A method for determining the husk retention of processed rice

Abstract

The invention discloses a method for determining the peel retention of processed rice, and relates to the technical field of crop processing. The method comprises the following steps: sample preparation, instrument preparation, sample testing, data analysis, using an absorption peak intensity algorithm to calculate the total absorption intensity A of the rice sample, then calculating the peak ratio R of the rice sample, and finally obtaining the peel retention D of the rice sample by using the peel retention algorithm and combining the total absorption intensity A and the peak ratio R. The method is verified by data, and has the advantages of introducing a noise coefficient in the total absorption intensity calculation process to adjust each peak value, reducing errors, and making subsequent measurement results more accurate. The peak ratio R can accurately identify and quantify each peak value by comprehensively considering background interference and peak weight. The peel retention algorithm uses the average value of the total absorption intensity A and the peak ratio R, and sets a threshold to adjust the weight coefficient, so as to accurately reflect the change of the sample and improve the effect of the accuracy of the peel retention determination.

Description

Technical Field

[0001] The invention relates to the technical field of crop processing, in particular to a method for determining the peel retention rate of processed rice. Background Art

[0002] Rice processing precision is an important indicator for evaluating rice quality and an important factor affecting the nutritional efficacy and economic value of rice. Rice processing refers to the process of removing the husk and cortex of rice to finally make rice of better edible quality. It is very important to scientifically, quickly and accurately detect the rice processing precision. It is the basis for judging the precise and appropriate processing of rice and one of the key factors restricting the loss of rice processing. At present, the spectral measurement method is usually used for the analysis of the husk retention.

[0003] During the spectral determination of rice skin retention, noise is likely to exist, which will affect the accuracy of the absorption intensity. The spectral peaks in the rice samples may overlap with each other, making it difficult to accurately identify and quantify each peak. At the same time, different samples have different spectral characteristics. Existing methods may not be able to fully reflect these changes, thereby affecting the accuracy of subsequent skin retention determination results. Summary of the invention

[0004] The object of the present invention is to provide a method for determining the peel retention of processed rice, which solves the problems raised in the above-mentioned background technology.

[0005] To achieve the above object, the present invention provides the following technical solution: a method for determining the bran retention of processed rice, comprising the following steps:

[0006] Sample preparation: select the rice sample to be tested, husk the rice sample, wash and dry it, and remove surface impurities and moisture;

[0007] Instrument preparation: prepare the infrared spectrometer and tray, place the rice sample evenly on the tray, calibrate the infrared spectrometer, and set the wavelength range and resolution of the infrared spectrometer;

[0008] Sample test: put the rice sample tray into the infrared spectrometer and start the measurement program. The infrared spectrometer will scan the rice sample and record the infrared absorption spectrum of the rice sample.

[0009] Data analysis, using an absorption peak intensity algorithm and combining the data in the infrared absorption spectrum to calculate the total absorption intensity A of the rice sample, then calculating the peak ratio R of the rice sample, and finally using a skin retention algorithm and combining the total absorption intensity A and the peak ratio R to obtain the skin retention D of the rice sample;

[0010] For data verification, the same rice sample was measured multiple times, and a control experiment was conducted using a rice sample with a known degree of husk retention to evaluate the accuracy and reliability of the measurement method. Finally, the measurement data was recorded and preserved.

[0011] Optionally, the infrared spectrometer in the instrument preparation step is a Fourier infrared spectrometer.

[0012] Optionally, the absorption peak intensity algorithm process in the data analysis step is as follows:

[0013]

[0014] Where A is the total absorption intensity;

[0015] n is the number of peaks;

[0016] BS i is the absorption intensity of the ith peak;

[0017] Z i is the noise factor of the ith peak;

[0018] Drift i is the response change value of the i-th peak;

[0019] Slope is the slope constant;

[0020] The absorption peak intensity algorithm can reduce the impact of abnormal data introduced by a single peak point by calculating the total absorption intensity A of multiple peak points, and can take into account the random errors in the measurement by introducing the noise coefficient. The noise coefficient is adjusted synchronously through the intensity of the noise in the spectral measurement process, so that the absorption intensity of the peak can reflect the actual situation and improve the accuracy of the absorption intensity.

