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

Abstract

The present invention belongs to the field of dairy cow performance measurement and milk quality detection, and discloses a method for rapid batch detection of polyunsaturated fatty acids in milk by mid-infrared spectroscopy. In terms of the selection of characteristic bands, it breaks the commonly used method of using algorithms to screen features, and instead uses a method of manual selection + multiple traversals. Finally, characteristic bands for modeling are selected, especially the absorption region 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 preprocessing and algorithm combination for the establishment of the polyunsaturated 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 polyunsaturated fatty acid content in raw milk is achieved, and rapid batch detection is realized.

Description

Technical Field

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

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

[0003] Polyunsaturated fatty acids (PUFA) are essential fatty acids (EFA) required by the human body. They are an important component of mitochondrial and cell membrane phospholipids, participate in lipid metabolism and sperm formation, and are also precursors for the synthesis of prostaglandins. They have a series of physiological functions such as clearing blood dirt, lowering serum cholesterol, enhancing cell membrane permeability, and promoting tissue repair. Studies have shown that supplementing with polyunsaturated fatty acids can have a certain therapeutic and preventive effect on rheumatoid arthritis, prostatitis, and atopic dermatitis. [1] .

[0004] Milk is an important food source for humans. Milk fat contains about 400 fatty acids, of which polyunsaturated fatty acids account for about 5% of the total fatty acids. [2-3] In addition to being directly related to milk flavor, physical and chemical properties, and cheese making, these fatty acids [4-6] , and is closely related to human health. Polyunsaturated fatty acids are considered to be beneficial to human health. [7] In addition, the fatty acid content in milk can also reflect the individual energy and health status of dairy cows. [9-10] 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 Foods (GB5009.168-2016). my country stipulates that fatty acids in milk and dairy products should be detected using gas chromatography (GB 5009.168-2016).

[11] This method can quantitatively detect the types and contents of fatty acids, but the sample pretreatment process is complex, 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, making it difficult to use them quickly and in batches in production practice. Mid-infrared spectroscopy (MIR) technology is a very economical and efficient detection tool. It predicts the properties or contents 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. In the animal husbandry industry, the use of MIR to detect nutritional and physiological indicators of dairy cows has gradually become popular in China. Indicators such as total protein and total fat can now be detected, but there is currently no rapid batch detection model with high accuracy for the content of polyunsaturated fatty acids.

[0005] At present, foreign countries have already started the research based on mid-infrared detection of material components in milk, but all have low precision, and the problems such as characteristic band is not accurate enough, 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 is a big difference with foreign dairy cows. Milk quality 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 measurement of important performance index polyunsaturated fatty acid content in dairy cow lactation traits, provide phenotypic data and theoretical basis for dairy cow genetic breeding, and detect and analyze polyunsaturated fatty acid content in raw milk and milk products, provide reference for dairy processing industry and consumer. The purpose of the present invention is to solve the problems currently existing, set up a rapid detection technology for polyunsaturated fatty acids in milk, and provide technical guarantee for the development of China's dairy industry. Summary of the Invention

[0006] The purpose of the present invention is to provide a method for rapid batch detection of polyunsaturated fatty acids in milk by mid-infrared spectroscopy, which has the advantages of being simple, rapid and batch-based.

[0007] Another object of the present invention is to provide an application of a method for rapid batch detection of polyunsaturated fatty acids in milk using mid-infrared spectroscopy.

[0008] In order to achieve the above object, the present invention adopts the following technical measures:

[0009] A method for rapid batch detection of polyunsaturated fatty acids in milk by mid-infrared spectroscopy comprises the following steps:

[0010] 1. The characteristic band of the infrared spectrum of the milk sample is: 933.64cm -1 -1049.38cm -1 、1053.23cm -1 -1192.12cm -1 、1253.85cm -1 -1415.89cm -1 、1477.61cm -1 -1651.22cm -1 、1793.97cm -1 -2075.60cm -1 、2110.33cm -1 -2291.65cm -1 、2303.23cm -1 -2650.45cm -1 、3016.96cm -1 -3109.55cm -1 、3140.41cm -1 -3244.58cm -1 、3395.04cm -1 -3641.95cm -1 、3931.30cm -1 -4077.91cm -1 、4101.05cm -1 -4232.23cm -1 、4274.66cm -1 -4510.00cm -1 、4618.03cm -1 -4810.93cm -1 、4861.08cm -1 -5015.40cm -1 MIR data in;

[0011] 2. Substitute the MIR data obtained from the measurement into the diff2+diff1+PLSR (n_component=5) model to output the prediction results of the polyunsaturated fatty acid content.

