JPH0629851B2 - Eating value estimation method - Google Patents
Eating value estimation methodInfo
- Publication number
- JPH0629851B2 JPH0629851B2 JP9393989A JP9393989A JPH0629851B2 JP H0629851 B2 JPH0629851 B2 JP H0629851B2 JP 9393989 A JP9393989 A JP 9393989A JP 9393989 A JP9393989 A JP 9393989A JP H0629851 B2 JPH0629851 B2 JP H0629851B2
- Authority
- JP
- Japan
- Prior art keywords
- value
- taste
- sample
- taste value
- stickiness
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Expired - Lifetime
Links
- 238000000034 method Methods 0.000 title claims description 23
- 235000019640 taste Nutrition 0.000 claims description 104
- 241000209094 Oryza Species 0.000 claims description 30
- 235000007164 Oryza sativa Nutrition 0.000 claims description 30
- 235000009566 rice Nutrition 0.000 claims description 30
- 238000004458 analytical method Methods 0.000 claims description 16
- 102000004169 proteins and genes Human genes 0.000 claims description 15
- 108090000623 proteins and genes Proteins 0.000 claims description 15
- IJGRMHOSHXDMSA-UHFFFAOYSA-N Atomic nitrogen Chemical compound N#N IJGRMHOSHXDMSA-UHFFFAOYSA-N 0.000 claims description 14
- 239000000470 constituent Substances 0.000 claims description 12
- 229910052757 nitrogen Inorganic materials 0.000 claims description 7
- 229920002472 Starch Polymers 0.000 claims description 5
- 235000019698 starch Nutrition 0.000 claims description 5
- 239000008107 starch Substances 0.000 claims description 5
- 235000019629 palatability Nutrition 0.000 claims 1
- 229920000856 Amylose Polymers 0.000 description 15
- 230000001953 sensory effect Effects 0.000 description 14
- 239000000126 substance Substances 0.000 description 6
- 238000002835 absorbance Methods 0.000 description 4
- 238000011088 calibration curve Methods 0.000 description 4
- 230000000875 corresponding effect Effects 0.000 description 4
- 238000011156 evaluation Methods 0.000 description 4
- FYYHWMGAXLPEAU-UHFFFAOYSA-N Magnesium Chemical compound [Mg] FYYHWMGAXLPEAU-UHFFFAOYSA-N 0.000 description 3
- ZLMJMSJWJFRBEC-UHFFFAOYSA-N Potassium Chemical compound [K] ZLMJMSJWJFRBEC-UHFFFAOYSA-N 0.000 description 3
- 230000007423 decrease Effects 0.000 description 3
- 239000011777 magnesium Substances 0.000 description 3
- 229910052749 magnesium Inorganic materials 0.000 description 3
- 239000011591 potassium Substances 0.000 description 3
- 229910052700 potassium Inorganic materials 0.000 description 3
- 230000015556 catabolic process Effects 0.000 description 2
- 238000012364 cultivation method Methods 0.000 description 2
- 235000013305 food Nutrition 0.000 description 2
- 239000004615 ingredient Substances 0.000 description 2
- SWHAQEYMVUEVNF-UHFFFAOYSA-N magnesium potassium Chemical compound [Mg].[K] SWHAQEYMVUEVNF-UHFFFAOYSA-N 0.000 description 2
- 239000000463 material Substances 0.000 description 2
- 238000005259 measurement Methods 0.000 description 2
- 238000001228 spectrum Methods 0.000 description 2
- ZCYVEMRRCGMTRW-UHFFFAOYSA-N 7553-56-2 Chemical compound [I] ZCYVEMRRCGMTRW-UHFFFAOYSA-N 0.000 description 1
- 229920000945 Amylopectin Polymers 0.000 description 1
- 206010011416 Croup infectious Diseases 0.000 description 1
- 235000019750 Crude protein Nutrition 0.000 description 1
- 244000269722 Thea sinensis Species 0.000 description 1
- 238000010411 cooking Methods 0.000 description 1
- 230000002596 correlated effect Effects 0.000 description 1
- 201000010549 croup Diseases 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000002329 infrared spectrum Methods 0.000 description 1
- 229910052500 inorganic mineral Inorganic materials 0.000 description 1
- 239000011630 iodine Substances 0.000 description 1
- 229910052740 iodine Inorganic materials 0.000 description 1
- 239000007788 liquid Substances 0.000 description 1
- 239000011707 mineral Substances 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000000704 physical effect Effects 0.000 description 1
- 238000004445 quantitative analysis Methods 0.000 description 1
- 229940100486 rice starch Drugs 0.000 description 1
- 238000002834 transmittance Methods 0.000 description 1
- 235000019583 umami taste Nutrition 0.000 description 1
Landscapes
- 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〕 本発明は食味値推定方法に係り、例えば、米の場合粘り
等食味に関連するパラメータでまず分類し、その後その
パラメータの分類に対応した食味推定式により食味推定
値を得る方法に関する。DETAILED DESCRIPTION OF THE INVENTION [Industrial application] The present invention relates to a method for estimating a taste value, for example, in the case of rice, the taste is first classified by parameters related to the taste, and then the taste corresponding to the classification of the parameters. The present invention relates to a method for obtaining an estimated taste value by an estimation formula.
