Brown sugar type identification method

Abstract

The invention discloses a brown sugar type identification method. The method comprises the following steps: preparing two identical mixed solutions, wherein the mixed solutions are formed by mixing K ml of a brown sugar solution to be detected and A ml of a 0.01 mol / L NaOH solution; detecting the two mixed solutions at the same time by adopting a chronoamperometry and a cyclic voltammetry respectively to obtain corresponding detection current sets I1 and I2, dropwise adding h ml of the brown sugar solution to be detected into the two mixed solutions at the same time every d seconds in the whole detection process, stirring, and dropwise adding for m times in total; performing normalization processing on the detection current sets I1 and I2 to obtain corresponding normalized data sets IG1 and IG2; performing staggered merging on the normalized data sets IG1 and IG2 to obtain a merged data set IE; inputting the merged data set IE as an input signal into a feature display analysis model, and outputting a feature value EKG by the feature display analysis model; and judging the type of the brown sugar according to the characteristic value EKG. The brown sugar type can be quickly and accurately identified, and the operation is simple.

Description

Technical Field

[0001] The invention relates to the technical field of brown sugar detection, and in particular to a method for identifying the type of brown sugar. Background Art

[0002] Traditional brown sugar is made from sugar cane and is crystallized through multiple steps. As a sweet substance with a long history, in addition to having the function of sugar, it also retains the polyphenols, flavonoids, vitamins and other substances of sugar cane to the maximum extent, so it has a high nutritional value. However, the quality of commercially available brown sugar products varies. Some unscrupulous merchants use other sugars for reprocessing to obtain second-grade brown sugar with similar appearance, impersonating first-grade brown sugar or premium brown sugar, which greatly reduces its nutritional value. Therefore, it is particularly important to distinguish the types of brown sugar.

[0003] The existing methods for detecting the types of brown sugar include instrumental analysis and chemical detection. Although the instrumental analysis method is simple to operate, it has the defects of large equipment size, high cost and low detection accuracy; the chemical detection method has the disadvantages of complicated operation and poor repeatability. Summary of the invention

[0004] In order to solve the above technical problems, the present invention provides a method for identifying the type of brown sugar, which can quickly and accurately identify the type of brown sugar and is easy to operate.

[0005] In order to solve the above problems, the present invention adopts the following technical solutions:

[0006] A method for identifying brown sugar types of the present invention comprises the following steps:

[0007] S1: prepare two identical mixed solutions, wherein the mixed solution is formed by mixing K ml of the brown sugar solution to be tested and A ml of 0.01 mol / L NaOH solution;

[0008] S2: The two mixed solutions are tested simultaneously by chronoamperometry and cyclic voltammetry, respectively, to obtain corresponding detection current sets I1 and I2. During the entire detection process, h ml of the brown sugar solution to be tested is added to the two mixed solutions at an interval of d seconds and stirred, for a total of m times;

[0009] S3: normalizing the detection current sets I1 and I2 to obtain corresponding normalized data sets IG1 and IG2;

[0010] S4: interleave and merge the normalized data sets IG1 and IG2 to obtain a merged data set IE;

[0011] S5: inputting the merged data set IE as an input signal into a feature display analysis model, and the feature display analysis model outputs a feature value EKG;

[0012] S6: Determine the type of brown sugar based on the characteristic value EKG.

[0013] In this scheme, the brown sugar to be tested is dissolved to obtain a brown sugar solution to be tested, and the brown sugar solution to be tested is mixed with A ml of 0.01 mol / L NaOH solution to obtain two identical mixed solutions. The detection current sets I1 and I2 obtained by chronoamperometry and cyclic voltammetry are normalized and then interlaced to obtain a combined data set IE, which is input into the feature display analysis model to obtain the eigenvalue EKG, and the type of brown sugar is determined according to the eigenvalue EKG.

[0014] In this method, the detection current set I2 obtained by cyclic voltammetry reflects the redox properties of brown sugar. The greater the content of effective ingredients in brown sugar, the more significant the signal of the redox reflection. The detection current set I2 is used to anchor the detection current set I1 obtained by chronoamperometry, thereby overcoming the errors caused by accidental factors and improving the accuracy of brown sugar type identification.

[0015] Preferably, step S2 comprises the following steps:

[0016] Three-electrode sensors were placed in the two mixed solutions. The two mixed solutions were detected simultaneously by chronoamperometry and cyclic voltammetry, respectively, to obtain corresponding detection current sets I1 and I2. During the entire detection process, h ml of the brown sugar solution to be tested was added to the two mixed solutions at an interval of d seconds and stirred, for a total of m times.

