Optimal Calibration Models Research Articles

Rapid and nondestructive quantitative analysis of natural rubber blends regardless of geographical origin and harvest time of the natural rubber

ABSTRACTNatural rubber (NR) blends are widely used in many industries because of their excellent integrated properties. However, a simple, easily operational, nondestructive, and accurate method for their quantitative analysis remains as a challenge. This has been always an important issue in the related industries, particularly for their daily quality control tests. One main reason is that NR ingredients vary according to their geographical origin and the harvest time, which renders it hard to set up a versatile analytical protocol for all NRs. Another reason is owing to the defects of the established methods themselves as having been revealed in those relying on TGA, Py‐GC/MS, FTIR, and ATR‐FTIR. In this study, a simple and feasible method based on near infrared spectroscopy combined with chemometric is proposed to solve this problem for the first time. NR/SBR (styrene‐butadiene rubber) rubber blend, the most widely used NR blend, is selected as a typical research subject. Spectral calibration region, factor, and several different pretreatment methods are applied on the spectra data to optimize calibration models. The result shows the optimized calibration model provides a good accuracy (0.135 wt %), intraday precision (0.121 wt %) and interday precision (0.132 wt %) for 3 months. © 2014 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2015, 132, 41423.

Automatic Selection for Optimal Calibration Model of Camera

When calibration model express camera status well, calibration result will be accurate. How to select optimal calibration model for different camera by program is significant to calibrate automatically. A method of selecting optimal calibration model was proposed in this paper. First, the models including enough possible status were selected from physical models and Chebyshev models. These models would be taken as the input of our method. Second, candidate calibration models were obtained by variance detection. Third, optimal calibration model was extracted by utilizing detection of minimum description length. Experimental results show that calibration residuals is lowest via optimal model, computation error of principle distance is no more than 0.2 pixels and variance lies in the bounds of 0.5 pixels. DOI : http://dx.doi.org/10.11591/telkomnika.v12i6.5447 Full Text: PDF

Determination of polymer blends composed of polycarbonate and rubber entities using near-infrared (NIR) spectroscopy and multivariate calibration

This paper presents an application of near-infrared NIR spectroscopy and multivariate calibration for compositional analysis of complex blends of polycarbonate (PC) and three copolymer components (C1, C2, and C3). Each of the copolymers is composed of 2–3 entities (sub-components) consisting of combinations of butadiene, styrene and acrylonitrile. The concentrations of the PC and three copolymer components were varied using a modified D-optimal design with criteria that minimized the inter-component correlations. To minimize non-chemical spectral variations, the acquired NIR spectra were pre-processed using standard normal variate (SNV and 2nd derivative Savitzky-Golay). Spectral range selection was explored in order to identify which optimal spectral regions were required to generate robust partial least-squares (PLS) models for each component. The optimal calibration models for PC, C1, C2 and C3 exhibited RMSEP values of 0.94%, 0.62%, 0.59% and 0.69% respectively. Using a set of external validation samples, the optimized calibration models for PC, C1, C2 and C3 exhibited bias values of −1.07%, 0.28%, −1.21% and −1.00%, and RMSEP of 2.43%, 1.44%, 1.51%, and 2.05%, respectively. Finally, using a set of 3 samples, the optimized model was successfully transferred to a secondary instrument located in a quality control (QC) laboratory.

Detecting macronutrients content and distribution in oilseed rape leaves based on hyperspectral imaging

