A model and method for accurately identifying royal jelly produced by different foods

Abstract

The present invention relates to the field of analysis and detection technology, and in particular to a model and method for accurately identifying royal jelly produced by different foods. The method for constructing the model includes selecting different foods and royal jelly produced by different foods as test samples, testing the carbon and nitrogen stable isotope values ​​of different test samples respectively, calculating the fractionation coefficient and fractionation value data of carbon and nitrogen isotopes between different foods and royal jelly, and using the data to construct a random forest model to obtain a model for accurately identifying royal jelly produced by different foods. The present invention provides scientific and accurate technical means for the traceability and authenticity of royal jelly, and also provides a scientific basis for the construction of a royal jelly food traceability isotope database and the screening and analysis of royal jelly food sources.

Description

Technical Field

[0001] The invention relates to the technical field of analysis and detection, and in particular to a model and method for accurately identifying royal jelly produced by different foods. Background Art

[0002] Royal jelly, also known as royal jelly, is a milky white, light yellow or light orange pulpy substance secreted by the pharyngeal glands and maxillary glands of 5-15-day-old nursery worker bees. It is mainly used to feed queen bees and bee larvae. It is rich in protein, antimicrobial peptides, minerals, amino acids and other substances, so it is called a functional food. In the life cycle of a bee colony, the basic nutrition required for its healthy development comes from plant nectar and pollen. Bees will only secrete royal jelly when there is enough honey and bee food in the nursery room. If there is a lack of pollen resources in the external environment or insufficient reserves in the hive, it will not only affect the development of bee larvae and the growth of adult bees, but also weaken their ability to secrete royal jelly, and the queen bee's ability to lay eggs will also decrease. In this case, timely supplementing sugar water or substitute pollen for bees is a key measure to maintain the health of the bee colony. However, due to the high price of pollen, when the natural pollen source is insufficient, most bee farms will use sugar water, soybean meal, soybean powder and other artificial feeds for feeding. The quality of these artificial feeds will affect the yield and quality of royal jelly to varying degrees. Therefore, how to distinguish the authentic source of royal jelly is an important issue facing the current royal jelly quality control.

[0003] At present, conventional analytical methods for identifying the authenticity of royal jelly include chemical analysis and pollen analysis, but these methods have certain limitations. Therefore, various instrumental methods such as mid-infrared spectroscopy, nuclear magnetic resonance, gas chromatography, liquid chromatography and stable isotope mass spectrometry have been developed for the authenticity identification of royal jelly. Among them, stable isotope mass spectrometry can provide more accurate and reliable traceability information, so it has been widely used in the research of royal jelly authenticity identification.

[0004] Stable isotope traceability refers to the use of the differences in the natural abundance of stable isotopes in different plants or feeds to achieve the purpose of identifying the source of plants. Stable carbon and nitrogen isotopes are key components of plants, and their composition and ratio are important indicators for studying stable isotope fractionation. There are certain differences in the isotopes of different plants or feeds. Therefore, there will be isotope fractionation in the process of bees collecting honey, bee food or feed and transforming and secreting it to form royal jelly. Studying the isotope fractionation effect transmitted to royal jelly through honey, bee food or feed will be a new and important research idea for conducting royal jelly traceability research.

[0005] The composition and activity of royal jelly are affected by many factors, among which different foods are one of the main factors affecting the quality and active ingredients of royal jelly. Gaëlle Daniele et al. (2011) used EA-IRMS to measure the stable carbon ratio of royal jelly produced from different sources and fed with sugar water. Their study provided data support for the authenticity range of royal jelly under traditional beekeeping practices, showing that the authenticity of royal jelly from traditional beekeeping practices is d 13 C values ​​ranged from -22.48‰ to -27.90‰, while commercial samples produced by feeding with sugar water ranged from -18.54‰ to -26.58‰. Wytrychowski et al. (2012) compared the isotope ratios of royal jelly produced by different diets and found that the isotope ratio of royal jelly fed with honey or C3 plant sugars was close to -25‰. In contrast, the isotope ratios of royal jelly fed with sucrose or corn hydrolysate were -17.52‰ and -17.74‰, respectively, indicating that the feed type has a significant effect on the isotopic composition of royal jelly. Kim et al. (2023) measured the carbon isotope ratios and sugar (fructose, glucose and sucrose) content in royal jelly produced by artificial feeding and determined the Pearson correlation between feeding method and royal jelly composition. The patent "Method for Identifying Royal Jelly Produced from Different Foods" (CN202210221269.7) determined the stable isotope values ​​of royal jelly produced from different food sources. d 13 C and d 18 O / d 15 The N value determines the source of royal jelly. These studies have shown that there are differences in the stable isotopes of royal jelly produced from different food sources. However, these studies mainly focus on the stable isotope ratio of royal jelly itself, and lack research on the stable isotopes of bee bread and feed during the formation of royal jelly. At the same time, there is no research on the isotope fractionation from honey, bee bread and feed to royal jelly using stable isotopes. In addition, the analysis of stable isotope values ​​in royal jelly is mostly carried out in combination with significance and correlation, but the identification accuracy is not high. Summary of the invention

