CN115561233A - Method for visually and intelligently detecting freshness of meat based on hydrogel material - Google Patents
Method for visually and intelligently detecting freshness of meat based on hydrogel material Download PDFInfo
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Abstract
The invention discloses a method for visually and intelligently detecting freshness of meat based on a hydrogel material, and belongs to the technical field of food detection. The method comprises the following steps: preparing methacrylic acid gelatin GelMA hydrogel with high degree of substitution; the obtained GelMA hydrogel is used as a carrier of bromocresol green, and the bromocresol green BCG is embedded to prepare a visual film as an indication label; placing the obtained indication label and the meat food to be detected in the same closed space, taking a picture by using a smart phone after standing to obtain an image of the indication label, integrating a VGG 16 algorithm and a watershed algorithm of a deep Convolutional Neural Network (CNN) into a mobile phone APP by utilizing deep learning of the CNN, and enabling a consumer to scan the indication label by using the mobile phone APP to identify the freshness of meat within 30 s. The method can realize sensitive, automatic, nondestructive and real-time detection of the freshness of the meat, and has low detection cost and simple and convenient operation.
Description
Technical Field
The invention relates to a method for visually and intelligently detecting freshness of meat based on a hydrogel material, and belongs to the technical field of food detection.
Background
The meat food has rich nutrient substances and is popular with consumers. However, during the production and sale of meat products, the meat products are easily contaminated by microorganisms, which leads to spoilage. Spoiled meat not only destroys the nutritional components of the meat, but also has the potential to produce toxic substances that can endanger the health and even life of the consumer. Therefore, it is of great importance to develop a new method for detecting the freshness of meat products.
Bromocresol green (BCG) is an acid-base indicator and has great potential in application of a food detection indicator label, but low-concentration bromocresol green cannot accurately indicate freshness of meat. Therefore, an efficient embedding carrier is needed to embed high-concentration bromocresol green so as to accurately and efficiently indicate the freshness of meat.
Disclosure of Invention
[ problem ] to
The existing meat freshness detection mode is time-consuming and labor-consuming, and cannot realize real-time, nondestructive and accurate detection in the face of a large number of detection samples.
Meanwhile, due to individual differences among people and other subjective or objective reasons, the color change of the label is insensitive and errors are easily caused, so that the color change of the indication label is combined with a neural network, and a method for detecting the freshness of the fish meat by using a deep learning model of preparing the indication label by using a methacrylated Gelatin (Gelatin methylated, gelMA) hydrogel and combining the indication label with the neural network to construct a smart phone platform is developed, so that the freshness of the fish meat can be better detected.
[ solution ]
The invention provides a method for visually and intelligently detecting freshness of meat based on a hydrogel material and an intelligent detection system APP. Volatile basic nitrogen released by meat with different freshness can enable a methylated gelatin-embedded bromocresol green hydrogel (GelMA-BCG) indicating label to generate a color reaction, the color change of the indicating label is combined with a deep learning model of a neural network, and a smart phone platform is constructed, so that the rapid, accurate, real-time and nondestructive detection of the freshness of the meat is realized, and the detection cost is low, and the operation is simple and convenient.
Specifically, the invention provides a method for visually and intelligently detecting the freshness of meat based on a hydrogel material, which utilizes volatile basic nitrogen released by meat with different freshness to enable a methylated gelatin bromcresol green-embedded hydrogel indicator label (GelMA-BCG) to generate a color reaction, thereby realizing the visual detection of the freshness of the meat.
A methylated gelatin-embedded bromocresol green hydrogel (GelMA-BCG) indicating label for visually and intelligently detecting freshness of meat is prepared by the following steps:
s1, dissolving gelatin in a phosphate buffer solution, and heating and dissolving to obtain a gelatin solution; then adding methacrylic anhydride into the gelatin solution, uniformly mixing and reacting, transferring to a dialysis bag for dialysis after the reaction is finished, and then drying to obtain methylated gelatin (GelMA);
s2, adding bromocresol green (BCG) and a photoinitiator into water, and uniformly mixing to obtain a BCG solution; then adding the methylated gelatin (GelMA) obtained in the step S1 into a BCG solution, dissolving and uniformly mixing, irradiating by an ultraviolet lamp to initiate reaction, and obtaining a bromocresol green-embedded methylated gelatin hydrogel (GelMA-BCG) after the reaction is finished;
and S3, adjusting the pH value of the methylated gelatin hydrogel (GelMA-BCG) embedded with bromocresol green obtained in the step S2 to 3-5 by using acid, dropwise adding the obtained product onto filter paper until the obtained product is completely immersed, irradiating the obtained product by using an ultraviolet lamp, and drying the obtained product to obtain the methylated gelatin hydrogel (GelMA-BCG) embedded with bromocresol green indicating label.