This study was carried out to investigate the potential of visible and near infrared (VIS–NIR) hyperspectral imaging system for rapid and non-destructive content determination and distribution estimation of nitrogen (N), phosphorus (P) and potassium (K) in oilseed rape leaves. Hyperspectral images of 140 leaf samples were acquired in the wavelength range of 380–1030 nm and their spectral data were extracted from the region of interest (ROI). Partial least square regression (PLSR) and least-squares support vector machines (LS-SVM) were applied to relate the nutrient content to the corresponding spectral data and reasonable estimation results were obtained. The regression coefficients of the resulted PLSR models with full range spectra were used to identify the effective wavelengths and reduce the high dimensionality of the hyperspectral data. LS-SVM model for N with RP of 0.882, LS-SVM model for P with RP of 0.710, and PLSR model for K with RP of 0.746 respectively got the best prediction performance for the determination of the content of these three macronutrients based on the effective wavelengths. Distribution maps of N, P and K content in rape leaves were generated by applying the optimal calibration models in each pixel of reduced hyperspectral images. The different colours represented indicated the change of nutrient content in the leaves under different fertiliser treatments. The results revealed that hyperspectral imaging is a promising technique to detect macronutrients within oilseed rape leaves non-destructively and could be applied to in situ detection in living plants.

Adaptive multiscale regression for reliable Raman quantitative analysis

This paper presents a novel methodology, adaptive multiscale regression (AMR), to adaptively process Raman spectra for quantitative analysis. The proposed methodology aims to construct an optimal calibration model for a Raman spectrum at hand, regardless of its structural characteristics, thus facilitating the application of Raman spectroscopy as a general tool for analytical chemistry. AMR firstly splits the spectra in a calibration set into frequency components at different scales using adaptive wavelet transform (AWT). Parallel member models constructed at different scales are then fused into a final prediction. The contributions of member models to a fusion model are straightforwardly estimated by a partial least square (PLS) model that emerges from a cross-validation results matrix (X) and reference values (Y). This procedure avoids information leakage by fully utilizing the multiscale nature of the input Raman spectra instead of arbitrarily removing some part of the spectral information by calibrating to selected features. Theoretically, we establish that AMR represents an automatic data-driven strategy that captures the Raman spectral structures adaptively and accurately. Our work tests and refines the AMR method by drawing upon the systematic analysis of spectra formulated to yield challenges representative of those encountered in common Raman analyses. AMR compares favorably with other popular preprocessing methods. Satisfactory calibration results suggest that AMR has the capacity to improve robustness and reliability of Raman spectral analysis, and may well extend to other spectroscopic techniques.

Determination of total content of ten ginsenosides in Yiqifumai lyophilized injection by near infrared spectroscopy

To determine the total content of 10 ginsenosides in Yiqifumai lyophilized injection by near infrared spectroscopy. Sixty samples were collected and determined of the total contents of ten ginsenosides by HPLC. The optimal calibration model was established by the contents of 10 ginsenosides in fifty samples and their NIR spectroscopy using the PLS. And the contents of 10 samples were successfully predicted. When using the pretreatment of the first derivative and MSC in the range of 4 246.8 - 4 602.2, 5 446.8 - 61 02.6 cm(-1), the best dimension was 9, and the quantitative model was accurate. The R2 was 94.2, and the RMSECV was 0.186. The RMSEP of ten samples was 0.234. This method is easy, rapaid and precise, and can be used to determine the content of 10 ginsenosides in Yiqifumai lyophilized injection.

Multivariate Calibration and Instrument Standardization for the Rapid Detection of Diethylene Glycol in Glycerin by Raman Spectroscopy

The transfer of a multivariate calibration model for quantitative determination of diethylene glycol (DEG) contaminant in pharmaceutical-grade glycerin between five portable Raman spectrometers was accomplished using piecewise direct standardization (PDS). The calibration set was developed using a multi-range ternary mixture design with successively reduced impurity concentration ranges. It was found that optimal selection of calibration transfer standards using the Kennard-Stone algorithm also required application of the algorithm to multiple successively reduced impurity concentration ranges. Partial least squares (PLS) calibration models were developed using the calibration set measured independently on each of the five spectrometers. The performance of the models was evaluated based on the root mean square error of prediction (RMSEP), calculated using independent validation samples. An F-test showed that no statistical differences in the variances were observed between models developed on different instruments. Direct cross-instrument prediction without standardization was performed between a single primary instrument and each of the four secondary instruments to evaluate the robustness of the primary instrument calibration model. Significant increases in the RMSEP values for the secondary instruments were observed due to instrument variability. Application of piecewise direct standardization using the optimal calibration transfer subset resulted in the lowest values of RMSEP for the secondary instruments. Using the optimal calibration transfer subset, an optimized calibration model was developed using a subset of the original calibration set, resulting in a DEG detection limit of 0.32% across all five instruments.

