Smartphone-assisted dual-mode sensor array and applications thereof

Abstract

This invention discloses a smartphone-assisted dual-mode sensor array and its application. Belonging to the field of food detection technology, this invention involves two sensing elements: Fe3O4 with high peroxidase-like activity and Fe3O4 / MoS2 biomimetic nanomaterials. Using 3,3',5,5'-dimethylbenzidine and rhodamine B isothiocyanate as indicators, the two sensing elements respond differently to different types and concentrations of sulfides in two modes, and an internal filtering effect exists between oxidized TMB and rhodamine B isothiocyanate. Therefore, sulfide content is reflected by changes in absorbance and fluorescence intensity, and the color change, combined with a smartphone, enables more intuitive and rapid detection. The sensor array of this invention is simple to operate, reacts rapidly, and detects accurately, overcoming the disadvantages of expensive instruments and cumbersome operation of traditional detection methods, and has significant application prospects in food safety detection.

Description

Technical Field

[0001] This invention belongs to the field of food testing technology, specifically relating to a smartphone-assisted colorimetric / fluorescence dual-mode sensor array and its application in the detection of sulfides in polysaccharide foods. Background Technology

[0002] Sulfides are widely used as food additives due to their antioxidant, preservative, bleaching, and enzymatic browning inhibition properties, especially in the processing of polysaccharide foods such as white sugar, starch, and biscuits. However, excessive intake of sulfides can cause health problems such as vomiting, diarrhea, intestinal flora imbalance, and vitamin deficiencies, and in severe cases, can damage the nervous system. The US FDA, European EFSA, and other agencies have established strict limits on sulfide residues in food; therefore, establishing rapid, sensitive, and accurate sulfide detection methods is crucial for ensuring food safety.

[0003] Traditional sulfide detection methods, such as the Monier-Williams method and distillation, suffer from drawbacks such as high detection limits, long processing times, and cumbersome operation. While modern analytical methods such as ion chromatography, electrochemical methods, and ratiometric fluorescence probe methods have improved sensitivity, they rely on expensive equipment, have long experimental cycles, and require professional operation, making them difficult to apply for rapid on-site detection. In recent years, colorimetric detection technology based on nanozymes has been developed due to its low cost and ease of operation; however, existing methods are mostly based on a single "lock-and-key" recognition mechanism, capable of detecting only a single sulfide and failing to meet the need for simultaneous detection of multiple sulfides in food. Furthermore, existing technologies often use specific numerical values ​​to limit raw material ratios and reaction parameters, resulting in narrow patent protection and susceptibility to circumvention.

[0004] Therefore, developing a multi-dimensional, low-cost, flexible, and wide-range sulfide sensor array suitable for rapid on-site detection has become an urgent technical problem to be solved in this field. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of the prior art and provide a smartphone-assisted colorimetric / fluorescence dual-mode sensor array. By using molar ratio and mass ratio to limit the raw material ratio and reaction parameter range, the scope of patent protection is expanded, and at the same time, rapid qualitative identification and accurate quantitative detection of various sulfides in polysaccharide foods are achieved.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a smartphone-assisted dual-mode sensing array, comprising a dual-mode sensing array, which includes two sensing elements: Fe3O4 nanomaterials with peroxidase-like activity and Fe3O4 / MoS2 nanocomposite materials; it also includes a colorimetric indicator 3,3',5,5'-tetramethylbenzidine and a fluorescent indicator Rhodamine B isothiocyanate.

[0007] Fe3O4 and Fe3O4 / MoS2 catalyze the oxidation of TMB by H2O2 to generate ox-TMB; the fluorescence of Rhodamine B isothiocyanate is quantitatively quenched by ox-TMB;

[0008] Fe3O4 and Fe3O4 / MoS2 achieve multi-dimensional qualitative identification and quantitative detection of sulfides by detecting the changes in absorbance at 652 nm, the changes in fluorescence intensity at 580 nm, and the brightness value L calculated from the RGB values ​​of the reaction system extracted by a smartphone.

[0009] The application of a smartphone-assisted dual-mode sensor array in sulfide detection includes the following steps:

[0010] Step G1: Construction of the colorimetric sensor array; Add NaAc-HAc buffer solution, Fe3O4 or Fe3O4 / MoS2 nanomaterial solution, H2O2 solution, and TMB solution to a 96-well plate, with a total reaction volume of 200 μL. After reacting in a water bath at 37℃ for 10–15 min, read the absorbance A0 at 652 nm using a microplate reader; Add 20 μL of the sulfide solution to be tested, react for 2–5 min, and read the absorbance A1 at 652 nm. Calculate the absorbance ratio A1 / A0; Set up 5 replicates for each sample to construct a dual-channel colorimetric sensor array with two sensing elements;

[0011] Step G2: Construction of the fluorescence sensor array; Rhodamine B isothiocyanate fluorescent dye solution was added to the reaction system of step G1, and the reaction was carried out at room temperature in the dark for 10-15 min. The fluorescence intensity F was measured by a multifunctional microplate fluorescence reader at an excitation wavelength of 560 nm and an emission wavelength of 580 nm to construct a dual-channel fluorescence sensor array with two sensing elements.

[0012] Step G3: Construction of the smartphone-assisted colorimetric sensor array; Place the 96-well plate from step G1 in an LED light box, take a picture at a fixed position to obtain image information, extract the RGB values ​​of the image, calculate the brightness value L according to the formula L=0.2126R+0.7152G+0.0722B, and construct the smartphone-assisted dual-channel colorimetric sensor array.

[0013] Step G4: Data analysis; Combine the data matrices of 2 types of nanomaterials × n samples × 5 parallel data obtained in steps G1 to G3 with principal component analysis for qualitative identification of sulfides; Quantitative detection of sulfides is achieved by establishing standard curves of sulfide concentration versus A1 / A0, F, and L.

[0014] Two nanomaterials with different peroxidase-like activities were selected as sensing elements: Fe3O4 nanomaterials and Fe3O4 / MoS2 nanocomposites. By limiting the molar and mass ratios of the raw materials, the reproducibility of the nanomaterial preparation and the stability of the catalytic performance were ensured. Among them, Fe3O4 / MoS2, due to the loading effect of MoS2 nanosheets, solved the problem of easy agglomeration of Fe3O4, and its catalytic activity was significantly higher than that of pure Fe3O4.

[0015] A colorimetric / fluorescence dual-mode detection system was constructed: TMB was used as a colorimetric indicator, and its oxidation product ox-TMB has a characteristic absorption at 652 nm; Rhodamine B isothiocyanate was used as a fluorescence indicator, and the internal filtering effect between ox-TMB and Rhodamine B isothiocyanate was utilized to achieve a quantitative response of the fluorescence signal; by limiting the ratio of each reagent to the sensing element, the stability and sensitivity of the detection system were ensured.

[0016] Introducing smartphone-assisted detection: The brightness value L is calculated by extracting the RGB values ​​of the reaction system, enabling visualized on-site detection.

[0017] By combining principal component analysis (PCA) to process multidimensional data, accurate differentiation of various sulfides can be achieved; and quantitative detection of sulfides can be achieved by establishing a standard curve.

[0018] Compared with the prior art, the beneficial effects of the present invention are:

[0019] (1) The raw material ratio is limited by molar ratio and mass ratio, and the reaction temperature, time and other parameters are ranged, which avoids patent circumvention caused by specific numerical limits, expands the scope of protection, and makes the process more operable and repeatable.

[0020] (2) Multi-dimensional detection with high accuracy: Simultaneously collect signals from three dimensions: absorbance, fluorescence intensity and image brightness. Cross-validation significantly reduces the probability of false positives and can distinguish 100% of the four sulfides: Na2S, Na2S2O3, Na2S2O5 and Na2S2O8.

