KR102434363B1 - Quality measurement system of rice - Google Patents

Abstract

The present invention relates to a quality measurement system using a germination rate, a sample alignment unit in which rice samples are aligned, and one or more light sources for irradiating light having a wavelength range of ultraviolet, visible light and near-infrared bands to rice samples on the sample alignment unit And, a spectroscopic measurement unit including sensors for detecting reflected light spectra of different bands in which the light irradiated from the light source is reflected by the rice samples, and the reflected light spectrum output from the sensors to compare and analyze the germination rate A germination rate prediction unit that binarizes the measured germination rate data to increase the degree of germination rate to predict the germination rate, and a food taste predictive value ( It is characterized in that it includes a food evaluation unit that evaluates and outputs the quality of rice as a value y).

Description

Rice quality measurement system {QUALITY MEASUREMENT SYSTEM OF RICE}

The present invention relates to a quality measurement system for rice, and more particularly, to a quality measurement system using a germination rate among factors affecting the taste of rice.

Quality factors that affect the taste of rice are very diverse, and many studies have been conducted on the correlation between quality factors and taste of rice at home and abroad. Ingredients are an important factor, and after harvest, quality degradation due to enzymatic action and polishing degree, whiteness, and appearance are known as important factors.

=0.755수준이다. 그러나, 이 식미수식은 미네랄을 변수로 개발한 식으로서 미네랄을 측정하기 위해서는 많은 시간이 필요한 이화학적방법이 필요하므로, RPC나 유통업체와 같은 생산현장에서 적용하기는 어려운 현실이다. 한편, 국내의 경우에는 본원 출원인에 의해 시중 유통 쌀 292점을 대상으로 품질과 관능적 식미 평가를 실시한 결과, 쌀 품질중 b값(도정도와 관계) 및 밥 경도가 식미에 가장 큰 영향을 미쳤고, 쌀과 밥의 품질인자 간 식미 수식에서 결정계수(

)는 0.2668 수준이었다. 또한, 발표되고 있는 대부분의 식미 수식은 현장에서 용이하게 측정이 어려운 식미인자를 포함하고 있고, 측정기준도 통일되어 있지 않다. In recent years, quality factors affecting the taste of rice have been investigated, and as various physicochemical/mechanical methods to measure these factors have been developed, a taste evaluation method using them is being introduced. In order to evaluate taste by such a physicochemical/mechanical method, the precision of the food formula using the quality factor that can express the taste as a variable is a very important indicator. The formula with the highest coefficient of determination among the food formulas published so far is the food formula described in the paper published by Horino et al.

= 0.755 level. However, this formula is a formula developed with minerals as a variable, and it is difficult to apply it in production sites such as RPC or distributors because it requires a long time-consuming physicochemical method to measure minerals. On the other hand, in the case of Korea, as a result of quality and sensory taste evaluation of 292 commercially distributed rice by the applicant of the present application, the b value (relationship with degree of degree) and rice hardness among rice quality had the greatest effect on taste, and rice The coefficient of determination in the food taste formula between the rice quality factor and

) was at the level of 0.2668. In addition, most of the published flavoring formulas contain flavor factors that are difficult to measure in the field, and the measurement standards are not uniform.

The development of a quality measurement system that can predict the taste of rice has been mainly conducted in Japan, and the quality measurement system that has been developed and is currently being distributed includes an external measurement device that measures external characteristics and a component analyzer that measures internal components. . Appearance measuring devices generally use an image processing system such as a camera to determine normal grains and bad grains of rice. The component analyzer measures protein, moisture, amylose, etc. using near-infrared spectroscopy, and indirectly predicts taste by applying these quality measurement systems. In other words, it quantitatively displays the taste of rice through the food taste, which is a function of various quality factors measured in the quality measurement system. However, according to Kawamura's review (1996) on the actual food-based quality measurement system that is being sold, the correlation coefficient (r) between the measured value of the food system and the sensory test result was very low, 0.31 to 0.45, and the quality factor announced so far It is a reality with very low precision to evaluate the taste of rice.

The present applicant conducted a quality change experiment according to the post-harvest treatment conditions, and the correlation between the quality factor and the sensory taste evaluation value was as shown in the table shown in FIG. 1 . As can be seen from the table shown in Figure 1, the quality factors with the highest correlation coefficient (r) were germination rate (r=0.8293) and TTC (r=0.6163), fatty acid value (r=0.5580), RVA characteristics, brown rice color factor a showed a high correlation coefficient (r). RVA characteristics such as breakdown and consistency are limited in simple measurement, and the fatty acid value measured by the AACC method has a disadvantage in that the measurement error in the quality measurement system increases due to the error of the measurement method itself. It is the most useful quality factor that can predict taste.

