A method for identifying calcium hydrogen phosphate and application thereof

Abstract

The application discloses a method for identifying calcium hydrogen phosphate and application, and belongs to the technical field of compound detection. The application constructs a qualitative and quantitative model of calcium hydrogen phosphate through near-infrared spectroscopy, so that adulteration of the calcium hydrogen phosphate can be rapidly screened. The application also realizes identification of stone powder, talcum powder and nitrogen-containing substances through the use of a dilute hydrochloric acid method, pH detection and an indantrione experiment.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of compound detection, in particular to a method for identifying dibasic calcium phosphate and application thereof. BACKGROUND

[0002] Phosphorus is the second most important nutrient after protein and amino acid in livestock nutrition cost, and is a very concerned nutrient element for feed manufacturers and breeders. Feed-grade dibasic calcium phosphate is the main mineral additive of phosphorus and calcium elements in livestock feed, and plays an important role in the feed industry in China. At the same time, the ratio of phosphorus and calcium in dibasic calcium phosphate is 1:1.29, which is the closest to the ratio of phosphorus and calcium in animal bones. It can be completely dissolved in gastric acid, easily absorbed and utilized, and participate in animal metabolism, preventing various diseases caused by calcium deficiency in animals and promoting animal growth. As one of the mineral raw materials, dibasic calcium phosphate is relatively high in value and price. In addition to considering the stability and safety of dibasic calcium phosphate quality, it is also necessary to consider whether dibasic calcium phosphate is adulterated.

[0003] At present, the quality stability and safety of dibasic calcium phosphate can be determined by the content of phosphorus, calcium and heavy metals, but the method for identifying adulteration is not perfect. It is mainly determined by human sense of smell and the apparent color of dibasic calcium phosphate, which lacks a certain rigor. At the same time, due to the high price of dibasic calcium phosphate, adulteration problems occur from time to time, especially for feed-grade dibasic calcium phosphate for pig industry. Once animal-derived bone meal or protein is added, it will bring corresponding production risks. Therefore, it is necessary to effectively apply a rapid adulteration identification method for dibasic calcium phosphate. SUMMARY

[0004] The technical problem to be solved by the present application is to provide a method for identifying dibasic calcium phosphate. The method can quickly screen and identify dibasic calcium phosphate.

[0005] In order to solve the above technical problems, the present application provides the following technical scheme:

[0006] In a first aspect, the present application provides a method for identifying dibasic calcium phosphate, comprising the following steps:

[0007] S1, establishment of a qualitative model of dibasic calcium phosphate:

[0008] Collecting near-infrared spectrum data of dibasic calcium phosphate standard, calculating the average value and standard deviation of absorbance of the standard spectrum at each wavelength point, and determining the confidence interval of the reference spectrum;

[0009] Collecting near-infrared spectrum data of the sample to be tested, calculating the eligibility index of the spectrum of the sample to be tested, and comparing it with the confidence interval of the reference spectrum;

[0010] S2, establishment of a quantitative model of dibasic calcium phosphate:

[0011] Collecting near infrared spectrum data of calcium hydrogen phosphate standard sample, pre-processing the spectrum data, selecting appropriate wave number range, extracting and processing spectrum characteristics, and establishing quantitative model of calcium and phosphorus in calcium hydrogen phosphate respectively;

[0012] S3, detecting and determining:

[0013] Analyzing near infrared spectrum of the sample to be tested through the qualitative model and the quantitative model, comparing the eligibility index of each wavelength point with the set limit value, if the eligibility index of the wavelength point exceeds the set range, determining it as an abnormal sample, otherwise determining it as consistent with the reference sample.

[0014] In an embodiment, the establishment of the calcium hydrogen phosphate qualitative model comprises the following steps:

[0015] (a) Collecting samples of calcium hydrogen phosphate for near infrared spectrum scanning, selecting wave number range 9172.4~4111.7cm -1 ;

[0016] (b) Calculating absorbance average value (A reference,i ) and standard deviation (σ reference,i ) of absorbance of each wavelength point i of the standard sample;

[0017] (c) Determining the confidence interval as: CI interval=A reference,i ±(CI limit value×σ reference,i ).

