A method for detecting ester content in soy sauce

Abstract

The invention provides a method for detecting the content of ester substances in soy sauce. The method comprises the following steps: using esters to react with hydroxylamine to produce hydroxamic acid under alkaline conditions, adopting the color change characteristics of an acid-base indicator, extracting the color change values ​​of RGB before and after the color change, and constructing a rapid detection model for the content of ester substances in the soy sauce detected by combining a chemometric method with a traditional GC-MS. The model established by the detection method can replace the traditional GC-MS method for detecting the content of ester substances in the soy sauce, and has the advantages of fast detection speed, accuracy, good repeatability, low cost, and the like.

Description

Technical Field

[0001] The invention belongs to the technical field of food detection, and particularly relates to a method for detecting the content of ester substances in soy sauce. Background Art

[0002] Soy sauce is an important traditional fermented condiment in my country, which is used in huge quantities in the market. Its brewing raw materials are mainly soybeans and wheat. The quality of soy sauce flavor is closely related to its quality. "Color, aroma, taste, and body" are the embodiment of the flavor characteristics of soy sauce. Among them, aroma is an important factor in determining the flavor of soy sauce, and rich ester substances can give soy sauce a good aroma. At present, there are two main ways to judge the total content of ester substances in soy sauce: one is to subjectively evaluate the ester aroma characteristics of soy sauce through sensory evaluation by evaluators. This method is fast to judge, but not objective and accurate enough; the other is to analyze the types and overall content of ester substances in soy sauce through GC-MS analysis. This method is accurate in detection, but the investment cost is high, time-consuming, and inefficient. For example, the invention patent with patent number ZL2016111450129 discloses "a method for improving the quality of soy sauce ester aroma and the products obtained therefrom", which uses anhydrous ether to extract esters in soy sauce, and then uses gas chromatography-mass spectrometry to detect the content of ethyl acetate and ethyl propionate. The processing steps are cumbersome and time-consuming. There is also a liquid phase method for detection. For example, the invention patent with the patent number ZL2016112537952, "A method for detecting the content of aromatic esters in liquor", uses high performance liquid chromatography to calculate the content of each aromatic ester through the peak area standard curve. Although the analysis results of this detection method are accurate, the cost is very high.

[0003] Therefore, it is urgent to innovate the detection method of soy sauce esters to improve detection efficiency and reduce detection costs. Summary of the invention

[0004] In order to overcome the defects of the prior art, the present invention is achieved through the following technical solutions:

[0005] The present invention provides a method for detecting the content of ester substances in soy sauce, comprising the following steps:

[0006] S1 uses headspace solid phase microextraction (HS-SPME)-gas chromatography (GC)-mass spectrometry (MS) to determine the volatile components in the sample:

[0007] S11: Treat the sample to be tested: add 5-10 ml of the crude soy sauce sample, 1.5-3.5 g of NaCl and a magnetic stirring rotor into a suitable headspace bottle, and add 5-10 mg / L of 1,4-methyl-2-pentanol as an internal standard to obtain a sample solution to be tested;

[0008] S12 SPME extraction-GC detection: insert the SPME extraction head into the head space of the headspace bottle and keep it for 30-60 minutes, then inject the SPME fiber into the GC inlet, use the crack-free mode, and desorb at 200-280℃ for 3-8 minutes;

[0009] Gas chromatography-mass spectrometry conditions:

[0010] Chromatographic column: capillary column (length 60m×diameter 0.25mm×inner diameter 0.25μm);

[0011] Carrier gas: helium, flow rate 1.0 mL / min;

[0012] Column temperature: a. The front injection temperature was set at 210-260°C, and the temperature program was as follows: initial temperature 35°C, increased to 90-120°C at a gradient of 3-5°C / min, maintained for 10-20min, then increased to 180-205°C at a gradient of 3-5°C / min, and finally increased to 210-260°C at a gradient of 10-12°C / min, maintained for 10-20min; b. The interface temperature was set at 210-260°C; the ion source temperature was set at 210-260°C;

[0013] Ionization mode: electron impact (EI source),

[0014] Mass spectrometry scanning range: 33-400 amu, 70 eV;

[0015] Retention index (RI): C7-C30 normal alkanes were used as standard for determination, and qualitative analysis was performed by comparison with the National Institute of Standards and Technology library. The concentration of volatile compounds was quantitatively calculated using the amount of internal standard added to obtain the total content of soy sauce esters;