Germination is the start of life for germination or the propagation of a species under a suitable environment, and the inactive DNA genetic information, various enzymes, and nutrients in the seed germ and endosperm of the seed are activated when the external environmental conditions improve and become active as a plant. It is the process of starting life. In rice, gibberellin, a plant growth material, is secreted under moderate temperature and moisture (30~35%), and biosynthesis occurs in the gene of the blastocyst epithelial tissue, resulting in massive replication of hydrolytic enzymes and carbohydrate transfer from the endosperm to the embryo. As they move in large quantities, respiration and metabolism become active, resulting in increased dry weight and germination. The ratio of seeds that can germinate among a certain amount of rice is called the germination rate, and the method of measuring the germination rate of rice is to measure the number of germinated 100 grains within 7 days at a temperature of 20℃. In addition, in order for germination to take place, the enzyme must be activated in the embryo. This phenomenon of enzyme activity can be measured for absorbance in a specific wavelength band by applying spectroscopy. In other words, although there is a difference in the degree of general raw material conditions, the quality deterioration due to the enzyme continues to occur in the same way as in the germination process, and atomic group and trace component content can be measured by irradiating light in various wavelength bands. Therefore, a high germination rate in rice means a high proportion of grains with live embryos, and live grains mean that there is enzyme activity. It is possible.

Japanese Patent Laid-Open No. 2007-232520 Japanese Patent Laid-Open No. 2009-139110

Accordingly, an object of the present invention is to provide a rice quality measurement system that can accurately evaluate the quality of rice by developing a flavoring formula with a high coefficient of determination as an invention devised to overcome the above-described limitations.

Furthermore, another object of the present invention is to develop a seasoning formula necessary to accurately evaluate the quality of rice, but it is possible to accurately evaluate the quality of rice by developing a seasoning formula that reflects quality factors as well as high measurement precision. In providing a quality measurement system of

In addition, another object of the present invention is to develop a flavoring formula centered on a quality factor (eg, germination rate) accompanied by a quality measurement method that can be used quickly and usefully in the field, so that the quality of rice can be accurately evaluated. to provide a quality measurement system of

Rice quality measurement system according to an embodiment of the present invention for solving the above-described technical problem,

A sample alignment unit in which rice samples are aligned, and

At least one light source irradiating light having a wavelength range of ultraviolet, visible and near-infrared bands to the rice samples on the sample alignment unit, and the reflected light spectrum of different bands in which the light irradiated from the light source is reflected by the rice samples a spectroscopic measurement unit including sensors for detecting

A germination rate prediction unit for predicting the germination rate of rice samples by comparing and analyzing the reflected light spectrum output from the sensors;

It is characterized by including a taste evaluation unit that evaluates and outputs the quality of rice with the value of the predicted value (y) obtained by substituting the predicted germination rate information into a function of the predicted value (y) of the germination rate (x1) expressed by the linear equation ,

In addition to the above configuration, the rice quality measurement system according to another modified embodiment further includes a driving control unit for controlling the movement of the sample alignment unit and controlling the amount of light of the light source.

In the rice quality measurement systems having the above configuration, the light source of the spectral measurement unit is characterized as a dual light source irradiating light having a wavelength range of 200 to 2200 nm,

Another feature of the spectrometer is a UV sensor for detecting a reflected light spectrum having a wavelength range of 200 to 400 nm, and a near-infrared sensor for detecting a reflected light spectrum having a wavelength range of 900 to 1700 nm.

In addition, the taste prediction value (y) used in the above-described rice quality measurement system is characterized in that it is calculated by the following equation.

y=2.1282+0.055x1 (x1 is the predicted germination rate)

According to the above-described problem solving means, the present invention has an effect of accurately evaluating the quality of rice by developing and using a food formula including information on the germination rate with a high coefficient of determination among quality factors of rice,

Furthermore, by using the food formula centered on the germination rate, there is an effect of providing a quality measurement method that can be used quickly and usefully in the field compared to other quality factors.

