CN107917982B - Citrus variety identification and systematic classification method based on 21 characteristic component contents - Google Patents
Citrus variety identification and systematic classification method based on 21 characteristic component contents Download PDFInfo
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Abstract
The invention belongs to the field of analytical chemistry, and particularly relates to a citrus variety identification and systematic classification method based on 21 characteristic component contents. Selecting a citrus standard sample, determining the content of 17 characteristic components in the standard sample and the percentage of 4 organic acid contents in the total acid content as indexes to establish a sample matrix, establishing the sample matrix for the indexes, applying a principal component analysis method and a cluster analysis method, selecting a chemical component with the highest weight in the principal components as a distinguishing factor, calculating the squared Euclidean distance between the same type of citrus standard samples by taking the distinguishing factor as an independent variable, obtaining the squared Euclidean distance range of the standard samples, determining the characteristic component contents of the distinguishing factor of the sample to be detected, respectively calculating the squared Euclidean distance ranges of the distinguishing factor and the citrus standard samples, and judging the type of the sample to be detected. The invention can realize effective differentiation of different citrus varieties, particularly pomelos, wide-peel oranges, oranges and other varieties, and provides an objective and reliable method for citrus classification.
Description
Technical Field
The invention belongs to the field of analytical chemistry, and particularly relates to a citrus variety identification and systematic classification method based on 21 characteristic component contents.
Background
The citrus is the first fruit in the world and is also one of the main cultivated fruits in China, the citrus cultivation area in China is the first in the world in 2015, and the yield is the third. The citrus area of the whole country in 2015 is 251.30 ten thousand hectares, and the yield of 3660.08 thousand tons occupies an important position in rural economy in the south of China. The citrus is a perennial woody plant, so that the species and the genus are easy to hybridize, the bud mutation frequency is high, the cultivation history is long, the citrus germplasm resources are quite rich and complex, the difference of the agronomic characters among a plurality of varieties with similar genetic relations is small, and even only the unobvious variation of a single character exists. The variety diversity and the complexity of characters of the citrus varieties also bring great difficulty to the identification and the recognition of the citrus varieties. Therefore, a worldwide uniform citrus plant classification method cannot be formed. Conventional plant classification and identification includes morphology, biochemistry, cytology, where it is difficult to effectively distinguish or identify numerous varieties due to environmental and time effects. At present, most of molecular biological methods such as ISSR, RAPD, AFLP, SSR, isoenzymes, PCR-RPLP of cytoplasmic genome segments and other various molecular marking technologies are applied to citrus germplasm identification and genetic diversity research of citrus such as Klimeldiphilus orange, pomelo and sweet orange, and new evidence is provided for the system evolution research of citrus plants. These methods require the detection of the sample by complex, costly and time-consuming assays. On the other hand, many citrus breeding parents are more and more concentrated on a few good varieties or lines, so that the bred new varieties are more similar in more characters and difficult to distinguish. How to effectively identify varieties in scientific research and production becomes a frequently encountered and very important problem. Therefore, the establishment of a rapid and reliable identification technical system of citrus varieties has important practical significance for early identification of seedlings, variety right protection, variety differentiation in production, research of genetic relationship and genetic diversity of citrus germplasm resources and the like. Meanwhile, an effective authenticity identification and quality evaluation method is established on the basis of the quality characteristics of the citrus fruit, and the method is an effective way and an effective way for protecting local high-quality citrus brands and has very important significance for controlling the quality of citrus products, protecting the benefits and enthusiasm of fruit growers and ensuring the sustainable and healthy development of the citrus industry.
The phenolic substances in the citrus fruits are main active substances in citrus, have strong oxidation resistance and are important research objects in the field of active substances and functional foods. Citrus phenolics include primarily flavanones, polymethoxylated flavones and phenolic acids, of which various phenolics including hesperetin, naringenin are believed to be widely present in the fruits of each representative citrus variety, but the composition and content of phenolics vary widely from citrus variety to citrus variety. Organic acids in citrus fruits play an important role in metabolism, are intermediates of carbohydrate, fat and protein metabolism in respiration, and are important associations of the three major substance metabolism. The content and the component of organic acid in the fruit are one of the important factors for determining the flavor quality of the fruit, so the quality can be compared by comparing the content of the organic acid in different fruits. The organic acid in the citrus fruit is mainly citric acid and malic acid, and also comprises acetic acid, pyruvic acid, tartaric acid, quinic acid, etc., wherein the citric acid and malic acid account for more than 90% of the total acid, and the content of other acids is less than 2%. The organic acid compositions and contents of different varieties of citrus fruits are not greatly different, so that no method for effectively carrying out citrus effective phenols by using the organic acid compositions and contents is available. The rapid and efficient measures for identifying varieties by using the content of phenolic substances and the proportion of organic acids as markers are not available for all varieties so far.
