Robust Calibration Transfer in Noninvasive Ethanol Measurements, Part I: Mathematical Basis for Spectral Distortions in Fourier Transform Near-Infrared Spectroscopy (FT-NIR)

Ridder, Trent D.; Steeg, Benjamin J. Ver; Price, Glenn L.

doi:10.1366/13-07422

Cited by 10 publications

(10 citation statements)

References 20 publications

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“…One novel approach has broad applicability to a variety of optical spectrometers for improvement of both multivariate calibration transfer and instrument standardization. 56…”

Section: Dispersive To Ft Transfermentioning

confidence: 99%

A Review of Calibration Transfer Practices and Instrument Differences in Spectroscopy

Workman

2017

Appl Spectrosc

148

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Calibration transfer for use with spectroscopic instruments, particularly for near-infrared, infrared, and Raman analysis, has been the subject of multiple articles, research papers, book chapters, and technical reviews. There has been a myriad of approaches published and claims made for resolving the problems associated with transferring calibrations; however, the capability of attaining identical results over time from two or more instruments using an identical calibration still eludes technologists. Calibration transfer, in a precise definition, refers to a series of analytical approaches or chemometric techniques used to attempt to apply a single spectral database, and the calibration model developed using that database, for two or more instruments, with statistically retained accuracy and precision. Ideally, one would develop a single calibration for any particular application, and move it indiscriminately across instruments and achieve identical analysis or prediction results. There are many technical aspects involved in such precision calibration transfer, related to the measuring instrument reproducibility and repeatability, the reference chemical values used for the calibration, the multivariate mathematics used for calibration, and sample presentation repeatability and reproducibility. Ideally, a multivariate model developed on a single instrument would provide a statistically identical analysis when used on other instruments following transfer. This paper reviews common calibration transfer techniques, mostly related to instrument differences, and the mathematics of the uncertainty between instruments when making spectroscopic measurements of identical samples. It does not specifically address calibration maintenance or reference laboratory differences.

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“…One novel approach has broad applicability to a variety of optical spectrometers for improvement of both multivariate calibration transfer and instrument standardization. 56…”

Section: Dispersive To Ft Transfermentioning

confidence: 99%

A Review of Calibration Transfer Practices and Instrument Differences in Spectroscopy

Workman

2017

Appl Spectrosc

148

View full text Add to dashboard Cite

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“…Transdermal alcohol sensors collect reliable data on alcohol consumption but lag behind breath alcohol levels by one or more hours [20]. Furthermore, spectroscopic techniques showed promising results due to an alternative blood and breath test [21][22][23]. However, until now, no procedure including bioimpedance for BAC estimation has entered the clinical routine, although BAC estimation by bioimpedance measurements is a non-invasive, cheap and easy approach.…”

Section: Discussionmentioning

confidence: 99%

Evaluation of a new non-invasive measurement technique based on bioimpedance spectroscopy to estimate blood alcohol content: a pilot study

Czaplik

Ulbrich

Hochhausen

et al. 2019

Biomedical Engineering / Biomedizinische Technik

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The gold standard for estimating blood alcohol content (BAC) after alcohol consumption is a blood sample analysis. An innovative technology to estimate BAC is based on impedance cardiography and bioimpedance spectroscopy (BIS). This study investigated whether it is possible to estimate increasing blood alcohol levels during a drinking trial with bioimpedance measurement techniques. Twenty-one healthy volunteers were assigned to a test (ethanol) group (ETH) or a reference group (H2O). After baseline measurements, the ETH group ingested 120 ml of vodka, followed by a resorption phase of 50 min. Then, bioimpedance and breath alcohol measurements were performed. Thereafter, 60 ml of vodka was ingested and another resorption phase of 50 min was followed by bioimpedance and breath alcohol measurements. This procedure was repeated until alcohol levels exceeded 0.4 mg/l. The H2O group performed in the same way with water. For all measurements, extracellular resistance (Re) and the base impedance (Z0) were computed. Regarding BIS, several parameters differed significantly between the ETH and the H2O group. Re increased in ETH (p=0.005), but not in the H2O group when comparing the first and last measurements. Z0 also increased significantly in the ETH group (p=0.001). To conclude, with BIS measurements, it is possible to measure increasing blood alcohol levels.

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“…28,29 Zheng et al 30 proposed a calibration transfer method that applies canonical correlation analysis to transfer the informative components extracted from a spectral dataset. Ridder et al 31,32 proposed a robust calibration transfer method based on Fourier transform spectra for non-invasive ethanol measurements. Most of the methods need a set of standard spectra measured using different instruments with a group of standard samples.…”

Section: Introductionmentioning

confidence: 99%

Correcting Multivariate Calibration Model for near Infrared Spectral Analysis without Using Standard Samples

Cai

Shao

2015

Journal of Near Infrared Spectroscopy

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Model transfer is essential for practical applications of near infrared (NIR) spectroscopy because differences may exist between the spectra measured using different instruments. For correcting the calibration models in cases where standard samples are unavailable, a method is proposed based on the relationship between the prediction error and the spectral difference of the instruments. In this method, a partial least squares (PLS) model, named the primary model, was constructed using the calibration spectra measured with a primary instrument, and the model was then used to predict the spectra from a secondary instrument. Error may arise in the prediction and the predicted error is obviously caused by the difference between the spectra of the two instruments. Assuming that there is a linear relationship between the spectral difference and the prediction error, a correction PLS model can be built to predict the error using the spectral difference. The predicted error obtained with the correction model can be used to correct the error of the primary model. Two NIR datasets, of pharmaceutical tablets and tobacco leaf samples, measured with two instruments were used to test the performance of the method. The results show that, for both datasets, the prediction error for the spectra of the secondary instrument of the primary model can be satisfactorily corrected.

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Robust Calibration Transfer in Noninvasive Ethanol Measurements, Part I: Mathematical Basis for Spectral Distortions in Fourier Transform Near-Infrared Spectroscopy (FT-NIR)

Cited by 10 publications

References 20 publications

A Review of Calibration Transfer Practices and Instrument Differences in Spectroscopy

A Review of Calibration Transfer Practices and Instrument Differences in Spectroscopy

Evaluation of a new non-invasive measurement technique based on bioimpedance spectroscopy to estimate blood alcohol content: a pilot study

Correcting Multivariate Calibration Model for near Infrared Spectral Analysis without Using Standard Samples

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