In one embodiment of the invention, the meat includes chicken, duck, fish and various seafood.
In one embodiment of the invention, the methylated gelatin is methacrylic anhydride modified gelatin with a degree of substitution of 90.9%.
In one embodiment of the invention, in S1, the concentration of the gelatin solution is 5 to 20wt%; specifically, 10wt% can be selected.
In one embodiment of the invention, the volume fraction of methacrylic anhydride relative to the gelatin solution in S1 is 0.1% to 2% (v/v); specifically, 0.8% (v/v) can be selected.
In one embodiment of the present invention, in S2, the mass ratio of bromocresol green to methylated gelatin is 1:80.
in one embodiment of the present invention, in S2, the concentration of the photoinitiator in the BCG solution is 0.3 to 0.8wt%; specifically, 0.5wt% can be selected.
In one embodiment of the invention, in S2, the photoinitiation is Irgacure 2959.
In one embodiment of the invention, in S2, the concentration of bromocresol green in the BCG solution is 0.05mg/mL-2mg/mL; specifically, 0.8mg/mL can be selected.
In one embodiment of the present invention, in S2, a 365nm ultraviolet lamp is used for the ultraviolet lamp irradiation.
In one embodiment of the present invention, in S2, the reaction time is 20 to 60min; specifically, 30min can be selected.
In one embodiment of the invention, in S3, the pH may be specifically adjusted to 3.5.
In one embodiment of the present invention, in S3, the ultraviolet lamp irradiation is performed for 30min using a 365nm ultraviolet lamp.
In one embodiment of the invention, in S3, the acid is hydrochloric acid.
In one embodiment of the present invention, in S3, the size of the filter paper is 2mm × 3mm.
In one embodiment of the present invention, the preparation step of the methylated gelatin-embedded bromocresol green hydrogel (GelMA-BCG) indicator label comprises the following steps:
s1, preparation of methylated gelatin (GelMA): dissolving gelatin in phosphate buffer solution with pH =7.5, heating in water bath at 50 ℃, slowly dropwise adding methacrylic anhydride solution into the gelatin solution, reacting for 1h, transferring into dialysis bag (MWCO 8000-14000), dialyzing in deionized water at 40 ℃ for 2 days, and freeze-drying to obtain methylated gelatin (GelMA) powder with substitution degree of 90.9%;
s2, preparation of methylated gelatin hydrogel embedding bromocresol green (GelMA-BCG): bromocresol green (BCG) was dissolved in deionized water containing 0.5% photoinitiator to obtain a BCG solution. Dissolving the methylated gelatin (GelMA) powder prepared in the step S1 in a bromocresol green (BCG) solution, and initiating for 30min by using a 365nm ultraviolet lamp to obtain a bromocresol green-embedded methylated gelatin hydrogel (GelMA-BCG);
s3, preparation of a bromocresol green-embedded methylated gelatin hydrogel (GelMA-BCG) indication label: adjusting the pH value of the methylated gelatin hydrogel (GelMA-BCG) embedded with bromocresol green prepared by S2 to 3.5 by using hydrochloric acid, dropwise adding the obtained mixture onto filter paper until the filter paper is completely immersed, irradiating the filter paper for 30min by using a 365nm ultraviolet lamp, and transferring the filter paper to an oven to be completely dried to obtain the methylated gelatin hydrogel (GelMA-BCG) embedded with bromocresol green.
The invention also provides application of the bromocresol green-embedded methylated gelatin hydrogel (GelMA-BCG) indicating label in detecting freshness of fish meat.
In one embodiment of the invention, a methylated gelatin hydrogel embedded with bromocresol green (GelMA-BCG) indicates that the tag can respond color to varying concentrations of ammonia in the environment. With the increase of the concentration of the ammonia gas, the color difference value delta E is increased; i.e. the indicator tag may be responsive to volatile ammonia for detecting the freshness of the pulp.
