CN108874732B - Method for judging and detecting pure phase similarity of dendrobium aphyllum - Google Patents
Method for judging and detecting pure phase similarity of dendrobium aphyllum Download PDFInfo
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Abstract
The invention relates to a method for judging and detecting pure similarity of dendrobium aphyllum, which is established in the following steps of S1: collecting dendrobium aphyllum samples consistent with the gene sequencing conclusion, and measuring the stem fourth internode length of each sample; s2: carrying out normality test on the length variable of the fourth internode of the sample stem; s3: standard interval: if the result obtained in the step S2 is that the normal distribution is obeyed, obtaining a 95% confidence interval of the mean value and a 95% confidence interval of the standard deviation according to a normal distribution overall calculation formula; the confidence interval can be used as a standard range for identifying the purity of an unknown sample. The judgment standard established according to the actually acquired data is as follows: the 95% confidence intervals for the mean and the 95% confidence intervals for the standard deviation were: (23.8377, 26.0996) and (4.4301, 6.0510), the tested Dendrobium aphyllum samples need only be compared to this interval. According to the method, the purity of the variety of the dendrobium aphyllum can be identified through morphological related characteristics of the dendrobium aphyllum, so that the value or the existence of the artificially planted dendrobium aphyllum can be simply judged; the identification accuracy is high, and the method has important practical significance.
Description
Technical Field
The invention relates to the field of medicine and biology, in particular to a method for judging and detecting the purity similarity of dendrobium aphyllum, namely a method for establishing the correlation between the morphological characteristics of dendrobium aphyllum and a gene sequencing conclusion and a method for identifying the purity of a detected dendrobium aphyllum variety.
Background
Dendrobe is a commonly used nourishing traditional Chinese medicine, and mainly belongs to the dendrobe plant. The plant of Dendrobium is the largest genus of Orchidaceae plants, and comprises herba Dendrobii, herba Dendrobii nobilis, and herba Dendrobii cronulati. There are about 1100 kinds of dendrobium in the world, and there are nearly hundreds of kinds found in China. The medicinal history of dendrobium has a long time, and the dendrobium is listed as a nourishing and tonifying product in the Shen nong's herbal Jing as early as possible, and the dendrobium is always regarded as a precious Chinese herbal medicine by people along with the development of the times, so that the dendrobium has very important nourishing efficacy. Clinically, dendrobium is used for treating various diseases and has the pharmacological effects of enhancing immunity, resisting oxidation, reducing blood sugar, inhibiting cancers and the like. The dendrobium including the dendrobium aphyllum has extremely important value in the fields of traditional Chinese medicine and health care.
However, due to the long-term unregulated mining and unreasonable utilization of dendrobium, wild resources are gradually reduced, artificial planting conditions are gradually increased, and even the dendrobium nobile is a main source for dendrobium aphyllum. However, the long-term artificial planting also brings false and bad phenomena to the dendrobium aphyllum, because the artificial planting changes the growth environment of wild dendrobium aphyllum; secondly, applying various fertilizers, pathological changes, pesticide application, appearance of new disease types and the like on the dendrobium aphyllum artificially in large quantity; thirdly, because the dendrobium has more varieties, the characters of the related varieties are crossed due to the hybridization among the varieties; and fourthly, other uncontrollable or unpredictable factors, which cause the change of the medicinal components of some artificially planted dendrobium aphyllum and even the disappearance of important medicinal components, and correspondingly, the gene sequence of the dendrobium aphyllum with the changed or disappeared medicinal components is substantially different from the original wild gene sequence. Once the medicinal value of dendrobium aphyllum is weakened or disappeared, the fruit is serious if the application is continued without self-knowledge in the field, and the fruit is serious if the dendrobium aphyllum disappears from the medical field without self-knowledge.
The applicant discovers that certain morphological related characteristics of dendrobium aphyllum are closely related to gene sequencing conclusion of the dendrobium aphyllum through long-term and great workload research, the gene sequencing conclusion is the gene sequencing result of the standard dendrobium aphyllum with traditional medicinal/nutritional value, and the wild dendrobium aphyllum is basically consistent with the gene sequencing conclusion. That is to say, the correlation degree of dendrobium aphyllum with the gene sequencing conclusion can be judged through the morphological characteristics of dendrobium aphyllum, the higher the correlation degree or the similarity degree is, the higher the purity degree of the dendrobium aphyllum variety is, the easier the dendrobium aphyllum is to maintain the traditional medicinal and health-care efficacy, the lower the correlation degree or the similarity degree is, the lower the purity degree of the dendrobium aphyllum variety is, namely, the larger the substantive difference between the dendrobium aphyllum and the gene sequencing conclusion is, the higher the possibility that the medicinal efficacy is reduced or disappears is.
The gene sequencing conclusion reflects the variety of the sample, and how to efficiently judge the purity of the variety of the sample by measuring morphologically related characteristics on the basis of the gene sequencing conclusion in order to identify the purity or the gene similarity of the variety is a problem which needs to be considered in practical application.
Disclosure of Invention
In view of the above problems in the prior art, the present invention is directed to solving the drawbacks of the prior art, and provides a method for determining and detecting the pure similarity of Dendrobium aphyllum.
The purpose of the invention is mainly realized by the following technical scheme.