米の食味試験を官能検査で行うには竹生新治郎著「米の
食味」頁58以降に記載されている様に少なくとも24人以
上のパネル要員を必要とするなどの様に短時間に簡単に
食味判定できない。To conduct a sensory test on the taste of rice, the taste is easy and quick, such as requiring at least 24 panel personnel as described in Shinjiro Takeo's “Taste of Rice” page 58 onwards. I can't judge.
これを解決すべく行われている理化学的推定による食味
判定方法は前記「米の食味」の頁70以降にも示されてい
る。例えば従来試みられている米または茶等の理化学的
食味判定方式は、食品中に含まれる蛋白質、アミロース
などの有機成分または、一部のミネラル等の含有率を物
理化学的に定量分析しそれらの値から総合的に食味値を
判定する方法または、これらの成分を近赤外分析器にて
測定できる様にして食味判定を行っていた。さらに、こ
れを一歩進めて官能試験した複数の試料のスペクトルを
前記近赤外分析器で直接校正して、食味値を予測できる
様にした試みもある。The taste determination method based on physicochemical estimation that is carried out to solve this problem is also shown on page 70 and subsequent pages of the above-mentioned “Taste of Rice”. For example, the physicochemical taste determination method of rice or tea that has been attempted in the past is based on a physicochemical quantitative analysis of the content rate of proteins, organic components such as amylose, or some minerals contained in foods, and those The taste was judged by a method of comprehensively judging the taste value from the values or by making it possible to measure these components by a near infrared analyzer. Furthermore, there is an attempt to take this one step further and directly calibrate the spectra of a plurality of samples subjected to a sensory test by the near infrared analyzer so that the taste value can be predicted.
近赤外分析法を使って直接米の食味値を推定する方法と
して、 (1)予め米の含有成分や物理・化学的特性を組み合わせ
ることにより、最も官能食味値を説明できる要素（目的
変数）を予め決定しておき、この数値を米の試料（標
本）群について求めておき、これをもとに近赤外分析法
により食味推定回帰式（検量線）を求めて、この検量線
を使用して未知試料の食味推定をする方法 (2)上記(1)項の目的変数を直接官能評価値を使用して検
量線を求める方法 があり、具体的には、(2)項の方法として下記の式が公
表されている。（日本作物学会第185回講演会、堀野
等、昭和63年４月４日） Ｔ：食味推定値 ｋ，ｌ：定数 〔発明が解決しようとする課題〕 食味を判別する上で考慮すべき点として下記事項があ
る。As a method of directly estimating the taste value of rice using near infrared analysis, (1) an element that can explain the most sensory taste value (target variable) by combining the ingredients contained in rice and physical / chemical characteristics in advance. Has been determined in advance, this value has been obtained for a group of rice samples (samples), and based on this, the regression equation (calibration curve) for estimating taste has been obtained by the near-infrared analysis method, and this calibration curve has been used. (2) There is a method to obtain a calibration curve by directly using the sensory evaluation values for the objective variables in (1) above, and specifically, as the method in (2) above. The following formula has been published. (The 185th Lecture Meeting of the Crop Science Society of Japan, Horino et al., April 4, 1988) T: estimated taste value k, l: constant [Problems to be solved by the invention] There are the following points to be considered in determining the taste.
