A d-pi-a colorimetric probe and its use in detecting food freshness

Abstract

The application belongs to the technical field of food detection, and particularly relates to a D-pi-A colorimetric probe and application thereof in detecting food freshness. The application further provides a method for detecting food freshness based on the D-pi-A colorimetric probe. The method establishes a quantitative corresponding relationship between a color characteristic value and a TVBN concentration, and realizes accurate, repeatable and digital detection of food freshness.

Description

Technical Field

[0001] This invention belongs to the field of food testing technology, specifically relating to a D-π-A colorimetric probe and its application in detecting food freshness. Background Technology

[0002] Fresh foods (such as shrimp, fish, and beef) are highly susceptible to spoilage and deterioration due to microbial growth during cold chain storage and transportation, leading to serious safety hazards. Therefore, effective monitoring of food freshness during cold chain storage and transportation is crucial.

[0003] Existing technologies for detecting biogenic amines and TVB-N primarily include high-performance liquid chromatography (HPLC), the Kjeldahl method, and laboratory physicochemical analysis. However, these traditional methods have significant limitations: firstly, they heavily rely on large, sophisticated instruments, failing to meet the on-site or real-time testing needs of cold chain logistics; secondly, these methods involve lengthy testing procedures, complex operations, and high costs. Furthermore, traditional methods are mostly destructive sampling, making them unsuitable for non-destructive online monitoring inside packaging and hindering continuous, visualized freshness assessment in long-distance cold chain environments.

[0004] To overcome the shortcomings of traditional methods, rapid food freshness detection technologies based on organic small molecule fluorescent / colorimetric probes or test strips have gradually been developed. These technologies primarily utilize the chemical reaction between the probe and biogenic amines or TVB-N in the headspace of food, producing color or fluorescence changes to achieve semi-quantitative detection of food spoilage. Some solutions further integrate smartphone image acquisition to assist in reading and judgment.

[0005] Currently, the specific implementation of the above-mentioned rapid detection technology is usually as follows: organic probes or commercial indicators with simple structures are directly coated on substrates such as test paper or filter paper; during detection, the probe changes color after contacting the headspace gas released by the food, and images are obtained by visual observation or by taking pictures with a smartphone. Finally, based on the change in color intensity, a qualitative or semi-quantitative judgment is made on whether the food is spoiled.

[0006] In summary, there is an urgent need to provide a novel food freshness detection method that is highly selective, highly stable, accurately quantifiable, and applicable to real cold chain scenarios. Summary of the Invention

[0007] This invention provides an in-situ detection system and method for the freshness of cold chain food based on a DπA colorimetric probe and a PCL electrospun membrane, integrated into a smartphone application. The probe specifically recognizes primary biogenic amines associated with spoilage through a nucleophilic addition-intramolecular cyclization mechanism, producing a noticeable color change. After electrospinning and immobilization, a stable, hydrophobic, and flexible electrospun nanofiber membrane is formed. The RGB colors of the electrospun nanofiber membrane are then extracted using a smart food freshness sensing platform, converted into color feature values, and finally substituted into a standard curve to calculate the TVBN content. This achieves non-destructive, real-time, visualized, and precise quantitative detection of the freshness of cold chain food.

[0008] One objective of this invention is to provide a D-π-A colorimetric probe, the chemical structure of which is shown below: ; R is selected from cyano or ethyl ester.

[0009] In some embodiments, the chemical structure of the D-π-A colorimetric probe is shown in formula (I): (I).

[0010] In some embodiments, the chemical structure of the D-π-A colorimetric probe is shown in formula (II): (II).

[0011] The second objective of this invention is to provide the use of the aforementioned D-π-A colorimetric probe in detecting food freshness.

[0012] In some implementations, the food is a cold chain food.

[0013] In some preferred embodiments, the cold chain food includes, but is not limited to: frozen fruits, frozen vegetables, frozen meat, frozen meat products, frozen milk, frozen dairy products, frozen eggs, frozen egg products, frozen aquatic products, frozen soy products, frozen beverages, frozen prepared foods, etc.

[0014] In some preferred embodiments, the cold chain food includes, but is not limited to: frozen fish, frozen shrimp, frozen crab, frozen poultry and livestock, quick-frozen food, etc.

[0015] In some embodiments, the D-π-A colorimetric probe is immobilized in a substrate material to form an electrospun nanofiber membrane.

[0016] In some preferred embodiments, the matrix of the substrate material is polycaprolactone (PCL).

[0017] A third objective of this invention is to provide the use of the aforementioned D-π-A colorimetric probe in the preparation of products for detecting food freshness.

[0018] In some embodiments, the product includes at least one of a detection reagent, a detection kit, and a detection system.

[0019] In some implementations, the food is a cold chain food.

[0020] In some preferred embodiments, the cold chain food includes, but is not limited to: frozen fruits, frozen vegetables, frozen meat, frozen meat products, frozen milk, frozen dairy products, frozen eggs, frozen egg products, frozen aquatic products, frozen soy products, frozen beverages, frozen prepared foods, etc.

[0021] In some preferred embodiments, the cold chain food includes, but is not limited to: frozen fish, frozen shrimp, frozen crab, frozen poultry and livestock, quick-frozen food, etc.

[0022] In some embodiments, the D-π-A colorimetric probe is immobilized in a substrate material to form an electrospun nanofiber membrane.

[0023] In some preferred embodiments, the matrix of the substrate material is polycaprolactone (PCL).

[0024] The fourth objective of this invention is to provide a product for detecting the freshness of food, the product comprising a D-π-A colorimetric probe, the chemical structure of which is as described above.

[0025] In some embodiments, the product includes at least one of a detection reagent, a detection kit, and a detection system.

[0026] In some embodiments, the product further comprises a substrate material immobilizing the D-π-A colorimetric probe.

