Mid-infrared rapid batch detection method for linoleic acid 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 linoleic acid in milk using mid-infrared spectroscopy. In terms of the selection of characteristic bands, the commonly used algorithm-based feature screening method is broken, and instead a method of manual selection + multiple traversals is used. 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 linoleic acid model is selected, the optimal parameters are determined, the accuracy of the model is improved, and the rapid, accurate and low-cost detection of the linoleic acid content in raw milk is achieved, thereby realizing rapid batch detection.

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 linoleic acid in milk using mid-infrared spectroscopy. Background Art

[0002] Milk is rich in nutrients and is a very important part of people's daily diet. As people's living standards improve, how to make the milk fat composition of milk higher quality and more beneficial to human health has always been a hot topic of research.

[0003] Milk is an important food source for humans. Milk fat contains about 400 fatty acids, of which unsaturated fatty acids account for about 30% of the total fatty acids. [1，2] , linoleic acid, an ω-6 unsaturated fatty acid [3] Linoleic acid is an essential fatty acid that cannot be synthesized by the human body. Modern pharmacological research shows that linoleic acid can lower cholesterol in human blood and prevent atherosclerosis, and is known as the "blood vessel scavenger"; it can also lower the levels of triglycerides, low-density lipoproteins and very low-density lipoproteins in the blood and maintain the metabolic balance of blood lipids; therefore, it is mainly used in medicine to prevent and treat diseases such as atherosclerosis, hypertension, and myocardial infarction. [4,5] Linoleic acid also has good anti-inflammatory and anti-allergic activity, and has a deep moisturizing effect on the skin, so it is often added to toiletries. [6,7] Researchers have conducted experiments hoping to improve milk quality and make it more beneficial by regulating the linoleic acid content in milk. There is currently a national standard for the determination of linoleic acid 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 determined using gas chromatography (GB 5009.168-2016). [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, so as to obtain 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 linoleic 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 all have low precision, and characteristic band is not accurate enough and other problems, 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, and milk quality also has its characteristics, and foreign detection model is not necessarily suitable for Chinese dairy cows, so it is necessary to set up the linoleic acid content detection method that is suitable for Chinese dairy cow milk with my country's independent property rights as soon as possible, not only can be used for fast, efficient, accurate and non-invasive measurement of important performance index linoleic acid content in dairy cow lactation traits, for dairy cow genetic breeding provides phenotypic data and theoretical basis, and can detect and analyze linoleic acid content in raw milk and milk products, for dairy processing industry and consumer provide reference. Object of the present invention is promptly to solve existing problems, set up the rapid detection technology of linoleic acid in milk, for the development of China's dairy industry provides technical guarantee. Summary of the Invention

[0005] The purpose of the present invention is to provide a method for rapid batch detection of linoleic acid in milk by mid-infrared spectroscopy, which has the advantages of being simple, rapid and batch-based.

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

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

[0008] A method for rapid batch detection of linoleic acid in milk by mid-infrared spectroscopy comprises the following steps:

[0009] 1. The characteristic band of the infrared spectrum of the milk sample is: 972.22cm -1 -1103.38cm -1 、1130.39cm -1 -1361.87cm -1 、1535.48cm -1 -1809.40cm -1 、1913.57cm -1 -2291.65cm -1 、2303.23cm -1 -2554.00cm -1 、2654.30cm -1-2878.07cm -1 、2993.81cm -1 -3248.44cm -1 、3302.45cm -1 -3460.63cm -1 、3468.34cm -1 -3819.42cm -1 、3912.01cm -1 -4224.51cm -1 、4251.52cm -1 -4425.13cm -1 、4533.15cm -1 -4594.88cm -1 、4737.62cm -1 -5015.40cm -1 MIR data in;

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

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

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

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

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

[0015] 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.

[0016] 2. The optimal preprocessing and algorithm combination for establishing the linoleic 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 linoleic 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

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

[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 selected thirteen characteristic bands (a) and the enlarged view of each characteristic band (b).

[0021] 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

[0022] 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.

[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. 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.

[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. 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 linoleic acid 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, and 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 acquired, the milk samples were frozen at -20°C for subsequent determination of linoleic acid content.

[0034] 3.2.2 Determination of linoleic acid content by gas chromatography

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

[0036] 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.

