A method for detecting sucrose solution concentration

Abstract

The present invention discloses a method for detecting the concentration of a sucrose solution. The method comprises the following steps: preparing a plurality of sucrose solutions of varying concentrations, detecting a characteristic value Y corresponding to each sucrose solution, and performing a linear fit on these values ​​to obtain a concentration calculation formula: Y = 1.2X-130, where X is the concentration of the sucrose solution; and obtaining a sucrose solution to be tested, detecting its corresponding characteristic value Y, and calculating the concentration of the sucrose solution to be tested according to the concentration calculation formula: Y = 1.2X-130. The present invention can quickly and accurately detect the concentration of a sucrose solution and is simple to operate.

Description

Technical Field

[0001] The present invention relates to the technical field of solution concentration detection, and in particular to a method for detecting the concentration of a sucrose solution. Background Art

[0002] Sucrose, the main component of table sugar, is a disaccharide formed by the condensation and dehydration of the hemiacetal hydroxyl group of one glucose molecule and the hemiacetal hydroxyl group of one fructose molecule. Sucrose has a sweet taste and is odorless. It is readily soluble in water and glycerol and slightly soluble in alcohol. Sucrose is found almost universally in the plant kingdom, in leaves, flowers, stems, seeds, and fruits. It is particularly abundant in sugarcane, sugar beets, and maple sap. Sucrose has a sweet taste and is an important food and sweetener.

[0003] Existing methods for detecting the concentration of sucrose solution include instrumental analysis and chemical detection. Although the instrumental analysis method is simple to operate, it has the disadvantages of large equipment size, high cost and low detection accuracy; the chemical detection method has the disadvantages of cumbersome operation and poor repeatability. Summary of the Invention

[0004] In order to solve the above technical problems, the present invention provides a method for detecting the concentration of a sucrose solution, which can quickly and accurately detect the concentration of a sucrose solution and is simple to operate.

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

[0006] A method for detecting the concentration of a sucrose solution of the present invention comprises the following steps:

[0007] Prepare multiple sucrose solutions of different concentrations, detect the characteristic value Y corresponding to each concentration of sucrose solution, and perform linear fitting on these values ​​to obtain the concentration calculation formula: Y = 1.2X - 130, where X is the concentration of the sucrose solution;

[0008] Take the sucrose solution to be tested, detect its corresponding characteristic value Y, and calculate the concentration of the sucrose solution to be tested according to the concentration calculation formula: Y = 1.2X-130.

[0009] Preferably, the method for detecting the characteristic value Y corresponding to a sucrose solution of a certain concentration comprises the following steps:

[0010] S1: input sucrose solution into the sample chamber;

[0011] S2: The infrared light source is activated to emit infrared light. The infrared light passes through the sucrose solution and is detected by the first infrared detection module. The first infrared detection module sends the detected spectral data set D1 to the central processing unit. The spectral data set D1 includes n spectral data. The infrared light passes through the reference optical fiber and is detected by the second infrared detection module. The second infrared detection module sends the detected spectral data set D2 to the central processing unit. The spectral data set D2 includes n spectral data. Each spectral data consists of a wave number wn and a corresponding spectral intensity sp.

[0012] S3: The central processing unit arranges the spectral data in the spectral data set D1 from large to small according to wavenumber to obtain a spectral data set L1, and arranges the spectral data in the spectral data set D2 from large to small according to wavenumber to obtain a spectral data set L2. The central processing unit subtracts the spectral intensity of the spectral data in the spectral data set L1 from the spectral intensity of the corresponding spectral data in the spectral data set L2 to obtain a spectral data set L3;

[0013] S4: The central processing unit calculates the intensity wavenumber ratio tr corresponding to each spectral data in the spectral data set L3, and obtains the intensity wavenumber ratio data set T, T = {tr(1), tr(2) ... tr(n)}, tr(i) is the intensity wavenumber ratio corresponding to the i-th spectral data G3(i) in the spectral data set L3, 1≤i≤n, Wherein, sp3(i) is the spectral intensity in the spectral data G3(i), and wn3(i) is the wave number in the spectral data G3(i);

[0014] S5: The central processing unit inputs the data in the intensity wavenumber ratio data set T into the nonlinear resonance model, and uses the nonlinear resonance model to calculate the characteristic signal-to-noise ratio SNR. The central processing unit establishes a rectangular coordinate system with the excitation noise intensity as the x-axis and the signal-to-noise ratio value as the y-axis, and draws a characteristic signal-to-noise ratio curve in the rectangular coordinate system. The signal-to-noise ratio value corresponding to the trough with the maximum excitation noise intensity on the characteristic signal-to-noise ratio curve is taken as the characteristic value Y.