[0027] In some embodiments, the D-π-A colorimetric probe forms an electrospun nanofiber membrane with the substrate material.

[0028] In some preferred embodiments, the matrix of the substrate material is polycaprolactone (PCL).

[0029] In some implementations, the product also includes an intelligent detection module.

[0030] In some preferred embodiments, the intelligent detection module includes a data acquisition unit, a processing unit, and an output unit; The acquisition unit is used to acquire images; the processing unit is used to identify color signals in the acquired images, convert the color signals into color feature values, and convert the color feature values ​​into analysis data; the output unit outputs food freshness judgment results based on the obtained analysis data.

[0031] In some implementations, the determination of food freshness includes fresh, qualified, and spoiled.

[0032] In some implementations, the food is a cold chain food.

[0033] In some preferred embodiments, the cold chain food includes, but is not limited to: frozen fruits, frozen vegetables, frozen meat, frozen meat products, frozen milk, frozen dairy products, frozen eggs, frozen egg products, frozen aquatic products, frozen soy products, frozen beverages, frozen prepared foods, etc.

[0034] In some preferred embodiments, the cold chain food includes, but is not limited to: frozen fish, frozen shrimp, frozen crab, frozen poultry and livestock, quick-frozen food, etc.

[0035] The fifth objective of this invention is to provide a method for detecting the freshness of food using the aforementioned product, the method comprising: (1) Place the electrospun nanofiber membrane and the food to be tested in the same enclosed space; (2) Images of the electrospun nanofiber membrane are continuously acquired by the acquisition unit; (3) The color signal in the image is identified by the processing unit, the color signal is converted into color feature value, and the color feature value is converted into analysis data; (4) The output unit outputs the food freshness judgment result based on the analyzed data; The electrospun nanofiber membrane comprises the aforementioned D-π-A colorimetric probe and a substrate material immobilizing the aforementioned D-π-A colorimetric probe; The analytical data are the volatile basic nitrogen (TVBN) values; When R is a cyano group, the color characteristic value is the G value / B value; When R is an ethyl ester group, the color characteristic value is (R value + G value) / (R value + G value + B value).

[0036] In some embodiments, the matrix of the substrate material is polycaprolactone (PCL).

[0037] In some implementations, the transformation of the changed data is based on a fitted equation: The fitting equation for the D-π-A colorimetric probe with R being cyano is y = 0.019x + 0.415, R² = 0.991; The fitting equation for the D-π-A colorimetric probe with R being ethyl ester group is y = 0.015x + 0.259, R² = 0.991; Where x is the color characteristic value of the electrospun nanofiber membrane, and y is the volatile basic nitrogen (TVBN) value (mg / 100 g).

[0038] In some implementations, the determination of food freshness includes fresh, qualified, and spoiled.

[0039] In some embodiments, the criteria for judging the freshness of the food are as follows: a volatile basic nitrogen value of <15 mg / 100g indicates freshness; a volatile basic nitrogen value of 15 mg / 100g to 30 mg / 100g indicates compliance; and a volatile basic nitrogen value >30 mg / 100g indicates inedibility.

[0040] The sixth objective of this invention is to provide a method for preparing a D-π-A colorimetric probe, the method comprising: Malononitrile or ethyl cyanoacetate was added to an anhydrous tetrahydrofuran solution of 7-(4-(bis(4-methoxyphenyl)amine)benzo)benzo[C][1,2,5]thiadiazole-4-aldehyde, and the reaction was carried out in the presence of ammonium acetate. The compound represented by formula (I) or formula (II) was then isolated.

[0041] This invention employs a specific colorimetric probe based on the DπA structure, composited with a PCL membrane to form an electrospun nanofiber membrane. Combined with a smartphone quantitative recognition algorithm, this constructs an in-situ, visualized, and quantitative intelligent sensing platform for food freshness that can be directly used in cold chain packaging. The chemical structure of the specific colorimetric probe is shown in formula (I) or formula (II). The probe specifically responds to spoilage biogenic amines through a nucleophilic addition-intramolecular cyclization mechanism, achieving a clear color gradient from blue-purple to purple to red-orange. The PCL membrane provides a hydrophobic, stable, and flexible immobilizing carrier. The smartphone converts the color signal into TVBN values, enabling non-destructive, real-time, and on-site detection.

[0042] Compared with the prior art, the present invention has the following beneficial effects: (1) The probe provided by this invention has extremely high selectivity and strong anti-interference. The molecular structure is novel, the response mechanism is clear, and the selectivity and sensitivity are superior to existing probes. The detection limits of the probe provided by this invention for biogenic amines are 13.0 μg / L (TPA-CN2) and 18.1 μg / L (TPA-CN-COOEt), respectively, which have reached the ppb level (parts per billion, used to describe the content of extremely low concentrations of substances). The detection limits are lower than those of most fluorescent probes reported in the literature (Adv. Mater.37, no. 44(2025): e10964 (3.7–9.1 µM, approximately 30 μg / L to 80 μg / L); Transactions of the Chinese Society of Agricultural Engineering, 2025, 41(24):320-328 (4.6693 μmol / L, approximately 40 μg / L). In addition, the probe can be observed with the naked eye without detection under ultraviolet light.

[0043] (2) Significantly improved stability, not only suitable for cold chain high-humidity environments, but also operable at room temperature. Electrospun nanofiber membranes significantly improve stability, hydrophobicity, and packaging compatibility. Compared to materials such as agar and hydrophilic polyvinyl alcohol (PVA) membranes, this invention immobilizes probes onto PCL membranes to form electrospun nanofiber membranes, which are hydrophobic, not easily photobleached, and can operate stably for more than 7 days under cold chain high-humidity conditions, and can be directly integrated into food packaging. The electrospun nanofiber membranes provided by this invention are superior to the probe / agar gel control group in terms of recovery rate (accuracy) and precision (stability), especially showing stability under long-term storage and room temperature conditions, as detailed in Table 1.

