JP4904446B2 - Prediction method and prediction apparatus for fruit component information - Google Patents

Description

  The present invention belongs to the field of technology for predicting information on the internal components of a fruit at the time of ripening, for example, in a fruit such as a tomato.

  A method is known in which the surface of a growing fruit or vegetable (fruit) is irradiated with light from a light projecting body, the reflected light is detected with a light receiver, the absorbance of the fruit is detected, and the current fruit maturity is judged. (See Patent Document 1).

In addition, a light projecting body and a light receiving body are provided so that light passes through the growing fruit, and the absorbance of the fruit is detected from the light received by the light receiving body to determine the current maturity (sugar content) of the fruit. There is a known method (see Patent Document 2).
JP-A-11-332378 Japanese Patent Laid-Open No. 7-229834

  In the above background art, only a method for detecting the current internal component information of the fruit during the growth is disclosed, and it can be used to determine the appropriate harvest time by judging the internal component information of the current fruit, There was no technical idea of predicting in-component information of fruits when harvesting was completed after harvesting, and the present in-component information of fruits could not be fed back and fully utilized for fruit cultivation.

  Therefore, an object of the present invention is to make it possible to predict internal component information at maturity with high accuracy from internal component information during fruit growth.

In order to solve the above problems, the present invention has taken the following technical means.
That is, the invention according to claim 1 determines the internal component information during the growth of the fruit, and predicts the internal component information at the time of fruit ripening based on the calculation model created in advance from the internal component information during the growth The calculation model uses different calculation equations depending on the cultivation week after the pollination of the fruit , and the calculation equation uses the dry matter rate and starch content as the internal component information during the growth. It is an equation for setting the soluble solid content as internal component information at the time of ripening and calculating the soluble solid content, where the dry matter rate is A, the starch content is B, and the soluble solid content is C. C = 1.554 + 0.488A + 0.361B when the cultivation week after pollination is 6 weeks, and C = 0.521 + 0.804A + 0. 049B, fruit C = 0.342 + 1.259A-0.099B when the cultivation week after pollination is 8 weeks, and C = 0.198 + 0. 0 when the cultivation week after pollination is 9 weeks. In the case of 675A-0.061B, and when the cultivation week after pollination of the fruit is 10 weeks old, it was set as the prediction method of the internal component information of the fruit as C = 0.589 + 0.826A + 0.055B .

Moreover, the invention which concerns on Claim 2 determines the internal component information at the time of fruit ripening based on the calculation means created beforehand from the judgment means which judges the internal component information in the middle of the growth of fruit, and this internal component information in the middle of the growth Predicting means for predicting is provided, and the predicting device for fruit internal component information using the predicting method according to claim 1 is provided .

Therefore, according to the present invention, the inner component information in the middle growth fruit, the inner component information at the time of fruit ripening on the basis of the calculation model using different calculation equations by cultivation weeks old after pollination fruit previously prepared high It can be predicted with accuracy, and the information on the internal components of the growing fruit can be used appropriately for cultivation so that a desired mature fruit can be obtained.