The invention also provides a detection system APP for visually and intelligently detecting the freshness of the meat based on the hydrogel material, which extracts a large number of characteristic structures of labels with different meat freshness colors through a deep learning model, and detects the freshness of the meat by scanning the color of the label of the meat to be detected through a mobile phone after continuously iteratively training the model.
In one embodiment of the invention, the indication label and the meat food to be detected are placed in the same closed space, the intelligent mobile phone is used for photographing after standing to obtain an image of the indication label, a VGG 16 algorithm and a watershed algorithm of a deep Convolutional Neural Network (CNN) are integrated into a mobile phone APP by utilizing deep learning of the CNN, and a consumer can identify the freshness of the meat within 30s by scanning the indication label with the mobile phone APP.
In an embodiment of the present invention, the detection system APP includes the following implementation steps:
s1, data acquisition: for meat samples of different freshness, after label color acquisition by the method of claim 1, each image of the color label is matched to its potential class according to a training factor and features are collected for classification. The acquired images are classified into three categories according to the volatile nitrogen value: fresh (< 15mg/100g means volatile basic nitrogen, corresponding to <150ppm ammonia), less fresh (15-30 mg/100g means volatile basic nitrogen, corresponding to 150-300ppm ammonia), and deteriorated (> 30mg/100g means volatile basic nitrogen, corresponding to >300ppm ammonia);
s2, label scanning and developing: according to the characteristics of the experimental image in the S1, segmenting a color label from the whole image by using a watershed algorithm based on a mark in an Open CV library;
s3, establishing a deep learning model: designing a three-class image classification network by using VGG-16, wherein the three-class image classification network comprises an input layer, a convolution layer, a Full Connection (FC) layer and an output layer, and a rectification linear unit (ReLU) function is used as an activation function of each convolution layer;
s4, APP development: combining color indicating labels with a marker-based watershed algorithm for image segmentation and integrating a VGG 16 algorithm for deep learning therewith forms a mobile application to provide automatic identification of freshness.
The detection system can be a smart phone APP, and the specific detection method comprises the following steps: during the production and sale processes of meat products, the methylated gelatin-embedded bromocresol green hydrogel (GelMA-BCG)) indicating label and meat are packaged, transported and stored together, and the color change of the indicating label is scanned by a smart phone APP in the process so as to accurately, real-timely and quickly reflect the freshness of the meat.
The invention has the remarkable advantages that:
the methylated gelatin hydrogel (GelMA-BCG) indicating label embedded with bromocresol green can make color response to the change of the ammonia gas concentration in the environment, and can be used for detecting the freshness of meat. After storage for various periods of time, bromocresol green embedded methylated gelatin hydrogel (GelMA-BCG) indicated that the label exhibited different colors as the freshness of the fish meat changed.
According to the invention, the GelMA hydrogel with high substitution degree is adopted to realize the embedding of the maximum concentration of bromocresol green so as to construct the indication label sensitive to the TVB-N content, and the indication label is combined with the deep learning smart phone APP, so that the error analysis of the indication label by naked eyes caused by subjective or objective reasons is reduced, the accuracy of the indication label is improved, no expensive instrument is required, the operation is simple, and the accurate, real-time and rapid detection of the freshness of meat products can be realized.
Drawings
FIG. 1 is a gel of methylated gelatin hydrogel embedded with bromocresol green (GelMA-BCG) indicating the color response of the label to ammonia gas;
FIG. 2 is a representation of a bromocresol green embedded methylated gelatin hydrogel (GelMA-BCG) indicating label change with fish freshness;
FIG. 3 is a diagram of the design and construction of a deep learning experiment for a Convolutional Neural Network (CNN);
fig. 4 is an operation flow of a smartphone APP;
FIG. 5 shows changes of bromocresol green sol-gel type fish freshness indicator card with freshness of fish meat at different times; wherein (a) is 0h; (b) 18h; (c) 22h; and (d) 24h.
Detailed Description
The present invention is further described below with reference to examples, but the embodiments of the present invention are not limited thereto.
Unless otherwise specified, the gelatin producers described in the following examples are Meclin, CAS No. 9000-70-8; the methacrylic anhydride is produced by Meclin, CAS number 760-93-0; the bromocresol green was manufactured as Maxin, CAS number 76-60-8.