[0012] The diff2 is the second-order difference, and diff1 is the first-order difference.

[0013] In the method described above, preferably, a gap of two wave points is allowed before and after each wave band in step 1.

[0014] The protection content of the present invention also includes: the above detection method is used to detect the content of polyunsaturated fatty acids in milk.

[0015] Compared with the prior art, the present invention has the following advantages:

[0016] 1. In terms of feature band selection, we broke away from the common algorithmic feature selection method and instead used a manual selection method with multiple iterations. Ultimately, we selected the feature bands for modeling, specifically the absorption region containing some water. We also demonstrated that adding some water absorption bands can improve model accuracy.

[0017] 2. The optimal preprocessing and algorithm combination for establishing the polyunsaturated fatty acid model was selected, the optimal parameters were determined, and the accuracy of the model was improved.

[0018] 3. It realizes the rapid, accurate and low-cost detection of the polyunsaturated fatty acid content in raw milk, and realizes rapid batch detection. The detection 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

[0019] Figure 1 (a) The infrared spectrum of the untreated milk sample and the average spectrum (b) are shown.

[0020] Figure 2 This is the mid-infrared spectrum of the milk sample after diff2+diff1 pretreatment.

[0021] Figure 3 The overall spectrum of the fifteen selected characteristic bands (a) and the enlarged view of each characteristic band (b).

[0022] Figure 4 This is the correlation between the true value and predicted value of the model milk data and the fitted straight line graph. DETAILED DESCRIPTION

[0023] Unless otherwise specified, the technical solutions described in the present invention are all conventional solutions in the field; the reagents or materials described are all from commercial channels unless otherwise specified.

[0024] 1. Experimental Materials

[0025] 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. Each milk sample was collected for 40 ml and divided 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. The bottles were slowly shaken to fully dissolve them. 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.

[0026] 2. Mid-infrared spectrum measurement and collection

[0027] 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. TM The 7RM milk composition detector extends the solid fiber optic probe into the liquid, mixes the sample and then scans it.

[0028] 3. Detection method of the true (reference) value of polyunsaturated fatty acids in milk

[0029] 3.1 Instruments, equipment, and reagents

[0030] 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, and helium.

[0031] 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.

[0032] 3.2 Experimental methods

[0033] 3.2.1 Collection of mid-infrared spectra

[0034] 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 to preheat 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 carried out in sequence. After the spectrum was acquired, the milk samples were frozen at -20°C for subsequent determination of the polyunsaturated fatty acid content.

[0035] 3.2.2 Determination of polyunsaturated fatty acid content by gas chromatography

[0036] (1) Lipid extraction: dichloromethanol method (commonly used at home and abroad, simpler) [12-13]

[0037] In a 15ml centrifuge tube, add 1.8ml of water and 8ml of CH2Cl2 / CH3OH (2:1, v / v) to 1ml of milk. Vortex for 10 minutes, centrifuge at 8000 rpm for 10 minutes, and remove the lower layer. Add 4ml of CH2Cl2 / CH3OH (2:1, v / v) to the remaining solution and extract again. Combine the extracts and blow down with liquid nitrogen.

[0038] (2) Fatty acid methylation: KOH methanol solution method

[0039] 50 mg of lipid was added with 1 mL of n-hexane and then 50 μL of KOH-MeOH solution (2 M) was added for methyl esterification. Vortex for 30 seconds, centrifuge at 8000 rpm for 5 minutes, and the supernatant was passed through a membrane for GC-MS analysis.

[14]

[0040] (3) Gas chromatography instrument parameters

[0041] Split injection mode

[0042] Chromatographic column: 100m×0.25mm×0.2μm, fused silica capillary column

[0043] Stationary phase: cyanopropyl-polysiloxane

[0044] Carrier gas: Helium

[0045] Column head pressure: 225kPa (175kPa-225kPa)

[0046] Total flow rate: 25.5ml / min

[0047] Split ratio: 10:1

[0048] Injector temperature: 250°C

[0049] Detector temperature: 275°C

[0050] Injection volume: 1 μl.