  First, based on FIG. 1, the outline of the near-infrared spectrum measuring apparatus 1 used in order to detect the dry matter content (DM) used as the internal component information of the fruit (tomato) in the middle of growth, and starch content is demonstrated. The near-infrared spectrum measuring apparatus 1 includes a light source 2 that generates light including near-infrared light that is easily absorbed by a substance, and an optical interference probe 3 that irradiates light from the light source 2 and receives diffusely reflected light. A spectroscopic analyzer 4 for spectroscopically analyzing the diffuse reflected light received by the optical interference probe 3 and measuring a near infrared spectrum (NIR spectrum); and an output for outputting a result analyzed by the spectroscopic analyzer 4 And a terminal (personal computer) 5. The light source 2, the light interference probe 3 and the spectroscopic analyzer 4 constitute judgment means for judging the internal component information during the fruit growth, and the output terminal (PC) 5 provides the internal component information during the growth. A predicting means for predicting internal component information at the time of fruit ripening based on a calculation model created in advance from the above is configured. The light source 2 and the spectroscopic analyzer 4 and the optical interference probe 3 are connected by a cable 6. The spectroscopic analyzer 4 and the output terminal 5 are connected by a cable 7. The light source 2 includes a manual shutter 8 and can be switched to a state in which light is not emitted from the optical interference probe 3. The light source 2 includes a light amount adjustment knob 9 for adjusting the light amount. In addition, since the penetration depth of near infrared light into the fruit (tomato) is about 8 mm at the maximum, the amount of light is small and the near infrared light into the fruit is detected accurately in order to detect the tomato absorbance. The penetration depth is set to 8 mm. As shown in FIG. 3, the tip of the optical interference probe 3 includes a circular light emitting ring 10 for irradiating light from the light source 2 to the outside and a light receiving hole 11 for receiving external light. The light projected on the fruit from the light emitting ring 10 is reflected, and the reflected light is received from the light receiving hole 11. Further, as shown in FIG. 2, a trumpet-shaped sample holder 12 is attached to the tip of the optical interference probe 3, and the fruit is put on the sample holder 12 (FIG. 2B). In this state, the fruit is irradiated with light by the light interference probe 3 to detect the absorbance (near infrared spectrum) of the fruit. Therefore, the sample holder 12 serves as a fruit positioning guide, and the distance between the fruit and the light interference probe 3 can be properly maintained to detect the absorbance of the fruit with high accuracy. Further, in order not to be affected by external light in the field (cultivation field), the tip portion of the light interference type probe 3 including the sample holder 12 portion and the fruit to be measured are covered with a black cloth cover. The light absorbance (near infrared spectrum) of the fruit is detected.

  In order to simplify handling in the field (cultivation field), a portable measuring instrument is provided in which the light source 2, the optical interference probe 3 and the spectroscopic analyzer 4 constituting the determining means are integrated. The portable measuring instrument is used with the cable 7 connected to the output terminal 5 removed, and at the same time, it is moved to an appropriate position in the outdoor cultivation field and the absorbance of the growing fruit on the tree at that position ( This measurement operation can be simplified by measuring the near infrared spectrum.

  The near-infrared spectrum of the tomato fruit obtained by the near-infrared spectrum measuring apparatus 1 is recorded in the wavelength region of 750 to 900 nm, and the dry matter rate (DM) and starch content (more precisely, starch content rate) are calculated from the recorded results. judge. The calculation of the dry matter ratio (DM) and starch content is executed by an arithmetic device (corresponding to the predicting means) provided in the output terminal (personal computer) 5. In addition, when the fruit (tomato) pattern is divided upward and divided into three equal parts, the fruit pattern part, equator part (intermediate part) and fruit top part are used from above, regardless of the maturity of the fruit (tomato). Since the inner component information of the equatorial part (intermediate part) shows the average value of the whole fruit with respect to the inner component information of the fruit part and the top part of the fruit (tomato), the near infrared spectrum of each fruit is Measured at two places (symmetric positions with respect to the fruit center) 180 degrees apart along the equator portion (intermediate portion) and averaged.