Example 1
The preparation method of the methylated gelatin-embedded bromocresol green hydrogel (GelMA-BCG) indicating label comprises the following steps:
s1, preparation of methylated gelatin (GelMA): dissolving gelatin in phosphate buffer with pH =7.5 to form 10% (w/v) gelatin solution, heating in water bath at 50 ℃, slowly dropwise adding methacrylic anhydride into the gelatin solution until the volume fraction of the methacrylic anhydride is 0.8% (v/v), transferring to a dialysis bag (MWCO 8000-14000) after reacting for 1h, dialyzing in deionized water at 40 ℃ for 2 days, and freeze-drying to obtain methylated gelatin (GelMA) powder with the substitution degree of 90.9%;
s2, preparation of methylated gelatin hydrogel embedding bromocresol green (GelMA-BCG): bromocresol green (BCG) was dissolved in deionized water containing 0.5% photoinitiator to obtain a 0.8mg/ml BCG solution. Dissolving the methylated gelatin (GelMA) powder prepared in the step S1 in a bromocresol green (BCG) solution, and initiating for 30min by using a 365nm ultraviolet lamp to obtain a bromocresol green-embedded methylated gelatin hydrogel (GelMA-BCG) (the mass ratio of the bromocresol green to the GelMA is 1;
s3, preparation of a bromocresol green-embedded methylated gelatin hydrogel (GelMA-BCG) indication label: adjusting the pH value of the methylated gelatin hydrogel (GelMA-BCG) embedded with bromocresol green prepared by S2 to 3.5 by using hydrochloric acid, dropwise adding the obtained product onto 2mm × 3mm filter paper until the obtained product is completely immersed, irradiating the obtained product for 30min by using a 365nm ultraviolet lamp, and transferring the obtained product to an oven until the obtained product is completely dried to obtain the methylated gelatin hydrogel (GelMA-BCG) embedded with bromocresol green.
The methylated gelatin hydrogel embedded with bromocresol green (GelMA-BCG) indication label is used for detecting the freshness of fish:
placing 40g fish meat in a culture dish of 90mm, sticking a methylated gelatin hydrogel (GelMA-BCG) indicating label embedded with bromocresol green on the inner side of the culture dish cover by using an adhesive tape, covering the culture dish, sealing, and placing in a refrigerator at 4 ℃. As shown in FIG. 1, bromocresol green embedded methylated gelatin hydrogel (GelMA-BCG) indicates that the label responds color to varying concentrations of ammonia in the environment. As the concentration of ammonia gas increases, the color difference value delta E also increases, so that the indicator label prepared by the method can respond to the volatile ammonia and be used for detecting the freshness of the meat. Fresh (< 15mg/100g means volatile basic nitrogen, corresponding to <150ppm ammonia), less fresh (15-30 mg/100g means volatile basic nitrogen, corresponding to 150-300ppm ammonia), and deteriorated (> 30mg/100g means volatile basic nitrogen, corresponding to >300ppm ammonia).
As shown in fig. 2, after storage for various periods of time, the bromocresol green-embedded methylated gelatin hydrogel (GelMA-BCG) indicated that the label exhibited different colors as the freshness of the fish meat changed, yellow indicated that the meat was fresh, green indicated that the meat was relatively fresh, and blue indicated that the meat had deteriorated.
Comparative example 1 comparison of existing methods for detecting freshness of fish meat based on bromocresol green
The preparation method of the bromocresol green sol-gel type fish freshness indicating card comprises the following steps:
s1, preparation of a silicon alkoxide precursor mixture: 1.206mL tetraethyl silicate (TEOS) and 1.259mL Methyltriethoxysilane (MTEOS) were mixed well;
s2, adding 20mg of bromocresol green into 3.676mL of ethanol and 0.85mL of hydrochloric acid solution (0.1 mol/L) to completely dissolve the bromocresol green, dropwise adding the silicon alkoxide precursor mixture prepared in the S1 into the solution, magnetically stirring the solution for 1h, and then adding deionized water and the silicon alkoxide precursor mixture (the molar ratio is 4:1) to obtain a sol-gel solution.
And S3, standing filter paper serving as an indicator substrate in the sol-gel solution obtained in the S2 overnight, and drying at room temperature to obtain the bromocresol green sol-gel type fish freshness indicator card.