Quantitative classification is the introduction of mathematical methods and computational techniques into plant taxonomic studies. The principal component analysis is to recombine the original indexes into a group of new comprehensive indexes which are independent of each other and have non-overlapping information by using the idea of dimension reduction; meanwhile, according to a certain principle and actual needs, a few comprehensive indexes are extracted from the data to reflect the high proportion of information carried by the original indexes. In recent years, quantitative classification and principal component analysis have been widely used in research on fruit trees, but are often applied to molecular markers, sporotrichosis, cytology, isoenzymes and the like in citrus research, so that many breakthroughs are brought to citrus classification, and the genetic relationship between citrus classification and species is clarified. For example, Liu Yong and the like also carry out AFLP and SSR genetic diversity analysis on pomelo germplasm resources, and carry out quantity classification research on 33 pomelo varieties. However, at present, most researchers only stay in the visual description of molecular marker results such as AFLP and ISSR, but do not perform deep quantitative analysis, so that it is difficult to judge the genetic relationship between species more precisely and accurately. The rapid and efficient measures for identifying varieties by using the combination of a clustering analysis method and a principal component analysis method by taking the content of phenolic substances and the proportion of organic acids as markers are not available for all varieties so far.
Disclosure of Invention
The invention aims to provide a method for identifying and systematically classifying citrus varieties, which comprises the steps of taking the content of 17 phenolic substances and the percentage of citric acid, tartaric acid, malic acid and quinic acid in total acid as indexes, and carrying out deep quantitative analysis by combining principal component analysis and cluster analysis to realize identification and variety classification of different varieties of citrus and identification of raw materials in mixed fruit juice. The method can effectively distinguish different citrus varieties, particularly pomelos and other varieties, improves the efficiency and accuracy of classification of different citrus varieties, and provides an objective and reliable method for classification of citrus.
In order to achieve the purpose, the invention adopts the following technical scheme:
a method for discriminating and systematically classifying the citrus varieties based on 21 characteristic component contents includes such steps as choosing different parts of citrus fruit, such as peel or pulp, measuring the contents of 17 phenolic components and 4 organic acids in standard sample, creating a sample matrix, analyzing the principal components by principal component analysis and cluster analysis, choosing the chemical component with the highest weight from the principal components as the differentiating factor, calculating the squared Euclidean distance between the standard samples of the same citrus fruit by using the differentiating factor as independent variable, obtaining the range of squared Euclidean distance of the standard sample, measuring the characteristic component contents of the differentiating factor of the sample to be measured, calculating the ranges of squared Euclidean distance between the standard sample and citrus fruit, and determining the type of the sample to be measured.
The 17 characteristic components are gallic acid, chlorogenic acid, protocatechuic acid, caffeic acid, p-coumaric acid, bengal, ferulic acid, sinapic acid, rutin, benzoic acid, naringin, hesperidin, diosmin, quercetin, kaempferol, nobiletin and hesperetin. The 4 organic acid proportion calculation modes are that the contents of citric acid, malic acid, quinic acid and tartaric acid account for the proportion of the total acid content.
The invention has the following remarkable advantages:
establishing a sample matrix by taking the content of phenolic substances in citrus peel or pulp and the proportion of different components in organic acid as indexes, applying a principal component analysis method and a cluster analysis method, selecting a chemical component with the maximum weight in the principal component as a distinguishing factor, taking the distinguishing factor as an independent variable, calculating the squared Euclidean distance between the same type of citrus standard samples to obtain the squared Euclidean distance range of the standard samples, measuring the characteristic component content of the distinguishing factor of the sample to be measured, calculating the squared Euclidean distance range of the distinguishing factor and the citrus standard samples respectively, and judging the type of the sample to be measured. The method can also establish the fingerprint of the sample to be detected, realizes quality detection, monitoring and quality evaluation of the citrus varieties sold in the market, is beneficial to identification and control of the authenticity, the producing area and the quality of the citrus fruits, overcomes the defect of poor identification accuracy only by the producing area, the fruit shape index and the taste characteristics, and provides a reliable basis for solving the problem of quality mixing of high-quality citrus varieties in China.