[0017] I1={I1(t1), I1(t2)…I1(t n )}, I2={I2(t1), I2(t2)...I2(t n )},t i is the i-th sampling moment, I1(t i ) is the sampling time t using the chronoamperometry i The absolute value of the detected current value, I2(t i ) is the sampling time t of cyclic voltammetry i The absolute value of the detected current value is 1≤i≤n, where n is the number of sampling times.

[0018] Preferably, step S3 comprises the following steps:

[0019] The detection current set I1 is normalized to the interval [1, 5] to obtain the corresponding normalized data set IG1, IG1 = {IG1(t1), IG1(t2)…IG1(t n )},IG1(t i ) is I1(t i ) corresponding normalized value;

[0020] The detection current set I2 is normalized to the interval [1, 5] to obtain the corresponding normalized data set IG2, IG2 = {IG2(t1), IG2(t2)…IG2(t n )},IG2(t i ) is I2(t i ) corresponds to the normalized value.

[0021] Preferably, the merged data set IE in step S4 is:

[0022] IE={IE(1), IE(2)...IE(2n-1), IE(2n)},

[0023] Among them, IG1(t i )=IE(2(i-1)+1),IG2(t i )=IE(2i).

[0024] Preferably, step S5 comprises the following steps:

[0025] The data IE(j) in the merged data set IE is used as the input signal x(j) to input the feature display analysis model, j = 1, 2...2n:

[0026]

[0027]

[0028] Among them, u(T) is an elastic system, is the excitation signal, ω is the frequency, θ is the initial angle, p is the intensity of the excitation signal, Q, a, and b are all constants;

[0029] Adjust the value of p from small to large, and the characteristic display analysis model approaches the transition condition. When the characteristic display analysis model reaches the transition state, stop adjusting p, record the current value of p as ps, and the characteristic display analysis model outputs the characteristic value EKG.

[0030]

[0031] Preferably, the scanning potential of the chronoamperometry is +0.5 V, the scanning range of the cyclic voltammetry is +0.4 V to +0.6 V, and the scanning rate is 0.10 V / s.

[0032] Preferably, the three-electrode sensor comprises a working electrode, a counter electrode and a reference electrode, the working electrode is a copper sheet electrode, the counter electrode is a platinum electrode, and the reference electrode is a saturated calomel electrode.

[0033] Preferably, step S6 comprises the following steps:

[0034] When W1≤EKG<W2, the brown sugar to be tested is judged to be industrial brown sugar;

[0035] When W2≤EKG<W3, the brown sugar to be tested is judged to be grade II brown sugar;

[0036] When W3≤EKG<W4, the brown sugar to be tested is judged to be first-grade brown sugar;

[0037] When W4≤EKG≤W5, the brown sugar to be tested is judged to be high-quality brown sugar.

[0038] The beneficial effects of the present invention are that the types of brown sugar can be identified quickly and accurately, and the operation is simple. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 is a flow chart of an embodiment. DETAILED DESCRIPTION

[0040] The technical solution of the present invention is further specifically described below through embodiments and in conjunction with the accompanying drawings.

[0041] Embodiment: A method for identifying the type of brown sugar in this embodiment, such as Figure 1 As shown, the following steps are included:

[0042] S1: Prepare two identical mixed solutions, wherein the mixed solution is formed by mixing K ml of the brown sugar solution to be tested and A ml of 0.01 mol / L NaOH solution.

[0043] S2: The two mixed solutions are detected simultaneously by chronoamperometry and cyclic voltammetry, respectively, to obtain corresponding detection current sets I1 and I2. During the entire detection process, h ml of the brown sugar solution to be tested is added to the two mixed solutions at an interval of d seconds and stirred, for a total of m additions.

[0044] Step S2 includes the following steps:

[0045] Three-electrode sensors were placed in the two mixed solutions. The two mixed solutions were detected simultaneously by chronoamperometry and cyclic voltammetry, respectively, to obtain corresponding detection current sets I1 and I2. During the entire detection process, h ml of the brown sugar solution to be tested was added to the two mixed solutions at an interval of d seconds and stirred, for a total of m times.

[0046] I1={I1(t1), I1(t2)…I1(t n )}, I2={I2(t1), I2(t2)...I2(t n )},t i is the i-th sampling moment, I1(t i) is the sampling time t using the chronoamperometry i The absolute value of the detected current value, I2(t i ) is the sampling time t of cyclic voltammetry i The absolute value of the detected current value is 1≤i≤n, where n is the number of sampling times.