[0016] Preparation of S2 visual sensor array and its color value data processing:

[0017] S21 Preparation of hydroxylamine solution: Dissolve an appropriate amount of hydroxylamine in 3 mg / mL NaOH solution to prepare a 0.5-2 g / mL solution and store at 0-4°C for later use;

[0018] S22 Preparation of indicator: S211 Preparation of bromocresol purple indicator: Take 1-3 mL, 0.5-5 mg / mL bromocresol purple ethanol solution, add 100-300 μL of hydroxylamine solution to form bromocresol purple indicator; S212 Preparation of neutral red indicator: Take 1-2 mL, 0.5-4 mg / mL neutral red ethanol solution and add 200-300 μL of hydroxylamine solution to form neutral red indicator;

[0019] S23 spot plate: Use a glass capillary to draw 5 μL of bromocresol purple and 5 μL of neutral red indicator onto the C2 reverse silica gel plate of the visualization array sensor to form a 1*2 visualization array;

[0020] S24 front image acquisition: The initial image of the reverse silicone plate of C2 of the visualization array sensor is scanned with a flatbed scanner and named “front image”;

[0021] Reaction of S25 esters: Take 5-15 mL of soy sauce stock solution in a beaker, fix the C2 reverse silica gel plate of the visualized array sensor after spotting on the top of the beaker, and react with the volatile gas in the sample solution at room temperature for 20-60 minutes;

[0022] S26 post-image acquisition: Scan the visualized array sensor silicone plate image after the reaction with a flatbed scanner, named “post-image”;

[0023] S27 color value processing: using colorAnalysis software, extract the color value on the reverse C2 silicone plate of the visualization array sensor, and decompose the three-channel color image into three single-channel grayscale images, respectively, obtain the red, green, and blue color intensity values ​​(i.e., RGB values) of the front image and the rear image, respectively, and use the image processing algorithm to locate the position of the sensitive unit, subtract the RGB value of the front image from the RGB value of the rear image, and the obtained chromaticity difference is the characteristic value of the sensitive unit. For each sample, 6 color variables are obtained from 2 color perception points, and the chromaticity difference calculation formula is as follows: ΔR=Ra-RbΔG=Ga-GbΔB=Ba-Bb, wherein Ra, Ga, and Ba are the grayscale values ​​of red, green, and blue after adding soy sauce crude oil, respectively; Rb, Gb, and Bb are the initial grayscale values ​​of red, green, and blue, respectively; in the 1×2 visualization array described in step S23, each sensitive unit obtains 3 characteristic variables, and the characteristic values ​​of all sensitive units are combined to obtain a characteristic matrix X with 6 variables. X is an m×n matrix, wherein m represents the number of samples and n represents the number of characteristic variables;

[0024]

[0025] S3 builds the model: the eigenvalue combination of all sensitive units in step S27 is used as the independent variable matrix X, and the headspace solid phase microextraction (HS-SPME)-gas chromatography (GC)-mass spectrometry (MS) measurement results in step S12 are used as the dependent variable matrix Y. Randomly select 2 / 3 of the matrix X as a training set and 1 / 3 as a prediction set in a 2:1 ratio to build a prediction model, use Matlab software, and use linear partial least squares (PLS) to establish the correlation between the visualized RGB color value and the ester content in soy sauce, that is, establish the correlation between the independent variable matrix X and the dependent variable matrix Y, and obtain the PLS model;

[0026] S4 unknown soy sauce sample detection: take the unknown soy sauce sample solution, measure its visualized RGB value, call the model built in step S3, input the visualized RGB value of the unknown soy sauce sample into the model, and then the total ester content in the unknown soy sauce sample solution can be output, thereby realizing the detection of the ester content in the soy sauce. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 The visualized sensor array prepared according to Example 1

[0028] Figure 2 The prediction results of the PLS model training set and test set for the ester content in Example 1

[0029] Figure 3 The prediction results of the PLS model training set and test set for the ester content in Example 2

[0030] Figure 4 The prediction results of the PLS model training set and test set for the ester content in Example 3

[0031] Beneficial Effects

[0032] The invention provides a method for detecting the content of ester substances in soy sauce. The method utilizes that esters can react with hydroxylamine to produce hydroxamic acid under alkaline conditions, and then adopts the color change characteristics of an acid-base indicator to extract the color difference of RGB before and after the color change and the content of ester substances to build a detection model. Only GC-MS modeling is used during modeling. After the model is established, only RGB on a visualization array plate needs to be used to predict the content during detection. Compared with a traditional GC-MS measurement method, detection is faster, more accurate and has lower cost. DETAILED DESCRIPTION

[0033] The applicant will describe the implementation and application of the present invention in detail below in conjunction with specific embodiments, so that those skilled in the art can clearly understand the present invention. However, it should be understood that the following embodiments should not be interpreted in any way as limiting the scope of protection of the claims of this application.

