Organic planting identification method based on plant circular RNA detection
By using circular RNA detection to screen key markers and build a classification model, the complexity and high cost of identifying organically grown products have been solved, enabling efficient and accurate identification of growing methods and supporting reliable traceability of organic products.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-23
- Publication Date
- 2026-03-17
AI Technical Summary
Existing methods for identifying organically grown products are complex, costly, and difficult to guarantee in terms of accuracy. Traditional regulatory methods rely on process records, which are susceptible to human interference and the risk of disruption in the traceability chain.
By collecting plant samples, performing circular RNA sequencing, screening key circular RNA markers, constructing a random forest classification model, determining planting methods based on the expression levels of circular RNA markers, and using circular RNA detection methods for product identification.
It simplifies the operation process, reduces testing costs, improves the convenience and stability of testing, enables objective and direct traceability judgment of products, and ensures the accuracy of identification results.
Smart Images
Figure CN121674541A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of agricultural product quality monitoring technology, and in particular to an organic farming identification method based on the detection of plant circular RNA. Background Technology
[0002] Organic agricultural products refer to agricultural products that strictly adhere to organic agricultural standards throughout the entire production process, prohibiting the use of chemically synthesized pesticides, fertilizers, growth regulators, and other substances, and are certified by independent certification bodies. With increasing global consumer focus on food safety, the ecological environment, and personal health, the organic agricultural product market is experiencing rapid growth. However, this booming market is accompanied by challenges related to product authenticity. Because organic products typically command a higher market premium, some conventionally grown products may be sold as organic, harming consumer interests, severely disrupting fair market competition, and discouraging genuine organic producers.
[0003] To ensure the authenticity of organic products, establishing a reliable traceability and identification system is crucial. Traditional regulatory methods primarily rely on reviewing documentation of the production process, such as agricultural operation records, input purchase and usage records, etc. This approach is process-based regulation, and its effectiveness is highly dependent on the completeness and authenticity of the records, making it susceptible to human interference and breaks in the traceability chain. Therefore, developing "product authenticity identification" technologies that can directly test the final product itself has become a key requirement to compensate for insufficient process regulation and ensure the healthy development of the organic industry. Academia and industry are also continuously exploring identification methods based on differences in the intrinsic components of products. Summary of the Invention
[0004] The purpose of this invention is to provide an organic farming identification method based on plant circular RNA detection, which solves the technical problems of existing organic farming product identification methods, such as complex operation, high cost, high requirements for sample condition, and difficulty in guaranteeing accuracy.
[0005] To achieve the above-mentioned objectives, the present invention provides the following technical solution: Instruction manual for an organic farming identification method based on plant circular RNA detection This invention provides a method for identifying organically grown plants based on the detection of circular RNA in plants. It includes the following steps: Collect plant samples from both organically and conventionally grown plants; Circular RNA sequencing was performed on the plant samples. Analyze and screen key circular RNA markers that showed differential expression between the organically grown and conventionally grown samples; The expression levels of all samples were determined based on the key circular RNA markers mentioned above. A classification model was constructed using the expression levels of the key circular RNA markers as features and predefined planting method categories as response variables. The expression levels of the key circular RNA markers in the plant samples to be tested are input into the classification model, and the planting method of the plant samples to be tested is determined based on the output of the classification model.
[0006] Preferably, in the above method, the plant sample is derived from fruit; The circular RNA sequencing involves digesting the extracted total plant RNA with ribonuclease R to enrich circular RNA, and constructing a strand-specific sequencing library for high-throughput sequencing.
[0007] Preferably, in the above method, screening the key circular RNA markers includes screening circular RNAs that show a significant difference in expression levels between the organic and conventional planting groups and whose fold change meets a preset threshold through differential expression analysis.
[0008] Preferably, in the above method, the criterion for determining the significant difference is that the adjusted p-value is less than 0.01; The preset threshold for the difference multiple is that the absolute value of the logarithmic difference multiple with base 2 is greater than 1; The classification model is a random forest classification model; Before building the classification model, all samples are divided into a training set and an independent test set according to a preset ratio. The training set is used for model building, and the independent test set is used for model performance verification. The training set comprises 70% of the total samples, and the independent test set comprises 30% of the total samples.
[0009] The present invention also provides the application of the above-mentioned organic cultivation identification method based on plant circular RNA detection in identifying the cultivation methods of fruit plants.
