JP2015084677A - Feeding content prediction method for cow, feed selection method for cow, and method for controlling quality of raw milk - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a feeding content prediction method for a cow that has produced raw milk, a feed selection method for a cow for acquiring a prescribed quality of raw milk using the prediction method, and a method for controlling raw milk to a prescribed quality using the feed selection method.SOLUTION: An appropriate combination of the processes (a) to (e) is used: (a) a process for performing one kind or more than one kind of a sensory test, a physicochemical test, and a flavor component test for raw milk whose feeding content is known; (b) a process for acquiring a correlation on one kind or more than one kind of a correlation between the feeding content and the result of the sensory test, the correlation between the feeding content and the result of the physicochemical test, and a correlation between the feeding content and the result of the flavor component test; (c) a process for predicting the feeding content of a prescribed quality of raw milk from the correlation determined in (b); (d) a process for selecting feed for cows from the feeding content predicted in (c); and (e) a process for growing cows with the feed selected in (d) and acquiring the prescribed quality of raw milk.

Description

  The present invention relates to a method for predicting dairy cow feeding content, a method for selecting dairy cow feed, and a method for controlling the quality of raw milk.

The quality of raw milk fluctuates under the influence of various feeding management factors such as feed, dairy cattle (production, strain), climate, and breeding environment. The quality of raw milk flavor and physicochemical properties is generally most affected by feed content. Feed is roughly divided into roughage and concentrated feed. Among these, roughage includes grazing grass, grass silage, hay, corn silage, and the like. In the dairy field, forage is suitable for the climate and soil of each region. For example, grazing grass and grass silage are typical examples in Hokkaido Neiso region, and corn silage is typical in Hokkaido Tokachi region. In other prefectures, the ratio of self-contained feed is small, and imported hay is mainly used for roughage.
Dairy farmers generally aim for high component ratios (fat ratio, SNF ratio (non-fat milk solid content ratio)) and high milk yield in order to increase the income from raw milk production. In order to achieve this, each dairy farmer has devised the content of the feed for the self-contained feed and concentrated feed available in each region, considering the nutritional and cost aspects of the feed. Therefore, the quality (flavor, physicochemical properties) of raw milk produced by each dairy farm is not constant and varies to some extent. Therefore, among dairy products manufactured at dairy factories, dairy products whose raw milk quality contributes directly due to low level of manufacturing processing, such as milk, cream, butter, etc., have their flavor and physicochemical properties. There are differences depending on the region and season. This difference can be a problem when producing products of uniform quality.

  So far, various methods for controlling the quality of raw milk have been studied in order to solve the above problems. For example, Japanese Patent Application Laid-Open No. 07-107919 (Patent Document 1) describes a composition for improving milk quality comprising a hot water extract of green tea. Japanese Patent Application Laid-Open No. 2010-162035 (Patent Document 2) describes a method of obtaining raw milk having a desired fatty acid composition based on genetic test results of dairy cows.

Japanese Patent Laid-Open No. 07-107919 JP 2010-162035 A

  This invention makes it a subject to provide the method of estimating the feeding content of the dairy cow which produced raw milk. It is another object of the present invention to provide a method for selecting dairy cow feed for obtaining raw milk of a predetermined quality using the method. Furthermore, it is an object to provide a method for controlling raw milk to a predetermined quality using the method.

  In order to solve the above problems, the present inventors, in the process of research and examination of the quality control of raw milk, the difference in the feeding content of dairy cows that produced raw milk, the sensory test results of raw milk, physicochemical test results, It was found that there is a relationship with the results of the odor component test. Therefore, the raw contents of dairy cows that produced raw milk by conducting sensory tests, physicochemical tests, and aroma component tests on raw milk with known feed contents, and statistically analyzing the results to obtain correlations I found a way to predict. Furthermore, the method of selecting the feed of dairy cows for obtaining raw milk of a predetermined quality using the method was found. Furthermore, the present invention was completed by finding a method for controlling raw milk to a predetermined quality using the method.

