JPH0640069B2 - Estimation method of taste value by near infrared - Google Patents
Estimation method of taste value by near infraredInfo
- Publication number
- JPH0640069B2 JPH0640069B2 JP11076589A JP11076589A JPH0640069B2 JP H0640069 B2 JPH0640069 B2 JP H0640069B2 JP 11076589 A JP11076589 A JP 11076589A JP 11076589 A JP11076589 A JP 11076589A JP H0640069 B2 JPH0640069 B2 JP H0640069B2
- Authority
- JP
- Japan
- Prior art keywords
- sample
- taste
- wavelength
- value
- spectrum
- 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 17
- 238000001228 spectrum Methods 0.000 claims description 37
- 238000002835 absorbance Methods 0.000 claims description 14
- 239000013598 vector Substances 0.000 claims description 12
- 238000012545 processing Methods 0.000 claims description 8
- 230000001953 sensory effect Effects 0.000 claims description 7
- 238000012360 testing method Methods 0.000 claims description 6
- 230000003595 spectral effect Effects 0.000 claims description 4
- 238000012935 Averaging Methods 0.000 claims description 3
- 230000001678 irradiating effect Effects 0.000 claims description 2
- 239000000523 sample Substances 0.000 description 47
- 241000209094 Oryza Species 0.000 description 8
- 235000007164 Oryza sativa Nutrition 0.000 description 8
- 235000009566 rice Nutrition 0.000 description 8
- 238000005259 measurement Methods 0.000 description 6
- 238000004364 calculation method Methods 0.000 description 4
- 235000013305 food Nutrition 0.000 description 4
- 239000000463 material Substances 0.000 description 4
- 230000004069 differentiation Effects 0.000 description 3
- 230000000694 effects Effects 0.000 description 3
- 238000004458 analytical method Methods 0.000 description 2
- 102000004169 proteins and genes Human genes 0.000 description 2
- 108090000623 proteins and genes Proteins 0.000 description 2
- 239000013074 reference sample Substances 0.000 description 2
- 238000004566 IR spectroscopy Methods 0.000 description 1
- AMQJEAYHLZJPGS-UHFFFAOYSA-N N-Pentanol Chemical compound CCCCCO AMQJEAYHLZJPGS-UHFFFAOYSA-N 0.000 description 1
- 238000004497 NIR spectroscopy Methods 0.000 description 1
- 244000269722 Thea sinensis Species 0.000 description 1
- 238000010521 absorption reaction Methods 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 125000000524 functional group Chemical group 0.000 description 1
- 238000002329 infrared spectrum Methods 0.000 description 1
- 229910052500 inorganic mineral Inorganic materials 0.000 description 1
- 239000011707 mineral Substances 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000010238 partial least squares regression Methods 0.000 description 1
- 230000000704 physical effect Effects 0.000 description 1
- 238000004445 quantitative analysis Methods 0.000 description 1
- 230000005855 radiation Effects 0.000 description 1
- 238000000611 regression analysis Methods 0.000 description 1
Landscapes
- Investigating Or Analysing Materials By Optical Means (AREA)
- Cereal-Derived Products (AREA)
- General Preparation And Processing Of Foods (AREA)
Description
【発明の詳細な説明】 〔産業上の利用分野〕 本発明は主として米の食味値の判定を、近赤外領域を連
続して走査または、所定の帯域を連続して走査できる近
赤外線分光器を使用して推定する方法に関するものであ
る。DETAILED DESCRIPTION OF THE INVENTION [Industrial field of application] The present invention is mainly a near-infrared spectroscope capable of continuously scanning the near-infrared region or continuously scanning a predetermined band in determining the taste value of rice. It is about the method of estimating using.
米の食味試験を官能検査で行うには竹生新治郎著の「米
の食味」Ｐ58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, it requires a panel of at least 24 people, as described in "Taste of Rice" by P. 58 of Shinjiro Takeo. 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Ｐ70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、食品中に含まれるたん白質，アミロー
ルなどの有機成分または、１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 by physicochemical estimation, which is carried out to solve this, is also shown in the above-mentioned "Rice taste of rice" P70 and thereafter. For example, the physicochemical taste determination method of rice or tea that has been attempted in the past is based on physicochemical quantitative analysis of the content rate of organic components such as protein and amylol contained in foods, or 1 part of minerals and the like. The taste was judged by the method of comprehensively judging the taste value from the value of 1 or by making it possible to measure these components with a near infrared analyzer. further,
There is also an attempt to take this one step further and calibrate the spectra of a plurality of samples subjected to a sensory test directly by the near infrared analyzer so that the taste value can be predicted.