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

[0038] 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]

[0039] (3) Gas chromatography instrument parameters

[0040] Split injection mode

[0041] Chromatographic column: 100m×0.25mm×0.2μm, fused silica capillary column Stationary phase: cyanopropyl-polysiloxane

[0042] Carrier gas: Helium

[0043] Column head pressure: 225kPa (175kPa-225kPa)

[0044] Total flow rate: 25.5ml / min

[0045] Split ratio: 10:1

[0046] Injector temperature: 250°C

[0047] Detector temperature: 275°C

[0048] Injection volume: 1 μl.

[0049] Temperature programming:

[0050] Time / min Temperature / ℃ Retention time / min Rate / ℃ / min 0 60 5 5 165 1 15 10 225 20 2

[0051] (4) The measured linoleic acid content in the mixed standard is the reference true value result.

[0052] 4. Selection of effective samples

[0053] 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.

[0054] Example 1:

[0055] Selection of prediction model algorithm for linoleic acid:

[0056] The purpose of this application is to establish a quantitative determination model for linoleic acid, 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:

[0057] 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.

[0058] 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.

[0059] Example 2:

[0060] Screening of preliminary model algorithm for mid-infrared spectroscopy and its optimal preprocessing combination:

[0061] 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.

[0062] 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:

[0063] The comparison results are as follows:

[0064]

[0065]

[0066] 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.

[0067] Example 3:

[0068] Establishment of a method for detecting linoleic acid content in milk using mid-infrared spectroscopy:

[0069] 1. Division of modeling dataset

[0070]

[0071] 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.

[0072] 2. Manual selection process and determination of modeling feature bands

[0073] 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.

[0074] This embodiment selects characteristic bands by manual selection, and the selection steps are as follows:

[0075] (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 linoleic acid.

[0076] (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 test set effect was also significantly improved, with greater optimization potential, 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).

[0077]

[0078] The manual selection process of modeling feature bands is as follows:

[0079] (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.

[0080] (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.

[0081] (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.

[0082] Finally, after thirteen rounds of screening, the optimal results were obtained, as shown in the table:

[0083]

[0084]

[0085] The final selected characteristic band result is: 972.22cm -1 -1103.38cm -1 、1130.39cm -1 -1361.87cm -1 、1535.48cm -1 -1809.40cm -1 、1913.57cm -1 -2291.65cm -1 、2303.23cm -1 -2554.00cm -1 、2654.30cm -1 -2878.07cm -1 、2993.81cm -1 -3248.44cm -1 、3302.45cm -1 -3460.63cm -1 、3468.34cm -1 -3819.42cm -1 、3912.01cm -1 -4224.51cm -1 、4251.52cm -1 -4425.13cm -1 、4533.15cm -1-4594.88cm -1 、4737.62cm -1 -5015.40cm -1 , a gap of two wave points is allowed before and after each segment. The results show that after adding part of the first and second segments of water absorption areas to the model, the model can achieve the best effect, indicating that the characteristic band of linoleic acid contains part of the water absorption area.

[0086] 3. Screening and determination of model parameters

[0087] 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):

[0088]

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

[0090] After comparative analysis, the optimal regression model for linoleic acid 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 0.8467 and 0.8402, respectively; the root mean square errors for the training and test sets were 0.2141 and 0.2160, respectively.

[0091] Example 4:

[0092] Application of rapid batch detection method of linoleic acid in milk using mid-infrared spectroscopy MIR:

[0093] The established optimal regression model of linoleic acid (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 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: 972.22cm -1 -1103.38cm -1 、1130.39cm -1 -1361.87cm -1 、1535.48cm -1 -1809.40cm -1 、1913.57cm -1 -2291.65cm -1、2303.23cm -1 -2554.00cm -1 、2654.30cm -1 -2878.07cm -1 、2993.81cm -1 -3248.44cm -1 、3302.45cm -1 -3460.63cm -1 、3468.34cm -1 -3819.42cm -1 、3912.01cm -1 -4224.51cm -1 、4251.52cm -1 -4425.13cm -1 、4533.15cm -1 -4594.88cm -1 、4737.62cm -1 -5015.40cm -1 MIR data in;

[0095] At the same time, gas chromatography was used to detect the true value of linoleic acid 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 linoleic 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 linoleic acid content in milk.

