JPH0727703A - Quantitative analysis of multiple component substance - Google Patents
Quantitative analysis of multiple component substanceInfo
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- JPH0727703A JPH0727703A JP16885693A JP16885693A JPH0727703A JP H0727703 A JPH0727703 A JP H0727703A JP 16885693 A JP16885693 A JP 16885693A JP 16885693 A JP16885693 A JP 16885693A JP H0727703 A JPH0727703 A JP H0727703A
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Abstract
Description
【０００１】[0001]
【産業上の利用分野】本発明は、スペクトルを用いた複
数成分の同時定量分析法に関するもので、特に、赤外分
光法を用いた血液生化学検査装置に用いられる定量方法
に関する。BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a simultaneous quantitative analysis method for a plurality of components using a spectrum, and more particularly to a quantitative method used in a blood biochemical test apparatus using infrared spectroscopy.
【０００２】[0002]
【従来の技術】赤外吸収スペクトルを用いた血液生化学
成分の同時定量分析に関しては、アナリティカル ケミ
ストリ（１９８９年）第２０１６ページから第２０２３
ページに述べられている。ここでは校正に使用する参照
データとして患者から採取した血漿を用いている。ここ
で述べられている校正手順は、まず血漿中の各目的生化
学成分の濃度を酵素法などの基準法で測定し、次にその
血漿の赤外吸収スペクトルを測定する。そして基準法で
測定した各成分濃度の数列と赤外吸収スペクトルの各デ
ータポイントの吸光度の数列を部分最小二乗法（ＰＬ
Ｓ）によって回帰し検量式を計算する。2. Description of the Related Art Analytical Chemistry (1989), pages 2016 to 2023, for simultaneous quantitative analysis of blood biochemical components using infrared absorption spectra.
Stated on the page. Here, plasma collected from a patient is used as reference data used for calibration. In the calibration procedure described here, first, the concentration of each target biochemical component in plasma is measured by a standard method such as an enzymatic method, and then the infrared absorption spectrum of the plasma is measured. Then, the sequence of the concentration of each component measured by the standard method and the sequence of the absorbance of each data point of the infrared absorption spectrum are calculated by the partial least squares method (PL
S) to regress and calculate the calibration formula.
【０００３】また多変量による定量分析法は、アナリテ
ィカル ケミストリ（１９８９年）第２００９ページか
ら第２０１５ページに述べられている。ここでは、単成
分のみの情報を持つスペクトルの集合を参照データとし
て使用するＫマトリクス法と対象成分全ての情報を持つ
スペクトルの集合を参照データとして使用するＱ及びＰ
マトリクス法について概述している。A multivariate quantitative analysis method is described in Analytical Chemistry (1989), pages 2009 to 2015. Here, the K-matrix method that uses a set of spectra having information of only a single component as reference data and Q and P that uses a set of spectra that has information of all target components as reference data
The matrix method is outlined.
【０００４】[0004]
【発明が解決しようとする課題】従来スペクトルを用い
た多成分定量分析法は、対象成分の一つもしくは全てを
含む物質のスペクトルを参照データとして校正を行って
いた。上述したＫマトリクス法では、分析対象物中の各
成分のそれぞれのスペクトルを羅列して数列化したもの
を参照データとして使用する。このため各成分の特徴ピ
ーク間に重なりがある場合、その重なりを予測できない
ため定量分析の正確度が低下した。また対象成分全てを
含む混合物スペクトルを参照データとして使用するＱ及
びＰマトリクスは、調整した混合物や成分濃度が既知な
分析対象物をスペクトルとして測定するのでＫマトリク
スよりはピークの重なりがある成分に対して有効である
が、対象成分が多い場合、その調整や測定が非常に煩雑
であった。また既存の濃度組成を持つ分析対象物をその
まま参照データとして用いると、成分濃度間に相関が生
じ分析の正確度が低下するという問題があった。In the conventional multi-component quantitative analysis method using spectra, calibration was performed by using the spectrum of a substance containing one or all of the target components as reference data. In the above-mentioned K matrix method, a spectrum obtained by listing the spectra of each of the components in the analysis target is used as reference data. Therefore, when there is an overlap between the characteristic peaks of each component, the accuracy cannot be predicted because the overlap cannot be predicted. In addition, the Q and P matrices, which use a mixture spectrum containing all the target components as reference data, measure the adjusted mixture or the analyte having a known concentration of the component as a spectrum, so that the components having peak overlaps more than the K matrix do. However, when there are many target components, the adjustment and measurement were very complicated. In addition, when an analysis target having an existing concentration composition is used as it is as reference data, there is a problem that a correlation occurs between component concentrations and the accuracy of analysis is reduced.