[0021] (3) Easy to operate and low cost: No large precision instruments are required; on-site testing can be completed with a smartphone; nanomaterials are easy to prepare, have good stability, and can be mass-produced.

[0022] (4) Rapid detection and high sensitivity: The entire detection process takes no more than 30 minutes, the linear range is 0 to 100 μmol / L, and the linear correlation coefficient R² ≥ 0.989.

[0023] (5) Highly practical: It can be applied to the detection of sulfides in various polysaccharide foods such as white sugar, corn starch, wheat starch, and biscuits. The spiked recovery rate is 92.25% to 107.43%, and the RSD is ≤7.97%, which meets the actual sample detection needs.

[0024] (6) Good biosafety: Fe3O4 / MoS2 nanomaterials do not show obvious hemolysis in the concentration range of 0 to 40 μg / mL, and the cell survival rate is ≥90% at a concentration of 8 μg / mL, making them safe to use. Attached Figure Description

[0025] Figure 1 This is a schematic diagram of the preparation process of Fe3O4 and Fe3O4 / MoS2 nanomaterials in Example 1 of this invention;

[0026] Figure 2 These are transmission electron microscope and scanning electron microscope images of the Fe3O4 / MoS2 nanomaterials in Example 1 of this invention;

[0027] Figure 3 The Fourier transform infrared spectra of Fe3O4 and Fe3O4 / MoS2 nanomaterials in Example 1 of this invention are shown below.

[0028] Figure 4 The X-ray diffraction patterns of Fe3O4 and Fe3O4 / MoS2 nanomaterials in Example 1 of this invention are shown below.

[0029] Figure 5 This is a hydration particle size distribution diagram of Fe3O4 and Fe3O4 / MoS2 nanomaterials in Example 1 of this invention;

[0030] Figure 6 The image shows the Zeta potential diagrams of Fe3O4 and Fe3O4 / MoS2 nanomaterials in Example 1 of this invention.

[0031] Figure 7 This is an elemental mapping diagram of the Fe3O4 / MoS2 nanomaterial in Example 1 of this invention;

[0032] Figure 8 This is a graph showing the effect of nanomaterial concentration on peroxidase-like activity in Example 2 of this invention;

[0033] Figure 9 This is a graph showing the effect of pH value on the activity of nanomaterial-based peroxidases in Example 2 of this invention;

[0034] Figure 10 This is a graph showing the effect of temperature on the activity of nanomaterial-based peroxidases in Example 2 of this invention;

[0035] Figure 11This is a graph showing the effect of reaction time on the activity of nanomaterial-based peroxidases in Example 2 of this invention;

[0036] Figure 12 This is a steady-state kinetic analysis diagram of the nanomaterial catalytic oxidation of TMB in Example 2 of this invention;

[0037] Figure 13 This is a catalytic stability diagram of the nanomaterial in Example 3 of this invention;

[0038] Figure 14 This is a hemolysis rate diagram of the Fe3O4 / MoS2 nanomaterial in Example 4 of this invention;

[0039] Figure 15 This is a graph showing the toxicity of Fe3O4 / MoS2 nanomaterials to human umbilical vein endothelial cells in Example 5 of this invention.

[0040] Figure 16 This is a schematic diagram of the process of using Fe3O4 and Fe3O4 / MoS2 nanomaterials as sensing elements in Example 6 of the present invention.

[0041] Figure 17 PCA diagram showing the colorimetric sensor array in Example 6 of this invention distinguishing four sulfides at different concentrations;

[0042] Figure 18 PCA diagram showing the fluorescence sensing array in Example 6 of this invention distinguishing four sulfides at different concentrations;

[0043] Figure 19 The PCA diagram showing the smartphone-assisted colorimetric sensor array in Example 6 of this invention distinguishing four sulfides at different concentrations;

[0044] Figure 20 This is a PCA diagram of the sensor array for selective analysis of sulfides in Example 7 of this invention;

[0045] Figure 21 This is a PCA analysis chromatogram of different concentrations of Na2S in Example 8 of this invention;

[0046] Figure 22 This is a linear regression equation graph for different concentrations of Na2S in Example 8 of this invention;

[0047] Figure 23 The PCA analysis chromatograms of different concentrations of Na2S2O3 in Example 8 of this invention are shown.

[0048] Figure 24 This is a linear regression equation graph for different concentrations of Na2S2O5 in Example 8 of this invention;

[0049] Figure 25The PCA analysis chromatograms of different concentrations of Na2S2O8 in Example 8 of this invention are shown.

[0050] Figure 26 This is a linear regression equation graph for different concentrations of Na2S2O8 in Example 8 of this invention;

[0051] Figure 27 The PCA diagram for the sensor array in Example 9 of this invention distinguishes four sulfides in white sugar. Detailed Implementation

[0052] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.

[0053] A smartphone-assisted dual-mode sensing array includes a dual-mode sensing array comprising two sensing elements: Fe3O4 nanomaterials with peroxidase-like activity and Fe3O4 / MoS2 nanocomposite materials.

[0054] It also includes the colorimetric indicator 3,3',5,5'-tetramethylbenzidine and the fluorescent indicator Rhodamine B isothiocyanate.

[0055] Fe3O4 and Fe3O4 / MoS2 catalyze the oxidation of TMB by H2O2 to generate ox-TMB; the fluorescence of Rhodamine B isothiocyanate is quantitatively quenched by ox-TMB;

[0056] Fe3O4 and Fe3O4 / MoS2 achieve multi-dimensional qualitative identification and quantitative detection of sulfides by detecting the changes in absorbance at 652 nm, the changes in fluorescence intensity at 580 nm, and the brightness value L calculated from the RGB values ​​of the reaction system extracted by a smartphone.

[0057] The preparation method of Fe3O4 nanomaterials includes the following steps:

[0058] Step S1: Under nitrogen protection, stir and mix deionized water with hydrochloric acid-acidified FeCl3 solution for 30-40 minutes;

[0059] Step S2: Add Na2SO3 solution dropwise. After the solution changes from reddish-brown to yellow, add NH3·H2O solution dropwise and stir vigorously. After a black precipitate is formed, continue stirring for 40-60 minutes.

[0060] Step S3: Wash the black precipitate three times with deoxygenated water and vacuum dry to obtain Fe3O4 nanomaterials;

[0061] The molar ratio of FeCl3 to NH3·H2O is 1:11.33, and the molar ratio of FeCl3 to Na2SO3 is 3:1.

[0062] The preparation method of Fe3O4 / MoS2 nanocomposite materials includes the following steps:

[0063] Step F1: Thiourea, ammonium molybdate tetrahydrate and Fe3O4 nanomaterial prepared according to claim 3 are ultrasonically mixed in deionized water, transferred to a Teflon high-pressure reactor, and reacted at 180-200℃ for 10-12 h to generate a black precipitate;

[0064] Step F2: After the formation of black precipitate, wash three times each with deionized water and anhydrous ethanol, and then vacuum dry to obtain Fe3O4 / MoS2 nanocomposite material.

[0065] The mass ratio of Fe3O4 to thiourea is 1:19, and the mass ratio of Fe3O4 to ammonium molybdate tetrahydrate is 1:8.75.

[0066] The components in the detection system of the dual-mode sensor array satisfy the following proportional relationship:

[0067] The mass ratio of Fe3O4 to Fe3O4 / MoS2 is 1:1;

[0068] The concentration ratio of Fe3O4 or Fe3O4 / MoS2 nanomaterials to NaAc-HAc buffer solution is 1 μg / mL: 0.05 mol / L;

[0069] The concentration ratio of Fe3O4 or Fe3O4 / MoS2 nanomaterials to H2O2 solution is 1 μg / mL: 0.004 mol / L;

[0070] The mass ratio of Fe3O4 or Fe3O4 / MoS2 nanomaterials to TMB is 1:5;

[0071] The concentration ratio of Fe3O4 or Fe3O4 / MoS2 nanomaterials to Rhodamine B isothiocyanate fluorescent dye is 4 μg / mL: 1 μg / mL;

[0072] The NaAc-HAc buffer solution has a pH of 3.6, and the Rhodamine B isothiocyanate fluorescent dye is dissolved in dimethyl sulfoxide.

