New Hybrid Algorithm for Transferring Multivariate Quantitative Calibrations of Intra-Vendor Near-Infrared Spectrometers

2002 ◽  
Vol 56 (7) ◽  
pp. 877-886 ◽  
Author(s):  
Christine M. Wehlburg ◽  
David M. Haaland ◽  
David K. Melgaard

A new prediction-augmented classical least-squares/partial least-squares (PACLS/PLS) hybrid algorithm is ideally suited for use in transferring multivariate calibrations between spectrometers. Spectral variations such as instrument response differences can be explicitly incorporated into the algorithm through the use of subset sample spectra collected on both spectrometers. Two current calibration transfer methods, subset recalibration and piecewise direct standardization (PDS), also utilize subset sample spectra to facilitate transfer of calibration. The three methods were applied to the transfer of quantitative multivariate calibration models for near-infrared (NIR) data of organic samples containing chlorobenzene, heptane, and toluene between a primary and three secondary spectrometers that were all the same model, called intra-vendor transfer of calibration. The hybrid PACLS/PLS method outperformed subset recalibration and provided predictions equivalent to PDS with additive background correction on the two secondary spectrometers whose instrument drift appeared to be dominated by simple linear baseline variations. One of the secondary spectrometers had complex instrument drift that was captured by repeatedly measuring the spectrum of a single repeat sample. In calculating a transfer function to correct prediction spectra, PDS assumes no instrumental drift on the secondary spectrometer. Therefore, PDS was unable to directly accommodate both the subset samples and the use of a single repeat sample to transfer and maintain a calibration on that secondary instrument. In order to implement the transfer of calibration with PDS in the presence of complex instrument drift, recalibrated PLS models that included the repeat spectra from the secondary spectrometer were used to predict the spectra transformed by PDS. The importance of correcting for drift on the secondary spectrometer during calibration transfer was illustrated by the improvements in prediction for all three methods vs. using only the instrument response differences derived from the subset sample spectra. When the effects of instrument drift were complex on the secondary spectrometer, the PACLS/PLS hybrid algorithm outperformed both PDS and subset recalibration. Through the explicit incorporation of spectral variations, due to instrument response differences and drift on the secondary spectrometer, the PACLS/PLS algorithm was successful at intra-vendor transfer of calibrations between NIR spectrometers.

2007 ◽  
Vol 61 (7) ◽  
pp. 747-754 ◽  
Author(s):  
Robert D. Guenard ◽  
Christine M. Wehlburg ◽  
Randy J. Pell ◽  
David M. Haaland

This paper reports on the transfer of calibration models between Fourier transform near-infrared (FT-NIR) instruments from four different manufacturers. The piecewise direct standardization (PDS) method is compared with the new hybrid calibration method known as prediction augmented classical least squares/partial least squares (PACLS/PLS). The success of a calibration transfer experiment is judged by prediction error and by the number of samples that are flagged as outliers that would not have been flagged as such if a complete recalibration were performed. Prediction results must be acceptable and the outlier diagnostics capabilities must be preserved for the transfer to be deemed successful. Previous studies have measured the success of a calibration transfer method by comparing only the prediction performance (e.g., the root mean square error of prediction, RMSEP). However, our study emphasizes the need to consider outlier detection performance as well. As our study illustrates, the RMSEP values for a calibration transfer can be within acceptable range; however, statistical analysis of the spectral residuals can show that differences in outlier performance can vary significantly between competing transfer methods. There was no statistically significant difference in the prediction error between the PDS and PACLS/PLS methods when the same subset sample selection method was used for both methods. However, the PACLS/PLS method was better at preserving the outlier detection capabilities and therefore was judged to have performed better than the PDS algorithm when transferring calibrations with the use of a subset of samples to define the transfer function. The method of sample subset selection was found to make a significant difference in the calibration transfer results using the PDS algorithm, while the transfer results were less sensitive to subset selection when the PACLS/PLS method was used.


2002 ◽  
Vol 56 (5) ◽  
pp. 605-614 ◽  
Author(s):  
Christine M. Wehlburg ◽  
David M. Haaland ◽  
David K. Melgaard ◽  
Laura E. Martin

Our newly developed prediction-augmented classical least-squares/partial least-squares (PACLS/PLS) hybrid algorithm can correct for the presence of unmodeled sources of spectral variation such as instrument drift by explicitly incorporating known or empirically derived information about the unmodeled spectral variation. We have tested the ability of the new hybrid algorithm to maintain a multivariate calibration in the presence of instrument drift using a near-infrared (NIR) spectrometer (7500–11 000 cm−1) to quantitate dilute aqueous solutions containing glucose, ethanol, and urea. The spectral variations required to update the multivariate models for both short- and long-term drift were obtained using a single representative midpoint sample whose spectrum was repeatedly measured during collection of calibration data and during collection of separate validation sample spectra on three subsequent days. The performance of the PACLS/PLS model for maintaining a calibration was compared to PLS with subset recalibration, a method that has previously been applied to maintenance and transfer of calibration. Without drift corrections, both PACLS/PLS and PLS had poor predictive ability on sample spectra collected on subsequent days. Unlike previous maintenance of calibration studies that corrected for long-term drift only, the PACLS/PLS and PLS models demonstrated the best predictive abilities when short-term drift was also corrected. The PACLS/PLS hybrid model outperformed PLS with subset recalibration for near real-time predictions when instrument drift was determined from the repeat samples closest in time to the measurement of the unknown. Near real-time standard errors of prediction (SEPs) for the hybrid model were comparable to the cross-validated SEPs obtained with the original calibration model.


