Method for detecting key taste substances of black garlic based on taste visual sensing technology

Abstract

The application discloses a black garlic key taste substance detection method based on a taste visual sensing technology and belongs to the field of food and agricultural product nondestructive detection technologies.According to the chemical structure and properties of key taste substances in the black garlic processing process, a 3*3 taste visual sensor array is designed and constructed.The sensor array is used to react with black garlic samples of different processing days, images before and after the reaction of the sensor array are acquired, taste characteristic information is extracted, and a quantitative prediction of the key taste substances in the black garlic processing process is realized in combination with a chemometrics method.The method in the application designs a rapid detection method suitable for the key taste substances in the black garlic processing process, has the advantages of low cost, simple operation and visualization, and provides a new idea for quality characterization and objective monitoring of the black garlic processing process.

Description

Technical Field

[0001] This invention relates to a method for detecting key flavor substances in black garlic based on taste visualization sensing technology, belonging to the field of non-destructive testing technology for food and agricultural products. Background Technology

[0002] Black garlic, also known as black garlic bulbs, is a new type of deep-processed garlic product made by processing fresh garlic in a high-temperature and high-humidity environment for a certain period of time. Compared with regular garlic, black garlic has a sweet and sour, soft and tender taste, and lacks the pungent and irritating garlic smell of fresh garlic, possessing unique flavor characteristics. It also has antioxidant, anti-tumor, and blood sugar and lipid-regulating functions, gradually becoming a part of people's daily lives. During the processing of black garlic, the Maillard reaction mainly occurs, a complex reaction between compounds containing carbonyl groups (reducing sugars, polysaccharides, etc.) and compounds containing free amino groups (free amino acids, lysine residues on peptides or proteins, etc.). This reaction is closely related to the degree of ripening and the overall flavor quality of black garlic, including its color, aroma, taste, and shape. Total acid, reducing sugar, and amino acid nitrogen are important quality indicators, reflecting the degree of ripening and taste quality of black garlic to a certain extent. Rapid detection of these parameters is crucial for the control of black garlic production processes and the healthy and high-quality development of related industries.

[0003] Currently, the detection of flavor compounds in black garlic mainly employs traditional physicochemical methods, such as potentiometric titration (GB12456-2021) for determining total acid content and the 3,5-dinitrosalicylic acid method for determining reducing sugar content. However, traditional physicochemical analysis methods are cumbersome, time-consuming, and inefficient, making it difficult to meet the requirements for rapid detection in food processing.

[0004] Visualization technology is a novel colorimetric sensing technique proposed in recent years. It primarily utilizes the principle of color change arising from the reaction between chemically responsive dyes and analytes to achieve the "visual" detection of target substances. Among these, taste visualization technology is a biomimetic taste sensing technology based on a liquid-sensitive sensor array. This array can fully contact the sample, thereby enabling the visual detection of the analyte. This method offers advantages such as low cost, simple operation, fast response speed, and large information capacity, and has been widely applied in the food, pharmaceutical, and environmental fields.

[0005] This invention addresses the shortcomings of existing detection methods by developing a method for detecting the content of key flavor compounds in black garlic based on taste visualization sensing technology. This method enables the detection and characterization of flavor quality during the processing of black garlic. This invention can provide a methodological reference for the quality detection of black garlic and other processed fruit and vegetable products, and is of great significance for promoting the standardization and healthy high-quality development of the black garlic industry. Summary of the Invention

[0006] The main objective of this invention is to provide a liquid sensor array for visually detecting key flavor compounds in black garlic, its preparation method, and its application. The liquid sensor provided by this invention offers a rapid and sensitive response, is easily observed with the naked eye, and, combined with chemometric methods, establishes a correlation between the sensor's response characteristic values ​​and the content of key flavor compounds in black garlic, thereby achieving quantitative detection of these compounds.