A method for judging the pure similarity of dendrobium aphyllum comprises the following steps:
s1: and (3) standard data acquisition: collecting dendrobium aphyllum samples consistent with the gene sequencing conclusion, wherein the sample capacity is n, and measuring the stem fourth internode length of each sample to obtain a measurement value of the stem fourth internode length variable;
s2: and (3) checking normality: carrying out normality test on the length variable of the fourth internode of the sample stem;
s3: standard interval: if the result obtained in the step S2 is that the normal distribution is obeyed, obtaining a 95% confidence interval of the mean value and a 95% confidence interval of the standard deviation according to a normal distribution overall calculation formula;
if the stem fourth internode length variable normality test result of the dendrobium aphyllum in the step S2 is not in accordance with normal distribution, if the sample capacity exceeds 30, the sample can still calculate the 95% confidence interval of the mean value and the 95% confidence interval of the standard deviation according to the normal general formula according to the central limit theorem;
the 95% confidence interval for the mean and the 95% confidence interval for the standard deviation obtained above can be used as a standard range for identifying the purity of an unknown sample.
Further, after obtaining the measured value of the stem fourth internode length variable in step S1, the basic statistics of the stem fourth internode length variable is calculated according to the measured value of the stem fourth internode length variable, the basic statistics includes the average level and the degree of dispersion, then the data is determined to have abnormal value according to the basic statistics, if the abnormal value exists, the investigation is performed, if the abnormal value belongs to the measurement error or the recording error, the abnormal point is deleted, if the abnormal value is not due to the error, the data should be retained.
Further, the mean level includes at least one of a mean, a median, and a mode, and the degree of dispersion includes a standard deviation, a mean absolute deviation, and a coefficient of variation;
the basic statistics further comprise making a histogram and/or a box plot based on the measured values of the fourth internode length variable of the stem to visualize the data and to facilitate the determination of the erroneous abnormal value.
Further, the normality test includes at least one of a visual image analysis and a hypothesis test.
Further, the normality test includes visual image analysis and hypothesis test.
Further, the method for analyzing the visual image comprises the following steps:
according to normal empirical distribution function
Drawing a normal empirical distribution function curve of the dendrobium aphyllum;
according to normal probability density function
Drawing a normal probability density curve of the dendrobium aphyllum; when μ is 0 and σ is 1, the normal distribution becomes a standard normal distribution:
② according to the measured value of the length variable of the fourth internode of the stem obtained in the step S1, and according to the formula
Drawing a real experience distribution function by the experience distribution function;
according to the measured value of the length variable of the fourth internode of the stem obtained in step S1 and according to the formula
The probability density function of drawing a true probability density functionA figure of numbers;
comparing the real experience distribution function graph with a distribution function curve of normal distribution, and preliminarily judging whether the sample data accords with the normal distribution or not by judging the deviation degree of the curve; comparing the real probability density function graph with a probability density curve of normal distribution, and judging whether the sample data is subjected to the normal distribution or not according to the deviation degree and the curve shape consistency degree;
if the deviation of the real empirical distribution function graph and the distribution function graph of the normal distribution or the deviation of the real empirical probability density function graph and the probability density function graph of the normal distribution is small and the shapes of the real empirical probability density function graph and the probability density function graph of the normal distribution are consistent, the stem fourth internode length of the dendrobium aphyllum sample to be detected is probably in accordance with the normal distribution, and if the deviation is obviously large and the shapes of the stem fourth internode length of the dendrobium aphyllum sample to be detected are obviously inconsistent, the stem fourth internode length of the dendrobium aphyllum.
Further, the hypothesis test includes any one of a JB test, a KS test, and a lillieford test.
Further, the hypothesis test is a lillieford test, and the statistic T ═ F of the lillieford test*(x) -S (x) |, where T is the Lilliefors test statistic, F*(x) Is a cumulative distribution function of normal distribution with a mean value of 0 and a standard deviation of 1, and S (x) is
Rejecting the original hypothesis H0 when the test statistic T exceeds the test threshold value at the significance level of α; otherwise, the original hypothesis cannot be rejected.
A detection method for pure similarity of Dendrobium aphyllum comprises the following steps:
(1) 81 wild dendrobium aphyllum samples consistent with the conclusion of gene sequencing are collected, the fourth internode length of the stem of each sample is determined, and the determination results are as follows: the stem length variation range of the dendrobium aphyllum is 16.05 mm-46.49 mm, the average level is 24.97 mm-25.06 mm, and the calculated result is as follows: mean value: 24.97mm, median: 25.06mm, mode: 25.06mm, standard deviation of the stem fourth internode length fluctuation of 5.11mm, mean absolute deviation: 3.72mm, coefficient of variation: 0.20;
(2) visual image analysis normal distribution: drawing an empirical distribution function graph and a probability density function graph according to the data in the step (1), wherein the result of comparing the real empirical distribution function graph with the normal empirical distribution function curve is as follows: the curves of the two are basically consistent; the result of comparing the true probability density function graph with the normal probability density curve is as follows: the shapes of the two curves are approximately the same, the two curves are bell-shaped curves, and the kurtosis and the skewness are basically consistent;
through visual analysis of the images, the sample data of the length between the fourth internode of the stem of the dendrobium aphyllum is most likely to accord with normal distribution;
(3) lillieford test: the original hypothesis is H0: the data obeyed normal distribution; let us assume H1: data do not follow normal distribution; the test results obtained from the data in step (1) were:
| statistics | Critical value | P value | Significance level alpha | Whether to accept the original hypothesis |
| 0.0818 | 0.0986 | 0.1962 | 0.05 | Is that |
The value of the statistic is 0.0818, less than the threshold value 0.0986; the P value is equal to 0.1962 and is greater than the significance level alpha, so that the original assumption is accepted, and the dendrobium aphyllum sample data can be confirmed to be in normal distribution;
(4) calculating a mean 95% confidence interval and a standard deviation 95% confidence interval of the sample data of the length between the fourth nodes of the dendrobium aphyllum stem according to a normal distribution overall calculation formula, wherein the mean 95% confidence interval and the standard deviation 95% confidence interval are respectively as follows:
| mean value | 95% confidence interval of mean | Standard deviation of | 95% confidence interval of standard deviation |
| 24.9686 | (23.8377，26.0996) | 5.1145 | (4.4301，6.0510) |
The 95% confidence interval for the mean and the 95% confidence interval for the standard deviation are: (23.8377, 26.0996) and (4.4301, 6.0510), which are standard intervals for determining the pure similarity of Dendrobium aphyllum.
