Mid-infrared rapid batch detection method for monounsaturated fatty acids in milk

Abstract

The present invention belongs to the field of dairy cow performance determination and milk quality detection, and discloses a method for rapid batch detection of monounsaturated fatty acids in milk by mid-infrared spectroscopy. In terms of the selection of characteristic bands, the commonly used algorithm screening feature is broken, and a method of manual selection + multiple traversals is used. Finally, the characteristic bands used for modeling are selected, especially the absorption area containing part of water is screened out, and it is proved that adding part of the water absorption band can improve the accuracy of the model. The optimal pretreatment and algorithm combination for the establishment of the monounsaturated fatty acid model is selected, the optimal parameters are determined, and the accuracy of the model is improved. Rapid, accurate, and low-cost detection of the monounsaturated fatty acid content in raw milk is achieved, and rapid batch detection is achieved.

Description

Technical Field

[0001] The invention belongs to the field of dairy cow performance determination and milk quality detection, and particularly relates to a mid-infrared spectrum rapid batch detection method for monounsaturated fatty acids in milk. Background Art

[0002] Milk has always been considered one of the most perfect foods naturally formed in nature. Milk is one of the natural sources of fatty acids. It is rich in linoleic acid, linolenic acid, and especially conjugated linoleic acid, which are beneficial to human health. It has an important impact on human health and has become one of the most important sources of nutrition for Chinese people.

[0003] Milk is an important food source for humans. Milk fat contains about 400 fatty acids, of which monounsaturated fatty acids account for about 25% of the total fatty acids. [1-2] Monounsaturated fatty acids have the effect of lowering blood cholesterol, triglycerides and low-density lipoprotein cholesterol (LDL-C). The main monounsaturated fatty acid in milk fat is oleic acid (C18:1). Some scholars have found that the higher the mass fraction of oleic acid in milk fat, the lower the melting point of butter processed from it. Dairy products such as cheese made from it have higher solubility and softer texture. [3] , and is closely related to human health. Monounsaturated fatty acids are considered beneficial to human health. [4-5] In addition, the fatty acid content in milk can also reflect the individual energy and health status of dairy cows. [6-7] There is currently a national standard for the determination of saturated fatty acids in milk, namely the National Food Safety Standard - Determination of Fatty Acids in Food (GB5009.168-2016). my country stipulates that fatty acids in milk and dairy products should be detected by gas chromatography (GB5009.168—2016). [8] This method can quantitatively detect the types and contents of fatty acids, but the sample pretreatment process is complicated, time-consuming, and costly. Researchers have tried to improve this method. The main methods for milk fat detection at home and abroad include gas chromatography, ultraviolet spectrophotometry, liquid chromatography, etc., but they all have problems such as high cost and low efficiency, and it is difficult to use them quickly in batches in production practice. Mid-infrared spectroscopy MIR (Mid-Infrared Spectroscopy) technology is a very economical and efficient detection tool. It predicts the properties or content of the substances and traits under study by the difference in the frequency and corresponding peaks of the absorption of mid-infrared light on specific chemical bonds in molecules, thereby obtaining information such as the physiological state of the organism. With the rise of Fourier Transform Mid-Infrared (FT-MIR) spectroscopy in the dairy testing industry and the continuous improvement of the accuracy of predicting the fatty acid content in milk [9-11]This technology has become a routine, rapid and effective method for large-scale detection of fatty acid content in milk, and also provides great potential for genetic analysis and selection of fatty acids.

[12] However, there is currently no high-precision rapid batch detection model for monounsaturated fatty acid content in the livestock industry.

[0004] At present, foreign countries have already started the research based on mid-infrared detection of material components in milk, but there are problems such as low precision and inaccurate characteristic bands, and there is no relevant report in China. Compared with other countries, Chinese dairy cows are affected by domestic climate, geographical environment and feeding conditions, and there are large differences with foreign dairy cows. The quality of milk also has its characteristics. Foreign detection models are not necessarily suitable for Chinese dairy cows. Therefore, it is necessary to establish a polyunsaturated fat content detection method suitable for Chinese dairy cow milk with my country's independent property rights as soon as possible, which can not only be used for fast, efficient, accurate and non-invasive determination of monounsaturated fatty acid content, an important performance index in dairy cow lactation traits, but also provide phenotypic data and theoretical basis for dairy cow genetic breeding, and can detect and analyze monounsaturated fatty acid content in raw milk and dairy products, and provide reference for dairy processing industry and consumers. The purpose of the present invention is to solve the existing problems, establish a rapid detection technology of monounsaturated fatty acids in milk, and provide technical guarantee for the development of China's dairy industry. Summary of the invention

[0005] The purpose of the present invention is to provide a mid-infrared spectrum rapid batch detection method for monounsaturated fatty acids in milk, which has the advantages of simplicity, rapidity and batch detection.