[0007] The technical solution adopted in this invention is as follows: A method for detecting the content of key flavor substances in black garlic based on taste visualization sensing technology, which is carried out according to the following steps:

[0008] Step 1: Use physicochemical analysis methods to detect key flavor substances during the processing of black garlic;

[0009] Step 2: Design and fabricate a taste visualization sensor array sensitive to key flavor compounds in black garlic.

[0010] Based on the chemical structure and properties of key flavor substances, a sensing unit was designed; a clean 96-well plate was taken, and an independent and complete sensing unit was constructed in each well according to the design of the sensing unit, and a 3×3 taste visualization sensor array was prepared.

[0011] Step 3: The prepared visualization sensor array is reacted with black garlic processed for different days, and images of the visualization sensor array before and after the reaction are collected;

[0012] Step 4: Process the images before and after the reaction, extract the color difference of the sensors before and after the reaction as the response feature value of the visualization sensor array, establish the correlation between the obtained visualization sensing data and the physicochemical analysis results, and realize the quantitative detection of key flavor substances of black garlic based on taste visualization sensing technology.

[0013] In step 1, the key flavor substances in the black garlic processing mainly include: total acid (sour taste), reducing sugar (sweet taste), and amino acid nitrogen (umami taste).

[0014] In step 1, the total acid content of the black garlic sample is determined by potentiometric titration according to GB 12456-2021. Based on the principle of acid-base neutralization, the acid in the test solution is titrated with a standard sodium hydroxide solution to neutralize the sample solution to pH 8.2, which is determined as the titration endpoint. The total acid content in the test sample is calculated based on the amount of alkali consumed. The amino acid nitrogen content of the black garlic sample is determined by pH meter method according to GB 5009.235-2016. Utilizing the amphoteric effect of amino acids, formaldehyde is added to fix the basicity of the amino group, making the carboxylic acid exhibit acidity. Titration is performed with a standard sodium hydroxide solution, and the endpoint is determined by pH meter. The amino acid nitrogen content in the sample is calculated. The reducing sugar content of the black garlic sample is determined by the 3,5-dinitrosalicylic acid (DNS) method.

[0015] In step 2, based on the key flavor substances determined in the previous stage, a 3×3 taste visualization sensor array was designed and constructed based on the pH indicator color change principle, indicator displacement principle, and silver nanoparticle aggregation color change principle. This includes three sensing units composed of bromothymol blue (BTB), bromocresol purple (BP), and chlorophenol red (CR) indicators respectively with 3-nitrophenylboronic acid (NPA); three sensing units composed of zinc acetate dihydrate, copper sulfate pentahydrate, and nickel chloride hexahydrate metal ions respectively with catechol purple (PV); and three sensing units composed of silver nanoparticle solutions synthesized with different formulations. A 4-hydroxyethylpiperazine ethanesulfonic acid (HEPES) buffer solution was used to provide the reaction environment for the first six sensing units.

[0016] In step 2, the HEPES buffer solution has a concentration of 10 mM, a pH of 6-7, and a volume of 50-100 μL; the indicator solutions are all prepared with ethanol and a concentration of 0.5-1 mM; the 3-nitrophenylboronic acid solution has a concentration of 10-20 mM and a volume of 50 μL; the catechol purple solution has a concentration of 0.6-1.6 mM and a volume of 40 μL; and the metal ion solution has a concentration of 0.3-3.2 mM and a volume of 40 μL.