And if an operator inputs the cultivation week age after pollination of the fruit measured with the said near-infrared spectrum measuring apparatus 1 into the output terminal (personal computer) 5, the dry matter rate (DM) and starch which were obtained with the near-infrared spectrum measuring apparatus 1 will be shown. From the content and the data of the cultivation week age, the soluble solid content of the fruit at the time of ripening predicted based on a predetermined arithmetic model (including the arithmetic equation shown below) by the arithmetic device provided in the output terminal (personal computer) 5 ( SSC) is calculated. Arithmetic equations for predicting the soluble solids content (SSC) of mature fruits according to the dry matter rate (DM) and starch content values used in the computing device are various weeks (in this example 6 weeks to 10 weeks). It depends on the order. The equation that can be calculated from the value of 6 weeks old is as follows.
(SSC) = 1.554 + 0.488 (6-week old DM) +0.361 (6-week-old starch content)
The equation that can be calculated from 7-week-old values is
(SSC) = 0.521 + 0.804 (7-week old DM) +0.049 (7-week old starch content)
The equation that can be calculated from 8-week old values is
(SSC) = 0.342 + 1.259 (8-week old DM) -0.099 (8-week old starch content)
The equation that can be calculated from 9-week-old values is
(SSC) = 0.198 + 0.675 (9-week old DM) -0.061 (9-week old starch content)
The equation that can be calculated from 10-week-old values is
(SSC) = 0.589 + 0.826 (10-week-old DM) +0.055 (10-week-old starch content)
It becomes. In addition, the soluble solid content (SSC) of the mature fruit in the said equation assumes the case where it harvests by 11 to 12 weeks old after fruit set by winter cultivation, and is equivalent to the sugar content of a mature fruit. Generally, the nutrient solution is controlled so that the sugar content becomes a target value (for example, 8%), and fruit of a desired quality is harvested. Moreover, the unit of the dry matter rate (DM) in the above equation is% W / W, and the unit of the starch content (starch content rate) is the weight percent (% W / W dry basis) based on the dry matter.

  As described above, the near infrared spectrum measuring apparatus 1 is used to irradiate the growing fruit (tomato) on the tree in the field (cultivation field) and detect the absorbance of the fruit from the diffuse reflected light. The dry matter content and starch content, which are the internal component information during fruit growth, are judged, and the internal component information at the time of fruit ripening based on the calculation model prepared in advance from the dry matter rate (DM) and starch content during the growth The soluble solids content (SSC) or sugar content can be predicted. Therefore, the equatorial part (intermediate part) of the growing fruit on the tree is irradiated with light, the absorbance of the fruit is detected from the diffuse reflected light, and the information on the internal components of the whole fruit is judged to determine when the fruit has matured. Since the sugar content is predicted, the sugar content of the whole fruit at the time of ripening can be predicted without damaging the growing fruit on the tree, and the sugar content can be predicted with high accuracy. Moreover, since the said sugar content at the time of a ripening is estimated based on the dry matter rate of the fruit in the middle of growth with a high correlation with the soluble solid content (sugar content) of a fruit, it can obtain the estimated sugar content value which is generally appropriate and stable. In addition, in predicting the sugar content at the time of fruit ripening, the starch content of the growing fruit having a high correlation with the sugar content is also used as a predicting factor, so that the accuracy of predicting the sugar content can be further improved.

  Therefore, this near-infrared spectrum measuring apparatus 1 becomes a near-infrared measuring apparatus for the field (cultivated field) for monitoring the dry matter rate (DM) and starch content of the cultured tomato fruit, and further monitoring the immature fruit to be monitored. The soluble solids content (SSC) of the mature fruit can be predicted from the dry matter rate (DM) and starch content. The near-infrared spectrum measuring apparatus 1 determines or predicts biological information such as dry matter rate (DM), starch content, and soluble solid content (SSC) at the time of ripening without destroying the fruit in real time. Information can be reflected in daily control of nutrient solution in cultivation, and fruits with high sugar content can be obtained.

Next, the process which proved the rationality of the calculation means (calculation equation) for the prediction of the soluble solid content (SSC) of the fruit at the time of ripening described above will be described.
Among the quality parameters to consider for tomato fruit, soluble solids content (SSC) is the most important ingredient and is extremely important in determining the price to be paid to the producer. It is well known that tomatoes grown under water stress and salinity conditions have higher levels of soluble solids content (SSC). In hydroponics, these conditions are achieved by intermittent circulation of the nutrient solution or an increase in the electrical conductivity of the nutrient solution. Under such conditions, the dry matter ratio (DM) is at a high level, and starch accumulation is emphasized and extended. Dry matter ratio (DM) contains soluble solids as the main component, and starch degradation in the ripening stage correlates with rapid accumulation of reducing sugars, so the dry matter ratio (DM) of immature fruits and starch content and mature fruits There is a high correlation with the soluble solids content (SSC). Therefore, in order to produce high quality tomato fruits, the state of nutrient solution must be controlled based on the dry matter rate (DM) and starch content of the growing fruits.

