CN113109291A - Rapid detection method for thiobarbituric acid value of infant complementary food nutrition package - Google Patents
Rapid detection method for thiobarbituric acid value of infant complementary food nutrition package Download PDFInfo
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- 230000035764 nutrition Effects 0.000 title claims abstract description 84
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- 238000010276 construction Methods 0.000 claims abstract description 8
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- VEXZGXHMUGYJMC-UHFFFAOYSA-N Hydrochloric acid Chemical compound Cl VEXZGXHMUGYJMC-UHFFFAOYSA-N 0.000 claims description 12
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- 238000012795 verification Methods 0.000 claims description 8
- YNJBWRMUSHSURL-UHFFFAOYSA-N trichloroacetic acid Chemical compound OC(=O)C(Cl)(Cl)Cl YNJBWRMUSHSURL-UHFFFAOYSA-N 0.000 claims description 6
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- G01N21/359—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light using near infrared light
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- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
- G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
- G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
- G01N21/3577—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light for analysing liquids, e.g. polluted water
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Abstract
一种婴幼儿辅食营养包硫代巴比妥酸值的快速检测方法，属于婴幼儿辅食营养包检测技术领域。该方法包括婴幼儿辅食营养包硫代巴比妥酸值的测量、婴幼儿辅食营养包近红外光谱的采集、婴幼儿辅食营养包硫代巴比妥酸值快速检测模型的构建、婴幼儿辅食营养包硫代巴比妥酸值的快速检测等5个基本步骤，其中模型预处理方法为一阶导数法，光谱范围为7504～4248，维数为9，R2为81.86，RMSECV为0.083，RPD为2.35。所述方法可以用于快速判定不同批次的婴幼儿辅食营养包中脂肪氧化程度，提高婴幼儿辅食营养包的优品率。具有成本低、测定效率高，适用于大批量检测的优点。
The invention discloses a rapid detection method for the thiobarbituric acid value of nutritional supplementary food for infants and young children, belonging to the technical field of detection of nutritional supplementary food for infants and young children. The method includes the measurement of the thiobarbituric acid value of the complementary food nutrition package for infants and young children, the collection of near-infrared spectra of the complementary food nutrition package for infants and young children, the construction of a rapid detection model for the thiobarbituric acid value of the complementary food nutrition package for infants and young children, the There are 5 basic steps such as rapid detection of thiobarbituric acid value in nutrition package, among which the model preprocessing method is the first derivative method, the spectral range is 7504-4248, the dimension is 9, the R 2 is 81.86, the RMSECV is 0.083, The RPD was 2.35. The method can be used to quickly determine the degree of fat oxidation in different batches of nutritional supplements for infants and young children, and to improve the quality rate of the nutritional supplements for infants and young children. It has the advantages of low cost, high measurement efficiency, and is suitable for mass detection.
Description
Technical Field
The invention belongs to the technical field of detection of infant complementary food nutrition packages, and particularly relates to a rapid detection method of a thiobarbituric acid value of an infant complementary food nutrition package.
Background
The infant complementary food nutrition bag mostly takes the soymilk powder as a base material, has rich nutrition and higher fat content, and is easily oxidized and deteriorated by external influence in the storage and processing processes. The final product of the peroxidation reaction of unsaturated lipid in the infant complementary food nutrition package is Malondialdehyde (MDA), the Malondialdehyde (MDA) and thiobarbituric acid (TBA) react to generate a red substance, the property of the red substance is stable, the red substance has the maximum absorption at 532nm, and Giozon's belief in food industry science and technology' research on quality deterioration in the storage process of infant formula milk powder 'is advanced, 2017, 38(28), and 330-334' indicate that the thiobarbituric acid value can be indirectly used as an index of the oxidation degree of the infant formula milk powder. At present, a main determination method for determining the degree of fatty oxidation by using the thiobarbituric acid value is a GB/T35252-2017 determination-direct method for determining the fatty oxidation degree by using the 2-thiobarbituric acid value of animal and vegetable fat, however, the conventional analysis method needs multiple reagents and multiple devices, consumes a large amount of manpower, has high cost and low working efficiency, and is not suitable for large-batch rapid nondestructive detection.
