Volatile amine and temperature synchronous sensing optical hydrogel array and preparation method and application thereof

Abstract

The application discloses a volatile amine and temperature synchronous sensing optical hydrogel array and a preparation method and application thereof, relates to the field of food safety, and aims to solve the problem of synchronous detection of volatile amine concentration and environmental temperature. The method comprises the following steps: preparing hyperbranched polyamide-amine, introducing glycidyl ether group tetraphenylethylene to obtain TPE-HPA; mixing TPE-HPA with a rhodamine B solution to obtain a TPE-HPA-RhB ratio fluorescent probe; taking acrylamide, N,N-methylene bisacrylamide and azobis (isobutylamidine) hydrochloride as polymerization monomers, a crosslinking agent and a catalyst to obtain a volatile amine sensing hydrogel; adding an initiator, a crosslinking agent and a catalyst to an N-isopropyl acrylamide solution, placing the volatile amine sensing hydrogel in a plurality of petal portions of a flower mold, and placing the volatile amine sensing hydrogel in a pistil portion to in-situ polymerize to obtain a synchronous sensing hydrogel array. The application can realize real-time synchronous monitoring of volatile amine concentration, freshness of fresh meat and environmental temperature.

Description

Technical Field

[0001] This invention relates to the field of food safety technology. More specifically, this invention relates to a volatile amine and temperature-synchronized sensing optical hydrogel array, its preparation method, and its applications. Background Technology

[0002] Currently, fresh meat has become the most important category in my country's meat consumption market. However, the loss rate of fresh meat during logistics in my country exceeds 5%, with losses during storage and transportation reaching hundreds of millions of yuan. Temperature monitoring is crucial for maintaining meat quality during storage and transportation. Temperature changes accelerate oxidative rancidity and microbial contamination, producing putrefactive gases such as NH3, which is a significant reason for the decline in fresh meat freshness. Therefore, developing an intelligent early warning system capable of real-time monitoring of NH3 concentration and temperature changes can provide technical support for the quality control of fresh meat.

[0003] Hydrogel optical sensor arrays have become one of the commonly used intelligent monitoring technologies for the freshness of fresh meat due to their advantages such as low cost, miniaturization, fast response, non-destructive nature, and real-time operation. The principle behind them is that when external and internal conditions change, the hydrogel undergoes changes in its physical or chemical properties, simultaneously emitting detectable or visible signal changes such as fluorescence, color, and transmittance. The functional component that enables these signal changes is the core of the sensor array; therefore, improving the responsiveness of the functional component is key to enhancing its sensitivity in monitoring the freshness of fresh meat.

[0004] In recent years, fluorescent probes have attracted much attention in the detection field due to their high sensitivity and rapid response characteristics. However, existing NH3-responsive fluorescent probes mostly adopt a single signal output mechanism, and their detection results are easily affected by temperature fluctuations, making it difficult to meet the needs of accurate monitoring of fresh meat freshness. To address this limitation, a dual-signal probe system based on the ratiometric fluorescence principle is constructed, which can effectively eliminate systematic errors introduced by excitation light intensity, detector sensitivity, and environmental factors, significantly improving detection accuracy. On the other hand, the phase transition temperature of the typical temperature-sensitive material N-isopropylacrylamide is 34 ℃. Given that the actual temperature range for fresh meat storage and transportation is 0-8 ℃, specific salt ions are introduced based on the Hofmeister effect to regulate intermolecular forces, achieving a precise migration of the phase transition temperature to the low-temperature region (<8 ℃), thus establishing a temperature sensing mechanism adapted to the cold chain environment. Finally, by integrating the above functional units into a hydrogel matrix, a multi-parameter synchronous sensing array can be constructed to achieve real-time synchronous monitoring of freshness and temperature during storage and transportation, providing key technical support for dynamic evaluation of fresh meat quality. Summary of the Invention

[0005] One objective of this invention is to provide a volatile amine and temperature synchronous sensing optical hydrogel array, its preparation method, and its application, which can realize real-time synchronous monitoring of volatile amine concentration and ambient temperature.

[0006] To achieve these objectives and other advantages of the present invention, according to one aspect of the present invention, a method for preparing a volatile amine and temperature-synchronized sensing optical hydrogel array is provided, comprising: S1: preparing a hyperbranched polyamide-amine using tri-(2-aminoethyl)amine and bisacryloylhexanediamine as raw materials, and introducing glycidyl ether tetraphenylethylene into it to obtain an aggregation-induced emission fluorescent probe TPE-HPA; S2: mixing TPE-HPA with a rhodamine B solution to obtain a TPE-HPA-RhB ratiometric fluorescent probe solution; S3: using acrylamide, N,N-methylenebisacrylamide, and azobisisobutylamidine hydrochloride as polymerization monomers, and using the TPE-HPA-RhB solution as a solvent. In step S3, a volatile amine-sensing hydrogel was polymerized at 65-75℃ for 50-70 minutes in the center of a flower-shaped mold. In step S4, sodium dodecahydrate and N-isopropylacrylamide were dissolved in deionized water to obtain an N-isopropylacrylamide solution. Ammonium persulfate aqueous solution was added to the N-isopropylacrylamide solution as an initiator, N,N-methylenebisacrylamide as a crosslinking agent, and N,N,N',N'-tetramethylethylenediamine as a catalyst. The mixed solution was placed in multiple petal parts of the flower-shaped mold, and the volatile amine-sensing hydrogel obtained in step S3 was placed in the center of the flower stamen part of the mold. After standing for 50-70 minutes, in-situ polymerization was completed to obtain a volatile amine and temperature synchronous sensing optical hydrogel array.

[0007] Further, in S1, glycidyl ether tetraphenylene and tri-(2-aminoethyl)amine are dissolved in methanol at a molar ratio of 1:(4-6) and refluxed at 65-75°C for 20-28 hours under nitrogen protection; then, bisacryloylhexanediamine at a molar ratio of (4-5):1 with glycidyl ether tetraphenylene is added, and the mixture is stirred at 20-30°C for 90-100 hours; after the reaction is completed, the pH is adjusted to 1.5-2.5 with hydrochloric acid methanol solution, diethyl ether is added to precipitate, the solid product is collected and dried under vacuum at 35-45°C to obtain TPE-HPA.

[0008] Further, in S2, a TPE-HPA stock solution with a concentration of 9.5~10.5 mg / mL and a Rhodamine B stock solution with a concentration of 0.095~0.105 mM are prepared; the TPE-HPA stock solution and the Rhodamine B stock solution are mixed so that the final concentration of TPE-HPA in the obtained TPE-HPA-RhB working solution is 0.95~1.05 mg / mL and the final concentration of Rhodamine B is 0.95~1.05 μM.

[0009] Furthermore, in S3, for every gram of acrylamide, 28-44 mg of N,N-methylenebisacrylamide, 28-44 mg of azobisisobutylamidine hydrochloride, and 16-20 ml of TPE-HPA-RhB aqueous solution are used.