[0015] In this scheme, a sucrose solution is first input into the sample chamber, and then the infrared light source is started to emit light. The first infrared detection module detects the spectral data of the infrared light passing through the sucrose solution, and the second infrared detection module detects the spectral data of the infrared light passing through the reference optical fiber. The two are subtracted to obtain a spectral data set L3 reflecting the sucrose solution. Then, the intensity wavenumber ratio tr corresponding to each spectral data in the spectral data set L3 is calculated to obtain an intensity wavenumber ratio data set T. The data in the intensity wavenumber ratio data set T is input into a nonlinear resonance model, and the characteristic signal-to-noise ratio SNR is calculated using the nonlinear resonance model. The central processing unit establishes a rectangular coordinate system with the excitation noise intensity as the x-axis and the signal-to-noise ratio value as the y-axis, and draws a characteristic signal-to-noise ratio curve in the rectangular coordinate system. The signal-to-noise ratio value corresponding to the trough with the maximum excitation noise intensity on the characteristic signal-to-noise ratio curve is taken as the characteristic value Y.

[0016] In the stage of lower excitation noise intensity, the characteristic signal-to-noise ratio curve shows insufficient stability. As the excitation noise intensity increases, the output stability is significantly enhanced. Therefore, the characteristic trough produced when the excitation noise intensity is large is selected as the characterization parameter of concentration, that is, the signal-to-noise ratio value corresponding to the trough with the largest excitation noise intensity on the characteristic signal-to-noise ratio curve (the trough on the far right of the characteristic signal-to-noise ratio curve) is selected as the characteristic value Y of the sucrose solution of the corresponding concentration.

[0017] Preferably, the step S1 includes the following steps: cleaning the sample chamber with clean water, introducing hot air into the sample chamber to dry the sample chamber, and introducing the sucrose solution into the sample chamber until the sucrose solution reaches a set height.

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

[0019] S31: The central processing unit arranges the spectral data in the spectral data set D1 from large to small according to the wave number to obtain the spectral data set L1, L1 = {G1(1), G1(2) ... G1(n)}, the i-th spectral data G1(i) in the spectral data set L1 = (wn1(i), sp1(i)), 1≤i≤n, sp1(i) is the spectral intensity in the spectral data G1(i), wn1(i) is the wave number in the spectral data G1(i);

[0020] S32: The central processing unit arranges the spectral data in the spectral data set D2 from large to small according to the wave number to obtain a spectral data set L2, L2 = {G2(1), G2(2) ... G2(n)}, the i-th spectral data G2(i) in the spectral data set L2 = (wn2(i), sp2(i)), sp2(i) is the spectral intensity in the spectral data G2(i), wn2(i) is the wave number in the spectral data G2(i);

[0021] S33: The central processing unit subtracts the spectral intensity of the spectral data in the spectral data set L1 from the spectral intensity of the corresponding spectral data in the spectral data set L2 to obtain the spectral data set L3, L3 = {G3(1), G3(2) ... G3(n)}, the i-th spectral data G3(i) in the spectral data set L3 = (wn3(i), sp3(i)),

[0022] Wherein, sp3(i)=sp1(i)-sp2(i), wn3(i)=wn1(i)=wn2(i), sp3(i) is the spectral intensity in the spectral data G3(i), and wn3(i) is the wave number in the spectral data G3(i).

[0023] Preferably, step S5 includes the following steps:

[0024] The central processing unit inputs the data in the intensity wavenumber ratio data set T into the nonlinear resonance model:

[0025]

[0026]

[0027] Where x is the position of the virtual particle in the nonlinear resonance model, V(x) is the nonlinear symmetric potential function, A is the input signal intensity, f0 is the modulation signal frequency, is the initial phase, D is the excitation noise intensity, a and b are coefficients, ξ(i) is the i-th Gaussian white noise, and its autocorrelation function is: E[ξ(i)ξ(0)]=2Dδ(i), δ(i) is the impulse function;

[0028] Adjust the value of D from small to large, and record the value of D when the nonlinear resonance model resonates as D k , thus obtaining the characteristic signal-to-noise ratio SNR,

[0029]

[0030] Where V0 is the potential barrier height;

[0031] The central processing unit establishes a rectangular coordinate system with the excitation noise intensity as the x-axis and the signal-to-noise ratio value as the y-axis, draws a characteristic signal-to-noise ratio curve in the rectangular coordinate system, and takes the signal-to-noise ratio value corresponding to the trough with the maximum excitation noise intensity on the characteristic signal-to-noise ratio curve as the characteristic value Y.