[0017] In step 2, a 3×3 taste visualization sensor array is prepared in a clean 96-well plate. The specific information of each color-sensitive sensing unit is as follows: S1: 50 μL BTB (1 mM) + 50 μL NPA (20 mM) + 50 μL HEPES buffer (pH=7); S2: 50 μL BP (1 mM) + 50 μL NPA (10 mM) + 50 μL HEPES buffer (pH=7); S3: 50 μL LCR (0.5 mM) + 50 μL NPA (20 mM) + 50 μL HEPES buffer (pH=7); S4: 40 μL LPV (0.6 mM) + 40 μL Cu 2+S5: (1.2mM) + 100μL HEPES buffer (pH=6); S6: 40μL LPV (1.6mM) + 40μL Zn 2+ S6: (3.2mM) + 100μL HEPES buffer (pH=7); S7: 40μL PPV (0.6mM) + 40μL Ni 2+ (1.2mM) + 100μL HEPES buffer (pH=7); S7: 100μL AgNPs (0.3ml NaBH4); S8: 100μL AgNPs (0.5ml NaBH4); S9: 100μL AgNPs (1.0ml NaBH4). Mix thoroughly by pipetting and equilibrate for 5 min before use.

[0018] In step 2, silver nanoparticles (AgNPs) are prepared using a chemical reduction method. The specific synthesis steps are as follows: At room temperature, 45.5 mL of pure water is poured into an Erlenmeyer flask, a magnetic stir bar is added, and the flask is placed on a magnetic stirrer. The mixture is stirred at a suitable speed. Then, 0.5 mL of silver nitrate AgNO3 (10 mM), 1 mL of sodium citrate C6H5Na3O7 (75 mM), 1.5 mL of polyvinylpyrrolidone PVP (2 mM), and 0.12 mL of H2O2 (30%) are added sequentially. The mixture is stirred for about 5 minutes until homogeneous. Subsequently, 0.5 mL of freshly prepared sodium borohydride NaBH4 (100 mM) solution in an ice-water bath is added. A light yellow silver seed solution is immediately generated. After stirring for 10 minutes, the mixture is allowed to stand for 15 minutes, during which a blue silver nanoparticle solution is gradually formed.

[0019] In step 3, sample pretreatment: Weigh 3g of the uniformly ground sample, dilute it to 100ml with distilled water, extract it at 25℃ for a certain time, filter it with qualitative filter paper, discard the filter residue, and take the filtrate for testing.

[0020] In step 3, the prepared visualization sensor array is reacted with the filtrate of black garlic samples processed for different days, and the original images of the visualization sensor array before and after the reaction are obtained by a scanner.

[0021] In step 4, the response feature extraction of the sensor array can be performed according to the following specific steps: A scanner is used to acquire the original images of the sensor array before and after reacting with the black garlic sample, i.e., RGB three-channel color images; a computer is used to decompose the acquired original images into three single-channel grayscale images, corresponding to the R, G, and B channels of the original image, respectively; an image processing algorithm is used to locate the position of each color-sensitive unit in the sensor array, and the difference between the mean grayscale values ​​of each color-sensitive unit before and after the reaction is obtained. The obtained difference is the feature value of each color-sensitive unit in the sensor array. The taste visualization sensor array prepared in step 2 is a 3×3 array, consisting of 9 color-sensitive units. Each color-sensitive unit corresponds to 3 feature variables, and the feature values ​​of all color-sensitive units are combined to obtain a feature matrix composed of 27 feature variables.

[0022] In step 4, a correlation is established between the obtained visual sensor response signal and the physicochemical analysis results of key flavor substances in black garlic. The characteristic values ​​of each color-sensitive unit in the obtained taste visualization sensor array are used as model input, and the measurement results from step 1 are used as model output. A quantitative prediction model is established using both linear partial least squares regression (PLSR) and nonlinear support vector machine regression (SVR) to characterize the correlation between taste visualization sensor data and key flavor quality indicators of black garlic samples. By calling the established quantitative prediction model and inputting the visual sensor data of an unknown sample, the content of key flavor substances in the unknown sample can be output, thereby achieving quantitative detection of key flavor substances in black garlic based on taste visualization sensor technology.