[0010] Furthermore, in S4, for every 100 mg of N-isopropylacrylamide, 19-35 mg of sodium phosphate dodecahydrate and 0.8-1.2 mL of deionized water are used; for every mL of N-isopropylacrylamide solution, 18-22 µL of 10 wt% ammonium persulfate aqueous solution, 3-5 mg of N,N'-methylenebisacrylamide, and 18-22 µL of N,N,N',N'-tetramethylethylenediamine solution are added.

[0011] According to another aspect of the invention, a volatile amine and temperature-synchronized sensing optical hydrogel array is also provided, prepared by the method described above.

[0012] Furthermore, sodium phosphate dodecahydrate of different concentrations was used when preparing multiple petal parts.

[0013] According to another aspect of the invention, the application of a volatile amine and temperature-synchronized sensing optical hydrogel array is also provided for monitoring the freshness and storage temperature of raw meat.

[0014] Furthermore, a volatile amine and temperature synchronous sensing optical hydrogel array is spaced above the fresh meat; an image of the volatile amine and temperature synchronous sensing optical hydrogel array is acquired and converted into a grayscale image; the grayscale values ​​of each petal portion in the grayscale image are extracted and input into a pre-established temperature prediction model to calculate the current ambient temperature; a fluorescence image of the volatile amine and temperature synchronous sensing optical hydrogel array is acquired; the RGB values ​​of the stamen portion in the fluorescence image are extracted and input into a pre-established volatile amine prediction model to predict the volatile amine concentration.

[0015] Furthermore, the method for constructing the temperature prediction model includes: placing a volatile amine and temperature-synchronized sensing optical hydrogel array at m different and known temperatures T. i Under the given conditions, i=1, 2...m, m≥20, images are acquired at various temperatures; the grayscale values ​​of n petal portions in each image are extracted to form an n-dimensional principal feature vector G. i =(G i1 G i2 ...G in Simultaneously, the R, G, and B channel intensity values ​​of the stamen and pistil were extracted as auxiliary features; the temperature value T was used as the basis for further analysis. i The output label is the main feature vector G. iThe fused features, consisting of auxiliary features, are used as input, and a machine learning regression algorithm is used to train a temperature prediction model. The method for constructing the volatile amine prediction model includes: exposing a volatile amine and temperature-synchronized sensing optical hydrogel array to p different and known concentrations of volatile amine environments. j In the model, j=1, 2...p, p≥15, fluorescence images are acquired under ultraviolet light excitation; the intensity values ​​of the R, G, and B channels of the stamen are extracted, and k ratio fluorescence characteristic values ​​F are calculated. j =(F j1 F j2 ...F_ jk The gray values ​​of n petal portions are extracted as auxiliary features, using the volatile amine concentration value C as the primary feature; j The output label is the main feature vector F. j The fused features, consisting of the main features and auxiliary features, are used as input, and a machine learning regression algorithm is used to train a volatile amine prediction model. The two prediction models adopt a weighted feature fusion strategy, with the main features assigned a weight of 0.6-0.8 and the auxiliary features assigned a weight of 0.2-0.4. The machine learning regression algorithm is selected from any one of multiple linear regression, random forest regression, support vector machine regression, or gradient boosting regression tree.

[0016] The present invention has at least the following beneficial effects:

[0017] This invention employs a unique flower-shaped mold design, integrating a volatile amine-responsive hydrogel as the core detection unit and a temperature-responsive hydrogel as the peripheral indicator unit to create a spatially ordered dual-function optical sensor array. In practical applications, only image acquisition and analysis of RGB and grayscale value changes are required to achieve real-time synchronous monitoring of volatile amine concentration (freshness index) and ambient temperature during fresh meat storage. This invention utilizes a simple and rapid dual-mode optical signal response to achieve quantitative detection of meat freshness and temperature, avoiding complex and cumbersome operating procedures, long detection times, and the consumption of expensive equipment and experimental consumables.

[0018] Other advantages, objectives and features of the present invention will become apparent in part from the following description, and in part from those skilled in the art through study and practice of the invention. Attached Figure Description

[0019] Figure 1 A schematic diagram of the TPE-HPA fluorescent probe structure obtained according to Embodiment 1 of the present invention is shown;

[0020] Figure 2 The proton NMR spectrum of the TPE-HPA fluorescent probe obtained according to Example 1 of the present invention is shown;

[0021] Figure 3 The structure and sensing principle of the NH3 and temperature synchronous sensing hydrogel array obtained according to Embodiment 1 of the present invention are shown.

[0022] Figure 4 The diagram shows a comparison of the fluorescence intensity of different concentrations of TPE-HPA and TPE-HPA-RhB fluorescent probes in response to NH3, obtained according to Example 1 of the present invention.

[0023] Figure 5 The fluorescence spectrum and fluorescence color change results of the TPE-HPA-RhB ratio fluorescent probe obtained according to Example 1 of the present invention in response to NH3 are shown.

[0024] Figure 6 The diagram shows a comparison and linear relationship of the NH3 response fluorescence color change of the NH3 and temperature synchronous sensing hydrogel array obtained according to Example 1 of the present invention.

[0025] Figure 7 The response temperature range diagram of the NH3 and temperature synchronous sensing hydrogel array obtained according to Embodiment 1 of the present invention is shown;

[0026] Figure 8 The figure shows the temperature response grayscale value change of the NH3 and temperature synchronous sensing hydrogel array obtained according to Embodiment 1 of the present invention;

[0027] Figure 9 The evaluation of the monitoring effect of the NH3 and temperature synchronous sensing hydrogel array obtained according to Example 1 of the present invention on the freshness and storage temperature of meat is shown. Detailed Implementation

[0028] The present invention will now be described in further detail so that those skilled in the art can implement it based on the description.

[0029] It should be understood that terms such as "having," "comprising," and "including" used in the embodiments of this application do not exclude the presence or addition of one or more other elements or combinations thereof. All directional indications (such as up, down, left, right, front, back, etc.) in the embodiments of this application are only used to explain the relative positional relationship and movement of components in a specific posture. If the specific posture changes, the directional indication will also change accordingly. When an element is referred to as "fixed to" or "set on" another element, it can be directly on the other element or may have an intervening element present. When an element is referred to as "connected to" another element, it can be directly connected to the other element or indirectly connected to the other element through an intervening element. Descriptions involving "first," "second," etc., in the embodiments of this application are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of indicated technical features. Therefore, a feature defined with "first" or "second" may explicitly or implicitly include at least one of those features.

[0030] It should be noted that the technical solutions of the various embodiments of this application can be combined with each other, but only if they are based on the ability of those skilled in the art to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be considered that such combination of technical solutions does not exist and is not within the scope of protection claimed by this application.