Calibration of self-reported oral health to clinically determined standards.

Self-report of oral health is an inexpensive approach to assessing an individual's oral health status, but it is heavily influenced by personal views and usually differs from that of clinically determined oral health status. To assist researchers and clinicians in estimating oral health self-report, we summarize clinically determined oral health measures that can objectively measure oral health and evaluate the discrepancies between self-reported and clinically determined oral health status. We test hypotheses of trends across covariates, thereby creating optimal calibration models and tools that can adjust self-reported oral health to clinically determined standards. Using National Health and Nutrition Examination Survey (NHANES) data, we examined the discrepancy between self-reported and clinically determined oral health. We evaluated the relationship between the degree of this discrepancy and possible factors contributing to this discrepancy, such as patient characteristics and general health condition. We used a regression approach to develop calibration models for self-reported oral health. The relationship between self-reported and clinically determined oral health is complex. Generally, there is a discrepancy between the two that can best be calibrated by a model that includes general health condition, number of times a person has received health care, gender, age, education, and income. The model we developed can be used to calibrate and adjust self-reported oral health status to that of clinically determined standards and for oral health screening of large populations in federal, state, and local programs, enabling great savings in resources used in dental care.

Determination of molecular weight of hyaluronic acid by near-infrared spectroscopy

This paper attempted the feasibility to determine the molecular weight of hyaluronic acid with near-infrared (NIR) diffuse reflectance spectroscopy. In this work, 46 experimental samples of hyaluronic acid powder were analyzed by partial least square (PLS) regression multivariate calibration method in the selected region of NIR spectra. The leave-one-out cross-validation method was used for the PLS model selection criterion. The accuracy of the final model was evaluated according to correlation coefficient of prediction set (Rp) and root mean square error of prediction set (RMSEP). The repeatability was verified through repeated measurement of spectra coupled with an appropriate chi-square test. Finally, the optimal calibration model was obtained with Rp = 0.9814 and RMSEP = 88.32 when using Savitzky-Golay first (SG-1st) derivative with 9 smoothing points spectral preprocessing method. The parameters above and repeatability of NIR spectroscopy obtained from chi-square test were both within the range of permissible error in factories. This study demonstrated that NIR spectroscopy was superior to conventional methods for the fast determination of molecular weight of hyaluronic acid.

Calibration sampling paradox in near infrared spectroscopy: A case study of multi-component powder blend

The objective of this study was to illustrate the sampling paradox resulting from the different strategies of spectral acquisition while preparing and implementing the calibration models for prediction of blend components in multi-component cohesive blends. A D-optimal mixture design was used to create 24 blending runs of the formulation consisting of chlorpheniramine maleate, lactose, microcrystalline cellulose and magnesium stearate. Three strategies: (a) laboratory mixing and static spectral acquisition, (b) IBC mixing and static spectral acquisition and (c) IBC mixing and dynamic spectral acquisition were investigated for obtaining the most relevant and representative calibration samples. An optical head comprising a sapphire window mounted on the lid of the IBC was used for static and dynamic NIR spectral acquisition of the powder blends. For laboratory mixed samples, powders were blended for fixed period of 30 min and later on scanned for NIR spectra. For IBC mixed blends, the spectral acquisition was carried out in-line for 2 min and stopped for static spectral acquisition. The same cycle was repeated for the next 28 min. Partial least square (PLS) calibration models for each component were built and ranked according to their calibration statistics. Optimal calibration models were selected from each strategy for each component and used for in-line prediction of blend components of three independent test runs. Although excellent statistics were obtained for the PLS models from the three strategies, significant discrepancies were observed during prediction of the independent blends in real time. Models built using IBC mixed blends and dynamic spectral acquisition resulted in the most accurate predictions for all the blend components, whereas models prepared using static spectral acquisition (laboratory mixed and IBC) showed erroneous prediction results. The prediction performance differences between the models obtained using the different strategies could be explained in the context of relevancy and representative sample collection at the initial stage of calibration model building.

