Quantitative detection method for five amino acids in rehmannia glutinosa based on mid-infrared spectrum technology

Abstract

According to the present invention, the high performance liquid chromatography (HPLC) is adopted to simultaneously determine the contents of the five amino acids such as serine, threonine, valine, cystine and phenylalanine in 50 batches of the Huai radix rehmanniae in the genuine production area, the infrared spectrogram of the Huai radix rehmanniae is collected by using the mid-infrared spectrometer, the HPLC and the mid-infrared spectrum technology are combined, the TQ analysis software is adopted to analyze the content of the five amino acids such as serine, threonine, valine, cystine and phenylalanine in the Huai radix rehmanniae in the genuine production area, and the content of the five amino acids such as serine, threonine, valine, cystine and phenylalanine in the Huai radix rehmanniae in the genuine production area is determined. A quantitative model of the five amino acids in the rehmannia glutinosa is established, serine is optimized in a PLS + CONSTANT + 1st Der + SG model, threonine is optimized in a SMLR + SNV + 2nd Der + ND model, valine is optimized in a PLS0 + CONSTANT + 1st Der + ND model, cystine is optimized in a PLS + SNV + 2nd Der + SG model, phenylalanine is optimized in a PLS + CONSTANT + 2nd Der + ND model, prediction correlation coefficients are respectively 0.8514, 0.8458, 0.8431, 0.9328 and 0.9290, average absolute deviations are respectively-0.03%, 0.02%, 0.01%,-0.02% and-0.01%, the established model is good, stable and reliable The research can provide reference for rapid evaluation of amino acids in rehmannia glutinosa.

Description

Technical Field

[0001] The invention belongs to the technical field of quantitative detection, and specifically relates to a method for quantitatively detecting five kinds of amino acids in Rehmannia glutinosa based on mid-infrared spectroscopy technology. Background Art

[0002] Rehmannia glutinosa Libosch. is the fresh or dried root of Rehmannia glutinosa, a plant of the Scrophulariaceae family. It contains chemical components such as iridoid glycosides and amino acids (Chen Jinpeng, Zhang Kexia, Liu Yi, et al. Research progress on the chemical components and pharmacological effects of Rehmannia glutinosa [J]. Chinese Herbal Medicine, 2021, 52(6): 1772-1782.). In August 2024, it was included in the catalog of substances that are traditionally both food and Chinese medicinal materials. Amino acids are essential nutrients for the human body. They not only participate in the Maillard reaction in food processing, but also affect its physiological activity. Among them, threonine, valine, and phenylalanine can maintain normal human metabolism and the health of the whole body. Cystine is an essential amino acid for premature children. Serine plays an important protective role in the central nervous system of mammals. The main amino acid analysis techniques include amino acid automatic analyzer analysis (Xue Jin, Qi Fan, Zhang Wenda, et al. Determination of protein and amino acid content and nutritional value evaluation in peach gum from different origins [J]. Journal of Food Safety and Quality, 2023, 14(18): 128-136.), high performance liquid chromatography (Yan Chunrong, Wei Jianqiang, Zhang Hairui, et al. Determination of 19 free amino acids in Gastrodia elata from different origins based on derivatization UPLC method [J]. Yunnan Chemical Industry, 2023, 50(12): 55-59.), liquid chromatography-mass spectrometry (Zhang Ji, Yang Sha, Luo Yuhang, et al. Simultaneous determination of 21 amino acids in liquor by UPLC-MS / MS [J]. China Brewing, 2024, 43(1): 250-254.), gas chromatography-mass spectrometry (Li Jingbo, Wen Rui, Zhang Xiaoying, et al. Solid phase extraction-gas chromatography-mass spectrometry (SPE-GC-MS / MS). Mass spectrometry is used to analyze the amino acids in protein hydrolysis in wine [J]. Western Archaeology, 2017 (2): 268-279.), capillary electrophoresis (Bai Xinwei, Chen Peng. Capillary zone electrophoresis for efficient separation and analysis of essential amino acids in bamboo fungus waste [J]. Yunnan Chemical Industry, 2021, 48 (1): 65-67.), nuclear magnetic resonance, etc. (Wu Zhengzong, Wang Fang, Xu Enbo, et al. Rapid detection of amino acid mass concentration in rice wine by Fourier transform infrared spectroscopy [J]. Journal of Food and Biotechnology, 2017, 36 (1): 34-40.), but these methods have the problems of high cost, long time consumption, and high equipment requirements (Chu Chu, Zhang Jingjing, Ding Lei, et al. Establishment and application of prediction model for three amino acid content in milk based on mid-infrared spectroscopy [J]. Journal of Animal Husbandry and Veterinary Medicine, 2023, 54 (8): 3299-3312.). Therefore, finding a low-cost, efficient and convenient amino acid detection method is crucial for the study of amino acids in food.