1 is an exemplary diagram of the correlation between quality factors and sensory taste according to post-harvest treatment conditions.
Figure 2 is an exemplary diagram of the comparative characteristics of the predicted value and the actual value when considering only the germination rate as a quality factor according to an embodiment of the present invention.
Figure 3 is an exemplary diagram of the comparative characteristics of the predicted value and the actual value when considering the germination rate and the fatty acid value as quality factors according to an embodiment of the present invention.
Figure 4 is an exemplary diagram of a prediction formula for brown rice using quality factors useful in raw materials, that is, brown rice through experiments for each treatment condition after harvest.
5 is an exemplary configuration diagram of a rice quality measurement system according to an embodiment of the present invention.
6 is experimental data for the development of a rice quality measurement system according to an embodiment of the present invention, and is an exemplary diagram of a raw spectrum acquisition and principal component analysis graph in the UV region.
7 is an exemplary graph of the significance analysis of the germination rate measurement in the UV region.
8 is an exemplary diagram of raw spectrum acquisition and principal component analysis in the NIR region.
9 is an exemplary graph of the significance analysis of the germination rate measurement in the NIR region.
10 is an exemplary view of the raw spectrum acquisition and principal component analysis graph in the UV-NIR region.
11 is a graph illustrating the significance of the measurement of the germination rate in the UV-NIR region.
12 is an exemplary graph comparing the actual value of the germination rate and the predicted value of the germination rate in the UV region of 200 to 400 nm.
13 is an exemplary graph comparing the actual value of the germination rate and the predicted value of the germination rate in the 900 to 1700 nm NIR region.
14 is a graph illustrating a comparison between the actual value of the germination rate and the predicted value of the germination rate in the UV-NIR region.

Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings. In the description of the present invention, if it is determined that a detailed description such as a related known function or configuration may unnecessarily obscure the gist of the present invention, a detailed description thereof will be omitted.

1) Experimental materials

First, experiments of various methods were conducted in order to obtain a taste formula of rice according to an embodiment of the present invention.

The rice sample used in the experiment is a mid-late cultivar Saenuri cultivar with 18.7% of domestic cultivation area (National Seed Resources, 2014), which is distributed as a high-quality variety, and was harvested in Yesan, Chungnam in October 2014. Private, Chungnam budget materials) were purchased and used.

Purchased rice was announced after screening foreign substances (chaff, straw, foreign matter, etc.) using a shipbuilding machine (IDS-30C, iGSP, Inc.) installed in the RPC, and the initial moisture content is 23.2% (w.b.).

2) Experimental method

In order to check the correlation between the quality of rice and the taste, the same kind of rice was used as a sample, and a storage experiment was conducted while changing the post-harvest treatment conditions to maximize the difference in taste and quality. Post-harvest treatment conditions were classified into three types: drying delay (required period from immediately after harvest to drying), drying moisture content (storage moisture content), and storage temperature, which are known to affect taste.

As for the drying delay, 1,000 kg of rice was placed in a polycon bag, and three levels of experimental plots were used: 0 days (dried immediately after harvest), 7 days (drying delay 7 days), and 14 days (drying delay 14 days). To exclude the effect of rain, polycon bags were stored in the warehouse, and the average temperature and relative humidity in the warehouse during the drying delay were 18.7°C and 54.0%. According to the drying delay, the rice grain temperature increased, showing 48.1℃ after 7 days of drying delay and 65.4℃ after 14 days of drying delay.

Samples with 3 levels of drying delay (drying delay of 0 days, 7 days, and 14 days) were dried at a drying temperature of 40~50℃ with a circulation-type grain dryer (2ton/batch, Hansung Industrial Co., Ltd., Gongju University) and the moisture content was reduced. It was dried while adjusting the drying time so that it became level 3 in the range of 12.0-17.0% (w.b.).

In order to minimize the change in moisture content during storage, samples prepared at 3 levels of drying delay (drying delay 0 days, 7 days, and 14 days) and 3 levels of drying moisture content (range of 12.0~17.0%) are filled in each PE film by about 10 kg. It was sealed and stored at a storage temperature of 3 levels (10, 20, 30 ℃) to measure the taste and quality deterioration over time.

The quality of rice during storage was measured by measuring the physical characteristics of the rice state, the physical characteristics of the brown rice state, the component characteristics and the quality characteristics, respectively. Quality change was measured once every 1 month and food sensory test was measured once every 2 months.

First, rice husks were removed with an impeller-type experimental brown rice machine (THU, Satake, Japan) to prepare brown rice as a brown rice sample. After milling the white rice to a level of 40, it was tested as a white rice sample.

The detailed quality and taste measurement methods in this case are as follows.