[0018] In an embodiment, the establishment of the calcium hydrogen phosphate quantitative model comprises the following steps:

[0019] (d) Collecting near infrared spectrum data of calcium hydrogen phosphate standard sample;

[0020] (e) Quantitative model of calcium: selecting wave number range 7506.1~6094.3cm -1 and 5454~4242.9 wave number range, and adopting first derivative+vector normalization (SNV) for spectrum processing;

[0021] (f) Quantitative model of phosphorus: selecting wave number range 7506.1~4597.7cm -1 , and adopting minimum-maximum normalization for spectrum processing;

[0022] (g) Adopting partial least squares method to establish quantitative model between infrared spectrum and calcium and phosphorus content respectively.

[0023] In an embodiment, the detecting and determining comprises:

[0024] (h) collecting near infrared spectrum data of the sample to be tested, selecting the wave number range of 9172.4~4111.7cm -1 ;

[0025] (i) calculating the absorbance (A sample,i ) of each wavelength point of the sample to be tested and calculating the qualification index, the qualification index = (A sample,i -A reference,i ) / σ reference,i ;

[0026] (j) comparing the qualification index of the sample to be tested with the preset CI limit value, if the qualification index of a certain wavelength point exceeds the limit value, it is determined as an abnormal sample;

[0027] (k) after the sample to be tested passes the qualitative test, quantitative prediction is carried out, and when the calcium or phosphorus or calcium-phosphorus result alarm occurs, it is determined as an abnormal sample.

[0028] In an embodiment, the method further comprises:

[0029] S4, detecting by dilute hydrochloric acid method:

[0030] Collecting the abnormal sample, adding dilute hydrochloric acid dropwise, if bubbles are generated in the sample, it is determined that there is stone powder and / or talc powder in the sample to be tested.

[0031] In an embodiment, the method further comprises:

[0032] S5, pH detection:

[0033] Collecting the abnormal sample and dissolving it in an aqueous solution, measuring the pH, and if the pH of the solution exceeds the range of 5.0-7.5, it is determined that there is an abnormality in the production process of calcium hydrogen phosphate.

[0034] In an embodiment, the method further comprises:

[0035] S6, nitrogen-containing substance detection:

[0036] Dissolving the abnormal sample in water and adjusting its pH value to the range of 5-7, adding indantrione reagent after heating reaction, observing the color change after cooling, if the sample shows blue-violet or yellow-brown color, it is determined that the sample contains nitrogen-containing substances; if there is no color change, it is determined that the sample does not contain nitrogen-containing substances.

[0037] In a second aspect, the present application also provides the use of the method of the first aspect in the identification of adulterated calcium hydrogen phosphate.

[0038] In an embodiment, the use comprises the identification of calcium hydrogen phosphate for feed.

[0039] In an embodiment, the identification comprises the quantitative or qualitative identification of calcium hydrogen phosphate for feed.

[0040] It should be understood that, within the scope of the present application, each of the technical features described above and each of the technical features described in detail below (such as the examples) can be combined with each other to form new or preferred technical solutions. Due to the limited space, they will not be listed one by one here.

[0041] Compared with the prior art, the present application has the following beneficial effects:

[0042] (1) The dicalcium phosphate adulteration identification experiment of the present application has simple operation process, high detection accuracy, less human influence factors, reduces the use of additional reagents, and reduces waste liquid pollution;

[0043] (2) The method of the present application can improve the detection efficiency of dicalcium phosphate, timely find quality problems, and eliminate economic losses and quality safety risks caused by raw material adulteration;

[0044] (3) For the adulteration of bone powder and other nitrogen-containing substances, the use of high-end instruments and equipment can be effectively reduced, and the detection cost can be reduced;

[0045] (4) The monitoring of the pH value of dicalcium phosphate can reflect whether the production process of dicalcium phosphate is abnormal;

[0046] (5) The method only needs to apply the established qualitative and quantitative model of dicalcium phosphate to realize the rapid scanning and abnormal screening of dicalcium phosphate. The qualitative identification method of the screened sample is simple and easy to operate, and is easy to popularize and use. BRIEF DESCRIPTION OF DRAWINGS

[0047] Figure 1 Flow chart of technical route of dicalcium phosphate adulteration identification method.

