JPH07117490B2 - Non-destructive method for determining the taste of fruits and vegetables by near infrared rays - Google Patents
Non-destructive method for determining the taste of fruits and vegetables by near infrared raysInfo
- Publication number
- JPH07117490B2 JPH07117490B2 JP4074088A JP4074088A JPH07117490B2 JP H07117490 B2 JPH07117490 B2 JP H07117490B2 JP 4074088 A JP4074088 A JP 4074088A JP 4074088 A JP4074088 A JP 4074088A JP H07117490 B2 JPH07117490 B2 JP H07117490B2
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- taste
- component
- content
- vegetables
- absorption spectrum
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- Expired - Lifetime
Links
- 238000000034 method Methods 0.000 title claims description 29
- 235000012055 fruits and vegetables Nutrition 0.000 title description 18
- 238000000862 absorption spectrum Methods 0.000 claims description 28
- 238000011088 calibration curve Methods 0.000 claims description 21
- 238000002835 absorbance Methods 0.000 claims description 7
- 238000000491 multivariate analysis Methods 0.000 claims description 7
- 235000013399 edible fruits Nutrition 0.000 claims description 6
- 235000013311 vegetables Nutrition 0.000 claims description 5
- 230000001678 irradiating effect Effects 0.000 claims description 2
- 230000001953 sensory effect Effects 0.000 claims description 2
- 238000004458 analytical method Methods 0.000 description 6
- 238000009614 chemical analysis method Methods 0.000 description 6
- 238000005259 measurement Methods 0.000 description 6
- 239000003365 glass fiber Substances 0.000 description 4
- 238000001028 reflection method Methods 0.000 description 4
- 238000000611 regression analysis Methods 0.000 description 4
- 239000000126 substance Substances 0.000 description 4
- 244000144730 Amygdalus persica Species 0.000 description 3
- 235000007688 Lycopersicon esculentum Nutrition 0.000 description 3
- 238000004497 NIR spectroscopy Methods 0.000 description 3
- 235000006040 Prunus persica var persica Nutrition 0.000 description 3
- 241000220324 Pyrus Species 0.000 description 3
- 240000003768 Solanum lycopersicum Species 0.000 description 3
- 238000010521 absorption reaction Methods 0.000 description 3
- 239000004615 ingredient Substances 0.000 description 3
- 235000000346 sugar Nutrition 0.000 description 3
- 241000555678 Citrus unshiu Species 0.000 description 2
- 230000007423 decrease Effects 0.000 description 2
- 239000000203 mixture Substances 0.000 description 2
- 235000021017 pears Nutrition 0.000 description 2
- 238000001228 spectrum Methods 0.000 description 2
- 244000291564 Allium cepa Species 0.000 description 1
- 235000002732 Allium cepa var. cepa Nutrition 0.000 description 1
- 241001672694 Citrus reticulata Species 0.000 description 1
- 244000068988 Glycine max Species 0.000 description 1
- 235000010469 Glycine max Nutrition 0.000 description 1
- 241000220225 Malus Species 0.000 description 1
- 235000011430 Malus pumila Nutrition 0.000 description 1
- 235000015103 Malus silvestris Nutrition 0.000 description 1
- 235000014443 Pyrus communis Nutrition 0.000 description 1
- 239000002253 acid Substances 0.000 description 1
- 235000001014 amino acid Nutrition 0.000 description 1
- 150000001413 amino acids Chemical class 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 230000001066 destructive effect Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 235000013305 food Nutrition 0.000 description 1
- 238000007689 inspection Methods 0.000 description 1
- 150000002632 lipids Chemical class 0.000 description 1
- 239000007788 liquid Substances 0.000 description 1
- 150000007524 organic acids Chemical class 0.000 description 1
- 235000005985 organic acids Nutrition 0.000 description 1
- 239000000843 powder Substances 0.000 description 1
- 235000018102 proteins Nutrition 0.000 description 1
- 102000004169 proteins and genes Human genes 0.000 description 1
- 108090000623 proteins and genes Proteins 0.000 description 1
- 238000007619 statistical method Methods 0.000 description 1
- 150000008163 sugars Chemical class 0.000 description 1
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 1
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
- G01N33/02—Food
- G01N33/025—Fruits or vegetables
-
- 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
Landscapes
- Physics & Mathematics (AREA)
- Spectroscopy & Molecular Physics (AREA)
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Chemical & Material Sciences (AREA)
- Analytical Chemistry (AREA)
- Biochemistry (AREA)
- General Health & Medical Sciences (AREA)
- General Physics & Mathematics (AREA)
- Immunology (AREA)
- Pathology (AREA)
- Engineering & Computer Science (AREA)
- Food Science & Technology (AREA)
- Medicinal Chemistry (AREA)
- Investigating Or Analysing Materials By Optical Means (AREA)
Description
【発明の詳細な説明】 〔産業上の利用分野〕 本発明は果実，野菜の糖類，有機酸，アミノ酸など化学
成分のバランスによって決まる食味を、近赤外線を用い
て非破壊的に、かつ迅速に判定する方法に関する。DETAILED DESCRIPTION OF THE INVENTION [Industrial field of application] The present invention uses a near infrared ray to nondestructively and rapidly determine the taste determined by the balance of chemical components such as sugars, organic acids and amino acids of fruits and vegetables. Regarding the determination method.