[0073] The application of a smartphone-assisted dual-mode sensor array in sulfide detection includes the following steps:

[0074] Step G1: Construction of the colorimetric sensor array; Add NaAc-HAc buffer solution, Fe3O4 or Fe3O4 / MoS2 nanomaterial solution, H2O2 solution, and TMB solution to a 96-well plate, with a total reaction volume of 200 μL. After reacting in a water bath at 37℃ for 10–15 min, read the absorbance A0 at 652 nm using a microplate reader; Add 20 μL of the sulfide solution to be tested, react for 2–5 min, and read the absorbance A1 at 652 nm. Calculate the absorbance ratio A1 / A0; Set up 5 replicates for each sample to construct a dual-channel colorimetric sensor array with two sensing elements;

[0075] Step G2: Construction of the fluorescence sensor array; Rhodamine B isothiocyanate fluorescent dye solution was added to the reaction system of step G1, and the reaction was carried out at room temperature in the dark for 10-15 min. The fluorescence intensity F was measured by a multifunctional microplate fluorescence reader at an excitation wavelength of 560 nm and an emission wavelength of 580 nm to construct a dual-channel fluorescence sensor array with two sensing elements.

[0076] Step G3: Construction of smartphone-assisted colorimetric sensor array; Place the 96-well plate from step G1 in an LED light box, take a picture with a smartphone at a fixed position to acquire image information, extract the RGB values ​​of the image to calculate the brightness value L, and construct a smartphone-assisted dual-channel colorimetric sensor array;

[0077] Step G4: Data analysis; Combine the data matrices of 2 types of nanomaterials × n samples × 5 parallel data obtained in steps G1 to G3 with principal component analysis for qualitative identification of sulfides; Quantitative detection of sulfides is achieved by establishing standard curves of sulfide concentration versus A1 / A0, F, and L.

[0078] The sulfides include at least one of Na2S, Na2S2O3, Na2S2O5, and Na2S2O8; the dual-mode sensing array can distinguish the four sulfides without cross-identification in the concentration range of 10 μmol / L to 100 μmol / L.

[0079] In step G3, the RGB values ​​are obtained by extracting them using the mobile app ColorMeter.

[0080] The linear range for quantitative detection is 0–100 μmol / L, and the linear correlation coefficient R² ≥ 0.989.

[0081] This is used to detect the sulfide content in polysaccharide foods; polysaccharide foods include at least one of white sugar, corn starch, wheat starch, and biscuits.

[0082] The pretreatment method for polysaccharide food samples is as follows:

[0083] Pretreatment of white sugar: Weigh the sample, dissolve it in pre-cooled ultrapure water, filter it through a 0.22μm microporous membrane, and dilute it 100 times with ultrapure water;

[0084] Corn starch / wheat starch pretreatment: Weigh the sample, add acetone and sonicate to mix, centrifuge to collect the supernatant, add pre-cooled ultrapure water, filter through a 0.22μm microporous membrane, and dilute with ultrapure water 100 times;

[0085] Cookie pretreatment: Weigh the uniformly ground sample, add pre-cooled ultrapure water and stir, centrifuge and collect the supernatant and repeat centrifugation; add petroleum ether to the supernatant for extraction, discard the organic phase and repeat extraction; filter with a 0.22μm microporous membrane and dilute 100 times with ultrapure water.

[0086] Example 1

[0087] See Figure 1 The sensing element of this invention is Fe3O4 and Fe3O4 / MoS2 biomimetic nanomaterials, and its preparation method is as follows:

[0088] Preparation of the sensing element Fe3O4: Under nitrogen protection, 30 mL of deionized water and 3 mL of FeCl3 solution (2 mol / L) acidified with hydrochloric acid were stirred for 30 min. Then, 2 mL of Na2SO3 solution (1 mol / L) was added dropwise to the system. When the solution color changed from reddish-brown to yellow, 80 mL of NH3·H2O solution (0.85 mol / L) was added dropwise while stirring vigorously. When a black precipitate was formed, stirring was continued for 40 min until the reaction was complete.

[0089] The black precipitate obtained after the reaction was washed with deoxygenated water until the pH of the black precipitate was 5-7.5. After washing, it was vacuum dried in a vacuum dryer at 45℃ for 12-16 hours to obtain Fe3O4 nanomaterials.

[0090] Preparation of Fe3O4 / MoS2 sensing element: 40 mg of Fe3O4 obtained in step a was weighed and added to 15 mL of deionized water, followed by 0.76 g of thiourea and 0.35 g of ammonium molybdate tetrahydrate. After ultrasonic dispersion, the resulting solution was transferred to a high-pressure reactor equipped with Teflon and heated to react at 180 °C for 10 h. After the reaction was completed, a black precipitate was formed. The black precipitate was washed three times with deionized water and anhydrous ethanol and then vacuum dried to obtain Fe3O4 / MoS2 nanomaterials.

[0091] Preparation and characterization of Fe3O4 and Fe3O4 / MoS2 biomimetic nanomaterials obtained in Example 1:

[0092] The morphology of the prepared Fe3O4 / MoS2 nanomaterials was characterized using transmission electron microscopy (TEM) and scanning electron microscopy (SEM). The results showed that the Fe3O4 / MoS2 nanomaterials exhibited a nanoflower-like structure with an average particle size of 400 nm. (See [link to relevant documentation]). Figure 2Fe3O4 is adsorbed on MoS2 two-dimensional nanosheets. This adsorption effectively solves the aggregation problem of Fe3O4NPs and enhances their catalytic performance. Furthermore, TEM and SEM images simultaneously demonstrate the successful synthesis of Fe3O4 and Fe3O4 / MoS2 biomimetic nanomaterials.

[0093] The composition of Fe3O4 and Fe3O4 / MoS2 nanomaterials was analyzed using Fourier transform infrared spectroscopy (FTIR). See Appendix. Figure 3 The FTIR spectrum of Fe3O4NPs is at 559 cm⁻¹ -1 The absorption peak at 3344 cm⁻¹ corresponds to the vibration of the Fe-O bond. -1 and 1620cm -1 The absorption peaks at 1596 cm⁻¹ represent the stretching and deformation vibrations of the OH bonds, respectively, proving the successful preparation of Fe₃O₄ nanomaterials; the FTIR spectrum of Fe₃O₄ / MoS₂ nanomaterials at 1596 cm⁻¹... -1 1398cm -1 and 1112cm -1 The absorption peaks at these locations represent OH. – In-plane bending vibration, out-of-plane bending vibration and tensile vibration, 1032cm -1 and 902cm -1 The absorption peak at 595 cm⁻¹ indicates the stretching vibration of the SS bond, confirming the successful preparation of MoS₂ nanosheets. Furthermore, Fe₃O₄ NPs were observed at 595 cm⁻¹ in the FTIR spectrum of the Fe₃O₄ / MoS₂ nanomaterials. -1 The absorption peak at [value missing] indicates that Fe3O4 nanomaterials were successfully adsorbed onto MoS2 nanosheets. These data demonstrate that the products prepared in Example 1 of this invention are Fe3O4 and Fe3O4 / MoS2 biomimetic nanomaterials.

