System and method for rapidly measuring nutritional ingredients of Chinese chestnuts based on spectral quantification

Abstract

The invention relates to the technical field of Chinese chestnut nutritional ingredient detection, and discloses a Chinese chestnut nutritional ingredient rapid determination system and method based on spectral quantitation, components of Chinese chestnuts are determined through a determination module, determination results are sent to a data acquisition module, and the data acquisition module numbers and records the determination results. The random Chinese chestnut sample data and the carefully selected high-quality Chinese chestnut sample data are classified and stored as a random sample data set and a carefully selected sample data set to be sent to the evaluation module, and the evaluation module calculates a random sample nutritional ingredient value, a carefully selected sample nutritional ingredient value and a comprehensive nutritional ingredient value according to the data sets; the evaluation module sends a calculation result to the comparison module, the comparison module compares a comprehensive nutritional ingredient value with a conventional nutritional ingredient value, judges the nutritional degree of the current Chinese chestnuts and sends the nutritional degree to the feedback module, and the feedback module generates a feedback report, so that the advantages that rapid detection is realized, waste of samples is avoided, and the detection efficiency is greatly improved are achieved.

Description

Technical Field

[0001] The invention relates to the technical field of nutrient component detection, and in particular to a system and method for rapid determination of chestnut nutrient components based on spectral quantification. Background Art

[0002] Chestnut, scientifically known as Castanea mollissima Blume, is a tree belonging to the genus Castanea of ​​the Fagaceae family. The bark is dark gray with irregular deep longitudinal cracks. The branches are gray-brown with longitudinal grooves, and the young branches are covered with gray-brown hairs. The leaves are elliptical or oblong-lanceolate, with acuminate or short-pointed tips, rounded or broadly cuneate bases, serrated edges, and awn-like tips at the ends of the teeth. The flowers are unisexual and monoecious; the male inflorescence is spike-shaped and grows in the axils of the lower part of new branches; the female flowers are sessile and often grow at the lower part of the male inflorescence, with a cup-shaped involucre on the outside, and are often cultivated in low mountains and hills, gentle slopes, and river beaches with low altitudes. It likes sunny and humid areas. It is a positive tree species, cold-resistant and drought-resistant. It has high soil requirements and prefers sandy soil. Chestnut nuts can be eaten raw or shelled and ground into powder to make cakes, tofu and other side dishes. Chestnut wood is yellow-brown, the sapwood is slightly lighter, the texture is straight, the structure is rough, hard, and resistant to water and humidity. It is a high-quality wood with high economic value. At the same time, chestnuts also have high medicinal value, such as invigorating qi, strengthening the stomach and intestines, and replenishing kidney qi.

[0003] During the production, processing and sales of chestnut food, in order to ensure that the quality of chestnut products meets the standards, regular nutritional testing is required. Traditional testing methods usually require multiple processing steps in the laboratory to obtain results, and this process is often time-consuming. For companies that need to respond quickly to market changes or deal with quality issues urgently, this inefficient testing method may not meet their needs. Since the testing process is complicated and time-consuming, traditional testing methods often cannot achieve real-time monitoring of product quality, which means that when problems are discovered, a large number of unqualified products may have already flowed into the market, causing losses to companies and consumers. Summary of the invention

[0004] 1. Technical issues to be resolved

[0005] In view of the shortcomings of the prior art, the present invention provides a system and method for rapid determination of the nutritional components of chestnuts based on spectral quantification, which can quickly determine the nutritional components of chestnuts while avoiding losses caused by sample damage, greatly improving the detection efficiency. Combined with random samples and high-quality samples, a more comprehensive understanding of the overall quality of chestnuts can be achieved.

[0006] (II) Technical solution

[0007] To achieve the above object, the present invention provides the following technical solutions: a system for rapid determination of chestnut nutrients based on spectral quantification, comprising a determination module, a data acquisition module, an evaluation module, a comparison module and a feedback module;

[0008] The determination module determines the components of the chestnut by spectral quantification and sends the determination results to the data acquisition module;

[0009] The data acquisition module records the measurement results by number, classifies and saves the random chestnut sample data and the selected high-quality chestnut sample data into a random sample data set and a selected sample data set, and sends the random sample data set and the selected sample data set to the evaluation module;

[0010] The evaluation module calculates the nutrient component values ​​of the random samples and the nutrient component values ​​of the selected samples according to the random sample data set and the selected sample data set, and calculates the comprehensive nutrient component values ​​according to the nutrient component values ​​of the random samples and the nutrient component values ​​of the selected samples, and the evaluation module sends the calculation results to the comparison module;

[0011] The comparison module compares the comprehensive nutritional value with the conventional nutritional value to determine the nutritional level of the current chestnuts, and sends the nutritional level of the current chestnuts to the feedback module;

[0012] The feedback module generates a feedback report based on the received data for feedback.