従来の果実，野菜の化学成分の分析は、細断した組織を
用いて特定の化学成分ごとに決められた方法を用いる、
いわゆる湿式化学分析法により行われてきた。しかし、
この方法は煩雑な操作と時間を要するとともに、測定対
象物を基本的に破壊してしまうため、例えば果実，野菜
の貯蔵試験において同一試料による品質の追跡調査がで
きなかったり、また選果場の特級選別において内容成分
の全数検査ができない等の問題点があった。Conventional analysis of chemical composition of fruits and vegetables uses a method determined for each specific chemical composition using finely divided tissue,
It has been carried out by the so-called wet chemical analysis method. But,
This method requires complicated operations and time, and basically destroys the object to be measured. Therefore, for example, in the storage test of fruits and vegetables, it is not possible to follow up on the quality of the same sample, There was a problem that 100% inspection of the content components was not possible in the special grade selection.
近年、果実，野菜の成分分析に近赤外線を用いる近赤外
分光法が実用化されつつあるが、穀物を対象に開発され
た技術であるため、測定対象物は粉末状あるいは液状の
ものに限られている。従って、近赤外分光法を果実，野
菜に適用するならば、上記の湿式化学分析法と同様に測
定対象物を破壊しなければならず、上記問題点を解決す
るには至っていない。ましてや、成分分析の結果から果
実や野菜の食味を判定することに関しては従来全く報告
されていない。In recent years, near-infrared spectroscopy, which uses near-infrared rays for the component analysis of fruits and vegetables, is being put to practical use, but since it is a technique developed for grains, the measurement target is limited to powder or liquid. Has been. Therefore, if the near-infrared spectroscopy is applied to fruits and vegetables, the measurement object must be destroyed as in the above wet chemical analysis method, and the above problems have not been solved. Moreover, there has been no report on determining the taste of fruits and vegetables from the results of component analysis.
そこで、本発明者らは果実，野菜を破壊せずに近赤外線
を用いて成分を測定し、さらにその測定結果から果実，
野菜の食味を判定する方法について鋭意研究した結果、
多変量解析手法により検量線を作成して利用することに
より目的を達成できることを知見し、本発明に到達し
た。Therefore, the present inventors measured the components using near-infrared rays without destroying fruits and vegetables,
As a result of earnest research on a method for determining the taste of vegetables,
The inventors have found that the object can be achieved by creating and using a calibration curve by a multivariate analysis method, and arrived at the present invention.