[0003] Quantitative analysis using mid-infrared spectroscopy is based on the spectral signal of the reference sample, using chemometrics to establish a quantitative model and verify it, and then using the established model to determine the content of the sample components (Fang Baoxia, Gun Daifen, Li Xiang, et al. Application progress of near-infrared spectroscopy technology in rapid quality control of chemical drugs [J]. Chinese Pharmacist, 2021, 24(10): 1894-1899.), which has the advantages of short time consumption, simple operation, and non-destructive samples. In recent years, mid-infrared spectroscopy technology has gradually been applied to food and traditional Chinese medicine quality assessment, authenticity identification, qualitative and quantitative analysis, etc. (Zou Shuhan, Xu Wenfen, He Shunzhi. Application and development of near-infrared spectroscopy technology in identification of traditional Chinese medicine [J]. Guizhou Science, 2015, 33(5): 65-69.), and its application in food quality control is increasing. Chu Chu et al. used mid-infrared technology to establish a prediction model for amino acid content in milk, Wu Zhengzong et al. used mid-infrared technology to quickly detect the amino acid content in rice wine, and Yu Ying (Yu Ying, Song Jinming, Yu Lidong, et al. Rapid quantitative determination of amino acids in oysters by Fourier transform near-infrared spectroscopy [J]. Marine Science, 2016, 40(12): 1-7) et al. used infrared technology to quickly detect the amino acid content in oysters. Literature research found that there are few studies on the quantitative analysis of amino acids in Rehmannia glutinosa using mid-infrared spectroscopy.

[0004] Based on this, this application was developed. Summary of the invention

[0005] The purpose of the present invention is to overcome the defects of the prior art and provide a method for quantitatively detecting five kinds of amino acids in Rehmannia glutinosa based on mid-infrared spectroscopy. The present invention adopts mid-infrared spectroscopy technology, combined with HPLC to measure the contents of serine, threonine, valine, cystine and phenylalanine in the sample, and uses TQ analyst 9 software for analysis to establish a quantitative model of amino acids in Rehmannia glutinosa, in order to achieve rapid quantitative determination of amino acids in Rehmannia glutinosa, and provide a reference for the quality evaluation of amino acids in food.

[0006] To achieve the above object, the present invention adopts the following technical solutions:

[0007] A method for quantitatively detecting five amino acids in Rehmannia glutinosa based on mid-infrared spectroscopy technology comprises the following steps:

[0008] 1) Preparation of reference solution

[0009] Accurately pipette 0.5 mL of the amino acid mixed standard solution for derivatization reaction to obtain an amino acid derivatized mixed standard solution for later use.

[0010] 2) Preparation of test solution

[0011] Accurately weigh 3g of sample, place in a 100mL conical flask, add 50mL of water, soak overnight, take 2mL of supernatant, add 2mL of 50g / L 5-sulfosalicylic acid, mix well, let stand for 1h, accurately add 1mL of EDTA, extract in boiling water bath for 1h, cool to room temperature, filter, collect the filtrate and make up to 100mL, accurately transfer 0.5mL for derivatization reaction to obtain the test solution, set aside;

[0012] 3) The samples were pressed into tablets and infrared spectra were collected to calculate the amino acid content.