  To determine the dry matter rate (DM) and starch content of growing fruits, a non-destructive test must be performed. The potential of near-infrared spectrophotometry in the non-destructive quality assessment of fruits has already been clarified over the last decade. The inventor has found that in laboratory experiments, an accurate calibration equation for measuring soluble solids content (SSC) is obtained by irradiating the whole fruit with near infrared radiation and detecting transmitted light from the bottom side of the fruit. It was shown to be obtained. However, the proposed measuring apparatus is extremely difficult to perform measurement in the field (cultivated field). Recently, it has been shown that near-infrared spectrophotometry can be used to determine the maturity of a mango that remains a tree in a field (cultivated field) using a portable near-infrared device.

  Thus, the inventors of the present application have focused on creating a calibration model with temperature compensation for dry matter ratio (DM) and starch content, and establishing a method for monitoring tomato fruit dry matter ratio (DM) and starch content.

  Next, a near infrared spectrum recording technique performed by the inventor of the present application when verifying the calculation means of the present case will be described. The near-infrared spectrum of the tomato fruit obtained by the near-infrared spectrum measuring apparatus 1 was recorded at intervals of 3.3 nm in the wavelength region of 305 to 1100 nm. As the near-infrared spectrum measurement conditions, the integration time was set to 20 ms, and light was scanned 50 times from the optical interference probe 3 in order to obtain an average value. As a reference measurement, the absorbance (near-infrared spectrum) of a white resin sphere having a diameter of 5 cm was measured instead of the fruit. When the pattern of the fruit (tomato) is divided upward and downward into three equal parts and the fruit pattern part, equator part (intermediate part), and top part of the fruit are taken from the top, the fruit regardless of the maturity of the fruit (tomato) Since the inner component information of the equatorial part (intermediate part) shows the average value of the whole fruit with respect to the inner component information of the fruit handle part and the fruit top part of (tomato), the near infrared spectrum of each fruit is Measurements were taken at two locations (symmetric positions with respect to the fruit center) 180 degrees apart along the equator (intermediate portion), and the measured values were averaged.

  In addition, in order to quickly measure the internal component information of the tomato fruit having a complicated internal structure in the field (cultivation field), a reasonable measurement position and number of measurement points must be determined. Previous studies have clarified methods for rational measurement with a small number of measurement points. That is, six measurement positions are taken every 60 degrees on the circumference of the equator part (intermediate part) of the fruit (tomato), and (1) one arbitrary point and (2) 180 degrees are opposed at these positions. Four measurement points were set: 2 points, (3) 3 points at an angle of 120 degrees, and (4) 6 points at all positions, and a calibration model was created and evaluated for each. . As a result, it has been concluded that a reasonable measurement method with a small number of measurement points and high accuracy may measure two points facing each other at 180 degrees in the equator as described above. Regardless of the maturity of the tomato fruit, the sugar content, which is the component information of the fruit, gradually increases as the fruit part, the equator part (intermediate part), and the fruit top part. It has been found that the sugar content in the equator (intermediate part) shows an average value with respect to the top of the fruit. Therefore, as described above, the measurement position is determined to be the equator part of the fruit.

