CN-122017055-A - Dynamic detection system and method for optimizing molecular weight distribution of bird's nest peptide

CN122017055ACN 122017055 ACN122017055 ACN 122017055ACN-122017055-A

Abstract

The invention relates to the technical field of bird's nest peptide detection, and discloses a dynamic detection system and a method for optimizing the molecular weight distribution of bird's nest peptide. The method comprises the steps of obtaining liquid chromatography mass spectrometry data of a bird's nest peptide sample, constructing a molecular weight dynamic distribution matrix based on the data, wherein the dimension of the molecular weight dynamic distribution matrix covers a time point index, a mass-to-charge ratio index and a signal intensity value, carrying out multi-scale decomposition on the matrix, extracting molecular weight distribution characteristics such as main peak positions, peak widths and peak area occupation ratios under different time scales, carrying out dynamic grouping on the characteristics by adopting an adaptive clustering algorithm to generate molecular weight distribution clusters comprising cluster centers, cluster boundaries and intra-cluster dispersion, constructing a dynamic evolution model based on the molecular weight distribution clusters, simulating the change trend of molecular weight distribution along with time, predicting merging or splitting behaviors of the molecular weight distribution clusters, and adjusting acquisition parameters of the liquid chromatography mass spectrometry data according to prediction results to optimize the molecular weight distribution resolution of subsequent detection.

Inventors

HE ZHIWEI
DOU XIULI
GUO YANYAN

Assignees

青岛正典生物科技有限公司

Dates

Publication Date: 20260512
Application Date: 20251113

Claims (10)

1. A dynamic detection method for optimizing the molecular weight distribution of bird's nest peptide is characterized by comprising the following steps: Acquiring liquid chromatography-mass spectrometry data of a bird's nest peptide sample, wherein the liquid chromatography-mass spectrometry data comprises time sequence signals and mass-to-charge ratio distribution information; constructing a molecular weight dynamic distribution matrix based on the liquid chromatography-mass spectrometry data, wherein the dimension of the molecular weight dynamic distribution matrix comprises a time point index, a mass-to-charge ratio index and a signal intensity value; carrying out multi-scale decomposition on the molecular weight dynamic distribution matrix, and extracting molecular weight distribution characteristics under different time scales, wherein the molecular weight distribution characteristics comprise main peak positions, peak widths and peak area occupation ratios; Dynamically grouping the molecular weight distribution characteristics by adopting an adaptive clustering algorithm to generate a molecular weight distribution cluster, wherein the molecular weight distribution cluster comprises a cluster center, a cluster boundary and intra-cluster dispersion; Constructing a dynamic evolution model based on the molecular weight distribution cluster, wherein the dynamic evolution model is used for simulating the change trend of molecular weight distribution along with time and predicting the merging or splitting behavior of the molecular weight distribution cluster; And adjusting acquisition parameters of the liquid chromatography-mass spectrometry combination data according to the prediction result of the dynamic evolution model, and optimizing the molecular weight distribution resolution of subsequent detection.
2. The method for dynamically detecting the molecular weight distribution of the optimized cubilose peptide according to claim 1, wherein the constructing a molecular weight dynamic distribution matrix based on the liquid chromatography mass spectrometry data comprises: Performing time alignment and noise suppression processing on the liquid chromatography-mass spectrometry combined data to generate a standardized time sequence signal; Extracting signal intensity values of the standardized time sequence signals in different mass-to-charge ratio intervals, and constructing a three-dimensional matrix, wherein row indexes of the three-dimensional matrix correspond to time points, column indexes correspond to the mass-to-charge ratio intervals, and matrix element values correspond to signal intensities; and carrying out normalization processing on the three-dimensional matrix, eliminating the signal intensity difference at different time points, and generating a molecular weight dynamic distribution matrix.
3. The method for dynamically detecting the molecular weight distribution of the optimized cubilose peptide according to claim 2, wherein the performing multi-scale decomposition on the molecular weight dynamic distribution matrix comprises: traversing the molecular weight dynamic distribution matrix by adopting a sliding window algorithm, and calculating the molecular weight distribution statistical characteristics in different time windows; constructing a multi-scale feature vector based on the molecular weight distribution statistical features, wherein the multi-scale feature vector comprises short-time peak shape features, medium-time trend features and long-time stability features; and extracting key molecular weight distribution characteristics through the multi-scale characteristic vector of the main component analysis dimension reduction.
4. The method for dynamically detecting the molecular weight distribution of the optimized bird's nest peptide according to claim 3, wherein the dynamic grouping of the molecular weight distribution features by adopting the adaptive clustering algorithm comprises: calculating an initial clustering center according to the similarity measurement of the key molecular weight distribution characteristics; Adjusting the number of clusters based on dynamic density peak detection, wherein the dynamic density peak detection is realized through local density and a minimum distance threshold; and iteratively optimizing the boundary of the cluster until the intra-cluster dispersion converges to a preset range.
5. The method for dynamically detecting the molecular weight distribution of the optimized cubilose peptide according to claim 4, wherein the constructing a dynamic evolution model based on the molecular weight distribution cluster comprises: calculating a similarity matrix of molecular weight distribution clusters of adjacent time points, wherein the similarity matrix is used for quantifying migration probability among clusters; simulating an evolution path of a molecular weight distribution cluster based on a Markov chain model, wherein the evolution path comprises cluster merging, cluster splitting and cluster stable states; And predicting the change trend of the molecular weight distribution cluster at the future time point according to the evolution path.
6. The method for dynamically detecting molecular weight distribution of optimized cubilose peptide according to claim 5, wherein the adjusting the acquisition parameters of the liquid chromatography-mass spectrometry data according to the prediction result of the dynamic evolution model comprises: If the prediction result indicates that the molecular weight distribution clusters are about to be combined, the mass spectrum resolution is improved to distinguish overlapping peaks; if the predicted result indicates that the molecular weight distribution cluster is about to split, prolonging chromatographic separation time to improve peak separation degree; if the prediction result indicates that the molecular weight distribution cluster is stable, the current acquisition parameters are maintained to reduce redundant data.
7. The method for dynamically detecting the molecular weight distribution of the optimized cubilose peptide according to claim 6, wherein the method further comprises: monitoring abnormal offset of a molecular weight distribution cluster in real time, wherein the abnormal offset comprises cluster center mutation or cluster boundary abnormal expansion; When abnormal deviation is detected, the liquid chromatography-mass spectrometry combined data are triggered to be collected again, and the molecular weight dynamic distribution matrix is updated.
8. The method for dynamically detecting the molecular weight distribution of the optimized cubilose peptide according to claim 7, wherein the method further comprises: training a deep learning model based on historical molecular weight distribution data, wherein the deep learning model is used for assisting in the prediction of a dynamic evolution model; And carrying out weighted fusion on the output of the deep learning model and the prediction result of the dynamic evolution model to generate a final molecular weight distribution prediction result.
9. The method for dynamically detecting the molecular weight distribution of the optimized cubilose peptide according to claim 8, wherein the method further comprises: Establishing a molecular weight distribution quality evaluation index, wherein the quality evaluation index comprises peak symmetry, baseline drift and signal to noise ratio; and dynamically adjusting parameters of a clustering algorithm and an evolution model according to the quality evaluation index, and optimizing detection accuracy.
10. A bird's nest peptide detection system comprising a memory, a processor and a computer program stored in the memory and running on the processor, characterized in that the processor, when executing the computer program, realizes the steps of the bird's nest peptide detection method according to any one of the preceding claims 1 to 9.

