Preliminary investigation of near-infrared spectroscopic measurements of urea, creatinine, glucose, protein, and ketone in urine

doi:10.1016/S0009-9120(01)00198-9

Clinical Biochemistry

Volume 34, Issue 3, May 2001, Pages 239-246

https://doi.org/10.1016/S0009-9120(01)00198-9 Get rights and content

Abstract

Objective: We investigated the use of near-infrared spectroscopy as an analytical tool to quantify concentrations of urea, creatinine, glucose, ketone, and protein in urine.

Design and Methods: FT-IR spectroscopy in conjunction with a polynomial based spectral smoothing method was applied to urine specimens. A partial factorial experimental design was employed to collect spectra using normal and spiked urine samples.

Results: Our results show that the spectral signatures of urea, creatinine, glucose, ketone, and protein in the 1350 to 1800 nm and 2050 to 2375 nm range are sufficiently strong and unique for accurate measurements.

Conclusions: The accuracy of near infrared for quantifying concentrations of urea and creatinine is only slightly less than our selected reference methods. Glucose, ketone and protein are sufficiently accurate to be useful as a screening tool for wellness. The method successfully accounts for biologic matrix variation. The advantages of near-infrared analysis are (1) no reagents, (2) ease of sample preparation, (3) speed, and (4) the ability to quantify multiple analytes with one spectra.

Introduction

For clinical laboratory measurements, quantitative methods must be suitably accurate and precise over the expected range of values required. In addition, it is often desirable that the method be inexpensive, reliable, rapid and easily automated. Near-infrared spectroscopy has the potential to satisfy these criteria. It needs no reagents, little or no sample preparation, it is rapid and nondestructive, and is suitable for complex matrices. Near-infrared spectroscopy has been applied to measuring urine composition [1], [2], serum composition [3], [4], fecal composition [5], [6], glucose in whole blood [7] and complex matrices [8]. The paper explores the feasibility of the use of near-infrared analysis for measuring five compounds of interest for urinalysis screening testing.

Urine contains a wide variety of substances. Urinalysis involves measuring critical components in a sample of urine to identify previously undetected diseases or medical conditions, or urinalysis may be used to determine if regulated substances (e.g., drugs) are being abused. All of these analytes represent breakdown products of metabolism from various organ systems. The pattern of excretion is indicative of various disease states. The history and utility of urinalysis have been reviewed [9], [10].

In current urinalysis systems, such as those provided by Bayer and Boehringer Mannheim, the analytes measured include glucose, bilirubin, blood (or hemoglobin), protein, urobilinogen, nitrites, leukocytes, specific gravity, and pH. In urine the major ketone components are 3-hydroxybutyrate (80%), acetoacetic acid (17%) and acetone (3%), but only the acetoacetic acid is determined by the current test systems. Refractive index may be substituted for specific gravity [11], [12]. In some cases, measurement of creatinine is suggested, but is not provided by Bayer’s or Boehringer Mannheim’s urinalysis systems [13], [14].

The majority of urinalysis testing is accomplished by means of dip and read strip technology supplied by Bayer and Boehringer Mannheim. Strip technology is well understood, and suffers from a number of limitations. Readings must be properly timed to obtain accurate results. Urine samples must be well mixed and at room temperature. Strips are sensitive to light and humidity, and must be stored and handled properly. Quantitative results are difficult to obtain. Interfering substances can cause incorrect readings.

This work demonstrates the utility of near-infrared analysis for measurement of glucose, ketone and protein (currently done with test strips), and urea and creatinine. The objective was to determine the accuracy of near-IR spectroscopy for determining concentrations of these analytes in urine.

Section snippets

Experimental protocol and instrumentation

Two experimental protocols, referred to as protocols 1 and 2, were used to prepare the urine samples and collect the NIR data. For measurements involving urea, creatinine, glucose, experimental protocol 1 was employed. For protein and ketone, both experimental protocols 1 and 2 were used.