[0023] The beneficial effects of this invention are as follows: This invention proposes a method for detecting the content of key flavor compounds in black garlic based on taste visualization sensing technology. This method is simple and easy to implement, and can quickly and accurately predict the content of key flavor compounds in an unknown black garlic sample. It has reference value and guiding significance for monitoring the ripening degree of black garlic and characterizing its flavor quality. This invention is a positive exploration of applying visualization sensing technology to the quality detection of processed fruit and vegetable products, and is applicable to the detection of key flavor compound content in all black garlic samples. On the one hand, it can provide a reference for black garlic production enterprises to improve their processing technology, and play a guiding role in online production control and final product quality detection of black garlic products; on the other hand, it can guide quality inspection departments in the quality inspection of black garlic products and the analysis of the quality distribution of commercially available black garlic products. Attached Figure Description

[0024] Figure 1 This is a schematic diagram of the detection process for key flavor compounds in black garlic based on taste visualization sensing technology.

[0025] Figure 2To visualize the prediction results of the PLSR model (a) and SVR model (b) of the total acid content in the black garlic processing by the sensor array.

[0026] Figure 3 To visualize the prediction results of the PLSR model (a) and SVR model (b) of the reducing sugar content in black garlic processing by the sensor array.

[0027] Figure 4 To visualize the prediction results of the PLSR model (a) and SVR model (b) of the amino acid nitrogen content in black garlic processing by the sensor array. Detailed Implementation

[0028] The specific embodiments of the present invention will be described in further detail below with reference to the accompanying drawings. The specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of the invention.

[0029] Example 1: Fabrication of a taste visualization sensor array

[0030] A 3×3 taste visualization sensor array was prepared in a clean 96-well plate. The specific information of each color-sensitive sensing unit is as follows: S1: 50 μL BTB (1 mM) + 50 μL NPA (20 mM) + 50 μL HEPES buffer (pH=7); S2: 50 μL BP (1 mM) + 50 μL NPA (10 mM) + 50 μL HEPES buffer (pH=7); S3: 50 μL LCR (0.5 mM) + 50 μL NPA (20 mM) + 50 μL HEPES buffer (pH=7); S4: 40 μL LPV (0.6 mM) + 40 μL Cu 2+ S5: (1.2mM) + 100μL HEPES buffer (pH=6); S6: 40μL LPV (1.6mM) + 40μL Zn 2+ S6: (3.2mM) + 100μL HEPES buffer (pH=7); S7: 40μL PPV (0.6mM) + 40μL Ni 2+ (1.2mM) + 100μL HEPES buffer (pH=7); S7: 100μL AgNPs (0.3ml NaBH4); S8: 100μL AgNPs (0.5ml NaBH4); S9: 100μL AgNPs (1.0ml NaBH4). Mix thoroughly by pipetting and equilibrate for 5 minutes before use.

[0031] Example 2: Taste visualization sensor array used to measure changes in total acid content during black garlic processing

[0032] (1) Samples for testing: The samples for this example were garlic samples from days 0 to 12 of the black garlic processing process, 3 batches, 5 samples per day, for a total of 195 samples. The black garlic samples were obtained by processing fresh garlic at an environment of 60℃~80℃ without any additives.

[0033] (2) The total acid content of black garlic samples was determined by potentiometric titration according to GB 12456-2021: 5g of uniformly ground black garlic sample was weighed, placed in a 100mL beaker, and 50mL of water was added and stirred thoroughly. The mixture was then transferred to a 100mL volumetric flask. The beaker was washed several times with a small amount of water, and the washes were added to the volumetric flask. Water was added to the mark, mixed well, and filtered to prepare a sample dilution. The total acid content of the black garlic samples was determined by potentiometric titration according to GB 12456-2021. Based on the principle of acid-base neutralization, the acid in the test solution was titrated with a standard sodium hydroxide solution. The sample solution was neutralized to pH 8.2, which was determined as the titration endpoint. The total acid content in the test sample was calculated based on the amount of alkali consumed.

