Multicomponent Assay for Human Serum Using Mid-Infrared Transmission Spectroscopy Based on Component-Optimized Spectral Region Selected by a First Loading Vector Analysis in Partial Least-Squares Regression

2002 ◽  
Vol 56 (5) ◽  
pp. 625-632 ◽  
Author(s):  
Yoen-Joo Kim ◽  
Gilwon Yoon
Keyword(s):  
Uric Acid ◽  
Spectral Region ◽  
Partial Least Squares Regression ◽  
Cross Validation ◽  
Hdl Cholesterol ◽  
Vector Analysis ◽  
Percentage Error ◽  
Least Squares Regression ◽  
Infrared Transmission ◽  
Mid Infrared

Mid-infrared transmission spectroscopy with partial least-squares regression was used to determine the concentrations of blood components such as total protein, albumin, globulin, total cholesterol, HDL (high density lipoprotein) cholesterol, triglycerides, glucose, BUN (blood urea nitrogen), and uric acid in human serum. The optimal spectral region for each component was selected by first loading vector analysis. Positive peaks with positive value were assigned by first loading vector analysis. Because blood components in serum show a correlation among several components, a useful spectral region for predicting a particular component was selected such that its spectral feature was not overlapped by those of other components. Several regions with positive peaks by first loading vector were used to establish calibration models. The proposed method proved to be effective for a multicomponent assay and can also be used even when a single component spectrum in aqueous solution for all components is not known. Total protein, albumin, globulin, total cholesterol, triglycerides, and glucose have a mean percentage error of cross-validation (MPECV) of less than 5%. But HDL cholesterol, BUN, and uric acid have MPECVs between 12 and 18%. In terms of both the percentage error of cross-validation and clinically allowable error, six serum components, excepting HDL-cholesterol, BUN, and uric acid, were determined successfully.

scholarly journals Rapid tannin profiling of tree fodders using untargeted mid-infrared spectroscopy and partial least squares regression

Plant Methods ◽  
2021 ◽  
Vol 17 (1) ◽  
Author(s):  
Jordi Ortuño ◽  
Sokratis Stergiadis ◽  
Anastasios Koidis ◽  
Jo Smith ◽  
Chris Humphrey ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Least Squares ◽  
Partial Least Squares ◽  
Partial Least Squares Regression ◽  
Multivariate Calibration ◽  
Plant Ecology ◽  
Coefficient Of Determination ◽  
Least Squares Regression ◽  
Mid Infrared ◽  
Mid Infrared Spectroscopy

Abstract Background The presence of condensed tannins (CT) in tree fodders entails a series of productive, health and ecological benefits for ruminant nutrition. Current wet analytical methods employed for full CT characterisation are time and resource-consuming, thus limiting its applicability for silvopastoral systems. The development of quick, safe and robust analytical techniques to monitor CT’s full profile is crucial to suitably understand CT variability and biological activity, which would help to develop efficient evidence-based decision-making to maximise CT-derived benefits. The present study investigates the suitability of Fourier-transformed mid-infrared spectroscopy (MIR: 4000–550 cm−1) combined with multivariate analysis to determine CT concentration and structure (mean degree of polymerization—mDP, procyanidins:prodelphidins ratio—PC:PD and cis:trans ratio) in oak, field maple and goat willow foliage, using HCl:Butanol:Acetone:Iron (HBAI) and thiolysis-HPLC as reference methods. Results The MIR spectra obtained were explored firstly using Principal Component Analysis, whereas multivariate calibration models were developed based on partial least-squares regression. MIR showed an excellent prediction capacity for the determination of PC:PD [coefficient of determination for prediction (R2P) = 0.96; ratio of prediction to deviation (RPD) = 5.26, range error ratio (RER) = 14.1] and cis:trans ratio (R2P = 0.95; RPD = 4.24; RER = 13.3); modest for CT quantification (HBAI: R2P = 0.92; RPD = 3.71; RER = 13.1; Thiolysis: R2P = 0.88; RPD = 2.80; RER = 11.5); and weak for mDP (R2P = 0.66; RPD = 1.86; RER = 7.16). Conclusions MIR combined with chemometrics allowed to characterize the full CT profile of tree foliage rapidly, which would help to assess better plant ecology variability and to improve the nutritional management of ruminant livestock.