すなわち、本発明は対象の果実又は野菜の近傍から、も
しくは接触した状態で近赤外線を照射して近赤外吸収ス
ペクトルを得、得られた近赤外吸収スペクトルから多変
量解析手法により対象物中のｎ個の成分含量を推定する
のに適したｎ個の検量線を求め、成分含量未知の対象物
について前記検量線に採用した波長の吸光度から前記検
量線を用いて各成分含量を算出すると共に、該算出値よ
りｎ次元空間における別途官能検査によって決定される
最高食味点からの距離を求めて対象物の食味を自動的に
判定することを特徴とする近赤外線による果実・野菜の
食味の非破壊判定法を提供するものである。That is, the present invention obtains a near-infrared absorption spectrum by irradiating near-infrared rays from the vicinity of a target fruit or vegetable, or in a state of contacting, and in a target object by a multivariate analysis method from the obtained near-infrared absorption spectrum. N calibration curves suitable for estimating the content of n components are obtained, and the content of each component is calculated from the absorbance of the wavelength adopted in the calibration curve for the target of unknown component content using the calibration curve. Along with the calculated value, the taste of the object is automatically determined by obtaining the distance from the highest taste point determined by a separate sensory test in the n-dimensional space. It provides a non-destructive judgment method.
本発明において果実，野菜に特に制限はないが、皮が薄
いものが好ましく、たとえばモモ，リンゴ，ナシ，ミカ
ン，オレンジ，トマト，タマネギなどを挙げることがで
きる。In the present invention, fruits and vegetables are not particularly limited, but those with thin skin are preferable, and examples thereof include peach, apple, pear, orange, tomato, and onion.
近赤外線とは、可視光線と赤外線の間の波長域にあっ
て、0.7μｍから2.5μｍまでの波長の電磁波をいう。食
品中の代表的成分の近赤外吸収スペクトルは、それぞれ
の成分に特徴のあるスペクトルである。従って、果実，
野菜の散乱光の近赤外吸収スペクトルも、それぞれの内
容成分に帰属した特徴のあるスペクトルを示す。これ
は、赤外域で生じる特定分子の基準振動の倍音または結
合振動による吸収に由来するものである。複数の成分か
ら成る果実，野菜では、それぞれの成分による吸収が相
互に干渉し、近赤外領域における吸収スペクトルはそれ
ぞれの成分量に比例して影響を受けることから、重回帰
分析などの多変量解析の手法により近赤外吸収スペクト
ルからそれぞれの成分含量を測定することができる。Near infrared rays are electromagnetic waves in the wavelength range between visible light and infrared rays and having a wavelength of 0.7 μm to 2.5 μm. The near-infrared absorption spectrum of a typical ingredient in food is a spectrum characteristic of each ingredient. Therefore, the fruit,
The near-infrared absorption spectrum of scattered light of vegetables also shows a characteristic spectrum attributed to each content component. This is due to the absorption of the reference vibration of the specific molecule generated in the infrared region by the overtone or the combined vibration. In fruits and vegetables consisting of multiple components, the absorption of each component interferes with each other, and the absorption spectrum in the near infrared region is affected in proportion to the amount of each component. Therefore, multivariate analysis such as multiple regression analysis is performed. The content of each component can be measured from the near infrared absorption spectrum by an analytical method.
本発明では、果実，野菜をそのままの形で近赤外吸収ス
ペクトルを得るために、市販の近赤外分析装置に次のよ
うな工夫を施して近赤外吸収スペクトルを測定する。In the present invention, in order to obtain the near-infrared absorption spectrum of fruits and vegetables as they are, the near-infrared absorption spectrum is measured by applying the following device to a commercially available near-infrared analyzer.
同軸のグラスファイバーを用いる方法 第１図に示したように、内側が光源につながり、外側が
検出器につながった同軸状グラスファイバーをゴム性の
クッションを介して果実赤道部に密着させて暗箱内で吸
収スペクトルを測定する。Method of using coaxial glass fiber As shown in Fig. 1, the coaxial glass fiber whose inner side is connected to the light source and whose outer side is connected to the detector is closely attached to the fruit equator through the rubber cushion and placed in the dark box. Measure the absorption spectrum with.
反射法（非積分球方式） 既存の検出器の下部にゴム性のクッションを張り付け、
クッションに果実，野菜を密着させて暗箱内で吸収スペ
クトルを測定する（第２図参照）。Reflection method (non-integrating sphere method) Attach a rubber cushion to the bottom of the existing detector,
The fruits and vegetables are brought into close contact with the cushion and the absorption spectrum is measured in a dark box (see Fig. 2).