[0013] Preferably, the specific method of the determination module for determining the components of chestnuts by spectral quantification is:

[0014] The measurement module is internally connected to a near-infrared spectrometer, which uses the spectral characteristics generated by the interaction between chestnuts and light for analysis, and calculates the content of specific elements or compounds in the chestnuts based on the intensity or absorption degree of the chestnut measured spectrum.

[0015] Preferably, the measurement results include the contents of water, sugar, starch and protein in the chestnut components, and the specific expression is:

[0016] BLcf=SF、TF、DF、DB

[0017] In the above expression, BLcf represents chestnut components, SF represents water, TF represents sugar, DF represents starch, and DB represents protein.

[0018] Preferably, the expression of the random sample data set is: Where i and n are both positive integers. represents the i-th random sample data in the random sample data set, and n represents that there are n data in the random sample data set.

[0019] Preferably, the expression of the selected sample data set is: Where j and m are both positive integers. represents the jth selected sample data in the selected sample data set, and m represents that there are m data in the selected sample data set.

[0020] Preferably, the expression of the nutrient value of the random sample is:

[0021]

[0022] In the above expression, SJyp represents the nutrient value of random samples. Represents the sum of all chestnut nutrients in the random sample data set starting from i=1.

[0023] Preferably, the expression of the nutrient content value of the selected sample is:

[0024]

[0025] In the above expression, JXyp represents the nutrient value of the selected sample. Represents the sum of all chestnut nutrients in the selected sample data set starting from j=1.

[0026] Preferably, the expression of the comprehensive nutritional value is:

[0027] ZHyp=SJyp*JXyp

[0028] In the above expression, ZHyp represents the comprehensive nutritional value.

[0029] Preferably, the comparison module compares the comprehensive nutritional value with the conventional nutritional value. When the comprehensive nutritional value is lower than the conventional nutritional value, it means that the current chestnut nutritional value is insufficient. When the comprehensive nutritional value is higher than the conventional nutritional value, it means that the current chestnut nutritional value is sufficient.

[0030] A method for rapid determination of chestnut nutrients based on spectral quantification, based on a system for rapid determination of chestnut nutrients based on spectral quantification, comprising the following steps:

[0031] Step 1: The determination module determines the components of chestnuts by spectral quantitative determination;

[0032] Step 2: The data collection module classifies and numbers the measurement results, saves and records them, and forms a random sample data set and a selected sample data set respectively;

[0033] Step 3: The evaluation module calculates the nutrient value of the random sample, the nutrient value of the selected sample, and the comprehensive nutrient value according to the random sample data set and the selected sample data set with the classification number;

[0034] Step 4: The comparison module compares the calculated results with the conventional nutritional value to determine the nutritional level of the current chestnuts;

[0035] Step 5: The feedback module generates a feedback report based on the current nutritional level of the chestnuts.

[0036] Compared with the prior art, the present invention provides a system and method for rapid determination of chestnut nutrients based on spectral quantification, which has the following beneficial effects:

[0037] 1. The present invention not only can realize the rapid detection of the nutritional components of chestnuts through near-infrared spectroscopy technology, but also can quickly obtain the key nutritional indicators of chestnuts such as moisture, sugar, starch and protein without destroying the samples during the detection process, thus avoiding the waste of samples, greatly improving the detection efficiency, and having many advantages over traditional detection methods.

[0038] 2. The present invention calculates the comprehensive nutrient value by combining the nutrient values ​​of random samples and the nutrient values ​​of selected samples, which can provide a more comprehensive understanding of the overall quality of chestnuts, reduce errors, and help to more accurately grasp the quality level of chestnuts, providing a scientific basis for subsequent planting, processing and sales. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 It is a schematic diagram of the system module of the present invention;

[0040] Figure 2 It is a schematic diagram of the method steps of the present invention. DETAILED DESCRIPTION

[0041] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0042] See also Figure 1-Figure 2 , a chestnut nutritional component rapid determination system based on spectral quantification, including a determination module, a data acquisition module, an evaluation module, a comparison module and a feedback module;

[0043] The determination module determines the components of the chestnut by spectral quantification and sends the determination results to the data acquisition module;

[0044] The specific method of measuring the components of chestnuts by spectral quantitative determination module is as follows:

[0045] The determination module is internally connected to a near-infrared spectrometer, which uses the spectral characteristics generated by the interaction between chestnuts and light for analysis, and calculates the content of specific elements or compounds in the chestnuts based on the intensity or absorption degree of the chestnut measured spectrum;

[0046] The near-infrared spectroscopy technology can not only realize the rapid detection of chestnut nutritional components, but also can quickly obtain the key nutritional indicators of chestnuts such as moisture, sugar, starch and protein without destroying the samples during the detection process, thus avoiding the waste of samples and greatly improving the detection efficiency. It has many advantages over traditional detection methods.