Determination of Wavelength Accuracy in the Near-Infrared Spectral Region Based on NIST's Infrared Transmission Wavelength Standard SRM 1921

2000 ◽  
Vol 54 (3) ◽  
pp. 450-455 ◽  
Author(s):  
Stephen R. Lowry ◽  
Jim Hyatt ◽  
William J. McCarthy
Keyword(s):  
Reference Material ◽  
Standard Reference Material ◽  
Spectral Region ◽  
Near Infrared ◽  
Cost Effective ◽  
Infrared Transmission ◽  
Internal Reference ◽  
Mid Infrared ◽  
Infrared Spectral Region ◽  
Infrared Spectral

A major concern with the use of near-infrared (NIR) spectroscopy in many QA/QC laboratories is the need for a simple reliable method of verifying the wavelength accuracy of the instrument. This requirement is particularly important in near-infrared spectroscopy because of the heavy reliance on sophisticated statistical vector analysis techniques to extract the desired information from the spectra. These techniques require precise alignment of the data points between the vectors corresponding to the standard and sample spectra. The National Institute of Standards and Technology (NIST) offers a Standard Reference Material (SRM 1921) for the verification and calibration of mid-infrared spectrometers in the transmittance mode. This standard consists of a 38 μm-thick film of polystyrene plastic. While SRM 1921 works well as a mid-infrared standard, a thicker sample is required for use as a routine standard in the near-infrared spectral region. The general acceptance and proven reliability of polystyrene as a standard reference material make it a very good candidate for a cost-effective NIR standard that could be offered as an internal reference for every instrument. In this paper we discuss a number of the parameters in a Fourier transform (FT)-NIR instrument that can affect wavelength accuracy. We also report a number of experiments designed to determine the effects of resolution, sample position, and optics on the wavelength accuracy of the system. In almost all cases the spectral reproducibility was better than one wavenumber of the values extrapolated from the NIST reference material. This finding suggests that a thicker sample of polystyrene plastic that has been validated with the SRM 1921 standard would make a cost-effective reference material for verifying wavelength accuracy in a medium-resolution FT-NIR spectrometer.

scholarly journals Multianalyte Serum Analysis Using Mid-Infrared Spectroscopy

1998 ◽  
Vol 35 (5) ◽  
pp. 624-632 ◽  
Author(s):  
R Anthony Shaw ◽  
Steven Kotowich ◽  
Michael Leroux ◽  
Henry H Mantsch
Keyword(s):  
Uric Acid ◽  
Total Protein ◽  
Correlation Coefficients ◽  
Standard Errors ◽  
Infrared Transmission ◽  
Serum Samples ◽  
Chemical Methods ◽  
Mid Infrared ◽  
Low Concentrations ◽  
Clinical Chemical

This study assesses the potential for using mid-infrared (mid-IR) spectroscopy of dried serum films as the basis for the simultaneous quantitation of eight serum analytes: Total protein, albumin, triglycerides, cholesterol, glucose, urea, creatinine and uric acid. Infrared transmission spectra were acquired for 300 serum samples, each analysed independently using accepted reference clinical chemical methods. Quantitation methods were based upon the infrared spectra and reference analyses for 200 specimens, and the models validated using the remaining 100 samples. Standard errors in the IR-predicted analyte levels (Sy/x) were 2.8 g/L (total protein), 2.2 g/L (albumin), 0.23 mmol/L (triglycerides), 0.28 mmol/L (cholesterol), 0.41 mmol/L (glucose) and 1.1 mmol/L for urea, with correlation coefficients (IR vs reference analyses) of 0.95 or better. The IR method emerged to be less suited for creatinine (S y/ x = μmol/L) and uric acid (S y/x = 140 μmol/L) due to the relatively low concentrations typical of these analytes.