That is, the present invention relates to the following [1] to [5].
[1] A method for predicting the content of dairy cows, comprising the following steps (a) to (c).
(A) A step of performing one or more of sensory inspection, physical and chemical inspection, and aroma component inspection on raw milk whose feeding contents are known.
(B) Correlation between one or more of the correlation between the contents of the feeding and the result of the sensory test, the correlation between the contents of the feeding and the result of the physical and chemical test, and the correlation between the contents of the feeding and the result of the aroma component test The process to seek.
(C) A step of predicting the content of raw milk of a predetermined quality from the correlation determined in (b) above.
[2] A method for selecting dairy cattle feed, comprising the following steps (a) to (d):
(A) A step of performing one or more of sensory inspection, physical and chemical inspection, and aroma component inspection on raw milk whose feeding contents are known.
(B) Correlation between one or more of the correlation between the contents of the feeding and the result of the sensory test, the correlation between the contents of the feeding and the result of the physical and chemical test, and the correlation between the contents of the feeding and the result of the aroma component test The process to seek.
(C) A step of predicting the content of raw milk of a predetermined quality from the correlation determined in (b) above.
(D) A step of selecting dairy cattle feed from the breeding content predicted in (c) above.
[3] A method for controlling the quality of raw milk, comprising the following steps (a) to (e).
(A) A step of performing one or more of sensory inspection, physical and chemical inspection, and aroma component inspection on raw milk whose feeding contents are known.
(B) Correlation between one or more of the correlation between the contents of the feeding and the result of the sensory test, the correlation between the contents of the feeding and the result of the physical and chemical test, and the correlation between the contents of the feeding and the result of the aroma component test The process to seek.
(C) A step of predicting the content of raw milk of a predetermined quality from the correlation determined in (b) above.
(D) A step of selecting dairy cattle feed from the breeding content predicted in (c) above.
(E) A step of growing a dairy cow with the feed selected in (d) above to obtain raw milk of a predetermined quality.
[4] The method according to any one of [1] to [3], wherein the correlation is determined by a multivariate analysis method.
[5] It is characterized by classifying the feeding contents as grazing grass, high moisture grass silage, hay and / or low moisture grass silage, corn silage, cereal seeds and / or mixed feed, beet pulp, and other feed. The method according to any one of [1] to [4].

  The quality of raw milk is greatly influenced by the feed (content of feeding) of each dairy farmer, but each dairy farmer has to use the feed that is available in each season or region. Therefore, the quality of raw milk varies depending on the feed (feeding content) used by each dairy farmer. On the other hand, according to the present invention, the content of feeding can be accurately predicted from the results (analysis results, analysis results) of various tests relating to raw milk, and dairy cow feed for obtaining raw milk of a predetermined quality Can be selected accurately, and raw milk can be controlled to a predetermined quality.

It is the list | wrist of the fatty acid made into the measuring object by the measurement of the fatty acid composition of Example 1. FIG. It is a list | wrist of the fragrance component made into the measurement object by measurement of the fragrance component of Example 1. FIG. The component described as DHS was measured by the DHS-GC / MS method, and the component described as SBSE was measured by the SBSE-GC / MS method. Principal component analysis was performed on the feed content of the 119 samples of raw milk of Example 2, and a cluster analysis was performed for each raw milk production area (Neiso region, Tokachi region) based on the obtained several principal components, for a total of 9 It is a graph which shows the analysis result of each feed content classified into the feed cluster. The vertical axis shows the composition ratio of each feed content. The horizontal axis indicates each feed cluster, “G” indicates raw milk in the Nemuro region, and “U” indicates raw milk in the Tokachi region. FIG. 10 is a plot diagram of the principal component analysis results in Example 3. A principal component map of six evaluation terms used in the sensory test is shown on the left, and a principal component map of nine feed clusters is shown on the right. FIG. 10 is a plot diagram of a principal component analysis result in Example 4. In the principal component analysis and cluster analysis of the feed of Example 2, the principal component map of 11 items of the physical and chemical inspection in which a significant difference was recognized is shown on the left, and the principal component map of nine feed clusters is shown on the right. In the map, “C4-C14” indicates the result of analysis using the total value of C4: 0, C6: 0, C8: 0, C10: 0, C12: 0, and C14: 0. FIG. 10 is a plot diagram of principal component analysis results in Example 5. In the principal component analysis and cluster analysis of the feed of Example 2, the 12 principal component maps of the aroma component test in which a significant difference was recognized are shown on the left, and the principal component map of 9 feed clusters is shown on the right.

