A method for rapid and nondestructive detection of corn seed quality based on near-infrared spectroscopy

Abstract

The present invention relates to the field of crop detection technology, and specifically discloses a method for rapid and non-destructive detection of corn seed quality based on near-infrared spectroscopy technology, comprising the following steps: cleaning the surface of a corn seed sample; using a near-infrared spectrometer to measure the moisture, starch, fat, and amino acid contents of the corn seeds; grouping the corn seeds into four groups based on the relative differences in moisture, starch, fat, and amino acid contents compared to the corresponding values of standard corn seeds; and classifying the corn seeds into multiple quality grades based on the groups in which individual corn kernels are grouped according to moisture content, starch content, fat content, and amino acid content. The method of the present invention does not cause damage or contamination to the tested corn seeds, is simple, rapid, accurate, reliable, and suitable for actual production.

Description

Technical Field

[0001] The present invention belongs to the technical field of crop detection, and in particular relates to a method for rapidly and non-destructively detecting corn seed quality based on near-infrared spectroscopy technology. Background Art

[0002] Agriculture is the foundation of a nation, and agriculture is primarily based on seeds. my country is a major agricultural producer and user. The crop seed industry is a strategic, fundamental, and core national industry, fundamental to promoting the long-term, stable development of agriculture and ensuring national food security. To promote the development of the seed industry, we must strengthen research on key technologies such as seed testing and quarantine, and resistance identification, and formulate and improve technical standards for testing and inspection of variety authenticity and seed quality.

[0003] Corn combines the nutritional qualities of both grain and fruit and vegetable crops, while also boasting a pleasant taste and low-glycemic properties. It is cultivated on a large scale and by a wide population worldwide. It is also a key food crop in my country, where corn cultivation is widespread throughout the country. With improved living standards, high-quality corn is increasingly sought after. However, the use of inferior seeds during the planting process results in significant time and financial losses. Therefore, developing a simple, rapid, efficient, economical, and accurate method for testing corn seed quality is crucial for the rapidly developing seed market.

[0004] Near-infrared spectroscopy analysis technology has a relatively fast development speed, the widest application range, and relatively mature technology due to its faster analysis speed and recognition ability. Near-infrared reflectance spectroscopy can analyze samples non-destructively, and it only takes 3 seconds to detect a sample. Near-infrared light is an electromagnetic wave between red light and visible light, and the near-infrared spectrum is a molecular vibration spectrum generated by the transition of molecular vibrations from the ground state to the higher state. The principle of near-infrared spectroscopy detection is to use the optical properties of seeds (light absorption characteristics, reflection characteristics, and transmission characteristics) due to their different internal components and external characteristics. When irradiated with light of different wavelengths, they will have different absorption or reflection characteristics. The spectral reflectance or absorption rate of the seeds will peak at a certain wavelength. The change in this peak is related to the internal physiological indicators of the seeds. At the same time, with the help of image processing and computer fuzzy recognition technologies and fuzzy mathematics and statistics, comprehensive analysis is carried out to obtain the relevant components of the test sample. Summary of the Invention

[0005] The present invention aims to provide a method for rapid and nondestructive detection of corn seed quality based on near-infrared spectroscopy technology, which overcomes the shortcomings of traditional seed quality inspection methods, such as many manual operations, large differences in test results, long test cycles, and complicated measurement methods.

[0006] To achieve the above object, the present invention provides a method for rapid and non-destructive detection of corn seed quality based on near-infrared spectroscopy technology, comprising the following steps:

[0007] 1. A method for rapid and non-destructive detection of corn seed quality based on near-infrared spectroscopy, comprising the following steps:

[0008] S1. Clean the surface of the corn seed sample;

[0009] S2. Take corn seed samples and use near-infrared spectrometry to measure the moisture, starch, fat, and amino acid contents of corn seeds;

[0010] S3. The corn seeds were grouped according to the relative difference between the moisture content of the test corn seeds and the moisture content of the standard corn seeds; the corn seeds were grouped according to the relative difference between the starch content of the test corn seeds and the starch content of the standard corn seeds; the corn seeds were grouped according to the relative difference between the fat content of the test corn seeds and the fat content of the standard corn seeds; the corn seeds were grouped according to the relative difference between the amino acid content of the test corn seeds and the amino acid content of the standard corn seeds; the germination rate of the standard corn seeds was greater than 99%, and the tested samples were of the same variety;

[0011] S4. The corn seeds are divided into multiple quality levels based on the groups of individual corn kernels according to moisture content, starch content, fat content and amino acid content.