[0013] Specifically, the derivatization reaction in steps 1) and 2) can be performed by accurately transferring 0.5 mL of sample into a 10 mL volumetric flask, adding 0.5 mol·L -1 Adjust the pH to alkaline with NaHCO3, add 0.5 mL of 1% DNFB derivatization reagent, mix well, and keep in a water bath at 60±5℃ for 60±10 min. After cooling, add 0.1 mol·L -1 The KH2PO4 solution was diluted to 10 mL and filtered through a 0.45 μm filter membrane.

[0014] Furthermore, the above-mentioned alkalinity refers to pH=8-9; the pH of the KH2PO4 test solution is pH=7.

[0015] Specifically, in step 3), the sample tableting process can be as follows: 2 mg of crushed Rehmannia glutinosa and 120 mg of KBr are placed in an agate mortar and ground evenly, then an appropriate amount of the evenly ground sample is placed in a tableting mold, and pressed at a pressure of 8 MPa for 30 seconds to form a uniform translucent thin sheet.

[0016] Further preferably, in step 3), the slice can be placed under an infrared lamp to keep dry, and a potassium bromide sheet can be used as a blank. The infrared spectrum of the sample can be collected by an infrared spectrometer, and the spectrum collection parameters are: the spectrum collection range is 4000-400cm -1 , resolution 4cm -1 Each spectrum was scanned 16 times / s, CO2 and H2O were deducted during scanning, the room temperature was 23-26℃, the relative humidity was 25%-35%, each sample was sampled three times in parallel, and each pressed film was scanned twice.

[0017] Compared with the prior art, the present invention has the following beneficial effects:

[0018] Amino acids are essential nutrients for the human body. Existing amino acid analysis technologies have the problems of high cost, long time consumption, and high equipment requirements. Therefore, it is crucial to find a low-cost, efficient, and convenient amino acid analysis technology for amino acid-related research. The present invention uses high performance liquid chromatography (HPLC) to simultaneously determine the contents of five amino acids, namely, serine, threonine, valine, cystine, and phenylalanine, in 50 batches of authentic production areas of Rehmannia glutinosa. The infrared spectrum of Rehmannia glutinosa is collected using a mid-infrared spectrometer. HPLC is combined with mid-infrared spectroscopy technology. TQ analyst analysis software is used to establish a quantitative model for the five amino acids in Rehmannia glutinosa. The PLS+CONSTANT+1stDer+SG model is optimal for serine, the SMLR+SNV+2nd Der+ND model is optimal for threonine, the PLS0+CONSTANT+1stDer+ND model is optimal for valine, and the PLS+SNV+2nd Der+ND model is optimal for cystine. The Der+SG model was the best, and the PLS+CONSTANT+2ndDer+ND model was the best for phenylalanine. The prediction correlation coefficients were 0.8514, 0.8458, 0.8431, 0.9328, and 0.9290, respectively, and the average absolute deviations were -0.03%, 0.02%, 0.01%, -0.02%, and -0.01%, respectively. It was verified that the established model had good, stable, and reliable prediction effect. This study can provide a reference for the rapid evaluation of amino acids in Rehmannia glutinosa. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 This is the HPLC chart of amino acids in Rehmannia glutinosa, in which: 1-serine, 2-threonine, 3-valine, 4-cystine, 5-phenylalanine;

[0020] Figure 2 The infrared spectra of 50 batches of Rehmannia glutinosa are shown below;

[0021] Figure 3 It is the linear relationship diagram between the actual value and the predicted value of amino acids. DETAILED DESCRIPTION

[0022] The technical solution of the present invention is further described in detail below in conjunction with the embodiments, but the protection scope of the present invention is not limited thereto.

[0023] 1. Instruments and Materials

[0024] 1.1 Instruments

[0025] ME204 1 / 10,000 balance (METTLER TOLEDO), AUW220D 1 / 100,000 balance (SHIMAD-ZU), LC-20AD high performance liquid chromatograph (Shimadzu, Japan), 111B high-speed Chinese medicine grinder (Ruian Yongli Pharmaceutical Machinery Co., Ltd.), XMTD-7000 electric constant temperature water bath, INVENIO-HYPERION mid-infrared spectrometer (Bruker Beijing Technology Co., Ltd.).