2017 ◽  
Vol 25 (4) ◽  
pp. 223-230 ◽  
Author(s):  
Joseph Dubrovkin

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.


2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Lin Zhang ◽  
Baohua Zhang ◽  
Jun Zhou ◽  
Baoxing Gu ◽  
Guangzhao Tian

Uninformative biological variability elimination methods were studied in the near-infrared calibration model for predicting the soluble solids content of apples. Four different preprocessing methods, namely, Savitzky-Golay smoothing, multiplicative scatter correction, standard normal variate, and mean normalization, as well as their combinations were conducted on raw Fourier transform near-infrared spectra to eliminate the uninformative biological variability. Subsequently, robust calibration models were established by using partial least squares regression analysis and wavelength selection algorithms. Results indicated that the partial least squares calibration models with characteristic variables selected by CARS method coupled with preprocessing of Savitzky-Golay smoothing and multiplicative scatter correction had a considerable potential for predicting apple soluble solids content regardless of the biological variability.


2008 ◽  
Vol 16 (5) ◽  
pp. 487-496 ◽  
Author(s):  
Oluwatosin Emmanuel Adedipe ◽  
Ben Dawson-Andoh

This study investigated the feasibility of using near infrared (NIR) spectroscopy and multivariate calibration to predict bulk density and stiffness of 3.2 mm thick yellow poplar veneer strips. Full-range (800–2500 nm) raw NIR spectra and spectra pre-treated using the first derivative method, along with spectra from three other different wavelength windows of 1200–2400 nm, 1800–2400 nm and 1400–2000 nm were regressed against the bulk density (kg m−3) values and the dynamic modulus of elasticity (stiffness; GPa) of the veneers using the projection to latent structures (PLS) method to develop calibration models. All predictive models developed performed well in the prediction of bulk density and stiffness of new test samples that were not included in the calibration models. R2 values ranged from 0.67-0.78 and 0.56-0.72, respectively, for bulk density and stiffness. There was significant improvement in models developed with first derivative spectra over models developed with raw spectra. The models developed using the first derivative used fewer latent variables to achieve predictive models with higher R2 values, lower root mean square errors of prediction (RMSEP) and standard errors of prediction (SEP). Models developed using the full NIR spectral range (800–2500 nm) and the NIR spectral region of 1200–2400 nm performed better than models developed using the restricted NIR wavelength regions of 1800–2400 nm and 1400–2000 nm. However, there was no clear distinction between models developed using the full NIR spectral range and the NIR spectral region of 1200–2400 nm. Overall, models developed with the first derivative pre-processed spectra using the whole NIR spectrum performed best in predictability. The results of this study show the potential of using multivariate data analysis coupled with NIR spectroscopy for on-line sorting and assessment of veneer stiffness prior to the lay-up process in the manufacturing of veneer-based engineered wood products such as plywood, Parallam and laminated veneer lumber.


2000 ◽  
Vol 54 (2) ◽  
pp. 294-299 ◽  
Author(s):  
Songbiao Zhang ◽  
Babs R. Soller ◽  
Shubjeet Kaur ◽  
Kristen Perras ◽  
Thomas J. Vander Salm

Hematocrit (Hct), the volume percent of red cells in blood, is monitored routinely for blood donors, surgical patients, and trauma victims and requires blood to be removed from the patient. An accurate, noninvasive method for directly measuring hematocrit on patients is desired for these applications. The feasibility of noninvasive hematocrit measurement by using near-infrared (NIR) spectroscopy and partial least-squares (PLS) techniques was investigated, and methods of in vivo calibration were examined. Twenty Caucasian patients undergoing cardiac surgery on cardiopulmonary bypass were randomly selected to form two study groups. A fiber-optic probe was attached to the patient's forearm, and NIR spectra were continuously collected during surgery. Blood samples were simultaneously collected and reference Hct measurements were made with the spun capillary method. PLS multivariate calibration techniques were applied to investigate the relationship between spectral and Hct changes. Single patient calibration models were developed with good cross-validated estimation of accuracy (∼ 1 Hct%) and trending capability for most patients. Time-dependent system drift, patient temperature, and venous oxygen saturation were not correlated with the hematocrit measurements. Multi-subject models were developed for prediction of independent subjects. These models demonstrated a significant patient-specific offset that was shown to be partially related to spectrometer drift. The remaining offset is attributed to the large spectral variability of patient tissue, and a significantly larger set of patients would be required to adequately model this variability. After the removal of the offset, the cross-validated estimation of accuracy is 2 Hct%.


Sign in / Sign up

Export Citation Format

Share Document