[0051] Temperature programming:

[0052] Time / min Temperature / ℃ Retention time / min Rate / ℃ / min 0 60 5 5 165 1 15 10 225 20 2

[0053] (4) The sum of the contents of all nine polyunsaturated fatty acids measured is the monounsaturated fatty acid content.

[0054] 4. Selection of effective samples

[0055] 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 30 abnormal samples were eliminated, and 236 samples were selected for model establishment and optimization.

[0056] Example 1:

[0057] Selection of prediction model algorithm for polyunsaturated fatty acids:

[0058] The purpose of this application is to establish a quantitative determination model for polyunsaturated fatty acids, so the modeling algorithm used is a regression algorithm. There are many types of regression algorithms. This example mainly uses Ridge Regression (Ridge) and Partial Least Squares Regression (PLSR) algorithms for model establishment and comparison for the following reasons:

[0059] Ridge regression is a type of linear regression. It is just that 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 (2) stochastic gradient descent is implemented. 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 the results are relatively stable. It is one of the more commonly used basic algorithms. Therefore, this algorithm is selected as the candidate algorithm in this embodiment.

[0060] Partial least squares regression (PLS) is a very effective algorithm for multi-feature samples. In mid-infrared spectral data, each sample corresponds to 1060 wave points, which is representative of multi-feature samples. PLS also rarely overfits, so many mid-infrared spectroscopy researchers choose to use PLS for model building. Therefore, this algorithm was selected as the candidate algorithm in this example.

[0061] Example 2:

[0062] Screening of preliminary model algorithm for mid-infrared spectroscopy and its optimal preprocessing combination:

[0063] Effective feature screening is a basic operation for processing spectral data. Its purpose is to eliminate noise and lay a good foundation for feature extraction. Effective feature screening mainly includes feature extraction, feature preprocessing, and feature dimensionality reduction. This embodiment mainly uses five processing methods: SG (convolution smoothing), MSC (multivariate scatter correction), SNV (standard normal variate transformation), diff1 (first-order difference), and diff2 (second-order difference) to perform feature preprocessing on spectral data.

[0064] In this example, each sample corresponds to a piece of MIR spectrum data. The full spectrum band was substituted into the model to compare the accuracy of the model. Two algorithms and six methods (including no preprocessing) were paired one-to-one to determine the initial optimal algorithm and preprocessing combination for the model. The results are shown in the following table:

[0065] The comparison results are as follows:

[0066]

[0067]

[0068] 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. Therefore, the initial optimal algorithm and preprocessing combination of the model was finally selected as: PLSR+diff2.

[0069] Example 3:

[0070] Establishment of a method for detecting polyunsaturated fatty acids in milk using mid-infrared spectroscopy:

[0071] 1. Division of modeling dataset

[0072]

[0073] In this embodiment, the modeling data set is divided into 80% training set and 20% test set. The ratio of training set to test set is 4:1. At the same time, the training set is also called the cross-validation set. 10-fold cross-validation is performed during the model training process.

[0074] 2. Manual selection process and determination of modeling feature bands

[0075] 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 disadvantage is that it ignores the synergy between adjacent wave points 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, and 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 disadvantage is slow selection speed and low efficiency.

[0076] This embodiment selects characteristic bands by manual selection, and the selection steps are as follows:

[0077] (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 polyunsaturated fatty acids.

[0078] (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 was found that a second preprocessing can better optimize the model, so the result of the combination of the preliminary model algorithm selection and the preprocessing was preprocessed for the second time to select the best second preprocessing method. After comparison, it was found that the second preprocessing using diff1 significantly improved the effect of the training set, and the effect of the test set did not drop seriously, and the optimization potential was large, while the overfitting of diff2 was more serious, so diff1 was finally selected as the best second preprocessing method (the results are shown in the following table).

[0079]

[0080] The manual selection process of modeling feature bands is as follows:

[0081] (1) The entire band was segmented into 100 wave points per segment, and finally divided into 11 segments. According to preliminary experiments, it was 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 were not removed.

[0082] (2) Taking 50 wave points as a group, the partial least squares regression algorithm is used. First, a group of wave points is added or subtracted 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.

[0083] (3) After the first traversal is completed, perform a second, third, or more manual traversals until all the wave points no longer change, which is the optimal characteristic band.