Drawings
FIG. 1 shows the results of clustering analysis of the peels of 33 citrus varieties;
FIG. 2 shows the analysis results of the main components of the peels of 33 citrus varieties;
FIG. 3 shows the results of 19 citrus fruit flesh cluster analysis;
fig. 4 shows the analysis results of the main components of 19 citrus fruit pulps.
Detailed Description
For further disclosure, but not limitation, the present invention is described in further detail below with reference to examples.
A citrus variety identification method comprises the steps of selecting a citrus sample, determining the content of 17 phenolic substances in the standard sample and the percentage of 4 organic acids in the total acid content respectively as indexes to establish a sample matrix, applying a principal component analysis method and a cluster analysis method, selecting a chemical component with the largest weight in the principal component as a distinguishing factor, calculating the squared Euclidean distance between the same type of citrus standard samples by taking the distinguishing factor as an independent variable to obtain the squared Euclidean distance range of the standard sample, determining the characteristic component content of the distinguishing factor of the sample to be detected, calculating the squared Euclidean distance range of the distinguishing factor and the citrus standard samples respectively, and judging the type of the sample to be detected.
The 17 phenolic substances are gallic acid, chlorogenic acid, protocatechuic acid, caffeic acid, p-coumaric acid, menenoside, ferulic acid, sinapic acid, rutin, benzoic acid, naringin, hesperidin, diosmin, quercetin, kaempferol, nobiletin and hesperetin. The 4 organic acid proportion calculation modes are that the contents of citric acid, malic acid, quinic acid and tartaric acid account for the proportion of the total acid content. The standard sample is respectively prepared into 1mg/mL standard solution by DMSO-methanol and 50wt.% ethanol, mixed standard samples with different concentrations are prepared by a stepwise dilution method, and the mixed standard samples are stored at the temperature of-20 ℃ for later use.
The conditions of the high performance liquid chromatography-diode array detector method for the phenolic substances are as follows: adopting a C18 chromatographic column, wherein the column temperature is 30-50 ℃, and gradient elution is carried out by taking a mixed solution of acetonitrile and 0.1-1 wt.% acetic acid solution as a mobile phase, and the flow rate of the mobile phase is 0.8 mL/min; the detection wavelength is as follows: the components are 280nm, the detection time is 70min, and the sample injection amount is 5-20 μ L.
The organic acid detection chromatographic conditions are as follows: an HPLC instrument consisting of a quaternary pump, column oven, DAD detector and workstation was used.
Phenolic substance detection conditions: adopting a C18 chromatographic column, wherein the column temperature is 30-50 ℃, and gradient elution is carried out by taking a mixed solution of acetonitrile and 1% acetic acid solution as a mobile phase, and the flow rate of the mobile phase is 0.8 mL/min; the detection wavelength is as follows: 280nm, the detection time is 80min, the sample amount is 5-20 mu L, and the content of 17 phenolic substances is determined by a gradient elution program.
Organic acid detection conditions: and (3) analyzing the column: a hydrogen ion exchange column; mobile phase: the water phase A is 0.005mol/L sulfuric acid solution; the organic phase B is acetonitrile; the volume percentage of A is 96 percent, and the volume percentage of B is 4 percent; flow rate: 0.4 mL/min; and (3) an elution mode: isocratic elution; and (3) detection: temperature at 210 nm: 50 ℃; sample introduction volume: 10 mu L of the solution;
principal component analysis
Performing principal component analysis by comparing the content of 17 phenolic substances in the step (1) and the percentage of 4 organic acids in the total acid content as indexes, taking each chemical component standard as a variable, and calculating the correlation coefficient of each principal component and the variable according to the following step (1) to obtain a correlation coefficient matrix; then, based on the correlation coefficient matrix, carrying out dimensionality reduction arrangement on the main components according to the variance contribution rate, and taking the first N main components with the accumulated variance contribution rate of more than 80%; and calculating the weight of each chemical component in the N main components, and respectively selecting the chemical component with the largest weight as a representative main component in each main component to serve as a distinguishing factor. And selecting the newly obtained variable to characterize the structural characteristics of the original variable data as much as possible without losing information.
Decomposing the measurement matrix Y according to Singular Value Decomposition (SVD)
Y＝USVt(1)
And t represents transposition, and USV is respectively represented as a score matrix, a characteristic value matrix and a load matrix, which are used as the basis for judging the citrus content.