[0012] Furthermore, in the above technical solution, step S1 also includes removing seeds that are scarred, rotten, or have physiological or infectious diseases.

[0013] Furthermore, in the above technical solution, each test item in step S2 is tested independently three times, and the average value of the three data is taken as the measurement result.

[0014] Furthermore, in the above technical solution, the absolute difference of the three independent test results is less than or equal to 10% of the arithmetic mean of the three measurement values as valid measurement data. If the absolute difference of the three independent test results is greater than 10% of the arithmetic mean of the three measurement values, after confirming and eliminating the cause of the abnormal measurement result, the near-infrared test and data processing are repeated according to the original method.

[0015] Furthermore, in the above technical solution, in step S3, the corn seeds are divided into two groups according to the moisture content of the corn seeds, one of which is a group in which the relative difference between the moisture content of the test corn seeds and the moisture content of the standard corn seeds is less than or equal to 8%, and the other group is a group in which the relative difference between the moisture content of the test corn seeds and the moisture content of the standard corn seeds is greater than 8%.

[0016] Furthermore, in the above technical solution, in step S3, the corn seeds are divided into two groups according to the starch content, one group is that the relative difference between the starch content of the test corn seeds and the starch content of the standard corn seeds is less than or equal to 1.5%, and the other group is that the relative difference between the starch content of the test corn seeds and the starch content of the standard corn seeds is greater than 1.5%.

[0017] Furthermore, in the above technical solution, in step S3, the corn seeds are divided into two groups according to the fat content of the corn seeds, one of which is a group in which the relative difference between the fat content of the test corn seeds and the fat content of the standard corn seeds is less than or equal to 14%, and the other group is a group in which the relative difference between the fat content of the test corn seeds and the fat content of the standard corn seeds is greater than 14%.

[0018] Furthermore, in the above technical solution, in step S3, the corn seeds are divided into two groups according to the amino acid content, one group being a group in which the relative difference between the amino acid content of the test corn seeds and the amino acid content of the standard corn seeds is less than or equal to 17%, and the other group being a group in which the relative difference between the amino acid content of the test corn seeds and the amino acid content of the standard corn seeds is greater than 17%.

[0019] Furthermore, in the above technical solution, the corn seeds are divided into five quality levels in step S4.

[0020] Beneficial effects of the present invention:

[0021] The present invention provides a method for rapid and non-destructive detection of corn seed quality based on near-infrared spectroscopy technology. The method uses near-infrared spectroscopy technology to detect the moisture content, starch content, fat content and amino acid content of corn seeds and processes the detection results to determine the quality of corn seeds. The method is simple, rapid, accurate and reliable.

[0022] This method does not cause any damage to the internal structure or external composition of the seeds, nor does it affect or contaminate the seed samples, effectively ensuring the authenticity and validity of corn seed testing results. Therefore, its application in seed testing not only improves the quality of seed testing, but also saves testing costs for farmers due to its non-destructive nature, protecting the economic benefits of enterprises while also having good environmental benefits. DETAILED DESCRIPTION

[0023] The invention will be described in detail below in conjunction with specific embodiments. However, it should be understood that the scope of protection of the invention is not limited by the specific embodiments.

[0024] Example 1:

[0025] 1. Selection of near-infrared spectrometer

[0026] Choose a diffuse reflectance near-infrared spectrometer with a continuously scanning monochromator or other products with a spectral range of 950nm to 1650nm, a wavelength accuracy of ≤0.3nm, and a wavelength reproducibility of <0.02nm for two consecutive times or less than <0.2nm per year. The software mainly considers the functions of collecting, storing, preprocessing, modeling and predicting NIR spectral data for easy use, such as the DA7200 near-infrared spectrometer from Germany's Perton Company.

[0027] 2. Calibration of the Near-Infrared Spectrometer and Parameter Setting

[0028] The instrument should be calibrated before measurement, and the parameters should be set as follows:

[0029] 1) Resolution: 0.1nm~10nm;

[0030] 2) Spectral collection rate: 100 times / second.

[0031] 3. Sample Preparation

[0032] The surface of the intact corn seed sample should be properly cleaned, and seeds with scars, rot, physiological diseases, infectious diseases, etc. should be removed. The main consideration is that the sample test results are not affected by other factors.

[0033] 4. Selection of calibration model

[0034] The calibration model is selected based on the principle that the NIR spectrum of the calibration sample is representative of the NIR spectrum of the sample being measured. By comparing the GH values of these spectra, if the GH value of the sample being measured is less than 3, the calibration model can be selected; if the GH value of the sample being measured is greater than 3, the calibration model cannot be selected.