[0021] Optionally, the peak ratio R calculation process in the data analysis step is as follows:

[0022]

[0023] Where R is the peak ratio;

[0024] BS P1 is the first peak absorption intensity, the first peak represents the endosperm of the rice sample;

[0025] BS P2 is the second peak absorption intensity, and the second peak represents the rice bran layer of the rice sample;

[0026] G p1 is the first peak background interference intensity, G p2 is the second peak background interference intensity;

[0027] W p1is the weight of the first peak, W p2 is the weight of the second peak;

[0028] K p1 is the first peak width, K p2 is the second peak width;

[0029] K ave is the average of the first peak and the second peak width;

[0030] By adjusting W p1 and W p2 , reducing the interference in the measurement data, making the measurement results more stable and suitable for different measurement environments, improving the practicality of the rice skin retention determination method. When the skin retention of mixed samples or rice samples with complex spectral characteristics is determined, the peak ratio R can distinguish the different components of the rice samples, accurately identify and quantify each peak, and improve the ability to evaluate the quality of different rice samples.

[0031] Optionally, the skin retention algorithm process in the data analysis step is as follows:

[0032]

[0033] Where D is the skin retention degree;

[0034] R is the peak ratio;

[0035] K is the correction factor, which is a constant;

[0036] is the average value of the total absorption intensity;

[0037]

[0038] Where n is the number of peaks;

[0039] A i is the absorption intensity of the ith peak;

[0040] The threshold of the rice skin retention D is set to Y. When the rice skin retention D is greater than the threshold Y, it means that the rice skin retention D is high. At this time, W in the calculation process of the peak ratio R is p1 and W p2 Changes occur, and NW is obtained according to the skin retention D p1 and NW p2 , specifically:

[0041]

[0042]

[0043] NW p1is the new weight of the first peak, NW p2 is the new weight of the second peak;

[0044] When the peel retention degree D is greater than the threshold Y, increasing the weight of important peaks and reducing the weights of other peaks can highlight the characteristic information in the rice sample, so that the peel retention degree D can better reflect the actual state of the rice sample.

[0045] Optionally, in the data verification step, when the same rice sample is measured multiple times, the tray must be cleaned after each measurement to check whether the infrared spectrometer is working properly to ensure that each measurement is performed in the same environment.

[0046] Optionally, in the instrument preparation step, the wavelength range of the infrared spectrometer is 400nm to 800nm, and the resolution is 2nm.

[0047] Optionally, the infrared spectrometer is maintained, and the maintenance includes cleaning optical components, checking spectrometer calibration and internal inspection of the instrument.

[0048] Compared with the prior art, the present invention has the following beneficial effects:

[0049] 1. The absorption peak intensity algorithm of the present invention calculates the total absorption intensity A of multiple peaks. The calculation of the total absorption intensity A is a basic step, and the noise coefficient is introduced to adjust each peak, which reduces the error and makes the subsequent measurement results more accurate. The peak ratio R calculation combines the absorption intensity of different peaks and provides the relative spectral characteristics of the rice sample, which reduces the error caused by spectral overlap. Finally, the rice skin retention D of the rice sample is obtained by combining the total absorption intensity A and the peak ratio R through the skin retention algorithm. The average value of the total absorption intensity A and the peak ratio R are used to provide a more comprehensive measurement result, which can reduce the impact of abnormal data that may be introduced by a single peak, improve the reliability of the data, and set a threshold to adjust the weight coefficient. By dynamically adjusting the weight coefficient in the calculation of the peak ratio R, the skin retention estimation D can more accurately reflect the changes in the sample. The above algorithms are interconnected and influence each other, and jointly improve the accuracy of the skin retention measurement.

[0050] 2. In the process of verifying the skin retention D result of rice samples, the present invention cleans the tray after each measurement to remove the pollutants remaining in the test process, and ensures that each measurement is carried out under the same environmental conditions to avoid interference with subsequent tests, thereby ensuring that the obtained skin retention D result has high consistency in repeated measurements, reducing the source of error, and improving the reliability of the verification of the skin retention D result. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 The figure is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0052] 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.

[0053] For example, see Figure 1 The present invention provides a method for determining the peel retention of processed rice, comprising the following steps:

[0054] Sample preparation: select the rice sample to be tested, then husk the rice sample, wash and dry it to remove surface impurities and moisture;

[0055] Specifically, dust impurities on the surface of rice samples will affect the accuracy of spectral measurement, and impurities may produce additional interference signals in the spectrum, thereby affecting the identification and quantitative analysis of peaks. At the same time, moisture will affect the spectral characteristics of rice samples, especially in near-infrared spectral analysis. The presence of moisture may cause interference or movement of absorption peaks. Therefore, drying the rice samples can ensure that the moisture is completely removed and improve the stability of spectral measurement.