[0026] 1.2 Materials

[0027] Acetonitrile (GR, Thermo Fisher Scientific Inc.),

[0028] Amino acid mixed solution (China Institute of Metrology, GBW(E)100062),

[0029] Sodium bicarbonate (Beijing Chemical Plant, 20200205),

[0030] 2,4-Dinitrofluorobenzene (DNFB, Shanghai Titan Technology Co., Ltd., P1906604),

[0031] Potassium dihydrogen phosphate (Tianjin Fengchuan Chemical Reagent Technology Co., Ltd., 20210110),

[0032] 5-Sulfosalicylic acid (Tianjin Zhiyuan Chemical Reagent Co., Ltd., 20190320),

[0033] Sodium acetate (Tianjin Hengxing Chemical Reagent Pharmaceutical Co., Ltd., 20190802),

[0034] Ethylenediaminetetraacetic acid disodium (EDTA, National Group Chemical Reagent Co., Ltd., 20161012),

[0035] N,N-dimethylformamide (Tianjin Dengke Chemical Reagent Co., Ltd., 20200610),

[0036] Serine (Hefei Bomei Biotechnology Co., Ltd., SS021655),

[0037] Threonine (Hefei Bomei Biotechnology Co., Ltd., ST170008),

[0038] Valine (Hefei Bomei Biotechnology Co., Ltd., XV20191127),

[0039] Cystine (Hefei Bomei Biotechnology Co., Ltd., BG001167),

[0040] Phenylalanine (Hefei Bomei Biotechnology Co., Ltd., CB210061).

[0041] In 2021, 50 batches of fresh Rehmannia glutinosa were collected in Jiaozuo, including 20 batches from Wen County and 30 batches from Wuzhi County. They were randomly numbered WX1-WX20 and WZ1-WZ30, sliced ​​and freeze-dried, and identified by Professor Dong Chengming as Rehmannia glutinosa Libosch. of the Scrophulariaceae family.

[0042] 2 Methods

[0043] 2.1 High performance liquid chromatography method for the detection of five amino acids

[0044] 2.1.1 Chromatographic conditions

[0045] The chromatographic column was Phenomenex Gemini NX C18 (4.6 mm × 250 mm, 5 μm), and the mobile phase A was 0.05 mol·L -1 Sodium acetate (pH = 6.4, containing 0.1% N, N-dimethylformamide), mobile phase B is acetonitrile-water (1:1, v / v), gradient elution is used, the elution program is shown in Table 1, the detection wavelength is 360nm, the column temperature is 35℃, the injection volume is 10μL, and the flow rate is 1mL·min -1 .

[0046] Table 1 HPLC gradient elution program

[0047]

[0048]

[0049] 2.1.2 Preparation of reference solution

[0050] Accurately pipette 0.5 mL of the amino acid mixed standard solution for derivatization reaction. The specific operation is as follows: accurately pipette 0.5 mL of the above sample into a 10 mL volumetric flask, add 0.5 mL of 0.5 mol·L -1 Adjust the pH to alkaline (pH=8.5) with NaHCO3, add 0.5 mL of 1% DNFB derivatization reagent, mix well and keep in a 60°C water bath for 60 min. After cooling, add 0.1 mol·L -1 The KH2PO4 (pH = 7) test solution was diluted to 10 mL and filtered through a 0.45 μm filter membrane (see reference: Cao Jingya, Zhang Lixian, Fan Yi, et al. Determination of 17 free amino acids in Gynostemma pentaphyllum tea by pre-column derivatization reversed-phase high performance liquid chromatography [J]. Amino Acids and Bioresources, 2016, 38(3): 16-20.) to obtain an amino acid derivative mixed standard solution for later use.

[0051] 2.1.3 Preparation of test solution

[0052] Accurately weigh 3 g of sample, place in a 100 mL conical flask, add 50 mL of water, soak overnight, take 2 mL of supernatant, add 2 mL of 50 g / L 5-sulfosalicylic acid, mix well, let stand for 1 hour, accurately add 1 mL of EDTA and extract in a boiling water bath for 1 hour, cool to room temperature, filter, collect the filtrate and make up to 100 mL, accurately transfer 0.5 mL, and derivatize according to the derivatization reaction method under "2.1.2" to obtain the test solution for use.

