Calibration Transfer and Measurement Stability of Near-Infrared Spectrometers

1992 ◽  
Vol 46 (5) ◽  
pp. 764-771 ◽  
Author(s):  
Yongdong Wang ◽  
Bruce R. Kowalski
Keyword(s):  
Near Infrared ◽  
Multivariate Calibration ◽  
Process Analysis ◽  
Starch Content ◽  
Industrial Process ◽  
Nir Spectroscopy ◽  
Calibration Model ◽  
Calibration Transfer ◽  
Small Set ◽  
Transfer Method

Near-infrared (NIR) spectroscopy has been widely accepted as a quantitative technique in which multivariate calibration plays an important role. The application of NIR to process analysis, however, has been largely limited by a problem identified as calibration transfer, the attempt to transfer a well-established calibration model from one instrument (e.g., located in the central laboratory) to another instrument of the same type (e.g., located on an industrial process). A calibration transfer method called piecewise direct standardization (PDS) is applied to a set of gasoline samples measured on two different NIR spectrometers. On the basis of the measurement of a small set of transfer samples on both instruments, a structured transformation matrix can be determined and applied to transform spectra between two instruments, enabling the transfer of calibration models. The effect of spectrum preprocessing on standardization is studied with the use of a set of gasoline samples. In a separate study, the day-to-day instrument variation as observed from the change in the polystyrene spectrum is related to the prediction of moisture, oil, protein, and starch content in corn samples, and then the possibility of using such generic standards to replace real samples in a transfer set is explored. In all cases, a standard error for prediction comparable to full set cross-validation is obtained through standardization.

Transfer of calibration model between near-infrared spectrometers for hematocrit measurement of grazing cattle

NIR news ◽  
2017 ◽  
Vol 28 (7) ◽  
pp. 16-21
Author(s):  
Xuan Luo ◽  
Akifumi Ikehata ◽  
Kunio Sashida ◽  
Shanji Piao ◽  
Tsutomu Okura ◽  
...  
Keyword(s):  
Near Infrared ◽  
Optimal Solution ◽  
Nir Spectroscopy ◽  
Final Diagnosis ◽  
Rapid Diagnosis ◽  
Calibration Model ◽  
Calibration Transfer ◽  
Current Time ◽  
Grazing Cattle ◽  
Robust Model

A major concern for the practical use of NIR spectroscopy is calibration transfer. In this study, different ways of calibration transfer were tried and compared to seek the optimal solution for our developed portable NIR spectrometers, which are designed for rapid diagnosis of bovine anemia due to parasites and are believed to be promising to replace the current time-consuming centrifugation way of measuring Hematocrit value (%) for final diagnosis. Our results show the importance of a robust model during the process of calibration transfer. It is risky to transfer a model which is not robust enough by using standardization algorithm.

scholarly journals Near-Infrared Spectroscopy Combined with Multivariate Calibration to Predict the Yield of Sesame Oil Produced by Traditional Aqueous Extraction Process

2017 ◽  
Vol 2017 ◽  
pp. 1-5
Author(s):  
Yong-Dong Xu ◽  
Yan-Ping Zhou ◽  
Jing Chen
Keyword(s):  
Near Infrared ◽  
Multivariate Calibration ◽  
Nir Spectroscopy ◽  
Extraction Process ◽  
Sesame Oil ◽  
Aqueous Extraction ◽  
Oil Yield ◽  
Support Vector ◽  
Calibration Model ◽  
Aqueous Extraction Process

Sesame oil produced by the traditional aqueous extraction process (TAEP) has been recognized by its pleasant flavor and high nutrition value. This paper developed a rapid and nondestructive method to predict the sesame oil yield by TAEP using near-infrared (NIR) spectroscopy. A collection of 145 sesame seed samples was measured by NIR spectroscopy and the relationship between the TAEP oil yield and the spectra was modeled by least-squares support vector machine (LS-SVM). Smoothing, taking second derivatives (D2), and standard normal variate (SNV) transformation were performed to remove the unwanted variations in the raw spectra. The results indicated that D2-LS-SVM (4000–9000 cm−1) obtained the most accurate calibration model with root mean square error of prediction (RMSEP) of 1.15 (%, w/w). Moreover, the RMSEP was not significantly influenced by different initial values of LS-SVM parameters. The calibration model could be helpful to search for sesame seeds with higher TAEP oil yields.