[0032] The beneficial effects of the present invention are that the concentration of the sucrose solution can be detected quickly and accurately, and the operation is simple. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 is a flow chart of an embodiment;

[0034] Figure 2 is a schematic diagram of the characteristic signal-to-noise ratio curve;

[0035] Figure 3 is a schematic diagram of linear fitting;

[0036] Figure 4 It is a structural diagram of the sample chamber.

[0037] In the figure: 1. sample chamber, 2. liquid inlet, 3. liquid outlet, 4. infrared light source, 5. infrared detection device, 6. reference optical fiber, 7. first valve, 8. second valve. DETAILED DESCRIPTION

[0038] The technical solution of the present invention will be further specifically described below through embodiments and in conjunction with the accompanying drawings.

[0039] Example: A method for detecting the concentration of a sucrose solution in this embodiment, such as Figure 1 As shown, the following steps are included:

[0040] Prepare multiple sucrose solutions of different concentrations, detect the characteristic value Y corresponding to each concentration of sucrose solution, and perform linear fitting on these values ​​to obtain the concentration calculation formula: Y = 1.2X - 130, where X is the concentration of the sucrose solution;

[0041] Take the sucrose solution to be tested, detect its corresponding characteristic value Y, and calculate the concentration of the sucrose solution to be tested according to the concentration calculation formula: Y = 1.2X-130.

[0042] The method for detecting the characteristic value Y corresponding to a sucrose solution of a certain concentration comprises the following steps:

[0043] S1: Clean the sample chamber with clean water, blow hot air into the sample chamber to dry it, and add sucrose solution into the sample chamber until the sucrose solution reaches the set height;

[0044] S2: The infrared light source is activated to emit infrared light. The infrared light passes through the sucrose solution and is detected by the first infrared detection module. The first infrared detection module sends the detected spectral data set D1 to the central processing unit. The spectral data set D1 includes n spectral data. The infrared light passes through the reference optical fiber and is detected by the second infrared detection module. The second infrared detection module sends the detected spectral data set D2 to the central processing unit. The spectral data set D2 includes n spectral data. Each spectral data consists of a wave number wn and a corresponding spectral intensity sp.

[0045] S3: The central processing unit arranges the spectral data in the spectral data set D1 from large to small according to the wave number to obtain the spectral data set L1, L1 = {G1(1), G1(2) ... G1(n)}, the i-th spectral data G1(i) in the spectral data set L1 = (wn1(i), sp1(i)), 1≤i≤n, sp1(i) is the spectral intensity in the spectral data G1(i), wn1(i) is the wave number in the spectral data G1(i);

[0046] The central processing unit arranges the spectral data in the spectral data set D2 from large to small according to the wave number to obtain the spectral data set L2, L2 = {G2(1), G2(2) ... G2(n)}, the i-th spectral data G2(i) in the spectral data set L2 = (wn2(i), sp2(i)), sp2(i) is the spectral intensity in the spectral data G2(i), wn2(i) is the wave number in the spectral data G2(i);

[0047] The central processing unit subtracts the spectral intensity of the spectral data in the spectral data set L1 from the spectral intensity of the corresponding spectral data in the spectral data set L2 to obtain the spectral data set L3, L3 = {G3(1), G3(2) ... G3(n)}, the i-th spectral data G3(i) in the spectral data set L3 = (wn3(i), sp3(i)),

[0048] Wherein, sp3(i)=sp1(i)-sp2(i), wn3(i)=wn1(i)=wn2(i), sp3(i) is the spectral intensity in the spectral data G3(i), and wn3(i) is the wave number in the spectral data G3(i);

[0049] S4: The central processing unit calculates the intensity wavenumber ratio tr corresponding to each spectral data in the spectral data set L3, and obtains the intensity wavenumber ratio data set T, T = {tr(1), tr(2) ... tr(n)}, tr(i) is the intensity wavenumber ratio corresponding to the i-th spectral data G3(i) in the spectral data set L3,