【０００５】[0005]
【課題を解決するための手段】上記課題を解決するため
に、本発明においては、校正段階で複数成分からなる混
合物の応答と前記混合物中の各成分の濃度から構成され
る参照データを用い、前記参照データの成分濃度と応答
の回帰によって検量線を得る定量分析法において、演算
処理によって得た単成分の推定応答の組合せ演算によっ
て混合物の応答を模倣し参照データとする。In order to solve the above problems, in the present invention, reference data composed of the response of a mixture composed of a plurality of components and the concentration of each component in the mixture are used in the calibration step, In the quantitative analysis method for obtaining a calibration curve by regression of the component concentration of the reference data and the response, the response of the mixture is imitated as the reference data by the combined calculation of the estimated response of the single component obtained by the calculation process.
【０００６】[0006]
【作用】参照データを単成分スペクトルの演算処理によ
って構成するために、参照データ用の混合物を調整する
必要が無く、スペクトル測定の回数が減少する。また成
分ピーク間に重なりがある場合でも、それぞれの成分ス
ペクトルを演算し一つの参照データとすることで、ピー
クの重なりの程度を予測でき、分析の正確度を低下させ
ることが無い。また計算機によって参照データを構成す
るので、校正に用いる一連の参照データの成分濃度組成
を望むように構成できるのため、成分濃度相関を無くす
ことができ、分析の正確性が向上する。Since the reference data is constructed by the arithmetic processing of the single component spectrum, it is not necessary to adjust the mixture for the reference data, and the number of spectrum measurements is reduced. Further, even if there is overlap between component peaks, the degree of peak overlap can be predicted by calculating each component spectrum and using it as one reference data, and the accuracy of analysis will not be reduced. Further, since the reference data is configured by a computer, the component concentration composition of the series of reference data used for calibration can be configured as desired, so that the component concentration correlation can be eliminated and the accuracy of analysis is improved.
【０００７】[0007]
【実施例】図１は、本発明の第一の実施例の検出装置で
あるフーリエ変換赤外分光光度計（ＦＴ−ＩＲ）の装置
構成である。光源１から出射した赤外光２は、干渉計３
を通過し位相差を生じ、試料室４内に設置された減衰全
反射プリズムセル５に入射する。更に光２はセル５に導
入された試料６によって特定波長の光を吸収された状態
で検出器７に入射する。検出器７によって赤外光は電気
信号に変換され、増幅器８，ＡＤ変換器９を経てデジタ
ル信号としてコンピュータ１０に入力される。コンピュ
ータ１０によって信号はフーリエ変換され、スペクトル
として出力される。DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS FIG. 1 shows a device configuration of a Fourier transform infrared spectrophotometer (FT-IR) which is a detecting device according to a first embodiment of the present invention. The infrared light 2 emitted from the light source 1 is interferometer 3
To generate a phase difference and enter the attenuating total reflection prism cell 5 installed in the sample chamber 4. Further, the light 2 is incident on the detector 7 in a state where the light having a specific wavelength is absorbed by the sample 6 introduced into the cell 5. The infrared light is converted into an electric signal by the detector 7, and is input to the computer 10 as a digital signal through the amplifier 8 and the AD converter 9. The signal is Fourier transformed by the computer 10 and output as a spectrum.
【０００８】図２は、本装置で測定したグルコース水溶
液の１８００〜９５０cm-1の波数における赤外吸収スペ
クトルである。赤外吸収は、成分分子を構成する官能基
の振動回転によって起こるので、そのスペクトルは分子
固有のものである。ＦＴ−ＩＲによって測定されたスペ
クトルは、分散型分光計で測定されたスペクトルとは異
なり、分解能に応じた波数に対する吸光度をそれぞれ線
で結んだことによって構成されている。FIG. 2 is an infrared absorption spectrum of a glucose aqueous solution measured by this apparatus at a wave number of 1800 to 950 cm -1 . Infrared absorption occurs due to vibrational rotation of functional groups constituting the component molecules, and therefore its spectrum is peculiar to the molecule. The spectrum measured by FT-IR is different from the spectrum measured by the dispersive spectrometer, and is configured by connecting the absorbances with respect to the wave number corresponding to the resolution with lines.
【０００９】図２のスペクトルは、分解能１６cm-1で測
定したので、１８００〜９５０cm-1の波数範囲では５３
のデータポイントを持つことになる。つまり、このスペ
クトルは、吸光度で表される５３個の要素からなる数列
として表示できる。[0009] spectrum in Figure 2, since the measurement resolution 16cm -1, 53 in the wavenumber range of 1800～950Cm -1
Will have data points. That is, this spectrum can be displayed as a sequence of 53 elements represented by the absorbance.