[0048] Figure 2 The maximum qualified index of the qualitative model of dicalcium phosphate is Y axis, which represents the CI range, and the red line represents the CI limit value of 7.2. The green dot represents the sample spectrum, which is within the CI value confidence range 7.2, and is a normal spectrum; the blue dot represents the sample spectrum, which exceeds the CI value confidence range, and is an abnormal spectrum.

[0049] Figure 3 It is a quantitative model of calcium (A) and phosphorus (B). A is the dicalcium phosphate index, the near-infrared cross-validation quantitative model, which represents the correlation between the predicted value and the true value of calcium, wherein the correlation coefficient R 2 is 0.9291, the RMSECV is 0.299, and the RPD is 3.76; B is the dicalcium phosphate index, the near-infrared cross-validation quantitative model, which represents the correlation between the predicted value and the true value of phosphorus, wherein the correlation coefficient R 2 is 0.9111, the RMSECV is 0.197, and the RPD is 3.35.

[0050] Figure 4 For the detection of samples by dilute hydrochloric acid method, whether there are stone powder, talc powder and other adulterants.

[0051] Figure 5 For the detection of samples by indene triketone experiment, whether there are nitrogen-containing substances, the left purple sample and the middle yellow sample are abnormal samples, and the right sample is a normal sample. DETAILED DESCRIPTION

[0052] The application will be further described in detail below with specific embodiments. The examples provided below are only to illustrate the application, and are not intended to limit the scope of the application. The examples provided below can serve as a guide for further improvement by those skilled in the art, and do not in any way constitute a limitation on the application.

[0053] In the following examples, the experimental methods are conventional methods, and are performed according to the techniques or conditions described in the literature in the art or according to the product instructions, unless otherwise specified. The materials and reagents used in the following examples can be obtained commercially, unless otherwise specified.

[0054] Example:

[0055] The flow of the calcium hydrogen phosphate identification method is shown in Figure 1 .

[0056] 1. Establishment of calcium hydrogen phosphate qualitative model and result determination

[0057] a: Principle of qualitative model, calculate the average value and standard deviation σ of absorbance at each wavelength point of the reference spectrum, take the average value ± CI limit value * standard deviation σ of each wavelength point as the confidence interval of the wavelength point, and the difference between the absorbance (A sample,i ) of the test spectrum at the wavelength point and the average value (A reference,i ) divided by the standard deviation σ (σ reference,i ) to obtain the eligibility index CI. By comparing the CI of the test spectrum with the set CI limit value, it can be quickly judged whether the test spectrum is consistent with the reference spectrum. Eligibility index: CI = (A sample,i -A reference,i ) / σ reference,i .

[0058] b: Establishment of calcium hydrogen phosphate qualitative model. Collect 165 reference spectra of calcium hydrogen phosphate, and calculate the average value and standard deviation σ of absorbance at each wavelength point of the reference spectrum. -1The second derivative was selected for pretreatment in the selected wave number range. The maximum value was calculated for the selected wave number range using the maximum eligibility index for the processed spectrum. The CI limit value was intuitively determined to be 7.2. Then, 15 calcium hydrogen phosphate samples were selected as the samples to be tested, and scanning was performed. After scanning, qualitative prediction was performed through the qualitative model, and it was found that 12 samples failed the qualitative test, and the CI values all exceeded 7.2, which were determined to be abnormal samples. As shown in Table 1 and Figure 2 Fig. 1: The red line represents the CI limit value of 7.2, the green color represents the reference spectrum, and the blue point represents the test spectrum. Among them, 12 samples all exceed the confidence range of the CI value, the qualitative result is unqualified, and it is directly determined to be an abnormal sample.