[0006] Another object of the present invention is to provide an application of a method for rapid batch detection of monounsaturated fatty acids in milk using mid-infrared spectroscopy.

[0007] In order to achieve the above object, the present invention adopts the following technical measures:

[0008] A mid-infrared spectroscopy rapid batch detection method for monounsaturated fatty acids in milk comprises the following steps:

[0009] 1. The characteristic band of the infrared spectrum of the milk sample is: 979.93cm -1 -1060.95cm -1 、1261.57cm -1 -1369.59cm -1 、1647.37cm -1 -1689.80cm -1 、2037.02cm -1 -2303.23cm -1 、2418.97cm -1 -2492.27cm -1、2704.46cm -1 -3074.83cm -1 、3090.26cm -1 -3460.63cm -1 、3468.34cm -1 -3645.81cm -1 、3830.99cm -1 -4232.23cm -1 、4305.53cm -1 -4452.13cm -1 、4621.88cm -1 -5015.40cm -1 MIR data in;

[0010] 2. Substitute the MIR data obtained by the measurement into the diff2+diff1+PLSR (n_component=5) model to output the prediction results of the monounsaturated fatty acid content.

[0011] The diff2 is the second-order difference, and diff1 is the first-order difference.

[0012] In the method described above, preferably, in step 1, a gap of two wave points is allowed before and after each wave band.

[0013] The protection content of the present invention also includes: the above detection method is used to detect the content of monounsaturated fatty acids in milk.

[0014] Compared with the prior art, the present invention has the advantages of:

[0015] 1. In terms of the selection of characteristic bands, we broke the common method of using algorithms to screen features, and instead used a method of manual selection + multiple traversals. Finally, we selected the characteristic bands for modeling, especially the absorption area containing some water, and proved that adding some water absorption bands can improve the accuracy of the model.

[0016] 2. The optimal preprocessing and algorithm combination for establishing the monounsaturated fatty acid model was selected, the optimal parameters were determined, and the accuracy of the model was improved.

[0017] 3. It realizes the rapid, accurate and low-cost detection of monounsaturated fatty acid content in raw milk and realizes rapid batch detection. The measurement time of each sample is only 10-15 seconds, which improves the detection efficiency and has strong practicality. It will be widely used in dairy cow performance measurement and milk quality detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1The infrared spectrum of the untreated milk sample (a) and the average spectrum (b).

[0019] Figure 2 This is the mid-infrared spectrum of the milk sample after diff2+diff1 pretreatment.

[0020] Figure 3 The overall spectrum of the eleven selected characteristic bands (a) and the enlarged view of each characteristic band (b).

[0021] Figure 4 This is the correlation diagram between the actual value and predicted value of the model milk data and the fitted straight line diagram. DETAILED DESCRIPTION

[0022] The technical solutions described in the present invention, unless otherwise specified, are all conventional solutions in the art; the reagents or materials described, unless otherwise specified, are all from commercial channels.

[0023] 1. Experimental Materials

[0024] The experimental materials came from 266 Chinese Holstein cows in 36 dairy farms in 11 provinces in five regions of my country. A milk sample was collected from each cow. The milk sample collection was completed using an automatic milking device. First, the milk room was wiped with a sterilized towel, and then the udder was disinfected with an iodine-glycerin mixed solution. After squeezing out the first three handfuls of milk, milk samples were collected from the entire milking process. 40 ml of each milk sample was collected and packaged into new cylindrical sampling bottles with a diameter of 3.5 cm and a height of 9 cm. They were numbered in sequence, and bronopol preservative was immediately added to each sampling bottle and shaken slowly to fully dissolve it. Ice bags (2-4°C) were placed around the milk samples on the way back to prevent deterioration. Spectra were collected immediately after the samples arrived at the laboratory.

[0025] 2. Mid-infrared spectrum measurement and collection

[0026] The sample was poured into a cylindrical sample tube with a diameter of 3.5 cm and a height of 9 cm, and then placed in a water bath at 42°C for 15-20 min. The sample was scanned using the MilkoScan TM The 7RM milk composition detector extends the solid fiber optic probe into the liquid, mixes the sample and then scans it.