[0056] Instrument preparation: prepare the infrared spectrometer and tray, place the rice sample evenly on the tray, calibrate the infrared spectrometer, and set the wavelength range and resolution;

[0057] Specifically, the wavelength range of the infrared spectrometer is 400nm to 800nm, and the resolution is 2nm. According to the spectral characteristics of the rice sample, selecting this wavelength range can ensure that the measurement covers all relevant absorption peaks, thereby fully capturing the characteristics of the sample and avoiding missing key spectral information. It can reduce measurement errors caused by incomplete range, and by setting the appropriate resolution, reduce measurement errors caused by spectral overlap and improve data accuracy.

[0058] Sample test: put the rice sample tray into the infrared spectrometer and start the measurement program. The infrared spectrometer will scan the rice sample and record the infrared absorption spectrum of the rice sample.

[0059] Data analysis: using the absorption peak intensity algorithm and combining the data in the infrared absorption spectrum to calculate the total absorption intensity A of the rice sample, then calculating the peak ratio R of the rice sample, and finally using the skin retention algorithm and combining the total absorption intensity A and the peak ratio R to obtain the skin retention D of the rice sample;

[0060] For data verification, the same rice sample was measured multiple times, and a control experiment was conducted using a rice sample with a known degree of husk retention to evaluate the accuracy and reliability of the measurement method. Finally, the measurement data was recorded and preserved.

[0061] Specifically, if the difference in the results of multiple measurements of the same rice sample is less than a preset range, the measurement method is judged to have high accuracy and reliability. If the difference in the results is greater than the preset range, the instruments and rice samples are inspected to reduce the impact of other factors on the measurement results.

[0062] In this embodiment: by washing and drying the rice sample, surface impurities can be removed and the quality of infrared spectrometer data can be improved. Then, the total absorption intensity A of the rice sample is calculated by the absorption peak intensity algorithm. The calculation of the total absorption intensity A is a basic step, and the noise coefficient is introduced to adjust each peak value, which reduces the error and makes the subsequent measurement results more accurate. In the process of calculating the peak ratio R, the peak width adjustment factor is introduced to make it more able to reflect the actual characteristics of the peak value. Finally, the skin retention degree D of the rice sample is obtained by combining the total absorption intensity A and the peak ratio R through the skin retention degree algorithm. The average value and peak ratio R of multiple data points are used to provide a more comprehensive measurement result, which can reduce the impact of abnormal data that may be introduced by a single peak value, improve the reliability of the data, and set a threshold to adjust the weight coefficient. By dynamically adjusting the weight coefficient in the calculation of the peak ratio R, the skin retention degree estimation D can more accurately reflect the changes in the sample, further improving the accuracy of the skin retention degree measurement.

[0063] Furthermore, in the instrument preparation step, the infrared spectrometer is a Fourier infrared spectrometer.

[0064] Specifically, the Fourier transform infrared spectrometer can provide high-resolution spectral data, which can accurately distinguish and quantify tiny absorption peaks in the sample, thereby improving the resolution and accuracy of the spectral data in the determination of rice husk retention, thereby being able to more accurately calculate the total absorption intensity A and peak ratio R. The Fourier transform infrared spectrometer has high sensitivity and can detect trace changes in components in rice samples, and is suitable for the determination of rice samples with different husk retention degrees.

[0065] Furthermore, the absorption peak intensity algorithm process in the data analysis step is as follows:

[0066]

[0067] Where A is the total absorption intensity;

[0068] n is the number of peaks;

[0069] BS i is the absorption intensity of the ith peak;

[0070] Zi is the noise factor of the ith peak;

[0071] Drift i is the response change value of the i-th peak;

[0072] Slope is the slope constant;

[0073] Specifically, the absorption peak intensity algorithm can reduce the impact of abnormal data introduced by a single peak point by calculating the total absorption intensity A of multiple peak points, and can take into account random errors in measurement by introducing a noise coefficient. The noise coefficient is adjusted synchronously according to the intensity of noise in the spectral measurement process, so that the absorption intensity of the peak can reflect the actual situation and improve the accuracy of the absorption intensity. The response change value and slope constant are introduced to allow compensation for the time change of the infrared spectrometer response, reduce errors caused by changes in the performance of the infrared spectrometer, and improve data accuracy.