(1)食味（うまみ）評価に共通した問題であるが、食味
は嗜好的な面があり画一的なものでなく個人差があるこ
と。(1) It is a problem common to the evaluation of taste (umami), but the taste is not uniform and has individual differences.
(2)個人的なばらつきを少なくするため、複数のパネラ
ー（食味テスト者）による米の官能食味値を求め、これ
を統計処理して代表値としての官能食味値を得ることが
行われているが、このようにして得られた官能食味値
と、この米の化学的諸成分および物理的諸成分との関係
を厳密に定義することは困難であること。(2) In order to reduce individual variations, sensory taste values of rice are obtained by multiple panelists (tasting testers) and statistically processed to obtain the sensory taste value as a representative value. However, it is difficult to precisely define the relationship between the sensory taste values thus obtained and the chemical and physical components of this rice.
(3)官能食味値を最も良く推定する化学的成分および物
理的特性を特定し、これを近赤外分析法により被測定試
料のスペクトルから推定する際に、この近赤外分析法に
よる特定波長群の吸光度群と官能食味値との間で、官能
食味範囲（基準となる食味を０とし、例えば−１〜0.8
の範囲の食味のものを考えた場合）にわたり必ずしも直
線関係がない。(3) When the chemical components and physical properties that best estimate the sensory taste value are specified and estimated from the spectrum of the sample to be measured by the near infrared analysis method, the specific wavelength by this near infrared analysis method is used. Between the absorbance group and the sensory taste value of the group, the sensory taste range (the reference taste is set to 0, for example, -1 to 0.8
There is not necessarily a linear relationship over the range of () when the taste is considered.
(2)項について詳説すると、食味推定に使われている化
学的諸成分として次のものがある。Explaining item (2) in detail, there are the following chemical components used for taste estimation.
アミロース、蛋白質、マグネシウム、カリウム、マグネ
シウムとカリウムの比、マグネシウムとカリウムおよび
窒素の積との比等、物理化学的な諸特性としては、粘
度、ブレークダウン、炊飯液のヨード呈色度等がある。Physical and chemical properties such as amylose, protein, magnesium, potassium, magnesium-potassium ratio, magnesium-potassium and nitrogen product ratios include viscosity, breakdown, iodine coloration of the cooking liquid, etc. .
上記の諸成分、諸特性はいずれも独立しているものでは
なく相互に依存している。例えば、アミロースが増加す
れば粘り（粘度）が減り米の食味値が低下する。蛋白質
が増加すれば粘りが減少するが、反面特定の蛋白質の増
加は呈味成分の増加になる。ブレークダウン値が大きい
と米の粘りが大きい等これらはいずれも米の食味あるい
は米の澱粉の特性をある一面から評価しているのみでそ
れぞれの評価は複合しているものもあるし、干渉してい
るものもある。The above components and characteristics are not independent of each other but depend on each other. For example, when amylose increases, the stickiness (viscosity) decreases and the taste value of rice decreases. When the amount of protein increases, the stickiness decreases, but on the other hand, the increase of specific protein increases the taste components. If the breakdown value is large, the stickiness of the rice is large, etc.In all of these, the taste of rice or the characteristics of rice starch are only evaluated from one aspect, and there are some cases where the respective evaluations are complex and there is interference. Some have.