  Next, a chemical analysis method will be described. A 3.3 cm thick portion was collected from the outer surface of the equator (intermediate portion) of fresh tomatoes, and the collected pieces were analyzed for chemical components of dry matter ratio (DM) and starch content using a measuring instrument. In addition, the sampling part by the chemical test is 3.3 cm thick from the outer surface at the equator part (intermediate part) of the fruit (tomato). The internal component information of the part is the average value of the whole fruit. It is because it shows. The dry matter rate (DM) was measured after the collected portion was dried at 70 degrees Celsius for 72 hours. The starch content was measured by pulverizing the dried specimen with a small blender and filtering through a 0.5 mm screen. About soluble solid content (SSC), the extract | collected part was grind | pulverized and filtered with the hand mixer, and the filtrate was measured using the digital refractometer.

  Then, considering that it is difficult to manage the temperature of the fruit in the field (cultivation field), an experiment for obtaining a calibration model with temperature compensation was performed (hereinafter, this experiment is referred to as Experiment 1). Since it is difficult to control the fruit temperature of the tomato sample, the near-infrared spectrum of the tomato fruit was measured under temperature control in the laboratory. A temperature-compensated calibration model was prepared for dry matter ratio (DM) and starch content, and 150 tomato fruits were used as samples. In order to achieve a wide range of dry matter ratio (DM) and starch content, these tomatoes set up two types of nutrient solution culture zones under nutrient solution conditions with EC values of nutrient solutions of 1 and 10 dS / m, Cultivated in each hydroponic zone. Hereinafter, fruits obtained with an EC value of 1 dS / m are referred to as “non-stressed” tomatoes, and fruits obtained with 10 dS / m are referred to as “stressed” tomatoes. These fruits were harvested weekly from 15 tomatoes from each hydroponic zone each week for 5 to 9 weeks after pollination from February to March. That is, a total of 30 tomato specimens were collected every week. Immediately before the near-infrared measurement, each specimen was immersed in a water tank maintained at 15 degrees Celsius, 25 degrees Celsius, and 35 degrees Celsius for 30 minutes to set the temperature of each specimen. In order to prevent the specimen (tomato) from getting wet, the water surface of the water tank was covered with a polyethylene film, and the specimen was placed on the film. Of the 150 specimens, 74 are used as calibration curve preparation samples for calibration of dry matter ratio (DM) and starch content, and the remaining specimens are calibration curves for determining the validity of the calibration. Used as a sample for evaluation (validation). A partial least squares (PLS) regression method was used to create a temperature-compensated calibration model for dry matter (DM) and starch content.

  As shown in FIG. 5, the near-infrared spectrum (absorbance) of each fruit (tomato) at 15 degrees Celsius, 25 degrees Celsius and 35 degrees Celsius and the second derivative spectrum (second derivative absorbance) obtained by secondarily differentiating it are as follows: In both cases, the effect of fruit temperature was observed around the absorption band of water having a wavelength of 971 nm. This is considered because the hydrogen bond of water is easily influenced by temperature. In order to deal with the effects of such fruit temperature, the final calibration model (calibration curve) is obtained by combining the temperature-dependent calibration models in the near-infrared spectrum of fruits (tomatoes) at 15 degrees, 25 degrees and 35 degrees Celsius. It was determined. In order to evaluate this final calibration model (calibration curve), dry matter (DM) and starch content were calculated by partial least squares (PLS) regression using second derivative spectra in the 750 to 1000 nm wavelength region. did. The calibration and validity determination (validation) results are shown in the chart of FIG. Calibration standard error (SEC) and predicted bias correction standard error (SEP) were relatively high in starch. At the same time, in starch, the ratio of standard error (RPD) of the reference data of the calibration curve evaluation (validation) sample to the standard error of prediction was relatively high at 2.66. The variation between the reference data and near-infrared spectrum measurement data is indicated by the standard deviation and the predicted bias correction standard error (SEP), respectively. From the RPD, the SEP is much lower than the standard deviation of the reference data. understood. Therefore, the created calibration model with a starch content of 0.57% dry matter (DM) and a starch content of 2.32% can be considered sufficiently accurate. Moreover, the dry matter rate (DM) and the starch content of the fruit (tomato) at different temperatures of 15 degrees Celsius, 25 degrees Celsius, and 35 degrees Celsius could be determined sufficiently accurately. Using a two-sample t-test with a 95% confidence level, no significant difference was found between the chemical analysis value and the near-infrared spectrum prediction value for both the dry matter rate (DM) and the starch content judgment value. This indicates that there is no bias deviation. Thus, the calibration model was found to be sufficiently accurate in determining the dry matter rate (DM) and starch content of fruits between 15 and 35 degrees Celsius. Therefore, this calibration model is suitable for application to a real tomato growing on a tree in a field (cultivation field) where temperature control is impossible.