[0034] Example 1

[0035] A method for detecting the content of ester substances in soy sauce comprises the following steps:

[0036] S1 uses headspace solid phase microextraction (HS-SPME)-gas chromatography (GC)-mass spectrometry (MS) to determine the volatile components in the sample:

[0037] S11: Prepare the samples to be tested: Take 6 types of soy sauce crude oil from Jiangsu Hengshun Vinegar Co., Ltd., 3 batches of each type, 5 parallel samples of each batch, a total of 90 samples; add 8 ml of each batch of soy sauce crude oil sample, 2.5 g of NaCl and a magnetic stirring rotor into a 15 ml headspace bottle, and add 5 mg / L of 1,4-methyl-2-pentanol as an internal standard;

[0038] S12 SPME extraction-GC detection: The SPME extraction head (75 μm, Supelco, Bellefonte, USA) was inserted into the head space of the headspace bottle and kept for 40 min. Then the SPME fiber was injected into the gas inlet of GC (HP6890-5973, Agilent, USA) and desorbed at 250 °C for 5 min in the crackless mode.

[0039] Gas chromatography-mass spectrometry conditions:

[0040] Chromatographic column: DB-WAX capillary column (specifications: length 60m×diameter 0.25mm×inner diameter 0.25μm);

[0041] Carrier gas: helium, flow rate 1.0 mL / min;

[0042] Column temperature: a. The front injection temperature was set at 250°C, and the temperature program was as follows: the initial temperature was 35°C, and the gradient was increased to 100°C at 5°C / min, and maintained for 10 min, then increased to 200°C at 3°C / min, and finally increased to 220°C at 10°C / min, and maintained for 10 min.

[0043] b. The interface temperature is set to 250°C; the ion source temperature is set to 230°C;

[0044] Ionization mode: electron impact (EI source),

[0045] Mass spectrometry scanning range: 33-400 amu, 70 eV;

[0046] Retention index (RI): C7-C30 normal alkanes were used as standard for determination, and qualitative analysis was performed by comparison with the National Institute of Standards and Technology library. The concentration of volatile compounds was quantitatively calculated using the amount of internal standard added to obtain the total content of esters in the crude soy sauce oil;

[0047] Preparation of S2 visual sensor array and its color value data processing:

[0048] S21 Preparation of hydroxylamine solution: Dissolve 0.1 g of hydroxylamine in 100 mL of 3 mg / mL NaOH solution to make a 1 mg / mL solution, and store at 4°C for later use;

[0049] S22 Preparation of indicator: S211 Preparation of bromocresol purple indicator: Take 2 mL of 0.5 mg / mL bromocresol purple ethanol solution, add 100 μL of hydroxylamine solution to form bromocresol purple indicator; S212 Preparation of neutral red indicator: Take 1 mL of 0.5 mg / mL neutral red ethanol solution and add 200 μL of hydroxylamine solution to form neutral red indicator;

[0050] S23 spot plate: Use glass capillaries to draw 5 μL of bromocresol purple and neutral red indicator onto the C2 reverse silica gel plate of the visualization array sensor to form a 1*2 visualization array;

[0051] S24 front image acquisition: The initial image of the reverse silicone plate of C2 of the visualization array sensor is scanned with a flatbed scanner and named “front image”;

[0052] Reaction of S25 esters: Take 10 mL of soy sauce crude oil in a 100 mL beaker, fix the C2 reverse silica gel plate of the visualized array sensor after spotting on the top of the beaker, and react with the volatile gas in the sample solution at room temperature for 30 minutes. Figure 1 As shown (sensing unit 1 changes from brown to yellow before treatment, and sensing unit 2 changes from red to reddish purple before treatment.);

