitative models rapidly using a NIR spectral library based on the idea of a universal model. The NIR spectral library of amoxicillin and potassium clavulanate ODFs was defined and comprised the spectra of 377 batches of samples produced by 26 domestic pharmaceutical companies, including tablets, dispersible tablets, chewable tablets, oral suspensions, and granules. The correlation coefficient (rT) was used to indicate the similarities of the spectra. The calibration sets of samples were selected from a spectral library according to the median rT of the samples to be analyzed. The rT of the samples selected was close to the median rT. The difference in rT of these samples was 1.0–1.5%. We concluded that sample selection was not a problem when constructing NIR quantitative models using a spectral library compared with conventional methods of determining universal models. Sample spectra with a suitable concentration range in NIR models were collected rapidly. In addition, the models constructed through this method were targeted readily.

Using near-infrared reflectance spectroscopy to predict physical parameters of beef

ABSTRACTThe objective of this study was to develop near-infrared reflectance spectroscopy models for predicting the physical parameters of beef from commercial cuts. Four hundred and forty-two minced beef samples from various commercial cuts of two cattle breeds were used for the modeling process and randomly divided into two subsets: a calibration set and an independent prediction set (75 vs. 25%). Reflectance spectra (1000–1800 nm) were collected from both subsets of samples, and calibration models were built using partial least square regression on the calibration set of samples. Different mathematical pretreatments were tested and mean centering or multiplicative scatter correction combined with first-derivative preprocessing gave the best calibration models on all the beef physical traits. Based on the selected calibration equations, the coefficients of determination in calibration and prediction for Warner-Bratzler shear force, pH, color L*, and a* were higher than 0.60, which means models in the calibration were acceptable. However, the ratio performance deviation for all parameters was less than 2.0, indicating that the prediction abilities on independent validation set were not adequate for routine analysis. Further studies are required to establish more robust models for practical application of using near-infrared reflectance spectroscopy to predict physical traits of beef.

Development of near infrared reflectance spectroscopy to predict chemical composition with a wide range of variability in beef

A total of 182 beef samples were minced and divided into calibration set (n=140) and independent validation set (n=42). Calibration models of NIRS (1000–1800nm) were built using partial least squares regression (PLSR) on the calibration set of samples. Both the coefficient of determination in calibration (R2C) and the coefficient of determination in prediction (R2P) were over 0.98 for all chemical compositions. The ratio performance deviation (RPD) was 17.37, 5.12 and 10.43 for fat, protein and moisture, respectively. The results of the present study indicate the outstanding ability of NIRS to predict chemical composition in beef.

Feasibility of NIR interactance hyperspectral imaging for on-line measurement of crude composition in vacuum packed dry-cured ham slices

There is a growing market for packaged slices of dry-cured ham. The heterogeneity of the composition of slices between packages is an important drawback when aiming to offer consumers a product with a known and constant composition which fits individual consumer expectations. The aim of this work was to test the feasibility of NIR interactance imaging for on-line analysis of water, fat and salt and their spatial distribution in dry-cured ham slices. PLSR models for predicting water, fat and salt contents with NIR spectra were developed with a calibration set of samples (n=82). The models were validated with an external validation set (n=42). The predictive models were accurate enough for screening purposes. The errors of prediction were 1.34%, 1.36% and 0.71% for water, fat and salt, respectively. The spatial distribution of these components within the slice was also obtained.

A closed‐form expression for computing the sensitivity in second‐order bilinear calibration

AbstractA general expression is derived for estimating the sensitivity of second‐order bilinear calibration models, particularly parallel factor analysis (PARAFAC) and bilinear least‐squares (BLLS), whether the second‐order advantage is required or not. In the latter case, the sensitivity is correctly estimated either if the advantage is achieved by processing the unknown sample together with the calibration set (PARAFAC), or by post‐calibration residual bilinearization (BLLS). The expression includes, as special cases, the sensitivity expressions already discussed by Messick, Kalivas and Lang (MKL) and by Ho, Christian and Davidson (HCD). The former one is the maximum achievable sensitivity in a given calibration situation, where all components are present in the calibration set of samples. The latter approach gives the lowest possible sensitivity, corresponding to only calibrating the analyte of interest, leaving the remaining components as uncalibrated constituents of the unknown sample. In intermediate situations, that is more than one calibrated analyte and presence of unexpected components in the unknown sample, only the present approach is able to provide a satisfactory sensitivity parameter, in close agreement with previously described Monte Carlo numerical simulations. Copyright © 2006 John Wiley & Sons, Ltd.

Windowing technique for determining the composition of organic samples by near-infrared reflectance

A method of windowing regions of interest in near-infrared reflectance spectra is described. Windows are bounded by wavelengths at which spectra from all samples are re-scaled to zero. These bounds are determined numerically to maximize the correlation between the scaled spectral values within a window and chemical composition, as determined by wet-chemical methods, of a calibration set of samples. It is shown that the method yields predictions of chemical composition of forages that are, on average, more accurate than comparable methods based on principal components regression.