[0050] S5: The central processing unit inputs the data in the intensity wavenumber ratio data set T into the nonlinear resonance model:

[0051]

[0052] Where x is the position of the virtual particle in the nonlinear resonance model, V(x) is the nonlinear symmetric potential function, A is the input signal intensity, f0 is the modulation signal frequency, is the initial phase, D is the excitation noise intensity, a and b are coefficients, ξ(i) is the i-th Gaussian white noise, and its autocorrelation function is: E[ξ(i)ξ(0)]=2Dδ(i), δ(i) is the impulse function;

[0053] Adjust the value of D from small to large, and record the value of D when the nonlinear resonance model resonates as D k (Nonlinear resonance model in D=D k Resonance occurs when the signal is equal to the given value), thus obtaining the characteristic signal-to-noise ratio SNR,

[0054]

[0055] Where V0 is the potential barrier height;

[0056] The central processing unit establishes a rectangular coordinate system with the excitation noise intensity as the x-axis and the signal-to-noise ratio value as the y-axis, draws a characteristic signal-to-noise ratio curve in the rectangular coordinate system, and takes the signal-to-noise ratio value corresponding to the trough with the maximum excitation noise intensity on the characteristic signal-to-noise ratio curve as the characteristic value Y.

[0057] In this scheme, a sucrose solution is first input into the sample chamber, and then the infrared light source is started to emit light. The first infrared detection module detects the spectral data of the infrared light passing through the sucrose solution, and the second infrared detection module detects the spectral data of the infrared light passing through the reference optical fiber. The two are subtracted to obtain a spectral data set L3 reflecting the sucrose solution. Then, the intensity wavenumber ratio tr corresponding to each spectral data in the spectral data set L3 is calculated to obtain an intensity wavenumber ratio data set T. The data in the intensity wavenumber ratio data set T is input into a nonlinear resonance model, and the characteristic signal-to-noise ratio SNR is calculated using the nonlinear resonance model. The central processing unit establishes a rectangular coordinate system with the excitation noise intensity as the x-axis and the signal-to-noise ratio value as the y-axis, and draws a characteristic signal-to-noise ratio curve in the rectangular coordinate system. The signal-to-noise ratio value corresponding to the trough with the maximum excitation noise intensity on the characteristic signal-to-noise ratio curve is taken as the characteristic value Y.

[0058] In the stage of lower excitation noise intensity, the characteristic signal-to-noise ratio curve shows insufficient stability. As the excitation noise intensity increases, the output stability is significantly enhanced. Therefore, the characteristic trough produced when the excitation noise intensity is large is selected as the characterization parameter of concentration, that is, the signal-to-noise ratio value corresponding to the trough with the largest excitation noise intensity on the characteristic signal-to-noise ratio curve (the trough on the far right of the characteristic signal-to-noise ratio curve) is selected as the characteristic value Y of the sucrose solution of the corresponding concentration.

[0059] For example, five sucrose solutions with different concentrations are prepared, namely 5mmol / L, 10mmol / L, 15mmol / L, 20mmol / L, and 25mmol / L. The five characteristic signal-to-noise ratio curves obtained by testing these five sucrose solutions with different concentrations are as follows: Figure 2As shown, the signal-to-noise ratio value corresponding to the trough with the largest excitation noise intensity on each characteristic signal-to-noise ratio curve is taken as the characteristic value Y, and linear fitting is performed, as shown in Figure 3 shown.

[0060] like Figure 4 As shown, a liquid inlet 2 is provided at the top of the sample chamber 1, a liquid outlet 3 is provided at the bottom of the sample chamber 1, a first valve 7 for opening / closing the liquid inlet 2 is provided on the liquid inlet 2, and a second valve 8 for opening / closing the liquid outlet 3 is provided on the liquid outlet 3. Infrared light sources 4 and infrared detection devices 5 are symmetrically provided on the left and right sides of the sample chamber 1. The infrared detection device 5 includes a first infrared detection module and a second infrared detection module. A reference optical fiber 6 is also provided in the sample chamber 1. Both ends of the reference optical fiber 6 are respectively connected to the infrared light source 4 and the second infrared detection module. The first infrared detection module is used to detect infrared light passing through the sucrose solution to be tested, and the second infrared detection module is used to detect infrared light passing through the reference optical fiber.