[0053] S26 post-image processing: Scan the visualized array sensor silicone plate image after the reaction with a flatbed scanner, named "post-image";

[0054] S27 color value processing: use colorAnalysis software to extract the color value on the reverse C2 silicone plate of the visualization array sensor, and decompose the three-channel color image into three single-channel grayscale images, respectively, obtain the red, green, and blue color intensity values ​​(i.e., RGB values) of the front image and the rear image, respectively, use the image processing algorithm to locate the position of the sensitive unit, subtract the RGB value of the front image from the RGB value of the rear image, and the obtained chromaticity difference is the eigenvalue of the sensitive unit. For each sample, 6 color variables are obtained from 2 color perception points, and the chromaticity difference calculation formula is as follows: ΔR=Ra-RbΔG=Ga-GbΔB=Ba-Bb, wherein Ra, Ga, and Ba are the grayscale values ​​of red, green, and blue after adding soy sauce crude oil, respectively; Rb, Gb, and Bb are the initial grayscale values ​​of red, green, and blue, respectively; in the 1×2 visualization array described in step S23, each sensitive unit obtains 3 characteristic variables, and the characteristic values ​​of all sensitive units are combined to obtain a characteristic matrix X with 6 variables. X is an m×n matrix, where m represents the number of samples and n represents the number of characteristic variables. In this embodiment, m=90 and n=6.

[0055]

[0056] S3 builds the model: the eigenvalue combination of all sensitive units in step S27 is used as the independent variable matrix X, and the headspace solid phase microextraction (HS-SPME)-gas chromatography (GC)-mass spectrometry (MS) measurement results in step S12 are used as the dependent variable matrix Y. 2 / 3 of the matrix X is randomly selected as a training set and 1 / 3 as a prediction set in a ratio of 2:1 to build a prediction model, and Matlab software is used to use the linear partial least squares (PLS) method to establish the correlation between the visualized RGB color value and the ester content in the soy sauce, that is, the correlation between the independent variable matrix X and the dependent variable matrix Y is established, and the PLS model is obtained. Figure 2 The prediction results of the PLS training set and test set for the ester content of the soy sauce crude oil sample. The correlation coefficient of the PLS model test set reached above 0.85, and the correlation coefficient R between the output value of the training set and the actual measured value of the GC-MS method was C is 0.9341, RMSECV is 0.1626; the correlation coefficient R P The PLS model is 0.9215 and the RMSEP is 0.1874, indicating that the PLS model can achieve quantitative prediction of the content of ester substances in soy sauce;

[0057] S4 sample testing: Hengshun delicious fresh soy sauce (special grade) was taken, and the ester content was detected using the visualization detection method and headspace solid phase microextraction (HS-SPME)-gas chromatography (GC)-mass spectrometry (MS) detection method. The error between the predicted value of the visualization method and the actual value of the headspace solid phase microextraction (HS-SPME)-gas chromatography (GC)-mass spectrometry (MS) detection was 6.3%, and the prediction accuracy was relatively high.

[0058] Example 2

[0059] A method for detecting the content of ester substances in soy sauce comprises the following steps:

[0060] S1 uses headspace solid phase microextraction (HS-SPME)-gas chromatography (GC)-mass spectrometry (MS) to determine the volatile components in the sample:

[0061] S11: Prepare the samples to be tested: Take 6 types of soy sauce crude oil from Jiangsu Hengshun Vinegar Co., Ltd., 3 batches for each type, 5 parallel samples for each batch, a total of 90 samples; add 8 ml of soy sauce crude oil sample, 2.5 g of NaCl and a magnetic stirring rotor into a 15 ml headspace bottle, and add 8 mg / L of 1,4-methyl-2-pentanol as an internal standard;

[0062] S12 SPME extraction-GC detection: The SPME extraction head (75 μm, Supelco, Bellefonte, USA) was inserted into the head space of the headspace bottle and kept for 40 min. Then the SPME fiber was injected into the gas inlet of GC (HP6890-5973, Agilent, USA) and desorbed at 250 °C for 5 min in the crackless mode.