[0034] (3) The taste characteristics of black garlic processed for different days were detected using the taste visualization sensor array prepared in Example 1. First, the taste visualization sensor array on the 96-well plate was scanned using a flatbed scanner to obtain the original image of the sensor array before the reaction. Then, an appropriate amount of black garlic sample taste extract was placed in the sensor array, diluted evenly with a pipette, and after sufficient reaction, the original image of the sensor array after the reaction was obtained using a flatbed scanner and saved to the computer. Using the image processing program developed by our research group, three single-channel grayscale images of each sensor unit before and after the reaction of the visualization sensor array were obtained. The difference between the mean grayscale values ​​of each color-sensitive sensor unit before and after the reaction was calculated to obtain the difference in mean grayscale values, i.e., ΔR = R 后 -R 前 ΔG=G 后 -G 前 ΔB=B 后 -B 前 These differences are the response characteristic values ​​of each color-sensitive sensing unit. Each color-sensitive unit corresponds to 3 characteristic variables, and the combination of the characteristic values ​​of all color-sensitive units yields a characteristic matrix consisting of 27 characteristic variables.

[0035] (4) Establish the correlation between the response information of the taste visualization sensor and the results of the national standard method for determining total acid content. The samples were divided into training set and prediction set at a ratio of 2:1. The correlation between the characteristic variables of the taste visualization sensor and the total acid content was established using two modeling methods: PLSR and SVR. Figure 2(a) and (b) show the prediction results of the PLSR and SVR models for the total acid content of black garlic, respectively. The correlation coefficients of both models on the test set exceeded 0.95, indicating that both the PLSR and SVR models can quantitatively predict the total acid content during black garlic processing. Comparison shows that the SVR model has a higher correlation coefficient and a lower root mean square error. The correlation coefficient R between the training set output value and the actual national standard method measured value is higher. C The correlation coefficient R between the predicted values ​​and the actual titration values ​​in the test set is 0.9696, and the RMSECV is 0.0105. P The value is 0.9666, and the RMSEP is 0.0114.

[0036] Example 3: Taste visualization sensor array used to measure changes in reducing sugar content during black garlic processing

[0037] (1) Samples for testing: The samples for this example were garlic samples from days 0 to 12 of the black garlic processing process, 3 batches, 5 samples per day, for a total of 195 samples. The black garlic samples were obtained by processing fresh garlic at an environment of 60℃~80℃ without any additives.

[0038] (2) Weigh 5g of evenly ground black garlic sample, place it in a 100mL beaker, add 50mL of water and stir thoroughly. Transfer the mixture to a 100mL volumetric flask, wash the beaker several times with a small amount of water, add the washes back to the volumetric flask, add water to the mark, mix well, and filter to prepare the sample dilution. The reducing sugar content of the black garlic sample was determined using the 3,5-dinitrosalicylic acid (DNS) method.

[0039] (3) The taste characteristics of black garlic processed for different days were detected using the taste visualization sensor array prepared in Example 1. First, the taste visualization sensor array on the 96-well plate was scanned using a flatbed scanner to obtain the original image of the sensor array before the reaction. Then, an appropriate amount of black garlic sample taste extract was placed in the sensor array, diluted evenly with a pipette, and after sufficient reaction, the original image of the sensor array after the reaction was obtained using a flatbed scanner and saved to the computer. Using the image processing program developed by our research group, three single-channel grayscale images of each sensor unit before and after the reaction of the visualization sensor array were obtained. The difference between the mean grayscale values ​​of each color-sensitive sensor unit before and after the reaction was calculated to obtain the difference in mean grayscale values, i.e., ΔR = R 后 -R 前 ΔG=G 后 -G 前 ΔB=B 后 -B 前These differences are the response characteristic values ​​of each color-sensitive sensing unit. Each color-sensitive unit corresponds to 3 characteristic variables, and the combination of the characteristic values ​​of all color-sensitive units yields a characteristic matrix consisting of 27 characteristic variables.