A method for judging the pure similarity of dendrobium aphyllum comprises the following steps:
A. collecting length data of a fourth internode of a stem of a dendrobium aphyllum sample to be detected, and eliminating abnormal values caused by measurement errors or recording errors in the sample;
B. calculating a mean 95% confidence interval and a standard deviation 95% confidence interval of the length data of the fourth internode of the stem of the dendrobium aphyllum sample to be detected in the step A, and if the mean 95% confidence interval and the standard deviation 95% confidence interval are both in the standard interval obtained in the step (4) (namely the standard intervals are that the mean 95% confidence interval and the standard deviation 95% confidence interval are respectively (23.8377, 26.0996) and (4.4301, 6.0510)), the similarity of the pure species of the dendrobium aphyllum to be detected is high, namely the purity of the dendrobium aphyllum to be detected is high; and (3) if at least one of the 95% confidence interval of the mean value and the 95% confidence interval of the standard deviation of the length data of the fourth internode of the sample stem of the dendrobium aphyllum to be detected is not in the standard interval obtained in the step (4), the similarity of the pure species of the dendrobium aphyllum to be detected is low, namely the purity of the dendrobium aphyllum to be detected is low.
The invention has at least the following beneficial effects:
according to the method, the morphological characteristics of the dendrobium aphyllum are connected with the sequencing conclusion of the dendrobium aphyllum gene, and the purity of the dendrobium aphyllum gene can be obtained through the morphological characteristics. The purity of the variety of the dendrobium aphyllum to be detected can be identified through the data of the stem fourth internode length of the dendrobium aphyllum; the method can simply and simply judge the value of the artificially planted dendrobium aphyllum, even whether the value exists.
The method establishes the standard for judging the purity, can judge the purity similarity of the detected dendrobium aphyllum by the 95% confidence interval of the mean value and the 95% confidence interval of the standard deviation, is simple and accurate, is learned in a large amount of researches of the applicant, can judge the purity of the dendrobium aphyllum sample by the accuracy of more than 90%, and has important application value.
The method can basically judge the medicinal value of certain batch of dendrobium aphyllum and reflect the essential characteristics of the dendrobium aphyllum by morphological characteristics, thereby having profound significance to the whole medical and plant communities. In addition, the invention may open up a more updated and more accurate morphological classification concept or idea.
Drawings
FIG. 1 is a schematic diagram of a histogram according to an embodiment of the present invention;
FIG. 2 is a schematic structural diagram of a box diagram according to an embodiment of the present invention;
FIG. 3 is a schematic structural diagram of an empirical distribution function curve according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of a probability density curve according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to specific embodiments of the present invention, and it is obvious that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example 1
A method for judging the pure similarity of dendrobium aphyllum comprises the following steps:
s1: and (3) standard data acquisition: collecting dendrobium aphyllum samples which completely meet the morphological description of dendrobium aphyllum, namely collecting dendrobium aphyllum samples consistent with a gene sequencing conclusion, wherein the sample capacity is n, and measuring the stem fourth internode length of each sample to obtain a measurement value of the stem fourth internode length variable;
s2: and (3) confirmation of data: calculating basic statistics of the fourth internode length variable of the stem according to the measured values of the fourth internode length variable of the stem, wherein the basic statistics comprise an average level and a dispersion degree, the average level comprises at least one of a mean value, a median and a mode, and the dispersion degree comprises a standard deviation, a mean absolute deviation and a variation coefficient; and a histogram and a box diagram are made according to the corresponding data to visualize the data, so that the distribution condition and the abnormal value of the variable can be observed, analyzed and judged more clearly. And then determining that the data has error abnormal values, checking if the data has the error abnormal values, deleting the abnormal points if the data belongs to measurement errors or recording errors, and if the data is not due to errors, retaining the data.
S3: and (3) checking normality: carrying out normality test on the length variable of the fourth internode of the sample stem; the normality test includes at least one of a visual image analysis and a hypothesis test, preferably both, capable of testing both subjectively and objectively.
The method for analyzing the visual image comprises the following steps:
according to normal empirical distribution function
Drawing a normal empirical distribution function curve of the dendrobium aphyllum; x in the formula is a random variable, namely a sample observed value of the dendrobium aphyllum; mu is the mean value of the obtained sample observed values; σ is the standard deviation of the sample observations; e is a natural constant having a value of about 2.71828; the function curve of the normal distribution is given, a normal empirical distribution curve can be simulated through a computer, the normal empirical distribution curve is not obtained according to the original data, and the normal empirical distribution curve is drawn by comparing the distribution function curve of the original data with the function curve of the normal distribution to test whether the original data obeys the normal distribution.