〔発明が解決しようとする課題〕 理化学的（近赤外線分析器）方法で食味値の判定に使わ
れるデータの１つとして試料中の各種含有成分を近赤外
線分析器を使用して迅速に定量測定することができる。
しかし、例えばたん白質等の含有量の予測を近赤外線分
析器を使って行う場合には、赤外分光学（例えばB.G.OS
BORNEら著，NEAR INFRARED SPECTROSCOPY IN FOOD ANAL
YSIS.Ｐ36以降）で示されている様に、それぞれの成分
に対し、所定の帰属波長があるし、またそれが確定して
いない場合でも含有成分の定量測定は試料数を増やし
て、前記近赤外分析器に通常付属している回帰分析法な
どの統計処理を行って、いわゆる測定波長を容易に特定
することができる。即ち、近赤外スペクトル放射から成
分の定量情報が得られるが、試料からの反射または透過
光量を表わすスペクトルを対数演算しただけのいわゆる
原スペクトルでは、各種官能基に依存して起こるスペク
トルの相互干渉または試料の物理的性状および試料の提
示方法等の影響を受けて、原スペクトルの特定の波長に
おける吸光度と試料の特定成分に対する相関は、食品の
場合一般的に高くない。しかしこの原スペクトルを１次
ないし２次微分等の演算処理を行うことによって、特定
成分の濃度に対する特定波長の吸光度との相関は著じる
しく高まることが知られている（例えば、岩元睦夫ら
著，日本食品工業学会誌，第32巻，第９号，Ｐ688）。
即ち、微分処理したスペクトルの特定波長における吸光
値から特定成分の濃度を推定できる。一方、本来の食味
判定は官能試験で行われるものであるから、おのずと含
有成分の定量測定とは趣を異にする。換言すれば現在定
義されている各種パラメータの定量値またはその組合さ
れたものでは、官能試料で求められた食味値と高い相関
関係を示すものは未だ発表されていない（前記「米の食
味」Ｐ73）。従って近赤外分析計を使用して食味判定を
行う場合の測定波長を特定できない。しかし、一方測定
しようとする試料の食味値の異なるものは近赤外波長
域、例えば 600−2500nmのいずれかの部分帯において何
らかの差異または形状変化が起っている。換言すれば食
味値の異なる試料はそれぞれのスペクトルも微妙に変化
している。[Problems to be Solved by the Invention] As one of the data used for determining the taste value by a physicochemical (near infrared analyzer) method, various components contained in a sample are rapidly quantitatively measured by using a near infrared analyzer. can do.
However, for example, when the content of protein etc. is predicted using a near-infrared analyzer, infrared spectroscopy (eg, BGOS
BORNE et al., NEAR INFRARED SPECTROSCOPY IN FOOD ANAL
As shown in YSIS.P36 onwards), each component has a predetermined attribution wavelength, and even if it is not confirmed, the quantitative measurement of the contained component increases the number of samples, A so-called measurement wavelength can be easily specified by performing a statistical process such as a regression analysis method usually attached to an infrared analyzer. That is, quantitative information of components can be obtained from near-infrared spectrum radiation, but in the so-called original spectrum where only the spectrum showing the amount of light reflected or transmitted from the sample is logarithmically calculated, mutual interference of spectra that occurs depending on various functional groups. Or, due to the physical properties of the sample and the method of presenting the sample, the correlation between the absorbance at a specific wavelength of the original spectrum and the specific component of the sample is generally not high in the case of food. However, it is known that the correlation with the absorbance of a specific wavelength with respect to the concentration of a specific component is remarkably enhanced by subjecting the original spectrum to arithmetic processing such as first-order or second-order differentiation (for example, Mutsuo Iwamoto et al. Author, Journal of Japan Food Industry Society, Volume 32, No. 9, P688).