[0098]

[0099] References

[0100] [1] H L.Fatty acids in bovine milk fat[J].Food Nutr Res,2008,52.https: / / doi:10.3402 / fnr.v52i0.1821.

[0101] [2]Grummer Ric R.Effect of feed on the composition of milk fat[J].JDairy Sci,1991,74(9):3244-3257.

[0102] [3] Wang Xingguo, Jin Qingzhe. Oil Chemistry[M]. Beijing: Science Press, 2012.

[0103] [4] Marangoni F, Agostoni C, Borghi C, et al. Dietary linoleic acid and human health: Fo cus on cardiovascular and cardiometabolic effects [J]. Atherosclerosis, 2020, 292: 90-98.

[0104] [5] Yoon SY, Ahn D, Hwang JY, et al. Linoleic acid exerts antidiabetic effects by inhi biting protein tyrosine phosphatases associated with insulinresistance[J]. J Funct Foods, 2021,83:104532.

[0105] [6]Innes JK,Calder P C.Omega-6 fatty acids and inflammation[J].Prostaglandins,Leu kotrienes and Essential Fatty Acids,2018,132:41-48.

[0106] [7]Innes JK,Calder P C.Omega-6 fatty acids and inflammation[J].Prostaglandins,Leuk otrienes and Essential Fatty Acids,2018,132:41-48.

[0107] [8] National Standard of the People’s Republic of China. GB 5009.168—2016 National Food Safety Standard Determination of Fatty Acids in Foods[S]. Beijing: China Standards Publishing House. 2016.

[0108] [9]Soyeurt H,Dardenne P,Dehareng F,et al.Estimating fatty acidcontent in cow milk u sing mid-infrared spectrometry[J].J Dairy Sci,2006,89(9):3690-3695.

[0109]

[10] Soyeurt H,Dehareng F,Mayeres P,et al.Variation ofΔ9-desaturaseactivity in dair y cattle[J].J Dairy Sci,2008,91(8):3211-3224.

[0110]

[11] Fleming A,Schenkel F S,Chen J,et al.Prediction of milk fattyacidcontent with mi d-infrared spectroscopy in canadian dairy cattle usingdifferently distributed model devel opment sets[J].J Dairy Sci,2017,100(6):5073-5081.

[0111]

[12] Marchi M D,Toffanin V,Cassandro M,et al.Invited review:mid-infrared spectrosco py as phenotyping tool for milk traits[J].J Dairy Sci,2014,97(3):1171-1186.

[0112]

[13] Deng L,Zou Q,Liu B,et al.Fatty acid positional distribution incolostrum and mat ure milk of women living in Inner Mongolia,North Jiangsuand Guangxi of China[J].Foo d Funct,2018,9(8):4234-4245.

[0113]

[14] Ten-Domenech I,Beltran-Iturat E,Herrero-Martinez J M,etal.Triacylglycerol Analys is in Human Milk and Other Mammalian Species:Small-Scale Sample Preparation,Charact erization,and Statistical ClassificationUsing HPLC-ELSD Profiles[J].J Agric Food Chem,2015,63(24):5761-70.

[0114]

[15] Zou X,Huang J,Jin Q,et al.Lipid composition analysis of milk fatsfrom different mammalian species:potential for use as human milk fatsubstitutes[J].J Agric Food Chem,2013,61(29):7070-80.

Claims

1. A method for rapid batch detection of linoleic acid in milk by mid-infrared spectroscopy, comprising the following steps: The characteristic band in the infrared spectrum of the milk sample is: 972.22cm -1 -1103.38cm -1 、1130.39cm -1 -1361.87cm -1 、1535.48cm -1 -1809.40cm -1 、1913.57cm -1 -2291.65cm -1 、2303.23cm -1 -2554.00cm -1 、2654.30cm -1 -2878.07cm -1 、2993.81cm -1 -3248.44cm -1 、3302.45cm -1 -3460.63cm -1 、3468.34cm -1 -3819.42cm -1 、3912.01cm -1 -4224.51cm -1 、4251.52cm -1 -4425.13cm -1 、4533.15cm -1 -4594.88cm -1 、4737.62cm -1 -5015.40cm -1 MIR data in; Substitute the MIR data obtained from the measurement into the diff2+diff1+PLSR (n_component=5) model to output the predicted results of linoleic 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. Application of the method according to claim 1 in detecting the content of linoleic acid in milk.

Images