Super-Sensitive Optical Biosensor with a Spectrometer on a Chip

Song, Jun; Chen, Linchun; Li, Bojun

doi:10.5504/bbeq.2013.0055

Cited by 2 publications

(1 citation statement)

References 10 publications

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“…Moreover, by comparing an unknown quantity of protein to known amounts of BSA, it is often used as a protein concentration standard, which is therefore of great importance to identify BSA quantitatively and qualitatively. Even though BSA in a solid state (pressed pellet) and in solution has been previously investigated using THz spectroscopy [22][23][24][25][26][27][28], a great challenge for the property identification of BSA thin-films is obvious because subwavelength sample thicknesses impose great difficulties to conventional terahertz spectroscopy [29]. This work presents the first THz time-domain spectroscopy investigation of the BSA thin-films with a support vector regression (SVR) method to learn the function of the frequency and the target concentration.…”

Section: Introductionmentioning

confidence: 99%

Quantitative characterization of bovine serum albumin thin-films using terahertz spectroscopy and machine learning methods

Sun¹,

Du²,

Lu³

et al. 2018

Biomed. Opt. Express

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The development of new spectral analysis methods in bio thin-film detection has generated intense interest in terahertz (THz) spectroscopy and its application in a wide range of fields. In this paper, it is the first time that machine learning methods are applied to the quantitative characterization of bovine serum albumin (BSA) deposited thin-films detected by terahertz time-domain spectroscopy. The spectra data of BSA thin-films prepared by solutions with concentrations ranging from 0.5 to 35 mg/ml are analyzed using the support vector regression method to learn the underlying model of the frequency against the target concentration. The learned mode successfully predicts the concentrations of the unknown test samples with a coefficient of determination R = 0.97932. Furthermore, aiming to identify the relevance of each frequency to the concentration, the maximal information coefficient statistical analysis is used and the three most discriminating frequencies in THz frequency are identified at 1.2, 1.1 and 0.5 THz respectively, which means a good prediction for BSA concentration can be achieved by using the top three relevant frequencies. Moreover, the top discriminating frequencies are in good agreement with the frequencies predicted by a long-wavelength elastic vibration model for BSA protein.

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