Cluster analysis
The method for measuring the square Euclidean distance is to adopt an interclass coupling method in hierarchical clustering analysis under an Analyze module of SPSS, take a distinguishing factor as an independent variable and calculate the square Euclidean distance between standard samples of the same producing area.
Clustering is carried out by taking the content of 17 phenolic substances and the percentage of 4 organic acids in the total acid content of the sample as indexes to obtain a clustering analysis dendrogram, and the dendrogram clearly reflects the genetic relationship among varieties so as to classify and identify the varieties and types of oranges such as pomelos, broad-peel oranges, sweet oranges, tangerines and the like. The method can also establish the fingerprint of a single variety, realize the quality detection, monitoring and quality evaluation of the citrus varieties sold in the market, is beneficial to the identification and control of the authenticity, the producing area and the quality of the citrus fruits, overcomes the defect of poor identification accuracy only by the producing area, the fruit shape index and the taste characteristics, and provides a reliable basis for solving the problem of the quality mixing of high-quality citrus varieties in China.
Example 133 Citrus Peel Cluster analysis and principal component analysis results
(1) Sampling: weighing 10 fruits, separating the peels and the pulps of the oranges, freeze-drying a peel sample for 72 hours, crushing the orange peel sample to 60 meshes, respectively adopting 50wt.% methanol and DMSO-methanol solution to perform ultrasonic extraction for 20min, adding water to a constant volume of 50mL, centrifuging at 8000rpm/min, and filtering supernate through an organic filter membrane of 0.22 mu m for later use;
(2) preparing a test solution: respectively weighing citric acid, tartaric acid, malic acid and quinic acid reagents with HPLC chromatographic purity of more than 99%, and then preparing standard sample solutions with different concentration gradients of the 4 organic acids of 0.01-1 mg/mL by using a mobile phase water phase; 17 characteristic components are gallic acid, chlorogenic acid, protocatechuic acid, caffeic acid, p-coumaric acid, bengal, ferulic acid, sinapic acid, rutin, benzoic acid, naringin, hesperidin, diosmin, quercetin, kaempferol, nobiletin and hesperetin which are dissolved in 50% methanol solution and are prepared into mixed standard of 0.5, 1, 5, 10, 20 and 50 mu g/mL;
(3) chromatographic analysis conditions: an HPLC instrument consisting of a quaternary pump, column oven, DAD detector and workstation was used.
Phenolic substance detection conditions: adopting a C18 chromatographic column, wherein the column temperature is 30-50 ℃, and gradient elution is carried out by taking a mixed solution of acetonitrile and 1% acetic acid solution as a mobile phase, and the flow rate of the mobile phase is 0.8 mL/min; the detection wavelength is as follows: 280nm, the detection time is 80min, the sample amount is 5-20 mu L, and the content of 17 phenolic substances is determined by a gradient elution program.
Organic acid detection conditions: and (3) analyzing the column: a hydrogen ion exchange column; mobile phase: the water phase A is 0.005mol/L sulfuric acid solution; the organic phase B is acetonitrile; the volume percentage of A is 96 percent, and the volume percentage of B is 4 percent; flow rate: 0.4 mL/min; and (3) an elution mode: isocratic elution; and (3) detection: temperature at 210 nm: 50 ℃; sample introduction volume: 10 mu L of the solution;
(4) the percentage of organic acid is calculated as follows: and then, adopting an external standard method to quantitatively analyze and calculate the contents of 17 phenolic substances and 4 organic acids in the fruits. Organic acid ratio (organic acid component content) 100%/total acid
(5) Clustering analysis:
the contents of 17 phenolic substances and the percentage of 4 organic acids in the total acid content are used as indexes, Euclidean distance measurement is carried out, the class distance is measured by a longest distance method (Furthest neighbor), and clustering analysis is carried out. And (3) calculating the squared Euclidean distance between the same type of standard samples by using an inter-group coupling method in hierarchical clustering analysis of an Analyze module of SPSS statistical software and using the distinguishing factors as independent variables.