Detecting the freshness of the fish meat by applying a bromocresol green sol-gel type fish freshness indicating card:
the testing process comprises the following steps: putting 40g of fish meat into a culture dish of 90mm, attaching a freshness indication card on a preservative film, covering the culture dish by the preservative film to enable the freshness indication card to be positioned at the headspace of a sample, observing the color change of the indication card and taking a picture, processing the picture into an RGB value by ImageJ image processing software, and converting the RGB value into an H value according to an HSV (hue saturation value) color model.
And (3) testing results: as shown in fig. 5, the bromocresol green sol-gel type fish freshness indicating card shows different colors according to the freshness of fish meat, yellow indicates fresh meat quality, green and blue indicate fresher meat quality, and deep blue indicates deteriorated meat quality. It can be seen from fig. 5 that the green, blue and dark blue colors are not clearly distinguished, and that the sub-fresh and deteriorated meat cannot be accurately and rapidly distinguished by the H value.
Comparative example 2 detection comparison of labels obtained from different mass ratios of bromocresol green to GelMA
The preparation method of the methylated gelatin-embedded bromocresol green hydrogel (GelMA-BCG) indicator label comprises the following steps:
s1, preparation of methylated gelatin (GelMA): dissolving gelatin in phosphate buffer solution with pH =7.5 to form 10% (w/v) gelatin aqueous solution, heating in water bath at 50 ℃, slowly dropwise adding methacrylic anhydride solution to the gelatin solution to 0.8% (v/v), reacting for 1h, transferring to dialysis bag (MWCO 8000-14000), dialyzing in deionized water at 40 ℃ for 2 days, and freeze-drying to obtain methylated gelatin (GelMA) powder;
s2, preparation of methylated gelatin hydrogel embedding bromocresol green (GelMA-BCG): bromocresol green (BCG) was dissolved in deionized water containing 0.5% photoinitiator to obtain a 0.8% (v/v) BCG solution. Dissolving the methylated gelatin (GelMA) powder prepared in the step S1 in a bromocresol green (BCG) solution, and initiating for 30min by using a 365nm ultraviolet lamp to obtain a bromocresol green-embedded methylated gelatin hydrogel (GelMA-BCG) (the mass ratio of the bromocresol green to the GelMA is 1;
s3, preparation of a bromocresol green-embedded methylated gelatin hydrogel (GelMA-BCG) indication label: adjusting the pH value of the methylated gelatin hydrogel (GelMA-BCG) embedded with bromocresol green prepared by S2 to 3.5 by using hydrochloric acid, dropwise adding the obtained product onto 2mm 3mm filter paper until the obtained product is completely immersed, irradiating the obtained product for 30min by using a 365nm ultraviolet lamp, and transferring the obtained product to an oven to be completely dried to obtain a methylated gelatin hydrogel (GelMA-BCG) indicating label embedded with bromocresol green;
the methylated gelatin hydrogel embedded with bromocresol green (GelMA-BCG) indication label is used for detecting the freshness of fish:
placing 40g fish meat in a culture dish of 90mm, sticking a methylated gelatin hydrogel (GelMA-BCG) indicating label embedded with bromocresol green on the inner side of the culture dish cover by using an adhesive tape, covering the culture dish, sealing, and placing in a refrigerator at 4 ℃. As shown in FIG. 1, bromocresol green-embedded methylated gelatin hydrogel (GelMA-BCG) indicates that the tag responds color to changes in the concentration of ammonia in the environment, and thus can be used to detect the freshness of the pulp. After storage for various periods of time, bromocresol green-embedded methylated gelatin hydrogel (GelMA-BCG) indicated that the label exhibited different colors as the freshness of the fish meat changed, yellow indicated that the meat was fresh, green indicated that the meat was relatively fresh, and blue indicated that the meat had deteriorated. The indicator label took 1-2 minutes to develop color and was less colored than example 1.
Embodiment 2 construct meat freshness intelligent detection system
The utility model provides a detection APP based on visual intellectual detection system meat freshness of hydrogel material lies in drawing the colour difference value delta E of a large amount of different meat freshness colour labels through degree of deep learning model, after the training model of constantly iterating, detects meat freshness through the label colour of the meat that the cell-phone scanning awaits measuring.