[0031] The embodiments of this application provide a method for preparing a volatile amine and temperature-synchronized sensing optical hydrogel array, comprising: S1: preparing a hyperbranched polyamide-amine using tri-(2-aminoethyl)amine and bisacryloylhexanediamine as raw materials, and introducing glycidyl ether tetraphenylethylene into it to obtain an aggregation-induced emission fluorescent probe TPE-HPA; S2: mixing TPE-HPA with rhodamine B solution to obtain a TPE-HPA-RhB ratiometric fluorescent probe solution; S3: using acrylamide, N,N-methylenebisacrylamide, and azobisisobutylamidine hydrochloride as polymerizing monomers, and using TPE-HPA-RhB solution as solvent, applying the solution to the center of a flower-shaped mold at 6... A volatile amine sensing hydrogel was prepared by polymerization at 5-75℃ for 50-70 minutes; S4: Sodium dodecahydrate and N-isopropylacrylamide were dissolved in deionized water to obtain an N-isopropylacrylamide solution. Ammonium persulfate aqueous solution was added to the N-isopropylacrylamide solution as an initiator, N,N-methylenebisacrylamide as a crosslinking agent and N,N,N',N'-tetramethylethylenediamine as a catalyst. The mixed solution was placed in multiple petal parts of a flower mold, and the volatile amine sensing hydrogel obtained in S3 was placed in the center of the flower stamen part of the mold. After standing for 50-70 minutes, in-situ polymerization was completed to obtain a volatile amine and temperature synchronous sensing optical hydrogel array.

[0032] For example, in S1, tri-(2-aminoethyl)amine (TAEA) serves as a polyamino initiator, and hexamethylene bisacryloyldiamine (HMBA) serves as a crosslinking monomer. In methanol solvent, the two react with amine and acryloyl groups to form a hyperbranched polyamide-amine backbone. The epoxy groups of glycidyl ether tetraphenylethylene (TPE-epoxide) react with the amino groups of the hyperbranched polyamide-amine to achieve grafting, forming TPE-HPA with aggregation-induced emission properties, which exhibits enhanced blue-green fluorescence in the aggregated state. In S2, the TPE-HPA stock solution can be prepared as a 10 mg / mL aqueous solution, and the Rhodamine B (RhB) stock solution can be prepared as a 0.1 mM aqueous solution. After mixing at a volume ratio of 10:1, the final concentration of TPE-HPA is 1 mg / mL, and the final concentration of RhB is 1 μM. The fluorescence signal ratio changes between the two through the FRET mechanism. In S3, acrylamide (AAm) is the main monomer, N,N-methylenebisacrylamide (BIS) is used as a crosslinking agent to construct a three-dimensional network, and azobisisobutylamidine hydrochloride (AIBA) decomposes at 65-75℃ to generate free radicals to initiate polymerization; the size of the flower-shaped mold's central stamen part is 0.5×0.5×0.5cm. After polymerization at 70℃ for 60 minutes, it is rinsed three times with a large amount of deionized water to remove unreacted monomers and initiators, and then sealed and stored in a desiccator. In S4, sodium phosphate dodecahydrate lowers the phase transition temperature of N-isopropylacrylamide (NIPAM) through the Hofmeister effect. 19-35 mg of sodium phosphate can be dissolved in 1 mL of deionized water for every 100 mg of NIPAM. Ammonium persulfate (APS) and N,N,N',N'-tetramethylethylenediamine (TEMED) form a redox initiation system. 20 μL of 10 wt% APS, 4 mg of BIS, and 20 μL of TEMED are added per mL of solution. The mixed solution is injected into five petal sections of the mold (0.8 × 0.5 × 0.3 cm in size), and the hydrogel prepared in S3 is placed in the center. In-situ polymerization is completed after standing at room temperature for 60 minutes. After rinsing three times, the mold is sealed and stored to form a bifunctional array with a flower-shaped structure.

[0033] It is important to emphasize that the definitions of "stamen" and "petal" in this application are not limited to general plant morphology, but are primarily function-oriented. The "stamen" is a volatile amine-sensing hydrogel placed at the center of the mold and loaded with a TPE-HPA-RhB ratio fluorescent probe; its core function is to respond to the concentration of volatile amines. The "petal" is an N-isopropylacrylamide thermosensitive hydrogel distributed on the periphery and loaded with different concentrations of sodium dodecahydrate; its core function is to respond to ambient temperature. The structural forms of both can be arbitrarily adjusted according to the detection scenario, such as the space in fresh meat packaging, without being confined to the conventional understanding of a small stamen and petal-like structure; they only need to perform their respective sensing functions.

[0034] In existing technologies, fresh meat monitoring commonly uses single-functional hydrogels, which require separate detection of temperature and volatile amines. Furthermore, the functional units are prepared in multiple steps and then assembled, making interface separation a common problem. This embodiment utilizes in-situ polymerization with a flower-shaped mold to integrate two sensing hydrogels into one unit. The center measures volatile amines, while the petals measure temperature, allowing for simultaneous signal acquisition. This avoids assembly separation issues and simplifies the monitoring process.

[0035] In another embodiment, in S1, glycidyl ether tetraphenylene and tri-(2-aminoethyl)amine are dissolved in methanol at a molar ratio of 1:(4-6) and refluxed at 65-75°C for 20-28 hours under nitrogen protection; then, bisacryloylhexamethylenediamine at a molar ratio of (4-5):1 with glycidyl ether tetraphenylene is added, and the mixture is stirred at 20-30°C for 90-100 hours; after the reaction is completed, the pH is adjusted to 1.5-2.5 with hydrochloric acid methanol solution, diethyl ether is added to precipitate, the solid product is collected and dried under vacuum at 35-45°C to obtain TPE-HPA.

[0036] For example, in S1, 200 mg (0.5 mmol) of glycidyl ether tetraphenylethylene and 362 mg (2.5 mmol) of tri-(2-aminoethyl)amine (molar ratio 1:5) were weighed and dissolved in 2 mL of methanol. The solution was poured into a three-necked flask, and after purging with nitrogen for 30 minutes to remove air, the mixture was refluxed in an oil bath at 70 °C for 24 hours. The reflux apparatus included a condenser to prevent solvent evaporation. After the reaction, the mixture was cooled to room temperature, and 499 mg (2.2 mmol) of bisacrylamide hexamethylenediamine (molar ratio of TPE-epoxide 4.4:1) was added. The mixture was then magnetically stirred at 25 °C for 96 hours to promote the growth of hyperbranched structures. After the reaction was completed, 2.0 M hydrochloric acid methanol solution was added dropwise to adjust the pH to 2, and the mixture was stirred vigorously for 10 minutes. Then, 10 times the volume of diethyl ether was slowly added dropwise, and a white solid precipitated. The product was collected by filtration and dried in a vacuum drying oven at 40 °C for 12 hours to obtain a pale yellow TPE-HPA solid with a yield of up to 87%.

[0037] In existing technologies, the synthesis of fluorescent probes often results in poor fluorescence performance due to improper raw material ratios or incomplete reactions, and the purification steps are simple but leave many impurities. This embodiment improves the purity and fluorescence stability of TPE-HPA by precisely controlling the molar ratio, using nitrogen-protected reflux, and thorough stirring, combined with hydrochloric acid acidification and diethyl ether precipitation purification.