[0035] 5. Monitoring sample measurement and instrument calibration

[0036] According to the requirements of the near-infrared spectrometer manual, corn seed monitoring samples were taken and measured using the near-infrared spectrometer, the measurement data were recorded, and the instrument was calibrated according to the moisture content, starch content, fat content, and amino acid content of the monitoring samples.

[0037] 6. Determination of corn seed samples

[0038] Perform self-inspection of the instrument according to the requirements of the near-infrared spectrometer manual; take corn seed samples and use the near-infrared spectrometer to measure the moisture content, starch content, fat content and amino acid content, and record the measurement data.

[0039] 7. Test result processing

[0040] (1) The absolute difference between the three independent test results obtained under repeated conditions is no more than 10% of the arithmetic mean of the three measured values, and the measured result should be within the content range covered by the calibration model. The average of the three data is taken as the measured result, and the measured result is rounded to 2 decimal places;

[0041] (2) If the absolute difference between the two test results is greater than 10% of the arithmetic mean of the three measured values, after confirming and eliminating the cause of the abnormal measurement result, the near-infrared test and data processing shall be repeated according to the original method.

[0042] For example, abnormal test results may be caused by:

[0043] a) The content of the sample test item exceeds the range of the instrument calibration model;

[0044] b) The sample species is significantly different from the species used in the calibration sample set for the instrument;

[0045] c) Using the wrong calibration model;

[0046] d) There are too many impurities in the sample;

[0047] e) The sample shifted during the spectral scanning process;

[0048] f) The sample temperature exceeds the temperature range specified by the calibration model.

[0049] 8. Grouping corn seeds according to their moisture content, for example, into two groups, named A1 and A2, and using 8% of the relative difference between the moisture content of the test corn seeds and the moisture content of the standard corn seeds as the dividing line, wherein Group A1 is when the relative difference between the moisture content of the test corn seeds and the moisture content of the standard corn seeds is less than or equal to 8%, and Group A2 is when the relative difference between the moisture content of the test corn seeds and the moisture content of the standard corn seeds is greater than 8%; Grouping corn seeds according to their starch content, for example, into two groups, named B1 and B2, and using 1.5% of the relative difference between the starch content of the test corn seeds and the starch content of the standard corn seeds as the dividing line, wherein Group B1 is when the relative difference between the starch content of the test corn seeds and the starch content of the standard corn seeds is less than or equal to 1.5%, and Group B2 is when the relative difference between the starch content of the test corn seeds and the starch content of the standard corn seeds is greater than 1.5%; Fat content grouping, for example, can be divided into two groups, named C1 and C2, with a 14% difference between the fat content of the test corn seeds and the fat content of the standard corn seeds as the dividing line. Group C1 refers to corn seeds with a fat content difference of less than or equal to 14% between the test corn seeds and the standard corn seeds, while Group C2 refers to corn seeds with a fat content difference of greater than 14%. Corn seeds can also be grouped according to amino acid content, for example, two groups, named D1 and D2, with a 8% difference between the amino acid content of the test corn seeds and the standard corn seeds as the dividing line. Group D1 refers to corn seeds with a fat content difference of less than or equal to 17% between the test corn seeds and the standard corn seeds, while Group D2 refers to corn seeds with a fat content difference of greater than 17% between the test corn seeds and the standard corn seeds. The standard corn seeds are the same variety as the tested samples, have healthy, plump kernels, and a germination rate greater than 99%.

[0050] The dividing line of the grouping of each detection item is obtained through experiments. The method is as follows: the corn seeds are mixed evenly, and the germination rate of the corn seeds in each range is detected according to the relative difference between the moisture content of the corn seeds and the moisture content of the standard corn seeds within 2%, 2-4%, 4-6% and gradually increased to 98-100%, and the two adjacent ranges with the largest change in germination rate are selected, and the overlapping number of their endpoints is taken as the dividing line, which is 8%; the corn seeds are mixed evenly, and the starch content of the corn seeds is increased according to the relative difference between the starch content of the corn seeds and the starch content of the standard corn seeds within 0.5%, 0.5-1%, 1-1.5% and gradually increased to 99.5-100%, and the germination rate of the corn seeds in each range is detected respectively, and the two adjacent ranges with the largest change in germination rate are selected, and the overlapping number of their endpoints is taken as the dividing line. The overlapping number is used as the dividing line, which is 1.5%. The corn seeds are mixed evenly, and the relative difference between the fat content of the corn seeds and the fat content of the standard corn seeds is increased gradually from 2% to 98% to 100%. The germination rate of the corn seeds in each range is detected respectively, and the two adjacent ranges with the largest change in germination rate are selected, and the overlapping numbers of their endpoints are used as the dividing line, which is 14%. The corn seeds are mixed evenly, and the relative difference between the amino acid content of the corn seeds and the amino acid content of the standard corn seeds is increased gradually from 1% to 99% to 100%. The germination rate of the corn seeds in each range is detected respectively, and the two adjacent ranges with the largest change in germination rate are selected, and the overlapping numbers of their endpoints are used as the dividing line, which is 17%.

