Editorial Feature

Basing Glucose Sensors on Infrared Spectroscopy

Diabetic patients require their blood glucose levels to be monitored frequently. To do this, it is often necessary to collect blood samples. This can be painful, especially when performed periodically throughout the day. A non-invasive approach to measuring blood glucose levels eliminates the need to frequently obtain blood from patients, thereby offering significant help to millions of diabetic patients worldwide.

Some of the most recently proposed non-invasive glucose monitoring devices are based on sensors that utilize near-infrared (near-IR), mid-infrared (mid-IR), Raman spectroscopy and photoacoustics. While technologies based on these sensors have tremendous potential, they often suffer from a lack of accuracy under various measurement conditions.


Problems Associated with Non-Invasive Glucose Sensors

The success of non-invasive glucose sensors which are based on mid-IR spectroscopy depends on the ability of these sensors to determine the correct set of wavenumbers suitable for non-invasive blood glucose measurement.

Due to the need for external cooling and other device configurations, the mid-IR based glucose sensors are often bulky and very expensive. While laser light sources, such as a quantum cascade lasers (QCL), can be used as an alternative, the number of wavenumbers for this type of configuration must be reduced to less than five for practical applications of the mid-IR based glucose sensors. Since several reports have indicated that near IR-based glucose sensors require more than five wavenumbers, the likelihood of achieving such a configuration is highly improbable. It is therefore desirable for researchers to develop IR-based glucose sensors with a minimum quantity of wavenumbers.

Several variable factors can affect the accuracy of this type of non-invasive glucose measurement. Among these factors is the time at which the blood glucose measurement is taken, as the patient may have just eaten a meal or be in a period of fasting. Inter-individual variance and the patient’s temperature at the time of measurement are also influencing factors. Although the accuracy of these sensors could be improved by calibration, this will often need to be performed repeatedly, which can become extremely time-consuming. Since the introduction of this class of sensors, various predictive models have been tested with the aim of improving their accuracy.

Mid-IR Spectroscopy-Based Glucose Sensors with Fewer Wavenumbers

Researchers at the Ricoh Institute of Information and Communication Technology in Japan have recently discovered a method capable of non-invasively detecting blood glucose levels. The principle of this method relies on determining the absorbance of an object using an attenuated total reflection (ATR) prism and hollow fibers, which can efficiently transmit mid-IR radiation to the object. Using a hollow fiber as the transmission line, these Japanese scientists were able to measure the absorbance of the oral mucosa by propagating radiation to an ATR prism sandwiched inside the mouth. To ensure this device was non-toxic, zinc sulfide (ZnS) was chosen as the material for the ATR prism.

The efficiency of their device was confirmed by comparing the glucose measurements obtained from their mid-IR spectroscopy based non-invasive glucose sensor with two reference devices that measure blood glucose from blood samples. In addition, the researchers took several blood glucose measurements before and after the different subjects ate various meals, while also analyzing the spectrum of the patients’ oral mucosa. Additional data sets were acquired from different ATR prisms and various FTIR devices.

Improving the Accuracy Using Predictive Modelling

By performing a series of cross-validation processes for the various data sets that were collected using the mid-IR sensors and reference devices, the Japanese team narrowed down the correlated wavenumbers and constructed a predictive model. This group of researchers chose not to use the commonly practiced leave-one-out cross-validation (LOOCV) method. Instead, they utilized wavenumber selection which was based on more stringent series cross-validation methods to establish the predictive model. As this method involved the analysis of different series of data groups that were obtained simultaneously for model estimation, their results can be considered both accurate and reliable. The variability of their readings, concerning a specific environment or specific data, was also reduced with a predictive model based on just three wavenumbers (1050 cm-1, 1070 cm-1, and 1100 cm-1).


  1. Kasahara, R., Kino, S., Soyama, S., 7 Matsuura Y. (2018). Noninvasive glucose monitoring using mid-infrared absorption spectroscopy based on a few wavenumbers. Biomedical Optics Express 9(1). DOI: 10.1364/BOE.9.000289.

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Benedette Cuffari

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Benedette Cuffari

After completing her Bachelor of Science in Toxicology with two minors in Spanish and Chemistry in 2016, Benedette continued her studies to complete her Master of Science in Toxicology in May of 2018. During graduate school, Benedette investigated the dermatotoxicity of mechlorethamine and bendamustine; two nitrogen mustard alkylating agents that are used in anticancer therapy.


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