それ故、現在いくつかの食味推定式が公開されている
が、必ずしも食味（官能）値を十分説明するものではな
い。この一つの理由として使用されている諸成分による
食味の評価が十分できないことが考えられる。例えば食
品の特性を表す重要な指標である蛋白質は、いわゆる粗
蛋白質であり化学分析では全窒素を定量している。しか
し食味評価では必ずしも窒素の総量ではなくどのような
形態で窒素が存在しているかの方が重要と考えられる。
本発明の目的は、試料の構成成分またはこれらの比の関
数として算出される食味推定値を、食味値と所定の関連
を有する特性値に応じて修正することにより、短時間に
精度よく試料の食味値を推定する食味値推定方法を提供
することにある。Therefore, some taste estimation formulas are currently published, but they do not always sufficiently explain the taste (sensory) value. As one of the reasons for this, it is conceivable that the taste of each ingredient used cannot be sufficiently evaluated. For example, protein, which is an important indicator of the characteristics of food, is a so-called crude protein, and total nitrogen is quantified by chemical analysis. However, in the evaluation of taste, it is considered that the form of nitrogen present is more important than the total amount of nitrogen.
An object of the present invention is to accurately estimate the taste of a sample in a short time by correcting the taste estimation value calculated as a function of the constituent components of the sample or a ratio thereof, depending on the characteristic value having a predetermined relation with the taste value. It is to provide a method for estimating a taste value for estimating a taste value.
食味値と特定の関連を有する特性値（例えば米の場合粘
り度）の大小により試料を複数の群に分け、その群ごと
に食味値推定式を求めておき、食味未知試料をその特性
値を調べて、その特性値に対応した食味値推定式で食味
値を推定するようにすればよく、すなわち本発明の食味
値推定方法は、予め食味値または食味値の代用特性が既
知の試料につき、近赤外分析法により該試料の特定構成
成分と食味値または食味値代用特性の相関を求め、この
特定構成成分の関数で表した食味値推定式を求め、食味
値未知試料の前記特定構成成分を近赤外分析法で求めて
前記食味値推定式により食味値を推定する食味値推定方
法において、食味値既知の試料について、食味値と関連
を有する特性値を求め、この特性値により試料を複数の
群に分け、試料の特定構成成分により食味値を表す食味
値推定式を前記群ごとに定めておき、食味値未知の試料
について、前記特性値を求め、該特性値に対応した前記
群の食味値推定式を選択し、食味値未知の試料の前記特
定構成成分を求めて選択した食味値推定式に代入し、食
味値を求めるものであり、また前記試料が米である場合
において、前記特性値を粘り度とするとよく、また前期
試料が米である場合において、前記特性値を蛋白質の量
と粘り度とすると更に精度が向上する。その際、試料の
澱粉中のＣＨ基により粘り度を推定するとよく、また蛋
白質の量として窒素の量を用いるとよい。Samples are divided into multiple groups according to the size of the characteristic value (eg, stickiness in the case of rice) that has a specific relationship with the taste value, and the taste value estimation formula is calculated for each group, The taste value may be estimated by a taste value estimation formula corresponding to the characteristic value, that is, the taste value estimation method of the present invention is a sample in which the taste value or a substitute characteristic of the taste value is known in advance, The correlation between the specific constituent component of the sample and the taste value or the taste value substitute characteristic by the near-infrared analysis method is calculated, and the taste value estimation formula represented by the function of the specific constituent component is calculated, and the specific constituent component of the taste value unknown sample is obtained. In the taste value estimation method of estimating the taste value by the taste value estimation formula by obtaining the near infrared analysis method, for a sample having a known taste value, a characteristic value having a relationship with the taste value is obtained, and the sample is obtained by this characteristic value. Divide into multiple groups and A tasting value estimation formula that represents the tasting value by the constant constituents is defined for each group, the characteristic value is obtained for a sample whose tasting value is unknown, and the tasting value estimation formula of the group corresponding to the characteristic value is selected. , Is to obtain the taste value by substituting the specific constituents of the sample of unknown taste value into the selected taste value estimation formula, and when the sample is rice, if the characteristic value is the stickiness Well, in the case where the early sample is rice, the accuracy is further improved when the characteristic values are the amount of protein and the stickiness. At that time, the stickiness may be estimated by the CH group in the starch of the sample, and the amount of nitrogen may be used as the amount of protein.