[0027] 3. Detection method of the true (reference) value of monounsaturated fatty acids in milk

[0028] 3.1 Instruments, equipment and reagents

[0029] Electric constant temperature water bath (Wuhan Yiheng Sujing Scientific Instrument Co., Ltd.); Agilent gas chromatograph, including autosampler, column oven, injection bottle, vortex oscillator, syringe filter, 0.22 μm nylon filter membrane, 100 m × 0.25 mm × 0.2 μm, fused silica capillary column, helium.

[0030] Undecanoic acid methyl ester C11:0FAME (CAS No. 1731-86-8): ≥99%, 94118-1ML, purchased from Supelco; tridecanoic acid triglyceride C13:0TAG (CAS No. 26536-12-9): ≥99%, T3882-500MG purchased from Sigma-Aldrich; FAME mixed standard (37 fatty acids): , 1269119-100MG, purchased from Sigma-Aldrich; other reagents were all domestically produced chromatographic grade.

[0031] 3.2 Experimental methods

[0032] 3.2.1 Collection of mid-infrared spectra

[0033] MilkoScanTM FT+ was used for spectrum acquisition. The specific acquisition steps were as follows: milk samples were placed in batches in a 45°C electric constant temperature water bath for preheating for 5 minutes, the preheated milk samples were placed on the detection rack and shaken up and down several times to mix the milk colloidal solution evenly, the detection rack was placed on the detection track, the bottle cap was opened, and the tests were performed in sequence. After the spectrum was collected, the milk samples were frozen at -20°C for subsequent determination of the monounsaturated fatty acid content.

[0034] 3.2.2 Determination of monounsaturated fatty acid content by gas chromatography

[0035] (1) Lipid extraction: dichloromethanol method (commonly used at home and abroad, simpler) [12-13]

[0036] (15ml centrifuge tube) 1ml milk was added with 1.8ml water and 8ml CH2Cl2 / CH3OH (2:1, v / v), vortexed for 10min, centrifuged at 8000rmp for 10min, and the lower layer was taken. The remaining solution was added with 4ml CH2Cl2 / CH3OH (2:1, v / v) and extracted again. The combined extracts were blown with liquid nitrogen.

[0037] (2) Fatty acid methyl esterification: KOH methanol solution method

[0038] 50 mg lipid was added with 1 mL of n-hexane, and then 50 μL of KOH-MeOH solution (2 M) was added for methyl esterification. Vortexed for 30 seconds, centrifuged at 8000 rpm for 5 minutes, and the supernatant was passed through a membrane for GC-MS analysis.

[14]

[0039] (3) Gas chromatography instrument parameters

[0040] Split injection mode

[0041] Chromatographic column: 100m×0.25mm×0.2μm, fused silica capillary column

[0042] Stationary phase: cyanopropyl-polysiloxane

[0043] Carrier gas: Helium

[0044] Column head pressure: 225kPa (175kPa-225kPa)

[0045] Total flow rate: 25.5ml / min

[0046] Split ratio: 10:1

[0047] Injector temperature: 250°C

[0048] Detector temperature: 275°C

[0049] Injection volume: 1μl.

[0050] Temperature programming:

[0051] Time / min Temperature / ℃ Retention time / min Rate / ℃ / min 0 60 5 5 165 1 15 10 225 20 2

[0052] (4) The sum of all nine measured monounsaturated fatty acid contents is the monounsaturated fatty acid content.

[0053] 4. Selection of effective samples

[0054] Among the 266 samples, invalid data caused by sample deterioration, loss, abnormal mid-infrared spectrum measurement, abnormal sample reference value measurement, etc. were eliminated. A total of 34 abnormal samples were eliminated, and 232 samples were selected for model establishment and optimization.

[0055] Embodiment 1:

[0056] Selection of prediction model algorithm for monounsaturated fatty acids:

[0057] The purpose of this application is to establish a quantitative determination model for monounsaturated fatty acids, so the modeling algorithm used is a regression algorithm. There are many types of regression algorithms. This embodiment mainly uses Ridge regression (Ridge) and partial least squares regression (PLSR) algorithms for model establishment and comparison, for the following reasons:

[0058] Ridge regression is a type of linear regression. When the algorithm establishes the regression equation, ridge regression adds regularization restrictions to achieve the effect of solving overfitting. There are two types of regularization, namely l1 regularization and l2 regularization. The advantages of l2 regularization over l1 regularization are: (1) cross-validation can be performed and (2) stochastic gradient descent is realized. Ridge regression is a linear regression model with l2 regularization added. It retains the advantages of linear regression, meets the requirements of model establishment, and has relatively stable results. It is one of the more commonly used basic algorithms. Therefore, this algorithm is selected as the candidate algorithm in this embodiment.