[0044] (3) Achieve dual-mode detection of visualization and quantification. Freshness level can be directly judged by the naked eye, while a high-linearity quantitative model (R²) can be established through a smartphone. 2 >0.99), the TVBN test results are highly consistent with the national standard method, with a relative error of <±5%, see Table 1 for details.

[0045] (4) Non-destructive, fast, low-cost and easy to use. No pretreatment, large instruments or professional personnel are required. The response time is 5 min to 15 min and the cost per test is extremely low, making it suitable for large-scale industrial applications.

[0046] (5) It is applicable to multiple food substrates and can be used in real cold chain scenarios. It has been verified to be effective in cold chain foods such as shrimp, yellow croaker, and beef, and has completed a 1100 km long-distance cold chain transportation test. It can be used for real-world monitoring of the entire logistics process. Attached Figure Description

[0047] Figure 1 The 1H NMR spectrum (500 MHz) of probe TPA-CN2 in CDCl3 is shown.

[0048] Figure 2 The carbon NMR spectrum (126 MHz) of probe TPA-CN2 in CDCl3 is shown.

[0049] Figure 3 The high-resolution mass spectrum of probe TPA-CN2 is shown.

[0050] Figure 4 The 1H NMR spectrum (500 MHz) of the probe TPA-CN-COOEt in CDCl3 is shown.

[0051] Figure 5 The carbon NMR spectrum (126 MHz) of the probe TPA-CN-COOEt in CDCl3 is shown.

[0052] Figure 6 The high-resolution mass spectrum of the probe TPA-CN-COOEt is shown.

[0053] Figure 7 The infrared spectra of pure PCL, TPA-CN-COOEt / PCL, and TPA-CN2 / PCL electrospun nanofiber membranes are shown.

[0054] Figure 8 The X-ray diffraction patterns of PCL and electrospun nanofiber membranes are shown.

[0055] Figure 9 Scanning electron microscope images of pure PCL and electrospun nanofiber membranes are shown.

[0056] Figure 10 The process of establishing the standard curve for the TPA-CN2 probe is illustrated: Data on the changes in TVB-N content and the color change of the TPA-CN2 probe in shrimp samples stored at 25℃ were recorded, and a standard curve was established based on these data. Specifically, a) shows the changes in TVB-N content during storage; b) shows the color change of the TPA-CN2 probe and the corresponding R, G, and B values; and c) shows the standard curve for the TPA-CN2 probe, demonstrating the correlation between the G / B ratio of the TPA-CN2 probe and the TVB-N content.

[0057] Figure 11The standard curve establishment process of the TPA-CN-COOEt probe is shown: Data on the changes in TVB-N content and the color change of the TPA-CN-COOEt probe in shrimp samples stored at 25℃ were recorded, and a standard curve was established based on these data. Wherein a) represents the changes in TVB-N content during storage; b) represents the color change of the TPA-CN-COOEt probe and the corresponding R, G, and B values; c) is the standard curve of the TPA-CN-COOEt probe, showing the correlation between the (R+G) / (R+G+B) ratio of the TPA-CN-COOEt probe and the TVB-N content.

[0058] Figure 12 A schematic diagram showing the continuous acquisition of color changes in an electrospun nanofiber membrane by a smartphone is presented.

[0059] Figure 13 A schematic diagram of the workflow of the FoodFreshAnalyzer intelligent food freshness sensing platform is shown.

[0060] Figure 14 A schematic diagram of the application of the detection system based on the DπA colorimetric probe provided by the present invention in cold chain transportation is shown. Detailed Implementation

[0061] To enable those skilled in the art to better understand and implement the technical solutions of the present invention, the present invention will be further described below in conjunction with specific embodiments and accompanying drawings.

[0062] Unless otherwise specified, all reagents used in this invention are commercially available, and all methods used are conventional techniques in the art.

[0063] Unless otherwise defined, all technical and scientific terms used in this invention have the same meaning as commonly used in the field to which this invention pertains. For the purposes of interpreting this specification, the following definitions will apply, and where appropriate, terms used in the singular will also include the plural forms, and vice versa.

[0064] Unless the context clearly indicates otherwise, the terms “a” and “an” as used herein include plural references. For example, reference to “a cell” includes multiple such cells and equivalents known to those skilled in the art, etc.

[0065] As used herein, the term "about" indicates a range of ±20% of the following value. In some embodiments, the term "about" indicates a range of ±10% of the following value. In some embodiments, the term "about" indicates a range of ±5% of the following value.

[0066] The numerical ranges used in this article should be understood as including all numbers within that range. For example, the range 1 to 20 should be understood to include any number, combination of numbers, or subrange from the following group: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20.

[0067] As used herein, the term "comprising" or "including" means "including, but not limited to." This term is intended to be open-ended to specify the presence of any of the stated features, elements, integers, steps, or components, but does not exclude the presence or addition of one or more other features, elements, integers, steps, components, or groups thereof. Therefore, the term "comprising" includes the more restrictive terms "consisting of" and "substantially consisting of." In one embodiment, the term "comprising" as used throughout the application, particularly in the claims, may be replaced by the term "consisting of."

[0068] As used herein, the terms “optional,” “any,” “arbitrary,” or “any one” mean that the event or situation described below may, but does not have to, occur, including the circumstances in which the event or situation occurs or does not occur. As used herein, “an” and “a” refer to one or more grammatical objects.

[0069] The term “and / or” as used herein should be understood to mean any one of the options or any combination of two or more of the options. Invention Overview This invention provides a smart sensing platform and method for food freshness based on a DπA colorimetric probe and a PCL membrane.