◎ Thousands

First, in the same way as the National Agricultural Products Quality Management Service, 1,000 grains except for abnormal grains were manually selected and measured with an electronic scale, and the measurement was repeated three times.

◎ Recognition rate

In the same way as the National Agricultural Products Quality Control Institute, 200 g of the sample was reduced to 50 g using a equalizer, and then completely removed with a rubber roller for testing. was measured to measure the weight, and the measurement was repeated three times.

◎ Degree

1,000 grains, excluding abnormal grains contained in brown and white rice, are manually selected, the weight of 1,000 grains is measured, and calculated by Equation 1 below.

◎ Whiteness and Lab value

Using a whiteness meter (CR 300-3, Kett, Japan) and a colorimeter (CM-2500d, Konica Minolta Sensing, INC., Japan), the normal grain was measured 5 times, and the average value of 3 times excluding the maximum and minimum values was measured. was used.

◎ moisture content

According to the Korea Agricultural Machinery Association and 山下律也 (1975) method, the moisture content was measured by the drying method at 10g-135℃-24 hours, and then converted to the standard measurement method 105℃ drying method, and the measurement was repeated three times.

◎ Amyogram

The results of peak viscosity, through, breakdown, consistency, setback, peak time, etc. were analyzed using a Rapid visco analyzer (RVA Super 4, Newport Scientific, Sydney, Australia) by AACC (Method 76-21, 2000) method using Thermocline window software. was analyzed with

◎ Protein, amylose

A sample of 300 g was measured repeatedly three times using a component analyzer (Infratec 1241, Foss Tecator, Hogenas, Sweden).

◎ Fatty acid

After placing 10 g of the sample in a cylindrical filter paper by the AACC method (Method 02-01A), lightly filling it with cotton wool, extracting it for 16 hours using petroleum ether as a solvent in a soxhlet extractor, and then using a rotary vacuum concentrator to take only the gas components. After re-dissolving in 50 ml of 0.02% Benzene Alcohol Phenolphthalein (BAP) solution, titrate with 0.0178N KOH until the standard color becomes pink, and then the obtained result is converted to a fatty acid value by Equation 2 below, and the average value after three measurements was used.

In Equation 2, T represents 0.0178N KOH consumption (mL) during sample titration, B represents 0.0178N KOH consumption (mL) during blank experiment titration, and W represents the moisture content (%, w.b.) of the sample, respectively.

◎ Germination rate

According to the National Agricultural Products Quality Management Service, 100 grains of rice were selected and placed in an incubator at 20°C (HK-B1028, Korea General Machinery Manufacturing, Korea), and the number of fine grains germinated within 7 days was used as the germination rate. was used.

◎ TTC

According to the National Seed Resources method, TTC (2,3,5-triphenyltetrazolium chloride (tetrazolium)) was soaked in 100 sized seeds for 6 hours, cut by 3/4 of the length of endosperm, and placed in TZ (tetrazolium) solution at 30℃ After immersion for 2 hours, germination was judged according to the coloration of the embryo and the degree of coloration.

◎Guayacol color reaction

According to the method of the National Agricultural Products Quality Control Institute, 4 g of the sample is put into a test tube, 4 mL of 1% guaiacol solution is added, 0.2 mL of 3% hydrogen peroxide is added, and after 2 minutes, the colored liquid is separated and UV Spectrophotometer (Jasco V-600 series) , Tokyo, Japan) was repeated three times using the color mode and the average value was used.

◎ Peroxidase activity

After homogenizing the sample and 3% NaCl solution at a ratio of 2:3 (W/V), filter through gauze and centrifuge at 13,000rpm, 4℃, for 30 minutes (LaboGene 1730R, Labogene, Korea) to separate the supernatant It was used as an enzyme solution. Add 0.15mL of the extracted enzyme solution to 2.4mL of 0.05M potassium phosphate buffer (pH 6.5), 0.3mL 150mM guaiacol, and 100mM H ₂ O ₂ , and mix them at 470 nm using a UV Spectrophotometer (Jasco V-600 series, Tokyo, Japan). The absorbance was measured three times, and the peroxidase activity was calculated as the vitality to increase the absorbance by 1.0 per minute.