反射法（積分球方式） 第３図に示したように、試料台を上下できる試料室を用
い、果実，野菜を測定部の直下の同じところに位置決め
して吸収スペクトルを測定する。Reflection Method (Integrating Sphere Method) As shown in FIG. 3, an absorption spectrum is measured by using a sample chamber in which a sample stand can be moved up and down and positioning fruits and vegetables at the same place directly under the measurement unit.
本発明では、まず上記〜のいずれかの方法により複
数検体、好ましくは30検体以上について各成分の近赤外
吸収スペクトルを測定し、得られた測定値をコンピュー
タを用いて多変量解析手段により各成分含量あるいは理
化学的特性を測定するのに最適な波長を決定し、検量線
を作成する。なお、近赤外分光法による成分分析の精度
はこの検量線の精度により決定するので、各成分に適す
る特異的な波長を用いることが大切である。検量線の作
成は次のような手順によって行われる。In the present invention, first, by measuring the near-infrared absorption spectrum of each component for a plurality of specimens, preferably 30 specimens or more by any of the above-mentioned method, the measured values obtained by a multivariate analysis means using a computer The optimum wavelength for measuring the content of ingredients or physicochemical properties is determined and a calibration curve is prepared. Since the accuracy of component analysis by near infrared spectroscopy is determined by the accuracy of this calibration curve, it is important to use a specific wavelength suitable for each component. The calibration curve is created by the following procedure.
（１） 検体を専用の試料室にセットする。(1) Set the sample in the dedicated sample chamber.
（２） コンピュータからデータの取り込みの指令を入
力し、検体の吸光度をとる。(2) Input the command of data acquisition from the computer and take the absorbance of the sample.
（３） データ取り込み後別に行う化学分析法で求めら
れる該検体の成分（複数でもよい）含量をコンピュータ
にキーボードより入力する。(3) The content of the component (s) of the sample, which is obtained by a chemical analysis method separately performed after data acquisition, is input to the computer from the keyboard.
（４） 上記（１）から（３）の操作を複数検体（30検
体以上）について行う。(4) The above operations (1) to (3) are performed on a plurality of samples (30 samples or more).
（５） 多変量解析の手法により、検量線を検体に含ま
れる各成分ごとに作成する。(5) A calibration curve is prepared for each component contained in the sample by the method of multivariate analysis.
例えば、大豆の蛋白質含量（CP％）の検量線は次の式で
近似できる。For example, the calibration curve of protein content (C P %) of soybean can be approximated by the following formula.
CP＝K0＋K1αＷ＋K2αＯ＋K3αＰ （１） ここで、αW,αO,αＰは水，脂質，蛋白質に特異的な波
長における吸収の強さであり、Ｋは比例定数である。こ
こでは化学分析を求めた蛋白質含量と（１）式で推定し
た値の相関係数が最も高くなるように、多変量解析の統
計手法を用いてＫの値を決定する。なお、この計算はコ
ンピュータにより自動的に行うことができる。C P = K 0 + K 1 α W + K 2 α O + K 3 α P (1) where α W , α O and α P are absorption strengths at wavelengths specific to water, lipids and proteins, K is a proportional constant. Here, the value of K is determined by using the statistical method of multivariate analysis so that the correlation coefficient between the protein content obtained by the chemical analysis and the value estimated by the equation (1) becomes the highest. Note that this calculation can be automatically performed by a computer.
検量線を作成した後は、検量線作成時と同様の方法によ
り対象の果実，野菜を各成分に最適の波長で吸光度を測
定し、得られた測定値を検量線と照らし合わせて各成分
（ｎ個）の含量を求めればよい。この方法によれば、果
実，野菜中の複数の成分含量を迅速に測定できる。After creating the calibration curve, the absorbance was measured at the optimum wavelength for each component of the target fruits and vegetables by the same method as when creating the calibration curve, and the measured values were compared with the calibration curve for each component ( The content of (n pieces) may be calculated. According to this method, the content of a plurality of components in fruits and vegetables can be quickly measured.