That is, the concentration of the specific component can be estimated from the absorption value at the specific wavelength of the spectrum subjected to the differential processing. On the other hand, since the original taste determination is performed by a sensory test, it is naturally different from the quantitative measurement of the contained components. In other words, among the quantitative values of various parameters currently defined or combinations thereof, those showing a high correlation with the taste value obtained by the sensory sample have not yet been published (the above-mentioned “Taste of Rice” P73. ). Therefore, it is impossible to specify the measurement wavelength when the taste determination is performed using the near infrared analyzer. On the other hand, samples having different taste values to be measured have some difference or shape change in the near infrared wavelength region, for example, in any partial band of 600-2500 nm. In other words, the spectra of the samples having different taste values slightly change.
しかし前述の様に特定の波長のみを使用して、その吸光
度から含有量の推定を行う場合や、理化学分析して定量
した含有分の定量値からでは、この食味値の違いが含ま
れているスペクトルの形状の変化を測定することが困難
である。従来の食味判定方式には本方式で採用している
定量的情報に加えてこの定性的情報が含まれていなかっ
た。However, as described above, when using only specific wavelengths to estimate the content from its absorbance, or from the quantitative value of the content quantified by physicochemical analysis, this difference in taste value is included. It is difficult to measure changes in the shape of the spectrum. The conventional taste determination method does not include this qualitative information in addition to the quantitative information used in this method.
本発明は上記の欠点を克服するために、光学的な成分分
析方法をさらに発展させて、食味値未知の試料の食味値
を測定するために、先づ官能試験で食味値が既にわかっ
ている多数の標本の各々について、近赤外線を照射しそ
の波長を順次変えて、その反射光又は照射光の吸光度を
測定して、これを波長に関して１次微分または２次微分
等の演算処理をしたスペクトルデータを記録集積してお
く。そして食味値未知の被測定資料に対しても前記の食
味値既知の資料に対してと同様に近赤外線を照射してそ
の波長を順次変えてその吸光度を測定し１次微分又は２
次微分等の演算処理を行う。その結果、標本のスペクト
ルデータと未知の被測定資料との合致指標を計算により
求めて、その合致指標が最も大きいものを標本の中から
選択し、その選択された標本の食味値をもって、食味値
未知の被測定資料の食味値であると推定するものであ
る。In order to overcome the above-mentioned drawbacks, the present invention further develops an optical component analysis method to measure the taste value of a sample of which taste value is unknown, and the taste value is already known by a sensory test. A spectrum obtained by irradiating each of a number of specimens with near-infrared rays, changing their wavelengths in sequence, measuring the absorbance of the reflected light or irradiated light, and subjecting this to the first derivative or second derivative of the wavelength. Record and accumulate data. Similarly to the material whose taste value is unknown, the material to be measured whose taste value is unknown is irradiated with near-infrared rays and its wavelength is sequentially changed to measure its absorbance, and the first derivative or 2
Performs arithmetic processing such as secondary differentiation. As a result, the matching index between the spectral data of the sample and the unknown measured material is calculated, and the one with the largest matching index is selected from the samples, and the taste value of the selected sample is used as the taste value. It is estimated to be the taste value of an unknown measured material.
上記合致指標の計算並びに標本の選択については、以下
の計算式によって求めるものである。The calculation of the above-mentioned matching index and the selection of the sample are obtained by the following calculation formulas.
標本試料のスペクトルＡの波長ｉについてのベクトルの
成分をＸｉ 被測定試料のスペクトルＢの波長ｉについてのベクトル
の成分をＹｉ とし、 とするとき、 合致指標 が最も１に近い標本を選択する。Let X i be the vector component for the wavelength i of the spectrum A of the sample sample and Y i be the vector component for the wavelength i of the spectrum B of the measured sample. And the match index Choose the sample that is closest to 1.