[0094] The crystal structures of the two nanomaterials were further precisely analyzed using X-ray diffraction. (See [link to relevant documentation]). Figure 4 . Figure 4The diffraction peaks at 2θ = 21.14°, 35.1°, 41.47°, 50.61°, 63.14°, 67.55° and 74.46° are shown, corresponding to the (111), (220), (311), (400), (422), (511) and (440) crystal planes of Fe3O4NPs. Based on this model, the synthesized nanomaterials possess a crystalline cubic spinel structure similar to the standard structure of Fe3O4 (JCPDS No. 19-0629). The X-ray diffraction pattern of the Fe3O4 / MoS2 nanomaterials shows diffraction peaks at 2θ = 17.87°, 37.81°, and 67.8° belonging to the (002), (100), and (110) crystal planes of hexagonal MoS2 (JCPDS No. 37-1492). This pattern also contains characteristic peaks of Fe3O4 NPs, but the peak intensity is reduced, which is attributed to the adsorption of Fe3O4 nanomaterials on the surface of MoS2 nanosheets. These results also indicate that the products prepared in Example 1 of this invention are Fe3O4 and Fe3O4 / MoS2 biomimetic nanomaterials.

[0095] The hydrated particle size distribution of Fe3O4 and Fe3O4 / MoS2 nanomaterials in deionized water was determined using a laser particle size analyzer (DLS). See [link to relevant documentation]. Figure 5 .like Figure 5 As shown, the average hydrated particle size of Fe3O4 nanomaterials is 330.22 nm, and the average hydrated particle size of Fe3O4 / MoS2 nanomaterials is 380.81 nm. The hydrated particle size gradually increases after Fe3O4 adsorbs onto MoS2 nanosheets, indicating the successful preparation of Fe3O4 / MoS2 nanomaterials. Particle size analysis shows that the hydrated particle size of Fe3O4 nanomaterials is larger than the particle size measured by TEM. This is because Fe3O4 nanomaterials are prone to aggregation in solution, thus the measured particle size is larger. The hydrated particle size of Fe3O4 / MoS2 nanomaterials is similar to the particle size measured by TEM, indicating that the successful adsorption of Fe3O4 NPs can effectively solve the problem of its easy aggregation in solution.

[0096] The zeta potential measurement results show that, see [reference] Figure 6 The charges of Fe3O4 and Fe3O4 / MoS2 nanomaterials were -13.16 mV and -25.90 mV, respectively, and the change in potential further proved the successful synthesis of the nanomaterials.

[0097] To accurately analyze the elemental composition of the two sensing elements, the synthesized Fe3O4 / MoS2 nanomaterials were characterized and elementally mapped (EDS-mapping). See [link to relevant documentation]. Figure 7 The uniform distribution of the main elements was also verified by EDS-element mapping.

[0098] Example 2

[0099] Using 3,3',5,5'-tetramethylbenzidine (TMB) as a chromogenic substrate, the POD-like catalytic activity of the Fe3O4 and Fe3O4 / MoS2 nanomaterials prepared in Example 1 was investigated. 50 μL of natural horseradish peroxidase (10 ng / mL) was used as a positive control. 50 μL of Fe3O4 and Fe3O4 / MoS2 nanomaterials (40 μg / mL, dissolved in NaAc-HAc buffer) were added to 500 μL of NaAc-HAc buffer solutions consisting of 20 μL of H2O2 (30%) and 10 μL of TMB (5 mg / mL), respectively. The solutions were incubated at 37 °C for 15 min. After the reaction, 200 μL was added to a 96-well plate, and the absorbance was read at 652 nm using a microplate reader. The catalytic activity of the natural enzyme or nanozyme was affected by the reaction concentration, pH, temperature, and time. The relative catalytic activities of Fe3O4 and Fe3O4 / MoS2 nanomaterials were investigated under different concentrations (0–32 μg / mL), pH values ​​(2.8–6.8), temperatures (20–70 °C), and reaction times (0–30 min). Furthermore, under optimal reaction conditions, the steady-state kinetic parameters of the catalytic oxidation of TMB by Fe3O4 and Fe3O4 / MoS2 nanomaterials were obtained by varying the concentrations of TMB or H2O2.

[0100] The concentration of the NaAc-Hac buffer was 0.2 mol / L, and the pH value was 3.6.

[0101] The results showed that the catalytic activity of Fe3O4 and Fe3O4 / MoS2 nanomaterials increased with increasing concentration, exhibiting a significant concentration dependence. Furthermore, the catalytic activity of Fe3O4 / MoS2 nanomaterials for TMB was significantly higher than that of Fe3O4 nanomaterials. (See [link to relevant documentation]). Figure 8 .

[0102] Fe3O4 and Fe3O4 / MoS2 nanomaterials maintain stable catalytic activity over a wide pH range (3.0–5.0), with the optimal reaction pH being 3.6. (See [link to relevant documentation]). Figure 9 .

[0103] Both Fe3O4 and Fe3O4 / MoS2 nanomaterials maintain high catalytic activity in the range of 30–60 °C, overcoming the limitations of natural enzymes in terms of temperature dependence and high restriction of catalytic activity. (See [link to relevant documentation]). Figure 10 .

[0104] Furthermore, Fe3O4 and Fe3O4 / MoS2 nanomaterials exhibit high catalytic activity over a period of time (0–30 min), see [link to relevant documentation]. Figure 11Furthermore, within 15 minutes, the catalytic activity of Fe3O4 and Fe3O4 / MoS2 nanomaterials increased significantly, and after 15 minutes, the catalytic activity gradually leveled off with increasing time.

[0105] Kinetic analysis showed that Fe3O4 and Fe3O4 / MoS2 nanomaterials had a positive effect on the K-axis of TMB. m (1.27mM and 1.38mM respectively) lower than the K of natural POD m (1.65 mM) indicates that Fe3O4 and Fe3O4 / MoS2 nanomaterials have a high affinity for TMB. Additionally, Fe3O4 and Fe3O4 / MoS2 nanomaterials exhibit a high K+ affinity for H2O2. m (0.14 mM and 1.43 mM respectively), compared with the K of natural POD. m The similarity (0.53 mM) indicates that the Fe3O4 and Fe3O4 / MoS2 nanomaterials prepared in this invention have good POD simulation activity. See Appendix. Figure 12 .

[0106] Example 3

[0107] The catalytic stability of Fe3O4 and Fe3O4 / MoS2 nanomaterials was evaluated by monitoring the changes in POD-like activity over time in NaAc-Hac buffer (0.2 mol / L, pH 3.6). 1 mL of Fe3O4 and Fe3O4 / MoS2 nanomaterials at a concentration of 100 μg / mL were prepared using NaAc-Hac buffer. 50 μL of each nanomaterial was added to 500 μL of NaAc-Hac buffer solution, consisting of 20 μL of H2O2 (30%) and 10 μL of TMB (5 mg / mL), respectively. The mixture was incubated at 37°C for 15 min. After the reaction, 200 μL was added to a 96-well plate, and the absorbance was read at 652 nm using a microplate reader. The Fe3O4 and Fe3O4 / MoS2 nanomaterial solutions were stored at 4°C. The stability assessment period was 30 days, with measurements taken every 5 days. (See reference...) Figure 13 The result is as follows Figure 13 As shown, the absorbance of Fe3O4 and Fe3O4 / MoS2 nanomaterials at 652 nm did not change significantly over 30 days, indicating that the Fe3O4 and Fe3O4 / MoS2 nanomaterials prepared in Example 1 have strong catalytic stability in NaAc-Hac buffer solution.

[0108] Example 4

[0109] The biocompatibility of Fe3O4 / MoS2 nanomaterials was monitored to investigate the biosafety of Fe3O4 and Fe3O4 / MoS2 nanomaterials. Fresh blood was dispersed in physiological saline and centrifuged at 2500 r / min for 5 min in centrifuge tubes, repeated 2-3 times until the supernatant was clear. The supernatant was discarded, and the obtained red blood cells were washed with physiological saline. The red blood cells were then diluted with an appropriate amount of physiological saline to prepare a 2% red blood cell suspension. Fe3O4 / MoS2 nanomaterials at concentrations of 2.5, 5, 10, 20, and 40 μg / mL were then mixed with an equal volume of the 2% red blood cell suspension. The negative control was 0.9% physiological saline, and the positive control was ultrapure water. After stabilization at room temperature for 4 h, 100 μL of the supernatant was placed in a 96-well plate, and the absorbance was measured at 540 nm and photographed.