Near infrared spectroscopy (NIRS) is a rapid, accurate, green and pollution-free detection and analysis technology and is widely applied to the fields of agriculture, medicine, tobacco and chemical analysis. The near infrared spectrum reflects the frequency doubling and frequency combining absorption of the vibration of hydrogen-containing groups such as C-H, N-H, O-H and the like, the near infrared absorption wavelengths and intensities of different groups and substances have obvious difference, and the freshness information of the original sample can be detected through the infrared absorption change of chemical bonds. Compared with the prior art, the near infrared spectrum technology can complete the measurement of the infrared spectrum once only in ten seconds, so that the complex pretreatment process and the use of a large amount of harmful reagents during chemical detection are avoided, the time is effectively saved, and the method has the characteristics of high efficiency, no pollution, low cost and environmental protection.
At present, the Chinese patent application No. CN11272697A is named as a method for detecting the content of thiobarbituric acid based on near-infrared hyperspectrum, a near-infrared technology is used for predicting the thiobarbituric acid value in chicken so as to evaluate the quality of a meat product during storage, the Chinese patent application No. CN104655586A is named as a method for rapidly monitoring fish fat oxidation in a non-contact way based on hyperspectral data fusion, the freshness of the fish is predicted by utilizing the near-infrared hyperspectral technology, the prediction index is mainly the thiobarbituric acid value, however, for infant supplementary food nutrition packages or powders taking soymilk powder as a base material, no related near-infrared technology is applied, only a chemical analysis method is used for detecting the thiobarbituric acid value, and the quality of the powders is evaluated. The method is time-consuming and labor-consuming, can also consume a large amount of samples and chemical reagents, is not on site, and cannot meet the requirements of packaging, transporting and storing the infant complementary food nutrition packages.
Therefore, a method which can combine the near infrared technology with the determination of the thiobarbituric acid value to realize the rapid determination of the thiobarbituric acid value in the infant nutrition package so as to predict the storage period and the shelf life of the infant nutrition package is urgently needed at present.
Partial Least Squares (PLS) is an algorithm widely used for near-infrared analysis, is listed in ASTM-E-1655 infrared multivariate quantitative analysis standard, and is used as a standard algorithm for near-infrared analysis.
The judgment of the degree of the model is generally carried out by three evaluation indexes: first is to determine the coefficient R for the model2It isThe degree of closeness of correlation between the predicted value and the measured value is determined by the size of the reference point; the second is a predicted root mean square error RMSECV which reflects the deviation degree between a predicted value and an actually measured value in the cross test; the third is relative analysis error RPD, which reflects the deviation degree between the predicted value and the measured value in the cross-examination. A good model should have a high R2And lower RMSECV value, when 1.0<RPD<1.4, the model prediction capability is poor; when 1.4<RPD<At 1.8, the model can be used for relevance assessment; when 1.8<RPD<At 2.0, the model can be used for quantitative prediction; when RPD>2, better quantitative prediction can be performed. Therefore, the invention uses R in the modeling process2RMSECV is used as an index, and a proper data preprocessing mode, a spectrum interval and a dimension are selected; with R2And establishing results of the RMSECV and RPD evaluation models.
Disclosure of Invention
The invention aims to provide a method for rapidly detecting the thiobarbituric acid value of an infant complementary food nutrition package. The method is combined with a near infrared spectrum and a spectrophotometry to construct a thiobarbituric acid value model capable of realizing rapid evaluation of the nutrition package, and the fat oxidation degree in the infant complementary food nutrition packages of different batches is predicted by applying the thiobarbituric acid value model, so that the high-quality rate of the infant complementary food nutrition packages is rapidly evaluated.