さらに好結果を得るためには、上記の食味値既知の複数
の標本のスペクトルデータすなわち、吸光度を波長に対
して１次微分又は２次微分等の演算処理したデータを加
算して平均値を求めたものを標本の参照スペクトルデー
タとし、一方この複数の標本の食味値も加算平均して標
本の参照食味データとし、これと食味値未知の試料のス
ペクトルデータとの合致指標を求めるとよい。この方法
により、より適合した食味値を推定することができる。In order to obtain a better result, spectral data of a plurality of samples with known taste values, that is, data obtained by arithmetically processing absorbance with respect to wavelength by first derivative or second derivative is added to obtain an average value. It is advisable to determine the coincidence index between the sample and the spectrum data of the sample of which taste value is unknown by adding and averaging the taste values of the plurality of samples as the reference spectrum data of the sample. By this method, a more suitable taste value can be estimated.
本方式によれば、近赤外波長帯域を連続してまたはある
所定部分帯を連続して記録してある前記演算処理をされ
た標本試料の代表参照スペクトル群と、未知試料のスペ
クトルを比較し最も近似する上位数個の参照試料名（番
号）と各々の合致具合を後述する計算方法等を使って求
めた数値（合致指標、M.I.）には前述の通り結果的に定
量および定性情報が含まれていることになる。この方式
で完全に合致した場合はにM.I.＝ 1.0となる。この場合
は参照スペクトル群にまったく同じものがあったことと
なる。その合致した試料にあらかじめ求められてある食
味値から未知試料の食味値を推定できる。もし、参照ス
ペクトル群中から選定された試料と未知試料との合致指
標M.I.値が低い場合には、代表参照スペクトル群中に含
まれていない新たなスペクトル形状をもった試料である
ということが分かるので、この試料の食味値を官能検査
して代表参照スペクトル群中に新たに加えれば標本試料
の数がそれだけ増加するので、これを繰り返すことによ
り、食味値の推定精度の向上をはかることができる。According to this method, the spectrum of the unknown sample is compared with the representative reference spectrum group of the sample sample which has been recorded in the near infrared wavelength band continuously or in a certain predetermined partial band continuously and which has been subjected to the arithmetic processing. Numerical values (matching index, MI) obtained by using the calculation method described later and the likeness of each of the reference sample names (numbers) of the most similar top-ranked samples and the matching condition of each of them, as a result, include quantitative and qualitative information. It is supposed to be. MI = 1.0 is obtained when a perfect match is obtained by this method. In this case, the reference spectrum group had exactly the same thing. The taste value of the unknown sample can be estimated from the taste value obtained in advance for the matched sample. If the agreement index MI value between the sample selected from the reference spectrum group and the unknown sample is low, it can be seen that the sample has a new spectrum shape not included in the representative reference spectrum group. Therefore, if the taste value of this sample is subjected to a sensory test and newly added to the representative reference spectrum group, the number of sample samples will increase by that much.By repeating this, it is possible to improve the estimation accuracy of the taste value. .
本発明に従えば、特定の食味判定部署における食味判定
精度を前記新規試料を代表参照スペクトル群中に追加す
るいわゆる学習効果で食味判定の精度をより確実にする
ことができる。According to the present invention, the accuracy of taste determination in a specific taste determination section can be further ensured by the so-called learning effect of adding the new sample to the representative reference spectrum group.
一例として６００〜２５００nmの間の赤外線を２nmずつ
波長を変えて試料を照射し、その反射光又は透過光の強
度を測定すると、１個の試料について９５０個の吸光度
のデータが得られる。通常はこの吸光度の対数を波長に
ついて１次微分又は２次微分した値をベクトルの各成分
とすると、１個の試料についてのスペクトルデータとし
て９５０次元のベクトルが得られる。As an example, when the sample is irradiated with infrared rays in the wavelength range of 600 to 2500 nm by changing the wavelength by 2 nm and the intensity of the reflected light or the transmitted light is measured, data of 950 absorbances can be obtained for one sample. Usually, if the value obtained by first-order or second-order differentiating the logarithm of this absorbance with respect to wavelength is used as each component of the vector, a 950-dimensional vector is obtained as the spectrum data for one sample.
ここで、 標本試料のスペクトルＡの波長ｉについてのベクトルの
成分をＸｉ 被測定試料のスペクトルＢの波長ｉについてのベクトル
の成分をＹｉ とし、２つのベクトルの作る角度のcosineを求める。Here, the components of the vector for the wavelength i of the spectral B components of the vector of the X i measured sample for the wavelength i of the spectrum A sample specimen and Y i, determining the cosine of the angle formed by the two vectors.