次に、本発明では上記の方法で得られたｎ個の成分の含
量と各成分のバランスによって対象とする果実，野菜の
食味（品質）を判定する。複数の成分から果実，野菜の
食味値を求めるには次のような方法がある。Next, in the present invention, the taste (quality) of the target fruit or vegetable is determined by the content of the n components obtained by the above method and the balance of each component. The following methods can be used to obtain the taste values of fruits and vegetables from multiple components.
（１） A,B2成分から食味値を求める方法 Ａ成分Ha,B成分Hbを最高食味値100点、Ａ成分La,B成分L
bを最低食味値50点とすると、Ａ成分Sa,B成分Sbの試料
の食味値は次のように算出される。▲▼および▲
▼の距離は となり、Ｓ試料の食味値をＸとすると、 で算出される。(1) Method to obtain taste value from A and B2 components A component H a and B component H b have maximum taste value of 100 points, A component L a and B component L
When b is the minimum taste value of 50 points, the taste values of the samples of the A component S a and the B component S b are calculated as follows. ▲ ▼ and ▲
▼ is the distance And the taste value of the S sample is X, It is calculated by.
（２） ｎ成分から食味値を求める方法 C1成分C1H,C2成分C2H,…Cn成分CnHを最高食味値100点、
C1成分C1L,C2成分C2L,…Cn成分CnLを最低食味値とする
と、C1成分C1X,C2成分C2X,…Cn成分CnXの試料の食味値
は次式で算出される。(2) Method to obtain the taste value from the n component C 1 component C 1H , C 2 component C 2H , ... C n component C nH has the highest taste value of 100 points,
C 1 component C 1L , C 2 component C 2L , ... C n component C nL is the minimum taste value, and the C 1 component C 1X , C 2 component C 2X , ... C n component C nX samples have the following taste values. It is calculated by the formula.
ｎ次空間における最高食味点および最低食味点からの距
離を▲▼，▲▼とすると、それぞれの
値は で表わされる。したがって、試料の食味値をＸとする
と、 で算出される。例えば、温州ミカンの食味は酸味と甘味
のバランスによって決まり、pH3.7,Brix13の組合わせの
場合に食味が最もよく、両成分ともこれにより増加して
も減少しても食味は低下する。第４図に示したように、
温州ミカンの食味はpHとBrixの直交平面における最高食
味点からの距離を求めることによって評価することがで
きる。これは、最高食味点からの距離と食味の関係が第
５図に示したように正比例するからである。言いかえれ
ば、ｎ個の成分によって食味が決定される場合、各成分
が各々の軸に相当するｎ次元空間において最高食味点の
座標が存在し、ｎ個の成分値を近赤外吸収スペクトルか
ら非破壊的に直接求め、それらの値をもとにｎ次元空間
における最高食味点からの距離をコンピュータを用いて
算出することによって果実，野菜の食味を自動的に判定
するわけである。If the distances from the highest and lowest tasting points in the nth space are ▲ ▼ and ▲ ▼, the respective values are It is represented by. Therefore, if the taste value of the sample is X, It is calculated by. For example, the taste of Satsuma mandarin is determined by the balance of acidity and sweetness, and the combination of pH 3.7 and Brix 13 has the best taste, and the taste of both components decreases or decreases even if they are increased. As shown in Figure 4,
The taste of Satsuma mandarin can be evaluated by determining the distance from the highest taste point on the orthogonal plane of pH and Brix. This is because the relationship between the distance from the highest taste point and the taste is directly proportional as shown in FIG. In other words, when the taste is determined by n components, the coordinates of the highest taste point exist in the n-dimensional space where each component corresponds to each axis, and the n component values are calculated from the near infrared absorption spectrum. The taste of fruits and vegetables is automatically determined by directly determining nondestructively and calculating the distance from the highest taste point in the n-dimensional space using a computer based on these values.
次に、実施例により本発明を説明する。 Next, the present invention will be described with reference to examples.