The steps of constructing the intelligent detection system APP are as follows:
s1, data acquisition: for meat samples of different freshness, after label color acquisition using the application method of example 1, each image of the color label is matched to its potential class according to the training factor and features are collected for classification. The acquired images are classified into three categories according to the volatile nitrogen value: fresh (< 15mg/100 g), less fresh (15-30 mg/100 g) and metamorphic (> 30mg/100 g);
s2, label scanning and developing: according to the characteristics of the experimental image in the S1, segmenting a color label from the whole image by using a watershed algorithm based on a mark in an Open CV library;
s3, establishing a deep learning model: designing a three-class image classification network by using VGG-16, wherein the three-class image classification network comprises an input layer, a convolution layer, a Full Connection (FC) layer and an output layer, and a rectifying linear unit (ReLU) function is used as an activation function of each convolution layer;
s4, APP development: combining color indicating labels with a marker-based watershed algorithm for image segmentation and integrating a VGG 16 algorithm for deep learning therewith forms a mobile application to provide automatic identification of freshness.
The specific method for detecting the intelligent detection system (mobile phone APP) comprises the following steps: during the production and sale processes of meat products, the methylated gelatin-embedded bromocresol green hydrogel (GelMA-BCG) indicating label and meat are packaged, transported and stored together, and the color change of the indicating label is scanned by a smart phone APP in the process so as to accurately, real-timely and quickly reflect the freshness of the meat within 30 s. By using the detection system shown in fig. 3 and the operation flow shown in fig. 4, color change of the tag is shot through the smartphone APP, and fresh, not fresh and deterioration information of meat is obtained.
Although the present invention has been described with reference to the preferred embodiments, it should be understood that various changes and modifications can be made therein by those skilled in the art without departing from the spirit and scope of the invention as defined in the appended claims.
Claims (10)
1. A preparation method of a methylated gelatin-embedded bromocresol green hydrogel indication label for visually and intelligently detecting freshness of meat is characterized by comprising the following steps of:
s1, dissolving gelatin in a phosphate buffer solution, and heating and dissolving to obtain a gelatin solution; then adding methacrylic anhydride into the gelatin solution, uniformly mixing and reacting, transferring to a dialysis bag for dialysis after the reaction is finished, and then drying to obtain methylated gelatin GelMA;
s2, adding bromocresol green BCG and a photoinitiator into water, and uniformly mixing to obtain a BCG solution; then adding the methylated gelatin GelMA obtained in the step S1 into a BCG solution, dissolving and uniformly mixing, irradiating by an ultraviolet lamp to initiate reaction, and obtaining the methylated gelatin hydrogel GelMA-BCG embedded with bromocresol green after the reaction is finished;
and S3, adjusting the pH value of the methylated gelatin hydrogel GelMA-BCG embedded with bromocresol green obtained in the step S2 to 3-5 by using acid, dropwise adding the obtained product onto filter paper until the product is completely immersed, irradiating the product by using an ultraviolet lamp, and drying the product to obtain the methylated gelatin hydrogel indicator label embedded with bromocresol green.
2. The method of claim 1, wherein the methylated gelatin is methacrylic anhydride modified gelatin with a degree of substitution of 90.9%.
3. The method according to claim 1, wherein the concentration of the gelatin solution in S1 is 5 to 20wt%.
4. The method according to claim 1, wherein the volume fraction of methacrylic anhydride in S1 relative to the gelatin solution is 0.1% to 2%.
5. The method according to claim 1, wherein the mass ratio of bromocresol green to methylated gelatin in S2 is 1:80.
6. the method as claimed in claim 1, wherein the concentration of the photoinitiator in the BCG solution in S2 is 0.3 to 0.8wt%.
7. The method as claimed in claim 1, wherein the concentration of bromocresol green in the BCG solution in S2 is 0.05mg/mL to 2mg/mL.
8. A methylated gelatin-embedded bromocresol green hydrogel indicator label prepared by the method of any one of claims 1-7 for visually and intelligently detecting the freshness of meat.
9. The use of the methylated gelatin-embedded bromocresol green hydrogel indicator label for visual intelligent detection of meat freshness of claim 8 in detecting fish freshness.