[0047] The test results include the moisture, sugar, starch and protein content in chestnut ingredients. The specific expression is:

[0048] BLcf=SF、TF、DF、DB

[0049] In the above expression, BLcf represents chestnut components, SF represents water, TF represents sugar, DF represents starch, and DB represents protein;

[0050] Water is one of the components with higher content in chestnuts, which is essential for maintaining the freshness and taste of chestnuts. Water not only helps to maintain the juicy taste of chestnuts, but also participates in the physiological metabolic process inside chestnuts. Sugar is the main source of sweetness of chestnuts and is also an important substance for providing energy. Sugar can be quickly absorbed and utilized by the human body after consumption to provide energy for the body. Starch is a complex carbohydrate, which can be converted into glucose after digestion and decomposition in the human body, providing the body with continuous and stable energy. Protein is the basic substance that constitutes human cells and tissues, and plays an important role in maintaining the normal physiological functions and growth and development of the body. By measuring these components, the nutritional components of chestnuts can be analyzed.

[0051] The data acquisition module records the measurement results by number, classifies and saves the random chestnut sample data and the selected high-quality chestnut sample data into a random sample data set and a selected sample data set, and sends the random sample data set and the selected sample data set to the evaluation module;

[0052] The expression for the random sample data set is: Where i and n are both positive integers. represents the i-th random sample data in the random sample data set, and n represents that there are n data in the random sample data set;

[0053] The expression for the selected sample data set is: Where j and m are both positive integers. represents the jth selected sample data in the selected sample data set, and m represents that there are m data in the selected sample data set;

[0054] The influence of accidental error or systematic error on single experimental data is reduced. Multiple experimental data can be statistically analyzed to reduce the influence of these errors, thereby improving the reliability of the measurement results. At the same time, the changing rules and trends of chestnut nutrients under different conditions can be revealed, making the measurement results more accurate.

[0055] The evaluation module calculates the nutrient value of the random sample and the nutrient value of the selected sample according to the random sample data set and the selected sample data set, and calculates the comprehensive nutrient value according to the nutrient value of the random sample and the nutrient value of the selected sample, and the evaluation module sends the calculation result to the comparison module;

[0056] The expression of the nutrient content value of random samples is:

[0057]

[0058] In the above expression, SJyp represents the nutrient value of random samples. represents the sum of all chestnut nutrients in the random sample data set starting from i=1;

[0059] The expression of the nutrient content value of the selected samples is:

[0060]

[0061] In the above expression, JXyp represents the nutrient value of the selected sample. represents the sum of all the chestnut nutrients in the selected sample data set starting from j=1;

[0062] The expression of comprehensive nutritional value is:

[0063] ZHyp=SJyp*JXyp

[0064] In the above expression, ZHyp represents the comprehensive nutrient value;

[0065] The comparison module compares the comprehensive nutritional value with the conventional nutritional value to determine the nutritional level of the current chestnuts, and sends the nutritional level of the current chestnuts to the feedback module;

[0066] The comparison module compares the comprehensive nutritional value with the conventional nutritional value. When the comprehensive nutritional value is lower than the conventional nutritional value, it means that the current chestnut nutritional value is insufficient. When the comprehensive nutritional value is higher than the conventional nutritional value, it means that the current chestnut nutritional value is sufficient.

[0067] By combining the nutrient values ​​of random samples and selected samples to calculate the comprehensive nutrient value, we can have a more comprehensive understanding of the overall quality of chestnuts, reduce errors, and help to more accurately grasp the quality level of chestnuts, providing a scientific basis for subsequent planting, processing and sales;

[0068] The feedback module generates a feedback report based on the received data for feedback.

[0069] The specific method of the above-mentioned chestnut nutritional component rapid determination system based on spectral quantification is as follows:

[0070] Step 1: The determination module determines the components of the chestnut by spectral quantitative determination;

[0071] Step 2: The data collection module classifies and numbers the measurement results, saves and records them, and forms a random sample data set and a selected sample data set respectively;

[0072] Step 3: The evaluation module calculates the nutrient value of the random sample, the nutrient value of the selected sample, and the comprehensive nutrient value according to the random sample data set and the selected sample data set with the classification number;

[0073] Step 4: The comparison module compares the calculated results with the conventional nutritional value to determine the nutritional level of the current chestnuts;

[0074] Step 5: The feedback module generates a feedback report based on the current nutritional level of the chestnuts.