[0053] 2.2 Infrared spectrum acquisition method

[0054] 2.2.1 Sample processing and infrared spectrum acquisition

[0055] 50 batches of Rehmannia glutinosa were crushed (No. 3 sieve), 2 mg of the sample and 120 mg of KBr were placed in an agate mortar and ground evenly, then an appropriate amount of the evenly ground sample was placed in a tablet pressing mold and pressed at 8 MPa for 30 seconds to form a uniform translucent thin slice. The slice was then placed under an infrared lamp to keep it dry, and a potassium bromide tablet was used as a blank. The infrared spectra of the samples were collected using an infrared spectrometer. The spectrum collection parameters were: spectrum collection range 4000-400 cm -1 , resolution 4cm -1 Each spectrum was scanned 16 times / s, CO2 and H2O were deducted during scanning, the room temperature was 23-26℃, the relative humidity was 25%-35%, each sample was sampled three times in parallel, and each pressed film was scanned twice.

[0056] 2.2.2 Spectral preprocessing

[0057] The infrared spectrum model is to construct a mathematical model through the infrared spectrum data of a large number of samples, and then use the model to predict unknown samples. The basic steps are as follows:

[0058] (1) Collect samples, collect infrared spectra of the samples and determine reference values. The determination reference value is the "true value" of the amino acid content determined by HPLC.

[0059] (2) Divide the collected samples into a calibration set and a test set (for final verification) in a certain proportion, and make the calibration set as representative as possible.

[0060] (3) Preprocess the calibration set spectra, remove abnormal samples, select bands, etc.

[0061] (4) constructing a quantitative correction model and continuously adjusting the model parameters to obtain the optimal model of the measured components. The model parameters adjusted by the present invention include partial least squares (PLS), stepwise multiple linear regression (SMLR), principal component regression (PCR), original spectrum (CONSTANT), multiplicative signal correction (MSC), standard normal variate (SNV), first derivative (1st Der), second derivative (2nd Der) and (Savitzky-Golayfler, SG) curling smoothing and Norris derivative filtering (ND).

[0062] (5) Model evaluation, using correlation coefficient (R 2 ), Root mean square error of calibration (RMSEC), and Root mean square error of prediction (RMSEP) are used as evaluation criteria, where R 2 The closer the value is to 1, the better the correlation between the sample amino acid value and the infrared predicted value. The smaller the RMSEC and RMSEP are, the better the prediction performance of the model.

[0063] (6) Predicting unknown samples. After the test set is substituted into the prediction model, its sample content will be predicted and compared with the "true value" of the amino acid content determined by HPLC. The smaller the difference, the more reliable the established model.

[0064] The present invention uses partial least squares (PLS), stepwise multiple linear regression (SMLR), principal component regression (PCR), original spectrum (CONSTANT), multiplicative signal correction (MSC), standard normal variate (SNV), first derivative (1st Der), second derivative (2nd Der) and Savitzky-Golayfler (SG) curling smoothing and Norris derivative filtering (ND) to process the spectrum and establish a model. The correlation coefficient (R 2 ), root mean square error of calibration (RMSEC), and root mean square error of prediction (RMSEP) are used as evaluation criteria, where R 2 The closer the value is to 1, the better the correlation between the sample amino acid value and the infrared predicted value. The smaller the RMSEC and RMSEP are, the better the prediction performance of the model.

[0065] 3. Results

[0066] 3.1 Amino acid content determination results

[0067] 3.1.1 Sample measurement results

[0068] According to the above detection method and chromatographic conditions, the amino acid reference substance, sample, and blank solvent were subjected to HPLC determination. The HPLC chromatograms of the amino acid reference substance, sample, and blank solvent are shown in Figure 1 The results of five amino acid contents determination are shown in Table 2.