◎ reducing sugar

It was measured by the DNS method by Miller (1959), and after grinding the brown rice sample, put it in a 1% NaCl solution, and left it in a shaking incubator (KSI-200L, G&S, Korea) at 150rpm, 30℃, for 3 hours with filter paper (Advantec no .1, Japan) was filtered and used as a sample solution. Add 3 mL of DNS solution to 1 mL of sample solution, react in a heating bath (bW-10G, Jeiotech, Korea) at 65° C. for 5 minutes, cool in cold water, quantify to 25 mL with distilled water, and quantify with a UV Spectrophotometer (SpectraMax i3, Molecular Devices, LLC., USA ) and the absorbance was measured three times at 550 nm. The standard solution was calculated using the formula obtained by obtaining a standard curve with a glucose solution.

◎ Sensory quality evaluation of rice

Meanwhile, sensory quality evaluation of rice was evaluated by the method of Kim et al. (Physical characteristics of chalky kernels and their effects on sensory quality of cooked rice. Cereal Chem 77(3):376-379") by a panel of 29 trained people. After cooking, mixing and cooling were repeated 3 times, put about 50 g of rice in a white porcelain bowl (diameter × height, 8.5 cm × 5 cm) and closed the lid. Samples were provided at room temperature after reaching about 2°C. Appearance, aroma, taste, texture, and overall quality were evaluated as sensory quality characteristics. As ancillary strength characteristics, gloss, color, odor other than rice, and rice were evaluated. The grain surface roughness, hardness, elasticity, grain cohesiveness, and adhesion were measured in the characteristic taste intensity and texture. There were a total of 14 evaluation items, and the evaluation method was a 9-point item scale (1 = very low, 5 = average). degree, 9 = very high) was used.

The texture characteristics of rice were evaluated by 2 bite compression described by Bourne et al. (1978) using a texture analyzer (model TA-XT2, Stable Micro System Ltd., Haslemere, England) by placing 12 g of rice in a cylindrical container. springiness, cohesiveness, adhesiveness, hardness, and chewiness were measured by using a plunger (diameter 50 mm) and crosshead speed 10 mm/sec. The sample was subjected to 40% compression twice.

Using the sensory taste evaluation value repeated twice, variance analysis was performed using the Statistical Analysis System (SAS, 1988) to verify the difference between samples in each analysis item. A comparison was performed to compare the average value of each sample.

▶ Correlation with food taste by quality factor

The correlation between the quality change experimental values and sensory taste evaluation values during the storage period by drying delay, drying moisture content, and storage temperature was as shown in the table shown in FIG. 1 . As can be seen from the table shown in Figure 1, the quality factors with the highest correlation coefficient (r) were germination rate (r=0.8293) and TTC (r=0.6163), fatty acid value (r=0.5580), RVA characteristics, brown rice color factor a showed a high correlation coefficient (r). RVA characteristics such as breakdown and consistency are limited in simple measurement, and the fatty acid value measured by the AACC method has a disadvantage in that the measurement error in the quality measurement system increases due to the error of the measurement method itself. It was judged to be the most useful quality factor to predict taste.

Figure 2 illustrates the comparative characteristics of the predicted value and the measured value when only the germination rate is considered as the quality factor according to the embodiment of the present invention, and Figure 3 is the quality factor when considering the germination rate and the fatty acid value according to the embodiment of the present invention Each of the comparative characteristics of the predicted value and the measured value is illustrated. And FIG. 4 illustrates a food taste prediction formula using quality factors useful in raw materials, that is, brown rice through experiments for each treatment condition after harvest.

2 and 3, the horizontal axis (x-axis) represents the sensory quality evaluation measurement value, and the vertical axis (y-axis) represents the food taste predicted value. In FIG. 2 , when a model equation is created using a statistical analysis program for the food taste predicted value and the measured value data considering only the germination rate, it can be expressed as a linear function as shown in FIG. 2 . That is, the food taste prediction value (y) can be expressed as a function expressed by the linear expression of the germination rate (x1). The food-prediction equation considering only the germination rate (x1) as a quality factor, that is, the food-prediction value (y), is modeled by the experiment in FIG. 4 and the following equation.

=0.69)y=2.1282+0.055x1 (

=0.69)

In addition, when only the fatty acid value (x2) is considered as a quality factor, the taste prediction value (y) is modeled as 6.0040-0.0530x2 as described in the table of FIG. 4, and both the germination rate (x1) and the fatty acid value (x2) are considered as quality factors. The food prediction value y in this case is modeled by FIG. 4 and the following equation.

(

=0.71)

(

=0.71)

As described above, it was confirmed through the experiment that the germination rate is a major quality factor that can evaluate the quality of rice.