とすると、 合致指標 この合致指標は、標本試料の吸光度特性の代表参照スペ
クトルと未知の被測定試料のスペクトルの角度の一致
度、すなわち両スペクトルの作る角度のｃｏｓｉｎｅを
求めて１に近いかを検証するものである。 Then, the match index This matching index is for verifying whether or not the matching angle between the representative reference spectrum of the absorbance characteristic of the sample sample and the spectrum of the unknown sample to be measured, that is, the cosine of the angle formed by both spectra, is close to 1.
方向の一致を求めるので、証明の照度や検出ゲインの変
動の影響がなくなる。上記ではＸｉＹｉを１個の試料に
ついての値としたが、より良好でノイズの少ない測定を
行うには、複数の同一群に属する標本試料について吸光
度の対数の１次微分又は２次微分を求め、これを波長ご
とに平均した値を参照スペクトルＡのデータＸｉとし、
Ｘｉを成分とする参照スペクトルのベクトルを標本のベ
クトルとし、一方同一群に属する標本試料の食味値も平
均値を求めてこれを参照食味データとする。Since the matching of the directions is obtained, the influence of the fluctuation of the proof illuminance and the detection gain is eliminated. In the above, X i Y i is a value for one sample, but in order to perform better measurement with less noise, the first or second derivative of the logarithm of the absorbance for a plurality of sample samples belonging to the same group. Is obtained, and a value obtained by averaging this is set as data X i of the reference spectrum A,
A vector of a reference spectrum having X i as a component is used as a sample vector, and mean values of the taste values of sample samples belonging to the same group are also obtained and used as reference taste data.
このようにして求めた 合致指標M.I.が1.0 に近いもの
の標本の食味値をもって未知の被測定試料の食味値とす
る。たとえばM.I.が0.9999以上となるとき、その被測定
試料の食味値は参照標本試料と食味値は同じであると判
断できる。The taste value of the sample whose agreement index MI obtained in this way is close to 1.0 is taken as the taste value of the unknown sample to be measured. For example, when the MI is 0.9999 or more, it can be determined that the taste value of the measured sample is the same as that of the reference sample sample.
前記の例では、ベクトル成分ＸｉＹｉとして吸光度の対
数を波長について１次微分又は２次微分した値を用いた
が、これに限定されるものではない。実施例としては、
３次微分・４次微分・フーリエ変換・Ｓavitsky Ｇola
y, Ｋubelka Ｍunk・部分最小２乗回帰などの演算を実
施した結果の値を用いる場合もある。ただし演算の複雑
さと効果の点からは２次微分が最も実用的である。In the above example, the vector component X i Y i is a value obtained by first-order or second-order differentiation of the logarithm of the absorbance with respect to wavelength, but the present invention is not limited to this. As an example,
Third derivative, fourth derivative, Fourier transform, Savitsky Gola
In some cases, a value obtained as a result of performing an operation such as y, Kubelka Munk or partial least squares regression may be used. However, the second derivative is the most practical in terms of complexity of calculation and effect.
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.
上記の例の場合には未知試料１の食味値は0.1666に近い
ものであることが推定できる。 In the case of the above example, it can be estimated that the taste value of the unknown sample 1 is close to 0.1666.
本発明によれば、近赤外線の特定波長における吸光度か
ら推測する食味判定方式は異なり、試料のスペクトルの
強度および形状を比較測定するので高精度の食味判定が
できる。According to the present invention, the taste determination method inferred from the absorbance at a specific wavelength of near infrared rays is different, and since the intensity and shape of the spectrum of the sample are comparatively measured, the taste determination can be performed with high accuracy.