  Peaks in the regression coefficient plot in which the regression coefficient was plotted against wavelength were observed at 765, 839, 904, and 987 nm for both dry matter ratio (DM) and starch content. The large negative peaks at 765 and 839 nm are related to water, and the peaks at 904 and 987 nm are related to carbohydrates. This is considered to be derived from the fact that both the dry matter content (DM) and the starch content chemical composition are carbohydrates.

  In addition, an experiment was conducted to establish a method for monitoring the dry matter rate (DM) and starch content of tree fruits in the field (cultivation field) (this experiment is referred to as Experiment 2). Using a total of 45 fruits obtained in the same field (cultivation field) as in Experiment 1 above, near-infrared measurements are made on the growing fruit every week from the 6th to 10th week after pollination in the field (cultivation field). It was. The dry matter rate (DM) and starch content were determined by applying the calibration model created in Experiment 1 to the near infrared spectrum measured in the field (cultivated field). Deviation (bias) generation due to the difference in near-infrared spectrum measurement conditions from Experiment 1 (Experiment 1 is measured in the laboratory, and measured in the field (cultivated field) in this experiment) was used in this experiment. A part of the specimen (tomato fruit, 6 per week, total 30) was harvested, and the dry specimen (DM) and starch content of the harvested specimen were checked by quantitative measurement using chemical analysis, and the measurement The deviation (bias) generated due to the difference in conditions was corrected later as bias (bias) correction. The remaining 15 fruits that were not harvested were cultivated until near maturation (until 12 weeks after pollination) after performing near-infrared measurements every week. From the near-infrared spectrum measured in the remaining 15 fruit fields (cultivation field), using the temperature-compensated calibration model obtained in Experiment 1 and the bias correction, the dry matter rate (DM) and The starch content was predicted. The remaining 15 ripe fruits were then analyzed by the usual method of measuring soluble solids content (SSC) using chemical analysis. A relational expression was prepared using chemical analysis values at various weeks, dry matter rate (DM) predicted from near-infrared measurement, and starch content. By using this relational expression, it became possible to predict the soluble solid content (SSC) of the mature fruit from the dry matter rate (DM) of the fruit during growth and the starch content.

  Based on the calibration model obtained in Experiment 1, continuously monitoring changes in the fruit components during fruit growth, the “stress” tomato dry matter ratio (DM) is maintained at a high and almost stable level, It was found that the dry matter rate (DM) of “non-stressed” tomatoes was reduced. This phenomenon has already been reported by other researchers. Also, because tomatoes accumulate large amounts of starch throughout most of their growth when water supply is limited, the calibration model is used to monitor tree fruit dry matter (DM) and starch content. It was confirmed that it was effective.

