JP2023125301A - Particle mass non-contact measuring method and device - Google Patents

Particle mass non-contact measuring method and device Download PDF

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JP2023125301A
JP2023125301A JP2022029306A JP2022029306A JP2023125301A JP 2023125301 A JP2023125301 A JP 2023125301A JP 2022029306 A JP2022029306 A JP 2022029306A JP 2022029306 A JP2022029306 A JP 2022029306A JP 2023125301 A JP2023125301 A JP 2023125301A
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particles
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infrared light
seeds
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修 松田
Osamu Matsuda
健一 小川
Kenichi Ogawa
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01G—WEIGHING
    • G01G17/00—Apparatus for or methods of weighing material of special form or property
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01G—WEIGHING
    • G01G9/00—Methods of, or apparatus for, the determination of weight, not provided for in groups G01G1/00 - G01G7/00
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume or surface-area of porous materials
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume or surface-area of porous materials
    • G01N15/10—Investigating individual particles
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
    • G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
    • G01N21/27—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands using photo-electric detection ; circuits for computing concentration
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
    • G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
    • G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
    • G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
    • G01N21/3563—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light for analysing solids; Preparation of samples therefor
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
    • G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
    • G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
    • G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
    • G01N21/359—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light using near infrared light

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Abstract

To measure mass of light-weight small particles.SOLUTION: A measurement device for mass or a mass distribution of particles including an organic material comprises: supply means for supplying particles; irradiation means for irradiating the particles with near infrared light; imaging means for acquiring data on reflectivity of near infrared light reflected from the particles at an m number of specific wavelengths; calculation means for calculating the mass of the particles on the basis of the data on the reflectivity of the near infrared light at the m number of specific wavelengths. Also, a measuring method for mass or mass distribution of particles including an organic material comprises the following steps of: (a) supplying particles to the measurement device; (b) irradiating the particles with near infrared light, and acquiring data on the reflectivity of near infrared light at an m number of specific wavelengths; and (c) calculating the mass of the particles on the basis of the acquired data.SELECTED DRAWING: None

Description

本発明は種子のような小粒子の質量の測定方法に関する。 The present invention relates to a method for measuring the mass of small particles such as seeds.

機械育苗による効率的な作物生産を実現するには、作物が均一に成長することが求められる。一方、苗生産に用いられる種子は不均質であり、このことが均一な苗の成長を妨げる一因となる。 In order to achieve efficient crop production through mechanical seedling raising, crops must grow uniformly. On the other hand, seeds used for seedling production are heterogeneous, which is one factor that prevents uniform seedling growth.

苗の成長を揃えるため、種子に含まれる栄養の粗密を簡便に揃える手段としては、粒径よりも質量を基準とすることが望ましい。しかし、農林作物の種子の多くは、一粒数ミリ～数十ミリグラムと軽量である。そして軽量な小粒子の秤量は風や振動の影響を受けやすい。そのため、既存の秤量計で種子を一つずつ高速・連続的に測定することは現実的には不可能である。 In order to uniformize the growth of seedlings, it is preferable to use mass as a standard rather than particle size as a means of easily equalizing the concentration of nutrients contained in seeds. However, most of the seeds of agricultural and forestry crops are lightweight, weighing between a few millimeters and several tens of milligrams each. The weight of small, lightweight particles is easily affected by wind and vibration. Therefore, it is practically impossible to measure seeds one by one at high speed and continuously using existing weighing scales.

秤量計以外の方法で物の質量を測定する方法がいくつか提案されている。例えば特許文献１には、コンベアにより山状に積まれて搬送されてくる複数の高温状態にある粒状物集合体の体積若しくは重量を測定する方法において、上記コンベアの上方側に配置されたＣＣＤエリアカメラによりコンベア及び各粒状物集合体から発せられる赤外若しくは近赤外線像を単位時間毎連続的に撮影して二次元の単位画像を得る撮影工程と、得られた各単位画像領域内の各画素濃度と閾値濃度とを比較して濃度の低いコンベア部と濃度の高い粒状物集合体部を２値化分離する分離工程と、１画素当たりの面積校正値により各単位画像領域内に存在する各粒状物集合体の面積をそれぞれ求め、かつ、予め求められている画像面積と粒状物集合体の体積若しくは重量の関係式に基づいて各粒状物集合体の体積若しくは重量を計測する計測工程と、これ等計測値を積算して全粒状物集合体の体積若しくは重量を測定する測定工程、の各工程を具備することを特徴とする高温状態にある粒状物集合体の体積若しくは重量測定法が記載されている。また特許文献２には、容器を配置可能な配置部と、近赤外光を含む照射光を前記配置部に配置された前記容器、及び、前記容器に載せられた食品に照射可能な照射部と、前記照射光の反射光を検出する第１検出部と、前記容器の吸光度、前記容器の輝度、前記容器の形状、前記容器の材質、及び、前記容器の大きさの少なくとも１つを含む特徴情報を含むデータベースを記憶する記憶部と、前記第１検出部の検出結果から得られる前記特徴情報、及び、前記データベースを用いて前記食品の重量を推定する推定部と、前記第１検出部の検出結果及び前記推定部により求められた前記食品の重量を用いて前記食品の成分を分析する分析部とを備える食品分析装置が記載されている。 Several methods have been proposed for measuring the mass of an object using methods other than a weighing scale. For example, Patent Document 1 describes a method for measuring the volume or weight of a plurality of granular aggregates in a high temperature state that are piled up in a mountain shape and transported by a conveyor, in which a CCD area arranged above the conveyor is used. A photographing step of obtaining a two-dimensional unit image by continuously photographing infrared or near-infrared images emitted from the conveyor and each particulate aggregate by a camera every unit time, and each pixel in each unit image area obtained. A separation process that compares the density with a threshold density and binarizes and separates the conveyor part with a low density and the particulate matter aggregate part with a high density, and the area calibration value per pixel is used to separate each part existing in each unit image area. a measuring step of determining the area of each particulate aggregate, and measuring the volume or weight of each particulate aggregate based on a relational expression between the image area determined in advance and the volume or weight of the particulate aggregate; A method for measuring the volume or weight of a particulate aggregate in a high temperature state is described, which is characterized by comprising the following steps: measuring the volume or weight of the entire particulate aggregate by integrating these measured values. has been done. Further, Patent Document 2 discloses an arrangement part in which a container can be placed, and an irradiation part capable of irradiating irradiation light including near-infrared light onto the container arranged in the arrangement part and the food placed on the container. a first detection unit that detects reflected light of the irradiation light; and at least one of the absorbance of the container, the brightness of the container, the shape of the container, the material of the container, and the size of the container. a storage unit that stores a database including characteristic information; an estimation unit that estimates the weight of the food using the characteristic information obtained from the detection result of the first detection unit; and the database; and the first detection unit. A food analysis device is described that includes an analysis section that analyzes the components of the food using the detection result and the weight of the food determined by the estimation section.

一方、本発明者らは、健全な種子と非健全な種子とで反射スペクトルが明確に異なる波長域を特定したことに基づき、近赤外光を用いた針葉樹の種子の選別方法を検討してきた（特許文献３、非特許文献４）。 On the other hand, the present inventors have investigated a method for sorting conifer seeds using near-infrared light, based on the identification of a wavelength range in which the reflection spectra of healthy seeds and unhealthy seeds are clearly different. (Patent Document 3, Non-Patent Document 4).

近赤外光を用いた種子の分析方法に関しては、例えば特許文献４には、穀物の子実、及び標準反射板に照射した光の反射光の中から選択した中間赤外域に含まれる２つの波長バンドの反射光量について、子実からの前記２波長バンドの反射光量の差と、標準反射板からの前記２波長バンドの反射光量の差を測定することによる、子実重量の推定方法が記載されているが、ここでは、前記中間赤外域に含まれる２つの波長バンドに加えて、赤色域から選択した波長バンドと、近赤外域から選択した波長バンドの反射光量の測定も組み合わせて使用できることが記載されている。また特許文献５は、異なる波長を持つ３本の発射光線（Ｓ１、Ｓ２、Ｓ３）を送出するための３つの光源（Ｌ１、Ｌ２、Ｌ３）と、前記３本の発射光線（Ｓ１、Ｓ２、Ｓ３）を共通の光路（２４）に統合するための装置（２２）と、粒子状物質を含有させたガスを内部に導入可能であり、該内部で発射光（Ｓ１、Ｓ２、Ｓ３）が測定対象ガスに当たって粒子状物質の表面で散乱される測定体積であって、散乱角（α）を定義する基点となる中心点（２９）が該内部に定められており、前方散乱方向の光軸が散乱角の０度を規定する測定体積（２５）と、非散乱光を捕らえる、０度の位置にある吸光装置と、吸光装置（３０）に隣接して配置された、前方向の散乱光を捕らえる第１の検出器（４０）、７度と４０度の間の第２の散乱角内に配置された第２の検出器（４２）、４１度と７０度の間の第３の散乱角内に配置された第３の検出器（４４）、７１度と１１５度の間の第４の散乱角内に配置された第４の検出器（４６）、１１６度と１４５度の間の第５の散乱角内に配置された第５の検出器（４８）、及び、１４６度と１８０度の間の第６の散乱角内に配置された第６の検出器（５０）であって、各々の角度内で散乱光を捕らえる、一群の検出器と、散乱光が各検出器（４０、４２、４４、４６、４８、５０）により波長選択的に捕らえられるように各光源（Ｌ１、Ｌ２、Ｌ３）を制御する制御及び評価ユニット（６０）と、検出された散乱光の強度を記憶するための記憶装置（６０）とを備え、前記制御及び評価ユニット（６０）が、前記散乱光の強度から粒子状物質の粒径分布及び粒子状物質の質量分級を測定できるように構成されていることを特徴とする、粒子状物質の測定を行うための光学的な分析装置（１０）が記載されている。ここでは光源が近赤外領域であってもよいことが記載されている。 Regarding a seed analysis method using near-infrared light, for example, Patent Document 4 describes two methods included in the mid-infrared region selected from the grains of grains and the reflected light of light irradiated on a standard reflector. Regarding the amount of reflected light in the wavelength bands, a method for estimating the weight of the grain is described by measuring the difference in the amount of reflected light in the two wavelength bands from the grain and the difference in the amount of reflected light in the two wavelength bands from the standard reflector. However, here, in addition to the two wavelength bands included in the mid-infrared region, measurement of the amount of reflected light of a wavelength band selected from the red region and a wavelength band selected from the near-infrared region can also be used in combination. is listed. Further, Patent Document 5 discloses three light sources (L1, L2, L3) for sending out three emitted light beams (S1, S2, S3) having different wavelengths, and three light sources (L1, L2, L3) for sending out three emitted light beams (S1, S2, S3), S3) into a common optical path (24), into which a gas containing particulate matter can be introduced, in which the emitted light (S1, S2, S3) is measured. This is the measurement volume that is scattered by the surface of the particulate matter when it hits the target gas, and the center point (29) that serves as the reference point for defining the scattering angle (α) is determined inside the volume, and the optical axis in the forward scattering direction is A measurement volume (25) defining a scattering angle of 0 degrees, a light absorption device located at 0 degrees that captures unscattered light, and a light absorption device (30) located adjacent to the light absorption device (30) that captures the scattered light in the forward direction. a first detector (40) that captures, a second detector (42) positioned within a second scattering angle between 7 degrees and 40 degrees, and a third scattering angle between 41 degrees and 70 degrees. a third detector (44) located within the scattering angle; a fourth detector (46) located within the scattering angle between 71 degrees and 115 degrees; a fourth detector (46) located within the scattering angle between 116 degrees and 145 degrees; a fifth detector (48) disposed within a scattering angle of 5 and a sixth detector (50) disposed within a sixth scattering angle between 146 and 180 degrees; A group of detectors captures the scattered light within each angle and each light source (L1, L2) such that the scattered light is wavelength selectively captured by each detector (40, 42, 44, 46, 48, 50). , L3); and a storage device (60) for storing the intensity of the detected scattered light. An optical analyzer (10) for measuring particulate matter is described, characterized in that it is configured to be able to measure the particle size distribution of particulate matter and the mass classification of particulate matter based on intensity. has been done. It is stated here that the light source may be in the near-infrared region.