[0063] Gas chromatography-mass spectrometry conditions:

[0064] Internal standard solution: 5-10 mg / L 1,4-methyl-2-pentanol;

[0065] Chromatographic column: DB-WAX capillary column (specifications: length 60m×diameter 0.25mm×inner diameter 0.25μm);

[0066] Carrier gas: helium, flow rate 1.0 mL / min;

[0067] Column temperature: a. The front injection temperature was set at 250°C, and the temperature program was as follows: the initial temperature was 35°C, and the gradient was increased to 100°C at 5°C / min, and maintained for 10 min, then increased to 200°C at 3°C / min, and finally increased to 220°C at 10°C / min, and maintained for 10 min.

[0068] b. The interface temperature is set to 250°C; the ion source temperature is set to 230°C;

[0069] Ionization mode: electron impact (EI source),

[0070] Mass spectrometry scanning range: 33-400 amu, 70 eV;

[0071] Retention index (RI): C7-C30 normal alkanes were used as standard for determination, and qualitative analysis was performed by comparison with the National Institute of Standards and Technology Library (NIST, 2014; USA). The concentration of volatile compounds was quantitatively calculated using the amount of internal standard added to obtain the total content of soy sauce esters;

[0072] Preparation of S2 visual sensor array and its color value data processing:

[0073] S21 Preparation of hydroxylamine solution: Dissolve 0.3 g of hydroxylamine in 150 mL of 7 mg / mL NaOH solution to make a 1 mg / mL solution and store at 4°C for later use;

[0074] S22 Preparation of indicator: S211 Preparation of bromocresol purple indicator: Take 2 mL, 2 mg / mL bromocresol purple ethanol solution, add 200 μL of hydroxylamine solution to form bromocresol purple indicator; S212 Preparation of neutral red indicator: Take 1.5 mL, 2 mg / mL neutral red ethanol solution and add 250 μL of hydroxylamine solution to form neutral red indicator;

[0075] S23 spot plate: Use glass capillaries to draw 5 μL of bromocresol purple and 5 μL of neutral red indicator onto the C2 reverse silica gel plate of the visualization array sensor to form a 1*2 visualization array;

[0076] S24 front image acquisition: The initial image of the reverse silicone plate of C2 of the visualization array sensor is scanned with a flatbed scanner and named “front image”;

[0077] Reaction of S25 esters: 10 mL of crude soy sauce oil was placed in a 100 mL beaker, and the C2 reverse silica gel plate of the visualized array sensor after spotting was fixed on the top of the beaker, and reacted with the volatile gas in the sample solution at room temperature for 30 min;

[0078] S26 post-image acquisition: Scan the visualized array sensor silicone plate image after the reaction with a flatbed scanner, named “post-image”;

[0079] S27 color value processing: use colorAnalysis software to extract the color value on the reverse C2 silicone plate of the visualization array sensor, and decompose the three-channel color image into three single-channel grayscale images, respectively, obtain the red, green, and blue color intensity values ​​(i.e., RGB values) of the front image and the rear image, respectively, use the image processing algorithm to locate the position of the sensitive unit, subtract the RGB value of the front image from the RGB value of the rear image, and the obtained chromaticity difference is the eigenvalue of the sensitive unit. For each sample, 6 color variables are obtained from 2 color perception points, and the chromaticity difference calculation formula is as follows: ΔR=Ra-RbΔG=Ga-GbΔB=Ba-Bb, wherein Ra, Ga, and Ba are the grayscale values ​​of red, green, and blue after adding soy sauce crude oil, respectively; Rb, Gb, and Bb are the initial grayscale values ​​of red, green, and blue, respectively; in the 1×2 visualization array described in step S23, each sensitive unit obtains 3 characteristic variables, and the characteristic values ​​of all sensitive units are combined to obtain a characteristic matrix X with 6 variables. X is an m×n matrix, where m represents the number of samples and n represents the number of characteristic variables. In this embodiment, m=90 and n=6.

[0080]

[0081] S3 builds the model: the eigenvalue combination of all sensitive units in step S27 is used as the independent variable matrix X, and the headspace solid phase microextraction (HS-SPME)-gas chromatography (GC)-mass spectrometry (MS) measurement results in step S12 are used as the dependent variable matrix Y. 2 / 3 of the matrix X is randomly selected as a training set and 1 / 3 as a prediction set in a ratio of 2:1 to build a prediction model, and Matlab software is used to use the linear partial least squares (PLS) method to establish the correlation between the visualized RGB color value and the ester content in the soy sauce, that is, the correlation between the independent variable matrix X and the dependent variable matrix Y is established, and the PLS model is obtained. Figure 3 The prediction results of the PLS training set and test set for the ester content in the soy sauce crude oil are shown in Figure 2. The correlation coefficients of the model test set are all above 0.85, and the correlation coefficients of the training set output value and the actual measured value are R C is 0.9525, RMSECV is 0.1661; the correlation coefficient R P The PLS model is able to quantitatively predict the content of esters in soy sauce.