The invention relates to a method for quickly detecting an acid value of thiobarbituric acid of an infant complementary food nutrition package, which comprises 5 basic steps of measuring the acid value of the thiobarbituric acid of the infant complementary food nutrition package, acquiring a near infrared spectrum of the infant complementary food nutrition package, constructing a model for quickly detecting the acid value of the thiobarbituric acid of the infant complementary food nutrition package, evaluating the model for quickly detecting the acid value of the thiobarbituric acid of the infant complementary food nutrition package, and quickly detecting the acid value of the thiobarbituric acid of the infant complementary food nutrition package, wherein the model preprocessing method is a first-order derivative method, the spectral range is 7504-4248, the dimension is 9, and R is a first derivative method281.86 with RMSECV of 0.083 and RPD of 2.35.
The invention relates to a method for rapidly detecting an acid value of thiobarbituric acid of an infant complementary food nutrition package, which comprises the following steps:
1) complementary food nutrition bag for infantsMeasurement of thiobarbituric acid value: selecting a plurality of batches (20-500) of infant complementary food nutrition bags, respectively weighing 1g of samples in the infant complementary food nutrition bags, adding 5mL of TBARS solution (aqueous solution containing 2-thiobarbituric acid, trichloroacetic acid and hydrochloric acid, wherein the concentration of the 2-thiobarbituric acid is 0.375 wt%, the concentration of the trichloroacetic acid is 15 wt%, and the concentration of the hydrochloric acid is 0.25mol/L), carrying out boiling water bath for 10-20 min, and carrying out running water cooling; centrifuging at 8000r/min at 4 deg.C for 10-20 min; taking 1-3 mL of supernatant, and measuring absorbance A at 532nm532Then, the thiobarbituric acid value (TBA, mg/kg) of the sample in the detected infant complementary food nutrition bag is calculated by the following formula,
TBA(mg/kg)＝A532×2.77
2) collecting a near infrared spectrum of an infant complementary food nutrition pack sample: taking infant supplementary food nutrition packages of the same batches as the infant supplementary food nutrition packages in the step 1), respectively taking 30-40 g of samples in the infant supplementary food nutrition packages, putting the samples into a quartz sample cup, paving the samples, and collecting the near infrared spectrum of the sample to be detected by using an integrating sphere diffuse reflection mode with air as a background; the collection range of the near-infrared spectrometer is 12000-4000 cm-1The resolution is 4-16 cm-1Scanning 16-64 times;
3) constructing a rapid detection model of the thiobarbituric acid value of the infant complementary food nutrition package: and (3) mixing the infant complementary food nutrition packages of multiple batches according to the ratio of 10-15: 1, randomly dividing the sample into a correction set sample (for establishing a model) and a verification set sample (for verifying the model); using a correction set sample as a data source, using OPUS software to perform data preprocessing on absorbance data at different wavelengths in a near infrared spectrum by adopting a first derivative method, selecting a spectral range of 7504-4248, setting a dimension of 9, and then constructing a rapid detection model of the thiobarbituric acid value of the infant complementary food nutrition package by adopting a partial least square method; r of the rapid detection model281.86, RMSECV 0.083, RPD 2.35, model file exported after construction q2 suffix; by utilizing the rapid detection model, the thiobarbituric acid value of a sample can be predicted through the obtained absorbance data at different wavelengths in the near infrared spectrum;
4) evaluation of a rapid detection model of thiobarbituric acid value of the infant complementary food nutrition package: taking a verification set sample as a data source, carrying out data preprocessing on absorbance data at different wavelengths in a near infrared spectrum by using an OPUS software through a first-order derivative method, then using the rapid detection model of the thiobarbituric acid value of the infant complementary diet nutrition package obtained in the step 3), predicting the thiobarbituric acid value of the infant complementary diet nutrition package through the obtained absorbance data at different wavelengths in the near infrared spectrum to obtain a predicted value of the verification set sample, comparing the predicted value with an actual measured value, and calculating a standard deviation;
5) quick detection of the thiobarbituric acid value of the infant complementary food nutrition package: collecting the near infrared spectrum of the sample to be detected according to the method in the step 2) by taking the infant complementary food nutrition bag to be detected; and then carrying out data preprocessing on absorbance data at different wavelengths in the near infrared spectrum by using OPUS software through a first-order derivative method, and predicting the thiobarbituric acid value of the sample through the obtained absorbance data at different wavelengths in the near infrared spectrum by using the rapid detection model of the thiobarbituric acid value of the infant dietary supplement nutrition package obtained in the step 3).