特開２００２－００５６３７号公報（特許第３６３７９６０号）JP2002-005637A (Patent No. 3637960) 特開２０１８－１２８４２７号公報（特許第６６８１６０６号）JP2018-128427A (Patent No. 6681606) 国際公開ＷＯ２０１６／０８４４５２（特許第６７８２４０８号）International Publication WO2016/084452 (Patent No. 6782408) 特開２００６－２７１２０２号公報（特許第４６２１８９１号）JP2006-271202A (Patent No. 4621891) 特開２０１９－１４８５８５号公報（特許第６８２４３０４号）JP2019-148585A (Patent No. 6824304)

Osamu Matsuda et al. Determination of Seed Soundness in Conifers Cryptomeria japonica and Chamaecyparis obtusa Using Narrow-Multiband Spectral Imaging in the Short-Wavelength Infrared Range. PLoS ONE 10(6): e0128358. doi: 10.1371/journal.pone.0128358Osamu Matsuda et al. Determination of Seed Soundness in Conifers Cryptomeria japonica and Chamaecyparis obtusa Using Narrow-Multiband Spectral Imaging in the Short-Wavelength Infrared Range. PLoS ONE 10(6): e0128358. doi: 10.1371/journal.pone.0128358

現在、軽量な小粒子を高速・連続的に秤量するための実用的な方法は存在しない。 Currently, there is no practical method for rapidly and continuously weighing small, lightweight particles.

本発明者らは、今般、二次元的な画像情報に加え、画素ごとに被写体の反射スペクトルデータを格納できる分光画像をハイパースペクトルカメラを用いて撮影し、個々のヒノキ種子の質量の実測値と分光画像データとの回帰分析を行った。種子の被覆面積は、通常のデジタルカメラ画像でも取得できるが、種子質量の実測値との相関（決定係数Ｒ2≒０．３７）は高くはなかった。一方、近赤外反射スペクトルから種子質量の予測式を求めると、決定係数は飛躍的に高まった（Ｒ2≒０．６９）。さらに、近赤外反射スペクトルに被覆面積のデータを加えると、決定係数はさらに上昇した（Ｒ2≒０．８３）。同様の結果は、ヒノキ種子の例以外の植物の種子についても確認することができた。すなわち、近赤外反射スペクトルに基づく種子重量の予測手法は、多様な作物種に汎用的に適用できるものであることを見出し、本発明を完成した。 In addition to two-dimensional image information, the present inventors used a hyperspectral camera to take spectroscopic images that can store reflection spectrum data of the subject for each pixel, and obtained the actual measured mass of each cypress seed. Regression analysis with spectroscopic image data was performed. Although the seed coverage area can be obtained using a normal digital camera image, the correlation with the actual measured seed mass (coefficient of determination R 2 ≈0.37) was not high. On the other hand, when a prediction formula for seed mass was obtained from the near-infrared reflection spectrum, the coefficient of determination increased dramatically (R 2 ≈0.69). Furthermore, when data on the coverage area was added to the near-infrared reflection spectrum, the coefficient of determination further increased (R 2 ≈0.83). Similar results were also confirmed for seeds of plants other than cypress seeds. That is, the present invention was completed based on the discovery that a method for predicting seed weight based on near-infrared reflectance spectra can be universally applied to a variety of crop species.

本発明は、以下を提供する：
［１］ 有機物を含む粒子の質量又は質量分布の測定装置であって、
粒子を供給する供給手段と、
粒子に近赤外光を照射する照射手段と、
粒子から反射されるｍ個の特定の波長における近赤外光の反射率のデータを取得する撮影手段と、
ｍ個の特定の波長における近赤外光の反射率のデータに基づき、粒子の質量を演算する演算手段と、
を備える、測定装置。
［２］ さらに、演算結果に基づき粒子を選別する選別手段を備える、１に記載の測定装置。
［３］ 供給手段が、粒子を一定の速度で連続的に供給するものである、１又は２のいずれか１項に記載の測定装置。
［４］ 演算手段において、近赤外光の反射率のデータと粒子の被覆面積データとに基づいて演算を行う、１から３のいずれか１項に記載の測定装置。
［５］ 下記の工程を含む、有機物を含む粒子の質量又は質量分布の測定方法：
（ａ）粒子を測定装置に供給し、
（ｂ）粒子に近赤外光を照射し、ｍ個の特定の波長における近赤外光の反射率のデータを取得し、
（ｃ）得られたデータに基づき、粒子の質量を算出する。
［６］ 工程（ｂ）が、粒子の全面からの反射スペクトルを取得するものであり、同時に種子の被覆面積データｘ0を取得するものであり、粒子の質量の算出が、近赤外光の反射率のデータと粒子の被覆面積データとに基づく、５に記載の方法。
［７］ 工程（ｃ）が、測定対象となる粒子について予め決定した回帰式を用いて算出するものである、５又は６に記載の方法。
［８］ 粒子が、食品粒子、医薬品粒子、化粧品粒子、又は植物の種子である、５から７のいずれか１項に記載の方法。
［９］ ６から８のいずれか１項に記載の方法で種子の質量又は質量分布を測定し；
測定結果に基づき、一定の質量範囲の種子を選別し；
選別した種子を播種し、栽培し、植物体を生産する
工程を含む、作物又は種苗の生産方法。
［１０］ ９の生産方法を実施するための、装置、設備又は工場。
［１１］ コンピュータに対し、
粒子から反射されるｍ個の特定の波長における近赤外光の反射率のデータを入力するデータ入力ステップと、入力されたデータに基づき、粒子の質量を演算する演算ステップと、
を実行させる、５から８のいずれか１項に記載の方法を実施するための、プログラム。
［１２］ コンピュータが、測定対象となる粒子について予め決定した回帰式を記憶する記憶部を備えており、演算ステップが、測定対象となる粒子について予め決定した回帰式を用いて演算するものである、１１に記載のプログラム。 The present invention provides:
[1] A device for measuring the mass or mass distribution of particles containing organic matter,
a supply means for supplying particles;
irradiation means for irradiating particles with near-infrared light;
Photographing means for acquiring reflectance data of near-infrared light at m specific wavelengths reflected from the particles;
Calculating means for calculating the mass of the particle based on reflectance data of near-infrared light at m specific wavelengths;
A measuring device comprising:
[2] The measuring device according to 1, further comprising a sorting means for sorting particles based on the calculation result.
[3] The measuring device according to any one of 1 and 2, wherein the supply means continuously supplies particles at a constant rate.
[4] The measuring device according to any one of 1 to 3, wherein the calculation means performs calculation based on near-infrared light reflectance data and particle coverage area data.
[5] Method for measuring the mass or mass distribution of particles containing organic matter, including the following steps:
(a) supplying particles to a measuring device;
(b) irradiate the particles with near-infrared light and obtain data on reflectance of near-infrared light at m specific wavelengths,
(c) Calculate the mass of the particles based on the obtained data.
[6] Step (b) is to obtain the reflection spectrum from the entire surface of the particle, and at the same time obtain the coverage area data x 0 of the seed, and the calculation of the mass of the particle is based on near-infrared light. 6. The method according to 5, which is based on reflectance data and particle coverage data.
[7] The method according to 5 or 6, wherein the step (c) is a calculation using a regression equation determined in advance for the particles to be measured.
[8] The method according to any one of 5 to 7, wherein the particles are food particles, pharmaceutical particles, cosmetic particles, or plant seeds.
[9] Measuring the mass or mass distribution of the seeds by the method described in any one of 6 to 8;
Based on the measurement results, select seeds within a certain mass range;
A method for producing crops or seedlings, which includes the steps of sowing selected seeds, cultivating them, and producing plants.
[10] Apparatus, equipment, or factory for carrying out the production method in 9.
[11] To the computer,
a data input step of inputting reflectance data of near-infrared light at m specific wavelengths reflected from the particles; and a calculation step of calculating the mass of the particles based on the input data;
9. A program for carrying out the method according to any one of 5 to 8.
[12] The computer is provided with a storage unit that stores a regression formula determined in advance for the particles to be measured, and the calculation step is performed using the regression formula determined in advance for the particles to be measured. , 11.