[0040] (4) Establish the correlation between the response information of the taste visualization sensor and the results of the DNS method for measuring reducing sugar content. The samples were divided into training set and prediction set at a ratio of 2:1. The correlation between the characteristic variables of the taste visualization sensor and the reducing sugar content was established using two modeling methods: PLSR and SVR. Figure 3 (a) and (b) show the prediction results of the PLSR and SVR models for the reducing sugar content of black garlic, respectively. In the PLS model, the correlation coefficient R between the training set output value and the actual titration determination value is... C The correlation coefficient R between the predicted values ​​of the test set and the values ​​determined by the DNS method is 0.9848, and the RMSECV is 1.8400; P The correlation coefficient was 0.9810, and the RMSEP was 2.2400. The SVR model has a high correlation coefficient and a low root mean square error. The correlation coefficient R between the training set output values ​​and the actual national standard method measured values ​​is 0.9810. C The correlation coefficient R between the predicted values ​​of the test set and the values ​​determined by the DNS method is 0.9904, and the RMSECV is 0.0075; P The correlation coefficient was 0.9863, and the RMSEP was 0.0095. Both the PLSR model and the SVR model can monitor the reducing sugar content during black garlic processing. The SVR model has a higher correlation coefficient and a lower root mean square error, making it the superior model.

[0041] Example 4: Taste visualization sensor array used to measure changes in amino acid nitrogen content during black garlic processing

[0042] (1) Samples for testing: The samples for this example were garlic samples from days 0 to 12 of the black garlic processing process, 3 batches, 5 samples per day, for a total of 195 samples. The black garlic samples were obtained by processing fresh garlic at an environment of 60℃~80℃ without any additives.

[0043] (2) Weigh 5g of evenly ground black garlic sample, place it in a 100mL beaker, add 50mL of water and stir thoroughly. Transfer the mixture to a 100mL volumetric flask, wash the beaker several times with a small amount of water, add the washes back to the volumetric flask, add water to the mark, mix well, and filter to prepare a sample dilution. The amino acid nitrogen content of the black garlic sample was determined using the pH meter method according to GB 5009.235-2016. Utilizing the amphoteric effect of amino acids, formaldehyde was added to fix the basicity of the amino group, making the carboxylic acid acidic. Titration was performed with sodium hydroxide standard solution, and the endpoint was determined using a pH meter. The amino acid nitrogen content in the sample was then calculated.

[0044] (3) The taste characteristics of black garlic processed for different days were detected using the taste visualization sensor array prepared in Example 1. First, the taste visualization sensor array on the 96-well plate was scanned using a flatbed scanner to obtain the original image of the sensor array before the reaction. Then, an appropriate amount of black garlic sample taste extract was placed in the sensor array, diluted evenly with a pipette, and after sufficient reaction, the original image of the sensor array after the reaction was obtained using a flatbed scanner and saved to the computer. Using the image processing program developed by our research group, three single-channel grayscale images of each sensor unit before and after the reaction of the visualization sensor array were obtained. The difference between the mean grayscale values ​​of each color-sensitive sensor unit before and after the reaction was calculated to obtain the difference in mean grayscale values, i.e., ΔR = R 后 -R 前 ΔG=G 后 -G 前 ΔB=B 后 -B 前 These differences are the response characteristic values ​​of each color-sensitive sensing unit. Each color-sensitive unit corresponds to 3 characteristic variables, and the combination of the characteristic values ​​of all color-sensitive units yields a characteristic matrix consisting of 27 characteristic variables.

[0045] (4) Establish the correlation between the response information of the taste visualization sensor and the results of the national standard method for determining amino acid nitrogen content. The samples were divided into training set and prediction set at a ratio of 2:1. The correlation between the characteristic variables of the taste visualization sensor and the amino acid nitrogen content was established using two modeling methods, PLSR and SVR. Figure 4 (a) and (b) show the prediction results of the PLSR and SVR models for amino acid nitrogen content in black garlic, respectively. The correlation coefficients of both models on the test set exceeded 0.84, indicating that both the PLSR and SVR models can quantitatively predict amino acid nitrogen content during black garlic processing. Comparison shows that the SVR model has a higher correlation coefficient and a lower root mean square error. The correlation coefficient R between the training set output values ​​and the actual values ​​measured using the national standard method is higher. C The correlation coefficient R between the predicted values ​​and the actual titration values ​​in the test set was 0.9779, and the RMSECV was 0.0067. P The value is 0.9232, and the RMSEP is 0.0245.

