Step-by-Step Guide to Seed Purity Verification

Seed purity is the core index to measure seed quality, which is directly related to crop yield stability, quality uniformity, and agricultural production efficiency. Accurate purity identification is not only a key link in quality control of seed production enterprises, but also an important technical support for market supervision, variety right protection, and international trade. Traditional identification relies on field observation, which is intuitive but time-consuming. Although modern laboratory technology is efficient, it needs standardized operation to ensure reliable results.

This paper systematically sorts out the standardized process of seed purity identification, from the basic principles of scientific sampling, the operating specifications of field planting identification and laboratory electrophoresis technology, to the quantitative methods of data interpretation and the prevention and control of common errors, and constructs a set of step-by-step guidelines with both accuracy and operability. By integrating international common standards and practical experience, it can provide technical reference for seed testing personnel in the whole process from sample collection to result report, and help improve the standardized level of seed purity identification.

Sampling Strategies for Seed Identification

The accuracy of seed purity identification begins with a scientific and reasonable sampling process, and the scientificity of sampling methods directly determines the representativeness of subsequent testing results. The sampling guidelines formulated by the International Seed Inspection Association (ISTA) provide a unified standard for global seed purity testing, and its core principle is to ensure that the samples can truly reflect the quality of the whole batch of seeds through random sampling.

• A. Basic principle of sampling and determination of sample size
  • a) The uniformity of the seed batch is the premise of sampling, so it is necessary to collect multiple secondary samples at different positions of the seed batch and then merge them into a mixed sample. According to ISTA regulations, the weight of seed batches is positively correlated with the sample size: for seed batches weighing less than 1000kg, the minimum sample size is 5000 (small crops such as rape) to 1000 (large crops such as corn); For seed batches exceeding 1000kg, 10% additional sample size is required for every 1000kg increase.
  • b) The determination of sample size also needs to consider crop types and testing purposes. The sample size for field planting identification (GOT) is usually larger than that for laboratory testing. For example, more than 400 plants should be planted for the GOT of maize hybrids to ensure that the rate of miscellaneous plants below 1% can be detected. However, the sample size of laboratory electrophoresis analysis can be reduced to 200, and the overall purity can be calculated by a statistical method.

Banding pattern and schematic distribution of LAP, EST, PER, and CAT zymograms in ELF expose and nonexposed maize seedlings (Al-Huqail et al., 2015)

• B. The operation flow of random sampling technology
  • a) The random sampling technique recommended by ISTA includes the following steps:
    • Seed batch preparation: Ensure that the seed batch is in a sampling state, remove surface impurities, and record the weight, packaging method, and storage conditions of the seed batch.
    • Distribution of sampling points: For bagged seeds, the "diagonal sampling method" is adopted, and 9 points are sampled at the upper, middle, and lower layers and four corners of the seed pile; For bulk seeds, sampling probes are used to collect samples at different depths and positions.
    • Secondary sample merging: Fully mix the secondary samples (not less than 100 grains per sample) collected at each point, and use a sampler (such as Zhong Ding sampler) to reduce them to the required sample size;
    • Sample packaging and marking: Put the final sample into a sealed bag, mark the seed batch number, sampling date, sampler, and other information, and keep the backup sample for re-inspection.
• C. Sampling adjustment of special seed batches
  • a) For seed batches with a high risk of mixing (such as mechanically harvested mixed seeds), it is necessary to increase the number of sampling points and sample size. For example, mechanical harvesting of rice may lead to the mixing of different varieties, and the number of sampling points should be increased to 12, and the sample size should be increased by 20%. For coated seeds or pelleted seeds, it is necessary to remove the coating layer before sampling to avoid the interference of coating materials on subsequent detection.

GOT and Electrophoresis Technology for Seed Identification

Planting identification method and electrophoresis technology represent the traditional phenotypic detection and biochemical detection methods of seed purity identification, respectively, and they complement each other in principle, operation, and applicable scenarios, and together form the basic technical system of seed purity detection.

Phenotypic purity evaluation of GOT

GOT is a method to determine the purity by planting seeds in the field and observing the phenotypic characteristics of plants, which is listed as the basic method for seed purity identification by ISTA. The core of this method is to identify the miscellaneous plants that do not conform to the target variety by using the morphological markers unique to the variety, such as plant height, leaf shape, color, and ear type.

The operation process of GOT includes:

• Experimental design: A random block design was adopted, with 3 replicates, and the plot area was determined according to the crop type (not less than 10m² for rice and not less than 20 m² for corn)
• Planting density: Keep the same density as the production field to ensure that the typical characteristics can still be observed after the plant is fully grown
• Observation period: Observe at least three times in the key growth period of crops (such as the seedling stage, the flowering stage, and the maturity stage), and record the stable characteristics such as leaf color at the seedling stage, anther color at the flowering stage, and grain morphology at the maturity stage
• Miscellaneous plant judgment: Mark the plants that do not conform to the typical characteristics of the variety as miscellaneous plants, calculate the miscellaneous plant rate (number of miscellaneous plants/total number of plants ×100%), and the seed purity = 1-miscellaneous plant rate

The advantage of GOT is that it can reflect the true performance of varieties under natural growth conditions, and is especially suitable for identifying phenotypic traits that are greatly affected by the environment. However, this method has some shortcomings, such as a long period (3-6 months), a large area, and is limited by climatic conditions, and is usually used for seed certification or dispute arbitration.