従来、散乱光や撮像により求めた粒子の大きさ（断面積、体積）に基づき粒子の質量を推定できるが、本発明の近赤外反射スペクトルに基づく方法は、それより精度が格段に高い。 Conventionally, the mass of a particle can be estimated based on the size (cross-sectional area, volume) of the particle determined by scattered light or imaging, but the method based on the near-infrared reflection spectrum of the present invention has much higher accuracy.

本発明により、植物の種子の質量、種子群の質量分布の、非接触・高速測定が行える。また、種子の質量による選別が行える。 According to the present invention, non-contact and high-speed measurement of the mass of plant seeds and the mass distribution of seed groups can be performed. In addition, sorting can be performed based on the mass of seeds.

本発明により、優良な種子集合体を選択することができる。また、それを用いた優良な作物の栽培が行える。 According to the present invention, it is possible to select an excellent seed aggregate. Moreover, excellent crops can be cultivated using it.

本発明により、選別された種子を用いた均質な植物の栽培を行うことができる。また、そのような均質な植物群を用いた研究・育種を行うことができる。 According to the present invention, homogeneous plants can be cultivated using selected seeds. Furthermore, research and breeding can be conducted using such a homogeneous plant group.

本発明の実施形態に係る装置の全体像の例An example of an overall image of a device according to an embodiment of the present invention 本発明の実施形態に係る装置の例Example of a device according to an embodiment of the present invention ハードウエア相関図Hardware correlation diagram 処理工程 ※１ 飽和画素がなくなるまで、カメラの露光時間や光源出力を自動調整することも 可能。但し、出力の安定までに時間のかかるハロゲンランプ等の近赤外光源使用時は 、光源出力の動的な調整は推奨されない。 ※２ 実際的には並列処理を行うため、この段階での待機時間は顕著には生じない。Processing process *1 It is also possible to automatically adjust the camera's exposure time and light source output until there are no saturated pixels. However, dynamic adjustment of the light source output is not recommended when using a near-infrared light source such as a halogen lamp, which takes time for the output to stabilize. *2 In practice, parallel processing is performed, so there is no noticeable waiting time at this stage. 処理工程（図４－１の続き）Treatment process (continued from Figure 4-1) 解析ソフト画面の例 種子の推定質量に応じて色分け表示している。Example of analysis software screen Color-coded display according to estimated mass of seeds. 回帰モデリングregression modeling 種子の反射スペクトルに由来する変換及び結合スペクトルの生成手順の例 ＊１ Ｓａｖｉｚｋｙ－Ｇｏｌａｙ法，５点・３～４次多項式近似 ＊２ 有効波長範囲は、設定は１，２５２～２，２９４ ｎｍの１３７バンド又は９７ ９～２，２００ ｎｍの２０１バンド ＊３ 説明変数の全個数（列数）は、Ｘ1～Ｘ30が（＊２）のバンド数、Ｘ31～Ｘ36が バンド数×４に一致Examples of conversion and combined spectrum generation procedures derived from seed reflection spectra *1 Savizky-Golay method, 5-point, 3rd to 4th degree polynomial approximation *2 Effective wavelength range is set to 1,252 to 2,294 nm. 137 bands or 201 bands from 979 to 2,200 nm *3 The total number of explanatory variables (number of columns) is: X 1 to X 30 is the number of bands (*2), and X 31 to X 36 is the number of bands x 4. matches 説明変数の選択個数ｋと決定係数Ｒ2及びＡＩＣの対プロットの例Example of a plot of the selected number of explanatory variables k versus the coefficient of determination R 2 and AIC ヒノキ、イネ、ダイズ、及びカラマツの種子の典型的な近赤外反射スペクトル の概形を示す。The outline of typical near-infrared reflectance spectra of cypress, rice, soybean, and larch seeds is shown. ヒノキ種子の画像上における占有画素数と質量（実測値）の散布図、及び占 有画素数から質量を求めるための線形回帰モデルを示す。A scatter diagram of the number of occupied pixels and mass (actual value) on the image of cypress seeds, and a linear regression model for calculating the mass from the number of occupied pixels are shown. イネ種子の画像上における占有画素数と質量（実測値）の散布図、及び占有 画素数から質量を求めるための線形回帰モデルを示す。This figure shows a scatter plot of the number of occupied pixels and mass (actually measured values) on an image of rice seeds, and a linear regression model for calculating the mass from the number of occupied pixels. ヒノキ種子の質量の実測値と画像上における占有画素数に基づく予測値の相 関図を示す。A correlation diagram is shown between the measured value of the mass of a cypress seed and the predicted value based on the number of pixels occupied on the image. イネ種子の質量の実測値と画像上における占有画素数に基づく予測値の相関 図を示す。A diagram showing the correlation between the measured value of the mass of rice seeds and the predicted value based on the number of occupied pixels on the image is shown. ヒノキ種子の質量の実測値と近赤外反射スペクトルに基づく予測値の相関図 を示す。A correlation diagram between the measured mass of cypress seeds and the predicted value based on the near-infrared reflectance spectrum is shown. イネ種子の質量の実測値と近赤外反射スペクトルに基づく予測値の相関図を 示す。A correlation diagram between the measured value of rice seed mass and the predicted value based on the near-infrared reflectance spectrum is shown. ヒノキ種子の質量の実測値と占有画素数及び近赤外反射スペクトルに基づく 予測値の相関図を示す。A correlation diagram between the measured value of the mass of cypress seeds and the predicted value based on the number of occupied pixels and near-infrared reflection spectrum is shown. イネ種子の質量の実測値と占有画素数及び近赤外反射スペクトルに基づく予 測値の相関図を示す。A correlation diagram is shown between the measured value of rice seed mass and the predicted value based on the number of occupied pixels and the near-infrared reflectance spectrum. ダイズ種子の質量の実測値と（ａ）近赤外反射スペクトル単独又は（ｂ）占 有画素数を併用して算出した予測値の相関図を示す。A correlation diagram is shown between the measured value of the mass of soybean seeds and the predicted value calculated using (a) the near-infrared reflectance spectrum alone or (b) the combined use of the number of occupied pixels. カラマツ種子の質量の実測値と（ａ）近赤外反射スペクトル単独又は（ｂ） 占有画素数を併用して算出した予測値の相関図を示す。A correlation diagram is shown between the measured value of the mass of larch seeds and the predicted value calculated using (a) the near-infrared reflectance spectrum alone or (b) the number of occupied pixels together. 分光画像撮影時にレンズ直下（画角０°）付近を通過したヒノキ種子の質量 の実測値と占有画素数及び近赤外反射スペクトルに基づく予測値の相関図を示す。A correlation diagram is shown between the measured value of the mass of a cypress seed that passed directly under the lens (angle of view of 0°) when taking a spectroscopic image, and the predicted value based on the number of occupied pixels and the near-infrared reflection spectrum.

［測定方法］
本発明は、下記の工程を含む、有機物を含む粒子の質量又は質量分布の測定方法に関する。
（ａ）各粒子を測定装置に供給し；
（ｂ）各粒子に近赤外光を照射し、種子の全面又は一部の面から反射されるスペクトルデータｘ1～m（ｍはバンド数）を取得し；そして
（ｃ）得られたデータに基づき、各粒子の質量を算出する。 [Measuring method]
The present invention relates to a method for measuring the mass or mass distribution of particles containing organic matter, which includes the following steps.
(a) supplying each particle to a measuring device;
(b) Irradiate each particle with near-infrared light and obtain spectrum data x 1 to m (m is the number of bands) reflected from the entire surface or a part of the seed; and (c) the obtained data Calculate the mass of each particle based on

（測定対象）
本発明の方法による質量又は質量分布の測定対象は、有機物を含む粒子である。本発明に関し、有機物というときは、生体を構成する有機物、例えば脂質、タンパク質等であって、赤外波長域における分光特性に基づき、非破壊的に検知することができるものをいう。本発明に関し、粒子（小粒子ということもある）というときは、特に記載した場合を除き、質量が一粒５０００ ｍｇ以下のものを指す。粒子の質量の下限値は特に限定されないが、本発明は、一粒０．０１ ｍｇ以上、好ましくは０．１ ｍｇ以上、より好ましくは０．５ ｍｇ以上、さらに好ましくは１ ｍｇ以上の粒子に用いるのに適している。軽量で風や振動の影響を受けやすく、そのため、既存の秤量計で一つずつ高速・連続的に測定することが現実的には不可能であるとの観点からは、本発明は、質量が一粒１０００ ｍｇ以下、好ましくは７００ ｍｇ以下、より好ましくは１００ ｍｇ以下、さらに好ましくは５０ ｍｇ以下の粒子に用いるのに適しているといえる。 (Measurement target)
The objects of mass or mass distribution measurement by the method of the present invention are particles containing organic matter. In the present invention, the term organic matter refers to organic matter constituting living organisms, such as lipids and proteins, which can be detected non-destructively based on spectral characteristics in the infrared wavelength range. In the present invention, the term "particles" (sometimes referred to as "small particles") refers to particles with a mass of 5000 mg or less, unless otherwise specified. The lower limit of the mass of the particles is not particularly limited, but in the present invention, particles each weighing 0.01 mg or more, preferably 0.1 mg or more, more preferably 0.5 mg or more, still more preferably 1 mg or more are used. suitable for use. It is lightweight and susceptible to the effects of wind and vibration, and therefore it is practically impossible to measure one item at a time continuously at high speed with existing weighing scales. It can be said that it is suitable for use in particles of 1000 mg or less, preferably 700 mg or less, more preferably 100 mg or less, even more preferably 50 mg or less.