A rapid detection model for an acid value of thiobarbituric acid of an infant complementary food nutrition package is characterized in that data preprocessing is carried out by using a first-order derivative, and the spectrum range is 7504-4248, R281.86 with RMSECV of 0.083 and RPD of 2.35>2, the standard deviation between the measured value and the predicted value is less than or equal to 0.03606.
The construction of the rapid detection model for the thiobarbituric acid value of the infant complementary food nutrition package is characterized in that: the near infrared spectrum of the infant complementary food nutrition bag uses vector normalization, multivariate scattering correction, first derivative + vector normalization, first derivative + multivariate scattering correction and other preprocessing methods and RMSECV, R in the data preprocessing process2And carrying out model optimization for the indexes. Experimental results show that the processing method of the first derivative can obtain the best prediction result.
The construction of the model for quickly detecting the thiobarbituric acid value of the infant complementary food nutrition package is characterized in that during the selection process of the near-infrared spectrum of the infant complementary food nutrition package in a spectral interval, the near-infrared spectrum is selected by RMSECV,R2As indexes, in spectral ranges of 7504-4248, 7504-6096, 5646-4248, 7504-5548, 4600-4248, 9400-6096, 9400-4248, 6104-4248, 5880-4248 and 5456-4248 cm-1Selecting one or more of the above-mentioned groups for optimization, and selecting the group with the smallest RMSECV, R2The spectral interval of (a).
The method is characterized in that the dimension range is 1-10 in the dimension selection process of the infant complementary food nutrition package near infrared spectrum.
The rapid detection model for the thiobarbituric acid value of the infant complementary diet nutrition package is characterized in that the thiobarbituric acid value of the nutrition package which can be used for detection ranges from 0 mg/kg to 2.5 mg/kg.
The method for rapidly detecting the thiobarbituric acid value of the infant complementary food nutrition package not only can be used for rapidly determining the content of the thiobarbituric acid value of the infant complementary food nutrition package in different batches, but also can monitor the deterioration degree of the infant complementary food nutrition package in storage and sale, and can be widely applied to the field of rapid detection of the thiobarbituric acid value in fortified food. Has the advantages of low cost, high measuring efficiency and suitability for mass detection.
Drawings
FIG. 1: raw near infrared spectra of 81 samples as described in example 1 (wave number on the abscissa, absorbance on the ordinate, different curves representing the near infrared spectra of the samples measured);
FIG. 2: the near infrared spectrum described in example 1 is subjected to first derivative preprocessing and a spectrum range screening result curve (the abscissa is the wave number, the ordinate is the absorbance, the graph is obtained after derivation of the curve in fig. 1, and interferences such as baseline drift, noise, spectral line shift and the like can be eliminated through data preprocessing);
FIG. 3: example 1 a correlation diagram between predicted values and actual values in a model correction set (data points in the diagram represent the comparison between predicted values and actual values, and the closer a data point is to a straight line in the diagram, the Y is equal to X, which means that the prediction result is more accurate, and 75 data points are in total);
FIG. 4: the near infrared spectrum described in example 2 is subjected to vector normalization preprocessing and a spectrum range screening result curve (the abscissa is the wave number, the ordinate is the absorbance, the graph is obtained after derivation of the curve in fig. 1, and interferences such as baseline drift, noise, spectral line shift and the like can be eliminated through data preprocessing);
FIG. 5: example 2 a correlation diagram between predicted values and actual values in a model correction set (data points in the diagram represent the comparison between predicted values and actual values, and the closer a data point is to a straight line in the diagram, the Y is equal to X, which means that the prediction result is more accurate, and 75 data points are in total).