10. The detection system APP based on the methylated gelatin embedded bromocresol green hydrogel indicator label for visually and intelligently detecting the freshness of meat according to claim 8, characterized in that the color features of the indicator label after detection of a large number of different freshness of meat are extracted through a deep learning model, and after the model is trained repeatedly, the freshness of meat is detected by scanning the color of the label of the meat to be detected through a mobile phone.
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Citations (23)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107290335A (en) * | 2017-04-07 | 2017-10-24 | 广东海洋大学 | A kind of intelligent label for indicating aquatic products freshness and preparation method thereof |
| KR20180037736A (en) * | 2016-10-05 | 2018-04-13 | 연세대학교 원주산학협력단 | Indicator for sensing freshness of food and preparation method thereof, method for monitoring freshness od food using the same |
| CN109089992A (en) * | 2018-06-04 | 2018-12-28 | 浙江海洋大学 | A kind of classification method and system of the Estimation of The Fish Freshness based on machine vision |
| CN109115758A (en) * | 2018-08-22 | 2019-01-01 | 广西大学 | A kind of preparation method and applications of freshness indicator type intelligent label |
| CN109575317A (en) * | 2017-09-29 | 2019-04-05 | 天津大学 | A kind of high intensity hydrogel and preparation method thereof with pH response |
| CN110327056A (en) * | 2019-05-24 | 2019-10-15 | 武汉大学 | A kind of intelligent aqueous gel and preparation method thereof for visualizing blood sugar test |
| CN110618116A (en) * | 2019-08-28 | 2019-12-27 | 江苏大学 | Preparation method and application of intelligent indication label for visually detecting freshness of meat |
| US20200072756A1 (en) * | 2018-09-05 | 2020-03-05 | University Of South Carolina | pH Indicator Swabs for Biomonitoring and Diagnostics |
| CN112465094A (en) * | 2020-11-23 | 2021-03-09 | 武汉大学 | Dynamic two-dimensional code intelligent sensing label system for monitoring food freshness in real time |
| US20210223223A1 (en) * | 2020-01-21 | 2021-07-22 | Taiwan Carbon Nano Technology Corporation | System for managing and trading fresh foods according to flavor and method thereof |
| CN113279287A (en) * | 2021-05-08 | 2021-08-20 | 荆楚理工学院 | Preparation method of visual freshness label based on lycium ruthenicum anthocyanin |
| CN113402734A (en) * | 2021-06-17 | 2021-09-17 | 东北农业大学 | Preparation method and application of hydrogel for indicating freshness of chilled meat |
| CN113549226A (en) * | 2020-04-24 | 2021-10-26 | 华中科技大学 | Photonic crystal hydrogel for visual bacteria detection and preparation and application thereof |
| CN113563609A (en) * | 2021-07-28 | 2021-10-29 | 华南理工大学 | Nano composite porous hydrogel and preparation and application thereof |
| CN113651748A (en) * | 2021-07-19 | 2021-11-16 | 华南理工大学 | Volatile basic nitrogen response type compound, portable detection test paper, preparation method and application |
| CN113674265A (en) * | 2021-08-27 | 2021-11-19 | 石河子大学 | Qualitative meat quality detection method based on fusion of thermal imaging and visual imaging in time sequence temperature changing process |
| CN114047184A (en) * | 2021-09-23 | 2022-02-15 | 蚌埠学院 | Preparation method and application of intelligent indication label for identifying freshness of meat |
| CN114349990A (en) * | 2022-01-18 | 2022-04-15 | 苏州大学 | Hydrogel with adjustable dynamic characteristics and preparation method and application thereof |
| CN114414566A (en) * | 2021-12-20 | 2022-04-29 | 北京市农林科学院信息技术研究中心 | Nondestructive testing method and device for freshness of fishes |
| CN114409940A (en) * | 2022-03-01 | 2022-04-29 | 南京鼓楼医院 | Preparation method and application of fish gelatin hydrogel inverse opal film with pH responsiveness |
| CN114456404A (en) * | 2022-01-24 | 2022-05-10 | 哈尔滨工业大学 | Liquid metal flexible wearable device and preparation method thereof |
| CN114814084A (en) * | 2022-03-24 | 2022-07-29 | 武汉大学 | Method for detecting pH by using microneedle |