[0074] The absorption intensity of each peak in the infrared spectrum data corresponds to the different chemical composition or structural information of the rice sample. The total absorption intensity A can comprehensively consider this information to more accurately evaluate the overall characteristics of the sample, and can be compared with other standard samples to better understand the characteristics or quality of the rice sample.

[0075] Furthermore, the peak ratio R calculation process in the data analysis step is as follows:

[0076]

[0077] Where R is the peak ratio;

[0078] BS P1 is the first peak absorption intensity, the first peak represents the endosperm of the rice sample;

[0079] BS P2 is the second peak absorption intensity, and the second peak represents the rice bran layer of the rice sample;

[0080] G p1 is the first peak background interference intensity, G p2 is the second peak background interference intensity;

[0081] W p1 is the weight of the first peak, W p2 is the weight of the second peak;

[0082] K p1 is the first peak width, K p2 is the second peak width;

[0083] K ave is the average of the first peak width and the second peak width.

[0084] More specifically, by comprehensively considering background interference and peak weight, a more accurate peak ratio can be provided, the error caused by instrument and environmental factors is reduced, and the determination accuracy of rice husk retention is improved. The first peak absorption intensity BS P1 and the second peak absorption intensity BS P2 They respectively represent different important components of rice samples, and can adjust the weights between different components in real time according to actual conditions, can adapt to the determination of different rice samples, and reduce interference in the measurement data by adjusting background interference and peak weights, making the measurement results more stable and suitable for different measurement environments, thereby improving the practicability of the rice skin retention determination method. When the skin retention of mixed samples or rice samples with complex spectral characteristics is determined, the peak ratio R can distinguish different components of the rice sample, accurately identify and quantify each peak, and improve the ability to evaluate the quality of different rice samples.

[0085] Furthermore, the skin retention algorithm process in the data analysis step is as follows:

[0086]

[0087] Where D is the skin retention degree;

[0088] R is the peak ratio;

[0089] K is the correction factor, which is a constant;

[0090] is the average value of the total absorption intensity;

[0091]

[0092] Where n is the number of peaks;

[0093] A i is the absorption intensity of the ith peak;

[0094] Specifically, a more comprehensive measurement result is provided by utilizing the average value of the total absorption intensity A and the peak ratio R, wherein the average value of the total absorption intensity A provides the intensity of each peak in the infrared absorption spectrum data, which is the basis for calculating the peak ratio R, and the result of the peak ratio R is used for skin retention estimation. At the same time, since the noise coefficient introduced by the total absorption intensity A is adjusted for each peak, the error is reduced, thereby improving the accuracy of the subsequent peak ratio R and skin retention D, and can reduce the impact of abnormal data that may be introduced by a single peak, thereby improving the reliability of the data.

[0095] The threshold of the rice skin retention D is set to Y. When the rice skin retention D is greater than the threshold Y, it means that the rice skin retention D is high. At this time, W in the calculation process of the peak ratio R is p1and W p2 Changes occur, and NW is obtained according to the skin retention D p1 and NW p2 , specifically:

[0096]

[0097] NW p1 is the new weight of the first peak, NW p2 is the new weight of the second peak;

[0098] When the skin retention D is greater than the threshold Y, the weight of the important peak is increased and the weight of other peaks is reduced, which can highlight the characteristic information in the rice sample, so that the skin retention D can better reflect the actual state of the rice sample. By setting the threshold Y, the W in the peak ratio R calculation process is dynamically adjusted. p1 and W p2 The skin retention degree D can adapt to the changes of different rice samples, making important features more prominent under high skin retention conditions, thereby enhancing the sensitivity to changes. For example, in rice samples at different processing stages, the skin retention degree D can more accurately detect the impact of different processing stages on sample characteristics, and the skin retention degree estimation D can more accurately reflect the changes in samples and improve the accuracy of skin retention degree determination.

[0099] Furthermore, in the data verification step, when the same rice sample is measured multiple times, the tray must be cleaned after each measurement to check whether the infrared spectrometer is working properly, ensuring that each measurement is carried out under the same environmental conditions, including temperature, humidity and light, to eliminate the potential impact of environmental factors on spectral data.

[0100] Specifically, in the process of verifying the skin retention D results of rice samples, cleaning the tray after each measurement can effectively remove residual samples or contaminants during the test process to prevent them from interfering with subsequent tests, ensure that the skin retention D results obtained are highly consistent in repeated measurements, reduce error sources, and ensure data stability.