Detailed Description
Example 1:
(1) 81 batches of samples in the infant complementary food nutrition bag (Ganzhou city full-standard biotechnology limited: infant complementary food nutrition bag (complementary food nutrition supplement)) are taken, 1g of powder is accurately weighed for each batch of samples, 5mL of TBARS solution (aqueous solution containing 2-thiobarbituric acid, trichloroacetic acid and hydrochloric acid is added into a glass test tube, wherein the concentration of the 2-thiobarbituric acid is 0.375 wt%, the concentration of the trichloroacetic acid is 15 wt%, and the concentration of the hydrochloric acid is 0.25mol/L) is subjected to boiling water bath for 15min, and the samples are cooled by running water. Placing the sample in a low-temperature high-speed centrifuge, setting the centrifugation temperature to be 4 ℃ and the centrifugation temperature to be 8000r/min, centrifuging for 15min, taking 2mL of supernatant, measuring the absorbance at 532nm, calculating the thiobarbituric acid value of the infant and baby complementary food nutrition package to be detected by using the following formula, obtaining the thiobarbituric acid values of 81 samples, and calculating to obtain the thiobarbituric acid values of the 81 samples, wherein the average value of the 81 samples is 1.05, the standard deviation is 0.194925, the variation coefficient is 18.45%, and the results are shown in Table 1:
TBA(mg/kg)＝A532×2.77
table 1: test of thiobarbituric acid value of 81 batches of samples
(2) Taking the 81 batches of infant complementary foodTaking 30g of infant complementary food nutrition bag powder from each batch of samples in a nutrition bag, putting the infant complementary food nutrition bag powder into a quartz sample cup and paving the infant complementary food nutrition bag powder, wherein the air is taken as a background, and the acquisition range of a near-infrared spectrometer is 12000-4000 cm-1Resolution of 16cm-1Scanning for 64 times, and acquiring the near infrared spectrum of the sample to be detected in an integrating sphere diffuse reflection mode to obtain a near infrared spectrogram as shown in figure 1, wherein the horizontal and vertical marks are wavelengths, and the vertical coordinate is absorbance; because the thiobarbituric acid value of each sample is different, the absorbance data of the obtained infrared spectrogram at different wavelengths are also different;
(3) the 81 samples to be tested of the infant complementary food nutrition package are randomly divided into 75 correction set samples (for establishing a model) and 6 verification set samples (for verifying the model). The method comprises the steps of taking 75 correction set samples as data sources, using OPUS software to conduct data preprocessing on absorbance data of a near infrared spectrum at different wavelengths by adopting a first derivative method, selecting a spectral range of 7504-4248, setting a dimension of 9, enabling a spectrum preprocessing result to be shown in figure 2, then adopting a partial least square method to establish a rapid detection model of the thiobarbituric acid value of the infant complementary food nutrition package, and exporting a model file with q2 as a suffix after construction. In a rapid detection model, the thiobarbituric acid value of a sample can be predicted through absorbance data at different wavelengths in the obtained near infrared spectrum.
For example, as shown in fig. 3, the predicted value and the measured value of the correction set data after model processing are mostly close to the straight line Y-X, which indicates that the predicted value is very close to the actual value. Fast detection of R of model281.86 with RMSECV of 0.083, RPD 2.35, RPD>2 a better quantitative prediction was performed, using a validation set to verify the model accuracy, with RSMEP 0.01589 as shown in table 2.
In the actual inspection process, the established thiobarbituric acid value rapid detection model file is called, 1 infant complementary food nutrition pack sample (except for 81 samples) with unknown thiobarbituric acid value is taken, the sample near infrared spectrum is obtained by scanning, the absorbance data at different wavelengths in the near infrared spectrum is subjected to data preprocessing by using an OPUS software through a first-order derivative method, the absorbance data is brought into the established thiobarbituric acid value rapid detection model of the complementary food nutrition pack, the thiobarbituric acid value of the sample is predicted, the predicted value is 1.201mg/kg, and the actual value is 1.249 mg/kg.