[0110] The method for calculating the hemolysis rate is as follows:

[0111] Hemolysis rate (%) = (mean OD value of test sample group - mean OD value of negative sample group) / (mean OD value of positive sample group - mean OD value of negative sample group) × 100% Formula 1;

[0112] The results show that, see Figure 14 As shown in the figure, no obvious hemolysis was observed in the Fe3O4 / MoS2 nanomaterials within the concentration range of 0–40 μg / mL, and the hemolysis rate was significantly lower than 5%.

[0113] Example 5

[0114] The biosafety of Fe3O4 and Fe3O4 / MoS2 nanomaterials was investigated by monitoring their cytotoxicity. The cytotoxicity of Fe3O4 / MoS2 nanomaterials was analyzed using normal human umbilical vein endothelial cells (HUVECs). Cells were cultured in RPMI-1640 medium containing 10% fetal bovine serum (FBS) and 1% penicillin and streptomycin. Cells were seeded into 96-well plates at a density of 5 × 10⁶ cells per well. 4 Approximately 100 cells were inoculated and then placed in a 37°C, 5% CO2 incubator for 24 hours. Once the cells had adhered and grown to a certain density, Fe3O4 / MoS2 nanomaterials (0.25, 0.5, 1, 2, 4, 8 μg / mL) were added to each well for treatment. After 24 hours, 20 μL of MTT solution was added to each well, and the cells were incubated in the dark for 4 hours. The absorbance at 492 nm was measured using a microplate reader, and the toxic effects of each concentration of nanomaterials on the cells were calculated.

[0115] The method for calculating cell viability is as follows:

[0116] Cell viability (%) = (OD value of treatment group - OD value of blank control group) / (OD value of control group - OD value of blank control group) × 100% (Formula 2)

[0117] The results show that, see Figure 15 As shown in the figure, Fe3O4 / MoS2 nanomaterials had no significant effect on the activity of HUVECs cells. At a concentration of 8 μg / mL, the cell survival rate remained above 90%, indicating that Fe3O4 / MoS2 nanomaterials have good cell compatibility.

[0118] Example 6

[0119] See Figure 16 Using the Fe3O4 and Fe3O4 / MoS2 nanomaterials prepared in Example 1 as sensing elements, this invention provides a colorimetric / fluorescence dual-mode sensor array for smartphone-assisted multi-dimensional differentiation and identification of sulfides. The specific steps are as follows:

[0120] Step G1: Construction of colorimetric sensor array; Sodium sulfide (Na2S), sodium thiosulfate (Na2S2O3), sodium metabisulfite (Na2S2O5), and sodium persulfate (Na2S2O8) were selected as sulfide detection samples, and sulfide solutions of different concentrations (100μmol / L, 50μmol / L, and 10μmol / L) were prepared using water as a solvent. 162 μL of NaAc-HAc buffer solution, 20 μL of Fe3O4 and Fe3O4 / MoS2 nanomaterials (40 μg / mL, dissolved in deionized water), 8 μL of H2O2 (30%), and 10 μL of TMB (2 mg / mL) were added sequentially to each well of a 96-well plate. The reaction mixture was 200 μL per well and incubated in a 37°C water bath for 15 min. After the reaction, the absorbance A0 at 652 nm was read using a microplate reader. 20 μL of different sulfide solutions were added to the 200 μL reaction mixture, and after reacting at room temperature for 2 min, the absorbance A1 at 652 nm was read using a microplate reader. The absorbance ratio A1 / A0 was calculated. Based on a 5-parallel arrangement of each sample, a dual-channel colorimetric sensor array based on two sensing elements was obtained.

[0121] Step G2: Construction of the fluorescence sensing array; Rhodamine B isothiocyanate solution (100 μg / mL) was prepared using dimethyl sulfoxide (DMSO) as solvent and added as a fluorescence indicator. 10 μL of the fluorescence indicator was added to each well of the colorimetric sensing array constructed in step a, and incubated at room temperature in the dark for 10 min. After the reaction, the fluorescence intensity value F (Ex = 560 nm, Em = 580 nm) was read using a multifunctional microplate fluorescence reader. A dual-channel fluorescence sensor array based on two sensing elements was obtained.

[0122] Step G3: Construction of the colorimetric sensor array assisted by a smartphone; Place the colorimetric sensor array from step G1 in an LED light box, use a smartphone to take a picture at a fixed position to obtain image information, and analyze and extract RGB values ​​through the mobile app "ColorMeter";

[0123] The brightness value is calculated as follows: L = 0.2126R + 0.7152G + 0.0722B (Formula 3);

[0124] The luminance value (L) is calculated using Formula 3. This yields a smartphone-assisted dual-channel colorimetric sensor array based on two sensing elements.

[0125] Furthermore, a dual-mode colorimetric / fluorescence sensor array for multi-dimensional monitoring of sulfides using a smartphone was obtained, forming a data matrix of 2 nanomaterials × 4 sulfides × 5 parallel sequences, and distinguishing and identifying the four sulfides at different concentration levels.

[0126] The data matrix obtained from the colorimetric sensor array in step G1 is used to plot a PCA score map using principal component analysis (PCA). (See attached document.) Figure 17 As shown in the figure, the four sulfide replicate samples at concentration levels of 100 μmol / L, 50 μmol / L and 10 μmol / L are clustered compactly, and the different sulfides achieve 100% non-crossing distribution in the feature space, indicating that the obtained colorimetric sensing array can accurately distinguish the four sulfides at different concentration levels.

[0127] The data matrix obtained from the fluorescence sensing array in step G2 was further analyzed using PCA. (See [reference needed]). Figure 18 As shown in the figure, the four sulfide replicates at concentration levels of 100 μmol / L, 50 μmol / L and 10 μmol / L showed compact clustering, and the different sulfides achieved 100% non-cross-distribution in the feature space, indicating that the obtained fluorescence sensing array can accurately distinguish the four sulfides at different concentration levels.

[0128] Further, the data matrix obtained from the G3 smartphone-assisted colorimetric sensor array was subjected to PCA analysis, see [reference needed]. Figure 19 As shown in the figure, the four sulfide replicate samples were clustered tightly at concentration levels of 100 μmol / L, 50 μmol / L and 10 μmol / L. The different sulfides achieved 100% non-crossing distribution in the feature space, indicating that the obtained smartphone-assisted colorimetric sensor array can accurately distinguish the four sulfides at different concentration levels.

[0129] Therefore, the colorimetric / fluorescence dual-mode sensor array for smartphone-assisted multidimensional monitoring of sulfides prepared in this invention has excellent sulfide identification capabilities.

[0130] Example 7

[0131] To verify the selectivity of the smartphone-assisted colorimetric / fluorescence dual-mode sensor array for sulfides, several representative substances were selected as proof-of-concept materials, including sodium tartrate, anhydrous sodium carbonate, CaCl2, NaCl, Na2S, Na2S2O3, Na2S2O5, and Na2S2O8. These substances were added to the smartphone-assisted colorimetric / fluorescence dual-mode sensor array obtained in Example 6, with five parallel samples for each substance. The absorbance ratio A1 / A0, fluorescence intensity F, and luminance L of each sample were read, and the corresponding data matrices were obtained. PCA analysis was performed on the three data matrices; see [reference needed]. Figure 20 The results showed that there was a clear separation and no overlap between SCC and the interfering substances, and that the interfering substances could not be classified in the feature space, which proved the excellent selectivity of the dual-mode sensor array in sulfide detection.