[0101] Furthermore, the infrared spectrometer is regularly maintained, which includes cleaning the optical components, checking the spectrometer calibration and internal inspection of the instrument.

[0102] Specifically, cleaning optical components includes using non-woven cloth and optical cleaning fluid to gently wipe dust off the optical lens and beam path to ensure the quality of spectral measurement data. Checking the calibration of the spectrometer includes using standardized calibration samples or standard light sources to check the calibration status of the instrument and recalibrate according to the instrument manual. If deviations are found, adjustments are made according to the manufacturer's recommended procedures to ensure measurement accuracy. Internal inspection of the instrument includes using compressed air to clean the internal components of the instrument to prevent internal contamination from affecting instrument performance and maintain the stability of the instrument.

[0103] 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 method for determining the husk retention of processed rice, characterized in that: The following steps are involved: Step S1: Sample preparation, selecting a rice sample to be tested, husking the rice sample, washing and drying it, and removing surface impurities and moisture; Step S2: instrument preparation, preparing an infrared spectrometer and a tray, placing the rice sample evenly on the tray, calibrating the infrared spectrometer, and setting the wavelength range and resolution of the infrared spectrometer; Step S3: Sample testing: placing the rice sample tray into the infrared spectrometer and starting the measurement program. The infrared spectrometer will scan the rice sample and record the infrared absorption spectrum of the rice sample. Step S4: data analysis, using the absorption peak intensity algorithm and combining the data in the infrared absorption spectrum to calculate the total absorption intensity A of the rice sample, then calculating the peak ratio R of the rice sample, and finally using the skin retention algorithm and combining the total absorption intensity A and the peak ratio R to obtain the skin retention D of the rice sample; Step S5: Data verification, multiple measurements are performed on the same rice sample, and a control experiment is performed using a rice sample with a known degree of husk retention to evaluate the accuracy and reliability of the measurement method, and finally the measurement data is recorded and saved; In the instrument preparation step, the infrared spectrometer is a Fourier infrared spectrometer; The absorption peak intensity algorithm process in the data analysis step is as follows: Where A is the total absorption intensity; n is the number of peaks; BS i is the absorption intensity of the i-th peak; Z i is the noise coefficient of the i-th peak; Drift i is the response change value of the i-th peak; Slope is the slope constant; The calculation process of the peak ratio R in the data analysis step is as follows: Where R is the peak ratio; BS P1 is the first peak absorption intensity, and the first peak represents the endosperm of the rice sample; BS P2 is the second peak absorption intensity, and the second peak represents the rice bran layer of the rice sample; G p1 is the first peak background interference intensity, G p2 is the second peak background interference intensity; W p1 is the weight of the first peak, W p2 is the weight of the second peak; K p1 is the first peak width, K p2 is the second peak width; K ave is the average value of the first peak width and the second peak width; The skin retention algorithm process in the data analysis step is as follows: Where D is the skin retention degree; R is the peak ratio; K is the correction factor, which is a constant; is the average value of the total absorption intensity; Where n is the number of peaks; A i is the absorption intensity of the i-th peak; The threshold of the rice skin retention D is set to Y. When the rice skin retention D is greater than the threshold Y, it means that the rice skin retention D is high. At this time, W in the calculation process of the peak ratio R is p1 and W p2 Changes occur, and NW is obtained according to the skin retention D p1 and NW p2 , specifically: NW p1 is the new weight of the first peak, NW p2 is the new weight of the second peak; When the peel retention degree D is greater than the threshold Y, the weight of the important peak is increased and the weight of other peaks is reduced to highlight the characteristic information in the rice sample, so that the peel retention degree D can better reflect the actual state of the rice sample.

2. The method for determining the peel retention of processed rice according to claim 1, wherein: In the data verification step, when the same rice sample is measured multiple times, the tray must be cleaned after each measurement to check whether the infrared spectrometer is working properly to ensure that each measurement is performed in the same environment.

3. The method for determining the peel retention of processed rice according to claim 1, wherein: In the instrument preparation step, the wavelength range of the infrared spectrometer is 400nm to 800nm, and the resolution is 2nm.

4. The method for determining the peel retention of processed rice according to claim 1, wherein: Infrared spectrometers are regularly maintained and maintenance includes cleaning the optical components, checking the spectrometer calibration and internal inspection of the instrument.

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