本発明の方法は、植物の種子の質量又は質量分布の測定に特に適している。植物の種類は特に限定されない。適用可能な植物には、イネ科、キク科、シソ科、アブラナ科、セリ科、及びヒユ科の植物が含まれる。イネ科の植物には、イネ、コムギ、オオムギ、カラスムギ、ライムギ、キビ、アワ、ヒエ、トウモロコシ、シコクビエ、モロコシが含まれる。種子の状態は特に限定されず、植物体の育成のためのもののほか、食品として利用するために焙煎の工程を経たものであってもよい。 The method of the invention is particularly suitable for measuring the mass or mass distribution of plant seeds. The type of plant is not particularly limited. Applicable plants include plants from the Poaceae, Asteraceae, Lamiaceae, Brassicaceae, Apiaceae, and Amaranthaceae families. Plants of the Poaceae family include rice, wheat, barley, oats, rye, millet, millet, millet, corn, finger millet, and sorghum. The state of the seeds is not particularly limited, and in addition to being used for growing plants, the seeds may be roasted for use as food.

種子植物の例として、下記が挙げられる。
穀類（アマランサス、あわ、えんばく、大麦、きび、小麦、ライ麦、とうもろこし、イネ）；
種実類（アーモンド、あさ、えごま、カシューナッツ、かや、ぎんなん、けし、ごま、しい、とち、はす、ひし、ピスタチオ、ブラジルナッツ、ヘーゼルナッツ、ぺカン、マカダミアナッツ、まつ、落花生、コーヒー豆、カカオ豆）；
豆類（あずき、いんげんまめ、えんどう、ささげ、そらまめ、ダイズ、コーヒー豆、カカオ豆）；
樹木（スギ、ヒノキ、アカマツ、カラマツ、グイマツ、トドマツ、エゾマツ）；
野菜類（アイスプラント、アズキ、アスパラガス、インゲン、エダマメ、エンサイ、エンドウ、オカヒジキ、オクラ、カブ、カボチャ、カラシナ、カリフラワー、カンピョウ、キャベツ、キュウリ、ケール、ゴーヤ、コールラビ、ゴボウ、ゴマ、コマツナ、サラダ菜、山東菜、シソ、シュンギク、シロウリ、スイカ、スイ－トコ－ン、ズッキーニ、セルリー、ソラマメ、ダイコン、ダイズ、タマネギ、チヂミナ、チマサンチュ、チンゲンサイ、漬け菜ほか、トウガラシ、トウガン、トマト、トレビス、ナス、ナバナ、ニラ、ニンジン、ネギ、ハーブ、ハクサイ、パセリ、ハナナ、ハ－ブ、ビーツ、ピ－マン、ヒョウタン、フダンナ、ブロッコリー、ヘチマ、ホウレンソウ、マクワ、ミズナ、ミツバ、ミブナ、メロン、モロヘイヤ、リーキ、レタス、ロマネスコ）；
花類（アークトチス、アガスターシャ、アグロステンマ、アゲラタム、朝顔、アサリナ、アスター、アマランサス、アリッサム、アルセア、アルメリア、アンゲロニア、イベリス、インパチェンス、エキナセア、エリシマム、エリンジューム、おじぎ草、おしろい花、オステオスペルマム、オルレア、オレガノ、オンファロデス、カーネーション、ガーベラ、ガイラルディア、ガザニア、かすみ草、カルサムス（べにばな）、観賞用とうがらし、カンナ、カンパニュラ、ききょう、キャンディタフト、金魚草、金盞花（カレンジュラ）、草花タネセット、クフェア、グラス、クラスペディア、クリサンセマム、クレオメ、鶏頭、ゲウム、コスモス、ゴデチャ、コリウス、コンボルブルス、サポナリア、サルビア、サンビタリア、ジギタリス、ジニア（百日草）、シネラリア、シャスターデージー、シレネ、シロタエギク、スイートピー、スカビオサ、スクテラリア、ストック、スピランサス、石竹、ゼラニューム、セリンセ、千日紅、ダイアンサス（ナデシコ）、ダイコンドラ、ダリア、チェイランサス、チトニア、千鳥草、ツンベルギア、ディディスカス、ティモフィラ、ディモルフォセカ、デージー、デルフィニウム、ドリチョス、トレニア、ニゲラ、ニコチアナ、ネペタ、ネメシア、ネモフィラ、バージニアストック、バーバスカム、バーベナ、葉げいとう、バコパ、バジル、初雪草、花菜、花菱草、花輪菊、葉牡丹、パンジー、ビオラ、ビスカリア、ビデンス、ヒマワリ、ビンカ、風船かずら、風船とうわた、フェリシア、ブプレウルム、ブラキカム、フロックス、ベゴニア、ペチュニア、ヘリクリサム（帝王貝細工）、ペンタス、ほうせんか、ほおずき、松葉牡丹、マトリカリア、マリーゴールド、ミナロバータ、ミント、ムギ、メランポディウム、モナルダ、矢車草、夕顔、ユーストマ（トルコ桔梗）、ユーフォルビア、ユリ、ラナンキュラス、ラベンダー、リクニス、リナム、リナリア、リビングストンデージー、リムナンテス、リモニューム、るこう草、ルドベキア、ルピナス、レウイシア、れんげ草、ローダンセ、ローレンティア、ロドキトン、ワイルドフラワー、忘れな草）。 Examples of seed plants include:
Cereals (amaranth, millet, oat, barley, millet, wheat, rye, corn, rice);
Seeds and fruits (almonds, morning beans, perilla, cashew nuts, chives, ginnan, poppy seeds, sesame seeds, horse chestnuts, horse chestnuts, lotus, caltrops, pistachios, Brazil nuts, hazelnuts, pecans, macadamia nuts, eyelashes, peanuts, coffee beans, cacao beans);
Legumes (adzuki beans, kidney beans, peas, cowpeas, fava beans, soybeans, coffee beans, cacao beans);
Trees (cedar, cypress, red pine, larch, pine, fir, spruce);
Vegetables (ice plant, adzuki beans, asparagus, green beans, edamame, red beans, peas, Japanese radish, okra, turnip, pumpkin, mustard greens, cauliflower, camphor, cabbage, cucumber, kale, bitter melon, kohlrabi, burdock, sesame, komatsuna, salad greens) , Shandong greens, perilla, Chinese chrysanthemum, white lily, watermelon, sweet corn, zucchini, celery, fava beans, radish, soybeans, onions, chijimina, chimasanchu, bok choy, pickled vegetables, etc., chili peppers, chili peppers, tomatoes, trevice, eggplants, nava, Chives, carrots, green onions, herbs, Chinese cabbage, parsley, Chinese cabbage, herbs, beets, green peppers, gourds, Swiss chard, broccoli, loofah, spinach, makuwa, mizuna, mitzvah, mibuna, melon, molukhiyah, leeks, lettuce, Romanesco);
Flowers (Arctothis, Agastasha, Agrosthema, Ageratum, Morning Glory, Asarina, Aster, Amaranthus, Alyssum, Alcea, Armeria, Angelonia, Iberis, Impatiens, Echinacea, Erysimum, Eryngium, Osteocele, White Flower, Osteospermum, Orlea, Oregano, Omphalodes, carnation, gerbera, gaillardia, gazania, gypsophila, carthamus, ornamental chili pepper, canna, campanula, bellflower, candytuft, goldfish grass, calendula, flowering seedset, cuphea, grass , Craspedia, Chrysanthemum, Cleome, Cockscomb, Geum, Cosmos, Godetya, Coleus, Convolvulus, Saponaria, Salvia, Sanvitalia, Foxglove, Zinnia, Cinellaria, Shasta daisy, Silene, White scutellaria, Sweet pea, Scabiosa, Scutellaria, Stock, Spiranthus, Stone Bamboo, Geranium, Serinthe, Crape, Dianthus (Dianthus), Dichondra, Dahlia, Cheilanthus, Tithonia, Chidori, Thunbergia, Didiscus, Timophila, Dimorphotheca, Daisy, Delphinium, Dorichos, Torenia, Nigella , nicotiana, nepeta, nemesia, nemophila, virginia stock, verbascum, verbena, leaf beetroot, bacopa, basil, first snow grass, flower mustard, flower rhododendron, wreath chrysanthemum, leaf peony, pansy, viola, viscaria, bidens, sunflower, vinca, balloon Kazura, balloon and wax, felicia, bupleurum, brachicum, phlox, begonia, petunia, helichrysum (emperor shellwork), pentas, balsam, cherry blossom, pine needle peony, matricaria, marigold, minarobata, mint, wheat, melanpodium, monarda , cornflower, evening glory, Eustoma (turkish bellflower), euphorbia, lily, ranunculus, lavender, lychnis, linum, linaria, livingstone daisy, limnanthes, limonium, limanium, rudbeckia, lupine, lewisia, astragalus, rhodanthe, laurentia, rhodochiton, wildflower, forget-me-not).

本発明の適用が特に適している植物の種子の具体例としては、イネ、ヒノキ、ダイズ、カラマツの種子が挙げられる。なお、以下では、本発明を、粒子が植物種子である場合を例に説明することがあるが、その説明は、他の粒子を用いた場合にも当てはまる。他の粒子の例として、粒状又は顆粒状である、食品（ラムネ菓子、米菓、駄菓子、調味料、トッピング類、顆粒状濃縮物、茶葉、等）、化粧品、医薬品、農薬（除草剤、肥料を含む。）等が挙げられる。 Specific examples of plant seeds to which the present invention is particularly suitable include seeds of rice, cypress, soybean, and larch. In addition, although the present invention may be explained below using an example in which the particles are plant seeds, the explanation also applies to cases where other particles are used. Examples of other particles include foods (ramune sweets, rice crackers, cheap sweets, seasonings, toppings, granular concentrates, tea leaves, etc.), cosmetics, pharmaceuticals, agricultural chemicals (herbicides, fertilizers, etc.) that are granular or granular. ), etc.