[0132] The concentrations of sodium tartrate, anhydrous sodium carbonate, CaCl2, and NaCl were 500 μmol / L, and the concentrations of Na2S, Na2S2O3, Na2S2O5, and Na2S2O8 were 50 μmol / L. 20 μL of the above solutions were added to the sensor array reaction.

[0133] Example 8

[0134] The smartphone-assisted colorimetric / fluorescence dual-mode sensor array obtained in this invention is used to detect different concentrations of Na2S, Na2S2O3, and Na2S2O8.

[0135] Different concentrations of Na2S solution (0, 20, 40, 60, 80 and 100 μmol / L) were used as test samples and added to the smartphone-assisted colorimetric / fluorescence dual-mode sensor array obtained in Example 6. Five parallel samples were set for each concentration, and the absorbance ratio A1 / A0, fluorescence intensity value F and brightness value L of each sample were read to obtain the corresponding data matrix.

[0136] PCA analysis was performed on the three obtained data matrices. (See attached document.) Figure 21 PCA score maps of different concentrations of Na2S in the colorimetric sensor array (PC1=99.82%, PC2=0.18%), the fluorescence sensor array (PC1=98.75%, PC2=1.25%), and the smartphone-assisted colorimetric sensor array (PC1=98.69%, PC2=1.31%) were obtained. The results show that different concentrations of Na2S can be clearly distinguished in the sensor array, and the sample points of each category do not overlap.

[0137] Furthermore, linear regression equations were constructed using different PC1 values ​​and Na2S standard concentrations, as described in [reference needed]. Figure 22 The regression equation for the colorimetric sensor array is y1 = -0.04056x + 2.03091 (R²). 2 =0.99959), and the regression equation for the fluorescence sensing array is y2=0.04057x–2.05389 (R = 0.99959). 2 =0.99572), the regression equation for the smartphone-assisted colorimetric sensor array is y3=0.03601x–1.83375 (R = 0.99572). 2 =0.98912), where y1, y2, and y3 correspond to the PC1 values ​​of each array. The above results indicate that the smartphone-assisted colorimetric / fluorescence dual-mode sensor array obtained in this invention has excellent monitoring capabilities for Na2S.

[0138] Different concentrations of Na2S2O3 solutions (0, 20, 40, 60, 80 and 100 μmol / L) were used as test samples and added to the smartphone-assisted colorimetric / fluorescence dual-mode sensor array obtained in Example 6. Five parallel samples were set for each concentration, and the absorbance ratio A1 / A0, fluorescence intensity value F and brightness value L of each sample were read to obtain the corresponding data matrix.

[0139] PCA analysis was performed on the three obtained data matrices. (See attached document.) Figure 23 PCA score maps of different concentrations of Na2S2O3 in the colorimetric sensor array (PC1=99.81%, PC2=0.19%), the fluorescence sensor array (PC1=99.35%, PC2=0.65%), and the smartphone-assisted colorimetric sensor array (PC1=98.14%, PC2=1.86%) were obtained. The results show that different concentrations of Na2S2O3 can be clearly distinguished in the sensor array, and the sample points of each category do not overlap.

[0140] Furthermore, linear regression equations were constructed using different PC1 values ​​and Na2S2O3 standard concentrations, as described in [reference needed]. Figure 24 The regression equation for the colorimetric sensor array is y1 = -0.03997x + 1.95567 (R²). 2 =0.99901), and the regression equation for the fluorescence sensing array is y2=0.04019x–1.90559 (R = 0.99901). 2 =0.98653), the regression equation for the smartphone-assisted colorimetric sensor array is y3=0.03833x–1.83405 (R = 0.98653). 2=0.99772), where y1, y2, and y3 correspond to the PC1 values ​​of each array. The above results indicate that the smartphone-assisted colorimetric / fluorescence dual-mode sensor array obtained in this invention has excellent monitoring capabilities for Na2S2O3.

[0141] Different concentrations of Na2S2O8 solutions (0, 20, 40, 60, 80 and 100 μmol / L) were used as test samples and added to the smartphone-assisted colorimetric / fluorescence dual-mode sensor array obtained in Example 6. Five parallel samples were set for each concentration, and the absorbance ratio A1 / A0, fluorescence intensity value F and brightness value L of each sample were read to obtain the corresponding data matrix.

[0142] PCA analysis was performed on the three obtained data matrices. (See attached document.) Figure 25 PCA score maps of different concentrations of Na2S2O8 in the colorimetric sensor array (PC1=99.47%, PC2=0.53%), the fluorescence sensor array (PC1=99.17%, PC2=0.83%), and the smartphone-assisted colorimetric sensor array (PC1=99.87%, PC2=0.13%) were obtained. The results show that different concentrations of Na2S2O8 can be clearly distinguished in the sensor array, and the sample points of each category do not overlap.

[0143] Furthermore, linear regression equations were constructed using different PC1 values ​​and Na2S2O8 standard concentrations, as described in [reference needed]. Figure 26 The regression equation for the colorimetric sensor array is y1 = 0.0477x – 1.93779 (R²). 2 =0.99742), and the regression equation for the fluorescence sensing array is y2=-0.04729x+1.86483 (R = 0.99742). 2 =0.99353), the regression equation for the smartphone-assisted colorimetric sensor array is y3=-0.0405x+2.01334 (R = 0.99353). 2 =0.99862), where y1, y2, and y3 correspond to the PC1 values ​​of each array. The above results indicate that the smartphone-assisted colorimetric / fluorescence dual-mode sensor array obtained in this invention has excellent monitoring capabilities for Na2S2O8.

[0144] Therefore, the above results demonstrate that the smartphone-assisted colorimetric / fluorescence dual-mode sensor array obtained in this invention has excellent monitoring capabilities for sulfides in multiple dimensions.

[0145] Example 9

[0146] Applications of smartphone-assisted colorimetric / fluorescence dual-mode sensor arrays

[0147] The present invention provides a smartphone-assisted colorimetric / fluorescence dual-mode sensor array for distinguishing sulfides in polysaccharide foods across multiple dimensions. The specific steps are as follows:

[0148] 1. To verify the ability of the sensor array of this invention to distinguish and identify sulfides in real samples from multiple dimensions, granulated sugar was selected as a representative sample. Granulated sugar was purchased from Zhengzhou Hamike Supermarket. 2.5g of granulated sugar was weighed and dissolved in 25mL of pre-cooled ultrapure water to prepare a solution with a concentration of 0.1g / mL. The resulting solution was filtered through a 0.22μm microporous membrane and then diluted 100 times with ultrapure water to obtain a granulated sugar matrix solution with a concentration of 0.001g / mL. Na2S, Na2S2O3, Na2S2O5, and Na2S2O8 were added to the diluted granulated sugar solution to bring the final concentration to 50μmol / L, thus obtaining four test solutions.

[0149] 2. Take 20 μL of the above test solution and add it to the smartphone-assisted colorimetric / fluorescence dual-mode sensor array constructed in Example 6. Set 5 parallel samples for each test solution, and read the absorbance ratio A1 / A0, fluorescence intensity value F and brightness value L of each sample to obtain the corresponding data matrix.

[0150] Furthermore, PCA analysis was performed on the obtained data matrix; see [reference needed]. Figure 27 In the three PCA score plots, the four sulfides are distributed in different regions, exhibiting obvious inter-class separation and intra-class aggregation. Furthermore, the 95% confidence ellipses corresponding to each class are independent and do not overlap. These results demonstrate that the smartphone-assisted colorimetric / fluorescence dual-mode sensor array obtained in this invention can effectively eliminate interference from the complex matrix of actual samples, achieving accurate differentiation and identification of sulfides in multiple dimensions.