実施例１ ナシ50個をそのままの形で近赤外吸収スペクトルを測定
した。同時に同試料の全糖，還元糖,Brix,酸を従来の湿
式化学分析法により求めた。測定した近赤外吸収スペク
トルから重回帰分析の手法を用いて各成分含量を推定す
るのに最適な波長を決定し、検量線を作成した。第１表
に、各成分の検量線用選択波長と、その波長の吸光度を
用いて果実内容成分を推定する場合の測定精度を示す。Example 1 Near infrared absorption spectra of 50 pears were measured as they were. At the same time, total sugar, reducing sugar, Brix, and acid of the same sample were obtained by the conventional wet chemical analysis method. The optimum wavelength for estimating the content of each component was determined from the measured near-infrared absorption spectrum using the method of multiple regression analysis, and a calibration curve was prepared. Table 1 shows the selection wavelengths for the calibration curve of each component and the measurement accuracy when estimating the fruit content component using the absorbance at that wavelength.
次いで、第１表に示した波長を用いてナシ10個の近赤外
吸収スペクトルを測定し、測定値から各成分の含量を求
めた。また、Brix11.6、酸度0.33を最高食味値100点、B
rix9.1,酸度0.17を最低食味値50点として各試料の食味
値も求めた。この結果を第２表に示す。 Then, near infrared absorption spectra of 10 pears were measured using the wavelengths shown in Table 1, and the content of each component was determined from the measured values. Also, Brix 11.6, acidity 0.33, maximum taste value 100 points, B
The taste value of each sample was also determined with rix9.1 and acidity 0.17 as the minimum taste value of 50 points. The results are shown in Table 2.
実施例２ モモ30個をそのままの形で近赤外吸収スペクトルを測定
した。同時に同試料のBrix,酸度を従来の湿式化学分析
法により求め、また硬度（kg）をユニバーサル・ハード
・メータを用いて直径5mmの円筒形の針で赤道部２ケ所
の硬さを測定した平均値として求めた。測定した近赤外
吸収スペクトルから重回帰分析の手法を用いて各成分含
量あるいは理化学的特性を推定するのに最適な波長を決
定し、検量線を作成した。第３表に各成分の検量線用選
択波長とその波長の吸光度を用いて内容成分あるいは理
化学的特性を推定する場合の測定精度を示す。 Example 2 Near infrared absorption spectra of 30 peaches were measured as they were. At the same time, the Brix and acidity of the same sample were obtained by the conventional wet chemical analysis method, and the hardness (kg) was measured by measuring the hardness at two equator parts with a cylindrical needle with a diameter of 5 mm using a universal hard meter. It was calculated as a value. From the measured near-infrared absorption spectrum, the optimum wavelength for estimating the content of each component or the physicochemical properties was determined using the method of multiple regression analysis, and a calibration curve was prepared. Table 3 shows the measurement accuracy when the content component or the physicochemical property is estimated using the selected wavelength for the calibration curve of each component and the absorbance at that wavelength.
次いで、第３表に示した波長を用いてモモ10個の近赤外
吸収スペクトルを測定し、測定値から各成分の含量を求
めた。またBrix15.5,酸度3.78を最高食味値100点、Brix
10.1,酸度1.21を最低食味値50点として各試料の食味値
を求めた。この結果を第４表に示す。 Then, near infrared absorption spectra of 10 peaches were measured using the wavelengths shown in Table 3, and the content of each component was determined from the measured values. Also, Brix 15.5, acidity 3.78, maximum taste value 100 points, Brix
The taste value of each sample was calculated with 10.1 and acidity of 1.21 as the minimum taste value of 50 points. The results are shown in Table 4.
実施例３ トマト40個をそのままの形で近赤外吸収スペクトルを測
定した。同時に同試料のBrix,酸度を従来の湿式化学分
析法により求めた。測定した近赤外吸収スペクトルから
重回帰分析の手法を用いて各成分含量を推定するのに最
適な波長を決定し、検量線を作成した。第５表に各成分
の検量線用選択波長とその波長の吸光度を用いて内容成
分を推定する場合の測定精度を示す。 Example 3 Near infrared absorption spectra of 40 tomatoes were measured as they were. At the same time, Brix and acidity of the same sample were obtained by the conventional wet chemical analysis method. The optimum wavelength for estimating the content of each component was determined from the measured near-infrared absorption spectrum using the method of multiple regression analysis, and a calibration curve was prepared. Table 5 shows the measurement accuracy when the content components are estimated using the selected wavelength for the calibration curve of each component and the absorbance at that wavelength.