RAPD products of DNA fragments extracted from ELF exposed and nonexposed maize seedlings using 5 random primers (Al-Huqail et al., 2015)

Protein Electrophoresis Analysis Based on Laboratory

Protein electrophoresis is a rapid detection method commonly used in the laboratory by separating storage proteins or isoenzymes from seeds and identifying the purity by using the differences in protein patterns among varieties. Compared with GOT, electrophoresis technology has the advantages of a short cycle (3-5 days), no environmental influence, and batch detection.

The extraction and electrophoresis process of seed protein is as follows:

• Sample preparation: take 20-50 seeds, grind them into powder, and extract soluble protein with the extraction buffer
• Electrophoretic separation: polyacrylamide gel electrophoresis (PAGE) or sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) was used to separate proteins according to their molecular weight and charge difference
• Dyeing and imaging: develop color by Coomassie brilliant blue or silver staining, and obtain a protein map by gel imaging system
• Map analysis: Compare the position and intensity of protein bands between samples and standard varieties, and calculate the purity (number of seeds conforming to the standard map/total number of seeds ×100%)

In the purity detection of maize seeds, gliadin electrophoresis can separate 20-30 characteristic bands, which can effectively distinguish different hybrids and their parents, and the accuracy of purity detection is over 95%.

Electrophoresis and Isozyme Analysis for Seed Purity

Electrophoresis and isoenzyme analysis are laboratory methods to identify purity by detecting biochemical markers in seeds. The principle is to use the genetic polymorphism of different varieties at the protein or enzyme level to achieve rapid and stable purity evaluation.

Principle of Isozyme Analysis

Isozymes are enzymes that catalyze the same reaction but have different molecular structures, and their differences are determined by genes, which have tissue specificity and developmental stage specificity. In the identification of seed purity, commonly used isoenzymes include peroxidase, esterase, amylase, etc. These enzymes have high activity during seed germination and are easy to detect.

The operation steps of isozyme analysis are similar to those of protein electrophoresis, but attention should be paid to:

• Sampling: Usually use seedlings that germinate for 2-3 days, when the isozyme expression is the most abundant
• Enzyme extraction: Low temperature grinding (0-4℃) is adopted to avoid the loss of enzyme activity
• Electrophoresis and dyeing: Use a specific substrate dyeing solution (such as peroxidase dyed with benzidine) to make the enzyme band develop color
• In potato seed detection, esterase isozyme can show 8-12 bands, which can effectively distinguish different varieties, and the sensitivity of purity detection can reach 0.5%.

A comparison of different definitions of pure seed unit (PSU) (Frischie et al., 2020)

Types and Selection of Electrophoresis Technology

Different electrophoresis technologies are suitable for different crops and detection requirements:

• SDS-PAGE: It is suitable for storage protein analysis of most crops, such as wheat gluten and soybean globulin, with moderate resolution and simple operation
• Isoelectric focusing electrophoresis (IEF): According to the difference in isoelectric point in protein, the resolution is higher than SDS-PAGE, which is suitable for distinguishing varieties with similar genetic background, such as the indica-japonica subspecies of rice
• Two-dimensional electrophoresis (2-DE): Combined with isoelectric focusing and SDS-PAGE, thousands of proteins can be separated, with the highest resolution, but the operation is complicated and the cost is high, which is mainly used for screening species-specific markers

In practical application, it is necessary to choose appropriate electrophoresis methods according to crop types. For example, SDS-PAGE is commonly used to analyze globulin in rape seed purity detection, while IEF is used to analyze seed protein in sunflowers to obtain the best detection effect.

DNA-Based Methods for Seed Variety Identification

In addition to phenotypic and biochemical assays, Seed Variety Authenticity and Purity Identification increasingly relies on DNA-based technologies. Widely applied approaches include SSR (microsatellite) markers, which provide high-resolution genetic fingerprints, PCR-based assays for rapid and specific variety confirmation, and next-generation sequencing (NGS) platforms for genome-wide analysis of seed purity and authenticity. These molecular methods complement traditional tests by offering greater sensitivity, reproducibility, and scalability, making them indispensable in modern seed quality assessment.

Seed Identification Data Interpretation and Reporting

The data interpretation of seed purity testing should follow the standardized process to ensure the accuracy and comparability of the results, and at the same time clarify the purity threshold, so as to provide a basis for seed quality evaluation.