（近赤外光の利用）
本発明の測定方法では、粒子に近赤外光を照射し、ｍ個の特定の波長における近赤外光の反射率のデータを取得する。生体組織を構成する分子が赤外領域の光エネルギーを吸収すると、その振動又は回転状態が変化する。これらの状態変化に必要なエネルギー量は分子構造により異なるため、生体組織の赤外分光特性は、それを構成する化学成分に依存した特徴的なものとなる。赤外光の中でも比較的エネルギーレベルの高い近赤外光は、より長波長の遠赤外及び中赤外光と比較して物質による吸収が小さいため、超薄切片等を作製することなく、粒子の構成成分を非破壊的に測定するのに適している。また、高い透過性をもつため、表面に顕在化していない内部の状態を診断する上でも有利である。 (Use of near-infrared light)
In the measurement method of the present invention, particles are irradiated with near-infrared light, and near-infrared reflectance data at m specific wavelengths is obtained. When molecules constituting living tissue absorb light energy in the infrared region, their vibration or rotational state changes. Since the amount of energy required for these state changes differs depending on the molecular structure, the infrared spectral characteristics of biological tissue are characteristic depending on the chemical components that make up the tissue. Near-infrared light, which has a relatively high energy level among infrared light, is absorbed by substances less than far-infrared and mid-infrared light with longer wavelengths, so it can be used without the need to create ultra-thin sections. Suitable for non-destructively measuring the constituent components of particles. Furthermore, since it has high transparency, it is also advantageous in diagnosing internal conditions that are not apparent on the surface.

（工程）
本発明の方法は、工程（ａ）として、粒子を測定装置に供給する工程を含む。工程（ａ）においては、各粒子が一定の速度で連続的に測定装置に供給されることが好ましい。 (Process)
The method of the present invention includes, as step (a), a step of supplying particles to a measuring device. In step (a), it is preferable that each particle is continuously supplied to the measuring device at a constant rate.

本発明の方法は、工程（ｂ）として、粒子に近赤外光を照射し、ｍ個の特定の波長における近赤外光の反射率のデータを取得する工程を含む。好ましい態様においては、粒子の全面からの平均反射スペクトルを取得するものであり、この工程では同時に各種子の被覆面積データｘ0を取得するものである。 The method of the present invention includes, as step (b), a step of irradiating the particles with near-infrared light and acquiring near-infrared reflectance data at m specific wavelengths. In a preferred embodiment, the average reflection spectrum from the entire surface of the particle is obtained, and at the same time, the coverage area data x 0 of each seed is obtained at the same time.

ｍ（バンド数）は、後述するように、測定対象となる粒子に応じて適宜設定することができる。比較的均一な粒子の場合は、ｍが小さくても、例えば、ｍが４であっても、精度よく測定ができる。また、ｍは、面積データｘ0を取得する場合は、より少なくすることができる。 m (number of bands) can be appropriately set depending on the particles to be measured, as described later. In the case of relatively uniform particles, accurate measurements can be made even if m is small, for example, even if m is 4. Moreover, m can be made smaller when acquiring area data x 0 .

測定対象が植物の種子であり、被覆面積データを用いない場合、ｍは、１５～５０とすることができ、２０～４０とすることが好ましい。測定対象が植物の種子であり、被覆面積データを用いる場合、ｍは、１０～４０とすることができ、１５～３０とすることが好ましい。 When the measurement target is a plant seed and coverage area data is not used, m can be set to 15 to 50, preferably 20 to 40. When the measurement target is a plant seed and covered area data is used, m can be set to 10 to 40, preferably 15 to 30.

測定する粒子が、植物の種子である場合、取得する各スペクトルの波長域（バンド）は、７８０～２５００ｎｍであることが好ましい。 When the particles to be measured are plant seeds, the wavelength range (band) of each spectrum to be obtained is preferably 780 to 2500 nm.

本発明の方法は、工程（ｃ）として、得られたデータに基づき、粒子の質量を算出する工程を含む。工程（ｃ）では、測定対象となる粒子について予め決定した回帰式を用いて算出することができる。 The method of the present invention includes, as step (c), a step of calculating the mass of the particles based on the obtained data. In step (c), calculation can be performed using a regression equation determined in advance for the particles to be measured.

本発明の方法は、上記の工程（ａ）～（ｃ）以外に、測定した粒子の質量に基づき粒子を選別する工程を含んでもよい。 In addition to the above steps (a) to (c), the method of the present invention may include a step of sorting particles based on the measured mass of the particles.

本発明の測定方法は、作物又は種苗の生産方法に適用することができる。この生産方法は、上記の測定のための工程、選別する工程のほか、選別した種子を播種し、栽培し、植物体を生産する工程を含みうる。本発明はまた、このような生産方法を実施するための、装置、設備又は工場を提供する。 The measuring method of the present invention can be applied to methods for producing crops or seedlings. This production method may include the steps of sowing the selected seeds, cultivating them, and producing plants, in addition to the above-mentioned measurement steps and screening steps. The invention also provides equipment, equipment or factories for carrying out such production methods.

［測定装置］
本発明は、下記の各手段を備える、有機物を含む粒子の質量又は質量分布の測定装置を提供する：
粒子に近赤外光を照射する照射手段と、
粒子から反射されるｍ個の特定の波長における近赤外光の反射率のデータを取得する撮影手段と、
ｍ個の特定の波長における近赤外光の反射率のデータに基づき、粒子の質量を演算する演算手段。
なお、演算に使用する特定の波長における近赤外光の反射率のデータは、取得したデータから選択してもよい。（使用波長数ｋ≦測定波長数ｍ）。 [measuring device]
The present invention provides an apparatus for measuring the mass or mass distribution of particles containing organic matter, which includes the following means:
irradiation means for irradiating particles with near-infrared light;
Photographing means for acquiring reflectance data of near-infrared light at m specific wavelengths reflected from the particles;
Calculating means for calculating the mass of particles based on near-infrared reflectance data at m specific wavelengths.
Note that near-infrared light reflectance data at a specific wavelength used for calculation may be selected from the acquired data. (Number of wavelengths used k≦number of measured wavelengths m).

図１、２、３は、本発明装置の一実施態様を示す図である。図１は、装置を横から見た状態、図２は斜め上から見た状態、図３はハードウエア相関図を示している。図１に示すように、本実施形態に係る測定装置は、制御・演算部１、撮影部２、３、４、照射部５、供給部６、搬送部７、選別部８により構成される。そして、その運用中においては、搬送部７には測定対象物である９が載置され、図１に示す搬送方向に搬送される。測定対象物９は、本実施形態に係る測定装置による測定の測定対象であって、容器等に入れた状態ではなく、そのものである。 1, 2, and 3 are diagrams showing one embodiment of the device of the present invention. 1 shows the device viewed from the side, FIG. 2 shows the device viewed diagonally from above, and FIG. 3 shows a hardware correlation diagram. As shown in FIG. 1, the measuring device according to this embodiment includes a control/calculation section 1, photographing sections 2, 3, and 4, an irradiation section 5, a supply section 6, a transport section 7, and a sorting section 8. During the operation, an object to be measured 9 is placed on the transport section 7 and is transported in the transport direction shown in FIG. The object to be measured 9 is the object to be measured by the measuring device according to the present embodiment, and is not placed in a container or the like.

制御・演算部１は、測定装置全体を制御する。図３に示すように、制御・演算部１は、制御手段記憶部、較正画像データ記憶部、種子認識モデル記憶部、回帰モデル記憶部、選別区分記憶部、解析結果記憶部から構成される。制御手段記憶部により、撮影部２、３、４、照射部５、供給部６、搬送部７、選別部８が制御される。 The control/calculation unit 1 controls the entire measuring device. As shown in FIG. 3, the control/arithmetic section 1 is comprised of a control means storage section, a calibration image data storage section, a seed recognition model storage section, a regression model storage section, a sorting classification storage section, and an analysis result storage section. The control means storage section controls the imaging sections 2, 3, 4, the irradiation section 5, the supply section 6, the transport section 7, and the sorting section 8.

撮影部２、３、４は、近赤外分光カメラ２ａ、近赤外レンズ２ｂ、電動シャッター３ａ、３ｂ、標準反射板挿入ユニット４ａ、標準反射板４ｂにより構成される。標準反射板挿入ユニット４ａは較正の際に、標準反射板４ｂがカメラ下に移動するよう、空圧シリンダ等の駆動機構を装備することができる。 The photographing units 2, 3, and 4 are composed of a near-infrared spectroscopic camera 2a, a near-infrared lens 2b, electric shutters 3a, 3b, a standard reflector insertion unit 4a, and a standard reflector 4b. The standard reflector insertion unit 4a can be equipped with a drive mechanism such as a pneumatic cylinder so that the standard reflector 4b moves below the camera during calibration.

照射部５は、近赤外光源５ａ、近赤外光源５ｂにより構成される。近赤外光源５ａ、近赤外光源５ｂは、近赤外光を測定対象物９に上方から照射する。近赤外光源５ａ、近赤外光源５ｂは、測定対象９に対し、進行方向の前方及び後方であって、それぞれの斜め上４５度から均一に照射できる位置に設置することができ、基本的に装置の動作中は常時点灯している。撮影部２、３、４は、測定対象物９により反射された近赤外光を受光して反射スペクトルデータを生成する。 The irradiation unit 5 includes a near-infrared light source 5a and a near-infrared light source 5b. The near-infrared light source 5a and the near-infrared light source 5b irradiate the measurement object 9 with near-infrared light from above. The near-infrared light source 5a and the near-infrared light source 5b can be installed at positions where they can uniformly illuminate the object 9 to be measured from 45 degrees diagonally above the object 9 in the forward and backward directions. The light is always on when the device is operating. The photographing units 2, 3, and 4 receive near-infrared light reflected by the measurement object 9 and generate reflection spectrum data.