Accordingly, it will be possible to effectively predict and evaluate the taste of rice by using the taste prediction formula described in the table shown in FIG. 4 .

The taste evaluation method for evaluating the taste of rice requires first obtaining information on the germination rate (x1), which is one of the quality factors of rice. In some cases, the step of additionally acquiring not only the germination rate (x1) information but also the fatty acid value (x2) information of rice may be further included.

The germination rate (x1) and the fatty acid value (x2) can be used as experimental value information obtained by taking a sample from a group of rice to be evaluated. This experimental value information is information on the germination rate (x1) and the fatty acid value (x2), and can be input by the administrator through the user interface unit of the computing system. Of course, if the germination rate (x1) and fatty acid value (x2) can be measured through reflected light or spectroscopic analysis, the germination rate (x1) and fatty acid value (x2) information are automatically acquired, so that the quality of rice can be measured automatically. This rice quality measurement system will be described in more detail with reference to FIG. 5 .

The computing system that receives the germination rate (x1) information and the fatty acid value (x2) information or the acquired rice quality measurement system calculates the food taste value (y) by the formula and outputs the taste (quality) of the rice.

The food taste prediction value (y) is calculated using one of the following equations according to the input quality factor information (germination rate, fatty acid value).

y=2.1282+0.055x1

Hereinafter, a system for measuring the quality of rice using a rice formula that includes information on the germination rate with a high coefficient of determination among the quality factors of rice will be described in more detail.

Figure 5 illustrates the configuration of the rice quality measurement system according to an embodiment of the present invention.

As shown in FIG. 5, the rice quality measurement system according to an embodiment of the present invention includes a spectrometry-based spectroscopic measurement unit 100 to measure quality factors such as germination rate in rice grains and product states; It includes a sample alignment unit 110 , a germination rate prediction unit 120 , and a food taste evaluation unit 130 . In some cases, the control unit may further include a driving control unit 140 for controlling the movement of the sample alignment unit 110 and controlling the amount of light.

The spectral measurement unit 100 uses a dual light source to measure spectra of an ultraviolet (UV) band, a visible light (VISible) band, and a near-infrared (NIR) band. For example, the spectral measuring unit 100 includes a deuterium lamp 102 that irradiates ultraviolet light having a wavelength range of an ultraviolet band (200 to 600 nm), and a wavelength of visible light and near infrared band (400 to 2200 nm). and a halogen lamp 104 for irradiating light having a range. The amount of light of these dual light sources (deuterium lamp and halogen lamp) may be adjusted by a light amount controller controlled by the driving controller 140 to be described later. The dual light source is located above the sample alignment unit 110 to be described later, and a near infrared (NIR) sensor 106 and an ultraviolet (UV) sensor 108 to be described below are also located above the sample alignment unit 110 .

Each of the near-infrared (NIR) sensor 106 and the ultraviolet (UV) sensor 108, which is another component of the spectroscopic measurement unit 100, is in the 200-400 nm band from the reflected light reflected from the sample (eg, rice). It detects ultraviolet light and near-infrared light spectrum in the 900~1,700nm band, and photoelectrically converts it and outputs it. The optical fiber may be configured as a type in which three optical cables are connected as shown in order to simultaneously measure the transmission of the light source and the absorbance in the ultraviolet (UV) and near-infrared (NIR) regions.

The sample stand of the sample aligning unit 110 can measure 30 grains (5×6) at a time in a single grain state of rice. The sample alignment unit 110 can be precisely moved (±1 μm) in the XYZ axis direction under the control of the driving control unit 140 .

The germination rate prediction unit 120 compares and analyzes the reflected light spectrum output from the sensors 106 and 108 of the spectral measurement unit 100 with the reference spectrum for discrimination stored in the internal memory to predict the germination rate of rice samples. The spectral reference values for discrimination are values that can be obtained by experiments mentioned below.

The taste evaluation unit 130 evaluates and outputs the quality of the rice by using the formula for calculating the food taste prediction value using the germination rate described above. That is, the taste evaluation unit 130 determines the quality of the rice with the value of the predicted value (y) of the germination rate (x1) obtained by substituting the predicted germination rate (x1) information to the calculation function of the predicted value (y) of the germination rate (x1). evaluation is printed out.

The food prediction value y is calculated as y=2.1282+0.055x1.

