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S Kotowich

Publications and source records attributed to S Kotowich.

4 recordsLinked to original sources
PubMed1998

Multianalyte serum analysis using mid-infrared spectroscopy.

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 (Sy/x = mumol/L) and uric acid (Sy/x = 140 mumol/L) due to the relatively low concentrations typical of these analytes.

Blood Chemical Analysis↗
PubMed1996

Quantitation of protein, creatinine, and urea in urine by near-infrared spectroscopy.

OBJECTIVES: To determine the feasibility of near-infrared analysis for quantitating urea, creatinine, and protein in urine. Practical advantages of this method include ease of sample presentation and the absence of reagents or disposables. DESIGN AND METHODS: The near-infrared methods were developed by first measuring the spectra of 123 different urine samples and, using independent clinical analyses, determining the protein, creatinine, and urea levels in each. Calibration models relating near-infrared spectroscopic features to those independently determined concentrations were optimized, and each model then validated using a set of 50 additional samples. RESULTS: Standard errors of calibration were 14.4 mmol/L, 0.66 mmol/L, and 0.20 g/L, and standard errors of prediction 16.6 mmol/L, 0.79 mmol/L, and 0.23 g/L, respectively, for urea, creatinine, and protein. CONCLUSIONS: Near-infrared urea quantitation is as accurate as the reference method, enzymatic (urease) conductivity, used here for calibration. Creatinine analysis is slightly less accurate relative to the reference (Jaffe rate) method; however, these errors can be minimized by careful attention to factors affecting precision. The accuracy of the near-infrared protein analysis cannot approach that of the reference method; nevertheless, the technique is potentially useful for coarse screening and for quantifying protein levels above 0.3 g/L.

Calibration↗
PubMed1995

Arthritis diagnosis based upon the near-infrared spectrum of synovial fluid.

Synovial fluid aspirates have been characterized by measuring their visible/near-infrared spectra (400-2500 nm). The hypothesis tested in this study is that the spectra contain sufficient information to serve as an aid in the diagnosis and/or staging of arthritic disorders. The concentrations of all major constituents are carried implicitly in the spectra, and in this sense this approach is similar in spirit to conventional synovial fluid analysis. The distinguishing feature of this method is that we have not converted the raw data (spectra) explicitly to analytical information. Rather, we have used automated pattern recognition methods to identify significant characteristics of the spectra themselves. A total of 109 spectra were measured and split into three classes according to the disease (osteoarthritis, rheumatoid arthritis, or spondyloarthropathy) affecting the patient from whom the synovial fluid sample was taken. An automated classification method was then trained by correlating features derived from these spectra to the clinical diagnoses. The robustness of the classification was validated using the leave-one-out cross-validation method, i.e., by training on all but one of the spectra and using the resulting model to predict the classification for the spectrum that is left out. The result derived by following this procedure for each of the spectra was that 105 of the 109 predicted classifications correctly matched the clinical diagnosis. These results suggest that the near-infrared spectrum of synovial fluid is sufficient to allow diagnosis of the disease affecting the joint from which the aspirate is drawn.

Arthritis, Rheumatoid↗