[0006] The present invention provides a method for constructing a model for accurately identifying royal jelly produced by different foods, comprising:

[0007] Different foods and royal jelly produced from different foods were selected as test samples, the carbon and nitrogen stable isotope values ​​of different test samples were tested respectively, the carbon and nitrogen isotope fractionation coefficients and fractionation value data between different foods and the royal jelly produced were calculated, and a random forest (RF) model was constructed using the data to obtain a model for accurately identifying royal jelly produced from different foods.

[0008] The present invention finds that by selecting the RF model, it is possible to use the carbon and nitrogen isotope fractionation data of different foods and the royal jelly produced thereby to construct a model for accurately identifying the royal jelly produced by different foods. When this model is used to identify the food source of royal jelly, it can achieve a 100% accuracy rate, thereby realizing rapid and accurate identification of the food source of royal jelly.

[0009] Preferably, the test sample comprises honey, bee feed, sugar water and substitute pollen.

[0010] Preferably, the sugar water is prepared by mixing sucrose and water.

[0011] More preferably, the sugar water is prepared by mixing sucrose and water in a mass ratio of 1:(0.7-1.2).

[0012] Preferably, the pollen substitute is prepared by mixing honey, water and plant protein powder (preferably soybean powder).

[0013] More preferably, the substitute pollen is prepared by mixing honey, water and plant protein powder (preferably soybean powder) in a mass ratio of 1: (0.7-1.2): (2.5-3.5).

[0014] Preferably, the calculation formula for the carbon and nitrogen stable isotope values ​​of different test samples is as follows:

[0015] d (‰) =( R 样品 - R 标准 ) / R 标准 × 1000

[0016] in, R 样品 and R 标准 The test samples and standards are 13 C / 12 C or 15 N / 14 N ratio;

[0017] The calculation formula of the isotope fractionation coefficient between different test samples is as follows:

[0018] α A-B = R A / R B

[0019] Among them, A and B represent the names of different test samples; α A-B is the isotope distribution coefficient between samples A and B;R It is a test sample 13 C / 12 C or 15 N / 14 N ratio;

[0020] The calculation formula of the fractionation value between different test samples is as follows:

[0021] △ A-B = d A - d B

[0022] Among them, A and B represent the names of different test samples; d A and d B is the stable isotope value of carbon and nitrogen.

[0023] Preferably, after the data is imported into the Pandas library, the category labels are converted into integer values ​​through LabelEncoder.

[0024] Preferably, when building the RF model, adjust the Number of Estimators parameter range from 1 to 200, adjust the Max Depth parameter range from 1 to 30, and adjust the Min Samples Split parameter range from 2 to 10.

[0025] Preferably, when building the RF model, adjust the Number of Estimators parameter to 160, adjust the Max Depth parameter to 30, and adjust the Min Samples Split parameter to 7.

[0026] Furthermore, the present invention provides a model for accurately identifying royal jelly produced by different foods, obtained by constructing the method in any of the above embodiments.

[0027] Furthermore, the present invention provides the application of the model in accurately identifying royal jelly produced by different foods.

