Quantitative differentiation of multiple virus in blood using nanoporous silicon oxide immunosensor and artificial neural network

• Published in: Biosensors & bioelectronics 2017-12, Vol.98, p.180-188
• Main Authors: Chakraborty, W., Ray, R., Samanta, N., RoyChaudhuri, C.
• Format: Article
• Language: English
• Subjects: Antibodies, Immobilized - chemistry; Antibodies, Immobilized - immunology; Artificial neural network; Biosensing Techniques; Differentiation; Hepacivirus - isolation & purification; Hepacivirus - pathogenicity; Hepatitis B - diagnosis; Hepatitis B - virology; Hepatitis B virus - isolation & purification; Hepatitis B virus - pathogenicity; Hepatitis C - diagnosis; Hepatitis C - virology; Humans; Immunoassay; Limit of Detection; Multiple virus; Nanopores; Nanoporous silicon oxide immunosensor; Neural Networks (Computer); Oxides - chemistry; Silicon - chemistry
• ISSN: 0956-5663; 1873-4235
• DOI: 10.1016/j.bios.2017.06.046

In spite of the rapid developments in various nanosensor technologies, it still remains challenging to realize a reliable ultrasensitive electrical biosensing platform which will be able to detect multiple viruses in blood simultaneously with a fairly high reproducibility without using secondary labels. In this paper, we have reported quantitative differentiation of Hep-B and Hep-C viruses in blood using nanoporous silicon oxide immunosensor array and artificial neural network (ANN). The peak frequency output (fp) from the steady state sensitivity characteristics and the first cut off frequency (fc) from the transient characteristics have been considered as inputs to the multilayer ANN. Implementation of several classifier blocks in the ANN architecture and coupling them with both the sensor chips, functionalized with Hep-B and Hep-C antibodies have enabled the quantification of the viruses with an accuracy of around 95% in the range of 0.04fM–1pM and with an accuracy of around 90% beyond 1pM and within 25nM in blood serum. This is the most sensitive report on multiple virus quantification using label free method. •This paper reports quantitative differentiation of Hep-B and Hep-C viruses in blood using immunosensor array and ANN.•The peak frequency output (fp) and the first cut off frequency (fc) have been considered as inputs to the multilayer ANN.•The ANN contains multiple classifier blocks and learning rate and loss function have been selected to avoid over-fitting.•Viruses can be quantified with an accuracy of around 95% at 0.04fM –1pM and around 90% beyond 1pM and within 25nM.