[0038] In another embodiment, in S2, a TPE-HPA stock solution with a concentration of 9.5~10.5 mg / mL is prepared, and a Rhodamine B stock solution with a concentration of 0.095~0.105 mM is prepared; the TPE-HPA stock solution and the Rhodamine B stock solution are mixed so that the final concentration of TPE-HPA in the resulting TPE-HPA-RhB working solution is 0.95~1.05 mg / mL, and the final concentration of Rhodamine B is 0.95~1.05 μM.

[0039] For example, in S2, 10.0 mg of TPE-HPA is weighed using an electronic balance, added to 1.0 mL of deionized water, and dissolved by sonication for 10 minutes to prepare a 10.0 mg / mL stock solution, which is stored in a brown bottle away from light. 4.79 mg of Rhodamine B (RhB) is weighed, dissolved in 100.0 mL of deionized water, and magnetically stirred for 30 minutes until completely dissolved to prepare a 0.1 mM stock solution. The two stock solutions are measured at a volume ratio of 10:1, i.e., 1.0 mL of TPE-HPA stock solution and 0.1 mL of RhB stock solution are mixed and stirred for 5 minutes. The resulting working solution has a final TPE-HPA concentration of 1.0 mg / mL and a final RhB concentration of 1.0 μM. At this concentration, the 480 nm fluorescence intensity of TPE-HPA matches the 585 nm fluorescence intensity of RhB, and the fluorescence ratio changes significantly in the NH3 response.

[0040] In existing technologies, ratiometric fluorescent probes often suffer from fluorescence quenching due to excessively high concentrations, or low FRET efficiency and weak response signals due to imbalanced ratios. This embodiment optimizes the concentration of the mother liquor and the mixing ratio to ensure an appropriate ratio of the two fluorescent molecules, thereby enhancing the fluorescence response sensitivity to volatile amines and reducing signal interference.

[0041] In another embodiment, in S3, each gram of acrylamide corresponds to 28-44 mg of N,N-methylenebisacrylamide, 28-44 mg of azobisisobutylamidine hydrochloride, and 16-20 ml of TPE-HPA-RhB aqueous solution.

[0042] For example, in S3, acrylamide (AAm) serves as a monomer to provide a hydrophilic network. Each gram of AAm is combined with 30 mg or 40 mg of N,N-methylenebisacrylamide (BIS). The amount of BIS determines the crosslinking density; too much BIS will make the hydrogel too hard, while too little will cause it to swell and crack easily. Azobisisobutylamidine hydrochloride (AIBA) is used as a thermal initiator, with 30 mg or 40 mg per gram of AAm. It decomposes at 70°C to generate free radicals that initiate polymerization. A TPE-HPA-RhB aqueous solution is used as a solvent, with 18 mL or 19 mL per gram of AAm to ensure complete dissolution of the monomer and initiator. For example, 85 mg of AAm (approximately 0.0012 mol) is added to 3 mg of BIS and 3 mg of AIBA, dissolved in 1.5 mL of TPE-HPA-RhB working solution, stirred until completely dissolved, poured into the center of a flower-shaped mold, and polymerized in a 70°C oven for 60 minutes to form a uniform volatile amine-sensing hydrogel.

[0043] In existing technologies, hydrogel preparation often results in insufficient gel strength or uneven distribution of fluorescent probes due to improper amounts of crosslinking agents or initiators. This embodiment, by limiting the proportions of each component, ensures a stable hydrogel network structure and uniform dispersion of fluorescent probes, guaranteeing consistent response.

[0044] In another embodiment, in S4, for every 100 mg of N-isopropylacrylamide, 19-35 mg of sodium phosphate dodecahydrate and 0.8-1.2 mL of deionized water are used; for every mL of N-isopropylacrylamide solution, 18-22 µL of 10 wt% ammonium persulfate aqueous solution, 3-5 mg of N,N'-methylenebisacrylamide, and 18-22 µL of N,N,N',N'-tetramethylethylenediamine solution are added.

[0045] For example, in S4, N-isopropylacrylamide (NIPAM) is a thermosensitive material whose phase transition temperature can be controlled by sodium dodecahydrate. 19.01 mg (0.05 M) or 34.21 mg (0.09 M) sodium phosphate is used per 100 mg NIPAM, dissolved in 1.0 mL of deionized water and magnetically stirred for 30 minutes until completely dissolved. Ammonium persulfate (APS) is used as an initiator, with 20 μL of a 10 wt% aqueous solution added per mL of NIPAM solution; 4 mg of N,N'-methylenebisacrylamide (BIS) is used as a crosslinking agent; 20 μL of N,N,N',N'-tetramethylethylenediamine (TEMED) accelerates the decomposition of APS. The resulting solution is pale yellow. The mixture is injected into the five petal sections of the mold, with the hydrogel prepared in S3 placed in the center. In-situ polymerization is completed by standing at room temperature for 60 minutes, allowing the petals and stamens to bond tightly.

[0046] In existing technologies, the phase transition temperature of thermosensitive hydrogels is difficult to adjust to the 0-8℃ required for cold chain processing, and in-situ polymerization is prone to weak bonding due to low initiation efficiency. This embodiment achieves lowering of the phase transition temperature by precisely controlling the sodium phosphate concentration and the amount of initiation system, while ensuring sufficient polymerization and stable array structure.

[0047] This application provides an optical hydrogel array for synchronous sensing of volatile amines and temperature, prepared by any of the methods described above. Exemplarily, the array is integrally formed by the aforementioned methods, with an overall flower-shaped structure. The central stamen is a volatile amine-sensing hydrogel, consisting of an acrylamide network supporting a TPE-HPA-RhB probe, measuring 0.5 × 0.5 × 0.5 cm, whose fluorescence changes from red to blue-green upon contact with NH3. The five outer petals are temperature-sensing hydrogels, composed of a NIPAM network and sodium phosphate of varying concentrations, measuring 0.8 × 0.5 × 0.3 cm. Their transmittance changes with temperature, and the grayscale value changes accordingly. The two hydrogels are polymerized in situ to form a seamless, interface-free whole, with the stamen and petals arranged in an orderly spatial arrangement, enabling synchronous response to volatile amine and temperature signals.

[0048] In existing technologies, most sensor array functional units are fabricated independently and then spliced ​​together, which can easily lead to signal crosstalk or loose structure. This embodiment, through integrated fabrication, ensures that the functional units are tightly integrated, that the signal responses do not interfere with each other, and that two parameters can be acquired simultaneously, making it suitable for fresh meat monitoring needs.

[0049] In another embodiment, sodium phosphate dodecahydrate of different concentrations was used when preparing multiple petal portions.

[0050] For example, when preparing the petals, the concentrations of sodium phosphate dodecahydrate were 0.05M, 0.06M, 0.07M, 0.08M, and 0.09M, corresponding to 19.01mg, 22.81mg, 26.61mg, 30.41mg, and 34.21mg of sodium phosphate added per 100mg of NIPAM, respectively. Different concentrations of sodium phosphate altered the intermolecular forces of NIPAM through the Hofmeister effect; the 0.05M petals had a higher phase transition temperature, while the 0.09M petals had a lower phase transition temperature, both covering the 0-8℃ storage and transportation temperature range for fresh meat. When the temperature changed, the transmittance of each petal changed asynchronously; the difference in grayscale values ​​allowed for a more accurate estimation of the ambient temperature.