According to normal probability density function
Drawing a normal probability density curve of the dendrobium aphyllum, wherein when mu is 0 and sigma is 1 (the mean value is 0 and the standard deviation is 1), the normal distribution becomes the standard normal distribution:
x in the formula is a random variable, namely a sample observed value of the dendrobium; e is a natural constant with a value of about 2.71828. Similarly, the probability density function curve of the normal distribution is given, and a normal empirical distribution curve can be simulated by a computer, and is not obtained from the raw data. Plotting the normal probability density curve is the probability density of the expected raw dataAnd comparing the degree function curve with the normal probability density function curve to test whether the original data obeys normal distribution.
The original data is the data obtained in step S1 of the summary of the invention.
Secondly, drawing a real Empirical Distribution function according to the measured value of the length variable of the fourth internode of the stem obtained in the step S1 and according to an Empirical Distribution Function (EDF);
the empirical distribution function formula is: let x1,x2,...,xnIs a group of sample measurement values with the sample capacity of n, and the n measurement values are rearranged from small to large
For any real number x (x being the measured value x for the sample)1,x2,...,xn) Defining a function
Then call Fn(x) An empirical distribution function of the population X. It can be abbreviated as Fn(x)＝1/n·*{x1,x2,...,xnTherein of*{x1,x2,...,xnDenotes x1,x2,...,xnIs not greater than the number of x. Another common representation is
Wherein I is an indicative function, i.e.
Thus, an empirical distribution function F is obtainedn(x) The value at a point x can be determined byN observations x of machine variable x1,x2,...,xnAnd (4) dividing the number of x which is less than or equal to x by the observation frequency n. Thus, Fn(x) That is, the frequency of occurrence of the event { X ≦ X } in n repeated independent experiments.
Drawing a real probability density function graph according to the measured value of the length variable of the fourth internode of the stem obtained in the step S1 and the probability density function;
the formula of the Probability Density Function (PDF) is: if the distribution function F (X) for the random variable X, there is a non-negative function f (X) such that for any real number there is a
Then, X is called (X is the measured value X of the sample1,x2,...,xn) Is a continuous random variable, wherein f (X) is a probability density function called X, abbreviated as probability density. The probability density function of random data represents the probability that the instantaneous amplitude falls within some specified range and is therefore a function of amplitude. Which varies with the amplitude of the range taken.
The probability density function has the following properties: f (x) is not less than 0;
since the probability density function is derived from the distribution function, the graph of the probability density function can be directly based on each data x of the sample measurement data1,x2,...,xnAnd (4) calculating.
Comparing the real empirical distribution function graph with a normal empirical distribution function curve, and preliminarily judging whether the sample data accords with normal distribution or not by judging the deviation degree of the curve; comparing the real probability density function graph with a normal probability density curve, and judging whether the sample data is subjected to normal distribution or not according to the deviation degree and the curve shape consistency degree;
if the deviation of the real empirical distribution function graph and the distribution function graph of the normal distribution or the deviation of the real empirical probability density function graph and the probability density function graph of the normal distribution is small and the shape is consistent, the length between the fourth nodes of the stem of the dendrobium aphyllum sample to be detected accords with the normal distribution, and if the deviation is obviously large and the shape is obviously inconsistent, the length between the fourth nodes of the stem of the dendrobium aphyllum sample to be detected does not accord with the normal distribution.
By drawing a sample empirical distribution function graph and a sample empirical probability density function graph and comparing the empirical distribution function graph and the corresponding function graph of normal distribution, the difference degree of the two curves is visually observed, so that whether the length variable of the fourth internode of the stem of the sample comes from normal distribution or not is judged.
Hypothesis testing
The hypothesis test includes any one of a JB test, a KS test, and a lillieford test, preferably the lillieford test.
The Lilliefors test statistic T ═ F*(x) -S (x) |, where T is the Lilliefors test statistic, F*(x) Is a cumulative distribution function of normal distribution with a mean value of 0 and a standard deviation of 1, and S (x) is
At the significance level of alpha, rejecting the original hypothesis H when the test statistic T exceeds the test threshold0(ii) a Otherwise, the original hypothesis cannot be rejected.
Whether the sample is from a normal distribution population can be judged more objectively by the Lilliefors normal distribution hypothesis testing method.
S4: standard interval: if the result obtained in the step S3 is that the normal distribution is obeyed, obtaining a 95% confidence interval of the mean value and a 95% confidence interval of the standard deviation according to a normal distribution overall calculation formula;
if the stem fourth internode length variable normality test result of dendrobium aphyllum in step S3 is not compliant with normal distribution, and considering the nature of a large sample, that is, when the sample size is large, that is, the general requirement is greater than 30, it can be known from the central limit theorem that the sample can still calculate the 95% confidence interval of the mean value and the 95% confidence interval of the standard deviation according to the formula of normal population.
The 95% confidence interval for the mean and the 95% confidence interval for the standard deviation obtained above can be used as a standard range for identifying the purity of an unknown sample.
In the present embodiment, the sample volume n is preferably not less than 80.