[0071] The term "cold chain food" as used in this article refers to packaged foods (or agricultural products) that are made primarily from agricultural products, livestock, aquatic products, fruits, and vegetables, and are produced under low-temperature (below 10°C, including cooling, freezing, and quick-freezing) processes after pretreatment or further mixing and preparation, and are stored, transported, sold, and distributed in a cold chain condition until consumed by consumers. Generally speaking, foods produced, stored, transported, and sold under a food cold chain system are also referred to as cold chain foods.

[0072] In some preferred embodiments, the cold chain food includes, but is not limited to: frozen fruits, frozen vegetables, frozen meat, frozen meat products, frozen milk, frozen dairy products, frozen eggs, frozen egg products, frozen aquatic products, frozen soy products, frozen beverages, frozen prepared foods, etc.

[0073] In some preferred embodiments, the cold chain food includes, but is not limited to: frozen fish, frozen shrimp, frozen crab, frozen poultry and livestock, quick-frozen food, etc.

[0074] The DπA colorimetric probe described in this invention refers to an organic small molecule / polymer sensing system with a "donor (D, electron-donating)-π conjugated bridge-acceptor (A, electron-withdrawing)" structure, which uses color (absorption spectrum) changes as readout; by regulating the intramolecular charge transfer (ICT) intensity, it achieves highly sensitive and visual detection of the analyte.

[0075] Core mechanism: After light excitation, "intramolecular charge transfer" occurs, forming a "push-pull" electronic system; after binding / reacting with the analyte, the ICT intensity or energy level changes, the absorption peak shift or intensity changes, and the color change is visible to the naked eye, i.e., the "colorimetric" signal.

[0076] The DπA colorimetric probe of this invention is shown in the structural formulas (I) and (II), wherein the electron donor is triphenylamine (TPA); the π conjugated bridge is an aromatic conjugated unit; and the electron acceptor A is a cyano or cyanoethyl ester, which provides a biogenic amine recognition site and can undergo a specific addition cyclization reaction with primary biogenic amines.

[0077] The substrate material of the electrospun nanofiber membrane of the present invention is polycaprolactone (PCL), and also contains the functional component DπA colorimetric probe, as shown in the structural formulas (I) and (II). Its structure is a nanofiber mesh membrane, which is porous, has a high specific surface area, is flexible, hydrophobic, and breathable. It mainly functions to fix the probe, improve stability, extend lifespan, and adapt to food packaging.

[0078] In some implementations, the present invention also provides an intelligent detection module, which includes a data acquisition unit, a processing unit, and an output unit.

[0079] In some implementations, the acquisition unit is used to acquire images. In some preferred implementations, the acquisition unit can be a common device such as a smartphone camera.

[0080] In some embodiments, the processing unit is used to identify color signals in the acquired image, convert the color signals into color feature values, and convert the color feature values ​​into analysis data. In some preferred embodiments, the processing unit includes an application that executes a color recognition algorithm to convert the color signals into color feature values, and a corresponding application that converts the color feature values ​​into analysis data according to a standard curve model.

[0081] In some embodiments, the color is the RGB color of the electrospun nanofiber membrane, the color characteristic values ​​are the R, G, and B values ​​of the identified color, and the analytical data is the volatile basic nitrogen value.

[0082] In some preferred embodiments, when R is a cyano group, the color characteristic value is the G value / B value.

[0083] In some preferred embodiments, when R is an ethyl acetate group, the color characteristic value is (R value + G value) / (R value + G value + B value).

[0084] In some implementation schemes, the standard curve model is a fitting equation (i.e., the quantitative relationship between color feature values ​​and TVBN content). The specific method for establishing the standard curve is as follows: (1) Prepare standard solutions / standard gases of different concentrations of putrescine; (2) Continuously acquire images of electrospun nanofiber membranes and record their R, G, and B values; (3) Fit a fitting equation that can calculate TVBN content based on the color feature values ​​corresponding to different putrescine concentrations, specifically as follows: Figure 10 and Figure 11 As shown.

[0085] In some implementations, the fitting equation includes: The fitting equation for the D-π-A colorimetric probe with R being cyano is y = 0.019x + 0.415, where R... 2 =0.991; The fitting equation for the D-π-A colorimetric probe with R being ethyl acetate is y = 0.015x + 0.259, R 2 =0.991; Where x is the color characteristic value of the electrospun nanofiber membrane, and y is the volatile basic nitrogen (TVBN) value (mg / 100 g).

[0086] The "volatile basic nitrogen" mentioned in this article is a key indicator for evaluating the freshness of animal-based foods such as meat and seafood. The unit is generally "mg / 100g". Volatile basic nitrogen is the sum of volatile alkaline nitrogenous substances such as ammonia, dimethylamine, and trimethylamine produced by the decomposition of proteins in meat / fish under the action of enzymes and bacteria; the higher the value, the less fresh the food.

[0087] In some implementations, the output unit outputs a food freshness judgment result based on the obtained analytical data. In some preferred implementations, the analytical data (i.e., volatile basic nitrogen value) is analyzed (the obtained volatile basic nitrogen value is compared with the threshold of the judgment standard) to output the food freshness judgment result.

[0088] In some implementations, the determination of food freshness includes fresh, acceptable, and spoiled.

[0089] In some implementation schemes, the criteria for judging the freshness of the food are as follows: a volatile basic nitrogen value of <15 mg / 100g indicates freshness; a volatile basic nitrogen value of 15 mg / 100g to 30 mg / 100g indicates compliance; and a volatile basic nitrogen value >30 mg / 100g indicates inedibility.