[0051] 9. The quality of corn seeds is comprehensively judged by grouping the moisture content, starch content, amino acid content, and fat content of individual corn seeds. Corn seed quality is divided into five grades: Grade 1, Grade 2, Grade 3, Grade 4, and Grade 5, with the quality decreasing in descending order. The specific classification method is as follows:

[0052] Corn seed quality is grade 1: (A1 B1 C1 D1);

[0053] Corn seed quality is divided into 2 grades: (A2 B1 C1 D1), (A1 B2 C1 D1), (A1 B1 C2 D1), (A1B1 C1D2);

[0054] Corn seed quality is divided into 3 grades: (A2 B2 C1 D1), (A1 B2 C2 D1), (A1 B1 C2 D2), (A1 B2 C1D2), (A2 B1 C2 D1), (A2 B1 C1 D2);

[0055] Corn seed quality is divided into 4 grades: (A1 B2 C2 D2), (A2 B1 C2 D2), (A2 B2 C1 D2), (A2B2 C2D1);

[0056] Corn seed quality is divided into 5 grades: (A2 B2 C2 D2).

[0057] Example 2

[0058] Accuracy test of corn seed moisture, starch, fat and amino acid content using near-infrared spectroscopy:

[0059] The experiment was conducted according to the test method of Example 1. Three corn seed samples were selected for measurement. Three parallel samples were weighed for each sample and tested and compared in two laboratories. The measurement results are shown in Tables 1 to 4. The relative standard deviations (RSD values) of the analysis results were as follows: moisture was 0.1248% to 1.1265%, starch was 0.0831% to 0.731%, fat was 0.7528% to 1.733%, and amino acids were 0.794% to 2.4743%. Moreover, the test results obtained by the two laboratories under repeated conditions for the same sample were highly consistent, indicating that the test data accuracy was good.

[0060] Table 1 Sample moisture content determination results

[0061]

[0062]

[0063] Table 2 Sample starch content determination results

[0064]

[0065] Table 3 Sample fat content determination results

[0066]

[0067]

[0068] Table 4 Amino acid content determination results of samples

[0069]

[0070] Example 3

[0071] Precision test of moisture content, starch content, fat content and amino acid content of corn seeds using near-infrared spectroscopy:

[0072] Corn seed samples were tested according to the method in Example 1. Two laboratories were selected for comparative testing, and the results of seven replicate samples were obtained. The results of these seven replicate samples are shown in Tables 5-8. The relative standard deviations (RSDs) for moisture ranged from 0.6940% to 0.9031%, starch ranged from 0.5780% to 0.6203%, fat ranged from 1.1956% to 1.8920%, and amino acids ranged from 1.3637% to 1.7989%, demonstrating the high precision of the method. Furthermore, the absolute differences in the replicated moisture, starch, fat, and amino acid test results obtained by the two laboratories, compared to the arithmetic mean of the two measured values, were 0.39% for moisture, 0.08% for starch, 0.6% for fat, and 5.2% for amino acids, further demonstrating the high precision and adaptability of the test.

[0073] Table 5 Sample moisture content determination results

[0074]

[0075] Table 6 Sample starch content determination results

[0076]

[0077]

[0078] Table 7 Sample fat content determination results

[0079]

[0080] Table 8 Amino acid content determination results of samples

[0081]

[0082]

[0083] Example 4

[0084] According to the corn seed quality detection and determination method in Example 1, the quality of the corn seeds was detected and graded, and a germination test was performed using a paper culture method in accordance with the crop inspection regulations (GB / T3534-1995). The germination rate was determined on the 7th day, and the germination rates of seeds of different quality levels were examined. The results are shown in Table 9. As can be seen from the table, the germination rate of grade 1 quality corn seeds was 100%, the germination rate of grade 2 quality corn seeds was 92.7%, the germination rate of grade 3 quality corn seeds was 85.6%, the germination rate of grade 4 corn seeds was 78.3%, and the germination rate of grade 5 corn seeds was 71.1%. The germination rates of corn seeds of different quality grades were significantly different, indicating that this method is accurate and effective in distinguishing the quality of corn seeds.