As described above, by using the germination rate (x1) and the fatty acid value (x2) of the rice sample, it is possible to measure the quality of rice more precisely. Accordingly, the system for measuring the quality of rice may further include a fatty acid value (x2) receiver for receiving the fatty acid value (x2) information of the rice sample. As such, if the system can receive the fatty acid value (x2) of rice, the food taste evaluation unit 130 determines the food taste prediction value (y) by a function expressed by the quadratic expression of the predicted germination rate (x1) and the fatty acid value (x2). can be calculated

The unpredicted value (y) calculated by a function expressed by the quadratic expression of the predicted germination rate (x1) and the fatty acid value (x2) may be calculated by the following equation.

The fatty acid value (x2) of the rice sample may be input into the rice quality measurement system by the administrator.

The basis for establishing a rice quality measurement system with the configuration described above is based on the following experimental methods and experimental results.

1) Experimental method

First, in order to check whether the germination rate can be measured in the spectroscopic analysis-based rice quality measurement system, the spectrum was measured for brown rice, and then the germination test was conducted to measure the accuracy.

The brown rice used in the germination rate measurement experiment were 2013 Chucheong and 2012 Hiami varieties stored in a 2℃ low-temperature storage. The initial moisture content was in the range of 14 to 15%. announced.

After dividing the 240 grains of the test material 8 times (30 grains × 8 times = 240 grains) and aligning 30 samples as single grains on the sample stand, the sample aligning unit 110 moves in the XYZ direction to measure the spectrum of each single grain. obtained. At this time, the spectrum was acquired in a total of 460 wavelength ranges with a difference of 2 to 30 nm in the ultraviolet (UV) region of 200-400 nm and the near-infrared (NIR) region of 900-1,700 nm.

Samples whose germination rate has been measured in the spectroscopic analysis-based rice quality measurement system are placed in an incubator at 20°C (HK-B1028, Korea General Equipment Manufacturing Co., Ltd., Korea) according to the method of the National Agricultural Products Quality Control Institute (Notice No. 2000-8) for 14 days. The number of fine particles germinated within the period was taken as the germination rate.

For spectrum analysis, the partial least square method (PLS) of Unscrambler (ver 7, Camo CO., USA), a commercial program, was used, and the wavelength range, mathematical treatment and transposition method were used for the spectrum indicated by absorbance and the germination rate value. It was set and analyzed with the coefficient of determination and the error of the correction unit, and analysis was performed in three areas: ultraviolet (UV) (200-400 nm), near-infrared (NIR) (900-1700 nm), and UV+NIR.

2) Results and considerations

In the principal component analysis for the spectrum of a total of 460 wavelengths acquired in the ultraviolet (UV) region of 200 to 400 nm and the near infrared (NIR) region of 900 to 1,700 nm, when there are 6 principal components, the variance is the lowest. As a result of analyzing the correlation of a total of 460 wavelength bands, it was found that there was a correlation (5% level) in more than 250 wavelength bands. The coefficient of determination was 0.87.

On the other hand, in the principal component analysis of a spectrum of a total of 204 wavelength bands obtained in the ultraviolet (UV) region of 200 to 400 nm, the variance was the lowest when there were 7 principal components, and the correlation of the total 204 wavelength bands As a result of analyzing the relationship, it was found that there was no correlation in a specific area, but that there was a correlation in a wide range. As a result of analyzing the measured germination rate and the predicted germination rate based on the spectrum with a calibration graph, the coefficient of determination was 0.85.

In addition, in the principal component analysis for a total of 256 wavelength band spectra acquired in the near infrared (NIR) region of 900 to 1,700 nm, the dispersion showed the lowest when there were 5 main components. Similarly, correlation in a specific area did not appear, but correlation was found in a wide range, and the coefficient of determination was 0.88 as a result of analyzing the measured germination rate and the predicted germination rate based on the spectrum with a calibration graph.

For reference, Figure 6 is experimental data for the development of a rice quality measurement system according to an embodiment of the present invention, and illustrates the raw spectrum acquisition and principal component analysis graph in the UV region, and Figure 7 is the significance of the measurement of the germination rate in the UV region Analysis graph, FIG. 8 is a raw spectrum acquisition and principal component analysis graph in the NIR region, FIG. 9 is a significance analysis graph of germination rate measurement in the NIR region, and FIG. 10 is a raw spectrum acquisition and principal component analysis graph in the UV-NIR region Figure 11 illustrates the significance analysis graph of the germination rate measurement in the UV-NIR region, respectively. 12 is an exemplary graph comparing the actual value of the germination rate and the predicted value of the germination rate in the UV region of 200 to 400 nm. In addition, FIG. 13 illustrates a graph comparing the measured germination rate and the predicted germination value in the 900 to 1700 nm NIR region, and FIG.