Scalable calibration transfer without standards via dynamic time warping for near-infrared spectroscopy

2019 ◽  
Vol 11 (35) ◽  
pp. 4481-4493 ◽  
Author(s):  
Congming Zou ◽  
Huimin Zhu ◽  
Junru Shen ◽  
Yue He ◽  
Jiaen Su ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Dynamic Time Warping ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Time Warping ◽  
Calibration Transfer ◽  
Transfer Method ◽  
Dynamic Time

A standard-free calibration transfer method has been developed for NIR spectroscopy based on variable penalty dynamic time warping.

scholarly journals Determination of the Lignocellulosic Components of Olive Tree Pruning Biomass by Near Infrared Spectroscopy

Energies ◽  
2019 ◽  
Vol 12 (13) ◽  
pp. 2497 ◽  
Author(s):  
José Luis Fernández ◽  
Felicia Sáez ◽  
Eulogio Castro ◽  
Paloma Manzanares ◽  
Mercedes Ballesteros ◽  
...  
Keyword(s):  
Near Infrared ◽  
Multivariate Calibration ◽  
Chemical Characterization ◽  
Nir Spectroscopy ◽  
Olive Tree ◽  
Calibration Method ◽  
Calibration Model ◽  
Alternative Method ◽  
Tree Pruning

The determination of chemical composition of lignocellulose biomass by wet chemistry analysis is labor-intensive, expensive, and time consuming. Near infrared (NIR) spectroscopy coupled with multivariate calibration offers a rapid and no-destructive alternative method. The objective of this work is to develop a NIR calibration model for olive tree lignocellulosic biomass as a rapid tool and alternative method for chemical characterization of olive tree pruning over current wet methods. In this study, 79 milled olive tree pruning samples were analyzed for extractives, lignin, cellulose, hemicellulose, and ash content. These samples were scanned by reflectance diffuse near infrared techniques and a predictive model based on partial least squares (PLS) multivariate calibration method was developed. Five parameters were calibrated: Lignin, cellulose, hemicellulose, ash, and extractives. NIR models obtained were able to predict main components composition with R2cv values over 0.5, except for lignin which showed lowest prediction accuracy.

Importance of Prediction Outlier Diagnostics in Determining a Successful Inter-Vendor Multivariate Calibration Model Transfer

2007 ◽  
Vol 61 (7) ◽  
pp. 747-754 ◽  
Author(s):  
Robert D. Guenard ◽  
Christine M. Wehlburg ◽  
Randy J. Pell ◽  
David M. Haaland
Keyword(s):  
Least Squares ◽  
Outlier Detection ◽  
Prediction Error ◽  
Near Infrared ◽  
Multivariate Calibration ◽  
Subset Selection ◽  
Calibration Model ◽  
Calibration Transfer ◽  
Significant Difference ◽  
Pls Method

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.

Calibration transfer based on the weight matrix (CTWM) of PLS for near infrared (NIR) spectral analysis

2018 ◽  
Vol 10 (18) ◽  
pp. 2169-2179 ◽  
Author(s):  
Feiyu Zhang ◽  
Ruoqiu Zhang ◽  
Jiong Ge ◽  
Wanchao Chen ◽  
Wuye Yang ◽  
...  
Keyword(s):  
Spectral Analysis ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Weight Matrix ◽  
Calibration Model ◽  
Calibration Transfer ◽  
Practical Applications

Calibration transfer is of great necessity for practical applications of near infrared (NIR) spectroscopy, since the original calibration model would become invalid when spectra are measured on different instruments or under different detection conditions.

Calibration Transfer between Miniature Photodiode Array-Based Spectrometers in the near Infrared Assessment of Mandarin Soluble Solids Content

2002 ◽  
Vol 10 (1) ◽  
pp. 27-35 ◽  
Author(s):  
C.V. Greensill ◽  
K.B. Walsh
Keyword(s):  
Near Infrared ◽  
Model Updating ◽  
Soluble Solids ◽  
Soluble Solid Content ◽  
Calibration Model ◽  
Calibration Transfer ◽  
Orthogonal Signal Correction ◽  
Significant Difference ◽  
Photodiode Array ◽  
Direct Standardisation

The transfer of predictive models among photodiode array based, short wave near infrared spectrometers using the same illumination/detection optical geometry has been attempted using various chemometric techniques, including slope and bias correction (SBC), direct standardisation (DS), piecewise direct standardisation (PDS), double window PDS (DWPDS), orthogonal signal correction (OSC), finite impulse transform (FIR) and wavelet transform (WT). Additionally, an interpolation and photometric response correction method, a wavelength selection method and a model updating method were assessed. Calibration transfer was attempted across two populations of mandarin fruit. Model performance was compared in terms of root mean squared error of prediction ( RMSEP), using Fearn's significance testing, for calibration transfer (standardisation) between pairs of spectrometers from a group of four spectrometers. For example, when a calibration model (Root Mean Square Error of Cross-Validation [ RMSECV = 0.26% soluble solid content (SSC)], developed on one spectrometer, was used with spectral data collected on another spectrometer, a poor prediction resulted ( RMSEP = 2.5% SSC). A modified WT method performed significantly better (e.g. RMSEP = 0.25% SSC) than all other standardisation methods (10 of 12 cases), and almost on a par with model updating (MU) (nine cases with no significant difference, one case and two cases significantly better for WT and MU, respectively).

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