Claims (2)
し、各波長に対する反射または透過光の吸光度を測定す
ることにより食味を評価するとき、 予め、官能試験により各々の食味値が既知である多数の
標本の各々について前記同様の各波長に対する吸光度を
測定しておき、波長に関して１次微分または２次微分等
の演算処理をしたスペクトルデータおよび食味値のデー
タを記録しておき、 前記未知の被測定試料に対しても前記標本のスペクトル
と同様の演算処理を行い、標本試料のスペクトルＡの波
長ｉについてのベクトルの成分をＸｉ 被測定試料のスペクトルＢの波長ｉについてのベクトル
の成分をＹｉ とし、 とするとき、 合致指標 が最も１に近い標本を選択し、その選択された標本の食
味値により、前記未知の被測定試料の食味値を推定する
近赤外線による食味値推定方法。1. When the taste is evaluated by irradiating a sample to be measured whose taste value is unknown with near infrared rays and measuring the absorbance of reflected or transmitted light at each wavelength, each taste value is known in advance by a sensory test. The absorbance for each wavelength similar to the above is measured for each of a large number of samples, and spectral data and tasting value data obtained by performing arithmetic processing such as first derivative or second derivative with respect to the wavelength are recorded. also performs the same processing and the spectrum of the sample with respect to an unknown sample to be measured, the components of the vector for the wavelength i of the spectrum a sample specimen of a vector for the wavelength i of the spectrum B of X i measured sample Let the component be Y i , And the match index Is selected, and the taste value estimation method by near-infrared ray is used to estimate the taste value of the unknown sample to be measured based on the taste value of the selected sample.
クトルデータを処理するとき、 食味値の類似した複数個の試料についての波長に対する
スペクトルデータをそれぞれ波長ごとに平均して標本の
参照スペクトルデータとし、一方これに対応する食味値
の値も平均して参照食味データとし、これと未知試料の
スペクトルデータとの合致指標を求めることにより、食
味値を推定する近赤外線による食味値推定方法。2. When processing the spectrum data of the sample according to claim 1, the spectrum data for the wavelengths of a plurality of samples having similar taste values are averaged for each wavelength, and the reference spectrum data of the sample. On the other hand, the taste value estimation method by near-infrared ray for estimating the taste value by averaging the corresponding taste value as reference taste data and obtaining a matching index between this and the spectrum data of the unknown sample.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| JP11076589A JPH0640069B2 (en) | 1989-04-30 | 1989-04-30 | Estimation method of taste value by near infrared |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| JP11076589A JPH0640069B2 (en) | 1989-04-30 | 1989-04-30 | Estimation method of taste value by near infrared |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| JPH02290537A JPH02290537A (en) | 1990-11-30 |
| JPH0640069B2 true JPH0640069B2 (en) | 1994-05-25 |
Family
ID=14544008
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| JP11076589A Expired - Lifetime JPH0640069B2 (en) | 1989-04-30 | 1989-04-30 | Estimation method of taste value by near infrared |
Country Status (1)
| Country | Link |
|---|---|
| JP (1) | JPH0640069B2 (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 (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH09184761A (en) * | 1995-12-29 | 1997-07-15 | Horiba Ltd | Method for calculating coincidence of two spectra |
| WO2009038206A1 (en) * | 2007-09-21 | 2009-03-26 | Suntory Holdings Limited | Visible/near-infrared spectrum analyzing method and grape fermenting method |
| CN113484272B (en) * | 2021-07-08 | 2022-08-19 | 云南中烟工业有限责任公司 | Method for rapidly predicting oil content in fresh tobacco leaves by adopting similarity analysis technology based on near infrared spectrum |
| JP7677117B2 (en) * | 2021-10-28 | 2025-05-15 | トヨタ自動車株式会社 | Unknown material identification system using material spectrum data |
| CN114220501A (en) * | 2021-11-24 | 2022-03-22 | 江苏大学 | Quick quantitative evaluation method for fried rice taste characteristics |
| CN118169068B (en) * | 2024-05-15 | 2024-08-16 | 奥谱天成（厦门）光电有限公司 | Brown rice taste value detection method, device, medium and equipment |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPS63241338A (en) * | 1986-09-05 | 1988-10-06 | Satake Eng Co Ltd | Rice taste measuring apparatus |
| JPH07104280B2 (en) * | 1987-07-07 | 1995-11-13 | 株式会社佐竹製作所 | Evaluation method of rice taste |
| JPS6449890A (en) * | 1987-08-20 | 1989-02-27 | Toshiba Corp | Heat transfer pipe |
-
1989
- 1989-04-30 JP JP11076589A patent/JPH0640069B2/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 |
|---|---|
| JPH02290537A (en) | 1990-11-30 |
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