scholarly journals Fitting and Cross-Validating Cox Models to Censored Big Data With Missing Values Using Extensions of Partial Least Squares Regression Models

2021 ◽  
Vol 4 ◽  
Author(s):  
Frédéric Bertrand ◽  
Myriam Maumy-Bertrand
Keyword(s):  
Big Data ◽  
Least Squares ◽  
Partial Least Squares ◽  
Partial Least Squares Regression ◽  
Missing Values ◽  
Cross Validation ◽  
Cox Model ◽  
R Package ◽  
Least Squares Regression ◽  
Cox Models

Fitting Cox models in a big data context -on a massive scale in terms of volume, intensity, and complexity exceeding the capacity of usual analytic tools-is often challenging. If some data are missing, it is even more difficult. We proposed algorithms that were able to fit Cox models in high dimensional settings using extensions of partial least squares regression to the Cox models. Some of them were able to cope with missing data. We were recently able to extend our most recent algorithms to big data, thus allowing to fit Cox model for big data with missing values. When cross-validating standard or extended Cox models, the commonly used criterion is the cross-validated partial loglikelihood using a naive or a van Houwelingen scheme —to make efficient use of the death times of the left out data in relation to the death times of all the data. Quite astonishingly, we will show, using a strong simulation study involving three different data simulation algorithms, that these two cross-validation methods fail with the extensions, either straightforward or more involved ones, of partial least squares regression to the Cox model. This is quite an interesting result for at least two reasons. Firstly, several nice features of PLS based models, including regularization, interpretability of the components, missing data support, data visualization thanks to biplots of individuals and variables —and even parsimony or group parsimony for Sparse partial least squares or sparse group SPLS based models, account for a common use of these extensions by statisticians who usually select their hyperparameters using cross-validation. Secondly, they are almost always featured in benchmarking studies to assess the performance of a new estimation technique used in a high dimensional or big data context and often show poor statistical properties. We carried out a vast simulation study to evaluate more than a dozen of potential cross-validation criteria, either AUC or prediction error based. Several of them lead to the selection of a reasonable number of components. Using these newly found cross-validation criteria to fit extensions of partial least squares regression to the Cox model, we performed a benchmark reanalysis that showed enhanced performances of these techniques. In addition, we proposed sparse group extensions of our algorithms and defined a new robust measure based on the Schmid score and the R coefficient of determination for least absolute deviation: the integrated R Schmid Score weighted. The R-package used in this article is available on the CRAN, http://cran.r-project.org/web/packages/plsRcox/index.html. The R package bigPLS will soon be available on the CRAN and, until then, is available on Github https://github.com/fbertran/bigPLS.

scholarly journals The development of green analytical methods to monitor adulteration in honey by UV-visible spectroscopy and chemometrics models

2020 ◽  
Vol 211 ◽  
pp. 02011
Author(s):  
Omar Elhamdaoui ◽  
Aimen El Orche ◽  
Houda Bouchafra ◽  
Miloud El Karbane ◽  
Amine Cheikh ◽  
...  
Keyword(s):  
Analytical Methods ◽  
Partial Least Squares Regression ◽  
Cross Validation ◽  
Essential Element ◽  
Support Vector ◽  
Least Squares Regression ◽  
Visible Spectroscopy ◽  
Uv Visible ◽  
Non Destructive

The development of green and environmentally friendly analytical methods for agri-food products is an essential element to be treated by green analytical chemistry. In this study, UV-Visible spectroscopy, combined with a mathematical and statistical or chemometrics algorithm, has been developed to monitor honey quality. Partial Least Squares Regression (PLS-R) and Support Vector Machine Learning Regression (SVM-R) showed an adequate quantification of the percentage of impurity. The use of these models demonstrates a high ability to predict the quality of honey. R-square’s high value shows this ability, and the low value of root mean square error of calibration and cross-validation (RMSECV, RMSEC). The results indicate that UV-Visible spectroscopy allied with the Chemometrics algorithms can provide a quick, non-destructive, green, and reliable method to control the quality and predict honey’s adulteration level.

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