In predicting the soluble solid content (SSC) of mature fruits, applying linear regression and multiple regression to the dry matter rate (DM) and starch content of growing fruits as independent variables respectively, Throughout the stages, it has been found that using both dry matter ratio (DM) and starch content gives better predictive results than using only one. Next, an operational equation for predicting the soluble solid content (SSC) of the mature fruit based on the dry matter rate (DM) and starch content values at various weeks (6 to 10 weeks in this example) is shown. And the equation that can be calculated from the value of 6 weeks old is
(SSC) = 1.554 + 0.488 (6-week old DM) +0.361 (6-week-old starch content)
The equation that can be calculated from 7-week-old values is
(SSC) = 0.521 + 0.804 (7-week old DM) +0.049 (7-week old starch content)
The equation that can be calculated from 8-week old values is
(SSC) = 0.342 + 1.259 (8-week old DM) -0.099 (8-week old starch content)
The equation that can be calculated from 9-week-old values is
(SSC) = 0.198 + 0.675 (9-week old DM) -0.061 (9-week old starch content)
The equation that can be calculated from 10-week-old values is
(SSC) = 0.589 + 0.826 (10-week-old DM) +0.055 (10-week-old starch content)
It becomes. In addition, the soluble solid content (SSC) of the mature fruit in the said equation assumes the case where it harvests by 11 to 12 weeks old after fruit set by winter cultivation, and is equivalent to the sugar content of a mature fruit. Generally, the nutrient solution is controlled so that the sugar content becomes a target value (for example, 8%), and fruit of a desired quality is harvested. Moreover, the unit of the dry matter rate (DM) in the above equation is% W / W, and the unit of the starch content (starch content rate) is the weight percent (% W / W dry basis) based on the dry matter. As can be seen from these equations, at various growth stages, the dry matter (DM) regression coefficient is extremely high compared to the starch content regression coefficient, which predicts the soluble solids content (SSC) of mature fruits. The dry matter rate (DM) is more important than the starch content. This confirms that soluble solids are the primary substrate for dry matter. Although starch in immature fruits is considered to be a source of sugar production in the maturation stage, starch degradation during the maturation process is extremely complicated and includes many factors other than starch, so the regression coefficient of starch content is relatively low. It is thought that it became.

  By determining the internal component information of the fruit in the middle of cultivation, it can be used for predicting the appropriate harvest time. Moreover, it can utilize for estimating the internal component of the fruit at the time of a harvest based on the internal component information of the fruit in the middle of cultivation. Furthermore, the fruit of the target sugar content can be harvested by controlling the concentration of the nutrient solution based on the internal component information of the fruit during cultivation in the nutrient solution cultivation. Moreover, it can utilize, for example in fruit selection machine etc., to judge the quality of a fruit and to select a fruit based on the quality.

The figure which shows the outline of the near infrared spectrum measuring device The figure which shows the state which detects the light absorbency of a fruit with a light interference type probe (a: before detection, b: at the time of detection) The figure which shows the front-end | tip part of an optical interference type probe Chart showing calibration (calibration) and validity (validation) results for dry matter rate and starch content of immature fruits Second derivative spectrum of tomato at each temperature

Claims (2)

A method for predicting internal component information of a fruit by determining internal component information during fruit growth and predicting internal component information at the time of fruit ripening based on a calculation model created in advance from the internal component information during growth The calculation model uses different calculation equations depending on the cultivation age after pollination of the fruit , and the calculation equation uses the dry matter rate and starch content as the internal component information during the growth, and as the internal component information at the time of ripening An equation for setting the soluble solid content and calculating the soluble solid content, where the dry matter rate is A, the starch content is B, and the soluble solid content is C, the cultivation age after pollination of the fruit is 6 weeks In the case of decree, C = 1.554 + 0.488A + 0.361B, and in the case where the cultivation week after fruit pollination is 7 weeks, C = 0.521 + 0.804A + 0.049B, the cultivation week after fruit pollination When the order is 8 weeks old Is C = 0.342 + 1.259A-0.099B, and when the cultivation week after pollination of the fruit is 9 weeks old, C = 0.198 + 0.675A-0.061B, cultivation after the pollination of the fruit When the week age is 10 weeks, C = 0.589 + 0.826A + 0.055B . A judging means for judging the internal component information during the growth of the fruit and a predicting means for predicting the internal component information at the time of fruit ripening based on a calculation model created in advance from the internal component information during the growth, A device for predicting information on the internal components of fruits using the prediction method described in 1.

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