[0069] Table 2 Content of five amino acids (%)

[0070]

[0071]

[0072] 3.1.2 Establishment of standard curve

[0073] Prepare the concentrations of serine, threonine, valine, cystine, and phenylalanine to be 47.52, 57, 55.55, 121.81, and 83.07 μg / mL, respectively. Accurately pipette 2.0, 4.0, 8.0, 10.0, 15.0, and 20.0 μL of the standard solution into the HPLC (see Table 3), and inject the sample according to the chromatographic conditions under "2.2.1". Take the injection mass of the reference substance as the abscissa (X) and the peak area integral value as the ordinate (Y). Draw the standard curve and calculate the regression equation, and the linear range is expressed in terms of the injection mass.

[0074] Table 3 Injection volume and injection mass of five amino acids

[0075]

[0076] According to the above detection method, the peak area of ​​each was determined, and the peak area integral value Y was used as the ordinate and the injection mass X was used as the abscissa to draw the standard curve, and the regression equation and linear range were calculated. The test results showed that the five amino acids had a good linear relationship within their respective linear ranges, as shown in Table 4.

[0077] Table 4 Linear relationship between five amino acids

[0078]

[0079] 3.2 Screening, establishment and verification of infrared spectra and quantitative models of Rehmannia glutinosa

[0080] 3.2.1 According to the above infrared spectrum collection and spectrum preprocessing method, the infrared spectrum of Rehmannia glutinosa is as follows: Figure 2 shown.

[0081] 3.2.2 Screening of quantitative models

[0082] The upper and lower limits of the target components of the quantitative model were set. The upper limit of the target parameter was set to 105% of the maximum content of the target component in the sample, and the lower limit was set to 95% of the minimum content of the target component in the sample (see Table 5).

[0083] Table 5 Upper and lower limits of target components

[0084]

[0085] 3.2.3 Establishment of quantitative model

[0086] As shown in Tables 6 and 7, the PLS+CONSTANT+1st Der+SG model is the best for serine, the SMLR+SNV+2nd Der+ND model is the best for threonine, the PLS+CONSTANT+1st Der+ND model is the best for valine, the PLS+SNV+2nd Der+SG model is the best for cystine, and the PLS+CONSTANT+2nd Der+ND model is the best for phenylalanine. 2 ) were 0.8514, 0.8458, 0.8431, 0.9328, and 0.9290, respectively. The R 2 reached 0.84-0.89, indicating that the established model is very good. 2 The linear relationship between the actual value and the predicted value of the five amino acids is shown in Figure 3 .

[0087] Table 6 Spectral preprocessing results of serine, threonine and valine

[0088]

[0089]

[0090] Table 7 Spectral preprocessing results of cystine and phenylalanine

[0091]

[0092]

[0093] 3.2.4 Validation of quantitative model

[0094] Ten samples were randomly selected as validation sets, and their infrared spectra were input into the established model to predict the contents of five amino acids in Rehmannia glutinosa. The established amino acid model for Rehmannia glutinosa had the average absolute deviations of -0.03%, 0.02%, 0.01%, -0.02% and -0.01% for serine, threonine, valine, cystine and phenylalanine in Rehmannia glutinosa, respectively. This indicated that the established amino acid model for Rehmannia glutinosa had good predictive ability and could accurately predict the contents of five amino acids in Rehmannia glutinosa (Tables 8 and 9).

[0095] Table 8 Determination results of validation set samples of serine, threonine and valine

[0096]

[0097]

[0098] Table 9 Determination results of validation set samples of cystine and phenylalanine

[0099]

[0100] 4 Conclusion

[0101] The present invention establishes a quantitative model of serine, threonine, valine, cystine and phenylalanine in Rehmannia glutinosa, and uses this model to quantitatively predict five kinds of amino acids in the sample. The research results show that the PLS+CONSTANT+1stDer+SG model is the best for serine, the SMLR+SNV+2nd Der+ND model is the best for threonine, the PLS+CONSTANT+1stDer+ND model is the best for valine, the PLS+SNV+2nd Der+SG model is the best for cystine, and the PLS+CONSTANT+2ndDer+ND model is the best for phenylalanine. The correlation coefficient range is 0.8431-0.9328, the average absolute deviation is -0.03%-0.02%, and the established five kinds of amino acid models in Rehmannia glutinosa have high accuracy. Among the five kinds of amino acids, four kinds of amino acid quantitative models adopt PLS spectral processing, and one kind of amino acid model adopts SMLR spectral processing, indicating that compared with SMLR and PCR spectral processing, PLS spectral processing is more suitable for the establishment of amino acid quantitative models.