The method for predicting the breeding content of dairy cows of the present invention,
(A) For raw milk whose feeding content is known, conduct one or more of sensory testing, physicochemical testing, and aroma component testing,
(B) Correlation between one or more of the correlation between the contents of the feeding and the result of the sensory test, the correlation between the contents of the feeding and the result of the physical and chemical test, and the correlation between the contents of the feeding and the result of the aroma component test Seeking
(C) Predicting the content of raw milk of a predetermined quality from the correlation determined in (b) above,
It is characterized by that. Here, the “feeding content” indicates a method of actually feeding a cow, and specifically includes a breeding form of the cow, a feed structure to be used, and the like. For example, paying attention to the content of the feed used, the “feeding content” is changed to “grazing grass”, “high moisture grass silage”, “hay and / or low moisture grass silage”, “corn silage”, “cereal seeds and / or mixed feed”, “ It is possible to classify into 7 types of “beat pulp” and “other feed”, and this classification can be preferably used in the present invention. Here, the “grazing grass” refers to, for example, raw grass directly grazed by livestock grazed on artificial grassland or wild grassland. “High-moisture glass silage” refers to silage in which grasses based on grasses are packed in silos and subjected to anaerobic fermentation and stored. “Dried hay and / or low moisture grass silage” refers to, for example, dried grass that has been trimmed using sunlight and wind to generally have a moisture content of about 12 to 20%. “Corn silage” refers to whole crop silage that is harvested from corn for feed such as dent corn, packed in a silo, and stored after anaerobic fermentation. “Formulated feed” refers to a feed that is prepared with, for example, cereals, oil cakes, bran, and produced potatoes, and has a low fiber content and high nutrient concentration such as starch and protein. “Beat pulp” refers to a feed comprising a residue obtained by chopping sugar and extracting sugar with warm water in a sugar production process from sugar beet (sugar radish), for example. The “feed content” as used in the present invention is not particularly limited to the above-described classification example, and “feeding content” can be indicated by a classification method other than this classification or a different expression. In order to know the “feeding content”, for example, a dairy farmer who is a producer of raw milk may conduct a questionnaire survey or interview survey regarding feeding mode, type of feed, preparation method, salary amount, salary method, and the like.

  The sensory test performed in the method for predicting the breeding content of dairy cows of the present invention is not particularly limited as long as it is an evaluation method using evaluation terms that are considered appropriate in evaluating raw milk. For example, evaluation terms include “milk fragrance”, “unpleasant odor”, “miscellaneous taste”, “grass fragrance”, “freshness”, “richness”, “stickiness of aftertaste”, etc. And a method of evaluating the degree of strength in seven stages. The evaluator is preferably an evaluator who has received special training, but is not particularly limited.

  The physicochemical test performed in the method for predicting the content of dairy cow feeding according to the present invention is not particularly limited as long as it evaluates evaluation items that are considered appropriate in evaluating raw milk. For example, as evaluation items, fat, protein, lactose, non-fat milk solids, total solids, urea nitrogen, somatic cell count, freezing point, retinol, lutein, β-carotene, vitamin E, fatty acid composition, fat globule particle size , Taste balance, color tone, and the like. Fat, protein, lactose, non-fat milk solids, total solids, urea nitrogen, somatic cells, and freezing point can be measured using, for example, a milk component analyzer “Mircoscan” (manufactured by Foss). . Retinol, lutein, β-carotene, and vitamin E can be measured by, for example, an HPLC method. The fatty acid composition can be measured, for example, by a GC method (GC-FID) equipped with a flame ion detector (FID). The fat globule particle size can be measured by, for example, a particle size distribution meter (manufactured by Beckman Coulter). The taste balance can be measured using, for example, a taste sensor (manufactured by Intelligent Sensor Technology). The color tone can be measured using, for example, a spectrocolorimeter (manufactured by Konica Minolta).

  The aromatic component test performed in the method for predicting the breeding content of dairy cows of the present invention is not particularly limited as long as it measures a measurement target component that is considered appropriate in evaluating raw milk. Examples of the measurement target component include 1-Pentanol, Pentanal, Dimethyl disulfide, Acetone, Hexanal, 2-Nonanone, Acetophenone, 1-Octanol, Dodecanoic acid, γ-Dodecalactone, Linoleic acid, (Z) -Dairy lactone, and the like. . These aroma components can be measured by, for example, DHS (dynamic headspace) -GC / MS method, SBSE (stirrer adsorption extraction) -GC / MS method, or the like.