官能食味は被測定試料の構成成分の組み合わせまたはそ
の比などの関数としてある程度推定できるが、例えば被
測定試料の近赤外分析法による特定波長群の吸光度群と
官能食味値との間で、官能食味範囲にわたり必ずしも直
線関係がないため、官能食味範囲全域にわたり十分な精
度が得られない。そこで、食味値と所定の関連を有する
特性値を見出し、特性値の値によって試料を複数の群に
分け、この群ごとに食味値推定式を定め、食味値未知の
試料の特性値を求め、この特性値に対応した群の食味値
推定式を選択し、食味値未知の試料の特定構成成分を求
めて選択した食味値推定式に代入して食味値を求めるこ
とにより推定精度が向上する。この特性値を複数とすれ
ば、さらに推定精度が向上する。The sensory taste can be estimated to some extent as a function of the combination of the constituent components of the sample to be measured or the ratio thereof, but for example, between the absorbance group of a specific wavelength group and the sensory taste value by the near infrared analysis method of the sample to be measured, Since there is not necessarily a linear relationship over the taste range, sufficient accuracy cannot be obtained over the entire sensory taste range. Therefore, a characteristic value having a predetermined relationship with the taste value is found, the sample is divided into a plurality of groups according to the value of the characteristic value, a taste value estimation formula is determined for each group, and a characteristic value of the sample with an unknown taste value is obtained. The estimation accuracy is improved by selecting the taste value estimation formula of the group corresponding to this characteristic value, obtaining the specific constituent component of the sample with the unknown taste value, and substituting it into the selected taste value estimation formula to obtain the taste value. If this characteristic value is set to a plurality, the estimation accuracy is further improved.
以下本発明の一実施例を第１図，第２図を用いて説明す
る。An embodiment of the present invention will be described below with reference to FIGS. 1 and 2.
現在市場に流通している米はコシヒカリやササニシキに
代表される。特に、コシヒカリは良食味米として定評が
ある。その組成を他の米と比較してみると一般的にアミ
ロース含量が少ない（従ってアミロペクチンの相対含量
が多い）ことが報告されている。しかしこのアミロース
含量のみでは食味を完全に説明できないことも報告され
ている（澱粉科学第32巻第１号頁51〜60、竹生等、1985
年）。そしてこのアミロース含量が少ないことが米の粘
りを増し結果的に良食味品種であることが認められてい
る。そこで本実施例ではこのような事実に着目して、前
述の(1)式を本発明を用いることにより修正した場合に
ついて説明する。The rice currently on the market is represented by Koshihikari and Sasanishiki. In particular, Koshihikari has a good reputation as a good-tasting rice. It has been reported that its composition is generally low in amylose content (and thus high in amylopectin content) when compared with other rice. However, it has been reported that the taste cannot be completely explained only by the amylose content (Starch Science, Vol. 32, No. 1, pages 51-60, Takeo et al., 1985.
Year). It has been recognized that the low amylose content increases the stickiness of rice, resulting in a good-tasting variety. Therefore, in the present embodiment, focusing on such a fact, a case will be described in which the above formula (1) is modified by using the present invention.
予め、アミロース含量または粘り度および（マグネシウ
ム）／（カリウム・蛋白質・アミロース）が既知の米試
料をアミロース含量の比較的低いまたは粘り度が比較的
大きいグループとそうでないグループに分類し、(1)式
を求める前記方法により、それぞれのクループ別の食味
推定式を求め、アミロース含量の比較的低いまたは粘り
度が比較的小さいグループより導いた式を(2)式、そう
でないグループより導いた式を(3)式とする。両式は係
数ｍ，ｎを異にするだけである。食味推定値Ｔは次式で
表される。In advance, rice samples with known amylose content or stickiness and (magnesium) / (potassium / protein / amylose) were classified into a group with a relatively low amylose content or a relatively high stickiness and a group with no such stickiness, (1) By the method for obtaining the formula, to obtain a texture estimation formula for each croup, the formula derived from the group having a relatively low amylose content or a relatively small stickiness (2), the formula derived from the other group Formula (3) is used. Both formulas differ only in the coefficients m and n. The estimated taste value T is expressed by the following equation.
アミロース含量または粘り度を判定する一つの方法とし
て次の式を算出する。 The following formula is calculated as one method for determining the amylose content or stickiness.