Table 2: verification set predicted value and actual value comparison data
Example 2:
(1) same as example 1, step (1);
(2) same as example 1, step (2);
(3) the 81 samples to be tested of the infant complementary food nutrition package are randomly divided into 75 correction set samples (for establishing a model) and 6 verification set samples (for verifying the model). The method comprises the steps of taking 75 correction set samples as data sources, using OPUS software to conduct data preprocessing on near infrared spectrum data, enabling the data preprocessing mode to be a vector normalization method (vector normalization, multivariate scattering correction, first-order derivatives, vector normalization, first-order derivatives and multivariate scattering correction are all applicable to the method, enabling the data preprocessing mode capable of obtaining the best prediction result to be the first-order derivatives after screening, enabling the spectrum preprocessing result to be as shown in figure 4, selecting a spectrum range of 6104-4248 and setting the dimension to be 9, building a rapid detection model of the thiobarbituric acid value of the infant complementary food nutrition package, and exporting a model file with q2 as a suffix after construction, enabling the predicted value and an actually measured value of the correction set data after model processing to be as shown in figure 5, enabling most of the prediction result to be close to a straight line Y (X), and enabling the predicted value to be close to the actual value. R of the model277.89, RMSECV 0.0917, RPD 2.13, RPD>2 a better quantitative prediction was performed, using a validation set to verify the model accuracy, with RSMEP 0.07037869 as shown in table 3.
Table 3: verification set predicted value and actual value comparison data
In the actual inspection process, the established thiobarbituric acid value rapid detection model file is called, 1 infant and child complementary food nutrition pack sample (except for 81 samples) with unknown thiobarbituric acid value is taken, the sample near infrared spectrum is obtained through scanning, the sample near infrared spectrum is brought into the established thiobarbituric acid value rapid detection model of the complementary food nutrition pack, the thiobarbituric acid value of the sample is predicted, the predicted value is 1.198mg/kg, and the actual value is 1.249 mg/kg.
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2000056452A (en) * | 1998-08-06 | 2000-02-25 | Mitsubishi Chemicals Corp | Positive photosensitive composition and positive lithographic printing plate |
| CN104655586A (en) * | 2015-02-28 | 2015-05-27 | 华南理工大学 | Hyperspectral-data-fusio-based fast non-contact fish fat oxidation monitoring method |
| CN105588819A (en) * | 2016-03-11 | 2016-05-18 | 江西出入境检验检疫局检验检疫综合技术中心 | Method for conducting near-infrared rapid detection on component content in infant formula rice flour |
| CN107655852A (en) * | 2017-09-29 | 2018-02-02 | 广东出入境检验检疫局检验检疫技术中心 | The near infrared spectrum quick determination method of essential nutrient in baby formula milk powder |
| CN109781722A (en) * | 2019-03-28 | 2019-05-21 | 中国林业科学研究院林业研究所 | A kind of determination method of malondialdehyde content in catalpa leaves |
| CN111272697A (en) * | 2020-04-27 | 2020-06-12 | 江苏益客食品集团股份有限公司 | Thiobaturic acid content detection method based on near-infrared hyperspectrum |
-
2021
- 2021-05-13 CN CN202110520637.3A patent/CN113109291A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2000056452A (en) * | 1998-08-06 | 2000-02-25 | Mitsubishi Chemicals Corp | Positive photosensitive composition and positive lithographic printing plate |
| CN104655586A (en) * | 2015-02-28 | 2015-05-27 | 华南理工大学 | Hyperspectral-data-fusio-based fast non-contact fish fat oxidation monitoring method |
| CN105588819A (en) * | 2016-03-11 | 2016-05-18 | 江西出入境检验检疫局检验检疫综合技术中心 | Method for conducting near-infrared rapid detection on component content in infant formula rice flour |
| CN107655852A (en) * | 2017-09-29 | 2018-02-02 | 广东出入境检验检疫局检验检疫技术中心 | The near infrared spectrum quick determination method of essential nutrient in baby formula milk powder |
| CN109781722A (en) * | 2019-03-28 | 2019-05-21 | 中国林业科学研究院林业研究所 | A kind of determination method of malondialdehyde content in catalpa leaves |
| CN111272697A (en) * | 2020-04-27 | 2020-06-12 | 江苏益客食品集团股份有限公司 | Thiobaturic acid content detection method based on near-infrared hyperspectrum |
Non-Patent Citations (1)
| Title |
|---|
| 孙群: "肉制品脂类氧化:硫代巴比妥酸试验测定醛类物质", 食品科学 * |
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