20 Medical Applications of NIR Spectroscopy
447
An extension to the last-mentioned approaches was presented by Han et al.,
describing a novel near-infrared-spectroscopy-based quantification method for
glycated hemoglobin (HbA1c), which is an important clinical diagnosis indicator
of diabetes. The analytical method was developed on the basis of simultaneous
determination of hemoglobin (Hb) and the absolute HbA1c content in human
hemolysate samples [18]. Several wavelength selection algorithms were tested for a
search range covered by spectral intervals not saturated by water absorption (780–
1880 nm [12,800–5320 cm
−1 ], 2090–2330 nm [4785 – 4290 cm
−1 ]). For Hb and
total HbA1c, only 6 and 14 wavelengths were selected with equidistant combination partial least squares (EC-PLS), respectively. The so-called competitive adaptive
reweighted sampling PLS (CARS-PLS) and a Monte Carlo uninformative variable
elimination PLS (MC-UVE-PLS) required 23 and 30 wavelengths, as well as 100 and
120 wavelengths, respectively. For details, the reader is referred to the publication.
Another area as mentioned above deals with disease diagnosis based on multivariate spectral classification algorithms, which has been more related to the
domain of mid-infrared and Raman spectroscopy. Recently, also blood analysis for
Alzheimer’s disease (AD) diagnosis has been presented, using multivariate classifiers based on NIR spectral signatures [19]. Robust and early diagnosis may be a
first step toward tackling this disease of mainly elderly people by allowing timely
intervention with novel synthesized pharmaceuticals. In the presented study, blood
plasma samples were analyzed with NIR spectroscopy as a minimally invasive
method to distinguish patients with AD from non-demented volunteers. Dry-film
spectra were recorded from 50 μL samples of blood plasma on IR-reflective glass
slides after overnight drying. Spectra were truncated to the biochemical fingerprint
region (1850–2150 nm [5400–4650 cm
−1 ]). By means of a multivariate classifier
(principal component analysis with quadratic discriminant analysis – PCA-QDA),
AD individuals were correctly identified with 93% accuracy, 87.5% sensitivity and
96% specificity. The results show the potential of NIR spectroscopy as a simple and
cost-effective diagnostic tool for AD.
Another bodyfluid is urine, which also contains valuable biomarkers for disease
diagnosis. Concerning urine analysis, not many publications are available. There is
a paper from the Mantsch group who analyzed protein, creatinine and urea in urine
[20], but other notable work has been carried out by the group around Mark Arnold
who investigated the use of NIR spectrometers for monitoring the dialysate during
hemodialysis of patients with renal failure, which is the pathway of a number of
kidney diseases [21]. The treatment choices for patients with impaired renal function are dialysis (hemo- or peritoneal dialysis) or renal transplantation. Accumulation of toxic end products of the nitrogen metabolism such as urea, creatinine and
uric acid in blood and tissue, a disturbed homeostasis of water and different salt
minerals are the complications resulting from renal failure. Whereas for their feasibility studies, an FT-NIR spectrometer had been used; they used a spectrometer with
a temperature-controlled acousto optical tunable filter (AOFT) in conjunction with a
thermoelectrically cooled extended wavelength Indium Gallium Arsenide (InGaAs)
photodetector, providing spectral measurements with a 20 cm
−1 resolution over the
combination band region (4000–5000 cm
−1 ) of the near-infrared spectrum. Their
447
An extension to the last-mentioned approaches was presented by Han et al.,
describing a novel near-infrared-spectroscopy-based quantification method for
glycated hemoglobin (HbA1c), which is an important clinical diagnosis indicator
of diabetes. The analytical method was developed on the basis of simultaneous
determination of hemoglobin (Hb) and the absolute HbA1c content in human
hemolysate samples [18]. Several wavelength selection algorithms were tested for a
search range covered by spectral intervals not saturated by water absorption (780–
1880 nm [12,800–5320 cm
−1 ], 2090–2330 nm [4785 – 4290 cm
−1 ]). For Hb and
total HbA1c, only 6 and 14 wavelengths were selected with equidistant combination partial least squares (EC-PLS), respectively. The so-called competitive adaptive
reweighted sampling PLS (CARS-PLS) and a Monte Carlo uninformative variable
elimination PLS (MC-UVE-PLS) required 23 and 30 wavelengths, as well as 100 and
120 wavelengths, respectively. For details, the reader is referred to the publication.
Another area as mentioned above deals with disease diagnosis based on multivariate spectral classification algorithms, which has been more related to the
domain of mid-infrared and Raman spectroscopy. Recently, also blood analysis for
Alzheimer’s disease (AD) diagnosis has been presented, using multivariate classifiers based on NIR spectral signatures [19]. Robust and early diagnosis may be a
first step toward tackling this disease of mainly elderly people by allowing timely
intervention with novel synthesized pharmaceuticals. In the presented study, blood
plasma samples were analyzed with NIR spectroscopy as a minimally invasive
method to distinguish patients with AD from non-demented volunteers. Dry-film
spectra were recorded from 50 μL samples of blood plasma on IR-reflective glass
slides after overnight drying. Spectra were truncated to the biochemical fingerprint
region (1850–2150 nm [5400–4650 cm
−1 ]). By means of a multivariate classifier
(principal component analysis with quadratic discriminant analysis – PCA-QDA),
AD individuals were correctly identified with 93% accuracy, 87.5% sensitivity and
96% specificity. The results show the potential of NIR spectroscopy as a simple and
cost-effective diagnostic tool for AD.
Another bodyfluid is urine, which also contains valuable biomarkers for disease
diagnosis. Concerning urine analysis, not many publications are available. There is
a paper from the Mantsch group who analyzed protein, creatinine and urea in urine
[20], but other notable work has been carried out by the group around Mark Arnold
who investigated the use of NIR spectrometers for monitoring the dialysate during
hemodialysis of patients with renal failure, which is the pathway of a number of
kidney diseases [21]. The treatment choices for patients with impaired renal function are dialysis (hemo- or peritoneal dialysis) or renal transplantation. Accumulation of toxic end products of the nitrogen metabolism such as urea, creatinine and
uric acid in blood and tissue, a disturbed homeostasis of water and different salt
minerals are the complications resulting from renal failure. Whereas for their feasibility studies, an FT-NIR spectrometer had been used; they used a spectrometer with
a temperature-controlled acousto optical tunable filter (AOFT) in conjunction with a
thermoelectrically cooled extended wavelength Indium Gallium Arsenide (InGaAs)
photodetector, providing spectral measurements with a 20 cm
−1 resolution over the
combination band region (4000–5000 cm
−1 ) of the near-infrared spectrum. Their