[0046] The above embodiments are merely examples to illustrate the detection process of the present invention and are not intended to limit the implementation. Those skilled in the art will recognize that other variations or modifications can be made based on the above description. It is neither necessary nor possible to exhaustively list all possible implementations here. However, obvious variations or modifications derived therefrom are still within the scope of protection of this invention.

Claims

1. A method for detecting the content of key flavor substances in black garlic based on taste visualization sensing technology, characterized in that Follow these steps: Step 1: Use physicochemical analysis methods to detect key flavor substances during the processing of black garlic; Step 2: Design and fabrication of a taste visualization sensor array sensitive to key flavor compounds in black garlic Based on the chemical structure and properties of key flavor substances, a sensing unit was designed; a clean 96-well plate was taken, and an independent and complete sensing unit was constructed in each well according to the design of the sensing unit, and a 3×3 taste visualization sensor array was prepared. Step 3: The prepared visualization sensor array is reacted with black garlic processed for different days, and images of the visualization sensor array before and after the reaction are collected; Step 4: Process the images before and after the reaction, extract the color difference of the sensors before and after the reaction as the response feature value of the visualization sensor array, establish the correlation between the obtained visualization sensing data and the physicochemical analysis results, and realize the quantitative detection of key flavor substances of black garlic based on taste visualization sensing technology. In step 1, the key flavor substances in the black garlic processing include: total acid - sour taste, reducing sugar - sweet taste, and amino acid nitrogen - umami taste; In step 2, based on the key taste substances determined in the previous stage, a 3×3 taste visualization sensor array was designed and constructed based on the pH indicator color change principle, indicator displacement principle, and silver nanoparticle aggregation color change principle. This array includes three sensing units composed of bromothymol blue (BTB), bromocresol purple (BP), and chlorophenol red (CR) indicators respectively with 3-nitrophenylboronic acid (NPA); three sensing units composed of zinc acetate dihydrate, copper sulfate pentahydrate, and nickel chloride hexahydrate metal ions respectively with catechol purple (PV); and three sensing units composed of silver nanoparticle solutions synthesized with different formulations. A 4-hydroxyethylpiperazine ethanesulfonic acid (HEPES) buffer solution was used to provide the reaction environment for the first six sensing units. In step 2, a 3×3 taste visualization sensor array is prepared in a clean 96-well plate. The specific information of each color-sensitive sensing unit is as follows: S1: 50 μL 1 mM BTB + 50 μL 20 mM NPA + 50 μL HEPES buffer at pH 7; S2: 50 μL 1 mM BP + 50 μL 10 mM NPA + 50 μL HEPES buffer at pH 7; S3: 50 μL 0.5 mM CR + 50 μL 20 mM NPA + 50 μL HEPES buffer at pH 7. S4: 40 μL 0.6 mM PV + 40 μL 1.2 mM Cu 2+ + 100 μL HEPES buffer at pH 6; S5: 40 μL 1.6 mM PV + 40 μL 3.2 mM Zn 2+ + 100 μL HEPES buffer at pH 7; S6: 40 μL 0.6 mM PV + 40 μL 1.2 mM Ni 2+ + 100 μL HEPES buffer at pH 7; S7: 100 μL AgNPs prepared with 0.3 ml NaBH4; S8: 100 μL AgNPs prepared with 0.5 ml NaBH4; S9: 100 μL AgNPs prepared with 1.0 ml NaBH4; homogenized by pipetting, equilibrated for 5 min and ready for use; In step 2, the AgNPs solution is prepared by chemical reduction. The specific synthesis steps are as follows: At room temperature, 45.5 mL of pure water is poured into an Erlenmeyer flask, a magnetic stir bar is added, and the flask is placed on a magnetic stirrer and stirred at a suitable speed. Then, 0.5 mL of 10 mM silver nitrate AgNO3, 1 mL of 75 mM sodium citrate C6H5Na3O7, 1.5 mL of 2 mM polyvinylpyrrolidone PVP, and 0.12 mL of 30% H2O2 are added sequentially and stirred for about 5 minutes until they are mixed evenly. Then, 0.5 mL of freshly prepared 100 mM sodium borohydride NaBH4 solution in an ice-water bath is added, and a light yellow silver seed solution is immediately generated. After stirring for 10 min, the solution is allowed to stand for 15 min, and a blue silver nanoparticle solution is gradually generated. In step 4, the response feature extraction of the sensor array is performed according to the following specific steps: The original images of the sensor array before and after reacting with the black garlic sample are acquired using a scanner, i.e., RGB three-channel color images; the acquired original images are decomposed into three single-channel grayscale images using a computer, corresponding to the R channel, G channel, and B channel of the original image, respectively; the position of each color-sensitive unit in the sensor array is located using an image processing algorithm; the difference between the mean grayscale values ​​of each color-sensitive unit before and after the reaction is calculated, and the difference is the feature value of each color-sensitive unit in the sensor array; the taste visualization sensor array prepared in step 2 is a 3×3 array, i.e., composed of 9 color-sensitive units, each color-sensitive unit corresponding to 3 feature variables; the feature values ​​of all color-sensitive units are combined to obtain a feature matrix composed of 27 feature variables. In step 4, the obtained visual sensor response signal is correlated with the physicochemical analysis results of key flavor substances in black garlic; the characteristic values ​​of each color-sensitive unit in the obtained taste visualization sensor array are used as model input, and the measurement results in step 1 are used as model output. Two modeling methods, linear partial least squares regression (PLSR) and nonlinear support vector machine regression (SVR), are used to establish a quantitative prediction model to characterize the correlation between taste visualization sensor data information and key flavor quality indicators of black garlic samples. By calling the established quantitative prediction model and inputting the visualized sensor data of the unknown sample, the content of key flavor substances in the unknown sample can be output, thereby realizing the quantitative detection of key flavor substances in black garlic based on taste visualization sensing technology.