[0028] Preferably, the present invention provides a method for accurately identifying royal jelly produced by different foods, comprising: respectively determining and calculating the carbon and nitrogen isotope fractionation coefficients and fractionation value data between the food to be identified and the royal jelly, inputting the data into a model constructed by any of the above-mentioned construction methods, wherein the model is constructed using the target food and the royal jelly produced by the target food as test samples to obtain a confusion matrix diagram of the identification results of the food source.

[0029] If the prediction results in the confusion matrix generated by the model are consistent with the actual label (target food), it means that the royal jelly to be identified belongs to the food source. If the prediction results in the confusion matrix generated by the model are inconsistent with the actual label (target food), it means that the royal jelly to be identified does not belong to the food source.

[0030] Preferably, the carbon and nitrogen isotopes are d 13 C and d 15 N.

[0031] In a specific implementation process, the carbon and nitrogen stable isotope values ​​include but are not limited to being determined using elemental analysis-isotope ratio mass spectrometry (EA-IRMS).

[0032] Compared with the prior art, the present invention has the following beneficial effects:

[0033] The present invention utilizes the carbon and nitrogen stable isotope fractionation effect between different foods and the royal jelly produced, accurately calculates the fractionation coefficient and fractionation value, and clarifies the relationship between the carbon and nitrogen stable isotopes in royal jelly and the isotope values ​​in its food. A random forest (RF) model is constructed based on the fractionation data to classify the food sources of royal jelly. This method requires a small amount of sample, has a high accuracy rate in model establishment, and reduces the amount of data processing. The present invention is based on stable isotope technology combined with machine learning to identify the food source of royal jelly, providing a scientific and accurate technical means for the traceability and authenticity of royal jelly, and also provides a scientific basis for the construction of a royal jelly food traceability isotope database and the identification and analysis of royal jelly food sources. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 It is the technical roadmap of the present invention to construct a model for accurately identifying royal jelly produced by different foods.

[0035] Figure 2 The accuracy (A) and parameter score diagram (B) of the RF model when identifying rapeseed royal jelly.

[0036] Figure 3 The accuracy (A) and parameter score diagram (B) of the RF model when identifying grape royal jelly.

[0037] Figure 4 The accuracy (A) and parameter score diagram (B) of the RF model in identifying royal jelly of Vitex truncatula.

[0038] Figure 5 This is the output of the model for identifying royal jelly from unknown food sources.

[0039] Figure 6 The accuracy (A) and parameter score graph (B) of the proportional SVM model identification. DETAILED DESCRIPTION

[0040] In order to make the purpose, technical scheme and advantages of the present invention clearer, the technical scheme in the present invention will be clearly and completely described below. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. The honey, bee food, feed and royal jelly samples in the embodiments of the present invention are directly collected from the same bee farm production site to ensure the authenticity of the experimental samples.

[0041] If no specific techniques or conditions are specified in the examples, all are carried out according to conventional methods or the techniques or conditions described in the literature in the field, or according to the product instructions. If no manufacturer is specified for the reagents and instruments used, all are conventional products that can be purchased through regular channels.

[0042] Example 1

[0043] This embodiment provides a model for accurately identifying royal jelly produced by different foods. The technical roadmap is as follows Figure 1 As shown, the construction method is as follows:

[0044] 1. Collection of honey, bee food, feed and royal jelly samples

[0045] The experiment was divided into three groups: natural production group, sugar water feeding group and substitute pollen feeding group. The bee species was healthy Italian honey bees ( Honey bee L.). Five colonies were selected for each group, with queens of similar ages.

[0046] The experiment of the natural production group was conducted during the rapeseed and sesame flowering periods. During this period, the bees relied entirely on honey and bee food (pollen) from natural plants as food sources, without adding any sugar water or substitute pollen. The samples collected by the natural production group included rapeseed royal jelly (RRJ), sesame royal jelly (SRJ), rapeseed bee food, sesame bee food, rapeseed honey and sesame honey.

[0047] The experiment of the sugar water feeding group was carried out during the flowering period of Baihua and grapes. During this period, because the bee food source was sufficient in the flowering period of Baihua and grapes, but the source of honey was less, the bees were fed with sugar water made of sucrose and water in a ratio of 1:1 (g / g). The samples collected in the sugar water feeding group included Baihua royal jelly (MRJ), grape royal jelly (GRJ), Baihua pollen, grape pollen and sugar water.