  In the method for predicting the content of dairy cow feeding according to the present invention, among the correlation between the feeding content and the result of the sensory test, the correlation between the feeding content and the result of the physical and chemical test, the correlation between the feeding content and the result of the aroma component test, 1 Find correlations for species or more than one species. This correlation is not particularly limited, but can be determined by, for example, a multivariate analysis method. Multivariate analysis is a statistical technique for analyzing the dependency and dependency of a plurality of variables (variables). In the present invention, the multivariate analysis method is not particularly limited. For example, principal component analysis and / or cluster analysis can be preferably used. Principal component analysis is a method of converting and aggregating multi-variable data into a new small number of variable data while minimizing information loss. Cluster analysis is a method of classifying and grouping data based on the distance perspective based on the distance between data calculated from the similarity of data structures. Both principal component analysis and cluster analysis are statistical methods well known to those skilled in the art.

  In the method for predicting the content of dairy cow feeding according to the present invention, the content of feeding raw milk of a predetermined quality can be predicted from the correlation obtained above. For example, by mapping the principal component analysis result and / or cluster analysis result of the feed content of the sample raw milk and the principal component analysis result of the sensory test result of the sample raw milk, to grasp the correlation, from the sensory test result of the sample raw milk The feed content of the raw sample milk can be accurately predicted. Similarly, the results of physicochemical examination of sample raw milk are ascertained by mapping the principal component analysis results and / or cluster analysis results of feed contents of sample raw milk and the principal component analysis results of physicochemical examination results of sample raw milk. Therefore, the feed content of the sample raw milk can be accurately predicted, and the principal component analysis result and / or cluster analysis result of the feed content of the sample raw milk and the principal component analysis result of the aroma component test result of the sample raw milk are mapped. By grasping the correlation, the feed content of the sample raw milk can be accurately predicted from the aroma component test result of the sample raw milk. Moreover, the feed content of the sample raw milk can be predicted with higher accuracy by combining two or more of these predictions. That is, the feed content can be predicted using both the sensory test result and the physicochemical test result of the sample raw milk, and the feed content can be predicted using both the sensory test result and the aroma component test result of the sample raw milk. The feed contents can also be predicted using both the physicochemical test results and the aroma component test results of the raw sample milk, and in these cases, the accuracy of the prediction is increased, which is a preferred embodiment. Furthermore, the feed content can also be predicted using the three types of sensory test results, physical and chemical test results, and aroma component test results of raw sample milk. In this case, since the accuracy of prediction further increases, this is a more preferable embodiment.

  In order to obtain raw milk of a predetermined quality using the method for predicting the breeding content of dairy cows of the present invention, a method for selecting dairy cow feed is provided. This method of selecting a dairy cattle feed is also included in the present invention. Specifically, the feed selection method for the dairy cow of the present invention may be selected in accordance with the feeding content predicted by the method for predicting the feeding content of the dairy cow of the present invention.

  A method for controlling the quality of raw milk is provided using the method for selecting a feed for dairy cows of the present invention. This method of controlling the quality of raw milk is also included in the present invention. Specifically, the raw milk quality control method of the present invention may be such that the dairy cow is fed with the feed selected by the method for selecting the feed for dairy cows of the present invention.

  Hereinafter, based on an Example, this invention is demonstrated more concretely. In addition, the result of this test example does not limit this invention.

[Example 1] Implementation of survey and inspection In order to clarify the relationship between the contents of feeding and the quality (flavor, physicochemical properties) of raw milk, the summer (2012 August) and twice in winter (February 2013), we conducted a breeding management survey, a sensory test on raw milk, a physical and chemical test, and a flavor component test.

The inspection contents are shown below.
(1) Number of surveys: Total 119 samples of raw milk root region: 60 samples Tokachi region: 59 samples (2) Feeding content survey: Survey using questionnaire paper Survey items: Raw milk production, feeding form, feed type, preparation method , Salary, salary method, type of mineral salt and additives.
(3) Sensory test evaluation terms: 6 items (milk aroma, off-flavor / miscellaneous taste, grass aroma, refreshing feeling, rich feeling, sticky aftertaste)
Evaluation method: 7-step evaluation Evaluator: Trained raw milk panel 5 (4) Milk components (milk fat concentration, non-fat milk solids concentration, milk protein concentration) by physical and chemical inspection and Mircoscan FT120 (manufactured by Foss) , Lactose concentration, total solids concentration, urea nitrogen concentration, somatic cell count, freezing point).
・ Measurement of retinol, lutein, β-carotene and vitamin E by HPLC (manufactured by Hitachi High-Technologies Corporation).
・ Measurement of fat sphere particle size (particle size average diameter, median diameter, mode diameter) with a laser diffraction scattering particle size distribution analyzer LS230 (manufactured by Beckman Coulter).
・ Measurement of fatty acid composition by GC-FID (manufactured by Shimadzu Corporation) method using fatty acid methyl esterification method (potassium hydroxide-methanol method). The fatty acids to be measured are shown in FIG.
(5) Aroma component inspection / DHS (dynamic headspace) -GC / MS method and SBSE (stirrer adsorption extraction) -GC / MS method for aroma component measurement. As the automatic extraction apparatus, an apparatus made by Gestell was used, and for GC / MS, an apparatus made by Agilent Technologies was used. The aroma components to be measured are shown in FIG.
The results of analysis from the above inspection results are shown below.