DEVELOPMENT OF A SENSITIVE SPECTROFLUOROMETRIC-MULTIVARIATE CALIBRATION METHOD FOR ENZYME KINETIC OF ALDEHYDE OXIDASE

Attempts to obtain experimental values for the kinetic parameters of phenanthridine oxidation by guinea pig or rabbit liver aldehyde oxidase using common spectrophotometric methods have not been successful due to a lower limit of detection. In the present study, a new spectrofluorimetric assay in combination with a multivariate calibration method for enzymatic kinetic study of aldehyde oxidase activity, using phenanthridine as the substrate, has been developed and validated. Phenanthridine and phenanthridinone binary mixtures were prepared in a dynamic linear range of 0.025-1 µM and the emission flourimetric spectra of the solutions recorded at the excitation and emission wavelengths of 236 and 320-450 nm, respectively. The optimized calibration model of partial least squares (PLS) method was applied for the simultaneous determination of the concentration of each chemical in the prediction set. The limits of detection for phenanthridine and phenanthridinone were found to be 2.13 ± 0.33 and 3.41 ± 0.34 nM (mean ± SD, n = 5), respectively. This method was then used for kinetic study of phenanthridine oxidation using guinea pig and rat hepatic aldehyde oxidase. The results were compared with those obtained from a univariate spectroscopic method. Using this new spectrofluorimetric-multivariate calibration method, the K m value for the oxidation of phenanthridine with guinea pig and rat liver aldehyde oxidase were obtained as 0.83 ± 0.08 and 2.20 ± 0.40, µM (mean ± SD, n = 3), respectively.

Determination of potency of heparin active pharmaceutical ingredient by near infrared reflectance spectroscopy

Potency is an important parameter for evaluation of quality of heparin active pharmaceutical ingredient (API). In this paper the feasibility to determine potency of heparin API with near infrared reflectance spectroscopy (NIRS) coupled with partial least squares (PLS) algorithm is attempted. PLS factors, correlation coefficient of calibration set ( R c), the root mean square of cross-validation (RMSECV), correlation coefficient of prediction set ( R p) and the root mean square of prediction (RMSEP) were used to evaluate the performance of the models. The optimal calibration model was obtained with R p = 0.9721 and RMSEP = 0.55 in the 1700–1898 nm spectral region when using SG-1st derivative spectral transform method and division of calibration/prediction samples was 1/1. Three other additional samples demonstrated good prediction capability of the final model and three validation samples gave good repeatability result. NIRS has the potential to be a final lot release test to be performed in a QC laboratory.

Application Fourier transform near infrared spectrometer in rapid estimation of soluble solids content of intact citrus fruits

Nondestructive method of measuring soluble solids content (SSC) of citrus fruits was developed using Fourier transform near infrared reflectance (FT-NIR) measurements collected through optics fiber. The models describing the relationship between SSC and the NIR spectra of citrus fruits were developed and evaluated. Different spectra correction algorithms (standard normal variate (SNV), multiplicative signal correction (MSC)) were used in this study. The relationship between laboratory SSC and FT-NIR spectra of citrus fruits was analyzed via principle component regression (PCR) and partial least squares (PLS) regression method. Models based on the different spectral ranges were compared in this research. The first derivative and second derivative were applied to all spectra to reduce the effects of sample size, light scattering, instrument noise, etc. Different baseline correction methods were applied to improve the spectral data quality. Among them the second derivative method after baseline correction produced best noise removing capability and yielded optimal calibration models. A total of 170 NIR spectra were acquired; 135 NIR spectra were used to develop the calibration model; the remaining spectra were used to validate the model. The developed PLS model describing the relationship between SSC and NIR reflectance spectra could predict SSC of 35 samples with correlation coefficient of 0.995 and RMSEP of 0.79 degrees Brix.