[0010] This invention also provides a method for identifying organically grown tomatoes based on the detection of plant circular RNA. It includes the following steps: Collect samples of organically grown and conventionally grown tomatoes; Circular RNA sequencing was performed on the tomato samples. The expression levels of the tomato samples were determined based on six key circular RNA markers, the nucleotide sequences of which are SEQ ID NO.1, SEQ ID NO.2, SEQ ID NO.3, SEQ ID NO.4, SEQ ID NO.5 and SEQ ID NO.6, respectively. Using the expression levels of the six key circular RNA markers as input features, a random forest classification model was constructed with organic farming corresponding to the first category label and conventional farming corresponding to the second category label. The expression levels of the six key circular RNA markers in the tomato samples to be tested are input into the random forest classification model, and the planting method of the tomato samples to be tested is determined based on the category labels output by the model.
[0011] Preferably, in the above-mentioned method for identifying organic tomato cultivation based on plant circular RNA detection, the first category label is 0 and the second category label is 1; The number of decision trees used to construct the random forest classification model is 50. Furthermore, all samples were divided into a 70% training set and a 30% independent test set.
[0012] This invention also provides a method for identifying organically grown strawberries based on the detection of plant circular RNA. It includes the following steps: Collect samples of organically grown and conventionally grown strawberries; Circular RNA sequencing was performed on the strawberry samples. The expression levels of the strawberry samples were determined based on seven key circular RNA markers, the nucleotide sequences of which are SEQ ID NO.7, SEQ ID NO.8, SEQ ID NO.9, SEQ ID NO.10, SEQ ID NO.11, SEQ ID NO.12 and SEQ ID NO.13, respectively. Using the expression levels of the seven key circular RNA markers as input features, a random forest classification model was constructed with organic farming corresponding to the first category label and conventional farming corresponding to the second category label. The expression levels of the seven key circular RNA markers in the strawberry sample to be tested are input into the random forest classification model, and the planting method of the strawberry sample to be tested is determined based on the category label output by the model.
[0013] Preferably, in the above-mentioned method for identifying organic strawberry cultivation based on plant circular RNA detection, the first category label is 0 and the second category label is 1; The number of decision trees used to construct the random forest classification model is 30. Furthermore, all samples were divided into a 70% training set and a 30% independent test set.
[0014] The present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements any of the above-described identification methods.
[0015] The beneficial effects of this invention are: This invention provides a simpler and more cost-effective method for identifying organic produce. This method is more adaptable to different storage and transportation conditions for plant samples, greatly improving the convenience and stability of actual testing. More importantly, its identification results are accurate and reliable, enabling objective and direct traceability of the product itself. This provides strong technical support for establishing a credible organic product authentication system, helping to regulate market order, protect consumer rights, and thus promote the healthy development of organic agriculture. Attached Figure Description
[0016] Figure 1 Image showing the results of RNA detection in tomato sequencing samples by 1% agarose gel electrophoresis; Figure 2 A two-dimensional scaled classification diagram of random forest for tomato samples under different planting methods; Figure 3 The image shows the results of RNA detection in strawberry sequencing samples by 1% agarose gel electrophoresis. Figure 4 This is a two-dimensional scaled random forest classification diagram of strawberry samples under different planting methods. Detailed Implementation
[0017] The technical solutions provided by the present invention will be described in detail below with reference to the embodiments, but they should not be construed as limiting the scope of protection of the present invention. Example 1
[0018] Organic Tomato Identification Based on Detection of Plant Circular RNA 1. Experimental Sample Preparation Widely cultivated tomato varieties, such as Jingcai No. 8 and Jingfan 309, were selected. Two experimental groups were set up: Organic cultivation group: Tomatoes were grown in strictly controlled organic plots, using only certified, well-rotted organic fertilizers (such as soybean meal and sheep manure) as the nitrogen source throughout the entire process, without using any chemically synthesized fertilizers or pesticides. Conventional cultivation group: Tomatoes were grown in the same geographical area and during the same season in neighboring plots, using chemically synthesized urea as the nitrogen source according to conventional agronomic practices.
[0019] Except for nitrogen source management, the two groups maintained the same planting conditions (such as water, light, and physical control of pests and diseases). At the fruit ripening stage, tomatoes of uniform maturity were collected from both groups, with each sample containing six fruits from six plants. Thirty biological replicates were collected from each group. Samples were immediately flash-frozen in liquid nitrogen after collection and then stored at -80°C for long-term preservation.
[0020] 2. Extraction and sequencing of circRNA Three representative samples were selected from each planting method. Total RNA was extracted from the samples using an improved plant total RNA extraction kit (e.g., one containing DNase I digestion). The RNA quality was then assessed. Figure 1 The image shows the results of 1% agarose gel electrophoresis on RNA extracted from representative samples.
[0021] To enrich circRNAs, total RNA was processed using ribonuclease R to digest linear RNA. Subsequently, strand-specific sequencing libraries were constructed using the fragmented RNA, and paired-end sequencing was performed using high-throughput sequencing. Bioinformatics analysis employed tools such as CIRI2 and find_circ to identify circRNAs from the raw sequencing data, and standardized quantification was performed using mapping per million reads.