[0051] In existing technologies, temperature-sensitive hydrogels often employ single-concentration control, resulting in a narrow response temperature range that struggles to cover the entire cold chain. This embodiment utilizes a multi-concentration petal design to broaden the temperature response range and improve detection accuracy at different temperatures.

[0052] Embodiments of this application also provide the application of a volatile amine and temperature-synchronized sensing optical hydrogel array for monitoring the freshness and storage temperature of raw meat.

[0053] For example, fresh meat (such as pork or beef) is cut into pieces of approximately 200g each and placed in a sealed packaging box. The hydrogel array is then attached to the inside of the box lid using food-grade tape, with the center of the array positioned 2-3cm above the meat pieces to avoid direct contact with meat juices. When fresh meat spoils, it releases NH3, causing changes in the RGB fluorescence value of the central stamen hydrogel; fluctuations in storage temperature cause changes in the grayscale value of the outer petals. Images of the array are periodically captured using a smartphone, and the freshness of the fresh meat and the ambient temperature are monitored in real time by analyzing the changes in fluorescence and grayscale.

[0054] In existing technologies, monitoring the freshness of fresh meat requires separate gas and temperature sensors, which is costly and cumbersome. The array in this embodiment integrates dual functions and can be directly attached to the inside of the packaging, enabling low-cost, real-time synchronous monitoring.

[0055] In another embodiment, a volatile amine and temperature synchronous sensing optical hydrogel array is spaced above the fresh meat; an image of the volatile amine and temperature synchronous sensing optical hydrogel array is acquired and converted into a grayscale image; the grayscale values ​​of each petal portion in the grayscale image are extracted and input into a pre-established temperature prediction model to calculate the current ambient temperature; a fluorescence image of the volatile amine and temperature synchronous sensing optical hydrogel array is acquired; the RGB values ​​of the stamen portion in the fluorescence image are extracted and input into a pre-established volatile amine prediction model to predict the volatile amine concentration.

[0056] For example, the array is placed 2 cm away from fresh meat, and images are taken with a smartphone under the same lighting conditions. The images are converted into 8-bit grayscale images using image processing software. The grayscale values ​​(X1-X5) of the five petals are extracted and input into a temperature prediction model (such as the multivariate linear equation y=0.12X1+0.20X2-0.14X3-0.28X4+0.13X5) to calculate the temperature. Simultaneously, fluorescence images are captured under 320nm ultraviolet light excitation, and the R, G, and B values ​​of the flower stamens are extracted. The G / R ratio is calculated and input into a volatile amine model (such as y=0.6369+0.0027x) to predict the NH3 concentration, thereby determining the freshness.

[0057] In existing technologies, fresh meat testing relies on laboratory instruments for analysis, making real-time on-site quantification difficult. This embodiment utilizes images captured by a smartphone combined with model prediction, enabling convenient on-site quantitative monitoring without the need for specialized equipment.

[0058] In another embodiment, the method for constructing the temperature prediction model includes: placing a volatile amine and temperature-synchronized sensing optical hydrogel array at m different and known temperatures T. i Under the given conditions, i=1, 2...m, m≥20, images are acquired at various temperatures; the grayscale values ​​of n petal portions in each image are extracted to form an n-dimensional principal feature vector G. i =(G i1 G i2 ...G in Simultaneously, the R, G, and B channel intensity values ​​of the stamen and pistil were extracted as auxiliary features; the temperature value T was used as the basis for further analysis. i The output label is the main feature vector G. i The fused features, consisting of auxiliary features, are used as input, and a machine learning regression algorithm is used to train a temperature prediction model. The method for constructing the volatile amine prediction model includes: exposing a volatile amine and temperature-synchronized sensing optical hydrogel array to p different and known concentrations of volatile amine environments. j In the model, j=1, 2...p, p≥15, fluorescence images are acquired under ultraviolet light excitation; the intensity values ​​of the R, G, and B channels of the stamen are extracted, and k ratio fluorescence characteristic values ​​F are calculated. j =(Fj1 F j2 ...F_ jk The gray values ​​of n petal portions are extracted as auxiliary features, using the volatile amine concentration value C as the primary feature; j The output label is the main feature vector F. j The fused features, consisting of the main features and auxiliary features, are used as input, and a machine learning regression algorithm is used to train a volatile amine prediction model. The two prediction models adopt a weighted feature fusion strategy, with the main features assigned a weight of 0.6-0.8 and the auxiliary features assigned a weight of 0.2-0.4. The machine learning regression algorithm is selected from any one of multiple linear regression, random forest regression, support vector machine regression, or gradient boosting regression tree.

[0059] For example, when constructing a temperature prediction model, 20 known temperature points T are selected. i The array covers the 0-8℃ range for fresh meat storage and transportation, set at 0.4℃ intervals (e.g., 0℃, 0.4℃, 0.8℃…8℃). The array is placed in a constant-temperature chamber at each temperature for 1 hour. Images are taken with a smartphone under the same lighting conditions, converted to 8-bit grayscale images using image analysis software, and the grayscale values ​​G of 5 petals (n=5) are extracted. i1 To G i5 A main feature vector is formed, and the R, G, and B channel values ​​of the flower's stamen are extracted as auxiliary features. A weighted fusion strategy is adopted, with the main feature (5 gray values) weighted at 0.7 and the auxiliary feature (3 RGB values) weighted at 0.3. The fused result is used as input. A random forest regression algorithm is used for training, with 100 decision trees, a maximum depth of 8, and a minimum number of leaf node samples of 5. The temperature value Ti is used as the output label. The model obtains the final output by averaging the prediction results from multiple decision trees. The cross-validation coefficient of determination R is used. 2 The concentration reached above 0.90. When constructing the volatile amine prediction model, 15 known concentrations of C were selected. j The environment is characterized by volatile basic nitrogen. Array fluorescence images were captured under 320 nm ultraviolet light excitation, and the R, G, and B values ​​of the stamen portion were extracted. The fluorescence characteristic values ​​F, representing the G / R and G / B ratios, were calculated. j1 F j2 The grayscale values ​​of five petals were extracted as primary features and secondary features as secondary features. The primary feature weight was 0.7, and the secondary feature weight was 0.3; these were then fused and used as input. A random forest regression algorithm was used for training, with 80 decision trees, a maximum depth of 6, and a minimum number of split samples of 10. The volatile basic nitrogen concentration C was used as the input. j To output the label, the model obtains the final result through ensemble prediction of multiple decision trees, and the cross-validation correlation coefficient R0 is used. 2The accuracy is above 0.98. Both models are implemented using computer software; inputting newly acquired feature values ​​will output the corresponding predicted temperature or volatile amine concentration.