[0085] Table 9 Corn seed germination rate determination results

[0086] Seed quality grade Germination rate / % Level 1 100 Level 2 92.7 Level 3 85.6 Level 4 78.3 Level 5 71.1

[0087] In summary, the method for rapid and nondestructive detection of corn seed quality based on near-infrared spectroscopy technology of the present invention realizes the determination of corn seed quality by detecting the moisture content, starch content, fat content and amino acid content of corn seeds using near-infrared spectroscopy technology and processing the test results. This method is simple, rapid, accurate and reliable and can be used in actual production.

[0088] The foregoing descriptions of specific exemplary embodiments of the present invention are for purposes of illustration and description. These descriptions are not intended to limit the invention to the precise forms disclosed, and it is apparent that many variations and modifications are possible in light of the foregoing teachings. The exemplary embodiments have been selected and described for the purpose of explaining the specific principles of the invention and their practical application, thereby enabling those skilled in the art to realize and utilize a variety of exemplary embodiments of the invention and various options and modifications. The scope of the invention is intended to be defined by the claims and their equivalents.

Claims

1. A method for rapid and non-destructive detection of corn seed quality based on near-infrared spectroscopy technology, characterized in that: The following steps are involved: S1. Clean the surface of the corn seed sample; S2. Take corn seed samples and use near-infrared spectrometry to measure the moisture, starch, fat, and amino acid contents of corn seeds; S3. The corn seeds were grouped according to the relative difference in moisture content between the test corn seeds and the standard corn seeds; the corn seeds were grouped according to the relative difference in starch content between the test corn seeds and the standard corn seeds; the corn seeds were grouped according to the relative difference in fat content between the test corn seeds and the standard corn seeds; the corn seeds were grouped according to the relative difference in amino acid content between the test corn seeds and the standard corn seeds; the germination rate of the standard corn seeds was greater than 99%, and the tested samples were of the same variety; S4. Classify corn seeds into multiple quality grades based on the moisture content, starch content, fat content, and amino acid content of individual corn kernels; In the step S3: Corn seeds were divided into two groups based on their moisture content. One group consisted of corn seeds with a relative moisture content difference of less than or equal to 8% compared to standard corn seeds, and the other group consisted of corn seeds with a relative moisture content difference of greater than 8% compared to standard corn seeds. Corn seeds were divided into two groups based on their starch content. One group included corn seeds with a relative difference of 1.5% between the starch content of the test corn seeds and the starch content of the standard corn seeds, and the other group included corn seeds with a relative difference of more than 1.5% between the starch content of the test corn seeds and the starch content of the standard corn seeds. Corn seeds were divided into two groups based on their fat content: one group had a relative difference of 14% or less between the fat content of the test corn seeds and the fat content of the standard corn seeds; the other group had a relative difference of 14% or more between the fat content of the test corn seeds and the fat content of the standard corn seeds; Corn seeds were divided into two groups according to their amino acid content. One group included corn seeds with a relative difference of less than or equal to 17% in amino acid content compared to that of standard corn seeds, and the other group included corn seeds with a relative difference of greater than 17% in amino acid content compared to that of standard corn seeds.

2. The method for rapid and nondestructive detection of corn seed quality based on near-infrared spectroscopy according to claim 1, characterized in that: The step S1 also includes removing seeds that are scarred, rotten, or have physiological or infectious diseases.

3. The method for rapid and nondestructive detection of corn seed quality based on near-infrared spectroscopy according to claim 1, wherein: In step S2, each test item is tested independently three times, and the average value of the three data is taken as the measurement result.

4. The method for rapid and nondestructive detection of corn seed quality based on near-infrared spectroscopy according to claim 3, characterized in that: The absolute difference of three independent test results is less than or equal to 10% of the arithmetic mean of the three measured values as valid measurement data. If the absolute difference of three independent test results is greater than 10% of the arithmetic mean of the three measured values, after confirming and eliminating the cause of the abnormal measurement result, the near-infrared test and data processing shall be repeated according to the original method.

5. The method for rapid and nondestructive detection of corn seed quality based on near-infrared spectroscopy according to claim 1, characterized in that: In step S4, the corn seeds are divided into five quality levels.

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