[0022] 3. Screening of key differentially expressed circRNA markers To obtain differentially expressed circRNAs under two planting methods as key circRNA markers, differential expression analysis was first performed using the R software package to screen circRNAs with significantly different expression levels between the two groups (adjusted p-value < 0.01). The fold change index (|log2(fold change)| > 1) was then used to further narrow down the range and identify the most discriminative core markers. Through primer design and sample validation on the screened circRNAs, an optimal feature combination consisting of six key circRNAs (named circ-Q1 to circ-Q6) was finally obtained. Specific information on these six key circRNAs is shown in Table 1. Based on these six key circRNAs, primers were designed to cross junctions, and circRNA expression levels were tested on all samples.
[0023] Table 1. Assembly sequence information of 6 key circRNAs in tomato
[0024] 4. Construction and Validation of the Identification Model Using the expression levels of the aforementioned six key differentially expressed circRNAs as input features and planting method (organic = 0, conventional = 1) as the response variable, a random forest classification model was constructed using software such as R. Model parameters were set as follows: number of decision trees 50, other parameters default. All 60 samples were randomly divided into a training set (70%, 42 samples) and an independent test set (30%, 18 samples). After the model was built on the training set, predictions were made on the independent test set. The results showed that the model achieved a discrimination accuracy of 94.4% and a Kappa coefficient of 0.889 on the independent test set, demonstrating extremely high classification performance. The model's confusion matrix showed that only one organic planting sample was misclassified as conventionally planted, indicating that the model has extremely high classification performance. See [see results below]. Figure 2 . Example 2
[0025] Identification of Organic Strawberries Based on Detection of Plant Circular RNA 1. Experimental Sample Preparation Widely cultivated strawberry varieties, such as Hongyan and Fenyu, were selected. Two experimental groups were set up: Organic cultivation group: Strawberries were grown in strictly controlled organic plots, using only certified, well-rotted organic fertilizers (such as soybean meal and sheep manure) as the nitrogen source throughout the entire process, without using any chemical fertilizers or pesticides. Conventional cultivation group: Strawberries were grown in the same geographical area and during the same season in neighboring plots, using chemically synthesized urea as the nitrogen source according to conventional agronomic practices.
[0026] Except for nitrogen source management, the two groups maintained the same planting conditions (such as water, light, and physical control of pests and diseases). At the fruit ripening stage, strawberries of uniform maturity were collected from both groups, with each sample containing six fruits from six plants. Thirty biological replicates were collected from each group. Samples were immediately flash-frozen in liquid nitrogen after collection and then stored at -80°C for long-term preservation.
[0027] circRNA extraction and sequencing Three representative samples were selected from each planting method. Total RNA was extracted from the samples using an improved plant total RNA extraction kit (e.g., one containing DNase I digestion). The RNA quality was then assessed. Figure 3 The image shows the results of 1% agarose gel electrophoresis on RNA extracted from representative samples. To enrich circRNAs, total RNA was processed using ribonuclease R to digest linear RNA. Subsequently, strand-specific sequencing libraries were constructed using the fragmented RNA, and paired-end sequencing was performed using high-throughput sequencing. Bioinformatics analysis was performed using tools such as CIRI2 and find_circ to identify circRNAs from the raw sequencing data, and standardized quantification was performed at a rate of one million mapping reads.
[0028] 3. Screening of key differentially expressed circRNA markers To obtain differentially expressed circRNAs under the two planting methods as key circRNA markers, differential expression analysis was first performed using the R software package to screen circRNAs with significant differences in expression levels between the two groups (adjusted p-value < 0.01). Simultaneously, the fold change index (|log2(fold change)| > 1) was used to further narrow down the range and identify the most discriminative core markers. Through primer design and sample validation on the screened circRNAs, an optimal feature combination consisting of 7 key circRNAs (named circ-C1 to circ-C7) was finally obtained. These 7 markers have non-zero coefficients and were determined by the model to be the features that contribute the most to classification. Specific information on these 7 key circRNAs is shown in Table 2.
[0029] Table 2. Assembly sequence information of 7 key circRNAs in strawberry.
[0030] 4. Construction and Validation of the Identification Model Using the expression levels of the aforementioned seven key circRNAs as input features and planting method (organic = 0, conventional = 1) as the response variable, a random forest classification model was constructed using software such as R. Model parameters were set as follows: number of decision trees 30, other parameters default. All 60 samples were randomly divided into a training set (70%, 42 samples) and an independent test set (30%, 18 samples). After the model was built on the training set, predictions were made on the independent test set. The results showed that the model achieved a discrimination accuracy of 88.89% on the independent test set, with a Kappa coefficient of 0.778. The confusion matrix showed that two organically grown samples were misclassified as conventionally grown, indicating that the model has high classification performance. See [see results below]. Figure 4 .