次いで、第５表に示した波長を用いてトマト10個の近赤
外吸収スペクトルを測定し、測定値から各成分の含量を
求めた。また、Brix7.3,酸度6.42を最高食味値100点、B
rix3.5,酸度2.08を最高食味値50点として各試料の食味
値を求めた。この結果を第６表に示す。 Next, the near infrared absorption spectra of 10 tomatoes were measured using the wavelengths shown in Table 5, and the content of each component was determined from the measured values. Also, Brix 7.3, acidity 6.42, the highest taste value 100 points, B
The taste value of each sample was determined with rix3.5 and acidity of 2.08 as the maximum taste value of 50 points. The results are shown in Table 6.
〔発明の効果〕 本発明によれば、果実，野菜を破壊することなく迅速に
その食味を判定できるので、同一試料の食味追跡調査
や、全試料の成分調査が可能である。 [Effects of the Invention] According to the present invention, the taste of fruits and vegetables can be quickly determined without destroying them, so that it is possible to trace the taste of the same sample and investigate the components of all samples.
第１図（１）は同軸のグラスファイバーを用いて近赤外
吸収スペクトルを測定する方法を示し、第１図（２）は
グラスファイバーのａ−ａ′の断面図を示す。第２図は
非積分球方式の反射法を用いた近赤外吸収スペクトルを
測定する方法を示し、第３図は積分球方式の反射法を用
いた近赤外吸収スペクトルを測定する方法を示す。な
お、図中のＡは検体,Bは光源,Cは検出器,Dはゴム性クッ
ションを指す。 第４図は温州ミカンのBrix,pHと食味の関係を示し、第
５図は最高食味点からの距離と食味の関係を示すもので
ある。FIG. 1 (1) shows a method for measuring a near-infrared absorption spectrum by using a coaxial glass fiber, and FIG. 1 (2) shows a sectional view of aa 'of the glass fiber. FIG. 2 shows a method for measuring a near infrared absorption spectrum using a non-integrating sphere reflection method, and FIG. 3 shows a method for measuring a near infrared absorption spectrum using an integrating sphere reflection method. . In the figure, A is a sample, B is a light source, C is a detector, and D is a rubber cushion. FIG. 4 shows the relationship between Brix, pH and taste of Wenshu mandarin orange, and FIG. 5 shows the relationship between the distance from the highest taste point and taste.
───────────────────────────────────────────────────── フロントページの続き (56)参考文献 特開 昭49−55392（ＪＰ，Ａ) 特表 昭60−501268（ＪＰ，Ａ) ─────────────────────────────────────────────────── ─── Continuation of the front page (56) References Japanese Patent Laid-Open No. 49-55392 (JP, A) Special Table 60-501268 (JP, A)
Claims (1)
接触した状態で近赤外線を照射して近赤外吸収スペクト
ルを得、得られた近赤外吸収スペクトルから多変量解析
手法により対象物中のｎ個の成分含量を推定するのに適
したｎ個の検量線を求め、成分含量未知の対象物につい
て前記検量線に採用した波長の吸光度から前記検量線を
用いて各成分含量を算出すると共に、該算出値よりｎ次
元空間における別途官能検査によって決定される最高食
味点からの距離を求めて対象物の食味を自動的に判定す
ることを特徴とする近赤外線による果実・野菜の食味の
非破壊判定法。1. A near-infrared absorption spectrum is obtained by irradiating near-infrared rays from the vicinity of a target fruit or vegetable or in a state of contact, and the near-infrared absorption spectrum is obtained from the obtained near-infrared absorption spectrum in a target object by a multivariate analysis method. N calibration curves suitable for estimating the content of n components are obtained, and the content of each component is calculated from the absorbance of the wavelength adopted in the calibration curve for the target of unknown component content using the calibration curve. Along with the calculated value, the taste of the object is automatically determined by obtaining the distance from the highest taste point determined by a separate sensory test in the n-dimensional space. Non-destructive judgment method.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| JP4074088A JPH07117490B2 (en) | 1988-02-25 | 1988-02-25 | Non-destructive method for determining the taste of fruits and vegetables by near infrared rays |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| JP4074088A JPH07117490B2 (en) | 1988-02-25 | 1988-02-25 | Non-destructive method for determining the taste of fruits and vegetables by near infrared rays |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| JPH01216265A JPH01216265A (en) | 1989-08-30 |