[0048] The experiment of the pollen substitute feeding group was carried out during the flowering period of Vitex truncatum. The pollen substitute was made of Vitex truncatum honey, water and soybean powder in a ratio of 1:1:3 (g / g / g). The samples collected in the pollen substitute feeding group included Vitex truncatum royal jelly (CRJ), Vitex truncatum honey and pollen substitute. The specific information of the collected samples is shown in Table 1. Honey samples were extracted from each beehive with a syringe and stored in a 250 mL brown bottle. Bee food was collected from each beehive with a disposable spoon and stored in a 50 mL centrifuge tube. All samples were stored in a refrigerator at -18°C before laboratory analysis.

[0049] Table 1 Sample information table

[0050]

[0051] 2. Sample pretreatment method

[0052] 1.0-1.5 mg of royal jelly and bee bread samples, as well as 1.0-2.0 mg of honey and sugar water samples, were accurately weighed and tightly wrapped in 8.0 mm × 5.0 mm tin paper cups, placed in the sample pan of the elemental analyzer (EA), and analyzed using EA-IRMS.

[0053] 3. Data processing

[0054] The calculation formula for the carbon and nitrogen stable isotope values ​​of different test samples is as follows:

[0055] d (‰) =( R 样品 - R 标准 ) / R 标准 × 1000

[0056] in, R 样品 and R 标准 The test samples and standards are 13 C / 12 C or 15 N / 14 N ratio;

[0057] Standard products adopt international standard materials d 13 C V-PDB = -26.39‰, d 15 N AIR = -4.52‰;

[0058] The calculation formula of the isotope fractionation coefficient between different test samples is as follows:

[0059] α A-B = R A / R B

[0060] Among them, A and B represent the names of different test samples; α A-B is the isotope distribution coefficient between samples A and B; R It is a test sample 13 C / 12 C or 15 N / 14 N ratio;

[0061] The calculation formula of the fractionation value between different test samples is as follows:

[0062] △ A-B = d A - d B

[0063] Among them, A and B represent the names of different test samples; d A and d B is the stable isotope value of carbon and nitrogen.

[0064] 4. Statistical Analysis of Isotope Fractionation

[0065] The fractionation coefficients and fractionation value data of carbon and nitrogen isotopes between different foods and the produced royal jelly were calculated according to the above calculation method. The results are shown in Table 2.

[0066] Table 2 Carbon and nitrogen isotope fractionation results from nectar and pollen sources to royal jelly

[0067]

[0068] Note: The numbers in the table are expressed as “mean ± standard deviation”.

[0069] 5. Classification Model Construction and Evaluation

[0070] Based on the fractionation coefficient and fractionation value of stable isotopes, an RF model was constructed to distinguish royal jelly from different food sources. In the data preprocessing stage, the Pandas library was used to import the dataset from the Excel spreadsheet. Since the machine learning model cannot directly process the category labels, these labels were converted to integer values ​​through LabelEncoder. For prediction, the dataset was randomly divided into a training set and a test set with a ratio of 70% and 30%. During the model construction, adjustment, and performance evaluation process, a confusion matrix was created to evaluate the performance of the model, and a 3D score plot was created to evaluate the hyperparameters of the model.

[0071] The optimization of the RF model is mainly achieved by adjusting three key hyperparameters: Number of Estimators, Max Depth, and Min Samples Split. The Number of Estimators parameter determines the number of trees in the random forest, directly affecting the accuracy and computational cost of the model, and its range is 1 to 200. The Max Depth parameter limits the maximum depth of the decision tree, effectively controls the complexity of the model, and reduces the risk of overfitting. Its range is 1 to 30. The Min Samples Split parameter is used to control the minimum number of samples required for node splitting, thereby affecting the complexity of the model and helping to prevent overfitting. Its range is 2 to 10.

[0072] By adjusting the above hyperparameters, this embodiment optimizes the training process and final performance of the RF model to determine the best parameter combination for a specific task. The best hyperparameter combination is determined by the 3D score graph as follows: the Number of Estimators parameter is 160, the Max Depth parameter is 30, and the Min Samples Split parameter is 7. After selecting the best hyperparameter combination, the 5-fold cross validation method is used to evaluate the model performance. All analyses are performed on the Python platform (version 3.6).