[0090] Example 1: Preparation of D-π-A colorimetric probe 1.1 Synthesis of probe TPACN2 Malononitrile (1.0 mmol) was added to an anhydrous tetrahydrofuran (5.0 mL) solution of 7-(4-(bis(4-methoxyphenyl)amine)benzo)benzo[C][1,2,5]thiadiazole-4-aldehyde (467 mg, 1.0 mmol), and 1 equivalent of ammonium acetate was added. The mixture was stirred at 25 °C for 12 hours. After the reaction was completed, the solvent was removed by rotary evaporation, and the product was separated by silica gel column chromatography (eluent: ethyl acetate and n-hexane mixed in a volume ratio of 1:9) to obtain the target product TPACN2 in 75% yield. The structure of the compound was characterized by 1H NMR, 1C NMR, and high-resolution mass spectrometry. 1 H NMR (500 MHz, Chloroform-d) δ 8.80 (s, 1H), 8.76(d, J = 7.9 Hz, 1H), 7.95 (d, J = 8.6 Hz, 2H), 7.81 (d, J = 7.9 Hz, 1H), 7.16(d, J = 8.4 Hz, 4H), 7.02 (d, J = 8.5 Hz, 2H), 6.89 (d, J = 8.5 Hz, 4H), 3.82 (s, 6H); 13 C NMR (126 MHz, Chloroform-d) δ 156.88, 154.72, 152.80, 152.68,150.71, 140.29, 139.56, 130.96, 130.69, 127.67, 126.57, 125.35, 121.10,118.56, 114.98, 114.12, 113.33, 81.54, 55.55; HR-MS (ESI), formula C 30 H 21 N5O2S1,[M+H] + m / z calcd. for 515.1416, found 515.1413.( Figures 1-3 ).

[0091] 1.2 Synthesis of probe TPACNCOOEt Ethyl cyanoacetate (1.0 mmol) was added to an anhydrous tetrahydrofuran (10 mL) solution of 7-(4-(bis(4-methoxyphenyl)amine)benzo)benzo[C][1,2,5]thiadiazole-4-aldehyde (467 mg, 1.0 mmol), followed by the addition of 1 equivalent of ammonium acetate. The mixture was then stirred at 25 °C for 12 hours. After the reaction was complete, the solvent was removed by rotary evaporation, and the product was separated by silica gel column chromatography (eluent: ethyl acetate and n-hexane in a volume ratio of 1:9) to obtain the target product TPACNCOOEt in 80% yield. The structure of the compound was characterized by 1H NMR, 1C NMR, and high-resolution mass spectrometry. 1 H NMR (500 MHz, Chloroform-d) δ 9.24(s, 1H), 8.84 (d, J = 7.8 Hz, 1H), 7.91 (d, J = 8.6 Hz, 2H), 7.80 (d, J = 7.7Hz, 1H), 7.15 (d, J = 8.4 Hz, 4H), 7.03 (d, J = 8.5 Hz, 2H), 6.88 (d, J = 8.4Hz, 4H), 4.44 (q, J = 7.1 Hz, 2H), 3.82 (s, 6H), 1.44 (t, J = 7.1 Hz, 3H); 13 CNMR (126 MHz, Chloroform-d) δ 162.43, 156.67, 155.29, 152.94, 150.23, 148.17,139.86, 138.86, 130.86, 130.41, 127.51, 127.18, 125.82, 121.97, 118.86,116.09, 114.92, 102.97, 62.79, 55.53, 14.25; HR-MS (ESI), formula C 32 H 26 N4O4S1,[M+H] + m / z calcd. for 562.1675, found 562.1671. ( Figures 4-6 ).

[0092] Example 2: Preparation of electrospun nanofiber membranes Electrospun nanofiber membranes were prepared using biocompatible and biodegradable polycaprolactone (PCL) as the substrate material. 2.0 g of PCL was dissolved in a hexafluoroisopropanol / formic acid mixed solvent (volume ratio 4:1) to prepare a 15% (w / w) spinning solution. 2.0 g of PCL was then mixed with 10 mg of probe (TPA-CN2 or TPA-CN-COOEt) and dissolved using the same method to obtain a homogeneous spinning solution. Electrospun nanofiber membranes were then prepared using electrospinning technology. The electrospinning conditions were: negative voltage -2 kV (needle tip), positive voltage +14 kV (receiver electrode), flow rate 1.5 mL / h, and roller speed 500 rpm. The obtained electrospun nanofiber membranes were collected on silicone paper, dried, and stored in a desiccator.

[0093] Example 3: Structure and property characterization of electrospun nanofiber membranes Fourier transform infrared (FT-IR) characterization results show: Pure PCL membranes, TPA-CN2 / PCL, and TPA-CN-COOEt / PCL electrospun nanofiber membranes all retain the characteristic peak of PCL: 2951 cm⁻¹. -1 (CH stretching), 1723 cm -1 (C=O stretching), 1257 / 1164 cm -1 (COC stretchable), 736 cm -1 (CH bending) proves the integrity of the PCL skeleton structure. TPA-CN2 / PCL at 2220 cm -1 A weak characteristic peak for cyano groups (C≡N) appears nearby; TPA-CN-COOEt / PCL has a peak at 1720-1730 cm⁻¹. -1 The peak intensities in the intervals were slightly increased. The positions and profiles of the main peaks of the three peaks were basically consistent, indicating that the probe loading did not change the chemical structure of PCL, and the probe was uniformly dispersed at a low content. Figure 7 ).

[0094] X-ray diffraction (XRD) characterization showed the following results: The pure PCL film exhibits a strong characteristic diffraction peak at 2θ = 21.64°, corresponding to the orthorhombic (110) crystal plane. After loading TPA-CN2 or TPA-CN-COOEt, the composite film shows obvious diffraction peaks at 2θ = 21.66° and 29.40° and 29.38°, respectively; the latter is attributed to the probe crystal signal. Figure 8 ).