[0059] c: The sample determined to be abnormal will be supplemented to the qualitative model database when subsequent verification does not exist adulteration (when the quantitative result is normal), for example, the sample to be tested 4 will be supplemented to the qualitative model database, and the modeling parameters will be optimized to expand the application range of the model.

[0060] Table 1: Detection accuracy summary of 15 calcium hydrogen phosphate samples to be tested

[0061]

[0062] 2. Establishment of calcium hydrogen phosphate quantitative model and result determination

[0063] a: Spectral pretreatment of the calcium hydrogen phosphate quantitative model: first, the reference spectrum of the collected 165 samples was subjected to spectral baseline correction and mathematical processing, and the specific process was as follows:

[0064] Calcium quantitative model: the wave number range of 7506.1~6094.3 cm -1 and 5454~4242.9 was selected, and the first derivative + vector normalization (SNV) was used for spectral processing.

[0065] Phosphorus quantitative model: the wave number range of 7506.1~4597.7 cm -1 was selected, and minimum-maximum normalization was used for spectral processing to eliminate the influence of spectral noise and baseline deviation.

[0066] b: Establishment of the calcium hydrogen phosphate quantitative model: the processed spectrum, the calcium and phosphorus quantitative models, were all subjected to internal cross-validation by the partial least squares method (PLS) for model establishment and optimization. The Mahalanobis distance, spectral residual, and concentration anomaly were used as the three indexes to remove abnormal samples. The feasibility of the calibration model was evaluated according to the directional coefficient R 2 , the root mean square error of internal validation (RMSECV), and RPD. R 2The closer to 1, the better the correlation between the predicted value and the true value; the smaller the RMSECV, the stronger the prediction ability of the model; the larger the RPD, the better the stability of the model; generally, when RPD>3, it indicates that the model is feasible.

[0067] ①R 2 =1-

[0068] ②RMSECV=

[0069] ③RPD =

[0070] ④MDI=

[0071] ⑤ SpecRes =

[0072] ⑥ F = F i > F 0.05 (1,n-1)

[0073] In the above formula ① and ②, R 2 is a directional coefficient, y i and represent the actual value and the predicted value of the i-th sample (single component) in modeling, represents the average value of all sample target true values, and n represents the sample quantity.

[0074] In the above formula ③, SD is the standard deviation of the validation set, and the predicted standard deviation.

[0075] In the above formula ④, MDI represents Mahalanobis distance. represents the spectral row vector of the i-th sample; represents the spectral row vector of the j-th sample; represents the inverse matrix of the covariance matrix of class X, represents the score.

[0076] In the above formula ⑤, SpecRes represents the spectral residual error. represents the spectrum (row vector) of the i-th sample in modeling; represents the spectrum (row vector) reconstructed from the partial least squares (PLS) vector V.

[0077] In the above formula ⑥, the concentration residual error is usually tested by F . The variance of the absolute error of the chemical value of the i-th sample to be tested, The variance of the absolute error of the entire sample set. The critical value probability is used F i Indicates, F The probability is generally set to 0.95, and greater than the threshold value can be determined as an abnormal sample.

[0078] The quantitative model constructed is shown in Figure 3 , and the calcium and phosphorus quantitative models are established after removing abnormal data from the calcium hydrogen phosphate sample through the three indicators of Mahalanobis distance, spectral residual, and concentration anomaly. Among them, example Figure 3 A is the quantitative model of calcium, and example Figure 3 B is the quantitative model of phosphorus. The quantitative model of calcium has a calibration evaluation index R 2 of 0.9291, an RMSECV of 0.299, and an RPD of 3.76; the quantitative model of phosphorus has a calibration evaluation index R 2 of 0.9111, an RMSECV of 0.197, and an RPD of 3.35, and the prediction error can meet the repeatability error requirements of national standards for calcium and phosphorus. According to the national standard GB / T 22549-2017, the absolute difference of calcium in calcium hydrogen phosphate is ≤0.3%, and the absolute difference of phosphorus is ≤0.2%.