生体組織の分光特性は、光ファイバー式の分光器による点計測が可能なほか、リモートセンサの一種であるハイパースペクトルカメラを用いることにより、座標情報（画像）と併せて計測（測定）することができる（面計測）。計測に際しては、対象とする波長域において高い検出感度を示す機器を選択する必要がある。本発明において着目する７８０～２５００ ｎｍ域（脂質やタンパク質の吸収波長域を含む）における分光特性を計測するためには、ＣＣＤ、ＣＭＯＳ、ＣＱＤ、ＩｎＧａＡｓ、ＨｇＣｄＴｅ（ＭＣＴ）、ＴｙｐｅＩＩ超格子（Ｔ２ＳＬ）などの赤外検出器を備えた機器を用いることができる。 Spectral characteristics of living tissues can be measured at points using a fiber optic spectrometer, and can also be measured along with coordinate information (images) using a hyperspectral camera, which is a type of remote sensor. (area measurement). When performing measurements, it is necessary to select equipment that exhibits high detection sensitivity in the target wavelength range. In order to measure the spectral characteristics in the 780 to 2500 nm region (including the absorption wavelength range of lipids and proteins), which is the focus of the present invention, CCD, CMOS, CQD, InGaAs, HgCdTe (MCT), Type II superlattice (T2SL) A device equipped with an infrared detector such as the following can be used.

供給部６、搬送部７、選別部８として、既存の装置を利用することができる。具体的には、供給部６、搬送部７として、既存のパーツフィーダ（自動部品供給装置）を用いてもよい。パーツフィーダは、自動車分野、電子部品分野、医薬品製造分野、化粧品製造分野、食品加工分野等で使用されている。一般に、パーツフィーダは、振動体、ボール（供給する粒子を入れる容器）、ホッパー（ボール内の粒子を一定に保ち、排出能力を安定させる装置）、アタッチメント（粒子を一定方向に整列させ、各ラインに供給する部分）、シュート（アタッチメント上で整列した粒子を次の工程に送る機構）、直進フィーダ（シュートに振動を与え、粒子を送る役割を担う部分）、架台（パーツフィーダを支える部分）、コントローラ（パーツフィーダの起動、停止、振動の調整等を行う部分）を含む。選別部８として、既存の選別機を用いてもよい。既存のものとして、例えば、カメラで変色品を他のものから識別し、圧縮エアで特定の品を吹き飛ばすことで選別する装置がある。 Existing devices can be used as the supply section 6, the transport section 7, and the sorting section 8. Specifically, an existing parts feeder (automatic parts feeding device) may be used as the supply section 6 and the transport section 7. Parts feeders are used in the automobile field, electronic parts field, pharmaceutical manufacturing field, cosmetics manufacturing field, food processing field, etc. In general, a parts feeder consists of a vibrating body, a ball (a container that holds the particles to be supplied), a hopper (a device that keeps the particles in the ball constant and stabilizes the discharge capacity), and an attachment (a device that aligns the particles in a certain direction and makes each line ), chute (mechanism that sends the particles aligned on the attachment to the next process), linear feeder (the part that gives vibration to the chute and sends the particles), pedestal (the part that supports the parts feeder), Contains a controller (a part that starts, stops, adjusts vibration, etc. of the parts feeder). As the sorting section 8, an existing sorting machine may be used. For example, there is an existing device that uses a camera to distinguish discolored items from other items and separates them by blowing them out with compressed air.

次に、本実施形態に係る測定装置が測定対象物９に対して近赤外光測定を行う際の処理について図４－１、４－２を参照して説明する。図４－１、４－２は、本実施形態に係る測定装置が測定対象物９に対して近赤外光測定を行う際の処理を説明するためのフローチャートである。 Next, the process when the measurement device according to this embodiment performs near-infrared light measurement on the measurement target 9 will be described with reference to FIGS. 4-1 and 4-2. 4-1 and 4-2 are flowcharts for explaining the process when the measurement device according to this embodiment performs near-infrared light measurement on the measurement target 9.

［回帰式導出］
本発明の粒子の質量の測定方法及び装置では、目的変数データＹ（粒子の質量）、説明変数データＸt（粒子の被覆面積Ｘ0、近赤外反射スペクトルＲ（＝Ｘ1）又はその派生スペクトルＸs（ｓは派生パターン）を、単独又は列結合したデータ）とする回帰式を用いることができる。回帰式の導出例を、以下、図６の各ステップ（ＳＴ）を引用しながら、種子の質量を測定する場合を例に説明する。 [Derivation of regression equation]
In the method and apparatus for measuring the mass of particles of the present invention, objective variable data Y (particle mass), explanatory variable data X t (particle coverage area X 0 , near-infrared reflection spectrum R (=X 1 ) or its derivative A regression equation can be used in which the spectrum X s (s is a derived pattern) is used alone or in column-combined data). An example of deriving the regression equation will be described below by referring to each step (ST) of FIG. 6 and using a case where the mass of a seed is measured as an example.

（データの取得）
測定を開始し、近赤外分光画像の撮影を行う（６－ＳＴ１）。必要に応じ、反射率補正画像を生成し（６－ＳＴ２）、種子認識モデルを適用する（６－ＳＴ３）。面積データＸ0は、同一条件下の撮影画像から各粒子の被覆面積として求めることができる。単位は実面積（ｍｍ2）であってもよく、画素数であってもよい。近赤外反射スペクトルデータＲは、近赤外分光器又はハイパースペクトルカメラを使用し、なるべく種子全面からの平均反射スペクトルを取得することが望ましい。後者を使用する場合、面積データも同時に取得できる（６－ＳＴ４）。 (data acquisition)
Measurement is started and a near-infrared spectroscopic image is taken (6-ST1). If necessary, a reflectance-corrected image is generated (6-ST2) and a seed recognition model is applied (6-ST3). The area data X 0 can be determined as the covered area of each particle from images taken under the same conditions. The unit may be the actual area (mm 2 ) or the number of pixels. As for the near-infrared reflection spectrum data R, it is desirable to use a near-infrared spectrometer or a hyperspectral camera to obtain the average reflection spectrum from the entire surface of the seed. When using the latter, area data can also be acquired at the same time (6-ST4).

一方、回帰式導出のため、精密天秤を用いて、測定対象物の質量を正確に秤量する（６－ＳＴ８、９）。なお、いったんその測定対象物について回帰式を導出した後は、同種の測定対象物に対しては質量を測定する必要はない。 On the other hand, in order to derive the regression equation, the mass of the object to be measured is accurately weighed using a precision balance (6-ST8, 9). Note that once a regression equation has been derived for the object to be measured, it is not necessary to measure the mass of the objects to be measured of the same type.

（データセットの準備）
次いで、回帰式導出に向けたデータセットの準備を行う。 (preparation of data set)
Next, a dataset is prepared for deriving the regression equation.

なお、小文字のｘ、ｙは個々の種子に関する説明変数および目的変数（質量）の値である。 Note that the lowercase letters x and y are the values of the explanatory variable and objective variable (mass) regarding each seed.

取得した近赤外反射スペクトルデータＲから自身Ｘ1を含めて、適切な数、例えば３６通りの変換及び、その結合スペクトルデータＸsを派生させる。回帰分析では、その全派生スペクトルの、説明変数としての使用を試みるが、目的変数との相関が同等の場合は、点線で示した範囲のなるべく単純な派生パターンを採択する。面積及びスペクトルデータは、一方又は両方を説明変数として使用できるが、両方を使用した方が相関の高い回帰式を導出できる。以下では上記３６通りのいずれかの派生スペクトルを使用した回帰式を例に説明することがある。 From the acquired near-infrared reflection spectrum data R, including itself X 1 , an appropriate number of transformations, for example, 36 ways, and the combined spectrum data X s are derived. In regression analysis, an attempt is made to use the entire derived spectrum as an explanatory variable, but if the correlation with the objective variable is equivalent, the simplest possible derived pattern within the range shown by the dotted line is adopted. Either or both of area and spectrum data can be used as explanatory variables, but a regression equation with a higher correlation can be derived by using both. In the following, a regression equation using any of the 36 derived spectra described above may be explained as an example.

（回帰式の導出手順）
データセットの最終準備を次のように行う。
１． ３６通りのスペクトルと、それに面積のデータ列を追加した計７２の説明変数データを準備する（６－ＳＴ５、６、７）。 (Procedure for deriving regression equation)
Perform the final preparation of the dataset as follows.
1. A total of 72 explanatory variable data, including 36 spectra and an area data string, is prepared (6-ST5, 6, 7).

２． 目的変数、説明変数ともに標準化を行う。 2. Standardize both objective variables and explanatory variables.

例えば、線形スパースモデリングによる回帰式の導出は次のように行うことができる。 For example, a regression equation can be derived using linear sparse modeling as follows.

３． 目的変数と説明変数データのすべての組み合わせについて、Ａｄａｐｔｉｖｅ ＬＡＳＳＯにより変数選択を行う（６－ＳＴ１０）。最初にＲｉｄｇｅ回帰により、各変数の重みの初期値を決定する（６－ＳＴ１１）。続いてＬＡＳＳＯ回帰により、正則化係数λと偏回帰係数、及び偏回帰係数が０とならない説明変数の個数の関係を表す解パスを算定する（６－ＳＴ１２）。回帰式の決定係数Ｒ2を最大化する説明変数の個数をｐとすると、説明変数をｐ個未満に削減する場合に適した説明変数の組み合わせの候補は、解パスより決定できる（６－ＳＴ１３）。 3. Variable selection is performed using Adaptive LASSO for all combinations of objective variables and explanatory variable data (6-ST10). First, the initial value of the weight of each variable is determined by Ridge regression (6-ST11). Next, a solution path representing the relationship between the regularization coefficient λ, the partial regression coefficient, and the number of explanatory variables for which the partial regression coefficient does not become 0 is calculated by LASSO regression (6-ST12). Assuming that the number of explanatory variables that maximizes the coefficient of determination R 2 of the regression equation is p, candidates for combinations of explanatory variables suitable for reducing the number of explanatory variables to less than p can be determined from the solution path (6-ST13 ).