粘り度またはアミロースに相関のある澱粉中の分子ＣＨ
基の量が既知である米試料を近赤外分析器で、前述の分
子ＣＨ基に主として感応がある1800nmあるいは2100nm近
辺の波長を照射し、この波長における米試料の吸光度
（log１／Ｒの一次または二次微分値、Ｒは反射光量）
と分子ＣＨ基の量との回帰式を求める。この式を粘り
（Ｖ）分類式とする。Molecule CH in starch correlated with stickiness or amylose
A near infrared analyzer is used to irradiate a rice sample with a known amount of groups with a wavelength near 1800 nm or 2100 nm, which is mainly sensitive to the above-mentioned molecular CH groups, and the absorbance (first order of log1 / R) of the rice sample at this wavelength. (Or second derivative, R is the amount of reflected light)
And the amount of CH groups in the molecule are found. This formula is a stickiness (V) classification formula.
Ｋ0，Ｋ1：回帰定数 λ：波長 ＯＤ：光学密度＝log 1/R なお、この例では粘りＶが単一回帰項の方程式で説明さ
れているが、これを多種項数からなる方程式としてこの
判定に使用することもできる。 K 0 , K 1 : Regression constant λ: Wavelength OD: Optical density = log 1 / R In this example, the stickiness V is explained by the equation of a single regression term. It can also be used for this determination.
次に粉砕された米の被測定試料に所定の範囲にわたり連
続的に近赤外線スペクトルを照射し、この反射（または
透過）光量の対数をとってlog１／Ｒ（またはlog１／
Ｔ，Ｒ＝反射率，Ｔ＝透過率）のデータを連続する各波
長について求める。次いでこのlog１／Ｒの一次微分ま
たは二次微分の演算を各波長に対して行う。この際、粘
りまたはアミロースに相関のある澱粉中の分子ＣＨ基に
主として感応がある1800nmあるいは2100nm近辺の波長を
設定する。この波長における被測定材料の吸光度（log
１／Ｒの一次または二次微分値）を(4)式に代入し粘り
Ｖ値を求める。またマグネシウム／（カリウム・蛋白質
・アミロース）値が得られるので、(2)式または(3)式に
代入すれば食味推定値が得られる。(4)式で得られる粘
りＶ値が一定の数値（例えば５とする）以上のものは
(3)式により食味推定値を求め、５に達しないものは(2)
式により食味推定値を得る。これを第１図のフローチャ
ートで示す。Next, the crushed rice sample to be measured is continuously irradiated with a near-infrared spectrum over a predetermined range, and the logarithm of the reflected (or transmitted) light amount is taken to obtain log1 / R (or log1 /
Data of T, R = reflectance, T = transmittance) is obtained for each continuous wavelength. Then, the operation of the first derivative or the second derivative of log1 / R is performed for each wavelength. At this time, a wavelength in the vicinity of 1800 nm or 2100 nm, which is mainly sensitive to the molecular CH groups in the starch that have a correlation with stickiness or amylose, is set. Absorbance of measured material at this wavelength (log
Substituting the 1 / R primary or secondary differential value into the equation (4), the stickiness V value is obtained. Further, since the magnesium / (potassium / protein / amylose) value is obtained, the estimated taste value can be obtained by substituting it into the equation (2) or the equation (3). If the tenacity V value obtained by equation (4) is a certain value (for example, 5),
If the estimated taste value is calculated by the equation (3) and the value does not reach 5, then (2)
An estimated taste value is obtained from the formula. This is shown in the flow chart of FIG.
また、近赤外分析法の特徴の一つは同時に複数成分を測
定することが可能であるから、被測定材料の蛋白質
（Ｐ）（または窒素）を測定しこれにて前記粘り測定と
同様にこの蛋白質の含量（Ｐ）に所定のレベル（いき
値、例えばＰ＝８％）を設定してこのレベルを超えない
試料については(2)式を用いて食味値推定を行い、この
レベルを超える試料については粘りＶで検定を行い、粘
りＶが所定のレベル（例えば５）を超えていたら(3)式
を用い、超えていなければ(2)式を用いて食味値を推定
する。これを第２図のフローチャートで示す。Further, one of the characteristics of the near infrared analysis method is that it is possible to measure a plurality of components at the same time. Therefore, the protein (P) (or nitrogen) of the material to be measured is measured and this is used in the same manner as in the viscosity measurement. The protein content (P) is set to a predetermined level (threshold value, eg P = 8%), and for samples that do not exceed this level, the taste value is estimated using equation (2) and the level is exceeded. The sample is tested for stickiness V, and if the stickiness V exceeds a predetermined level (for example, 5), the expression (3) is used, and if the stickiness V is not exceeded, the tasting value is estimated using the expression (2). This is shown in the flow chart of FIG.
