Quantitative analysis of Raman spectra for glucose concentration in human blood using Gramian angular field and convolutional neural network

Wang, Qiaoyun; Pian, Feifei; Wang, Mingxuan; Song, Shuai; Li, Zhigang; Shan, Peng; Ma, Zhenhe

doi:10.1016/j.saa.2022.121189

Cited by 23 publications

(7 citation statements)

References 23 publications

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“…The other group of blood samples is put into a quartz cuvette. The blood spectra are acquired by a Bruker MultiRam spectroscopy (Bruker Optics), 31,34 the wavelength of the Nd:YAG laser is 1064 nm and the power used in the experiment was limited to 90 mW. A Germanium diode detector is used to detect the Raman signal.…”

Section: Instrument and Samplementioning

confidence: 99%

Quantitative analysis of blood glucose by FT‐Raman spectroscopy and multivariate statistical analysis

Sun,

Song,

Qian

et al. 2023

Micro & Optical Tech Letters

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Diabetes is one of the common chronic diseases in worldwide and increasing among young children. Testing glycated hemoglobin levels as well as blood glucose levels is a method for detecting diabetes. In this article, with multivariate statistical analysis was used to monitor the blood glucose levels by the Fourier Transform Raman spectroscopy. Principal component regression with direct orthogonal signal correction is developed and applied to analyze the glucose concentration with the blood Raman spectroscopy. The root means square error of calibration is 3.0824 mg/dL (0.1712 mmol/L), the root means square error of prediction is 3.2688 mg/dL (0.1816 mmol/L), the correlation coefficients (R2) is .9946 and ratio of performance to deviation is 6.4789 and, respectively. The results show that this method can be used as a strong potential diagnostic tool for diabetes mellitus.

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Section: Instrument and Samplementioning

confidence: 99%

Quantitative analysis of blood glucose by FT‐Raman spectroscopy and multivariate statistical analysis

Sun,

Song,

Qian

et al. 2023

Micro & Optical Tech Letters

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“…They showed the high performance with error of prediction (REMSP = 0.065) and coefficient of predication (0.999). These algorithms might be good option to predict of glucose concentration with Raman signal 41 , 42 .…”

Section: Glucose Monitoring Techniquesmentioning

confidence: 99%

Nanozyme-based colorimetric biosensor with a systemic quantification algorithm for noninvasive glucose monitoring

Jeon¹,

Kim²,

Chung³

et al. 2022

Theranostics

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Diabetes mellitus accompanies an abnormally high glucose level in the bloodstream. Early diagnosis and proper glycemic management of blood glucose are essential to prevent further progression and complications. Biosensor-based colorimetric detection has progressed and shown potential in portable and inexpensive daily assessment of glucose levels because of its simplicity, low-cost, and convenient operation without sophisticated instrumentation. Colorimetric glucose biosensors commonly use natural enzymes that recognize glucose and chromophores that detect enzymatic reaction products. However, many natural enzymes have inherent defects, limiting their extensive application. Recently, nanozyme-based colorimetric detection has drawn attention due to its merits including high sensitivity, stability under strict reaction conditions, flexible structural design with low-cost materials, and adjustable catalytic activities. This review discusses various nanozyme materials, colorimetric analytic methods and mechanisms, recent machine learning based analytic methods, quantification systems, applications and future directions for monitoring and managing diabetes.

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“…1 (a) and (b). Results demonstrated that there are two types of generated patterns, in which the patterning at the bottom of the tube is parallel to the tube direction with different angles in relation to the IDTs, but the patterning in the middle of the tube is perpendicular to the tube direction [24]. Additionally, they demonstrated the use of glass capillary tubes with flexible thin film SAW devices.…”

Section: Introductionmentioning

confidence: 96%

“…Manipulation using SAWs can be realized by either using two opposing SAWs that interfere with each other to generate standing SAWs (SSAWs) [3,5,[8][9][10][11][12], by using a traveling SAWs (TSAWs) propagating in one direction [4], or by using a combination of both of these waves such as in multi-stage devices [13,14]. SSAW-based manipulation typically relies on the generation of acoustic radiation forces (ARFs) to actuate objects toward the generated nodes and anti-nodes [15][16][17][18], for separating blood components and isolation of circulating tumor cells [19] and cancer cells [20], bacteria [21] and other biological cells [22][23][24][25]. However, the patterns generated by SSAWs are often restricted to half-wavelength periodic distances between nodes or anti-nodes.…”

Section: Introductionmentioning

confidence: 99%

Acoustofluidic patterning in glass capillaries using travelling acoustic waves based on thin film flexible platform

Wang,

Maramizonouz,

Stringer Martin

et al. 2024

Ultrasonics

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Quantitative analysis of Raman spectra for glucose concentration in human blood using Gramian angular field and convolutional neural network

Cited by 23 publications

References 23 publications

Quantitative analysis of blood glucose by FT‐Raman spectroscopy and multivariate statistical analysis

Quantitative analysis of blood glucose by FT‐Raman spectroscopy and multivariate statistical analysis

Nanozyme-based colorimetric biosensor with a systemic quantification algorithm for noninvasive glucose monitoring

Acoustofluidic patterning in glass capillaries using travelling acoustic waves based on thin film flexible platform

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