Example 2
A detection method for pure similarity of Dendrobium aphyllum comprises the following steps:
(1) collecting wild dendrobium aphyllum samples consistent with the gene sequencing conclusion, wherein the sample capacity is 81, and determining the stem fourth internode length of each sample, wherein the determination results are as follows: the length variation range of the stem fourth internode of the dendrobium aphyllum is approximately between 16.05mm and 46.49mm, the average level is approximately between 24.97mm and 25.06mm, and the calculated result is as follows: mean value: 24.97mm, median: 25.06mm, mode: 25.06mm, standard deviation of the stem fourth internode length fluctuation of 5.11mm, mean absolute deviation: 3.72mm, coefficient of variation: 0.20.
and a histogram and a box plot were made based on the above data to visualize the data, as shown in fig. 1 and 2. As can be seen from the box diagrams 1-2, there is an abnormal point in the collected values, and the abnormal point is found through re-examination and is not caused by a measurement error or a recording error, so that the abnormal point also belongs to a normal value, and this data is retained.
(2) Standard interval for identifying purity of dendrobium aphyllum sample to be determined
Plotting a real empirical distribution function from the measured quantitative values of the fourth internode stem length for each of the 81 samples and from the empirical distribution function, as shown by the solid line in figure 3; the curve of the empirical distribution function is a stepped curve with a jump rise. The smooth curve is a graph of the theoretical distribution function of the population X.
The true probability density function was plotted against the quantitative measure of the fourth internode length of the stem for each of the 81 samples determined and against the probability density function, as shown by the solid line in figure 4.
And drawing a normal empirical distribution function curve of the dendrobium aphyllum according to the data of the length of the fourth internode of the stem of the dendrobium aphyllum and the formula of the normal empirical distribution function, as shown in fig. 3.
And drawing a normal probability density function curve of the standard dendrobium aphyllum according to the data of the length of the fourth internode of the stem of the dendrobium aphyllum and the formula of the normal probability density function, as shown in fig. 4.
The ordinate of fig. 3 represents the cumulative probability of occurrence of an event { X ≦ X } in 81 repeated independent experiments, the ordinate represents the sum of probabilities of sample events less than or equal to a certain value, the probability distribution of variables can be described at a uniform angle by the cumulative empirical distribution function, for normal distribution, the cumulative probability distribution function has a fixed curve, and then the difference between the cumulative empirical distribution function graph drawn by sample data and the cumulative normal empirical distribution function graph is compared, so that it can be seen intuitively whether the sample data conforms to normal distribution. The ordinate of fig. 4 represents the density of the probability, and the greater the density, the greater the probability at that range. The abscissa of fig. 3 and fig. 4 represents the range of random variables, i.e., the abscissa represents the range of the length of the fourth internode of the stem of dendrobium aphanidermatum, the ordinate of fig. 3 represents the cumulative sum of probabilities that sample data is less than or equal to a certain value, and the ordinate of fig. 4 represents the density of probabilities, and the larger the density, the larger the probability in this range.
According to the drawn empirical distribution function diagram and the drawn probability density function diagram, whether the distribution of the sample data of the length between the fourth nodes of the stem is subject to normality or not is observed more intuitively from a subjective angle. Empirical distribution function curves can be used to evaluate the fit of the distribution to the data, estimate percentiles and compare different sample distributions. The distribution of the length data of the fourth internode of the dendrobium aphyllum can be visually seen through the graph 3.
Specifically, as can be seen from the empirical distribution function diagram of fig. 3, the true empirical distribution function curve is substantially consistent with the normal empirical distribution function curve; as can be seen from the probability density function diagram of fig. 4, the shape of the true probability density curve is approximately the same as that of the normal probability density curve, both the true probability density curve and the normal probability density curve are bell-shaped curves, and both the kurtosis and the skewness are substantially the same. Through visual analysis of the curves, the fact that the sample data of the length between the fourth section of the stem of the dendrobium aphyllum most possibly accords with normal distribution can be basically considered.
In actual life, many data meet the characteristics of normal distribution, so that the real distribution of original data can be judged by the method to be compared with the normal distribution, and the data obviously conforming to the normal distribution can be detected without adopting hypothesis.
Next, it is determined whether the sample actually comes from a normal distribution population by a hypothesis test.
Validation was performed using the lillieford test method, assuming H0: the data obeyed normal distribution; let us assume H1: data do not follow a normal distribution.
And (3) calculating test statistic and P value according to the collected data of the length of the fourth internode of the stem in the dendrobium aphyllum sample, wherein the test results are shown in table 1.
TABLE 1 Lilliefors test results
The cut-off values in table 1 are not subjectively given, but are determined by the test method and the sample size. The role of the P-value is to determine whether the original hypothesis should be rejected, and if the P-value is less than the significance level (typically 0.05), we reject the original hypothesis, i.e. the data is considered not to follow a normal distribution. As can be seen from the test results in table 1, the value of the statistic was 0.0818, which is less than the critical value 0.0986; p value equal to 0.1962, greater than significance level α; therefore, the original hypothesis is accepted, and the sample data of the length between the fourth section of the dendrobium aphyllum stem is considered to be in normal distribution.
The above P value is calculated by the formula: when the alternative assumption is that H1 is μ ≠ μ0When p is 2[1- Φ (Z)0)](ii) a When choosing to assume H1 is mu > mu0When p is 1-phi (Z)0) (ii) a When choosing to assume H1 as mu < mu0When p is phi (Z)0). Wherein, phi (Z)0) Is a function of the normal distribution of the experience,to be obtained by looking up a table, μ is a calculated statistic, μ0Is the assumed value of the statistic that,
the P value can be directly calculated by software, such as Matlab software.