米の食味値の測定例は次の通りである。The measurement example of the eating quality value of rice is as follows.
次に前述の(1)式と本実施例との食味値推定精度につい
て説明する。前記文献（日本作物学会第185回講演昭和
６３年４月４日）によると(1)式の推定精度は高くない
（“総合”に対して相関係数ｒ＝0.581）。本方式では
この検量線作成段階において、標本を予め前記の粘り
（Ｖ）につき近赤外分析法で測定し、この粘りのレベル
について分類してから、それぞれについて近赤外分析法
を使って食味推定回帰式を作成することにより食味値推
定精度を向上させており、このようにグループ別に推定
することにより“総合”で相関係数ｒ＝0.8程度が得ら
れた。 Next, the taste value estimation accuracy of the above equation (1) and this embodiment will be described. According to the above-mentioned document (Agricultural Science Society of Japan, 185th lecture, April 4, 1988), the estimation accuracy of the equation (1) is not high (correlation coefficient r = 0.581 for “general”). In this method, at the stage of preparing this calibration curve, the sample is measured in advance by the near-infrared analysis method for the above-mentioned stickiness (V), the stickiness level is classified, and then the taste is evaluated by using the near-infrared analysis method. The estimation accuracy was improved by creating an estimated regression formula, and by estimating the groups in this way, a correlation coefficient r = 0.8 was obtained in the "total".
前記実施例では、米の粘り（Ｖ）蛋白質（Ｐ）を近赤外
分析法を用いた測定の例を示したが、近赤外分析法以外
の方法でしてもよい。さらに(2)式適用か(3)式適用かを
識別するためには、品種や栽培方法により粘り（Ｖ）又
は蛋白質（Ｐ）の大小が既知の場合には、各々の品種や
栽培方法によって適用式を決定しても差し支えない。ま
た新米、古米によって適用式を区分することもできる。In the above-mentioned Examples, an example of measuring the stickiness (V) protein (P) of rice using the near infrared analysis method was shown, but a method other than the near infrared analysis method may be used. Furthermore, in order to discriminate whether the formula (2) is applied or the formula (3) is applied, if the size of the stickiness (V) or the protein (P) is known depending on the variety and the cultivation method, it is determined according to each variety and the cultivation method. There is no problem in determining the applicable formula. It is also possible to divide the applicable formula by new rice and old rice.
なお、前記実施例において粘り（Ｖ）分類式として回帰
式(4)を近赤外分析法で求める方法を例示したが、粘り
度又はアミロース量を１個づつの既知試料について測定
することは必ずしも必要でない。品種等により粘り度の
大小が判明している２つのグループの各グループごとの
代表的な値１個づつ用いて回帰式を求めてもよい。Although the method of obtaining the regression formula (4) as the stickiness (V) classification formula by the near-infrared analysis method has been illustrated in the above examples, the stickiness or the amylose amount is not necessarily measured for each known sample. Not necessary. The regression equation may be obtained using one representative value for each of the two groups of which the degree of stickiness is known depending on the type of product.
本発明によれば、被測定試料を予め特性値により分類し
たのち、その分類されたグループに最適な食味推定式を
用いて食味を推定するので、精度の高い推定値を短時間
で得ることができる。According to the present invention, after the sample to be measured is classified in advance by the characteristic value, the taste is estimated using the optimum taste estimation formula for the classified group, so that a highly accurate estimated value can be obtained in a short time. it can.