| JPH07117490B2 true JPH07117490B2 (en) | 1995-12-18 |
Family
ID=12589030
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| JP4074088A Expired - Lifetime JPH07117490B2 (en) | 1988-02-25 | 1988-02-25 | Non-destructive method for determining the taste of fruits and vegetables by near infrared rays |
Country Status (1)
| Country | Link |
|---|---|
| JP (1) | JPH07117490B2 (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2022047369A (en) * | 2020-09-11 | 2022-03-24 | Tdk株式会社 | Method for making sense-of-taste estimation model, sense-of-taste estimation system, and sense-of-taste estimation program |
Families Citing this family (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2882824B2 (en) * | 1989-11-17 | 1999-04-12 | 三菱重工業株式会社 | Fruit and vegetable ingredient measuring device |
| US5089701A (en) * | 1990-08-06 | 1992-02-18 | The United States Of America As Represented By The Secretary Of Agriculture | Nondestructive measurement of soluble solids in fruits having a rind or skin |
| JPH06249776A (en) * | 1992-07-10 | 1994-09-09 | Toyo Noki Kk | Detection of root vegetables by use of near-infrared ray |
| JPH07318429A (en) * | 1994-05-20 | 1995-12-08 | Yanmar Agricult Equip Co Ltd | Optical rotation angle measuring method |
| US5708271A (en) * | 1994-12-28 | 1998-01-13 | Sumitomo Metal Mining Co., Ltd. | Non-destructive sugar content measuring apparatus |
| US5726750A (en) * | 1995-06-29 | 1998-03-10 | Sumitomo Metal Mining Co., Ltd. | Non-destructive taste characteristics measuring apparatus and tray used in the apparatus |
| US5844678A (en) * | 1995-06-29 | 1998-12-01 | Sumitomo Metal Mining Co. Ltd. | Non-destructive taste characteristics measuring apparatus and tray used in the apparatus |
| FR2775345B1 (en) * | 1998-02-26 | 2000-05-19 | Cemagref | PROCESS AND PLANT FOR MEASURING THE CONTENT, IN PARTICULAR OF SUGAR, OF FRUITS AND VEGETABLES |
| JP2002014042A (en) | 2000-04-24 | 2002-01-18 | Sumitomo Metal Mining Co Ltd | Nondestructive sugar-level measuring apparatus |
| KR100414186B1 (en) | 2001-07-24 | 2004-01-07 | 대한민국 | the automatic calibration system and for measuring internal qualities of fruits on both side |
| ES2285961A1 (en) * | 2007-05-07 | 2007-11-16 | Universidad Politecnica De Madrid | Device for carton box forming machines, presents diverse cavities by terminal angles, where cavities adjust device longitudinally, and device has swinging arm for ceiling of support during packing |
| JP5441703B2 (en) * | 2007-09-21 | 2014-03-12 | サントリーホールディングス株式会社 | Visible / Near-Infrared Spectroscopy and Grape Brewing Method |
| CN110530818B (en) * | 2019-09-04 | 2024-01-05 | 中国计量大学 | Sample vessel for containing cherry products and application of sample vessel in origin tracing |
| CN114136887B (en) * | 2021-12-07 | 2023-10-20 | 广东省农业科学院蔬菜研究所 | Method for rapidly detecting white gourd taste determining factor malic acid based on near infrared spectrum technology |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US3770111A (en) * | 1972-05-03 | 1973-11-06 | Fmc Corp | Apparatus for sorting fruit according to color |
-
1988
- 1988-02-25 JP JP4074088A patent/JPH07117490B2/en not_active Expired - Lifetime
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2022047369A (en) * | 2020-09-11 | 2022-03-24 | Tdk株式会社 | Method for making sense-of-taste estimation model, sense-of-taste estimation system, and sense-of-taste estimation program |
Also Published As
| Publication number | Publication date |
|---|---|
| JPH01216265A (en) | 1989-08-30 |
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