Confidence intervals and the like of the dendrobium aphyllum to be detected are calculated according to the data of the stem fourth internode length of the dendrobium aphyllum to be detected collected in the embodiment, and specific results are shown in table 2.
TABLE 2 calculation results of mean, standard deviation and confidence interval of fourth internode length of Dendrobium aphyllum
| Mean value | 95% confidence interval of mean | Standard deviation of | 95% confidence interval of standard deviation |
| 24.9686 | (23.8377，26.0996) | 5.1145 | (4.4301，6.0510) |
The 95% confidence intervals for the mean and the 95% confidence intervals for the standard deviation were: (23.8377, 26.0996) and (4.4301, 6.0510), the interval is a standard interval for determining the purity of Dendrobium aphyllum, i.e. the interval is a standard interval for identifying the purity of Dendrobium aphyllum.
Example 3
A method for judging the pure similarity of dendrobium aphyllum comprises the following steps:
A. collecting length data of a fourth internode of a stem of a dendrobium aphyllum sample to be detected, and eliminating abnormal values caused by measurement errors or recording errors in the sample;
B. calculating a mean 95% confidence interval and a standard deviation 95% confidence interval of the length data of the fourth internode stem of the dendrobium aphyllum sample to be detected in the step A, wherein if the mean 95% confidence interval and the standard deviation 95% confidence interval are both in the standard interval obtained in the embodiment 2, the similarity of the pure species of the dendrobium aphyllum to be detected is high, namely the purity of the dendrobium aphyllum to be detected is high; if at least one of the 95% confidence interval of the mean and the 95% confidence interval of the standard deviation of the length data of the fourth internode of the sample stem of dendrobium aphyllum to be detected is not within the standard interval obtained in example 2, the similarity of the pure species of dendrobium aphyllum to be detected is low, that is, the purity of dendrobium aphyllum to be detected is low.
As a further preferred embodiment, in step a, the histogram and the box plot described in example 1 or example 2 can be used to assist in determining the presence or absence of an abnormal value.
As a further preferred embodiment, the sample volume for collecting the dendrobium nobile lindl sample to be detected is not less than 65.
Example 4
The inventor has long studied the correlation between the morphological characteristics and the variety purity of dendrobium aphyllum, and found that the dendrobium aphyllum with low purity has large influence on some morphological characteristics, and basically obtains the following results through data of a large amount of wild dendrobium aphyllum and artificially planted samples, and summary, comparison, research and the like of the samples: the deviation and shape of the empirical distribution function curve, probability density function curve and the like of the stem fourth internode length of the dendrobium aphyllum with high purity (i.e. high similarity of pure species) are basically consistent with the deviation and shape of the normal distribution function curve, the mean 95% confidence interval and the standard deviation 95% confidence interval are both in the standard interval obtained in the example 2, and at least one of the mean 95% confidence interval and the standard deviation 95% confidence interval of the dendrobium aphyllum sample to be tested with low purity (i.e. low similarity of pure species) is not in the standard interval obtained in the example 2, namely the mean 95% confidence interval (23.8377, 26.0996) and the standard deviation 95% confidence interval (4.4301, 6.0510).
In addition, in the past decade, the present inventors have conducted numerous times of sample collection, calculation, comparison, etc. at various locations throughout the country, and conducted gene sequence measurement many times, and found that the accuracy of the determination method in the present invention was 90% or more by big data comparison. If the mean 95% confidence interval and the standard deviation 95% confidence interval of the dendrobium aphyllum sample to be detected are not in the standard interval, the similarity of the gene sequences of the detected dendrobium aphyllum and the real dendrobium aphyllum is below 95%, and the similarity of the gene sequences of the detected dendrobium aphyllum sample in the standard interval and the real dendrobium aphyllum is basically above 95%.
Some tested Dendrobium aphanidermatum samples recorded in the record are randomly selected, and the 95% confidence interval of the mean and the 95% confidence interval of the standard deviation are calculated according to the method of example 3, as shown in Table 3 below.
TABLE 3 partial case schematic case table (Stem fourth internode long)
In the above table, the location is the province where the tested sample is located, and both the 95% confidence interval of the mean and the 95% confidence interval of the standard deviation refer to the obtained interval of the tested dendrobium aphyllum sample, and whether the value of the tested dendrobium aphyllum sample in the standard interval is in the 95% confidence interval of the mean obtained by the invention or not; the similarity is the percentage of the number of the tested dendrobium aphyllum samples in the interval to the ratio of the sample volume.
As for the samples, the accuracy of the method is high and the method has important application reference value through actual measurement.
In specific implementation, the formula needed by the invention is as follows:
mean value: the mean value is an arithmetic mean value (mean) calculated by
Wherein n is the sample size of dendrobe.
Median: for a finite number set, the median can be found by ranking all observed values in order of magnitude, and if there are even numbers of observed values, the median is usually the average of the two most median values.
When N is an odd number, m0.5＝X(N+1)/2(ii) a When N is an even number, the number of bits in the bit line is,
mode: by M in general0This means that the number is the most proportional of the set of data.
Standard deviation: the standard deviation is a measure of the degree of dispersion of the mean values of a set of data, and a larger standard deviation represents a larger difference between the majority of values and their mean values; a smaller standard deviation indicates that these values are closer to the mean. Is calculated by the formula
Where μ is the mean. Because we have a large number of samples in contact, the sample standard deviation is commonly calculated, which can be understood as an unbiased estimate of the total standard deviation given, by the formula
Wherein
Is the sample mean.