[0151] Example 10

[0152] Applications of smartphone-assisted colorimetric / fluorescence dual-mode sensor arrays

[0153] To further verify the application performance of the smartphone-assisted colorimetric / fluorescence dual-mode sensor array for monitoring sulfides in polysaccharide foods, white sugar was selected as the actual sample, with Na2S as the representative. White sugar was purchased from Zhengzhou Hamike Supermarket. 2.5g of white sugar was weighed and dissolved in 25mL of pre-cooled ultrapure water to prepare a solution with a concentration of 0.1g / mL. The resulting solution was filtered through a 0.22μm microporous membrane and then diluted 100 times with ultrapure water to obtain a white sugar matrix solution with a concentration of 0.001g / mL. Different concentrations of Na2S (0, 40, 60, and 80μmol / L) were added to the diluted white sugar solution to obtain four test solutions. 20μL of each test solution was added to the smartphone-assisted colorimetric / fluorescence dual-mode sensor array constructed in Example 6. Five parallel samples were set for each test solution. The absorbance ratio A1 / A0, fluorescence intensity value F, and brightness value L of each sample were read, and the corresponding data matrix was obtained.

[0154] The obtained data matrix was subjected to PCA analysis. Different PC1 values ​​were obtained for the test solutions with different spiking concentrations in the principal component score diagram. The PC1 values ​​obtained by the sensor array at each spiking concentration were substituted into the corresponding linear regression equations (y1, y2, y3) established in Example 8 to calculate the Na2S content in white sugar. The results are shown in Table 1.

[0155] Furthermore, corn starch was selected as a representative actual sample to verify the application performance of the smartphone-assisted colorimetric / fluorescence dual-mode sensor array of the present invention. Corn starch was purchased from Zhengzhou Hamike Supermarket. 2.5g of corn starch was weighed, 15mL of acetone solvent was added, and the mixture was ultrasonically shaken. The mixture was then centrifuged at 8000rpm for 5min to remove the precipitate. 25mL of pre-cooled ultrapure water was added to the supernatant after centrifugation, filtered through a 0.22μm microporous membrane, and diluted 100 times with ultrapure water. Different concentrations of Na2S (0, 40, 60, and 80μmol / L) were added to the diluted corn starch solution to obtain four test solutions. 20μL of each test solution was added to the smartphone-assisted colorimetric / fluorescence dual-mode sensor array constructed in Example 6. Five parallel samples were set for each test solution. The absorbance ratio A1 / A0, fluorescence intensity value F, and brightness value L of each sample were read, and the corresponding data matrix was obtained.

[0156] The obtained data matrix was subjected to PCA analysis. Different PC1 values ​​were obtained for the test solutions with different spiking concentrations in the principal component score diagram. The PC1 values ​​obtained by the sensor array at each spiking concentration were substituted into the corresponding linear regression equations (y1, y2, y3) established in Example 8 to calculate the Na2S content in corn starch. The results are shown in Table 1.

[0157] Furthermore, wheat starch was selected as a representative sample to verify the application performance of the sensor array. Corn starch was purchased from Zhengzhou Hamike Supermarket. 2.5g of wheat starch was weighed, 15mL of acetone solvent was added, and the mixture was ultrasonically shaken. The mixture was then centrifuged at 8000rpm for 5min to remove the precipitate. 25mL of pre-cooled ultrapure water was added to the supernatant after centrifugation, filtered through a 0.22μm microporous membrane, and diluted 100 times with ultrapure water. Different concentrations of Na2S (0, 40, 60, and 80μmol / L) were added to the diluted wheat starch solution to obtain four test solutions. 20μL of each test solution was added to the smartphone-assisted colorimetric / fluorescence dual-mode sensor array constructed in Example 6. Five parallel samples were set for each test solution. The absorbance ratio A1 / A0, fluorescence intensity value F, and brightness value L of each sample were read, and the corresponding data matrix was obtained.

[0158] The obtained data matrix was subjected to PCA analysis. Different PC1 values ​​were obtained for the test solutions with different spiking concentrations in the principal component score diagram. The PC1 values ​​obtained by the sensor array at each spiking concentration were substituted into the corresponding linear regression equations (y1, y2, y3) established in Example 8 to calculate the Na2S content in wheat starch. The results are shown in Table 1.

[0159] Furthermore, biscuits were selected as a representative sample to verify the application performance of the sensor array. Biscuits were purchased from Zhengzhou Hamike Supermarket. 2.5g of biscuits were weighed, ground evenly, and then stirred in 25mL of pre-cooled ultrapure water for 15min. After centrifugation at 10,000rpm for 10min, the supernatant was collected, and the centrifugation was repeated twice. Additionally, 10mL of petroleum ether was added to the supernatant, vortexed for 1min, and then centrifuged at 10,000rpm for 10min. The top organic phase was discarded, and the extraction procedure was repeated twice more. The solution was filtered through a 0.22μm microporous membrane and diluted 100-fold with ultrapure water. Different concentrations of Na₂S (0, 40, 60, and 80μmol / L) were added to the diluted biscuit matrix solution to obtain four test solutions. Take 20 μL of the test solution and add it to the smartphone-assisted colorimetric / fluorescence dual-mode sensor array constructed in Example 6. Set up 5 parallel samples for each test solution, and read the absorbance ratio A1 / A0, fluorescence intensity value F and brightness value L of each sample to obtain the corresponding data matrix.

[0160] The obtained data matrix was subjected to PCA analysis. Different PC1 values ​​were obtained for the test solutions with different spiking concentrations in the principal component score diagram. The PC1 values ​​obtained by the sensor array at each spiking concentration were substituted into the corresponding linear regression equations (y1, y2, y3) established in Example 8 to calculate the Na2S content in the biscuits. The results are shown in Table 1.

[0161] Table 1 shows the detection of Na2S in actual samples by a smartphone-assisted colorimetric / fluorescence dual-mode sensor array. The actual samples were white sugar, corn starch, wheat starch, and biscuits.

[0162] Table 1

[0163]

[0164]

[0165] The calculation process for RSD (Relative Standard Deviation) is as follows: In the actual sample testing process, each sample test is repeated 5 times in parallel, corresponding to 5 PC1 values, and the standard deviation is calculated. The formulas for calculating RSD and spiked recovery rate are as follows:

[0166] RSD = (Standard deviation / Arithmetic mean) × 100% Formula 4;

[0167] Spike recovery rate = (total amount determined / amount spiked) × 100% (Formula 5)

[0168] As shown in Table 1, this invention utilizes a smartphone-assisted colorimetric / fluorescence dual-mode sensor array to detect the sulfide content in polysaccharide foods from multiple dimensions. Different spiking amounts were set for each group and the tests were repeated. The average value was taken to obtain the final spike recovery rate and RSD. The spike recovery rate was 92.25%-107.43%, and the RSD was 1.29%-7.97%, which showed good reproducibility.

[0169] In summary, this invention provides a smartphone-assisted colorimetric / fluorescence dual-mode sensor array for the identification and monitoring of sulfides in polysaccharide foods. The sensor's response mechanism is based on the following principle: sulfides differentially inhibit the peroxidase-like activity of Fe3O4 and Fe3O4 / MoS2 nanoparticles, while utilizing the internal filtration effect (IFE) between oxidized TMB (ox-TMB) and Rhodamine B isothiocyanate. This invention uses Fe3O4 and Fe3O4 / MoS2 nanoparticles as sensing elements. Under the same reaction conditions, both catalyze the oxidation of colorless TMB to blue ox-TMB, producing different levels of light absorption intensity, showing significant differences at a wavelength of 652 nm. When sulfides are present in the system, some ox-TMB is reduced, forming a characteristic response pattern that reflects the differences in the reducing power of different sulfides. Meanwhile, the residual ox-TMB in the reaction system quantitatively quenches the fluorescence of rhodamine B isothiocyanate through an internal filtration effect. The fluorescence intensity of rhodamine B isothiocyanate is negatively correlated with the concentration of residual ox-TMB, thus enabling the identification of sulfides using the fluorescence signal. Furthermore, the smartphone-assisted colorimetric sensing platform constructed in this invention provides a more intuitive and convenient on-site detection capability for sulfides in food samples. This comprehensive detection strategy significantly improves the accuracy and reliability of the sensor array in sulfide content analysis. Experimental results show that this sensor array has been successfully applied to the identification and monitoring of sulfides in polysaccharide foods, verifying its practicality and robustness in complex sample matrices, overcoming the limitations of traditional detection methods, and demonstrating broad application prospects in on-site monitoring of sulfides and other food additives.