第１図は試料の粘りによりグループ分けして食味値を推
定するフローチャート、第２図は試料の蛋白質量および
粘りによりグループ分けして食味値を推定するフローチ
ャートである。FIG. 1 is a flowchart for estimating taste values by grouping according to sample stickiness, and FIG. 2 is a flowchart for estimating taste values by grouping according to protein mass and stickiness of the sample.
Claims (5)
の試料につき、近赤外分析法により該試料の特定構成成
分と食味値または食味値代用特性の相関を求め、この特
定構成成分の関数で表した食味値推定式を求め、食味値
未知試料の前記特定構成成分を近赤外分析法で求めて前
記食味値推定式により食味値を推定する食味値推定方法
において、食味値既知の試料について、食味値と関連を
有する特性値を求め、この特性値により試料を複数の群
に分け、試料の特定構成成分により食味値を表す食味値
推定式を前記群ごとに定めておき、食味値未知の試料に
ついて、前記特性値を求め、該特性値に対応した前記群
の食味値推定式を選択し、食味値未知の試料の前記特定
構成成分を求めて選択した食味値推定式に代入し食味値
を求めることを特徴とする食味値推定方法。1. A sample for which a taste value or a substitute characteristic of a taste value is known in advance, a correlation between a specific constituent component of the sample and a taste value or a substitute value of the taste value is obtained by a near-infrared analysis method. Obtaining the taste value estimation formula represented by a function, in the taste value estimation method of estimating the taste value by the taste value estimation formula by obtaining the specific constituents of the taste value unknown sample by the near infrared analysis method, the taste value is known. For the sample, determine the characteristic value that is related to the taste value, divide the sample into a plurality of groups by this characteristic value, and define the taste value estimation formula that expresses the taste value for each group according to the specific constituents of the sample. For the sample of unknown value, the characteristic value is obtained, the taste value estimation formula of the group corresponding to the characteristic value is selected, and the specific constituents of the sample of unknown taste value are obtained and substituted into the selected taste value estimation formula. Special to find the taste value Palatability value estimation method to be.
性値が粘り度であることを特徴とする請求項１記載の食
味値推定方法。2. The taste value estimating method according to claim 1, wherein when the sample is rice, the characteristic value is stickiness.
性値が蛋白質の量と粘り度であることを特徴とする請求
項１記載の食味値推定方法。3. The method according to claim 1, wherein when the sample is rice, the characteristic values are the amount of protein and the stickiness.
することを特徴とする請求項２，３のいずれかに記載の
食味値推定方法。4. The taste value estimation method according to claim 2, wherein the stickiness is estimated by the CH group in the starch of the sample.
特徴とする請求項３記載の食味値推定方法。5. The taste value estimation method according to claim 3, wherein the amount of nitrogen is used as the amount of protein.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| JP9393989A JPH0629851B2 (en) | 1989-04-13 | 1989-04-13 | Eating value estimation method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| JP9393989A JPH0629851B2 (en) | 1989-04-13 | 1989-04-13 | Eating value estimation method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| JPH02271254A JPH02271254A (en) | 1990-11-06 |
| JPH0629851B2 true JPH0629851B2 (en) | 1994-04-20 |
Family
ID=14096402
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| JP9393989A Expired - Lifetime JPH0629851B2 (en) | 1989-04-13 | 1989-04-13 | Eating value estimation method |
Country Status (1)
| Country | Link |
|---|---|
| JP (1) | JPH0629851B2 (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 (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH0792433B2 (en) * | 1990-12-03 | 1995-10-09 | 青森県 | Measuring method for sugar content of fruits and vegetables and sugar content measuring device |
| JP3191340B2 (en) * | 1991-09-03 | 2001-07-23 | 井関農機株式会社 | Rice quality judgment device |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPS6367547A (en) * | 1986-09-09 | 1988-03-26 | Satake Eng Co Ltd | Taste measuring instrument for rice |
| JPS646746A (en) * | 1987-01-20 | 1989-01-11 | Satake Eng Co Ltd | Rice taste measuring apparatus |
-
1989
- 1989-04-13 JP JP9393989A patent/JPH0629851B2/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 |
|---|---|
| JPH02271254A (en) | 1990-11-06 |
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