Mean absolute deviation: commonly denoted as mad (mean Absolute development), the sum of the distances of the observations from the mean is calculated and then averaged. Is calculated by the formula
Coefficient of variation: when it is desired to compare the magnitude of the two sets of data discrete levels,if the difference between the measurement scales of the two sets of data is too large or the data dimensions are different, it is not suitable to directly use the standard deviation for comparison, and then the influence of the measurement scales and the dimensions should be eliminated, and the Coefficient of Variation (CV) can do this, which is the ratio of the standard deviation of the original data to the average of the original data. The coefficient of variation is calculated by
Confidence interval: the previously calculated mean and standard deviation are point estimates of the parameters, which are a value calculated from the sample to estimate the unknown parameter. However, the point estimate is only an approximation of the unknown parameter and does not reflect the error range of this approximation, which is usually given in the interval. We wish to define an interval which is believed to contain the true parameter value with a relatively high degree of confidence, generally referred to as the confidence level, denoted 1- α, where α is referred to as the significance level, and is a small positive number, typically taken to be α ═ 0.025,0.05,0.1, etc.
When variance σ2When known, the statistic is
Confidence intervals for the mean μ are:
when variance σ2When unknown, the statistic is
Confidence intervals for the mean μ are:
when the mean μ is unknown, the statistic is
Confidence interval of standard deviation sigma of：
Lillieford test:
the hypothesis test is a statistical inference method used to determine whether sample-to-sample, sample-to-population differences are caused by sampling errors or substantial differences. The rationale is to make some assumption about the characteristics of the population and then to infer whether this assumption should be rejected or accepted by statistical reasoning from sampling studies. The normal distribution test in hypothesis testing includes three types: JB test, KS test, and Lilliefors test, for testing whether a sample is from a normal distribution population.
Among them, the lilliefos test is an improvement of the KS test, which compares a sample with a standard normal distribution (mean 0, variance 1), and the lilliefos test is not aimed at a standard normal but at a normal distribution with the same mean and variance as the sample. The method is suitable for normal distribution test of small samples and unknown parameters, so that the Lilliefors test is most suitable for the normality test of the dendrobium sample data.
The inspection principle and method are as follows:
checking and assuming that: h0: the data obeyed normal distribution; h1: data do not follow a normal distribution. Significance level α ═ 0.05.
Testing statistics:
T＝sup|F*(x)-S(x)|
wherein T is the Lilliefors test statistic, F*(x) Is a cumulative distribution function of normal distribution with a mean value of 0 and a standard deviation of 1, and S (x) is
The function value is distributed empirically. The sample size and raw data values are needed for calculating s (x).
Judging the principle: rejecting original hypothesis H0 when test statistic T exceeds a test threshold at a significance level of α; otherwise, the original hypothesis cannot be rejected.
In specific implementation, the applicant obtains that the length of the fourth internode of the stem of the dendrobium aphyllum is closely related to the purity of the dendrobium aphyllum through years of gene sequencing conclusion and morphological characteristic research of the dendrobium aphyllum, and the judgment accuracy rate is more than 90% in conservation
In the present invention, the stem fourth internode means that the number is counted from the bottom end of the stem of Dendrobium aphyllum, the fourth internode is between the fourth node and the third node, and the length of the fourth internode is the length of the fourth internode.
Finally, it should be noted that: the above-mentioned embodiments are only used for illustrating the technical solution of the present invention, and not for limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.
Claims (5)
1. A method for judging the pure similarity of dendrobium aphyllum is characterized by comprising the following steps: the judging method comprises the following steps:
s1: and (3) standard data acquisition: collecting dendrobium aphyllum samples consistent with the gene sequencing conclusion, wherein the sample capacity is n, and measuring the stem fourth internode length of each sample to obtain a measurement value of the stem fourth internode length variable;
s2: and (3) checking normality: carrying out normality test on the length variable of the fourth internode of the sample stem; the method for testing adopts an intuitive image analysis method and a hypothesis testing method, and the method is explained as follows:
according to normal empirical distribution function
Drawing a normal empirical distribution function curve of the dendrobium aphyllum;
according to normal probability density function
Drawing a normal probability density curve of the dendrobium aphyllum; when μ is 0 and σ is 1, the normal distribution becomes a standard normal distribution:
② according to the measured value of the length variable of the fourth internode of the stem obtained in the step S1, and according to the formula
Drawing a real experience distribution function by the experience distribution function;
according to the measured value of the length variable of the fourth internode of the stem obtained in step S1 and according to the formula
Drawing a real probability density function graph by using the probability density function;
comparing the real experience distribution function graph with a distribution function curve of normal distribution, and preliminarily judging whether the sample data accords with the normal distribution or not by judging the deviation degree of the curve; comparing the real probability density function graph with a probability density curve of normal distribution, and judging whether the sample data is subjected to the normal distribution or not according to the deviation degree and the curve shape consistency degree;
if the deviation of the real empirical distribution function graph and the distribution function graph of the normal distribution or the deviation of the real empirical probability density function graph and the probability density function graph of the normal distribution is small and the shapes of the real empirical probability density function graph and the probability density function graph of the normal distribution are consistent, the stem fourth internode length of the dendrobium aphyllum sample to be detected is probably in accordance with the normal distribution, and if the deviation is obviously large and the shapes of the stem fourth internode length of the dendrobium aphyllum sample to be detected are obviously inconsistent, the stem fourth internode length of the dendrobium aphyllum;
fourthly, any hypothesis test method of JB test, KS test and Lilliefors test can be adopted to confirm whether the sample conforms to normal distribution;
s3: standard interval: if the result obtained in the step S2 is that the normal distribution is obeyed, obtaining a 95% confidence interval of the mean value and a 95% confidence interval of the standard deviation according to a normal distribution overall calculation formula;
if the stem fourth internode length variable normality test result of the dendrobium aphyllum in the step S2 is not in accordance with normal distribution, if the sample capacity exceeds 30, the sample can still calculate the 95% confidence interval of the mean value and the 95% confidence interval of the standard deviation according to the normal general formula according to the central limit theorem;
the 95% confidence interval for the mean and the 95% confidence interval for the standard deviation obtained above can be used as a standard range for identifying the purity of an unknown sample.