Claims

1. A method for detecting the concentration of a sucrose solution, characterized in that: The following steps are involved: Prepare multiple sucrose solutions of different concentrations, detect the characteristic value Y corresponding to each concentration of sucrose solution, and perform linear fitting on these values ​​to obtain the concentration calculation formula: Y = 1.2X - 130, where X is the concentration of the sucrose solution; Take the sucrose solution to be tested, detect its corresponding characteristic value Y, and calculate the concentration according to the formula: Y = 1.2X - 130, calculate the concentration of the sucrose solution to be tested; The method for detecting the characteristic value Y corresponding to each concentration of sucrose solution comprises the following steps: S1: input sucrose solution into the sample chamber; S2: The infrared light source is activated to emit infrared light. The infrared light passes through the sucrose solution and is detected by the first infrared detection module. The first infrared detection module sends the detected spectral data set D1 to the central processing unit. The spectral data set D1 includes n spectral data. The infrared light passes through the reference optical fiber and is detected by the second infrared detection module. The second infrared detection module sends the detected spectral data set D2 to the central processing unit. The spectral data set D2 includes n spectral data. Each spectral data consists of a wave number wn and a corresponding spectral intensity sp. S3: The central processing unit arranges the spectral data in the spectral data set D1 from large to small according to wavenumber to obtain a spectral data set L1, and arranges the spectral data in the spectral data set D2 from large to small according to wavenumber to obtain a spectral data set L2. The central processing unit subtracts the spectral intensity of the spectral data in the spectral data set L1 from the spectral intensity of the corresponding spectral data in the spectral data set L2 to obtain a spectral data set L3; S4: The central processing unit calculates the intensity wavenumber ratio tr corresponding to each spectral data in the spectral data set L3, and obtains the intensity wavenumber ratio data set T, T = {tr(1), tr(2) ... tr(n)}, tr(i) is the intensity wavenumber ratio corresponding to the i-th spectral data G3(i) in the spectral data set L3, 1≤i≤n, Wherein, sp3(i) is the spectral intensity in the spectral data G3(i), and wn3(i) is the wave number in the spectral data G3(i); S5: The central processing unit inputs the data in the intensity wavenumber ratio data set T into the nonlinear resonance model, and uses the nonlinear resonance model to calculate the characteristic signal-to-noise ratio SNR. The central processing unit establishes a rectangular coordinate system with the excitation noise intensity as the x-axis and the signal-to-noise ratio value as the y-axis, and draws a characteristic signal-to-noise ratio curve in the rectangular coordinate system. The signal-to-noise ratio value corresponding to the trough with the maximum excitation noise intensity on the characteristic signal-to-noise ratio curve is taken as the characteristic value Y.

2. A method for detecting the concentration of sucrose solution according to claim 1, characterized in that, The step S1 includes the following steps: cleaning the sample chamber with clean water, introducing hot air into the sample chamber to dry the sample chamber, and introducing sucrose solution into the sample chamber until the sucrose solution reaches a set height.

3. A sucrose solution concentration detection method according to claim 1, characterized in that, The step S3 comprises the following steps: S31: The central processing unit arranges the spectral data in the spectral data set D1 from large to small according to the wave number to obtain the spectral data set L1, L1 = {G1(1), G1(2) ... G1(n)}, the i-th spectral data G1(i) in the spectral data set L1 = (wn1(i), sp1(i)), 1≤i≤n, sp1(i) is the spectral intensity in the spectral data G1(i), wn1(i) is the wave number in the spectral data G1(i); S32: The central processing unit arranges the spectral data in the spectral data set D2 from large to small according to the wave number to obtain a spectral data set L2, L2 = {G2(1), G2(2) ... G2(n)}, the i-th spectral data G2(i) in the spectral data set L2 = (wn2(i), sp2(i)), sp2(i) is the spectral intensity in the spectral data G2(i), wn2(i) is the wave number in the spectral data G2(i); S33: The central processing unit subtracts the spectral intensity of the spectral data in the spectral data set L1 from the spectral intensity of the corresponding spectral data in the spectral data set L2 to obtain the spectral data set L3, L3 = {G3(1), G3(2) ... G3(n)}, the i-th spectral data G3(i) in the spectral data set L3 = (wn3(i), sp3(i)), Wherein, sp3(i)=sp1(i)-sp2(i), wn3(i)=wn1(i)=wn2(i), sp3(i) is the spectral intensity in the spectral data G3(i), and wn3(i) is the wave number in the spectral data G3(i).

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