４． 最終的な回帰式はＰＬＳ回帰分析により導出する（６－ＳＴ１５）。ＬＡＳＳＯ回帰における解パスより決定した、２～ｐ個の説明変数を使用して回帰式を導出する（６－ＳＴ１６）。各回帰式について、決定係数Ｒ2と情報量規準ＡＩＣやＢＩＣなどを求める（６－ＳＴ１７）。 4. The final regression equation is derived by PLS regression analysis (6-ST15). A regression equation is derived using 2 to p explanatory variables determined from the solution path in LASSO regression (6-ST16). For each regression equation, determine the coefficient of determination R 2 and the information criteria AIC, BIC, etc. (6-ST17).

５． 導出されたすべての回帰式に対し、説明変数の個数と情報量規準との関係を調べる。両者の関係が単調増加又は減少ではなく、極小値が求まる情報量規準に着目する。どの情報量規準がこの条件を満たすかは試料数などに依存する。両者の関係をもとに、使用する説明変数と回帰式を決定する。 5. For all derived regression equations, examine the relationship between the number of explanatory variables and the information criterion. We focus on the information amount criterion in which the relationship between the two is not a monotonous increase or decrease, but a local minimum value. Which information criterion satisfies this condition depends on the number of samples, etc. Based on the relationship between the two, determine the explanatory variables and regression equation to be used.

好ましい態様においては、回帰モデルは、ＡＩＣが極小化する説明変数の個数を特定し（６－ＳＴ２１）、Ｒ2が高値となる回帰モデルを選択する（６－ＳＴ２２）。 In a preferred embodiment, the regression model specifies the number of explanatory variables for which AIC is minimized (6-ST21), and selects a regression model with a high value of R 2 (6-ST22).

近赤外光センサは総じて高価であるが、感度域が９５０～１，７００ ｎｍのＩｎＧａＡｓセンサは比較的安価で安定しているという観点からは、この波長範囲の反射スペクトルデータから、高精度な質量予測の回帰式が導出されることが望まれる。 Near-infrared light sensors are generally expensive, but InGaAs sensors with a sensitivity range of 950 to 1,700 nm are relatively inexpensive and stable, so it is possible to obtain high-precision sensors based on reflection spectrum data in this wavelength range. It is desired that a regression equation for mass prediction be derived.

［ヒノキ・イネ・ダイズ・カラマツ種子の大きさ及び近赤外反射スペクトルの測定と質量予測］
（材料）
供試試料として、岐阜県２０１７～２０１９年産のヒノキ種子、岡山県２０２０年産のイネ（籾）及びダイズ種子、北海道２０１６年産のカラマツ種子を用いた。 [Measurement of size and near-infrared reflectance spectra of cypress, rice, soybean, and larch seeds and mass prediction]
(material)
As test samples, we used cypress seeds produced in Gifu Prefecture from 2017 to 2019, rice (paddy) and soybean seeds produced in Okayama Prefecture in 2020, and larch seeds produced in Hokkaido in 2016.

近赤外光の照射は、２４Ｖ ２５０Ｗのアルミミラー付ハロゲンランプ（河北ライティングソリューションズ社製ＪＴＲ２４Ｖ２５０Ｗ１０Ｈ／５－ＡＬ、ＧＸ５．３口金）２灯への直流電圧印加により行い、近赤外分光画像は、ラインスキャン型ハイパースぺクトルカメラ（カラマツ種子はＥｍｅｒｇｉｎｇ Ｔｅｃｈｎｏｌｏｇｙ（現ＰｈｉＬｕｍｉｎａ）社ＳＷＩＲ－２００Ｒ、それ以外は住友電工社製ＣＶ－Ｎ８０１ＨＳ）を用いて撮影した。近赤外レンズは、焦点距離が３０ ｍｍの像側テレセントリックレンズ（Ｓｐｅｃｉｍ社又は住友電工社製）であり、撮影時の作動距離は２８ ｃｍ、空間分解能は９０ ｐｐｉ、波長サンプリング間隔は６ ｎｍであった。また、使用したカメラの波長感度域は１，２５０～２，５００ ｎｍ（ＳＷＩＲ－２００Ｒ）又は、９８０～２，３５０ ｎｍ（ＣＶ－Ｎ８０１ＨＳ）であった。各波長における反射率は、９９％標準反射板（米Ｌａｂｓｐｈｅｒｅ社ＳＲＴ－９９－０５０）及び暗電流（反射率０％）の撮影画像をもとに、拡散反射率相当に較正した。図９に、ヒノキ、イネ、ダイズ、及びカラマツの種子の典型的な近赤外反射スペクトルの概形を示す。 Irradiation of near-infrared light was performed by applying a DC voltage to two 24V 250W halogen lamps with aluminum mirrors (JTR24V250W10H/5-AL, GX5.3 base manufactured by Kawakita Lighting Solutions), and the near-infrared spectroscopic image was Images were taken using a line-scan hyperspectral camera (Larch seeds SWIR-200R from Emerging Technology (now PhiLumina), others CV-N801HS from Sumitomo Electric Industries). The near-infrared lens was an image-side telecentric lens (manufactured by Specim or Sumitomo Electric) with a focal length of 30 mm, a working distance of 28 cm, a spatial resolution of 90 ppi, and a wavelength sampling interval of 6 nm. there were. Furthermore, the wavelength sensitivity range of the camera used was 1,250 to 2,500 nm (SWIR-200R) or 980 to 2,350 nm (CV-N801HS). The reflectance at each wavelength was calibrated to be equivalent to the diffuse reflectance based on a photographed image of a 99% standard reflector (SRT-99-050, Labsphere, USA) and dark current (reflectance 0%). FIG. 9 shows the outline of typical near-infrared reflectance spectra of cypress, rice, soybean, and larch seeds.

種子の質量は、風防及び除電器を備え、防振台上に設置したマイクロ電子天秤（最小表示０．０１ ｍｇ）を用いて測定した。実測の最小値は、カラマツ種子の１．１ ｍｇ、最大値はダイズ種子の６６６ ｍｇであった。 The mass of the seeds was measured using a microelectronic balance (minimum display: 0.01 mg) equipped with a windshield and a static eliminator and placed on a vibration-proof table. The minimum value actually measured was 1.1 mg for larch seeds, and the maximum value was 666 mg for soybean seeds.

（方法）
（１）種子に対し、ハロゲンランプより光を均一に照射し、近赤外波長域の分光情報を含むハイパースペクトル画像を撮影した。
（２）種子の大きさ及び反射スペクトルは、分光画像内において各種子が占有する領域の画素数と、画素ごとに記録された近赤外反射スペクトルの領域内平均を利用した。なお、種子の実占有面積は、上述の空間分解能の値を用いて算出できるが、撮影条件を同一に保つ限り、この操作は本発明技術の利用に必須ではない。
（３）分光画像を撮影した種子の質量を１粒ごとにマイクロ電子天秤により測定した。
（４）種子の質量を目的変数、種子の大きさ及び近赤外反射スペクトルの、いずれか又は両方を説明変数とした線形回帰モデルを、単回帰分析又は、Ａｄａｐｔｉｖｅ ＬＡＳＳＯ（Least Absolute Shrinkage and Selection Operator）及びＰＬＳ（Partial Least Squares 又は Projection to Latent Structures）回帰分析を組み合わせた多変量スパースモデリングの手法を用いて導出した。反射スペクトル（Ｒ）には、逆数（Ｒ-1）及び対数（疑似吸光度、－ｌｏｇＲ）変換のほか、標準正規化（Standard Normal Variate, ＳＮＶ； 波長範囲は１，２５０～２，３００ ｎｍ（ＳＷＩＲ－２００Ｒ使用時）又は９８０～２，２００ ｎｍ（ＣＶ－Ｎ８０１ＨＳ使用時））変換を施した後、Ｓａｖｉｔｚｋｙ－Ｇｏｌａｙ？フィルタによる平滑化及び１～３次の平滑化微分（いずれも５点・３～４次多項式近似）を加え、またこれらを列結合することにより、計３６通りの派生スペクトルを生成した。これらすべてを回帰モデルの導出に適用し、最適モデルの選択にはＡＩＣ（赤池情報量規準）及び、予測－実測値間の決定係数（Ｒ2）を指標とした。
（５）回帰モデルの導出に際しては、訓練及び検証用データを適切に分割し、最終的に選択されたモデルが訓練用データに過剰適合していないことを確認した。 (Method)
(1) The seeds were uniformly irradiated with light from a halogen lamp, and a hyperspectral image containing spectral information in the near-infrared wavelength region was photographed.
(2) For the size and reflection spectrum of the seeds, the number of pixels in the area occupied by each seed in the spectroscopic image and the average within the area of the near-infrared reflection spectra recorded for each pixel were used. Note that although the actual occupied area of the seeds can be calculated using the above-mentioned spatial resolution value, this operation is not essential to use the technique of the present invention as long as the imaging conditions are kept the same.
(3) The mass of each seed whose spectroscopic images were photographed was measured using a microelectronic balance.
(4) A linear regression model with seed mass as an objective variable and either or both of seed size and near-infrared reflectance spectrum as explanatory variables was analyzed using simple regression analysis or Adaptive LASSO ( Least A bsolute S shrinkage It was derived using a multivariate sparse modeling technique that combines PLS ( P artial L east Squares or Projection to L atent Structures) regression analysis . The reflection spectrum (R) is subjected to reciprocal (R -1 ) and logarithmic ( pseudo absorbance, -logR) conversion, as well as standard normalization (SNV); the wavelength range is 1,250 to 2,300. nm (when using SWIR-200R) or 980 to 2,200 nm (when using CV-N801HS)), Savitzky-Golay? A total of 36 derived spectra were generated by adding filter smoothing and 1st to 3rd order smoothing differentials (all 5-point, 3rd to 4th order polynomial approximations), and by column-combining these. All of these were applied to derive the regression model, and the AIC (Akaike Information Criterion) and the coefficient of determination (R 2 ) between predicted and measured values were used as indicators to select the optimal model.
(5) When deriving the regression model, we appropriately divided the training and verification data and confirmed that the finally selected model did not overfit the training data.