Protocol 1 was designed to produce samples of varying analyte concentration and to provide a variable matrix from which an accurate partial least squares (PLS) model could be derived and to provide an

Sample preparation and reference methods

Fresh urine samples from normal, healthy volunteers were collected for initial calibration and model validation testing.

Urea (Aldrich Chemical Co., Milwaukee, WI) powder was weighed out to the nearest 0.1 mg, and dissolved into 10 ml of sample so that the sample set’s concentrations were of sufficient dynamic range for calibration. Urinary Urea Nitrogen concentration of both native urine and modified urine was determined by the Vision Instrument (Abbott) after predilution with distilled water.

Data analysis

The first derivative of each spectrum was taken to enhance the separation and uniqueness of the spectral features of the individual analytes, and to eliminate baseline drift. The first-derivative spectra were calculated using a sliding spectral segment of 10 cm⁻¹ with a gap of 2 cm⁻^1. We determined that the first-derivative was preferred over higher order derivatives based on the following observations: [1] the baseline drift of our spectrometer was spectrally flat, and its first-derivative was

Regression

The near-infrared absorption of biologic materials are due to the overtone and combination bands of the molecular vibrations of C-H, O-H and N-H bonds, stretching vibrations, and O-H bending vibrations. These absorption bands are wide (tens of nanometers) and weak (a few percent of the absorption of water). When several biologic compounds are present with comparable concentrations in a matrix, the absorption bands overlap one another. At any given wavelength, many substances contribute to the

Calibration and model validation

Choosing the appropriate number of PLS factors to use in the calibration equation is critical issue in PLS calibration. If too few factors are used, then the model will not adequately describe the system. If too many factors are used, the model will over-fit the system, and will not adequately predict concentrations for samples outside the model. One method for determining the appropriate number of PLS factors is cross-validation. In this method the data set is divided into samples used for

Urea

We determined that the optimal wavelengths for use in a PLS calibration and model validation was between 2050 nm and 2275 nm. Wavelengths between 1800 nm and 2050 nm were excluded because of the strong dependence of the water spectra on temperature. The spectral signature of urea is much larger than the components that make up the background matrix in urine. In fact a strong correlation between urea concentration and the derivative spectra at one wavelength can be found at 2137 nm. The

Summary

Urinary urea, creatinine, glucose, ketone, and protein can be quantified using near-infrared spectroscopy. PLS models, with an optimal number of factors can quantitate urea, creatinine, glucose, ketone and protein in urine with SEP’s of 0.93 g/L, 0.13 g/L, 4.3 mmoL/L, 0.2 g/L and 0.18 g/L, respectively. The precision of the method improves significantly with calibration samples that include very high concentrations. The high analyte concentrations emphasize the spectral features of the analyte

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AnalyticalPreliminary investigation of near-infrared spectroscopic measurements of urea, creatinine, glucose, protein, and ketone in urine

Abstract

Introduction

Section snippets

Experimental protocol and instrumentation

Sample preparation and reference methods

Data analysis

Regression

Calibration and model validation

Urea

Summary

Clin Biochem

Clin Biochem

Office Practice of Laboratory Medicine

Trends Anal Chem

Assay of glucose in urine by near-infrared spectrophotometry

Clin Chem

Near-infrared spectophotometryA new dimension in clinical chemistry

Clin Chem

Value of a spectroscopic “fecalogram” in determining the etiology of steatorrhea

Clin Chem

Estimating fat in feces by near-infrared reflectance spectroscopy

Clin Chem

Near-infrared spectroscopic measurement of physiological glucose levels in variable matrices of protein and triglycerides

Anal Chem

Multivariate determination of glucose in whole blood by attenuated total reflection infrared scpectroscopy

Anal Chem

Analytical
Preliminary investigation of near-infrared spectroscopic measurements of urea, creatinine, glucose, protein, and ketone in urine