[0079] c: Abnormal sample determination: using the quantitative model to quantitatively detect the phosphorus and calcium content of the 15 calcium hydrogen phosphate samples detected by the qualitative result above, the results are shown in Table 1. According to the combined qualitative and quantitative results, among the 15 selected test samples, 12 abnormal samples are excluded by qualitative testing, and 3 samples pass the qualitative test. Among them, sample 1 and sample 3 have a phosphorus or calcium result alarm when detected by the quantitative model, and are determined as abnormal samples. When the subsequent verification of the abnormal samples does not exist (determined by the following step 3 detection that there is no adulteration), for example, test samples 1 and 3, supplement the type of sample to the quantitative model database, optimize the modeling parameters, and expand the application range of the model.

[0080] 3. Abnormal sample verification

[0081] a: Dilute hydrochloric acid method detection

[0082] Among the samples detected in steps 1 and 2 that show abnormalities, dilute hydrochloric acid is added, the ratio of hydrochloric acid to water is 1:1 (the concentration of dilute hydrochloric acid can be adjusted regularly), and after adding, as shown in Figure 4 , bubbles can be seen in the beaker sample, and the reaction is violent, which can be determined as the presence of stone powder, talc powder, and other adulterants in the calcium hydrogen phosphate.

[0083] b: pH detection

[0084] Take 10g of the sample of calcium hydrogen phosphate which is detected to be abnormal in steps 1 and 2 into a beaker, add 25mL of distilled water, stir until uniform, and then measure the pH value with a pH meter or pH test paper to observe the pH change. The pH value of the calcium hydrogen phosphate sample in the spot check is from 3.95 to 9.56, which is beyond the normal pH value range of 5.0-7.5 of calcium hydrogen phosphate, indicating that the calcium hydrogen phosphate processing process is abnormal.

[0085] c: Indantrione experiment

[0086] According to the principle of the indantrione reaction, all α-amino acids and all proteins can react with indantrione to generate blue-purple substances, in addition to proline and hydroxyproline which react with indantrione to generate yellow substances. The sample is dissolved in water and indantrione reagent is added, and whether it contains nitrogen-containing substances is determined according to the color reaction. If the sample appears blue-purple or yellow-brown, it is determined to contain nitrogen-containing substances; if the sample does not appear blue-purple or yellow-brown, it is determined to not contain nitrogen-containing substances.

[0087] Take 10g of the sample of calcium hydrogen phosphate which is detected to be abnormal in steps 1 and 2 into a 100mL beaker, add 25mL of distilled water, and stir for 5min on a magnetic stirrer. 1) If the pH test paper shows yellow-green when the pH is in the range of 5-7, add 3mL of indantrione reagent, continue to stir for 1min, place the beaker on an electric stove (or light wave stove) to boil for 1min, and then observe the color reaction of the sample after cooling and standing; 2) if the pH test paper shows other colors, adjust the pH to be in the range of 5-7 with dilute hydrochloric acid or alkali solution, and then continue the experiment. As shown in Figure 5 the results, the left purple sample and the middle yellow sample are both abnormal samples, and the right sample does not contain nitrogen-containing substances.

[0088] According to the above procedures (calcium hydrogen phosphate qualitative model, calcium hydrogen phosphate quantitative model, and abnormal sample verification), the sample is detected, and the time consumed in the detection is shown in Table 2.

[0089] Table 2 Detection efficiency of abnormal calcium hydrogen phosphate samples

[0090]

[0091] Although the present application has been disclosed with reference to the preferred embodiments as above, it is not intended to limit the present application, and any person skilled in the art can make various modifications and modifications without departing from the spirit and scope of the present application, and therefore the protection scope of the present application should be defined by the claims.