[0082] S4 sample testing: Hengshun delicious fresh soy sauce (special grade) was taken, and the ester content was detected using the visualization detection method and headspace solid phase microextraction (HS-SPME)-gas chromatography (GC)-mass spectrometry (MS) detection method. The error between the predicted value of the visualization method and the actual value of the headspace solid phase microextraction (HS-SPME)-gas chromatography (GC)-mass spectrometry (MS) detection was 5.7%, and the prediction accuracy was relatively high.

[0083] Example 3

[0084] A method for detecting the content of ester substances in soy sauce comprises the following steps:

[0085] S1 uses headspace solid phase microextraction (HS-SPME)-gas chromatography (GC)-mass spectrometry (MS) to determine the volatile components in the sample:

[0086] S11: Prepare the samples to be tested: Take 6 types of soy sauce crude oil from Jiangsu Hengshun Vinegar Co., Ltd., 3 batches for each type, 5 parallel samples for each batch, a total of 90 samples; add 8 ml of soy sauce crude oil sample, 2.5 g of NaCl and a magnetic stirring rotor into a 15 ml headspace bottle, and add 8 mg / L of 1,4-methyl-2-pentanol as an internal standard;

[0087] S12 SPME extraction-GC detection: The SPME extraction head (75 μm, Supelco, Bellefonte, USA) was inserted into the head space of the headspace bottle and kept for 40 min. Then the SPME fiber was injected into the gas inlet of GC (HP6890-5973, Agilent, USA) and desorbed at 250 °C for 5 min in the crackless mode.

[0088] Gas chromatography-mass spectrometry conditions:

[0089] Internal standard solution: 5-10 mg / L 1,4-methyl-2-pentanol;

[0090] Chromatographic column: DB-WAX capillary column (specifications: length 60m×diameter 0.25mm×inner diameter 0.25μm);

[0091] Carrier gas: helium, flow rate 1.0 mL / min;

[0092] Column temperature: a. The front injection temperature was set at 250°C, and the temperature program was as follows: the initial temperature was 35°C, and the gradient was increased to 100°C at 5°C / min, and maintained for 10 min, then increased to 200°C at 3°C / min, and finally increased to 220°C at 10°C / min, and maintained for 10 min.

[0093] b. The interface temperature is set to 250°C; the ion source temperature is set to 230°C;

[0094] Ionization mode: electron impact (EI source),

[0095] Mass spectrometry scanning range: 33-400 amu, 70 eV;

[0096] Retention index (RI): C7-C30 normal alkanes were used as standard for determination, and qualitative analysis was performed by comparison with the National Institute of Standards and Technology Library (NIST, 2014; USA). The concentration of volatile compounds was quantitatively calculated using the amount of internal standard added to obtain the total content of soy sauce esters;

[0097] Preparation of S2 visual sensor array and its color value data processing:

[0098] S21 Preparation of hydroxylamine solution: Dissolve 0.5 g of hydroxylamine in 200 mL of 10 mg / mL NaOH solution to prepare a 4 mg / mL hydroxylamine solution, and store at 4°C for later use;

[0099] S22 Preparation of indicator: S211 Preparation of bromocresol purple indicator: Take 3 mL, 5 mg / mL bromocresol purple ethanol solution, add 300 μL of hydroxylamine solution to form bromocresol purple indicator; S212 Preparation of neutral red indicator: Take 2 mL, 4 mg / mL neutral red ethanol solution and add 300 μL of hydroxylamine solution to form neutral red indicator;

[0100] S23 spot plate: Use a glass capillary to draw 5 μL of bromocresol purple and 5 μL of neutral red indicator onto the C2 reverse silica gel plate of the visualization array sensor to form a 1*2 visualization array;

[0101] S24 front image acquisition: The initial image of the reverse silicone plate of C2 of the visualization array sensor is scanned with a flatbed scanner and named “front image”;