【００１０】図３は、グルコース，尿素，アルブミン，
クレアチニンの混合水溶液の赤外吸収スペクトルであ
る。混合水溶液のスペクトルは、このように各成分の特
徴ピークが、複雑に重なっている。FIG. 3 shows glucose, urea, albumin,
It is an infrared absorption spectrum of a mixed aqueous solution of creatinine. In the spectrum of the mixed aqueous solution, the characteristic peaks of the respective components thus overlap in a complicated manner.
【００１１】図４は第一の実施例のフローチャートであ
る。ここでは便宜上、混合水溶液中の成分を任意にｉと
して表示する。定量分析法は、検量線を求める校正段階
１１と、校正段階で求めた検量線をもとに濃度予測を行
う予測段階１２の二段階に大きく分けることができる。FIG. 4 is a flow chart of the first embodiment. Here, for convenience, the components in the mixed aqueous solution are arbitrarily represented as i. The quantitative analysis method can be roughly divided into two steps: a calibration step 11 for obtaining a calibration curve and a prediction step 12 for predicting the concentration based on the calibration curve obtained in the calibration step.
【００１２】校正段階はおもに五つの処理ステップから
構成されている。このうち成分回帰式の算出を行うステ
ップ１３と成分スペクトルの算出を行うステップ１４
は、各対象成分に対して一回、すなわち一校正に対して
ｉ回の処理が行われる。以下各処理ステップに関して詳
述する。The calibration stage mainly consists of five processing steps. Of these, step 13 for calculating the component regression equation and step 14 for calculating the component spectrum
Is processed once for each target component, i.e., i times for one calibration. Each processing step will be described in detail below.
【００１３】まずステップ１３では、ｉ成分のみを溶解
した各々濃度が異なるｋ個の水溶液を参照物質１５と
し、各参照物質の赤外スペクトルの測定１６を行う。測
定した各スペクトルは、その分解能に応じた各波数に対
してそれぞれある吸光度を持つ。従って対象成分が持つ
特徴的なピークが存在する波長帯域のスペクトルを波数
ｊに対する吸光度の数列１７として表現することができ
る。この数列１７と参照物質の成分濃度の数列１８を回
帰分析し成分回帰式１９を算出する。First, in step 13, k pieces of aqueous solution having different concentrations, in which only the i component is dissolved, are used as the reference substance 15, and the infrared spectrum 16 of each reference substance is measured 16. Each measured spectrum has a certain absorbance for each wave number according to its resolution. Therefore, the spectrum of the wavelength band in which the characteristic peak of the target component is present can be expressed as a sequence 17 of the absorbance with respect to the wave number j. The component regression equation 19 is calculated by performing a regression analysis on the sequence 17 and the component concentration sequence 18 of the reference substance.
【００１４】一方、ステップ２０では、校正に直接使用
する参照データの成分濃度組成を算出する。この成分濃
度組成は、分析対象物ｌ中のｉ個の対象成分濃度をそれ
ぞれ人為的に算出したものである。このときの算出条件
は、各成分間の濃度に相関が無いことである。さらにこ
の濃度組成の中から、一個の成分ｉの濃度を抽出し（ス
テップ２１）、ステップ１４において成分回帰式１９に
代入して一個の成分ｉの吸光度数列２２を算出する。On the other hand, in step 20, the component concentration composition of the reference data used directly for calibration is calculated. This component concentration composition is obtained by artificially calculating the concentration of each of i target component components in the analysis target l. The calculation condition at this time is that there is no correlation between the concentrations of the components. Further, the concentration of one component i is extracted from this concentration composition (step 21) and is substituted in the component regression equation 19 in step 14 to calculate the absorbance sequence 22 of one component i.
【００１５】次にステップ２３において、参照データｌ
の濃度組成に応じたｉ個の成分吸光度数列を足し合わ
せ、参照データｌの吸光度数列２４とする。Next, in step 23, the reference data l
The i-number component absorbance sequences corresponding to the concentration composition of are added up to form the absorbance sequence 24 of the reference data l.
【００１６】ステップ２５では、ステップ２３で算出し
た一個の参照データを数列化し（ステップ２６）、この
数列２６とステップ２０で算出した成分濃度数列２７を
回帰することで検量用回帰式２８を得る。In step 25, one piece of reference data calculated in step 23 is converted into a sequence (step 26), and the sequence 26 and the component concentration sequence 27 calculated in step 20 are regressed to obtain a calibration regression equation 28.