Using a prototype of a spectroscopic analysis-based rice quality measurement system, the measured values of the germination rate and the predicted values of the germination rate in UV-NIR, UV and NIR regions are shown in FIGS. 12 to 14 . The solid lines marked up and down in FIGS. 12 to 14 are the numbers generated between the germinated case (1) and the non-germinated case (0) in order to increase the degree of measurement of the germination rate of the prototype of the quality measurement system, each binarized based on 0.5, As a result, as shown in Table 1 below, the accuracy of the measurement of the germination rate in the UV-NIR region was about 97.1%, which was slightly higher than that of 96.9% and 94.6% in the UV and NIR region.

division Wavelength range (nm) Number of grains to be measured number of discriminable accuracy(%) UV-NIR 200-400
900~1700 240 233 97.1 UV 200-400 240 232 96.7 NIR 900~1700 240 227 94.6

Based on the above experimental results and Table 1, considering that the results of measuring the germination rate using a prototype of a spectroscopic analysis-based quality measurement system (germination rate predicted value) and the actual value of the germination rate are showing high accuracy, wheat, soybean In other grains such as , barley, corn, and oats, it is expected that the germination rate can be measured through binarization.

Based on the above-described experimental method and experimental results, a rice quality measurement system having the configuration as shown in FIG. 5 was developed, and the germination rate of a rice sample is predicted using this rice quality measurement system, and the predicted germination rate information is used to determine the germination rate. By substituting into the new food taste predictive value calculation function expressed by the linear expression of , the quality of rice can be evaluated normally.

Accordingly, the present invention has the effect of accurately evaluating the quality of rice by developing and using a seasoning formula including information on the germination rate with a high crystallinity coefficient among the quality factors of rice, and furthermore, using a seasoning formula centered on the germination rate This has the effect of providing a quality measurement system that can be used quickly and usefully in the field compared to other quality factors.

The above has been described with reference to the embodiments shown in the drawings, which are merely exemplary, and those skilled in the art will understand that various modifications and equivalent other embodiments are possible therefrom. Therefore, the true technical protection scope of the present invention should be defined only by the appended claims.

Claims (8)

a sample alignment unit in which rice samples are aligned;
In order to measure the germination rate of rice on the sample alignment unit, a first light source for irradiating ultraviolet light having a wavelength range of an ultraviolet band to the samples, and a second light source for irradiating light having a wavelength range of a visible light and a near-infrared band a dual light source;
a spectral measurement unit including sensors that detect ultraviolet light and near-infrared light spectrum of different bands from the reflected light reflected by the rice samples from the light irradiated from the dual light source and photoelectrically convert and output;
a driving control unit for controlling the movement of the sample alignment unit and controlling the amount of light of the dual light source;
a germination rate predictor for predicting the germination rate of rice samples by comparing and analyzing the reflected light spectrum output from the sensors with a reference value for discrimination stored in an internal memory;
A taste evaluation unit that evaluates and outputs the quality of rice as a value of the predicted value (y) obtained by substituting the predicted germination rate (x1) information into the formula for calculating the predicted value (y) of the germination rate (x1) expressed by the linear equation; Rice quality measurement system, characterized in that
delete The method according to claim 1, wherein the first light source is a deuterium lamp irradiating ultraviolet light having a wavelength range of 200 to 600 nm, and the second light source is a halogen lamp irradiating light having a wavelength range of 400 to 2200 nm. Rice quality measurement system.
The method according to claim 1, wherein the spectroscopic measuring unit of the rice characterized in that it comprises a UV sensor for detecting a reflected light spectrum having a wavelength range of 200 to 400 nm, and a near-infrared sensor for detecting a reflected light spectrum having a wavelength range of 900 to 1700 nm. quality measurement system.
delete The system for measuring the quality of rice according to claim 1, wherein the food taste prediction value (y) as a function of the germination rate (x1) is calculated by the following equation.
y=2.1282+0.055x1
The method according to claim 1, wherein the fatty acid value (x2) receiving unit for receiving the fatty acid value (x2) information of the rice sample; further comprising, when receiving the fatty acid value of rice (x2), the taste evaluation unit is the predicted germination rate (x1) and the fatty acid value ( A system for measuring the quality of rice, characterized in that the food taste predicted value (y) is calculated by the following function expression expressed as a quadratic expression of x2).

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