[0031] As demonstrated by the above embodiments, this invention provides a method for identifying organic plant cultivation based on specific circular RNA markers. This method, through the established identification model, can effectively distinguish between products of the same species grown under organic and conventional methods, exhibiting good discriminative ability in actual sample testing. This indicates that the method system constructed by this invention can achieve rapid and effective identification of the organic cultivation status of economic crops such as tomatoes and strawberries, providing a new and feasible technical solution for the authenticity certification of related products.
[0032] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.
Claims
1. A method for identifying organic cultivation based on detection of plant circular RNA, characterized by, comprising the following steps: collecting plant samples of organic and conventional planting; sequencing circular RNAs of the plant samples; analyzing and screening key circular RNA markers differentially expressed between the organic and conventional planting samples; determining expression levels of all samples based on the key circular RNA markers; constructing a classification model with the expression levels of the key circular RNA markers as features and predefined planting method categories as response variables; inputting expression level data of the key circular RNA markers of the plant sample to be tested into the classification model and determining the planting method of the plant sample to be tested according to the output of the classification model.
2. The method of claim 1, wherein, the plant samples are derived from fruits; the circular RNA sequencing comprises ribonuclease R digestion of extracted total plant RNA to enrich circular RNAs and construction of a strand-specific sequencing library for high-throughput sequencing.
3. The method according to claim 1 or 2, characterized in that, screening the key circular RNA markers comprises screening circular RNAs with significant differences in expression levels between the organic and conventional planting groups and satisfying a preset threshold for the difference fold change through differential expression analysis.
4. The method of claim 3, wherein, the criterion for determining the significant difference is an adjusted p value less than 0.01; the preset threshold for the difference fold change is an absolute value of the logarithmic difference fold change with a base of 2 greater than 1; the classification model is a random forest classification model; before constructing the classification model, all samples are divided into a training set and an independent test set according to a preset ratio, the training set is used for model construction, and the independent test set is used for model performance verification; the training set accounts for 70% of the total number of samples, and the independent test set accounts for 30% of the total number of samples.
5. The method of claim 1 is used for identifying the planting method of fruit plants.
6. A method for identifying organic cultivation of tomatoes based on detection of plant circular RNAs, characterized by comprising the following steps: collecting tomato samples of organic and conventional planting; sequencing circular RNAs of the tomato samples; determining expression levels of the tomato samples based on 6 key circular RNA markers, the nucleotide sequences of which are SEQ ID NO. 1, SEQ ID NO. 2, SEQ ID NO. 3, SEQ ID NO. 4, SEQ ID NO. 5 and SEQ ID NO. 6, respectively; constructing a random forest classification model with the expression levels of the 6 key circular RNA markers as input features and the organic planting corresponding to a first category label and the conventional planting corresponding to a second category label; inputting expression level data of the 6 key circular RNA markers of the tomato sample to be tested into the random forest classification model and determining the planting method of the tomato sample to be tested according to the category label output by the model.
7. The method of claim 6, wherein, the first category label is 0, and the second category label is 1; the number of decision trees used for constructing the random forest classification model is 50; and all samples are divided into a 70% training set and a 30% independent test set.
8. A strawberry organic cultivation identification method based on plant circular RNA detection, characterized in that, comprising the following steps: collecting strawberry samples of organic and conventional planting; sequencing circular RNAs of the strawberry samples; The expression of the 7 key circular RNA markers, whose nucleotide sequences are SEQ ID NO. 7, SEQ ID NO. 8, SEQ ID NO. 9, SEQ ID NO. 10, SEQ ID NO. 11, SEQ ID NO. 12 and SEQ ID NO. 13 respectively, is determined for the strawberry samples; A random forest classification model is constructed by taking the expression of the 7 key circular RNA markers as input features and taking the first class label corresponding to organic planting and the second class label corresponding to conventional planting as labels; The expression data of the 7 key circular RNA markers of the to-be-tested strawberry sample is input into the random forest classification model, and the planting method of the to-be-tested strawberry sample is determined according to the class label output by the model.
9. The method of claim 8, wherein, The first class label is 0, and the second class label is 1; The number of decision trees used to construct the random forest classification model is 30; And all samples are divided into 70% training set and 30% independent test set.
10. A computer-readable storage medium having stored thereon a computer program, characterized in that, When the computer program is executed by the processor, the identification method of any one of claims 1-9 is realized.