(6) And (3) main component analysis:
taking the contents of 17 phenolic substances and the percentage of 4 organic acids in the total acid content as indexes, taking the first three main components, calculating the weight of each chemical component under the 6 main components, selecting the chemical component with the maximum weight in the main components as a distinguishing factor, taking the distinguishing factor as an independent variable, calculating the squared Euclidean distance between the same type of citrus standard samples to obtain the squared Euclidean distance range of the standard samples, measuring the characteristic component content of the distinguishing factor of the samples to be measured, calculating the squared Euclidean distance range of the distinguishing factor and the citrus standard samples respectively, and judging the type of the samples to be measured. The invention can realize effective differentiation of different citrus varieties, particularly pomelos and other varieties, and provides an objective and reliable method for citrus classification.
And (3) clustering the citrus varieties by taking the content of 17 phenolic substances and the percentage of 4 organic acids in the total acid content as indexes to obtain a clustering analysis dendrogram, wherein the dendrogram clearly reflects the genetic relationship among the varieties so as to distinguish the citrus varieties and varieties such as pomelos, broad-peel oranges, sweet oranges and the like.
Table 133 varieties of Citrus pericarp phenolic content (μ g/gDW)
Table 24 ratio of organic acids to total acids (%)
Clustering analysis and principal component analysis of 21 peel actives index for 33 different citrus varieties of peel as shown in fig. 1 and 2. The method adopts a class averaging method, and as can be seen from the figure, 21 characteristic component contents are taken as independent variables, citrus types are classified, 33 sample units are taken as area units, and the distance between the 33 sample units is measured by adopting Squared Euclidean distance (Squared Euclidean distance), so that the range of the Squared Euclidean distance of pomelos and other varieties is obtained, and the method specifically comprises the following steps: the Euclidean distance is 4, and the Euclidean distance is divided into 2 groups. Wherein one of the first group is: pomelo; the second group includes: broad-peel oranges and oranges. From the analysis chart of the main components in fig. 2, it can be seen that the citrus grandis such as Yunnan crisp and sweet, Taiwan dwarf and late citrus grandis, juniper citrus grandis, HB citrus grandis and the like are classified into one category, for example, No. 1 of Japanese south and Xingjin belong to Wenzhou mandarin orange variants, which are classified into one category with wide-peel citrus tangerines such as mandarin orange, dream navel orange, bright orange, dark orange, red orange, clear orange, green orange and the like and sweet orange varieties, and the citrus tangerines for establishing Yang are hybrid varieties, and the characteristic components are displayed between the citrus grandis and the wide-peel citrus tangerines, and the main component analysis result and the cluster. The analysis result can better distinguish the pomelo, the broad-peel orange and the sweet orange.
Table 333 names of citrus varieties
Example 219 Citrus pulp clustering analysis and principal component analysis results
(1) Sampling: weighing 10 fruits, separating the peels and the pulps of the oranges, freeze-drying a pulp sample for 72h, crushing the pulp sample to 60 meshes, respectively adopting 50wt.% methanol and DMSO-methanol solution to perform ultrasonic extraction for 20min, adding water to a constant volume of 50mL, centrifuging at 8000rpm/min, and filtering supernatant through a 0.22-micrometer organic filter membrane for later use;
(2) preparing a test solution: respectively weighing citric acid, tartaric acid, malic acid and quinic acid reagents with HPLC chromatographic purity of more than 99%, and then preparing standard sample solutions of the 4 organic acids with different concentration gradients of 0.01-1 mg/mL by using a mobile phase water phase; 17 characteristic components are gallic acid, chlorogenic acid, protocatechuic acid, caffeic acid, p-coumaric acid, bengal, ferulic acid, sinapic acid, rutin, benzoic acid, naringin, hesperidin, diosmin, quercetin, kaempferol, nobiletin and hesperetin which are dissolved in a 50% methanol solution;
(3) chromatographic analysis conditions: an HPLC instrument consisting of a quaternary pump, column oven, DAD detector and workstation was used.
Phenolic substance detection conditions: adopting a C18 chromatographic column, wherein the column temperature is 30-50 ℃, and gradient elution is carried out by taking a mixed solution of acetonitrile and 1% acetic acid solution as a mobile phase, and the flow rate of the mobile phase is 0.8 mL/min; the detection wavelength is as follows: 280nm, detection time of 80min, sample amount of 5-20 μ L, and gradient elution procedure.