[0084] Finally, after fifteen rounds of screening, the optimal results were obtained, as shown in the table:

[0085]

[0086]

[0087] The final selected characteristic band result is: 933.64cm -1 -1049.38cm -1 、1053.23cm -1 -1192.12cm -1 、1253.85cm -1 -1415.89cm -1 、1477.61cm -1 -1651.22cm -1 、1793.97cm -1 -2075.60cm -1 、2110.33cm -1 -2291.65cm -1 、2303.23cm -1 -2650.45cm -1 、3016.96cm -1 -3109.55cm -1 、3140.41cm -1 -3244.58cm -1 、3395.04cm -1 -3641.95cm -1 、3931.30cm -1 -4077.91cm -1 、4101.05cm -1 -4232.23cm -1 、4274.66cm -1 -4510.00cm -1 、4618.03cm -1 -4810.93cm -1 、4861.08cm -1 -5015.40cm -1 , allowing a gap of two wave points before and after each segment. The results showed that after adding part of the first and second segments of water absorption regions to the model, the model can achieve the best effect, indicating that the characteristic band of polyunsaturated fatty acids contains part of the water absorption region.

[0088] 3. Screening and determination of model parameters

[0089] Model parameters include those of the preprocessing method and the algorithm. 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):

[0090]

[0091] According to the comparison results, the main component (n_component) is finally selected as 5.

[0092] After comparative analysis, the optimal regression model for saturated fatty acids was the diff2 (second-order difference) + diff1 (first-order difference) + PLSR (n_component = 5) model. The correlation coefficients for the training and test sets were 0.8431 and 0.8308, respectively; the root mean square errors for the training and test sets were 0.2776 and 0.2979, respectively.

[0093] Example 4:

[0094] Application of rapid batch detection method of polyunsaturated fatty acids in milk using mid-infrared spectroscopy MIR:

[0095] The established optimal regression model of polyunsaturated fatty acids (diff2 (second-order difference) + diff1 (first-order difference) + PLSR (n_component = 5)) was used to predict 5 randomly selected external milk samples (not one of the 236 experimental materials), and the predicted results were compared with the true values.

[0096] 1. The characteristic band of the infrared spectrum of the milk sample is: 933.64cm -1 -1049.38cm -1 、1053.23cm -1 -1192.12cm -1 、1253.85cm -1 -1415.89cm -1 、1477.61cm -1 -1651.22cm -1 、1793.97cm -1 -2075.60cm -1 、2110.33cm -1 -2291.65cm -1 、2303.23cm -1 -2650.45cm -1 、3016.96cm-1 -3109.55cm -1 、3140.41cm -1 -3244.58cm -1 、3395.04cm -1 -3641.95cm -1 、3931.30cm -1 -4077.91cm -1 、4101.05cm -1 -4232.23cm -1 、4274.66cm -1 -4510.00cm -1 、4618.03cm -1 -4810.93cm -1 、4861.08cm -1 -5015.40cm -1 MIR data in;

[0097] At the same time, gas chromatography was used to detect the true value of polyunsaturated fatty acids in the same batch of milk.

[0098] 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 polyunsaturated fatty acid content.

[0099] 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 polyunsaturated fatty acid content of milk.

[0100]

[0101] References

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Claims

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

1. The characteristic band of the infrared spectrum of the milk sample is: 933.64cm -1 -1049.38cm -1 、1053.23cm -1 -1192.12cm -1 、1253.85cm -1 -1415.89 cm -1 、1477.61cm -1 -1651.22cm -1 、1793.97cm -1 -2075.60cm -1 、2110.33cm -1 -2291.65cm -1 、2303.23cm -1 -2650.45cm -1 、3016.96cm -1 -3109.55 cm -1 、3140.41cm -1 -3244.58cm -1 、3395.04cm -1 -3641.95cm -1 、3931.30cm -1 -4077.91 cm -1 、4101.05cm -1 -4232.23cm -1 、4274.66cm -1 -4510.00cm -1 、4618.03cm -1 -4810.93 cm -1 、4861.08cm -1 -5015.40cm -1 MIR data in; 2. Substitute the MIR data obtained from the measurement into the diff2+diff1+PLSR (n_component=5) model to output the predicted results of polyunsaturated fatty acid content.

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

3. Use of the method according to claim 1 in detecting the content of polyunsaturated fatty acids in milk.

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