US-20260128170-A1 - BIOMOLECULAR DETECTION METHOD AND DETECTION SYSTEM THEREOF

US20260128170A1US 20260128170 A1US20260128170 A1US 20260128170A1US-20260128170-A1

Abstract

A biomolecular detection method is disclosed. The method includes following steps: providing a surface-enhanced Raman spectroscopy (SERS) substrate; applying a biological sample onto the SERS substrate, wherein the biological sample is a medical specimen; acquiring a SERS signal on the SERS substrate through using a SERS spectrometer; and applying a trained machine learning model of a detection system to analyze the full spectral range of the SERS signal, or one or more key feature regions of the full spectral range, and output a biomolecular detection result of the biological sample.

Inventors

Jiunn-Der Liao
Han Lee
Kuan-Hung Liu

Assignees

NATIONAL CHENG KUNG UNIVERSITY

Dates

Publication Date: 20260507
Application Date: 20251107

Claims (15)

1 . A biomolecular detection method, comprising following steps: providing a surface-enhanced Raman spectroscopy (SERS) substrate; applying a biological sample onto the SERS substrate, wherein the biological sample is a medical specimen; acquiring a SERS signal on the SERS substrate through using a SERS spectrometer; and using a trained machine learning model of a detection system to analyze a full spectral range of the SERS signal, or one or more key feature regions of the full spectral range, and output a biomolecular detection result of the biological sample.
2 . The biomolecular detection method as claimed in claim 1 , wherein a type of the biological sample includes a viral envelope protein or a middle molecule toxin, and the biomolecular detection result includes a viral morphology detection result or a concentration or morphology detection result of a target molecule.
3 . The biomolecular detection method as claimed in claim 1 , wherein the SERS substrate comprises a bulk material and a plurality of gold nanoparticles (Au NPs) or a plurality of silver nanoparticles (Ag NPs), the plurality of gold nanoparticles or silver nanoparticles cover the substrate bulk material, and the substrate bulk material is of a fiber structure or a bowl-shaped structure.
4 . The biomolecular detection method as claimed in claim 3 , wherein a diameter of each gold or silver nanoparticle is between 1 and 100 nanometers (nm), a length of the fiber structure is between 1 and 5 micrometers (μm), and a diameter of the fiber structure is between 20 and 200 nanometers.
5 . The biomolecular detection method as claimed in claim 3 , wherein the biological sample is derived from urine of a test subject, and the SERS substrate is a substrate covered by the plurality of gold nanoparticles, silver nanoparticles, or the combination thereof.
6 . The biomolecular detection method as claimed in claim 1 , wherein a type of the trained machine learning model comprises a Support Vector Machine (SVM), Random Forest model, K-Nearest Neighbors (KNN) model, Convolutional Neural Network (CNN), Kernel model, ensemble learning model, or transformer architecture model.
7 . The biomolecular detection method as claimed in claim 1 , wherein the trained machine learning model is formed by training an initial model in a training phase, where training data used in the training phase includes SERS signals from multiple biological samples taken from the throat or nasal cavity of a human body, and each training data has a label that corresponds to either a positive (CoV(+)) or negative (CoV(−)) COVID-19 result.
8 . The biomolecular detection method as claimed in claim 1 , wherein the trained machine learning model is formed by training an initial model in a training phase, where training data used in the training phase includes SERS signals from multiple biological samples of urine taken from human bodies, and each training data has a label that corresponds to presence or absence of a middle molecule toxin, or to a concentration level of the middle molecule toxin, in which the middle molecule toxin is β2-m protein, or leptin protein.
9 . The biomolecular detection method as claimed in claim 1 , wherein key feature segments of the full spectral range of the SERS signal correspond to viral mutation sites, tryptophan (Trp) residue regions, or specific biological toxins.
10 . The biomolecular detection method as claimed in claim 1 , wherein source of the biological sample includes nasopharyngeal swabs, throat swabs, plasma, urine, saliva, or blood samples
11 . The biomolecular detection method as claimed in claim 1 , wherein the biomolecular detection method is integrated into a point-of-care testing (POCT) device including: an automated sample collection module, a sample processing and transport module, a SERS spectral analysis module, a data analysis software platform, a report output module, or a remote clinical decision support system, or a combination thereof.
12 . The biomolecular detection method as claimed in claim 1 , wherein the biomolecular detection method is applied to identification of pathogen outer membrane proteins and typing of variant strains, or real-time monitoring of middle molecule toxin concentrations related to kidney disease.
13 . A detection system, adapted for a biomolecular detection method including providing a surface-enhanced Raman spectroscopy (SERS) substrate; applying a biological sample onto the SERS substrate, wherein the biological sample is a medical specimen; acquiring a SERS signal on the SERS substrate through using a SERS spectrometer; and using a trained machine learning model of a detection system to analyze a full spectral range of the SERS signal, or one or more key feature regions of the full spectral range, and output a biomolecular detection result of the biological sample, comprising: a signal preprocessing unit to perform a signal preprocessing procedure on the SERS signal; and a trained machine learning model to analyze the preprocessed SERS signals.