Characterization and Quantitation of a Tertiary Mixture of Salts by Raman Spectroscopy in Simulated Hydrothermal Vent Fluid

This article will demonstrate that Raman spectroscopy can be a useful tool for monitoring the chemical composition of hydrothermal vent fluids in the deep ocean. Hydrothermal vent systems are difficult to study because they are commonly found at depths greater than 1000 m under high pressure (200-300 bar) and venting fluid temperatures are up to 400 degrees C. Our goal in this study was to investigate the use of Raman spectroscopy to characterize and quantitate three Raman-active salts that are among the many chemical building blocks of deep ocean vent chemistry. This paper presents initial sampling and calibration studies as part of a multiphase project to design, develop, and deploy a submersible deep sea Raman instrument for in situ analysis of hydrothermal vent systems. Raman spectra were collected from designed sets of seawater solutions of carbonate, sulfate, and nitrate under different physical conditions of temperature and pressure. The role of multivariate analysis techniques to preprocess the spectral signals and to develop optimal calibration models to accurately estimate the concentrations of a set of mixtures of simulated seawater are discussed. The effects that the high-pressure and high-temperature environment have upon the Raman spectra of the analytes were also systematically studied. Information gained from these lab experiments is being used to determine design criteria and performance attributes for a deployable deep sea Raman instrument to study hydrothermal vent systems in situ.

Simplifying the kinematic calibration of parallel mechanisms using vision-based metrology

In this paper, a vision-based measuring device is proposed and experimentally demonstrated to be an accurate, flexible, and low-cost tool for the kinematic calibration of parallel mechanisms. The accuracy and ease of use of the proposed vision sensor are outlined, with the suppression of the need for an accurate calibration target, and adequacy to the kinematic calibration process is investigated. In particular, identifiability conditions with the use of such an exteroceptive sensor are derived, considering the calibration with inverse or implicit models. Extensive results are given, with the evaluation of the measuring device and the calibration of an H4 robot. Using the full-pose measurement, an experimental analysis of the optimal calibration model is achieved, with study of the kinematic behavior of the mechanism. The efficiency of the provided method is thus evaluated, and the applicability of vision-based measuring devices to the context of kinematic calibration of parallel mechanisms is discussed.

Near-infrared spectroscopic measurement of urea in dialysate samples collected during hemodialysis treatments.

Single-beam spectra were collected over the combination region of the near-infrared spectrum for 80 samples collected from 15 people over a two-week period. Partial least-squares (PLS) regression was used to generate an optimized calibration model for urea. PLS calibration models accurately measure urea in the spent dialysate matrix. Prediction errors are on the order of 0.15 mM, which is sufficient for the clinical assessment of the dialysis process. In addition, the feasibility of a global calibration model is demonstrated by generating a calibration model from samples and spectra obtained from 12 people to predict the level of urea in samples collected from 3 different people. In this case, the standard error of prediction is 0.09 mM. Spectra were modified in order to systematically examine the impact of resolution and noise. Little impact is observed by altering the spectral resolution from 4 to 32 cm-1. Spectral noise, however, plays an important role in the accuracy of these calibration models. Increasing the magnitude of the spectral noise increases the prediction errors and increases the width of the spectral range necessary for extracting the analytical information. The utility of the method is demonstrated by analyzing dialysate samples collected during actual dialysis treatments. In addition, the necessary resolution and spectral quality necessary for reliable on-line urea monitoring is identified. These findings indicate that a dedicated, on-line urea spectrometer must posses a resolution of 16 cm-1 coupled with a sample thickness of 1.5 mm and spectral noise levels on the order of 25 micro-absorbance units when measured as the root-mean-square (RMS) noise of 100% lines.