[0102] The present invention combines HPLC with mid-infrared spectroscopy to establish a quantitative model for five amino acids in Rehmannia glutinosa. The established model is stable and reliable, and can realize the rapid content determination of amino acids in Rehmannia glutinosa. Compared with other detection methods, mid-infrared spectroscopy has the advantages of fast scanning speed, low cost, simple operation, and non-destructive samples. It can realize accurate and rapid determination of large batches of samples, providing a research basis for ensuring food quality and safety. In the later stage, this research group will apply mid-infrared spectroscopy technology to the analysis of amino acids in other foods, and provide a technical reference for the quality evaluation of amino acids in foods.

Claims

1. A method for quantitatively detecting five amino acids in Rehmannia glutinosa based on mid-infrared spectroscopy, characterized in that: The steps include: 1) Preparation of reference solution Accurately pipette 0.5 mL of the amino acid mixed standard solution for derivatization reaction to obtain the amino acid derivatized mixed standard solution for later use; 2) Preparation of test solution Accurately weigh 3 g of sample, place in a 100 mL conical flask, add 50 mL of water, soak overnight, take 2 mL of supernatant, add 2 mL of 50 g / L 5-sulfosalicylic acid, mix well, let stand for 1 h, accurately add 1 mL of EDTA, extract in a boiling water bath for 1 h, cool to room temperature, filter, collect the filtrate and make up to 100 mL, accurately transfer 0.5 mL for derivatization reaction to obtain the test solution for standby use; 3) The samples were pressed into tablets and infrared spectra were collected to calculate the amino acid content.

2. The method for quantitatively detecting five amino acids in Rehmannia glutinosa based on mid-infrared spectroscopy technology as claimed in claim 1, characterized in that: The specific operation of the derivatization reaction in steps 1) and 2) is as follows: accurately transfer 0.5 mL of sample into a 10 mL volumetric flask, add 0.5 mol·L -1 Adjust the pH to alkaline with NaHCO3, add 0.5 mL of 1% DNFB derivatization reagent, mix well, and keep in a water bath at 60±5℃ for 60±10 min. After cooling, add 0.1 mol·L -1 KH2PO4 solution was diluted to 10 mL, 0.45 μ m membrane filtration.

3. The method for quantitatively detecting five amino acids in Rehmannia glutinosa based on mid-infrared spectroscopy as claimed in claim 2, characterized in that: Alkalinity refers to pH=8-9; the pH of KH2PO4 test solution is 7.

4. The method for quantitatively detecting five amino acids in Rehmannia glutinosa based on mid-infrared spectroscopy as claimed in claim 1, characterized in that: In step 3), the sample tableting process is as follows: 2 mg of crushed Rehmannia glutinosa and 120 mg of KBr are placed in an agate mortar and ground evenly, then an appropriate amount of the evenly ground sample is placed in a tableting mold and pressed at a pressure of 8 MPa for 30 s to form a uniform translucent thin sheet.

5. The method for quantitatively detecting five amino acids in Rehmannia glutinosa based on mid-infrared spectroscopy as claimed in claim 4, characterized in that: In step 3), the slice was placed under an infrared lamp to keep it dry, and a potassium bromide sheet was used as a blank. The infrared spectrum of the sample was collected by an infrared spectrometer. The spectrum collection parameters were: spectrum collection range 4000~400 cm -1 , resolution 4 cm -1 .

6. The method for quantitatively detecting five amino acids in Rehmannia glutinosa based on mid-infrared spectroscopy as claimed in claim 5, characterized in that: Each spectrum was scanned 16 times / s, CO2 and H2O were deducted during scanning, the room temperature was 23~26 ℃, the relative humidity was 25%~35%, each sample was sampled three times in parallel, and each pressed film was scanned twice.

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