Abstract
HighlightsAn ultrasensitive multiplex biosensor was designed to quantify magnetic nanoparticles on immunochromatography test strips.A machine learning model was constructed and used to classify both weakly positive and negative samples, significantly enhancing specificity and sensitivity.A waveform reconstruction method was developed to appropriately restore the distorted waveform for weak magnetic signals.
Highlights
Acute diseases such as acute myocardial infarction (AMI) occur rapidly and can cause severe damage to health, making them a major public health problem worldwide [1, 2]
We developed a novel data processing method based on a support vector machine (SVM) classifier and custom waveform reconstruction method for weak signals, thereby greatly improving the sensitivity and accuracy
The raw data provided by the magnetic immunoassay reader (MIR) apparatus comprised the digital signal, as shown in Fig. 6a, where spiked noise interfered with the magnetic signal
Summary
Acute diseases such as acute myocardial infarction (AMI) occur rapidly and can cause severe damage to health, making them a major public health problem worldwide [1, 2]. Various methods have been developed for the simultaneous analysis of diseases, such as enzyme-linked immunosorbent assay (ELISA) [4], electrochemiluminescence immunoassay (ECLIA) [5], electrochemical immunoassay [6, 7], fluorescence detection [8, 9], and label-free methods [10, 11]. Some of these methods are time-consuming and require well-equipped facilities, complex operations, well-trained technicians, and long analysis times [12,13,14]. POCT has been widely developed for early diagnosis over the past decade [15,16,17,18,19,20]
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