[0059] The partial least squares regression algorithm is one of the most effective algorithms in multi-feature samples. In the mid-infrared spectral data, each sample corresponds to 1060 wave points, which is a representative of multi-feature samples. At the same time, the partial least squares regression algorithm rarely overfits, so many mid-infrared spectroscopy researchers will choose to use the partial least squares regression algorithm to establish the model. Therefore, this embodiment selects this algorithm as the candidate algorithm.

[0060] Embodiment 2:

[0061] Screening of preliminary model algorithm and optimal preprocessing combination for mid-infrared spectroscopy:

[0062] Effective feature screening is a basic operation for processing spectral data, the purpose is to eliminate noise and lay a good foundation for feature extraction. Effective feature screening mainly includes feature extraction, feature preprocessing and feature dimension reduction. This embodiment mainly uses five processing methods such as SG (convolution smoothing), MSC (multiple scatter correction), SNV (standard normal variable transformation), diff1 (first order difference) and diff2 (second order difference) to perform feature preprocessing on spectral data.

[0063] In this embodiment, each sample corresponds to a piece of MIR spectrum data. The full spectrum band is substituted into the model to compare the accuracy of the model. Two algorithms and six methods (including no preprocessing) are used for one-to-one pairing to determine the initial optimal algorithm and preprocessing combination of the model. The results are as follows:

[0064] The comparison results are as follows:

[0065]

[0066]

[0067] After comparison, it was found that the combination of partial least squares regression and second-order difference (PLSR+diff2) had better overall effect and was less overfitting than the Ridge algorithm, so the initial optimal algorithm and preprocessing combination of the model was finally selected: PLSR+diff2.

[0068] Embodiment 3:

[0069] Establishment of a method for detecting monounsaturated fatty acid content in milk using mid-infrared spectroscopy:

[0070] 1. Division of modeling data set

[0071]

[0072] In the modeling data set division in this embodiment, 80% is the training set and 20% is the test set. The ratio of the training set to the test set is 4:1. At the same time, the training set is also called the cross-validation set. A 10-fold cross-validation is performed during the model training process.

[0073] 2. Manual selection process and determination of modeling feature bands

[0074] There are many methods for selecting feature bands, mainly including algorithmic feature selection and manual feature selection. The principle of algorithmic feature selection is mainly derived from the correlation between each wave point and the reference value. The advantages are fast speed and high efficiency, but the disadvantages are that the synergy between adjacent wave points is ignored and the idea is relatively simple. The advantage of manual feature selection is that the role of the band (that is, adjacent wave points) can be strengthened during the selection process. At the same time, the original information state of the spectrum can be retained more in the process of improving the model. It has stronger inclusiveness and generalization ability and accurate band selection. The disadvantages are slow selection speed and low efficiency.

[0075] This embodiment selects the characteristic band by manual selection, and the selection steps are as follows:

[0076] (1) Determine the basic algorithm and pretreatment combination. From Example 2, it can be seen that the PLSR+diff2 combination has a better overall effect, so the PLSR+diff2 combination is finally selected as the preliminary model algorithm and pretreatment combination for monounsaturated fatty acids.

[0077] (2) Determine the best second preprocessing. During the modeling process, sometimes only one preprocessing will limit the generalization ability and optimization potential of the model. In the modeling process of this embodiment, it is found that a second preprocessing can better optimize the model, so the result of the preliminary model algorithm selection and preprocessing combination is subjected to a second preprocessing to select the best second preprocessing method. After comparison, it is found that the second preprocessing using diff1 has a more obvious improvement on the effect of the training set, and the effect of the test set has not declined seriously, the optimization potential is large, and the overfitting of diff2 is more serious, so diff1 is finally selected as the best second preprocessing method (the results are shown in the following table).

[0078]

[0079] The manual selection process of modeling feature bands is as follows:

[0080] (1) The whole band is segmented into 100 wave points per segment, and finally divided into 11 segments. According to the preliminary experiment, it is found that retaining some spectral regions related to water absorption can improve the accuracy of the model. Therefore, in this step, the spectral regions related to water absorption are not removed.

[0081] (2) Taking 50 wave points as a group, the partial least squares regression algorithm is used to first increase or decrease a group of wave points at both ends of the critical point of the first band to find the optimal effect. Based on this, similar operations are performed on the second band. Finally, the first traversal is completed after all 11 bands have completed a round of operations.

[0082] (3) After the first traversal is completed, perform a second, third, or more manual traversals until all wave points no longer change, which is the optimal characteristic band.

