Blood hyperviscosity identification with reflective spectroscopy of tongue tip based on principal component analysis combining artificial neural network.

Background: With spectral methods, noninvasive determination of blood hyperviscosity in vivo is very potential and meaningful in clinical diagnosis. In this study, 67 male subjects (41 health, and 26 hyperviscosity according to blood sample analysis results) participate.

Methods: Reflectance spectra of subjects' tongue tips is measured, and a classification method bases on principal component analysis combined with artificial neural network model is built to identify hyperviscosity.

[Characteristics analysis of human tongue reflectance spectra].

The present paper presents the spectroscopic analysis method. Eighty samples of spectra data of tongue parts with coating and without coating were collected by Usb4000 spectrometer of Ocean Optics, then comparing the spectra data of the different parts of tongue we found that there was a relation between the spectra characteristics and tongue coating, and further analysis of the spectra data showed that there was a big difference between the two parts within the wavelength range between 500 and 600 nm. It was also found that the biggest differences appear when the wavelength is 579.

[Noninvasive detection of hematocrit and the mean corpuscular hemoglobin concentration levels by Vis-NIR spectroscopy].

Hematocrit (HCT) and mean hemoglobin concentration(MCHC) play a very important role in preventing cardiovascular disease and anemia. A method was developed on the basis of spectroscopy to detect HCT and MCHC non-invasively and accurately. The anatomical study showed that the blood rheology abnormalities and blood viscosity's changes can cause the changes of tongue, so there is a certain correlation between tongue and blood components.

[Rapid diagnosis of fatty liver by glossoscopy based on spectrometry].

In order to diagnose fatty liver noninvasively, rapidly and accurately, this article presented a new method based on spectroscopy to diagnose fatty liver. This method is non-invasive, rapid. Because tongue can objectively reflect physiological and pathological changes, so this experiment collected reflectance spectrum on the tongue tips of volunteers at first, then analyzed the above data, calculated the normalized reflection ratio, and built a three-layer BP network model.

[Study on near infrared spectrum in the application of the BP neural network in the tongue diagnosis for hepatitis patients].

For a quick and noninvasive examination of the tongue for potential hepatitis patients, a study was conducted on the relation between the reflectivity of near infrared spectrum on the tongues of the healthy people and the hepatitis patients. Spectral data, 25 items for each case, are to be collected from the left and right side tongue, left and right sublingual venae, and tip of the tongue from the healthy people and the hepatitis patients. Then a three-layer neural network structure was established with all the data input after normalization reflectivity pretreatment.