[0102] Reaction of S25 esters: Take 10 mL of soy sauce crude oil in a 100 mL beaker, fix the C2 reverse silica gel plate of the visualized array sensor after spotting on the top of the beaker, and react with the volatile gas in the sample solution at room temperature for 30 minutes. Figure 1 As shown;

[0103] S26 post-image processing: Scan the visualized array sensor silicone plate image after the reaction with a flatbed scanner, named "post-image";

[0104] S27 color value processing: use colorAnalysis software to extract the color value on the reverse C2 silicone plate of the visualization array sensor, and decompose the three-channel color image into three single-channel grayscale images, respectively, obtain the red, green, and blue color intensity values ​​(i.e., RGB values) of the front image and the rear image, respectively, use the image processing algorithm to locate the position of the sensitive unit, subtract the RGB value of the front image from the RGB value of the rear image, and the obtained chromaticity difference is the eigenvalue of the sensitive unit. For each sample, 6 color variables are obtained from 2 color perception points, and the chromaticity difference calculation formula is as follows: ΔR=Ra-RbΔG=Ga-GbΔB=Ba-Bb, wherein Ra, Ga, and Ba are the grayscale values ​​of red, green, and blue after adding soy sauce crude oil, respectively; Rb, Gb, and Bb are the initial grayscale values ​​of red, green, and blue, respectively; in the 1×2 visualization array described in step S23, each sensitive unit obtains 3 characteristic variables, and the characteristic values ​​of all sensitive units are combined to obtain a characteristic matrix X with 6 variables. X is an m×n matrix, where m represents the number of samples and n represents the number of characteristic variables. In this embodiment, m=90 and n=6.

[0105]

[0106] S3 builds the model: the eigenvalue combination of all sensitive units in step S27 is used as the independent variable matrix X, and the headspace solid phase microextraction (HS-SPME)-gas chromatography (GC)-mass spectrometry (MS) measurement results in step S12 are used as the dependent variable matrix Y. 2 / 3 of the matrix X is randomly selected as a training set and 1 / 3 as a prediction set in a ratio of 2:1 to build a prediction model, and Matlab software is used to use the linear partial least squares (PLS) method to establish the correlation between the visualized RGB color value and the ester content in the soy sauce, that is, the correlation between the independent variable matrix X and the dependent variable matrix Y is established, and the PLS model is obtained. Figure 4 The prediction results of the PLS training set and test set for the ester content in the soy sauce crude oil sample. The correlation coefficient of the model test set is above 0.85, and the correlation coefficient R between the training set output value and the actual measured value is C is 0.9645, RMSECV is 0.1521; the correlation coefficient R P The PLS model is 0.9534 and the RMSEP is 0.1990, which shows that the PLS model can achieve quantitative prediction of the content of esters in soy sauce.

[0107] S4 sample testing: Hengshun delicious fresh soy sauce (special grade) was taken, and the ester content was detected using the visualization detection method and headspace solid phase microextraction (HS-SPME)-gas chromatography (GC)-mass spectrometry (MS) detection method. The error between the predicted value of the visualization method and the actual value of the headspace solid phase microextraction (HS-SPME)-gas chromatography (GC)-mass spectrometry (MS) detection was 4.9%, and the prediction accuracy was relatively high.

[0108] It is obvious that those skilled in the art can easily make various modifications to these embodiments and apply the general principles described herein to other embodiments without creative work. Therefore, the present invention is not limited to the above embodiments, and improvements and modifications made by those skilled in the art based on the disclosure of the present invention without departing from the scope of the present invention should be within the protection scope of the present invention.