[0047] t1 is the 0th second from the start of detection, t n The time is (m+1)*d seconds from the start of detection. The scanning potential of the chronoamperometry is +0.5V constant potential; the scanning range of the cyclic voltammetry is +0.4V~+0.6V, and the scanning rate is 0.10V / s. The three-electrode sensor includes a working electrode, a counter electrode, and a reference electrode. The working electrode is a copper electrode, the counter electrode is a platinum electrode, and the reference electrode is a saturated calomel electrode. During the entire detection process, the constant temperature automatic stirrer continuously stirs the mixed solution.

[0048] S3: Normalize the detection current set I1 to the interval [1, 5] to obtain the corresponding normalized data set IG1, IG1 = {IG1(t1), IG1(t2)…IG1(t n )},IG1(t i ) is I1(t i ) corresponding to the normalized value;

[0049] The detection current set L2 is normalized to the interval [1, 5] to obtain the corresponding normalized data set IG2, IG2 = {IG2(t1), IG2(t2)…IG2(t n )}, IG2(t i ) is I2(t i ) corresponds to the normalized value.

[0050] S4: Interleave and merge the normalized data sets IG1 and IG2 to obtain a merged data set IE, where IE = {IE(1), IE(2) ... IE(2n-1), IE(2n)}, where IG1(t i )=IE(2(i-1)+1),IG2(t i )=IE(2i).

[0051] The method for interlaced merging of normalized data sets IG1 and IG2 is: generate an empty merged data set IE, first put the first element of the normalized data set IG1 into the merged data set IE, then put the first element of the normalized data set IG2 into the merged data set IE, then put the second element of the normalized data set IG1 into the merged data set IE, then put the second element of the normalized data set IG2 into the merged data set IE, and repeat this cycle until all elements in the normalized data sets IG1 and IG2 are put into the merged data set IE.

[0052] For example: IG1={IG1(t1), IG1(t2), IG1(t3)},

[0053] IG2={IG2(t1), IG2(t2), IG2(t3)},

[0054] The normalized data sets IG1 and IG2 are interleaved and merged to obtain a merged data set IE={IG1(t1), IG2(t1), IG1(t2), IG2(t2), IG1(t3), IG2(t3)}.

[0055] S5: Input the merged data set IE as an input signal into the feature display analysis model, and the feature display analysis model outputs a feature value EKG.

[0056] Step S5 includes the following steps:

[0057] The data IE(j) in the merged data set IE is used as the input signal x(j) to input the feature display analysis model, j = 1, 2...2n:

[0058]

[0059] Among them, u(T) is an elastic system, is the excitation signal, ω is the frequency, θ is the initial angle, p is the intensity of the excitation signal, Q, a, and b are all constants, cos(ωj+θ) is the cosine frequency component of the input signal, and sin(ωj+θ) is the sine frequency component of the input signal;

[0060] Adjust the value of p from small to large, and the characteristic display analysis model approaches the transition condition. When the characteristic display analysis model reaches the transition state, stop adjusting p, record the current value of p as ps, and the characteristic display analysis model outputs the characteristic value EKG.

[0061]

[0062] S6: When W1≤EKG<W2, the brown sugar to be tested is judged to be industrial brown sugar;

[0063] When W2≤EKG<W3, the brown sugar to be tested is judged to be grade II brown sugar;

[0064] When W3≤EKG<W4, the brown sugar to be tested is judged to be first-grade brown sugar;

[0065] When W4≤EKG≤W5, the brown sugar to be tested is judged to be high-quality brown sugar.

[0066] In this scheme, the brown sugar to be tested is dissolved to obtain a brown sugar solution to be tested, and the brown sugar solution to be tested is mixed with A ml of 0.01 mol / L NaOH solution to obtain two identical mixed solutions. The detection current sets I1 and I2 obtained by chronoamperometry and cyclic voltammetry are normalized and then interlaced to obtain a combined data set IE, which is input into the feature display analysis model to obtain the eigenvalue EKG, and the type of brown sugar is determined according to the eigenvalue EKG.

[0067] In this method, the detection current set I2 obtained by cyclic voltammetry reflects the redox properties of brown sugar. The greater the content of effective ingredients in brown sugar, the more significant the signal of the redox reflection. The detection current set I2 is used to anchor the detection current set I1 obtained by chronoamperometry, which overcomes the errors caused by accidental factors, improves the accuracy of brown sugar type identification, and is simple to operate.

[0068] In this embodiment, the initially configured mixed solution is formed by mixing 100 ml of a 20 g / L brown sugar solution to be tested and 20 ml of a 0.01 mol / L NaOH solution. During the entire detection process, 50 μL of the brown sugar solution to be tested is added to the two mixed solutions at the same time every 25 seconds and stirred, for a total of 20 additions.