[0060] For example, the main feature vector (from petal grayscale): G raw =[g1, g2, ..., gn]; Auxiliary feature vector (from RGB): Araw=[a1, a2, a3]; Preset weights: Main feature weight Wp (values ​​0.6-0.8), auxiliary feature weight Wa (values ​​0.2-0.4), and usually satisfy Wp+Wa=1. Standardize each feature dimension of the entire dataset separately to eliminate the influence of units. Main feature standardization G norm =(G raw -μ g ) / σ g ;where μ g and σ g These are the mean and standard deviation of the main feature vectors of all samples, respectively. Auxiliary feature standardization: A norm =(A raw -μ a ) / σ a , where μ a and σ a These are the mean and standard deviation of all auxiliary feature vectors for the samples, respectively. The standardized feature vectors are scaled according to weight coefficients and then concatenated into a fused feature vector, which is then weighted into a principal feature vector G. weighted =Wp×G norm Weighted auxiliary feature vector A weighted =WaA norm The final fused feature vector F fused =[Wpg1 norm ,……,Wpgn norm ,Waa1 norm Waa2 norm Waa3 norm The same applies to the inputs for the temperature prediction model and the volatile amine prediction model.

[0061] In existing technologies, prediction models for temperature and volatile amine concentration often suffer from insufficient accuracy due to difficulties in capturing the nonlinear relationship between features and target values, reliance on single features, and susceptibility to interference. One approach is to use a random forest regression algorithm, leveraging the ensemble properties of multiple decision trees to fit the complex correlation between features (such as petal grayscale and stamen RGB) and target values, avoiding the bias of a single model. Another approach is to introduce a weighted feature fusion strategy. Although the stamen is the core of the volatile amine response (loaded with TPE-HPA-RhB probes, its RGB values ​​mainly change with amine concentration), its acrylamide hydrogel carrier exhibits a thermo-induced swelling and shrinkage effect. Temperature changes fine-tune the gel network density and water content, altering the probe molecule distribution and indirectly affecting the fluorescence signal. Furthermore, the fluorescence quantum yield of TPE-HPA and RhB varies with temperature (higher temperatures result in stronger molecular motion and slight fluctuations in fluorescence intensity). These two effects mean that while the stamen RGB values ​​are primarily amine-responsive, they also exhibit quantifiable fluctuations with temperature, serving as an auxiliary signal for temperature prediction and complementing the petal grayscale values ​​(the main feature; the petals are thermosensitive hydrogels, and their grayscale values ​​change significantly with temperature). Therefore, the temperature model assigns a weight of 0.6-0.8 to the main feature (petal grayscale) and a weight of 0.2-0.4 to the auxiliary feature (stamen RGB), which integrates multi-dimensional information, reduces interference, and improves the model's generalization ability and stability in cold chain scenarios.

[0062] The following is a description of a specific embodiment.

[0063] Example 1: A method for preparing a volatile amine and temperature-synchronized sensing optical hydrogel array, comprising the following steps:

[0064] Step 1: As Figure 1 As shown, 200.0 mg (0.5 mmol) of glycidyl ether tetraphenylethylene and 362.0 mg (2.5 mmol) of tri-(2-aminoethyl)amine (TAEA) were weighed and dissolved in 2 mL of methanol. The mixture was refluxed at 70 °C for 24 h under a N2 atmosphere, and then cooled to room temperature. 499.0 mg (2.2 mmol) of HMBA and 2 mL of methanol solution were added to the reaction mixture, and the mixture was stirred at room temperature for 96 h. After the reaction was complete, 2.0 M HCl / CH3OH was added to the reaction mixture to adjust the pH to 2. The mixture was then stirred vigorously, and a large amount of diethyl ether was added dropwise to the acidified solution. The solid product was collected and dried under vacuum at 40 °C to obtain a pale yellow solid product, TPE-HPA, with a yield of 87.0%. Its structure was characterized by 1H NMR spectroscopy, as shown in the figure. Figure 2 As shown.

[0065] Step 2: Dissolve 10.0 mg TPE-HPA in 1.0 mL of pure water to prepare a 10.0 mg / mL TPE-HPA stock solution. Dissolve 4.79 mg Rhodamine B (RhB) in 100.0 mL of pure water to prepare a 0.1 mM RhB stock solution. Mix the two fluorescent probes, TPE-HPA and RhB, at a volume ratio of 10:1 to achieve a final concentration of 1.0 mg / mL TPE-HPA and 1.0 μM RhB, which will serve as the TPE-HPA-RhB working solution.

[0066] Step 3: Dissolve acrylamide (AAm, 85 mg), N,N-methylenebisacrylamide (BIS, 3 mg), and azobisisobutylamidine hydrochloride (AIBA, 3 mg) in 1.5 mL of TPE-HPA-RhB aqueous solution. Place the mixed solution in the center of a flower-shaped mold (0.5 × 0.5 × 0.5 cm) and polymerize at 70 °C for 1 hour to obtain an NH3-sensing hydrogel. Rinse the obtained hydrogel repeatedly with plenty of deionized water three times to remove impurities, and seal for later use.

[0067] Step 4: Dissolve 19.01, 22.81, 26.61, 30.41, and 34.21 mg of sodium phosphate dodecahydrate and 100 mg of N-isopropylacrylamide (NIPAM) in 1 mL of deionized water, respectively, and stir until completely dissolved to form a homogeneous NIPAM solution. Add 20 µL of 10 wt% ammonium persulfate (APS) aqueous solution, 4 mg of N,N'-methylenebisacrylamide (BIS), and 20 µL of N,N,N',N'-tetramethylethylenediamine (TEMED) solution to the solution. Place the mixed solution into five petal sections (0.8 × 0.5 × 0.3 cm) of a flower-shaped mold, and place the prepared NH3-sensing hydrogel into the stamen section of the mold. Let it stand for 1 hour to complete the in-situ polymerization reaction, obtaining an NH3 and temperature-synchronized sensing hydrogel array. Rinse repeatedly with plenty of deionized water three times to remove impurities, and seal for storage until use. The obtained hydrogel array is shown below. Figure 3 As shown.

[0068] Example 2: The fluorescence intensity changes of TPE-HPA and TPE-HPA-RhB fluorescent probes at different concentrations were recorded before and after reacting with 300 μM NH3 for 1 min using a fluorescence spectrophotometer (excitation wavelength: 320 nm, slit width: 5 nm) to determine the optimal concentration of the ratiometric fluorescent probe.

[0069] Figure 4(a) shows the fluorescence intensity changes at an emission wavelength of 480 nm for different concentrations of TPE-HPA fluorescent probes before and after reacting with NH3. The horizontal axis represents the concentration of TPE-HPA, the vertical axis of the bar chart represents the fluorescence intensity, and the vertical axis of the line graph represents the fluorescence intensity changes before and after reacting with NH3. Figure 4 (b) The ratio fluorescence changes of TPE-HPA-RhB fluorescent probes with different concentrations of RhB before and after reaction with NH3 at emission wavelengths of 480 and 585 nm are shown. The horizontal axis represents the concentration of RhB, the vertical axis of the bar chart represents the ratio fluorescence intensity, and the vertical axis of the line graph represents the fluorescence intensity change before and after reaction with NH3. Based on Figure 4 It can be seen that the fluorescence change is more significant after the mixed ratio fluorescent probe reacts with NH3, thus it can be deduced that the mixed ratio fluorescent probe has better NH3 response capability.