[0073] Example 2

[0074] Another batch of rapeseed honey, rapeseed bee food and rapeseed royal jelly corresponding to the rapeseed flowering period was collected, and the rapeseed honey to rapeseed royal jelly was measured and calculated according to the test method of Example 1. d 13 The fractionation value and fractionation coefficient of C were 1.1267 and 0.9607 respectively; from rapeseed bee food to rapeseed royal jelly d 13 The fractionation value and fractionation coefficient of C were 1.6833 and 0.9424 respectively; from rapeseed bee feed to rapeseed royal jelly d 15The fractionation value and fractionation coefficient of N are -2.5606 and 0.4398 respectively. Adding it to the RF model constructed in Example 1, the results are as follows Figure 2 As shown, it was found that the accuracy rate was still 100%, and rapeseed royal jelly could be accurately identified.

[0075] Example 3

[0076] The sugar water, grape bee food and grape royal jelly corresponding to the sugar water fed to grapes during the flowering period were collected, and the sugar water to grape royal jelly ratio was determined and calculated according to the test method of Example 1. d 13 The fractionation value and fractionation coefficient of C are -6.1897 and 1.4802 respectively; from grape bee food to grape royal jelly d 13 The fractionation value and fractionation coefficient of C are 5.11 and 0.7888 respectively; from grape bee food to grape royal jelly d 15 The fractionation value and fractionation coefficient of N are 0.23 and 0.8940 respectively. Put it into the RF model constructed in Example 1, and the results are as follows Figure 3 As shown, it was found that the accuracy rate was still 100%, and grape royal jelly could be accurately identified.

[0077] Example 4

[0078] Another batch of vitex honey, vitex substitute pollen and vitex royal jelly corresponding to vitex feeding substitute pollen during the flowering period were collected, and the vitex honey to vitex royal jelly was measured and calculated according to the test method of Example 1. d 13 The fractionation value and fractionation coefficient of C were 2.7708 and 0.8931 respectively; d 13 The fractionation value and fractionation coefficient of C were 1.3601 and 0.9444 respectively; d 15 The fractionation value and fractionation coefficient of N are -1.3527 and -0.7736 respectively. Put it into the RF model constructed in Example 1, and the results are as follows Figure 4 As shown, royal jelly of Vitex truncatum can be accurately identified.

[0079] Example 5

[0080] Royal jelly (XRJ) of unknown food origin was collected from another apiary, and its carbon and nitrogen stable isotope values ​​were determined to be -23.61‰ and 0.38‰, respectively.

[0081] Assuming XRJ is rape royal jelly (RRJ), calculate rape honey to XRJ d 13The fractionation value and fractionation coefficient of C were 4.99‰ and 0.83‰ respectively; d 13 The fractionation value and fractionation coefficient of C were 5.37‰ and 0.81‰ respectively; d 15 The fractionation value and fractionation coefficient of N were -3.2‰ and -0.13‰, respectively;

[0082] Assuming XRJ is sesame royal jelly (SRJ), calculate sesame honey to XRJ d 13 The fractionation value and fractionation coefficient of C were 1.42‰ and 0.94‰ respectively; sesame bee food to XRJ d 13 The fractionation value and fractionation coefficient of C were 0.93‰ and 0.96‰ respectively; sesame bee food to XRJ d 15 The fractionation value and fractionation coefficient of N were -2.05‰ and -0.22‰, respectively;

[0083] Assuming XRJ is Vitex royal jelly (CRJ), calculate Vitex honey to XRJ d 13 The fractionation value and fractionation coefficient of C were 2.07‰ and 0.91‰ respectively; d 13 The fractionation value and fractionation coefficient of C were 1.07‰ and 0.92‰ respectively; d 13 The fractionation value and fractionation coefficient of C are 0.04‰ and 0.88‰ respectively;

[0084] Assuming XRJ is Baihua Royal Jelly (MRJ), calculate the sugar water to XRJ d 13 The fractionation value and fractionation coefficient of C were -10.9‰ and 1.85‰ respectively; d 13 The fractionation value and fractionation coefficient of C were 2.79‰ and 0.89‰ respectively; d 15 The fractionation value and fractionation coefficient of N were -1.4‰ and -0.37‰, respectively;

