Quantum-dot-spectrometer-based virtual barcode for the sensitive colorimetric urinalysis

Colorimetric sensing methods are extensively utilized for rapid and sensitive detection of various biomedical and environmental targets, with higher dimensional spectra resulting in more accurate results. Miniature reconstructive spectrometers, as portable colorimetric sensing devices, show promise in capturing high-dimension spectra signals, while facing the challenges of noise-sensitive spectrum reconstruction and complex pre-calibration. To address these issues, we present a virtual barcode method, which is directly based on the utilization of a high-dimension quantum dot (QD) spectrometer intensity vector. Any spectral changes of the analytes can be reflected in the corresponding barcode, without the redundant operation for spectral analysis. We demonstrate the QD barcode method in quantitatively detecting multiple biomarkers in the artificial human urine, including urinary calcium, glucose, nitrite, and creatinine, with lower limits of detection compared to the RGB sensing method (2.4-14.4-fold). To simplify the preparation of the QD spectrometer, we optimize both the number and the spectral distribution of QD filters. Furthermore, an artificial neural network model is also established to achieve 8-fold improved quantitative recognition performance. This QD barcode method can greatly broadens the biosensing application of miniaturized reconstructive spectrometers.