[0095] Scanning electron microscopy (SEM) characterization showed the following results: Pure PCL, TPA-CN2 / PCL, and TPA-CN-COOEt / PCL membranes all exhibited continuous, uniform, bead-free, and well-interwoven nanofiber morphologies. Pure PCL fibers exhibited a random-oriented, nonwoven structure; both composite membranes maintained the intact morphology of PCL fibers, with no significant phase separation or probe aggregation. This indicates that the addition of the probe does not disrupt the electrospinning process, and the electrospun nanofiber membrane possesses a stable fiber microstructure. Figure 9 ).

[0096] Example 4: Correlation between probe color and TVBN content 4.1 Method for Establishing Standard Curves Establish a quantitative relationship between color feature values ​​and TVBN.

[0097] (1) Sample preparation and gradient culture Fresh shrimp samples were collected and placed at 25°C for different times (e.g., 0 h, 2 h, 4 h, 6 h, 8 h, 11 h, 15 h) to obtain gradient samples with different putrescine concentrations. The TVBN content of each group of samples was determined according to the national standard GB / T 18108-2019, and color images of the corresponding electrospun nanofiber membranes were acquired simultaneously. Figure 10 a and b, Figure 11 (a and b). The results showed that the TVBN content in the shrimp samples gradually increased over time. Figure 10 b, Figure 11 (b) indicates that the freshness of the shrimp sample gradually decreased.

[0098] (2) Color feature value extraction The color images of the electrospun nanofiber membranes were processed using the Python Imaging Library to extract the gray values ​​of the R, G, and B channels, and the feature parameters were calculated respectively. For TPACNCOOEt, the color feature value (R+G) / (R+G+B) was found to have a good linear relationship with the TVBN value measured by the national standard method; while for TPACN2, the color feature value G / B was found to have a good linear relationship with the TVBN value measured by the national standard method.

[0099] (3) Linear fitting and standard curve establishment Using TVBN content as the x-axis and color feature values ​​as the y-axis, a least-squares linear fit was performed to obtain the standard curve: TPACN2: y = 0.019x + 0.415, R 2 = 0.991 ( Figure 10 c); TPACNCOOOEt: y = 0.015x + 0.259, R 2= 0.991 ( Figure 11 c).

[0100] The established standard curve can be used for rapid quantitative calculation of TVBN in subsequent unknown samples.

[0101] Those skilled in the art should understand that regardless of the technical means used to obtain the color characteristic value of the electrospun nanofiber membrane, as long as it is substituted into the standard curve provided by this invention, the TVBN content of the sample to be tested can be obtained, and then the freshness of the sample to be tested can be determined by the TVBN content.

[0102] This invention utilizes data on the color change of a collected probe, converts it into color feature values, and ultimately calculates the TVBN content, thereby achieving non-destructive, visual detection of food freshness. During the storage of shrimp samples, the probe color changed significantly with increasing TVBN content, conforming to the grading standards of GB / T 18108-2019: TVBN < 15 mg / 100 g is fresh, 15–30 mg / 100 g is acceptable, and > 30 mg / 100 g is inedible.

[0103] Experimental results show that both probes can quantify TVBN through digital colorimetric analysis, with TPACNCOOEt exhibiting superior colorimetric differentiation.

[0104] 4.2 Application of Standard Curve in Food Freshness Testing Shrimp and beef samples were tested at 4℃ and 25℃, and the probe color change was consistent with the TVBN standard judgment result. Based on the independently developed intelligent food freshness sensing platform: FoodFreshAnalyzer (the public can freely obtain this application from the GitHub platform and use it to implement the detection method of this invention, link: https: / / github.com / foodfox123 / FoodFreshAnalyzer_2.apk.git), the RGB values ​​of the electrospun nanofiber membrane were extracted by taking a picture with a mobile phone camera, the color characteristic value of the probe was obtained, and it was substituted into a pre-fitted standard curve to automatically calculate the TVBN content. Figure 12 As shown, in the upper half of the image, from left to right, the color of the electrospun nanofiber membrane gradually deepens, indicating that the degree of sample spoilage gradually increases. This is consistent with the detection data displayed in the lower half, from left to right, showing that the TVBN content gradually increases over time. This demonstrates that the detection results are in good agreement with the national standard method, enabling low-cost, non-destructive, real-time, and portable freshness monitoring.

[0105] Example 5: Application of Intelligent Sensor Platform for Food Freshness in Food Freshness Detection The process of detecting food freshness based on the FoodFreshAnalyzer intelligent food freshness sensing platform is as follows: Figure 13 As shown, the specific process is as follows: 5.1 Smartphone Image Acquisition Process 5.1.1 Data Acquisition Equipment A smartphone (example model: Honor 70 / HUAWEI P40) is used, employing a rear camera, i.e., the acquisition unit described in this invention, with a fixed resolution of 4096×3072.

[0106] 5.1.2 Data Collection Conditions The experiment was conducted under a uniform white LED light source with an illuminance of approximately 500 lux to 800 lux, with no reflections or shadows; the distance between the probe film and the lens was fixed at 15 cm, and the images were taken vertically.

[0107] 5.1.3 Data Collection Steps (1) Place the electrospun nanofiber membrane after the response in the center of a white background; (2) Open the phone camera and turn off the flash, HDR, beauty mode and auto zoom; (3) After focusing, take a high-resolution photo of the front of the electrospun nanofiber membrane; (4) Take three parallel photos of each group of samples and take the average value.

[0108] (5) Compare the TVB-N value obtained by the national standard method and calculate the recovery rate (%) and standard deviation (RSD) (%).