[0069] In this embodiment, W1=0.2, W2=0.38, W3=0.46, W4=0.54, W5=0.61, that is:

[0070] When 0.2≤EKG<0.38, the brown sugar to be tested is judged to be industrial brown sugar;

[0071] When 0.38≤EKG<0.46, the brown sugar to be tested is judged to be second-grade brown sugar; when 0.46≤EKG<0.54, the brown sugar to be tested is judged to be first-grade brown sugar; when 0.54≤EKG≤0.61, the brown sugar to be tested is judged to be premium-grade brown sugar.

Claims

1. A method for identifying the type of brown sugar, characterized in that: The following steps are involved: S1: prepare two identical mixed solutions, wherein the mixed solution is formed by mixing K ml of the brown sugar solution to be tested and A ml of 0.01 mol / L NaOH solution; S2: The two mixed solutions are tested simultaneously by chronoamperometry and cyclic voltammetry, respectively, to obtain corresponding detection current sets I1 and I2. During the entire detection process, h ml of the brown sugar solution to be tested is added to the two mixed solutions at an interval of d seconds and stirred, for a total of m times; S3: normalizing the detection current sets I1 and I2 to obtain corresponding normalized data sets IG1 and IG2; S4: interleave and merge the normalized data sets IG1 and IG2 to obtain a merged data set IE; S5: inputting the merged data set IE as an input signal into a feature display analysis model, and the feature display analysis model outputs a feature value EKG; S6: Determine the type of brown sugar based on the characteristic value EKG.

2. A method for identifying brown sugar types according to claim 1, characterized in that: The step S2 comprises the following steps: Three-electrode sensors were placed in the two mixed solutions. The two mixed solutions were detected simultaneously by chronoamperometry and cyclic voltammetry, respectively, to obtain corresponding detection current sets I1 and I2. During the entire detection process, h ml of the brown sugar solution to be tested was added to the two mixed solutions at an interval of d seconds and stirred, for a total of m times. I1={I1(t1), I1(t2)…I1(t n )}, I2={I2(t1), I2(t2)...I2(t n )},t i is the i-th sampling moment, I1(t i ) is the sampling time t using the chronoamperometry i The absolute value of the detected current value, I2(t i ) is the sampling time t of cyclic voltammetry i The absolute value of the detected current value is 1≤i≤n, where n is the number of sampling times.

3. A method for identifying brown sugar types according to claim 2, characterized in that: The step S3 comprises the following steps: The detection current set I1 is normalized to the interval [1, 5] to obtain the corresponding normalized data set IG1, IG1 = {IG1(t1), IG1(t2)…IG1(t n )},IG1(t i ) is I1(t i ) corresponding to the normalized value; The detection current set I2 is normalized to the interval [1, 5] to obtain the corresponding normalized data set IG2, IG2 = {IG2(t1), IG2(t2)…IG2(t n )}, IG2(t i ) is I2(t i ) corresponds to the normalized value.

4. A method for identifying brown sugar types according to claim 3, characterized in that: The merged data set IE in step S4 is: IE={IE(1), IE(2)...IE(2n-1), IE(2n)}, Among them, IG1(t i )=IE(2(i-1)+1), IG2(t i ) = IE(2i).

5. A method for identifying brown sugar types according to claim 4, characterized in that: The step S5 comprises the following steps: The data IE(j) in the merged data set IE is used as the input signal x(j) to input the feature display analysis model, j = 1, 2...2n: Among them, u(T) is an elastic system, is the excitation signal, ω is the frequency, θ is the initial angle, p is the intensity of the excitation signal, Q, a, and b are all constants; Adjust the value of p from small to large, and the characteristic display analysis model approaches the transition condition. When the characteristic display analysis model reaches the transition state, stop adjusting p, record the current value of p as ps, and the characteristic display analysis model outputs the characteristic value EKG.

6. A method for identifying brown sugar types according to claim 2, characterized in that: The scanning potential of the chronoamperometry is +0.5V, the scanning range of the cyclic voltammetry is +0.4V to +0.6V, and the scanning rate is 0.10V / s.

7. A method for identifying brown sugar types according to claim 2, characterized in that: The three-electrode sensor includes a working electrode, a counter electrode, and a reference electrode.

8. A method for identifying brown sugar types according to claim 1, characterized in that: The step S6 comprises the following steps: When W1≤EKG<W2, the brown sugar to be tested is judged to be industrial brown sugar; When W2≤EKG<W3, the brown sugar to be tested is judged to be grade II brown sugar; When W3≤EKG<W4, the brown sugar to be tested is judged to be first-grade brown sugar; When W4≤EKG≤W5, the brown sugar to be tested is judged to be high-quality brown sugar.

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