[0070] Example 4: The NH3 fluorescence response characteristics of TPE-HPA-RhB were recorded using a fluorescence spectrophotometer (excitation wavelength: 320 nm, slit width: 5 nm). Figure 5 As shown.

[0071] based on Figure 5 It can be seen that the probe can achieve a fluorescence color change from red to blue-green in response to NH3, demonstrating good responsiveness.

[0072] The response principle can be explained as follows: the protonated -NH2 in the TPE-HPA molecule is deprotonated, the positive charge is neutralized, and the electrostatic repulsion is weakened, thereby promoting the aggregation of TPE-HPA small aggregates and TPE groups, and enhancing the blue-green fluorescence.

[0073] Example 5: Quantitative detection of ammonia using a hydrogel array, including the following steps:

[0074] Step 1: Add different volumes of 1.0 mM NH3·H2O solution to the sample cell and calculate the ammonia concentration;

[0075] Step 2: The process for fabricating the synchronous sensor array is the same as in Example 1;

[0076] Step 3: By attaching the synchronous sensing array to the headspace of the sample cell, different volumes of the above-mentioned NH3·H2O solution are added, and then distilled water is used to ensure the solution volume is 2 mL, thus obtaining an aqueous solution with a gradient of NH3 concentration. The fluorescence RGB values ​​are then extracted using a mobile phone image, ultimately revealing a linear relationship between NH3 concentration and fluorescence RGB values. Figure 6 As shown.

[0077] Figure 6 The horizontal axis represents the NH3 gas concentration, and the vertical axis represents the G / R ratio of the sensor array. Figure 6It can be seen that as the concentration of NH3 gas increases, the G / R ratio increases, and the two show a good linear relationship.

[0078] Example 6: Controlling the response temperature of a sensor array by adding phosphate, including the following steps:

[0079] Step 1: The process for fabricating the synchronous sensor array is the same as in Example 1;

[0080] Step 2: By measuring the transmittance with a UV spectrophotometer, the temperature response range of the sensor array is finally obtained. For example... Figure 6 As shown.

[0081] Figure 7 The horizontal axis represents ambient temperature, and the vertical axis represents the transmittance of pNIPAM gels with different phosphate additions. Figure 7 It can be seen that as the temperature increases, the transmittance gradually decreases to 0; and as the concentration of phosphate increases, the response temperature range gradually decreases, indicating good temperature response capability.

[0082] Example 7: Quantitative temperature monitoring via hydrogel array, including the following steps:

[0083] Step 1: The process for fabricating the synchronous sensor array is the same as in Example 1;

[0084] Step Two: By attaching the synchronous sensing array to the headspace of the sample cell and placing it at different ambient temperatures for 1 hour, the grayscale values ​​of the array are extracted using a mobile phone photo, ultimately obtaining a multivariate linear relationship between temperature and array grayscale values. For example... Figure 7 As shown.

[0085] Figure 8 The horizontal axis represents the ambient temperature, and the vertical axis represents the grayscale value of the sensor array. Figure 8 It can be seen that the grayscale value of the sensor array gradually increases with the increase of ambient temperature. According to... Figure 7 The data shown was used to construct a multiple linear regression model, resulting in the equation y = 0.12X1 + 0.20X2 - 0.14X3 - 0.28X4 + 0.13X5, with R². 2 The value is 0.8835. In the formula, y represents the temperature, and X1~X5 represent the gray values ​​corresponding to the temperature sensing arrays with five different phosphates added.

[0086] Example 8: Real-time monitoring of the freshness and temperature of fresh meat using a hydrogel array, including the following steps:

[0087] Step 1: The process for fabricating the synchronous sensor array is the same as in Example 1;

[0088] Step 2: Place the fresh meat in a packaging box, then attach a synchronous sensor array to the inside of the packaging box lid and store it at different ambient temperatures. Monitor the freshness of the fresh meat and changes in storage temperature based on the RGB and grayscale values ​​of the array fluorescence.

[0089] Actual fresh meat monitoring results are as follows Figure 9 As shown in (a). From Figure 9 As shown in (a), with the increase of storage temperature, the petal part of the synchronous sensor array changes from transparent to cloudy, and the color changes significantly. Meanwhile, with the increase of storage time, the fluorescence of the stamen part of the synchronous sensor array changes from red to blue-green. Therefore, the storage temperature and freshness of fresh meat can be determined in real time by observing the fluorescence and color changes of the smart tag.

[0090] Example 9: Temperature Prediction Model and Freshness Prediction Model

[0091] When constructing the temperature prediction model, 21 known temperature points T were selected. i The array covers the 4-25℃ range for fresh meat storage and transportation, set at 1.0℃ intervals (e.g., 4℃, 5℃, 6℃...25℃). The array is placed in a constant-temperature chamber at each temperature for 1 hour. Images are taken with a smartphone under the same lighting conditions, converted to 8-bit grayscale images using image analysis software, and the grayscale values ​​G of 5 petals (n=5) are extracted. i1 To G i5 A main feature vector is formed, and the R, G, and B channel values ​​of the flower's stamen are extracted as auxiliary features. A weighted fusion strategy is adopted, with the main feature (5 gray values) weighted at 0.7 and the auxiliary feature (3 RGB values) weighted at 0.3. The fused vector is used as input. A random forest regression algorithm is selected for training, with 100 decision trees, a maximum depth of 8, and a minimum number of leaf node samples of 5, using the temperature value T as the input. i To output the label, the model averages the predictions from multiple decision trees to obtain the final output, and the cross-validation determination coefficient R0 is used. 2 The value reached above 0.90. When constructing a fresh meat freshness prediction model, a fresh meat storage experiment was conducted. Volatile basic nitrogen content was measured at specific storage times, and array fluorescence images were captured using 320nm ultraviolet light excitation. R, G, and B values ​​of the stamen portion were extracted, and the G / R and G / B ratio fluorescence characteristic values ​​F were calculated. j1 F j2 As the primary feature, the grayscale values ​​of five petals were extracted as auxiliary features. The primary feature was weighted at 0.7, and the auxiliary features at 0.3, and the resulting fusion was used as input. A random forest regression algorithm was used for training, with 80 decision trees, a maximum depth of 6, and a minimum number of split samples of 10. Volatile basic nitrogen content was used as the output label, and the model obtained the final result through ensemble prediction of multiple decision trees. Figure 9As shown in (b), the collinearity between the actual experimental data and the predicted data of the synchronous sensor array for volatile basic nitrogen (TVB-N), an indicator of fresh meat freshness, under different storage temperatures, is shown by the coefficient of determination R. 2 The accuracy reached 0.97 or higher. Therefore, the above results indicate that, compared with Examples 7 and 8, the prediction accuracy of temperature and fresh meat freshness can be further improved by using temperature prediction models and freshness prediction models.