2. The method for determining the pure proximity of Dendrobium aphyllum according to claim 1, wherein: step S1 is to obtain the measured value of the stem fourth internode length variable, then calculate the basic statistic of the stem fourth internode length variable according to the measured value of the stem fourth internode length variable, the basic statistic includes average level and discrete degree, then determine whether the data has abnormal value according to the basic statistic, if the abnormal value exists, check, if the data belongs to the measurement error or the recording error, delete the abnormal point, if the data is not due to the error, the data should be retained.
3. The method for determining the pure proximity of Dendrobium aphyllum according to claim 2, wherein: the mean level comprises at least one of a mean, a median, and a mode, the degree of dispersion comprises a standard deviation, a mean absolute deviation, and a coefficient of variation;
the basic statistics further comprise making a histogram and/or a box plot based on the measured values of the fourth internode length variable of the stem to visualize the data and to facilitate the determination of the erroneous abnormal value.
4. The method for determining the pure proximity of Dendrobium aphyllum according to claim 1, wherein: the hypothesis test is a Lilliefors test, and the statistic T ═ F of the Lilliefors test*(x) -S (x) |, where T is the Lilliefors test statistic, F*(x) Is a cumulative distribution function of normal distribution with a mean value of 0 and a standard deviation of 1, and S (x) is
At the significance level of alpha, rejecting the original hypothesis H when the test statistic T exceeds the test threshold0(ii) a Otherwise, the original hypothesis cannot be rejected.
5. A method for detecting the pure phase similarity of dendrobium aphyllum is characterized by comprising the following steps: the detection method comprises the following steps:
(1) 81 wild dendrobium aphyllum samples consistent with the conclusion of gene sequencing are collected, the fourth internode length of the stem of each sample is determined, and the determination results are as follows: the stem length variation range of the dendrobium aphyllum is 16.05 mm-46.49 mm, the average level is 24.97 mm-25.06 mm, and the calculated result is as follows: mean value: 24.97mm, median: 25.06mm, mode: 25.06mm, standard deviation of the stem fourth internode length fluctuation of 5.11mm, mean absolute deviation: 3.72mm, coefficient of variation: 0.20;
(2) visual image analysis normal distribution: drawing an empirical distribution function graph and a probability density function graph according to the data in the step (1), wherein the result of comparing the real empirical distribution function graph with the normal empirical distribution function curve is as follows: the curves of the two are basically consistent; the result of comparing the true probability density function graph with the normal probability density curve is as follows: the curve shapes of the two are approximately same and are bell-shaped curves, and the kurtosis and the skewness are basically consistent;
through visual analysis of the images, the sample data of the length between the fourth internode of the stem of the dendrobium aphyllum is most likely to accord with normal distribution;
(3) lillieford test: the original hypothesis is H0: the data obeyed normal distribution; let us assume H1: data do not follow normal distribution; the test results obtained from the data in step (1) were:
The value of the statistic is 0.0818, less than the threshold value 0.0986; the P value is equal to 0.1962 and is greater than the significance level alpha, so that the original assumption is accepted, and the dendrobium aphyllum sample data can be confirmed to be in normal distribution;
(4) calculating a mean 95% confidence interval and a standard deviation 95% confidence interval of the sample data of the length between the fourth nodes of the dendrobium aphyllum stem according to a normal distribution overall calculation formula, wherein the mean 95% confidence interval and the standard deviation 95% confidence interval are respectively as follows:
The 95% confidence interval for the mean and the 95% confidence interval for the standard deviation are: (23.8377, 26.0996) and (4.4301, 6.0510), wherein the interval is a standard interval for judging the pure similarity of dendrobium aphyllum;
(5) collecting length data of a fourth internode of a stem of a dendrobium aphyllum sample to be detected, and eliminating an abnormal value caused by a measurement error or a recording error in the sample; calculating a mean 95% confidence interval and a standard deviation 95% confidence interval of the length data of the fourth internode of the stem of the dendrobium aphyllum sample to be detected, and if the mean 95% confidence interval and the standard deviation 95% confidence interval are both in the standard interval, the similarity of the pure species of the dendrobium aphyllum to be detected is high, namely the purity of the dendrobium aphyllum to be detected is high; if at least one of the 95% confidence interval of the mean value and the 95% confidence interval of the standard deviation of the length data of the fourth internode of the sample stem of the dendrobium aphyllum to be detected is not in the standard interval, the similarity of the pure species of the dendrobium aphyllum to be detected is low, namely the purity of the dendrobium aphyllum to be detected is low.
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