Quantitative Method of Purity Percentage

The purity calculation methods of different detection methods are slightly different:

• GOT: Purity (%) = (total number of plants-number of miscellaneous plants)/total number of plants ×100%, and miscellaneous plants include heterotypic plants, mutant plants, and other varieties of plants;
• Electrophoresis analysis: Purity (%) = (number of seeds consistent with the standard map/total number of tested seeds) ×100%. If there are more than two differences between the bands of a single seed and the standard map, it is judged as miscellaneous.
• Molecular marker detection: Calculate the purity according to the segregation ratio of polymorphic sites. For example, in SSR markers, if the heterozygote ratio exceeds 5%, it is judged that the purity is not up to standard.

In the calculation process, we should pay attention to the influence of sample size on the results. The larger the sample size, the higher the confidence of the results. For example, when testing 100 seeds, the confidence interval of 95% purity is 4.3%; When testing 1000 seeds, the confidence interval can be reduced to 1.4%.

The result obtained of 83 genotypes (23 parents and 60 hybrids)molecular markers using Structure Harvester analysis (Ahmed et al., 2022)

Specification Content of the Test Report

A complete seed purity test report shall contain the following information:

• Sample information: Seed batch number, variety name, sampling date, and sample size
• Detection method: Explain in detail the detection technology (such as GOT, SDS-PAGE), operation steps, and key parameters
• Results data: Total detection number, number of miscellaneous plants/grains, purity percentage, and confidence interval
• Judgment conclusion: Whether it meets the relevant standards, and if it is unqualified, explain the main reasons
• Additional notes: The possible influence of abnormal conditions (such as low seed germination rate and blurred electrophoresis bands) on the results

The report needs to be signed by inspectors and auditors to ensure its authority and traceability.

Common Errors and How to Avoid Them

In the process of seed purity testing, improper operation may lead to deviation of results, so it is necessary to identify potential risks and take preventive measures to ensure the quality of testing.

Pollution risk, Prevention, and Control

Pollution is the main reason for the distortion of detection results, including:

• Sample contamination: The sampling tools are not clean, resulting in mixed samples of different seed batches. The solution is to disinfect tools with 75% alcohol before sampling and replace gloves after each batch of samples
• Laboratory contamination: Cross-contamination of samples during electrophoresis, such as DNA or protein left before pipette; Prevention and control measures include using pipette tips with filter elements and replacing the tips after each sample is processed
• Field pollution: Pollen spread in adjacent plots in GOT leads to hybridization, especially for cross-pollinated crops (such as corn); Sufficient isolation distance (at least 50 meters) or planting barrier crops should be set

Avoiding False Positive and False Negative Results

False positives (false positives) and false negatives (false negatives) will seriously affect the accuracy of the results. The reasons and preventive measures are as follows:

• Phenotypic misjudgment: Phenotypic variation caused by environmental factors in GOT is mistaken for miscellaneous plants. The avoidance method is to combine the observation results of multiple growth periods, rather than the phenotype of a single period.
• Abnormal electrophoresis bands: Insufficient protein extraction led to band deletion, which was misjudged as miscellaneous grains. The formula of the extraction buffer should be optimized to ensure the full dissolution of the protein.
• Improper marker selection: The polymorphism of molecular markers is insufficient to distinguish related varieties; Verified core markers, such as 24 pairs of SSR core primers for rice, should be selected.

Prevention of Operational Errors

Human error is a common preventable error, including:

• Uneven sampling: The sample is not sampled according to the random principle, which leads to a certain part of the sample. It is necessary to strictly follow ISTA sampling guidelines and use a sampler to ensure uniform samples
• Counting error: The miscellaneous plant count in GOT is omitted or re-recorded; It is suggested that a two-person counting check should be adopted to jointly judge the disputed plants after marking them
• Improper calibration of equipment: Band deviation caused by unstable voltage of the electrophoresis instrument. It is necessary to calibrate the equipment regularly and check the instrument status before each experiment

Genetic relatedness of individuals from 83 sunflower populations using Structure software (Ahmed et al., 2022)

Conclusion

Seed purity identification is the key link to ensure the quality of seed industry, and its process needs to strictly follow the scientific and standardized operation process: starting with representative sampling, selecting appropriate methods according to the detection purpose (GOT is suitable for arbitration, electrophoresis is suitable for rapid detection), forming a reliable report through standardized data interpretation, and being alert to risks such as pollution and operational errors.

With the development of technology, modern technologies such as molecular markers are gradually merging with traditional methods, but no matter which technology is adopted, strict operation and quality control are the core to ensure accurate results. In the future, it is necessary to further improve the testing standards, promote the mutual recognition of the results of different methods, and provide solid technical support for the healthy development of the seed industry.

References

1. Al-Huqail AA, Abdelhaliem E. "Evaluation of Genetic Variations in Maize Seedlings Exposed to Electric Field Based on Protein and DNA Markers." Biomed Res Int. 2015 2015: 874906.
2. Frischie S, Miller AL., et al. "Ensuring seed quality in ecological restoration: nativeseed cleaning and testing." Restoration Ecology. 2020 28(3): 239-248.
3. Ahmed HGM, Rizwan M, Naeem M, et al. "Molecular characterization and validation of sunflower (Helianthus annuus L.) hybrids through SSR markers." PLoS One. 2022 17(5): e0267383.