[0170] The above embodiments are merely preferred embodiments of the present invention and should not be construed as limiting the scope of protection of the present invention. Any non-substantial changes and substitutions made by those skilled in the art based on the present invention shall fall within the scope of protection claimed by the present invention.

Claims

1. A smartphone-assisted dual-mode sensor array, comprising a dual-mode sensor array, characterized in that: The dual-mode sensing array includes two sensing elements: Fe3O4 nanomaterials with peroxidase-like activity and Fe3O4 / MoS2 nanocomposite materials.

2. The dual-mode sensor array assisted by a smartphone according to claim 2, characterized in that: It also includes the colorimetric indicator 3,3',5,5'-tetramethylbenzidine and the fluorescent indicator Rhodamine B isothiocyanate. Fe3O4 and Fe3O4 / MoS2 catalyze the oxidation of TMB by H2O2 to generate ox-TMB; the fluorescence of Rhodamine B isothiocyanate is quantitatively quenched by ox-TMB; Fe3O4 and Fe3O4 / MoS2 achieve multi-dimensional qualitative identification and quantitative detection of sulfides by detecting the changes in absorbance at 652 nm, the changes in fluorescence intensity at 580 nm, and the brightness value L calculated from the RGB values ​​of the reaction system extracted by a smartphone.

3. A smartphone-assisted dual-mode sensor array according to any one of claims 1-2, characterized in that: The preparation method of the Fe3O4 nanomaterial includes the following steps: Step S1: Under nitrogen protection, stir and mix deionized water with hydrochloric acid-acidified FeCl3 solution for 30-40 minutes; Step S2: Add Na2SO3 solution dropwise. After the solution changes from reddish-brown to yellow, add NH3·H2O solution dropwise and stir vigorously. After a black precipitate is formed, continue stirring for 40-60 minutes. Step S3: Wash the black precipitate three times with deoxygenated water and vacuum dry to obtain Fe3O4 nanomaterials; The molar ratio of FeCl3 to NH3·H2O is 1:11.33, and the molar ratio of FeCl3 to Na2SO3 is 3:

1.

4. A smartphone-assisted dual-mode sensor array according to any one of claims 1-2, characterized in that: The preparation method of the Fe3O4 / MoS2 nanocomposite material includes the following steps: Step F1: Thiourea, ammonium molybdate tetrahydrate and Fe3O4 nanomaterial prepared according to claim 3 are ultrasonically mixed in deionized water, transferred to a Teflon high-pressure reactor, and reacted at 180-200℃ for 10-12 h to generate a black precipitate; Step F2: After the formation of black precipitate, wash three times each with deionized water and anhydrous ethanol, and then vacuum dry to obtain Fe3O4 / MoS2 nanocomposite material. The mass ratio of Fe3O4 to thiourea is 1:19, and the mass ratio of Fe3O4 to ammonium molybdate tetrahydrate is 1:8.

75.

5. A smartphone-assisted dual-mode sensor array according to any one of claims 1-2, characterized in that: The components in the detection system of the dual-mode sensor array satisfy the following proportional relationship: The mass ratio of Fe3O4 to Fe3O4 / MoS2 is 1:1; The concentration ratio of Fe3O4 or Fe3O4 / MoS2 nanomaterials to NaAc-HAc buffer solution is 1 μg / mL: 0.05 mol / L; The concentration ratio of Fe3O4 or Fe3O4 / MoS2 nanomaterials to H2O2 solution is 1 μg / mL: 0.004 mol / L; The mass ratio of Fe3O4 or Fe3O4 / MoS2 nanomaterials to TMB is 1:5; The concentration ratio of Fe3O4 or Fe3O4 / MoS2 nanomaterials to Rhodamine B isothiocyanate fluorescent dye is 4 μg / mL: 1 μg / mL; The NaAc-HAc buffer solution has a pH of 3.6, and the Rhodamine B isothiocyanate fluorescent dye is dissolved in dimethyl sulfoxide.

6. The application of a smartphone-assisted dual-mode sensor array as described in any one of claims 1-2 in sulfide detection, characterized in that, Includes the following steps: Step G1: Construction of the colorimetric sensor array; Add NaAc-HAc buffer solution, Fe3O4 or Fe3O4 / MoS2 nanomaterial solution, H2O2 solution, and TMB solution to a 96-well plate, with a total reaction volume of 200 μL. After reacting in a water bath at 37℃ for 10–15 min, read the absorbance A0 at 652 nm using a microplate reader; Add 20 μL of the sulfide solution to be tested, react for 2–5 min, and read the absorbance A1 at 652 nm. Calculate the absorbance ratio A1 / A0; Set up 5 replicates for each sample to construct a dual-channel colorimetric sensor array with two sensing elements; Step G2: Construction of the fluorescence sensor array; Rhodamine B isothiocyanate fluorescent dye solution was added to the reaction system of step G1, and the reaction was carried out at room temperature in the dark for 10-15 min. The fluorescence intensity F was measured by a multifunctional microplate fluorescence reader at an excitation wavelength of 560 nm and an emission wavelength of 580 nm to construct a dual-channel fluorescence sensor array with two sensing elements. Step G3: Construction of smartphone-assisted colorimetric sensor array; Place the 96-well plate from step G1 in an LED light box, take a picture at a fixed position to acquire image information, extract the RGB values ​​of the image to calculate the brightness value, and construct a smartphone-assisted dual-channel colorimetric sensor array; Step G4: Data analysis; Combine the data matrices of 2 types of nanomaterials × n samples × 5 parallel data obtained in steps G1 to G3 with principal component analysis for qualitative identification of sulfides; Quantitative detection of sulfides is achieved by establishing standard curves of sulfide concentration versus A1 / A0, F, and L.

7. The application according to claim 6, characterized in that: The sulfides include at least one of Na2S, Na2S2O3, Na2S2O5, and Na2S2O8; the dual-mode sensing array can distinguish the four sulfides without cross-identification in the concentration range of 10 μmol / L to 100 μmol / L.

8. The application according to claim 6, characterized in that: In step G3, the RGB values ​​are obtained by extracting them using the mobile app ColorMeter.

9. The application according to claim 6, characterized in that: The linear range for quantitative detection is 0–100 μmol / L, and the linear correlation coefficient R² ≥ 0.

989.

10. The application according to claim 6, characterized in that: This is used to detect the sulfide content in polysaccharide foods; polysaccharide foods include at least one of white sugar, corn starch, wheat starch, and biscuits.

11. The application according to claim 10, characterized in that: The pretreatment method for polysaccharide food samples is as follows: Pretreatment of white sugar: Weigh the sample, dissolve it in pre-cooled ultrapure water, filter it through a 0.22μm microporous membrane, and dilute it 100 times with ultrapure water; Corn starch / wheat starch pretreatment: Weigh the sample, add acetone and sonicate to mix, centrifuge and take the supernatant, add pre-cooled ultrapure water, filter through a 0.22μm microporous membrane, and dilute with ultrapure water 100 times; Cookie pretreatment: Weigh the uniformly ground sample, add pre-cooled ultrapure water and stir, centrifuge and collect the supernatant and repeat centrifugation; add petroleum ether to the supernatant for extraction, discard the organic phase and repeat extraction; filter with a 0.22μm microporous membrane and dilute 100 times with ultrapure water.

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