[0075] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A rapid determination system of chestnut nutrients based on spectral quantification, characterized by: It includes a determination module, a data collection module, an evaluation module, a comparison module and a feedback module; The determination module determines the components of the chestnut by spectral quantification and sends the determination results to the data acquisition module; The data acquisition module records the measurement results by number, classifies and saves the random chestnut sample data and the selected high-quality chestnut sample data into a random sample data set and a selected sample data set, and sends the random sample data set and the selected sample data set to the evaluation module; The evaluation module calculates the nutrient value of the random sample and the nutrient value of the selected sample according to the random sample data set and the selected sample data set, and calculates the comprehensive nutrient value according to the nutrient value of the random sample and the nutrient value of the selected sample, and the evaluation module sends the calculation result to the comparison module; The comparison module compares the comprehensive nutritional value with the conventional nutritional value to determine the nutritional level of the current chestnuts, and sends the nutritional level of the current chestnuts to the feedback module; The feedback module generates a feedback report based on the received data for feedback.

2. The system for rapid determination of chestnut nutrients based on spectral quantification according to claim 1 is characterized in that: The specific method of the determination module for determining the components of chestnuts by spectral quantification is: The measurement module is internally connected to a near-infrared spectrometer, which uses the spectral characteristics generated by the interaction between chestnuts and light for analysis, and calculates the content of specific elements or compounds in the chestnuts based on the intensity or absorption degree of the chestnut measured spectrum.

3. A chestnut nutritional component rapid determination system based on spectral quantification according to claim 2, characterized in that: The measurement results include the moisture, sugar, starch and protein content in the chestnut components, and the specific expression is: BLcf=SF、TF、DF、DB In the above expression, BLcf represents chestnut components, SF represents water, TF represents sugar, DF represents starch, and DB represents protein.

4. The system for rapid determination of chestnut nutrients based on spectral quantification according to claim 3 is characterized in that: The expression of the random sample data set is as follows: 0 < i ≤ n, where both i and n are positive integers, represents the i-th random sample data in the random sample data set, and n represents that there are n data in the random sample data set.

5. The system for rapid determination of chestnut nutrients based on spectral quantification according to claim 4 is characterized in that: The expression of the selected sample dataset is as follows: 0 < j ≤ m, where both j and m are positive integers, represents the j-th selected sample data in the selected sample dataset, and m represents that there are m pieces of data in the selected sample dataset.

6. The system for rapid determination of chestnut nutrients based on spectral quantification according to claim 5, characterized in that: The expression of the nutrient content value of the random sample is: In the above expression, SJyp represents the nutrient value of random samples. DB) represents the sum of all chestnut nutrients in the random sample data set starting from i=1.

7. The system for rapid determination of chestnut nutrients based on spectral quantification according to claim 6, characterized in that: The expression of the nutrient content value of the selected sample is: In the above expression, JXyp represents the nutrient value of the selected sample. Represents the sum of all chestnut nutrients in the selected sample data set starting from j=1.

8. The system for rapid determination of chestnut nutrients based on spectral quantification according to claim 7, characterized in that: The expression of the comprehensive nutritional value is: ZHyp=SJyp*JXyp In the above expression, ZHyp represents the comprehensive nutritional value.

9. A system for rapid determination of chestnut nutrients based on spectral quantification according to claim 8, characterized in that: The comparison module compares the comprehensive nutritional value with the conventional nutritional value. When the comprehensive nutritional value is lower than the conventional nutritional value, it means that the current chestnut nutritional value is insufficient. When the comprehensive nutritional value is higher than the conventional nutritional value, it means that the current chestnut nutritional value is sufficient.

10. A method for rapid determination of chestnut nutrients based on spectral quantification, based on any one of the system for rapid determination of chestnut nutrients based on spectral quantification according to claims 1 to 9, characterized in that: The following steps are involved: Step 1: The determination module determines the components of chestnuts by spectral quantitative determination; Step 2: The data collection module classifies and numbers the measurement results, saves and records them, and forms a random sample data set and a selected sample data set respectively; Step 3: The evaluation module calculates the nutrient value of the random sample, the nutrient value of the selected sample, and the comprehensive nutrient value according to the random sample data set and the selected sample data set with the classification number; Step 4: The comparison module compares the calculated results with the conventional nutritional value to determine the nutritional level of the current chestnuts; Step 5: The feedback module generates a feedback report based on the current nutritional level of the chestnuts.

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