（結果・考察）
以下ではヒノキ及びイネ種子に関する解析結果について重点的に説明し、ダイズ及びカラマツについては、部分的なデータの提示にとどめる。 (Results/Discussion)
Below, we will mainly explain the analysis results for cypress and rice seeds, and will only present partial data for soybean and larch.

（１）種子の大きさと質量との相関には、植物種ごとに高低差が認められた。一般に均質な物質の質量は占有面積ではなく体積に比例することから、種子の大きさ（占有画素数）の１．５乗値と質量との関係についても調べたが、相関の顕著な向上は認められなかった。
（２）いずれの植物種においても、種子の大きさのみを説明変数に用いた場合に比べ、近赤外反射スペクトルを併用した場合に、種子の質量がより高い精度で予測できることが確認された。ヒノキ及びイネでは、近赤外反射スペクトルのみを説明変数に用いた場合の種子質量の予測精度は、種子の大きさのみを説明変数に用いた場合を上回っていた。
（３）説明変数に種子の大きさを含めるか否かによらず、種子の質量予測に最適な回帰モデルは、対象とする植物種ごとに異なっていた。しかし、ひとたび決定した回帰モデルは、採種年の異なる同一植物種の種子に対し、再導出の必要なく適用可能であった。
（４）種子内部の構造や成分を反映した近赤外反射スペクトルが得られにくい、粒径の大きなダイズでは、他の植物種を対象とした場合に比べ、種子質量の予測精度が劣る傾向が認められた。本発明技術は、秤による計量が困難な、小径又は偏平な種子において、より適合性が高いことが示唆された。
（５）回帰モデルの導出及び、これを用いた種子質量の予測において、分光画像上の水平座標、すなわちレンズからの画角が近接した種子を選択的に用いた場合、水平座標を考慮しない場合に比べ、予測精度は顕著に向上した。光源と被写体である種子、レンズ間の位置関係の一定性を高める工夫を分光測定装置に施すことにより、本発明技術による種子質量の予測精度をより高められる可能性が示唆された。 (1) Differences in height were observed for each plant species in the correlation between seed size and mass. Generally, the mass of a homogeneous substance is proportional to its volume rather than its occupied area, so we also investigated the relationship between the seed size (occupied pixel count) to the 1.5th power and its mass, but we found that the correlation was not significantly improved. I was not able to admit.
(2) For all plant species, it was confirmed that seed mass could be predicted with higher accuracy when near-infrared reflectance spectra were used in combination than when only seed size was used as an explanatory variable. . For cypress and rice, the accuracy of predicting seed mass when using only the near-infrared reflectance spectrum as an explanatory variable was higher than when using only seed size as an explanatory variable.
(3) Regardless of whether or not seed size was included as an explanatory variable, the optimal regression model for predicting seed mass differed depending on the target plant species. However, once the regression model was determined, it could be applied to seeds of the same plant species in different seeding years without the need for re-derivation.
(4) For soybeans with large grain sizes, where it is difficult to obtain near-infrared reflectance spectra that reflect the internal structure and components of the seeds, the accuracy of predicting seed mass tends to be lower than when targeting other plant species. Admitted. It was suggested that the technology of the present invention is more suitable for small-diameter or flat seeds that are difficult to weigh using a scale.
(5) When deriving a regression model and predicting seed mass using it, when seeds with horizontal coordinates on the spectral image, that is, the angle of view from the lens are close to each other, are selectively used, or when the horizontal coordinates are not considered The prediction accuracy was significantly improved. It has been suggested that the accuracy of predicting seed mass using the technology of the present invention may be further improved by adding a device to the spectrometer to improve the consistency of the positional relationship between the light source, the subject (seed), and the lens.

表１に、図１２～２０の回帰モデリングに使用したデータセットの詳細及び、種子質量の予測－実測値間の決定係数（Ｒ2）及び二乗平均平方根誤差（Root Mean Square Error, ＲＭＳＥ）を示す。また表２に、図１２～２０で使用した線形回帰モデルの詳細を示す。 Table 1 shows details of the datasets used for the regression modeling in Figures 12 to 20 , as well as the coefficient of determination ( R 2 ) and the root mean square error between predicted and measured seed mass. RMSE). Table 2 also shows details of the linear regression models used in Figures 12-20.

１ 制御・演算部（コンピュータ）
２、３、４ 撮影部
２ａ 近赤外分光カメラ
２ｂ 近赤外レンズ
３ａ 電動シャッター
３ｂ 電動シャッター
４ａ 標準反射板挿入ユニット
４ｂ 標準反射板
５ 照射部
５ａ 近赤外光源
５ｂ 近赤外光源
６ 供給部
６ａ 精密供給ユニット
６ｂ 連続供給ユニット
７ 搬送部（搬送ユニット）
８ 選別部
８ａ 回収ユニット
８ｂ 回収ユニット
８ｃ 回収ユニット
９ 測定対象物（粒子） 1 Control/calculation section (computer)
2, 3, 4 Photographing unit 2a Near-infrared spectroscopic camera 2b Near-infrared lens 3a Electric shutter 3b Electric shutter 4a Standard reflector insertion unit 4b Standard reflector 5 Irradiation unit 5a Near-infrared light source 5b Near-infrared light source 6 Supply unit 6a Precision supply unit 6b Continuous supply unit 7 Transport section (transport unit)
8 Sorting section 8a Collection unit 8b Collection unit 8c Collection unit 9 Object to be measured (particles)

Claims (12)

有機物を含む粒子の質量又は質量分布の測定装置であって、
粒子を供給する供給手段と、
粒子に近赤外光を照射する照射手段と、
粒子から反射されるｍ個の特定の波長における近赤外光の反射率のデータを取得する撮影手段と、
ｍ個の特定の波長における近赤外光の反射率のデータに基づき、粒子の質量を演算する演算手段と、
を備える、測定装置。 A device for measuring the mass or mass distribution of particles containing organic matter,
a supply means for supplying particles;
irradiation means for irradiating particles with near-infrared light;
Photographing means for acquiring reflectance data of near-infrared light at m specific wavelengths reflected from the particles;
Calculating means for calculating the mass of the particle based on reflectance data of near-infrared light at m specific wavelengths;
A measuring device comprising: さらに、演算結果に基づき粒子を選別する選別手段を備える、請求項１に記載の測定装置。 The measuring device according to claim 1, further comprising a sorting means for sorting particles based on the calculation result. 供給手段が、粒子を一定の速度で連続的に供給するものである、請求項１又は２のいずれか１項に記載の測定装置。 3. The measuring device according to claim 1, wherein the supply means continuously supplies particles at a constant rate. 演算手段において、近赤外光の反射率のデータと粒子の被覆面積データとに基づいて演算を行う、請求項１から３のいずれか１項に記載の測定装置。 The measuring device according to any one of claims 1 to 3, wherein the calculation means performs calculation based on near-infrared light reflectance data and particle coverage area data. 下記の工程を含む、有機物を含む粒子の質量又は質量分布の測定方法：
（ａ）粒子を測定装置に供給し、
（ｂ）粒子に近赤外光を照射し、ｍ個の特定の波長における近赤外光の反射率のデータを取得し、
（ｃ）得られたデータに基づき、粒子の質量を算出する。 A method for measuring the mass or mass distribution of particles containing organic matter, comprising the following steps:
(a) supplying particles to a measuring device;
(b) irradiate the particles with near-infrared light and obtain data on reflectance of near-infrared light at m specific wavelengths,
(c) Calculate the mass of the particles based on the obtained data. 工程（ｂ）が、粒子の全面からの反射スペクトルを取得するものであり、同時に種子の被覆面積データｘ0を取得するものであり、粒子の質量の算出が、近赤外光の反射率のデータと粒子の被覆面積データとに基づく、請求項５に記載の方法。 Step (b) is to acquire the reflection spectrum from the entire surface of the particle, and at the same time acquire the coverage area data x 0 of the seed, and the calculation of the mass of the particle is based on the reflectance of near-infrared light. 6. The method of claim 5, based on data and particle coverage data. 工程（ｃ）が、測定対象となる粒子について予め決定した回帰式を用いて算出するものである、請求項５又は６に記載の方法。 The method according to claim 5 or 6, wherein the step (c) is calculated using a regression equation determined in advance for the particles to be measured. 粒子が、食品粒子、医薬品粒子、化粧品粒子、又は植物の種子である、請求項５から７のいずれか１項に記載の方法。 8. A method according to any one of claims 5 to 7, wherein the particles are food particles, pharmaceutical particles, cosmetic particles, or plant seeds. 請求項６から８のいずれか１項に記載の方法で種子の質量又は質量分布を測定し；
測定結果に基づき、一定の質量範囲の種子を選別し；
選別した種子を播種し、栽培し、植物体を生産する
工程を含む、作物又は種苗の生産方法。 Measuring the mass or mass distribution of seeds by the method according to any one of claims 6 to 8;
Based on the measurement results, select seeds within a certain mass range;
A method for producing crops or seedlings, which includes the steps of sowing selected seeds, cultivating them, and producing plants. 請求項９の生産方法を実施するための、装置、設備又は工場。 An apparatus, equipment, or factory for carrying out the production method according to claim 9. コンピュータに対し、
粒子から反射されるｍ個の特定の波長における近赤外光の反射率のデータを入力するデータ入力ステップと、入力されたデータに基づき、粒子の質量を演算する演算ステップと、
を実行させる、請求項５から８のいずれか１項に記載の方法を実施するための、プログラム。 to the computer,
a data input step of inputting reflectance data of near-infrared light at m specific wavelengths reflected from the particles; and a calculation step of calculating the mass of the particles based on the input data;
A program for carrying out the method according to any one of claims 5 to 8. コンピュータが、測定対象となる粒子について予め決定した回帰式を記憶する記憶部を備えており、演算ステップが、測定対象となる粒子について予め決定した回帰式を用いて演算するものである、請求項１１に記載のプログラム。 Claim: The computer comprises a storage unit that stores a regression equation predetermined for the particles to be measured, and the calculation step is performed using the regression equation predetermined for the particles to be measured. The program described in 11.
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