| CN114835844A (en) * | 2022-05-26 | 2022-08-02 | 仲恺农业工程学院 | Intelligent packaging hydrogel material with fruit and vegetable metabolic gas product carbon dioxide response performance and application thereof |
-
2022
- 2022-09-20 CN CN202211141185.9A patent/CN115561233A/en active Pending
Patent Citations (23)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20180037736A (en) * | 2016-10-05 | 2018-04-13 | 연세대학교 원주산학협력단 | Indicator for sensing freshness of food and preparation method thereof, method for monitoring freshness od food using the same |
| CN107290335A (en) * | 2017-04-07 | 2017-10-24 | 广东海洋大学 | A kind of intelligent label for indicating aquatic products freshness and preparation method thereof |
| CN109575317A (en) * | 2017-09-29 | 2019-04-05 | 天津大学 | A kind of high intensity hydrogel and preparation method thereof with pH response |
| CN109089992A (en) * | 2018-06-04 | 2018-12-28 | 浙江海洋大学 | A kind of classification method and system of the Estimation of The Fish Freshness based on machine vision |
| CN109115758A (en) * | 2018-08-22 | 2019-01-01 | 广西大学 | A kind of preparation method and applications of freshness indicator type intelligent label |
| US20200072756A1 (en) * | 2018-09-05 | 2020-03-05 | University Of South Carolina | pH Indicator Swabs for Biomonitoring and Diagnostics |
| CN110327056A (en) * | 2019-05-24 | 2019-10-15 | 武汉大学 | A kind of intelligent aqueous gel and preparation method thereof for visualizing blood sugar test |
| CN110618116A (en) * | 2019-08-28 | 2019-12-27 | 江苏大学 | Preparation method and application of intelligent indication label for visually detecting freshness of meat |
| US20210223223A1 (en) * | 2020-01-21 | 2021-07-22 | Taiwan Carbon Nano Technology Corporation | System for managing and trading fresh foods according to flavor and method thereof |
| CN113549226A (en) * | 2020-04-24 | 2021-10-26 | 华中科技大学 | Photonic crystal hydrogel for visual bacteria detection and preparation and application thereof |
| CN112465094A (en) * | 2020-11-23 | 2021-03-09 | 武汉大学 | Dynamic two-dimensional code intelligent sensing label system for monitoring food freshness in real time |
| CN113279287A (en) * | 2021-05-08 | 2021-08-20 | 荆楚理工学院 | Preparation method of visual freshness label based on lycium ruthenicum anthocyanin |
| CN113402734A (en) * | 2021-06-17 | 2021-09-17 | 东北农业大学 | Preparation method and application of hydrogel for indicating freshness of chilled meat |
| CN113651748A (en) * | 2021-07-19 | 2021-11-16 | 华南理工大学 | Volatile basic nitrogen response type compound, portable detection test paper, preparation method and application |
| CN113563609A (en) * | 2021-07-28 | 2021-10-29 | 华南理工大学 | Nano composite porous hydrogel and preparation and application thereof |
| CN113674265A (en) * | 2021-08-27 | 2021-11-19 | 石河子大学 | Qualitative meat quality detection method based on fusion of thermal imaging and visual imaging in time sequence temperature changing process |
| CN114047184A (en) * | 2021-09-23 | 2022-02-15 | 蚌埠学院 | Preparation method and application of intelligent indication label for identifying freshness of meat |
| CN114414566A (en) * | 2021-12-20 | 2022-04-29 | 北京市农林科学院信息技术研究中心 | Nondestructive testing method and device for freshness of fishes |
| CN114349990A (en) * | 2022-01-18 | 2022-04-15 | 苏州大学 | Hydrogel with adjustable dynamic characteristics and preparation method and application thereof |
| CN114456404A (en) * | 2022-01-24 | 2022-05-10 | 哈尔滨工业大学 | Liquid metal flexible wearable device and preparation method thereof |
| CN114409940A (en) * | 2022-03-01 | 2022-04-29 | 南京鼓楼医院 | Preparation method and application of fish gelatin hydrogel inverse opal film with pH responsiveness |
| CN114814084A (en) * | 2022-03-24 | 2022-07-29 | 武汉大学 | Method for detecting pH by using microneedle |
| CN114835844A (en) * | 2022-05-26 | 2022-08-02 | 仲恺农业工程学院 | Intelligent packaging hydrogel material with fruit and vegetable metabolic gas product carbon dioxide response performance and application thereof |
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