[0109] 5.2 Usage Process of the Intelligent Sensing Platform for Food Freshness 5.2.1 Processing Software The invention employs a self-developed intelligent food freshness sensing platform: FoodFreshAnalyzer, which supports Android / iOS and integrates the processing unit and output unit described in this invention.

[0110] 5.2.2 Operating Procedures (1) Open FoodFreshAnalyzer and click “Detection”; (2) Import / real-time captured images of electrospun nanofiber membranes; (3) The software automatically selects the effective detection area and removes background interference; (4) Automatically extract the mean values ​​of R, G, and B, and calculate the corresponding color feature values.

[0111] When using the TPACNCOOEt probe, the formula for calculating the color feature value is: (R value + G value) / (R value + G value + B value); When using the TPACN2 probe, the formula for calculating the color feature value is: G value / B value.

[0112] (5) Call the built-in standard curve equation to automatically calculate the TVBN content; (6) Output results according to national standards: Fresh: TVBN < 15 mg / 100 g; Acceptable: 15 ≤ TVBN < 30 mg / 100 g; Not for consumption: TVBN ≥ 30 mg / 100 g.

[0113] Example 7: Practical Application of Cold Chain Transportation A simulated long-distance cold chain transportation was conducted between two cities in China, 1100 km apart. TPACNCOOEt / PCL electrospun film labels were placed in shrimp packaging, and tests were performed using air transport (12 h) and land transport (48 h). Results showed that the air-transported sample had a TVBN of 12.66 mg / 100 g, indicating freshness; the land-transported sample had a TVBN of 27.72 mg / 100 g, indicating compliance. The results from this smartphone platform are consistent with the actual degree of spoilage, proving that the detection method provided by this invention can be used in real-world cold chain logistics scenarios. Figure 14 ).

[0114] Comparative Example Weigh an appropriate amount of agar powder and dissolve it in deionized water. Heat and stir until completely dissolved to prepare a 0.5 wt% to 1.0 wt% agar solution. Keep the solution at 50℃ to 60℃. Add the TPA-CN-COOEt probe stock solution to the incubated agar solution and mix well. Control the final probe concentration to 10 μmol / L. Then, add the mixture to a 48-well plate, with a volume of 100 μL to 200 μL per well. Allow the plate to stand at room temperature until completely solidified to obtain the probe-agar gel sensing membrane. Store at 4℃ in the dark for later use.

[0115] The TVB-N determination method was compared with the national standard method using TPA-CN-COOEt agarose gel and TPA-CN-COOEt / PCL membrane (i.e., the electrospun nanofiber membrane of this invention) under the same experimental conditions. The results are shown in Table 1, indicating that the TVB-N recovery rate (92.01%~109.67%) measured by the probe / PCL membrane has a smaller deviation from the national standard method, and the overall result is closer to the TVB-N value of the national standard method, with higher precision.

[0116] As shown in Table 1, during the storage period at 4℃, the TVB-N concentration of shrimp samples increased from 10.79 mg / 100g to 38.26 mg / 100g using the national standard method, indicating accelerated spoilage. Using a TPA-CN-COOEt / PCL membrane for detection, the recovery rate ranged from 92.01% (2 days) to 104.54% (0 days), generally between 92% and 105%, which is close to the results of the national standard method. The precision ranged from 0.49% (4 days) to 4.34% (0 days), with the lowest deviation at 4 days (0.49%), indicating that the detection results were more stable under long-term storage. The TPA-CN-COOEt / agar gel control group showed a recovery rate of 86.34% (2 days) to 108.16% (0 days), which fluctuated significantly (only 86.34% at 2 days, lower than 90%). The precision ranged from 0.7% (4 days) to 4.07% (2 days). Although the deviation was low at 4 days, the overall stability of the detection was not as good as that of the TPA-CN-COOEt / PCL membrane.

[0117] As shown in Table 1, during storage at 4℃, the TVB-N concentration in beef samples increased from 12.41 mg / 100g to 36.52 mg / 100g using the national standard method as storage time increased, indicating accelerated spoilage. Using a TPA-CN-COOEt / PCL membrane for detection, the recovery rate was 98.31% (8 hours) to 109.67% (0 hours), all within the range of 98% to 110%, indicating good accuracy. The precision was a relative standard deviation of 1.74% (15 hours) to 5% (0 hours), with a significant decrease in deviation after 8 hours (1.82%), indicating that the TPA-CN-COOEt / PCL membrane method provides more stable results for food freshness detection in the later stages of storage. In contrast, the TPA-CN-COOEt / agar gel control group showed a recovery rate of 107.28% (15 hours) to 118.05% (0 hours), generally exceeding 100% (reaching 118.05% at 0 hours), indicating poor accuracy. The TPA-CN-COOEt / agar gel control group had a precision of 3.83% (15 hours) to 4.85% (0 hours), which was relatively large, especially at 0 hours (4.85%).

[0118] As shown in Table 1, during storage at 25℃, the TVB-N concentration in beef samples increased from 11.32 mg / 100 g to 34.56 mg / 100 g with prolonged storage time, indicating a significantly faster spoilage rate than at 4℃. Using a TPA-CN-COOEt / PCL membrane for detection, the recovery rate ranged from 100.79% (3 days) to 106.71% (0 days), all within the range of 100% to 107%, demonstrating excellent accuracy. The precision ranged from 1.33% (0 days) to 3.39% (3 days), with the lowest deviation (1.51%) at 5 days, indicating that the TPA-CN-COOEt / PCL membrane of this invention maintains stable detection performance even at room temperature. The TPA-CN-COOEt / agar gel control group showed a recovery rate of 107.1% (3 days) to 119.79% (0 days), with a high recovery rate of 119.79% on day 0, indicating poor accuracy. The relative standard deviation was 1.45% (5 days) to 9.07% (0 days), with the largest deviation (9.07%) on day 0, indicating extremely poor precision.