[Example 2] Analysis of breeding content Based on the results of the questionnaire survey, the breeding content of each raw milk sample was determined as “grazing grass”, “high moisture grass silage”, “hay and / or low moisture grass silage”, “corn silage”, “ It was classified into 7 categories: “cereal seeds and / or mixed feed”, “beet pulp” and “other feed”. Principal component analysis was performed using the composition ratios (%) of the seven classifications, and a cluster analysis was performed for each raw milk production area (Negyo region, Tokachi region) based on the obtained several principal components, for a total of nine. Classified into feed clusters. FIG. 3 shows the analysis results of the feed content. Table 1 shows a breakdown of the number of raw milk samples for each feed cluster. In both FIG. 3 and Table 1, “G” indicates raw milk in the Neiso region, and “U” indicates raw milk in the Tokachi region.

[Example 3] Analysis of sensory test results and prediction example of breeding content of dairy cows Principal component analysis was carried out using the panel average value of each evaluation point for the six evaluation terms used in the sensory test. Six evaluation terms and nine feed clusters were plotted on the principal component map. The principal component map of evaluation terms is shown on the left of FIG. 4, and the principal component map of the feed cluster is shown on the right of FIG. Since the principal component map can be interpreted as having the same tendency if the orientation from the origin of the plot is the same, for example, in the left side of FIG. 4, “refreshing” is leftward from the origin (on the negative side of the X axis). In the right side of FIG. 4, “UA” and “UB” are plotted in the left direction from the origin (that is, the same direction as the “feeling of refreshing” in the left side of FIG. 4). The breeding content of dairy cows that produce raw milk with a “clean feeling” could be predicted to be close to the contents of “UA” and “UB”.

[Example 4] Analysis of physics and chemistry test results and prediction of dairy cow feeding contents In the principal component analysis and cluster analysis of the feed of Example 2, using 11 items of physics and chemistry tests that showed significant differences, Component analysis was performed. Eleven items of physical examination and nine feed clusters were plotted on the principal component map. The principal component map of the physical and chemical inspection items used on the left of FIG. 5 is shown, and the principal component map of the feed cluster is shown on the right of FIG. Since the principal component map can be interpreted as having the same tendency if the directions from the origin of the plot are the same, for example, in the left side of FIG. 5, “β-carotene” is in the upper right direction from the origin (X axis, Y axis). Both are plotted in the area on the plus side), and in the right side of FIG. 5, “GA” is plotted in the upper right direction from the origin (that is, the same direction as “β-carotene” on the left side of FIG. 5). The breeding content of dairy cows producing raw milk with a high concentration of “β-carotene” could be predicted to be close to the content of “GA”.

[Example 5] Analysis of aroma component test results and prediction example of feeding contents of dairy cows In the main component analysis and cluster analysis of the feed of Example 2, 12 items of aroma component test in which significant differences were recognized were used. A principal component analysis was performed. Twelve items of the aroma component test and 9 feed clusters were plotted on the principal component map. The main component map of the aroma component test item used on the left side of FIG. 6 is shown, and the main component map of the feed cluster is shown on the right side of FIG. Since the principal component map can be interpreted as having the same tendency if the orientation from the origin of the plot is the same, for example, “(Z) -Dairy lactone” in the left direction of FIG. In the right side of FIG. 6, “UA” and “UB” are the left direction from the origin (that is, “(Z) -Dairy lactone” on the left side of FIG. 6). Direction), the content of dairy cows that produce raw milk with a high concentration of “(Z) -Dairy lactone” is close to the content of “UA” and “UB”. I was able to predict.

  According to the present invention, it is possible to predict the breeding content of a dairy cow that produced raw milk. Furthermore, the method can be used to select dairy cow feed for obtaining raw milk of a predetermined quality. In addition, the method can be used to control raw milk to a predetermined quality.