[0083] Finally, after twelve rounds of screening, the optimal results were obtained, as shown in the table:

[0084]

[0085] The final selected characteristic band result is: 979.93cm -1 -1060.95cm -1 、1261.57cm -1 -1369.59cm -1 、1647.37cm -1 -1689.80cm -1 、2037.02cm -1 -2303.23cm -1 、2418.97cm -1 -2492.27cm -1 、2704.46cm -1 -3074.83cm -1 、3090.26cm -1 -3460.63cm -1 、3468.34cm -1 -3645.81cm -1 、3830.99cm -1 -4232.23cm -1 、4305.53cm -1 -4452.13cm -1 、4621.88cm -1 -5015.40cm -1, a gap of two wave points is allowed before and after each segment. The results show that after adding the first segment water absorption and part of the second segment water absorption area to the model, the model can achieve the optimal effect, indicating that the characteristic band of monounsaturated fatty acids contains part of the water absorption area.

[0086] 3. Screening and determination of model parameters

[0087] Model parameters include parameters of preprocessing methods and algorithm parameters. The diff2 and diff1 preprocessing methods involved in this model have no parameters; the main parameters are the parameters of the partial least squares regression algorithm: principal component (n_component). The comparison of parameter selection results is as follows (partial):

[0088]

[0089] According to the comparison results, the main component (n_component) is finally selected as 5.

[0090] After comparative analysis, the best regression model for saturated fatty acids was: diff2 (second-order difference) + diff1 (first-order difference) + PLSR (n_component = 5) model. The correlation coefficients of the training set and the test set were 0.8566 and 0.8558 respectively; the root mean square errors of the training set and the test set were 1.9292 and 1.9186 respectively.

[0091] Embodiment 4:

[0092] Application of rapid batch detection method of mid-infrared spectroscopy MIR for monounsaturated fatty acids in milk:

[0093] The established optimal regression model of monounsaturated fatty acids (diff2 (second-order difference) + diff1 (first-order difference) + PLSR (n_component = 5)) was used to predict 6 randomly selected external milk samples (not one of the 232 experimental materials), and the predicted results were compared with the true values.

[0094] 1. The characteristic band of the infrared spectrum of the milk sample is: 979.93cm -1 -1060.95cm -1 、1261.57cm -1 -1369.59cm -1 、1647.37cm -1 -1689.80cm -1 、2037.02cm -1 -2303.23cm -1 、2418.97cm -1 -2492.27cm -1 、2704.46cm-1 -3074.83cm -1 、3090.26cm -1 -3460.63cm -1 、3468.34cm -1 -3645.81cm -1 、3830.99cm -1 -4232.23cm -1 、4305.53cm -1 -4452.13cm -1 、4621.88cm -1 -5015.40cm -1 MIR data in;

[0095] At the same time, gas chromatography was used to detect the true value of monounsaturated fatty acids in the same batch of milk.

[0096] 2. Substitute the MIR data obtained by the measurement into the diff2 (second-order difference) + diff1 (first-order difference) + PLSR (n_component = 5) model constructed in Example 3 to output the prediction result of the monounsaturated fatty acid content.

[0097] The results predicted by the model are very close to the actual results (as shown in the table below), so the model has high accuracy and can be used to predict the monounsaturated fatty acid content in milk.

[0098]

[0099] References

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Claims

1. A method for rapid batch detection of monounsaturated fatty acids in milk by mid-infrared spectroscopy, comprising the following steps:

1. The characteristic band of the infrared spectrum of the collected milk sample is: 979.93cm -1 -1060.95cm -1 、1261.57cm -1 -1369.59cm -1 、1647.37cm -1 -1689.80cm -1 、2037.02cm -1 -2303.23cm -1 、2418.97cm -1 -2492.27cm -1 、2704.46cm -1 -3074.83cm -1 、3090.26cm -1 -3460.63cm -1 、3468.34cm -1 -3645.81cm -1 、3830.99cm -1 -4232.23 cm -1 、4305.53cm -1 -4452.13cm -1 、4621.88cm -1 -5015.40cm -1 MIR data in; 2. Substitute the MIR data obtained by the measurement into the diff2+diff1+PLSR model to output the prediction results of monounsaturated fatty acid content; The diff2 is the second-order difference, diff1 is the first-order difference, and PLSR is a partial least squares regression algorithm; wherein the principal component of the partial least squares regression algorithm is 5.

2. The method according to claim 1, characterized in that: In step 1, a gap of two wave points is allowed before and after each band.

3. Application of the method according to claim 1 in detecting the content of monounsaturated fatty acids in milk.

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