2. The black garlic key taste substance content detection method based on taste visualization sensing technology according to claim 1, characterized in that In step 1, physicochemical analysis methods are used to detect key flavor substances during the processing of black garlic. The total acid content of the black garlic sample is determined by potentiometric titration according to GB 12456-2021. Based on the principle of acid-base neutralization, the acid in the test solution is titrated with a standard sodium hydroxide solution to neutralize the sample solution to pH 8.2, which is determined as the titration endpoint. The total acid content in the test sample is calculated according to the amount of alkali consumed. The amino acid nitrogen content of the black garlic sample is determined by pH meter method according to GB 5009.235-2016. Utilizing the amphoteric effect of amino acids, formaldehyde is added to fix the basicity of the amino group, making the carboxylic acid acidic. The solution is titrated with a standard sodium hydroxide solution, and the endpoint is determined by pH meter. The amino acid nitrogen content in the sample is calculated. The reducing sugar content of the black garlic sample is determined by 3,5-dinitrosalicylic acid method.

3. The black garlic key taste substance content detection method based on taste visualization sensing technology according to claim 1, characterized in that In step 3, 3g of uniformly ground sample is weighed, diluted to 100ml with distilled water, extracted at 25℃ for a certain time, filtered with qualitative filter paper, the filter residue is discarded, and the filtrate is collected; the prepared visualization sensor array is used to react with the filtrate of black garlic samples with different processing days, and the original images of the visualization sensor array before and after the reaction are obtained by a scanner.

Images