The method for predicting the breeding content of dairy cows of the present invention,
(A) For raw milk whose feeding content is known, conduct one or more of sensory testing, physicochemical testing, and aroma component testing,
(B) Correlation between one or more of the correlation between the contents of the feeding and the result of the sensory test, the correlation between the contents of the feeding and the result of the physical and chemical test, and the correlation between the contents of the feeding and the result of the aroma component test Seeking
(C) Predicting the content of raw milk of a predetermined quality from the correlation determined in (b) above,
It is characterized by that. Here, the “feeding content” indicates a method of actually feeding a cow, and specifically includes a breeding form of the cow, a feed structure to be used, and the like. For example, paying attention to the content of the feed used, "feeding content" a "herbage""Silages""drygrass""Cornsilage""Kokumi acids and / or mixed feed""beetpulp""Otherfeed" In the present invention, this classification can be preferably used. Here, the “grazing grass” refers to, for example, raw grass directly grazed by livestock grazed on artificial grassland or wild grassland. “Grass silage” refers to silage that is stored, for example, by grazing grass-based grass in a silo and subjected to anaerobic fermentation, and the amount of water varies depending on the preparation method. "Dry grass" is, for example, the blue mowing the grass is dried by using a sunlight and wind, it is generally moisture refers to the things that were about 12 to 20 percent. “Corn silage” refers to whole crop silage that is harvested from corn for feed such as dent corn, packed in a silo, and stored after anaerobic fermentation. “Formulated feed” refers to a feed that is prepared with, for example, cereals, oil cakes, bran, and produced potatoes, and has a low fiber content and high nutrient concentration such as starch and protein. “Beat pulp” refers to a feed comprising a residue obtained by chopping sugar and extracting sugar with warm water in a sugar production process from sugar beet (sugar radish), for example. The “feed content” as used in the present invention is not particularly limited to the above-described classification example, and “feeding content” can be indicated by a classification method other than this classification or a different expression. In order to know the “feeding content”, for example, a dairy farmer who is a producer of raw milk may conduct a questionnaire survey or interview survey regarding feeding mode, type of feed, preparation method, salary amount, salary method, and the like.

Claims (5)

A method for predicting the breeding content of a dairy cow, comprising the following steps (a) to (c).
(A) A step of performing one or more of sensory inspection, physical and chemical inspection, and aroma component inspection on raw milk whose feeding contents are known.
(B) Correlation between one or more of the correlation between the contents of the feeding and the result of the sensory test, the correlation between the contents of the feeding and the result of the physical and chemical test, and the correlation between the contents of the feeding and the result of the aroma component test The process to seek.
(C) A step of predicting the content of raw milk of a predetermined quality from the correlation determined in (b) above. A method for selecting a feed for dairy cows, comprising the following steps (a) to (d).
(A) A step of performing one or more of sensory inspection, physical and chemical inspection, and aroma component inspection on raw milk whose feeding contents are known.
(B) Correlation between one or more of the correlation between the contents of the feeding and the result of the sensory test, the correlation between the contents of the feeding and the result of the physical and chemical test, and the correlation between the contents of the feeding and the result of the aroma component test The process to seek.
(C) A step of predicting the content of raw milk of a predetermined quality from the correlation determined in (b) above.
(D) A step of selecting dairy cattle feed from the breeding content predicted in (c) above. A method for controlling the quality of raw milk, comprising the following steps (a) to (e):
(A) A step of performing one or more of sensory inspection, physical and chemical inspection, and aroma component inspection on raw milk whose feeding contents are known.
(B) Correlation between one or more of the correlation between the contents of the feeding and the result of the sensory test, the correlation between the contents of the feeding and the result of the physical and chemical test, and the correlation between the contents of the feeding and the result of the aroma component test The process to seek.
(C) A step of predicting the content of raw milk of a predetermined quality from the correlation determined in (b) above.
(D) A step of selecting dairy cattle feed from the breeding content predicted in (c) above.
(E) A step of growing a dairy cow with the feed selected in (d) above to obtain raw milk of a predetermined quality.   The method according to claim 1, wherein the correlation is determined by a multivariate analysis method.   Categorized as grazing grass, high moisture grass silage, hay and / or low moisture grass silage, corn silage, cereal and / or mixed feed, beet pulp, other feed, characterized in that Item 5. The method according to any one of Items 1 to 4.

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