14 . The detection system as claimed in claim 13 , further comprising a key feature segment extraction unit for extracting data corresponding to one or more key feature segments from the full spectral range of the SERS signal after the signal preprocessing procedure.
15 . The detection system as claimed in claim 13 , wherein the signal preprocessing procedure comprises a sub-procedure for spike removal, baseline correction, smoothing, normalization, or a combination thereof.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS This application claims the benefit of filing date of U.S. Provisional Application Ser. No. 63/717,494, entitled “A method that combines label-free SERS spectroscopy and artificial intelligence assistance to accurately detect the outer membrane of specific pathogens or biomedical molecular structures” filed Nov. 7, 2024 under 35 USC § 119 (e)(1). BACKGROUND OF THE INVENTION Field The present application relates to a detection method and detection system, and particularly to a detection method and detection system for biomolecules. Description of Related Art Currently, the most commonly used biomolecular detection method (e.g. PCR (Polymerase chain reaction), ELISA (enzyme linked immunosorbent assay) and LC-MS (Liquid chromatography mass spectrometry)) is labeling detection, which has high accuracy and strong identification ability. However, most of these methods require “labels” for detection, such as antibodies, enzymes, or probes. These testing procedures are not only complex and time-consuming, but also require expensive equipment and professional operating people, making them unsuitable for scenarios requiring rapid response, such as clinical settings or telemedicine. Surface-enhanced Raman spectroscopy (Hereinafter referred to as SERS) is a technique that can directly detect molecular signals, theoretically without the need for labels, and with very high sensitivity. However, in practical applications, the uneven distribution of “hot spots” on the nanomaterials can lead to unstable and difficult-to-reproduce signals, further affecting the reliability of the diagnosis. In addition, while machine learning (such as deep learning) can help analyze spectral data, current machine learning methods often require significant analysis time and are not easily adapted to clinical equipment. Due to technological limitations, spectral analysis still faces many challenges in applications such as rapid diagnosis and remote health monitoring. Therefore, a novel biomolecular detection method and system combining SERS and artificial intelligence is needed to improve the above-mentioned problems. SUMMARY OF THE INVENTION One objective of the present application is to provide a biomolecular detection method. The biomolecular detection method, comprises following steps: providing a SERS substrate; applying a biological sample onto the SERS substrate, wherein the biological sample is a medical specimen; acquiring a SERS signal on the SERS substrate through using a SERS spectrometer; and using a trained machine learning model of a detection system to analyze a full spectral range of the SERS signal, or one or more key feature regions of the full spectral range, and output a biomolecular detection result of the biological sample. Another objective of the present application is to provide a detection system, adapted for a biomolecular detection method including providing a SERS substrate; applying a biological sample onto the SERS substrate, wherein the biological sample is a medical specimen; acquiring a SERS signal on the SERS substrate through using a SERS spectrometer; and using a trained machine learning model of a detection system to analyze a full spectral range of the SERS signal, or one or more key feature regions of the full spectral range, and output a biomolecular detection result of the biological sample, comprising: a signal preprocessing unit to perform a signal preprocessing procedure on the SERS signal; and a trained machine learning model to analyze the preprocessed SERS signals. Other novel features of the disclosure will become more apparent from the following detailed description when taken in conjunction with the accompanying drawings. BRIEF DESCRIPTION OF DRAWINGS FIG. 1 is a flowchart of the steps of a biomolecular detection method combining SERS and artificial intelligence according to an embodiment of the present application. FIG. 2 is a system architecture diagram of a detection system according to an embodiment of the present application. FIG. 3 is a flowchart of the signal preprocessing procedure of an embodiment of the present application. FIG. 4 is a flowchart of the key feature search method of an embodiment of the present application. FIG. 5 is a flowchart of the detection method of the first example of the present application. FIG. 6 is a flowchart of the training process of the trained machine learning model in the first example of the present application. FIG. 7 is a flowchart of the detection method of the second example of the present application. FIG. 8 is a flowchart of the training process of the trained machine learning model in the second example of the present application. DETAILED DESCRIPTION OF THE INVENTION The exemplary embodiments of the present application will now be described in detail with reference to the accompanying drawings. Whenever possible, identical component symbols are used in diagrams and descriptions to represent the same or