Organic acid detection conditions: and (3) analyzing the column: a hydrogen ion exchange column; mobile phase: the water phase A is 0.005mol/L sulfuric acid solution; the organic phase B is acetonitrile; the volume percentage of A is 96 percent, and the volume percentage of B is 4 percent; flow rate: 0.4 mL/min; and (3) an elution mode: isocratic elution; and (3) detection: temperature at 210 nm: 50 ℃; sample introduction volume: 10 mu L of the solution;
(4) the percentage of organic acid is calculated as follows: and then, adopting an external standard method to quantitatively analyze and calculate the contents of 17 phenolic substances and 4 organic acids in the fruits. Organic acid ratio (organic acid component content) 100%/total acid
(5) Clustering analysis:
the contents of 17 phenolic substances and the percentage of 4 organic acids in the total acid content are used as indexes, Euclidean distance measurement is carried out, the class distance is measured by a longest distance method (Furthest neighbor), and clustering analysis is carried out. And (3) calculating the squared Euclidean distance between the same type of standard samples by using an inter-group coupling method in hierarchical clustering analysis of an Analyze module of SPSS statistical software and using the distinguishing factors as independent variables.
(6) And (3) main component analysis:
taking the content of 17 phenolic substances and the percentage of 4 organic acids in the total acid content as indexes, selecting the chemical component with the maximum weight in the main component as a distinguishing factor, taking the distinguishing factor as an independent variable, calculating the squared Euclidean distance between the same type of citrus standard samples to obtain the squared Euclidean distance range of the standard samples, measuring the characteristic component content of the distinguishing factor of the sample to be measured, calculating the squared Euclidean distance range of the distinguishing factor and the citrus standard samples respectively, and judging the type of the sample to be measured. The invention can realize effective differentiation of different citrus varieties, particularly pomelos and other varieties, and provides an objective and reliable method for citrus classification.
Cluster analysis of 21 active-substance indices for 19 different citrus varieties of pulp, as shown in fig. 4. The method adopts a class averaging method, and as can be seen from the figure, the method takes 21 characteristic component contents as independent variables, classifies the citrus types, takes 18 sample units as area units, measures the distance between the 18 sample units by adopting the squared Euclidean distance, and obtains the range of the squared Euclidean distance between the pomelo and other varieties, and specifically comprises the following steps: and 3 groups are formed when the Euclidean distance is 2.5. Wherein one of the first group is: the pomelo includes Yunnan crisp, fragrant and sweet, Taiwan dwarf late pomelo, june pomelo, HB pomelo, etc.; the second group comprises wide-peel oranges and oranges, including No. 1 Rinan and Xingjin which belong to Wenzhou mandarin orange variants, such as tangerine, dream navel orange, bright orange, dark orange, tangerine, clear orange, green orange and the like. The result obtained by calculating by using a benzyl component analysis method shows that pomelo varieties such as Yunnan crisp and sweet, Taiwan short and late pomelo, juniper pomelo, HB pomelo and the like are classified into one category, for example, No. 1 of Rinan and Xingjin belong to Wenzhou mandarin orange variant varieties which are classified into one category with wide-peel oranges such as tangerine, navel dream orange, bright orange, dark orange, red orange, clear orange, green orange and the like, and the main component analysis result is consistent with the cluster analysis result. The difference between the pulps is less than that between the peels.
The method comprises the steps of clustering citrus varieties by taking the content of 17 phenolic substances in citrus fruits and the percentage of 4 organic acids in the total acid content as indexes to obtain a clustering analysis dendrogram, and clearly reflecting the genetic relationship among the varieties so as to distinguish the citrus varieties and varieties such as pomelos, broad-peel oranges, sweet oranges and the like.
