CN-121994732-A - Soup component rapid detection method and spectrum analyzer thereof

CN121994732ACN 121994732 ACN121994732 ACN 121994732ACN-121994732-A

Abstract

The application discloses a soup component rapid detection method which comprises the steps of S1, preprocessing soup to be detected, S2, collecting data of the preprocessed soup, wherein the data collection comprises collection of multi-channel spectrum data, conductivity data and/or temperature data of the soup, S3, analyzing the soup by adopting a soup analysis model according to the multi-channel spectrum data, the conductivity data and/or the temperature data to generate an analysis result, and the analysis result comprises a qualitative analysis result and a semi-quantitative analysis result. The method can be used at any time in a living scene without laboratory-level pretreatment, can be used for nondestructive testing, does not influence the quality of soup, can be used for testing the content level of purine, protein, fat and salinity in the soup, eliminates mutual interference of components on the same characteristics by using big data and an AI model, and improves the reliability and accuracy of the test by using the difference of the components on different physical quantities.

Inventors

YANG KE
LIU WEI
ZHANG HAN
HUANG HAIXIA
CHEN ZHIFENG
Gong Xurong

Assignees

深圳市威视佰科科技有限公司

Dates

Publication Date: 20260508
Application Date: 20260216

Claims (10)

1. A method for rapidly detecting soup ingredients is characterized by comprising the following steps: S1, preprocessing soup to be detected, wherein the preprocessing comprises filtering, adsorbing and/or reverse osmosis; S2, carrying out data acquisition on the preprocessed soup, wherein the data acquisition comprises acquisition of multichannel spectrum data, conductivity data and/or temperature data of the soup; S3, analyzing the soup by adopting a soup analysis model according to the multi-channel spectrum data, the conductivity data and/or the temperature data to generate an analysis result, wherein the analysis result comprises a qualitative analysis result and a semi-quantitative analysis result.
2. The method for rapidly detecting soup ingredients according to claim 1, wherein the data acquisition of the pretreated soup is performed, specifically comprising the steps of: S21, directly placing a spectrum analyzer in the pretreated soup, wherein the spectrum analyzer comprises a processing module, an emission light source and a photoelectric receiver, and the optical path length of an acquisition optical path between the emission light source and the photoelectric receiver is 3-6 mm; S22, an emission light source of the spectrum analyzer emits ultraviolet visible near infrared light, and the photoelectric receiver receives the ultraviolet visible near infrared light penetrating through the soup; S23, the processing module analyzes and processes the ultraviolet visible near infrared light received by the photoelectric receiver according to the soup analysis model to obtain the multichannel spectrum data, the conductivity data and/or the temperature data.
3. The rapid detection method of soup ingredients according to claim 1, wherein the center frequency of the multi-channel spectral data acquisition comprises 260nm, 280nm, 360nm, 530nm, 890nm±10nm, 400nm, 930nm, 1160nm, 1200nm, 1400nm, 1500nm, 1600nm±10nm.
4. The method for rapid detection of soup ingredients according to claim 1, wherein the step of constructing the soup analysis model comprises: S100, preparing a sample library, wherein the sample library comprises a plurality of standard liquid samples, mixed liquid samples and real soup samples with different gradients, each sample is provided with a corresponding label, and each sample is used for preparing a parallel sample; S200, carrying out data acquisition and labeling on each sample of the sample library, wherein the data acquisition comprises acquisition of multichannel spectrum data, conductivity data and/or temperature data of the soup; s300, performing data quality control and data cleaning on the data; S400, putting the cleaned data into a regression model for training, and establishing a mapping model which takes multichannel spectrum, conductivity and temperature data as input and takes protein concentration, purine concentration, fat concentration and sodium chloride concentration as output through a supervised learning algorithm; S500, setting a qualitative analysis threshold and a semi-quantitative analysis threshold according to the training of the regression model. And (3) carrying out qualitative analysis and semi-quantitative analysis on the regression model obtained by realizing regression transformation through a decision transformation layer.
5. The method of claim 4, wherein the algorithm comprises a multi-layer perceptron regressive, an elastic regression network, a minimum absolute contraction and selection operator, and linear and nonlinear support vector regressions.
6. The method for rapid detection of soup ingredients according to claim 4, wherein the data quality control and data cleaning are performed on the data, specifically, invalid bad samples are removed, wherein the bad samples comprise data missing, label missing, repeated acquisition of single samples and/or excessive fluctuation rate change.
7. The method for rapid detection of soup ingredients according to claim 4, wherein the step of manufacturing the real soup sample comprises: s101, taking soup in the middle layer of the soup; S102, filtering out particle impurities and floating oil in the soup by using a filter screen with 100-400 meshes; s103, analyzing in a laboratory to obtain concentration values of purine, protein, fat and salinity in the soup.
8. The rapid detection method of soup ingredients according to claim 7, wherein the laboratory analysis method comprises high performance liquid chromatography, kjeldahl nitrogen determination method, acid hydrolysis method, silver nitrate titration method, conductivity measurement method.
9. The method for rapidly detecting soup ingredients according to claim 1, wherein the qualitative analysis results include pass and fail, and the semi-quantitative analysis results include low, medium and high.
10. The spectrum analyzer is characterized by comprising a processing module, an emission light source and a photoelectric receiver; The processing module includes a memory, one or more processors; The memory for storing one or more computer programs, the one or more processors for executing the one or more computer programs stored by the memory, to cause the one or more processors to perform the soup ingredient rapid detection method as claimed in any one of claims 1-9.