[0119] The above content can be summarized as follows: (1) The TPA-CN-COOEt / PCL membrane provided by the present invention: Accuracy: The recovery rate was 92.01%~109.67%, mostly between 95% and 105%, which is highly consistent with the results of the national standard method.

[0120] Precision: Relative standard deviation is 0.49%~5%, especially in the later stages of storage (4 days, 8 hours, 5 days) the deviation is less than 2%, and the stability is good.

[0121] Applicability: Stable at both 4℃ and 25℃, suitable for sample detection at different temperatures and storage times. The recovery rate of beef at 25℃ (TPA-CN-COOEt / PCL membrane method 100.79%~106.71%) was slightly higher than that at 4℃ (98.31%~109.67%), but the precision (1.33%~3.39%) was comparable to that at 4℃, indicating that the TPA-CN-COOEt / PCL membrane method is not sensitive to temperature and has stronger temperature adaptability.

[0122] (2) TPA-CN-COOEt / agar gel control group: Accuracy: Recovery rate ranged from 86.34% to 119.79%, with large fluctuations (e.g., below 90% or above 110% for 2-day shrimp and 0-hour beef), showing significant differences from the national standard method.

[0123] Precision: Relative standard deviation 0.7%~9.07%, especially poor repeatability for 0-day beef (9.07%) and 0-hour beef (4.85%).

[0124] Limitations: The instability of the agar gel carrier leads to large deviations in the test results, making it unsuitable as a reliable detection method.

[0125] In summary, the TPA-CN-COOEt / PCL membrane provided by this invention outperforms the TPA-CN-COOEt / agar gel control group in both recovery rate (accuracy) and precision (stability), especially exhibiting stability under long-term storage and room temperature conditions, making it a more reliable method for TVB-N detection. The control group, due to poor carrier stability, shows large fluctuations in results, making it unreliable for practical detection.

[0126] Table 1 *Recovery rate, specifically, refers to the ratio (percentage) of the TVB-N content measured in different treatment groups (TPA-CN-COOEt / PCL membrane group, TPA-CN-COOEt / agar gel group) to the TVB-N content measured by the national standard method. It is an indicator reflecting the degree of loss of the analyte during sample analysis; the less loss, the higher the recovery rate, and it is closely related to the true composition and the accuracy of the analysis.

[0127] It should be noted that when numerical ranges are involved in this invention, it should be understood that both endpoints of each numerical range and any value between the two endpoints can be selected. Since the steps and methods used are the same as in the embodiments, preferred embodiments are described in this invention to avoid redundancy. Although preferred embodiments of this invention have been described, those skilled in the art, once they understand the inventive concept of this invention, can make other changes and modifications to these embodiments, and all such changes and modifications fall within the scope of this invention.

[0128] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. If such modifications and variations fall within the scope of equivalents of this invention, then this invention also intends to include these modifications and variations.

Claims

1. A D-π-A colorimetric probe, characterized in that, The chemical structure of the D-π-A colorimetric probe is shown below: ； R is selected from cyano or ethyl ester.

2. The use of the D-π-A colorimetric probe according to claim 1 in detecting food freshness.

3. The use of the D-π-A colorimetric probe according to claim 1 in the preparation of products for detecting food freshness, characterized in that, The product includes at least one of a testing reagent, a testing kit, and a testing system.

4. The use according to claim 2 or 3, characterized in that, The food in question is a cold chain food.

5. A product for detecting the freshness of food, characterized in that, The product comprises the D-π-A colorimetric probe according to claim 1; and / or The product also includes a substrate material on which the D-π-A colorimetric probe is immobilized, and the D-π-A colorimetric probe and the substrate material form an electrospun nanofiber membrane.

6. The product according to claim 5, characterized in that, The matrix of the substrate material is polycaprolactone.

7. The product according to claim 5, characterized in that, The product also includes an intelligent detection module, which comprises a data acquisition unit, a processing unit, and an output unit; The acquisition unit is used to acquire images; The processing unit is used to identify color signals in the acquired image, convert the color signals into color feature values, and convert the color feature values ​​into analysis data; The output unit outputs the food freshness judgment result based on the analyzed data.

8. A method for detecting food freshness using the product described in claim 7, characterized in that, The method includes: (1) Place the electrospun nanofiber membrane and the food to be tested in the same enclosed space; (2) Images of the electrospun nanofiber membrane are continuously acquired by the acquisition unit; (3) The color signal in the image is identified by the processing unit, the color signal is converted into color feature value, and the color feature value is converted into analysis data; (4) The output unit outputs the food freshness judgment result based on the analyzed data; The electrospun nanofiber membrane comprises the D-π-A colorimetric probe and a substrate material immobilizing the D-π-A colorimetric probe; The analytical data are volatile basic nitrogen values; When R is a cyano group, the color characteristic value is the G value / B value; When R is an ethyl ester group, the color characteristic value is (R value + G value) / (R value + G value + B value).

9. The method according to claim 8, characterized in that, The process of converting the color feature values ​​into the analytical data is based on a fitting equation: The fitting equation of D-π-A colorimetric probe with R as cyano group is y=0.019x+0.415, R 2 =0.

991. The fitting equation for the D-π-A colorimetric probe with R being ethyl acetate is y = 0.015x + 0.259, R 2 =0.991; Where x is the color characteristic value of the electrospun nanofiber membrane, and y is the volatile basic nitrogen value.

10. The method according to claim 8 or 9, characterized in that, The criteria for judging the freshness of food are as follows: a volatile basic nitrogen value of <15 mg / 100 g indicates freshness; a volatile basic nitrogen value of 15 mg / 100 g to 30 mg / 100 g indicates compliance; and a volatile basic nitrogen value >30 mg / 100 g indicates inedibility.

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