[0085] Assuming XRJ is grape royal jelly (GRJ), calculate the sugar water to XRJ d 13 The fractionation value and fractionation coefficient of C were -10.87‰ and 1.84‰ respectively; d 13 The fractionation value and fractionation coefficient of C were 0.18‰ and 0.99‰ respectively; the grape bee food was added to XRJ d 15 The fractionation value and fractionation coefficient of N were -1.94‰ and -0.24‰, respectively;

[0086] Put it into the RF model constructed in Example 1, and the result is as follows Figure 5 As shown, the unknown royal jelly can be accurately identified as Vitex royal jelly.

[0087] Comparative Example

[0088] This comparative example provides a model for identifying royal jelly produced from different foods. The only difference between the construction method and Example 1 is that: based on the fractionation coefficient and fractionation value of stable carbon and nitrogen isotopes from food to the produced royal jelly, an SVM model is constructed to distinguish royal jelly from different food sources; the key parameters for constructing the SVM model are set as follows: the cost parameter is 4.2, the gamma parameter is 0.03, and the kernel function is 0.1. The accuracy of the SVM model constructed in this comparative example is 90%.

[0089] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for constructing a model for accurately identifying royal jelly produced by different foods, characterized in that: include: Different foods and royal jelly produced from different foods were selected as test samples, and the carbon and nitrogen stable isotope values ​​of different test samples were tested respectively. The carbon and nitrogen isotope fractionation coefficients and fractionation value data between different foods and the royal jelly produced were calculated. The fractionation coefficients and fractionation value data of each food and its corresponding royal jelly produced and the labels of different food categories were input into the random forest model. The labels of different food categories were converted into integer values ​​through LabelEncoder, and a confusion matrix diagram was generated to obtain a model for accurately identifying royal jelly produced from different foods. The calculation formula for the carbon and nitrogen stable isotope values ​​of different test samples is as follows: δ (‰) =( R 样品 - R 标准 ) / R 标准 × 1000； in, R 样品 and R 标准 The test samples and standards are 13 C / 12 C or 15 N / 14 N ratio; The calculation formula of the isotope fractionation coefficient between different test samples is as follows: α A-B = R A / R B； Among them, A and B represent the names of different test samples; α A-B is the isotope distribution coefficient between samples A and B; R It is a test sample 13 C / 12 C or 15 N / 14 N ratio; The calculation formula of the fractionation value between different test samples is as follows: △ A-B = δ A - δ B； in, δ A and δ B is the carbon and nitrogen stable isotope value.

2. The model building method according to claim 1, characterized in that: The test samples included honey, bee feed, sugar water and pollen substitute.

3. The model building method according to claim 1, characterized in that: After importing the data into the Pandas library, the category labels are converted into integer values ​​through LabelEncoder.

4. The model building method according to claim 3, characterized in that: When building the RF model, the parameter for adjusting the number of estimators ranges from 1 to 200, the parameter for adjusting the maximum depth ranges from 1 to 30, and the parameter for adjusting the minimum number of split samples ranges from 2 to 10.

5. The model building method according to claim 4, characterized in that: When building the RF model, the parameter for adjusting the number of estimators is 160, the parameter for adjusting the maximum depth is 30, and the parameter for adjusting the minimum number of split samples is 7.

6. A method for accurately identifying royal jelly produced by different foods, characterized in that: include: Respectively measure and calculate the fractionation coefficient and fractionation value data of carbon and nitrogen isotopes between the food to be identified and the royal jelly, input the data and the predicted food category label into the model constructed by the construction method described in any one of claims 1 to 5, the model uses the target food and the royal jelly produced by the target food as test samples for training and testing, if the predicted food category label and the target food category label in the confusion matrix diagram generated by the model are consistent, it means that the royal jelly to be identified belongs to the food source; If there is inconsistency between the predicted food category label and the target food category label in the confusion matrix diagram generated by the model, it means that the royal jelly to be identified does not belong to this food source.

7. The method according to claim 6, characterized in that The carbon and nitrogen isotopes are δ 13 C and δ 15 N.

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