Description

Soup component rapid detection method and spectrum analyzer thereof Technical Field The application relates to the technical field of soup detection, in particular to a soup component rapid detection method and a spectrum analyzer thereof. Background In the vast ocean of Chinese food, the soup clearly occupies an important position. The soup is a common dish type in life, in the continuous stewing process, as tissue cell walls are broken and a large amount of nucleotide is dissolved into the soup, the concentration of purine can be continuously increased, and fat and protein are cooperated to carry out emulsification reaction, so that the fat is distributed more uniformly and is not easy to separate, and as the fat is evaporated, the concentration of various components in the soup can be further increased. When Shang Pincheng is detected separately, the traditional laboratory method needs professional laboratory equipment and reagents, can be used for pretreatment by being matched with professional operation, has a single measurement mode, has interference among different components, cannot realize on-site rapid nondestructive detection, and is not suitable for resident life scenes. With the rise of living standard and health consciousness of residents and the aging of population, especially for patients with basic diseases such as hyperuricemia, hyperlipidemia, hypertension, etc., excessive intake of purine, protein, fat, salinity, etc. should be controlled on diet. When the purines, proteins, fats and salinity of the soup are detected and analyzed in a laboratory, a high performance liquid chromatography method for detecting purines, a Kjeldahl method for detecting proteins, a Coomassie brilliant blue method for detecting fats and the like are all required to use special reagents and equipment, the operation is troublesome, the household nondestructive detection cannot be realized, and the refraction type and conductivity type salinity meter for detecting the salinity in the soup can be interfered by substances such as grease and the like, for example, a single-channel sensor can respond to certain indexes, but cannot resist interference. When electric products with soup cooking function are developed, the purine of soup is controlled by using factors such as time, temperature and the like, and the scheme cannot adapt to the difference of various food materials and cannot judge the state of the final soup. Such as using a camera approach for viewing, there are drawbacks in metering accuracy and consistency between devices due to the lack of a light source. Disclosure of Invention The application aims to overcome the defects of the prior art and provides a rapid detection method for soup ingredients and a spectrum analyzer thereof. In order to achieve the above purpose, the present application provides the following technical solutions: the application provides a rapid detection method for soup ingredients, which comprises the following steps: S1, preprocessing soup to be detected, wherein the preprocessing comprises filtering, adsorbing and/or reverse osmosis; S2, carrying out data acquisition on the preprocessed soup, wherein the data acquisition comprises acquisition of multichannel spectrum data, conductivity data and/or temperature data of the soup; S3, analyzing the soup by adopting a soup analysis model according to the multi-channel spectrum data, the conductivity data and/or the temperature data to generate an analysis result, wherein the analysis result comprises a qualitative analysis result and a semi-quantitative analysis result. Preferably, the step of collecting data of the pretreated soup specifically includes the steps of: S21, directly placing a spectrum analyzer in the pretreated soup, wherein the spectrum analyzer comprises a processing module, an emission light source and a photoelectric receiver, and the optical path length of an acquisition optical path between the emission light source and the photoelectric receiver is 3-6 mm; S22, an emission light source of the spectrum analyzer emits ultraviolet visible near infrared light, and the photoelectric receiver receives the ultraviolet visible near infrared light penetrating through the soup; S23, the processing module analyzes and processes the ultraviolet visible near infrared light received by the photoelectric receiver according to the soup analysis model to obtain the multichannel spectrum data, the conductivity data and/or the temperature data. Preferably, the center frequency of the multi-channel spectrum data acquisition comprises 260nm, 280nm, 360nm, 530nm, 890nm + -10 nm, 400nm, 930nm, 1160nm, 1200nm, 1400nm, 1500nm, 1600nm + -10 nm. Preferably, the step of constructing the soup analysis model includes: S100, preparing a sample library, wherein the sample library comprises a plurality of standard liquid samples, mixed liquid samples and real soup samples with different gradients, each sample is provided with a corresponding la