[0092] Although embodiments of the present invention have been disclosed above, they are not limited to the applications listed in the specification and embodiments. They can be applied to various fields suitable for the present invention. For those skilled in the art, other modifications can be easily made. Therefore, without departing from the general concept defined by the claims and their equivalents, the present invention is not limited to the specific details and embodiments shown and described herein.

Claims

1. A method for preparing a volatile amine and temperature-synchronized sensing optical hydrogel array, characterized in that, include: S1: Using tri-(2-aminoethyl)amine, glycidyl ether tetraphenylethylene, and bisacryloylhexanediamine as raw materials, hyperbranched polyamide-amine was prepared by reacting tri-(2-aminoethyl)amine with glycidyl ether tetraphenylethylene first, and then with bisacryloylhexanediamine, and glycidyl ether tetraphenylethylene was introduced to obtain aggregation-induced emission fluorescent probe TPE-HPA. S2: Mix TPE-HPA with Rhodamine B solution to obtain TPE-HPA-RhB ratio fluorescent probe solution; S3: Using acrylamide as the polymerizing monomer, N,N-methylenebisacrylamide as the crosslinking agent, azobisisobutylamidine hydrochloride as the initiator, and TPE-HPA-RhB solution as the solvent, the volatile amine sensing hydrogel was polymerized in the center of the flower mold at 65-75℃ for 50-70 minutes to obtain the volatile amine sensing hydrogel. S4: Sodium dodecahydrate and N-isopropylacrylamide are dissolved in deionized water to obtain an N-isopropylacrylamide solution. Ammonium persulfate aqueous solution is added to the N-isopropylacrylamide solution as an initiator, N,N-methylenebisacrylamide as a crosslinking agent, and N,N,N',N'-tetramethylethylenediamine as a catalyst. The mixed solution is placed on multiple petal parts of a flower mold, and the volatile amine sensing hydrogel obtained in S3 is placed on the stamen part of the center of the mold. After standing for 50-70 minutes, in-situ polymerization is completed, and a volatile amine and temperature synchronous sensing optical hydrogel array is obtained. In S1, glycidyl ether tetraphenylene and tri-(2-aminoethyl)amine are dissolved in methanol at a molar ratio of 1:(4-6) and refluxed at 65-75°C for 20-28 hours under nitrogen protection; then bisacryloyl hexamethylenediamine is added at a molar ratio of (4-5):1 to glycidyl ether tetraphenylene, and the mixture is stirred at 20-30°C for 90-100 hours. After the reaction was completed, the pH was adjusted to 1.5-2.5 with hydrochloric acid methanol solution, diethyl ether was added to precipitate, the solid product was collected and dried under vacuum at 35-45℃ to obtain TPE-HPA; In S4, for every 100 mg of N-isopropylacrylamide, use 19-35 mg of sodium phosphate dodecahydrate and 0.8-1.2 mL of deionized water; for every mL of N-isopropylacrylamide solution, add 18-22 µL of 10 wt% ammonium persulfate aqueous solution, 3-5 mg of N,N-methylenebisacrylamide, and 18-22 µL of N,N,N',N'-tetramethylethylenediamine solution.

2. The method for preparing the volatile amine and temperature-synchronized sensing optical hydrogel array as described in claim 1, characterized in that, In S2, a TPE-HPA stock solution with a concentration of 9.5~10.5 mg / mL was prepared, and a Rhodamine B stock solution with a concentration of 0.095~0.105 mM was prepared. The TPE-HPA stock solution and the Rhodamine B stock solution were mixed to obtain a final concentration of 0.95~1.05 mg / mL for TPE-HPA and 0.95~1.05 μM for Rhodamine B in the resulting TPE-HPA-RhB working solution.

3. The method for preparing the volatile amine and temperature-synchronized sensing optical hydrogel array as described in claim 1, characterized in that, In S3, each gram of acrylamide corresponds to 28-44 mg of N,N-methylenebisacrylamide, 28-44 mg of azobisisobutylamidine hydrochloride, and 16-20 ml of TPE-HPA-RhB aqueous solution.

4. A volatile amine and temperature-synchronized sensing optical hydrogel array, characterized in that, Prepared by the method described in any one of claims 1-3.

5. The volatile amine and temperature synchronous sensing optical hydrogel array as described in claim 4, characterized in that, Different concentrations of sodium dodecahydrate were used when preparing multiple petal parts.

6. The application of the volatile amine and temperature synchronous sensing optical hydrogel array as described in claim 5, characterized in that, Used to monitor the freshness and storage temperature of raw meat.

7. The application of the volatile amine and temperature synchronous sensing optical hydrogel array as described in claim 6, characterized in that, A volatile amine and temperature-synchronized sensing optical hydrogel array is spaced above the fresh meat. Images of the optical hydrogel array that synchronously senses volatile amines and temperature are acquired, converted into grayscale images, and the grayscale values ​​of each petal portion in the grayscale images are extracted and input into a pre-established temperature prediction model to calculate the current ambient temperature. Fluorescence images of a volatile amine and temperature-synchronized sensing optical hydrogel array are acquired. The RGB values ​​of the stamen portion in the fluorescence image are extracted and input into a pre-established volatile amine prediction model to predict the concentration of volatile amines.

8. The application of the volatile amine and temperature synchronous sensing optical hydrogel array as described in claim 7, characterized in that, Methods for constructing temperature prediction models include: A volatile amine and temperature-synchronized sensing optical hydrogel array is placed at m different and known temperatures T. i Under the given conditions, i=1, 2...m, m≥20, images are acquired at various temperatures; the grayscale values ​​of n petal portions in each image are extracted to form an n-dimensional principal feature vector G. i =(G i1 G i2 ...G in Simultaneously, the R, G, and B channel intensity values ​​of the stamen and pistil were extracted as auxiliary features; the temperature value T was used as the basis for further analysis. i The output label is the main feature vector G. i The fused features, consisting of auxiliary features, are used as input, and a temperature prediction model is trained using a machine learning regression algorithm. Methods for constructing volatile amine prediction models include: A volatile amine-temperature synchronous sensing optical hydrogel array was exposed to p different and known concentrations of volatile amine environments. j In the model, j=1, 2...p, p≥15, fluorescence images are acquired under ultraviolet light excitation; the intensity values ​​of the R, G, and B channels of the stamen are extracted, and k ratio fluorescence characteristic values ​​F are calculated. j =(F j1 F j2 ...F_ jk The gray values ​​of n petal portions are extracted as auxiliary features, using the volatile amine concentration value C as the primary feature; j The output label is the main feature vector F. j The fused features, consisting of auxiliary features, are used as input, and a machine learning regression algorithm is used to train a volatile amine prediction model. The two prediction models employ a weighted feature fusion strategy, assigning a weight of 0.6-0.8 to the main features and a weight of 0.2-0.4 to the auxiliary features; The machine learning regression algorithm is selected from any one of multiple linear regression, random forest regression, support vector machine regression, or gradient boosting regression tree.

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