Claims

1. A method for identifying calcium hydrogen phosphate, characterized by, The method comprises the following steps: S1, establishment of a qualitative model of calcium hydrogen phosphate: Collecting near-infrared spectral data of calcium hydrogen phosphate standard samples, calculating the average absorbance and standard deviation of the spectral data of the standard samples at each wavelength point, and determining the confidence interval of the reference spectrum; Collecting near-infrared spectral data of the sample to be tested, calculating the eligibility index of the spectrum of the sample to be tested, and comparing it with the confidence interval of the reference spectrum; S2, establishment of a quantitative model of calcium hydrogen phosphate: Collecting near-infrared spectral data of calcium hydrogen phosphate standard samples, pre-processing the spectral data, selecting an appropriate wave number range, and establishing a quantitative model of calcium and phosphorus in calcium hydrogen phosphate through spectral feature extraction and processing; S3, detection and determination: Through the qualitative model and the quantitative model, the near-infrared spectrum of the sample to be tested is analyzed, the eligibility index of each wavelength point is compared with the set limit value, if the eligibility index of the wavelength point exceeds the set range, it is determined to be an abnormal sample, otherwise it is determined to be consistent with the reference sample; The establishment of the qualitative model of calcium hydrogen phosphate comprises the following steps: (a) Collecting near-infrared spectral data of calcium hydrogen phosphate standard samples, selecting a wave number range of 9172.4-4111.7 cm⁻¹; (b) calculating A for each wavelength point i of the standard reference,i and σ reference,i ; (c) determining a confidence interval as: CI interval = A reference,i ± (CI limit value x σ reference,i ); The establishment of the quantitative model of calcium hydrogen phosphate comprises the following steps: (d) Collecting near-infrared spectral data of calcium hydrogen phosphate standard samples; (e) Calcium quantitative model: selecting a wave number range of 7506.1-4242.9 cm⁻¹, and processing the spectrum by vector normalization; (f) Phosphorus quantitative model: selecting a wave number range of 7506.1-4597.7 cm⁻¹, and processing the spectrum by minimum-maximum normalization; (g) Using the partial least squares method to establish a quantitative model between the infrared spectrum and the contents of calcium and phosphorus, respectively; The detection and determination comprises: (h) Collecting near-infrared spectral data of the sample to be tested, selecting a wave number range of 9172.4-4111.7 cm⁻¹; (i) Calculate A sample,i and calculate the eligibility index, eligibility index = (A sample,i - A reference,i ) / σ reference,i ; (j) Comparing the eligibility index of the sample to be tested with the preset CI limit value, if the eligibility index of a wavelength point exceeds the limit value, it is determined to be an abnormal sample; (k) After the sample to be tested passes the qualitative test, quantitative prediction is performed, and when the calcium or phosphorus or calcium-phosphorus result alarm occurs, it is determined to be an abnormal sample.

2. The method of claim 1, wherein, The method further comprises: S4, detection by dilute hydrochloric acid method: Collecting abnormal samples, adding dilute hydrochloric acid dropwise, and if bubbles are generated in the sample, it is determined that the sample to be tested contains stone powder and / or talc powder.

3. The method of claim 1, wherein, The method further comprises: S5, pH detection: Collecting abnormal samples and dissolving them in an aqueous solution, measuring the pH, and if the pH of the solution exceeds the range of 5.0-7.5, it is determined that the calcium hydrogen phosphate production process is abnormal.

4. The method of claim 1, wherein, The method further comprises: S6, nitrogen-containing substance detection: Dissolving the abnormal sample in water and adjusting its pH value to the range of 5-7, adding ninhydrin reagent after heating reaction, observing the color change after cooling, if the sample shows blue-purple or yellow-brown color, it is determined that the sample contains nitrogen-containing substances; if there is no color change, it is determined that the sample does not contain nitrogen-containing substances.

5. Use of calcium hydrogen phosphate adulteration identification characterized by, The method of any one of claims 1-4 is used for the adulteration identification of calcium hydrogen phosphate.

6. Use according to claim 5, characterized in that, The application comprises the identification of calcium hydrogen phosphate for feed.

7. Use according to claim 6, characterized in that, The identification comprises the qualitative or quantitative identification of calcium hydrogen phosphate for feed.

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