Table 419 Citrus pulp phenolic content (μ g/gDW)
Table 519 Citrus pulp phenolic content (μ g/gDW)
Claims (1)
1. A citrus variety identification and systematic classification method based on 21 characteristic component contents is characterized in that: selecting a standard citrus peel or pulp sample, establishing a sample matrix by taking the content of 17 characteristic components of phenolic substances in the standard sample and the content of 4 organic acids of citric acid, tartaric acid, quinic acid and malic acid accounting for the percentage of the total acid content as indexes, and judging the type of the sample to be detected by applying a principal component analysis method and a cluster analysis method; the citrus is wide-peel orange, orange or pomelo; the method comprises the following specific steps:
(1) sample pretreatment: weighing 10 fruits, freeze-drying samples of citrus peel and citrus pulp, crushing the samples to 60 meshes, weighing 0.5g of the samples, adding 20mL of 50wt.% methanol solution, carrying out ultrasonic extraction for 20min, repeating the ultrasonic extraction twice, combining the supernatants, adding 50wt.% methanol to the combined supernatants, fixing the volume to 50mL, centrifuging at 8000rpm/min, and filtering the supernatant through a 0.22-micron organic filter membrane for later use;
(2) preparation of a standard solution: respectively weighing citric acid, tartaric acid, malic acid and quinic acid reagents with HPLC chromatographic purity of more than 95%, and then preparing standard sample solutions with different concentration gradients of 0.01-1 mg/mL of 4 organic acids of citric acid, tartaric acid, quinic acid and malic acid by using 0.005mol/L sulfuric acid solution; 17 phenolic substances of gallic acid, chlorogenic acid, protocatechuic acid, caffeic acid, p-coumaric acid, bengal, ferulic acid, sinapic acid, rutin, benzoic acid, naringin, hesperidin, diosmin, quercetin, kaempferol, nobiletin and hesperetin are dissolved in 50% methanol solution to prepare 1-100 mu g/mL standard solution;
(3) chromatographic analysis conditions: HPLC instrument consisting of quaternary pump, column oven, DAD detector and workstation was used:
phenolic substance detection conditions: adopting a C18 chromatographic column, wherein the column temperature is 30-50 ℃, and gradient elution is carried out by taking a mixed solution of acetonitrile and 1% acetic acid solution as a mobile phase, and the flow rate of the mobile phase is 0.8 mL/min; the detection wavelength is as follows: 280nm, detection time of 80min, sample amount of 5-20 μ L, and gradient elution procedure of 0-80 min; acetonitrile: 1% acetic acid =12:88-50: 50;
organic acid detection conditions: and (3) analyzing the column: a hydrogen ion exchange column; mobile phase: the water phase A is 0.005mol/L sulfuric acid solution; the organic phase B is acetonitrile; the volume percentage of A is 96 percent, and the volume percentage of B is 4 percent; flow rate: 0.4 mL/min; and (3) an elution mode: isocratic elution; and (3) detection: temperature at 210 nm: 50 ℃; sample introduction volume: 10 mu L of the solution;
(4) and (3) index calculation: quantitatively analyzing and calculating the contents of 17 phenolic substances and 4 organic acids in the fruits by adopting an external standard method, wherein the proportion of the organic acids = 100% of the content of the organic acid components/total acid;
(5) clustering analysis and principal component analysis:
the method comprises the steps of establishing a sample matrix for indexes by taking the contents of 17 characteristic components and 4 organic acids in a standard sample as percentages of total acid content as indexes, establishing the sample matrix for the indexes, applying a principal component analysis method and a cluster analysis method, selecting a chemical component with the largest weight in the principal component as a distinguishing factor, taking the distinguishing factor as an independent variable, calculating the squared Euclidean distance between standard samples of the same type of oranges, obtaining the squared Euclidean distance range of the standard samples, measuring the characteristic component content of the distinguishing factor of the samples to be measured, calculating the squared Euclidean distance ranges of the distinguishing factor and the standard samples of the oranges respectively, and judging the type of the samples to be measured.
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| CN109884227B (en) * | 2019-03-29 | 2021-08-13 | 福建省农业科学院农业工程技术研究所 | Method for distinguishing purple fruit species and yellow fruit species passion fruit based on phenolic substances and amino acids |
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| CN104914190A (en) * | 2015-06-23 | 2015-09-16 | 福建省农业科学院农业工程技术研究所 | Method for identifying tea species and determining contents of 21 characteristic components |
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| CN102706980A (en) * | 2012-06-06 | 2012-10-03 | 福建省农业科学院农业工程技术研究所 | Method utilizing HPLC to test seventeen phenol substances contained in grapes and oranges |
| CN102818872A (en) * | 2012-08-08 | 2012-12-12 | 福建省农业科学院农业工程技术研究所 | High performance liquid chromatography for simultaneously detecting content of ten organic acids in fruit |
| CN104914190A (en) * | 2015-06-23 | 2015-09-16 | 福建省农业科学院农业工程技术研究所 | Method for identifying tea species and determining contents of 21 characteristic components |
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| 超高效液相色谱法同时测定柑橘中主要酚酸和类黄酮物质;郑洁等;《中国农业科学》;20141231;第47卷(第23期);第4706～4717页 * |
| 高效液相色谱法同时测定柑橘果实中18种类黄酮的含量;张元梅等;《中国农业科学》;20121231;第45卷(第17期);第3558～3565页 * |
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