NMR and Bayesian regularized neural network regression for impurity determination of 4-aminophenol

A method for the determination of 4-aminophenol as an impurity in paracetamol ( N-(4-hydroxyphenyl)-acetamide) by proton nuclear magnetic resonance ( 1H-NMR) spectroscopy has been developed. The 13C-satellite from the protons in the ortho position from the hydroxyl group in paracetamol was used as an internal standard, although these peaks interfered with the peaks from the protons in 4-aminophenol. Because of interference in the spectra and non-linearity over a wide calibration range, a Bayesian regularized neural network model was used for calibration. Various kinds of data preprocessing were examined: zero filling, multiplication by a negative exponential function (line broadening), followed by Fourier transformation of the free induction decay (FID). The NMR spectral data were automatically phased and shift-adjusted by means of a genetic algorithm. Multiplicative scatter correction and data compression by wavelets and sequential zeroing of weights variable selection were performed to obtain an optimal calibration model. Neither zero filling of the FID nor line broadening improved the calibration models with regard to error of prediction, so these processes were excluded in the final model. The generated Bayesian regularized network model was evaluated with an independent test set. Four different models with different test sets were constructed to explore the quality of the calibration. The mean error of the optimal calibration model was 25.3×10 −6 weight of 4-aminophenol per weight paracetamol. The method is characterized by being relative fast, simple and sufficient sensitive for typical pharmaceutical impurity determinations.

Hydraulic Network Calibration Using Genetic Optimization

The validity of a hydraulic network model depends not only on the accuracy of its physical and geometric data but also on the accuracy of certain parametric data such as pipe roughness coefficients and nodal demands. Difficulties associated with economical and reliable measurements for these parameters often dictate estimation of these parameters through model calibration. This paper describes an optimization approach to calibrate a network model for pipe roughness coefficients, and spatial as well as temporal demand adjustment factors. The proposed model obtains an optimal solution by minimizing a nonlinear objective function subject to a set of linear and nonlinear constraints using a powerful search technique based on a genetic algorithm. Application of the optimal calibration model to water distribution systems using synthetic calibration data demonstrates capabilities of the proposed algorithm to generate good solutions in an efficient and robust manner.

Determination of free fatty acids in crude palm oil and refined‐bleached‐deodorized palm olein using fourier transform infrared spectroscopy

AbstractA rapid direct Fourier transform infrared (FTIR) spectroscopic method using a 100 µ BaF2 transmission cell was developed for the determination of free fatty acid (FFA) in crude palm oil (CPO) and refined‐bleached‐deodorized (RBD) palm olein, covering an analytical range of 3.0–6.5% and 0.07–0.6% FFA, respectively. The samples were prepared by hydrolyzing oil with enzyme in an incubator. The optimal calibration models were constructed based on partial least squares (PLS) analysis using the FTIR carboxyl region (C=O) from 1722 to 1690 cm−1. The resulting PLS calibrations were linear over the range tested. The standard errors of calibration (SEC) obtained were 0.08% FFA for CPO with correlation coefficient (R2) of 0.992 and 0.01% FFA for RBD palm olein with R2 of 0.994. The standard errors of performance (SEP) were 0.04% FFA for CPO with R2 of 0.998 and 0.006% FFA for RBD palm olein with R2 of 0.998, respectively. In terms of reproducibility (r) and accuracy (a), both FTIR and chemical methods showed comparable results. Because of its simpler and more rapid analysis, which is less than 2 min per sample, as well as the minimum use of solvents and labor, FTIR has an advantage over the wet chemical method.

Optimization of Two Fourier Transform Infrared Least-Squares Multivariate Analysis Methods for the Simultaneous Quantitation of Mixtures of Three Metal-Carbonyl Complexes in the Picomole Range

In order to develop a simultaneous multi-carbonyl metallo immuno assay (multi-CMIA) associated with Fourier transform infrared spectroscopy as the detection method, we thoroughly studied two multivariate analysis methods for the quantitation of mixtures of metal-carbonyl complexes from their infrared spectra restricted to the region of the upsilon co vibration modes (1850-2200 cm-1). These two methods were based on classical least-squares (CLS) and partial least-squares (PLS) algorithms. The influence of some of the parameters involved in the calibration stage (spectral intervals, nature and number of calibration spectra) was studied for both multivariate methods. With optimized calibration models, we showed that either artificial or real mixtures of three metal-carbonyl complexes could be simultaneously quantified in the 10-100 pmol range, which is the working area of CMIA experiments, with relative errors around 6%.