Description

Dynamic detection system and method for optimizing molecular weight distribution of bird's nest peptide Technical Field The invention relates to the technical field of bird's nest peptide detection, in particular to a dynamic detection system and a method for optimizing bird's nest peptide molecular weight distribution. Background Along with the continuous progress of technology, the bird's nest peptide gradually enters the field of vision of people. The bird's nest peptide is a hydrolysate of bird's nest, and has proved to have more excellent biological activity in some fields than the traditional bird's nest. At present, research on bird's nest peptide is mainly focused on the preparation method and biological activity research. In terms of preparation methods, researchers have tried a variety of enzymolysis techniques to obtain bird's nest peptides of different structures and functions. In the field of biological activity research, the effects of antioxidation, anti-aging, whitening, immunity improvement, anti-inflammatory and the like of the bird's nest peptide have been widely reported. However, detection methods for the molecular weight distribution of bird's nest peptides have been relatively less studied. Conventional methods for detecting the molecular weight of the bird's nest peptide, such as gel filtration chromatography, polyacrylamide gel electrophoresis and the like, can analyze the molecular weight of the bird's nest peptide to a certain extent, but have a plurality of limitations. First, they have difficulty in accurately acquiring information on the change in the molecular weight distribution of the bird's nest peptide over time. In the actual production and research process, the molecular weight distribution of the bird's nest peptide can be influenced by various factors, such as enzymolysis time, temperature, pH value and the like, and the traditional method cannot track the changes in real time. Secondly, the existing method has a defect in detection resolution. The bird's nest peptide is a complex mixture containing peptide fragments with various molecular weights, and the traditional detection method is difficult to finely separate and analyze the peptide fragments, so that detailed information of the molecular weight distribution of the bird's nest peptide cannot be accurately obtained. In addition, for some complex bird's nest peptide samples, the existing methods have limited analysis capability and are easily affected by impurities and interfering substances, so that the accuracy and reliability of detection are reduced. In recent years, as the interest of people in health and beauty is continuously increasing, the market demand of bird's nest peptide products is on the trend of rapid increase. From nutritional health products to skin care products, bird's nest peptides are becoming more common in body shadow. In the field of the skin care products, the bird's nest peptide is added into products such as a mask, essence and the like to realize the effects of whitening, moisturizing, resisting wrinkles and the like. Along with the expansion of the market, higher requirements are put on the quality control and detection precision of the bird's nest peptide products. The accurate control of the molecular weight distribution of the bird's nest peptide is important for evaluating the quality of products, optimizing the production process and guaranteeing the rights and interests of consumers. The traditional detection method can not meet the market demand, and a new technology is urgently needed to realize dynamic and accurate detection of the molecular weight distribution of the bird's nest peptide. The technology of the patent is exactly under the background, aims to solve the defects of the existing detection method, and provides powerful support for quality control and research and development of bird's nest peptide products. Disclosure of Invention The invention aims to provide a system and a method for optimizing dynamic detection of nidus Collocaliae peptide molecular weight distribution, so as to solve the problems in the background art. In order to achieve the above object, the present invention provides a method for optimizing dynamic detection of molecular weight distribution of bird's nest peptide, the method comprising: Acquiring liquid chromatography-mass spectrometry data of a bird's nest peptide sample, wherein the liquid chromatography-mass spectrometry data comprises time sequence signals and mass-to-charge ratio distribution information; constructing a molecular weight dynamic distribution matrix based on the liquid chromatography-mass spectrometry data, wherein the dimension of the molecular weight dynamic distribution matrix comprises a time point index, a mass-to-charge ratio index and a signal intensity value; carrying out multi-scale decomposition on the molecular weight dynamic distribution matrix, and extracting molecular weight distribution characteristics und