Claims

1. A method for detecting the content of ester substances in soy sauce, characterized in that: The following steps are involved: S1 uses headspace solid phase microextraction-gas chromatography-mass spectrometry to determine the volatile components in the sample: S11: Prepare the sample to be tested: add 5-10 ml of the crude soy sauce sample, 1.5-3.5 g of NaCl and a magnetic stirring rotor into a suitable headspace bottle, and add 5-10 mg / L of 1, 4-methyl-2-pentanol as an internal standard to obtain the sample solution to be tested; S12 SPME extraction-GC detection: insert the SPME extraction head into the head space of the headspace bottle and keep it for 30-60 minutes, then inject the SPME fiber into the GC inlet, use the crack-free mode, and desorb at 200-280℃ for 3-8 minutes; Gas chromatography-mass spectrometry conditions: Chromatographic column: DB-WAX capillary column; Carrier gas: helium, flow rate 1.0 mL / min; Column temperature: a. The front injection temperature was set at 210~260℃, and the temperature program was as follows: initial temperature 35℃, increased to 90~120℃ at 3~5℃ / min, maintained for 10~20 min, then increased to 180~205℃ at 3~5℃ / min, and finally increased to 210~260℃ at 10~12℃ / min, maintained for 10~20 min; b. The interface temperature was set at 210~260℃; the ion source temperature was set at 210~260℃; Ionization mode: electron impact, Mass spectrometer scanning range: 33 ~ 400 amu, 70 eV; Retention index: C7-C 30 n-Alkanes were used as standard for determination and qualitatively compared with the National Institute of Standards and Technology library, and the concentration of volatile compounds was quantitatively calculated using the amount of internal standard added to obtain the total content of soy sauce esters; Preparation of S2 visual sensor array and its color value data processing: S21 Preparation of hydroxylamine solution: Dissolve an appropriate amount of hydroxylamine in 3 mg / mL NaOH solution to prepare a 0.5-2 g / mL solution and store at 0-4°C for later use; Preparation of S22 indicator: Preparation of S211 bromocresol purple indicator: Take 1~3 mL of 0.5~5 mg / mL bromocresol purple ethanol solution and add 100~300 μ L of hydroxylamine solution to form bromocresol purple indicator; S212 preparation of neutral red indicator: take 1~2 mL, 0.5~4 mg / mL neutral red ethanol solution and add 200~300 μ L of hydroxylamine solution forms a neutral red indicator; S23 spot plate: Use glass capillaries to draw 5 μ L bromocresol purple and 5 μ L neutral red indicator is placed on the C2 reverse silicone plate of the visualization array sensor to form a 1×2 visualization array; S24 front image acquisition: Scan the initial image of the reverse silicone plate of visualization array sensor C2 with a flatbed scanner, named "front image"; Reaction of S25 esters: Take 5-15 mL of soy sauce stock solution in a beaker, fix the C2 reverse silica gel plate of the visualized array sensor after spotting on the top of the beaker, and react with the volatile gas in the sample solution at room temperature for 20-60 min; S26 post-image acquisition: Scan the visualized array sensor silicone plate image after the reaction with a flatbed scanner, named "post-image"; S27 color value processing: S27 color value processing: Use color Analysis software to extract the color value on the reverse C2 silicone plate of the visualization array sensor, and decompose the three-channel color image into three single-channel grayscale images, respectively, and obtain the red, green, and blue color intensity values ​​of the front image and the rear image, respectively. Use the image processing algorithm to locate the position of the sensitive unit, subtract the RGB value of the front image from the RGB value of the rear image, and the obtained chromaticity difference is the eigenvalue of the sensitive unit. For each sample, 6 color variables are obtained from 2 color perception points, and the chromaticity difference calculation formula is as follows: ΔR = Ra−Rb ΔG = Ga−Gb ΔB = Ba−Bb, where Ra, Ga, and Ba are the grayscale values ​​of red, green, and blue after adding soy sauce crude oil, respectively; Rb, Gb, and Bb are the initial grayscale values ​​of red, green, and blue, respectively; In the 1×2 visualization array described in step S23, each sensitive unit obtains 3 characteristic variables. After the eigenvalues ​​of all sensitive units are combined, a characteristic matrix X with 6 variables is obtained, and X is an m×n matrix, where m represents the number of samples and n represents the number of characteristic variables. S3 establishes a model: the eigenvalue combination of all sensitive units in step S27 is used as the independent variable matrix X, the headspace solid phase microextraction-gas chromatography-mass spectrometry measurement results in step S12 are used as the dependent variable matrix Y, 2 / 3 of the matrix X is randomly selected as a training set in a ratio of 2:1, and 1 / 3 is used as a prediction set to build a prediction model, Matlab software is used, and the linear partial least squares method is used to establish the correlation between the visualized RGB color value and the ester content in the soy sauce, that is, the correlation between the independent variable matrix X and the dependent variable matrix Y is established, and the PLS model is obtained; S4 unknown soy sauce sample detection: take the unknown soy sauce sample solution, measure its visualized RGB value, call the model built in step S3, input the visualized RGB value of the unknown soy sauce sample into the model, and then the total ester content in the unknown soy sauce sample solution can be output, thereby realizing the detection of the ester content in the soy sauce.

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