【００１７】次に予測段階１２では、サンプルの赤外吸
収スペクトルを測定し（ステップ２９）、これを前述の
ように数列化してサンプル吸光度数列３０とし、回帰式
２８に代入することによって予測濃度３１を算出する。Next, in the predicting step 12, the infrared absorption spectrum of the sample is measured (step 29), and the infrared absorption spectrum of the sample is sequenced as described above into the sample absorbance sequence 30, which is substituted into the regression equation 28 to obtain the predicted concentration 31. To calculate.
【００１８】図５は本発明の第二の実施例を用いた血液
生化学成分のＦＴ−ＩＲによる定量分析法のフローチャ
ートである。本実施例は、既存の血液の赤外スペクトル
をベースとして、それに実施例１で算出したものと同様
にして求めた成分スペクトルを加減したものである。FIG. 5 is a flow chart of the FT-IR quantitative analysis method for blood biochemical components using the second embodiment of the present invention. The present example is based on the existing infrared spectrum of blood, and the component spectrum obtained in the same manner as that calculated in Example 1 is added or subtracted.
【００１９】まずステップ３２では、ｎ個の採血済み血
液３３の赤外吸収スペクトルを測定し（ステップ３
４）、吸光度数列３５とする。さらに血液３３は酵素な
どの基準法によって血液中の対象成分濃度を測定する
し、基準法による成分濃度数列３６とする。First, in step 32, the infrared absorption spectra of the n blood-collected bloods 33 are measured (step 3
4) The absorbance sequence is set to 35. Further, the concentration of the target component in the blood 33 is measured by a standard method such as an enzyme, and the concentration series 36 of the component by the standard method is used.
【００２０】ステップ３７では、ステップ３２で求めた
ｎ個のベーススペクトルのうち一個を無作為抽出し（ス
テップ３８）、その濃度数列Ｃｂと参照データｌの濃度
数列Ｃｒ３９の差を取り（ステップ４０）、これを成分
ｉの回帰式１９に算入して成分スペクトル４１を算出す
る。このｉ個の成分スペクトルの和と３８で抽出したベ
ーススペクトルの吸光度数列Ａｂを加えて（ステップ４
２）、参照データｌの吸光度数列４３とする。ステップ
３７で求めた一個の参照データの吸光度数列と成分濃度
数列２７を回帰し、検量用回帰式２８を算出する。この
回帰式２８をもとに実施例１と同様に予測段階１２にお
いて検体の各成分濃度３１を算出する。In step 37, one of the n base spectra obtained in step 32 is randomly extracted (step 38), and the difference between the density sequence Cb and the density sequence Cr39 of the reference data 1 is taken (step 40). , And this is included in the regression equation 19 of the component i to calculate the component spectrum 41. The sum of the i component spectra and the absorbance sequence Ab of the base spectrum extracted in 38 are added (step 4
2), the absorbance sequence 43 of the reference data 1 is used. The absorbance number sequence and the component concentration number sequence 27 of one piece of reference data obtained in step 37 are regressed to calculate a calibration regression equation 28. Based on this regression equation 28, the concentration 31 of each component of the sample is calculated in the prediction step 12 as in the first embodiment.
【００２１】図６及び図７は、実施例２の効果を示した
ものである。6 and 7 show the effect of the second embodiment.
【００２２】図６は、同一検体にグルコースを順次添加
したときの尿素の予測濃度の誤差を表したものである。
従来方式では、参照データにおいてグルコースと尿素の
濃度の間に相関が生じており、グルコースの添加によっ
て尿素の予測濃度の正確性が影響を受けるというクロス
トークが生じていた。本実施例では、人為的に相関の無
い参照データを算出することによってクロストークが無
い、正確な濃度予測が行えるようになった。FIG. 6 shows the error in the predicted concentration of urea when glucose was sequentially added to the same sample.
In the conventional method, there is a correlation between the concentrations of glucose and urea in the reference data, and crosstalk occurs in which the accuracy of the predicted concentration of urea is affected by the addition of glucose. In this embodiment, it is possible to perform accurate concentration prediction without crosstalk by artificially calculating reference data having no correlation.
【００２３】図７は、実施例２におけるグルコースの測
定結果と基準法である酵素法との相関を示している。酵
素法との相関はγ＝０.９９ であり、濃度予測が正しく
行えていることを示している。FIG. 7 shows the correlation between the measurement result of glucose in Example 2 and the enzyme method which is the reference method. The correlation with the enzymatic method was γ = 0.99, which shows that the concentration can be predicted correctly.
【００２４】[0024]
【発明の効果】本発明に依れば、煩雑な参照物質の調整
が無く、しかもクロストークの無い正確度の高い多成分
定量分析を行うことができる。According to the present invention, it is possible to perform highly accurate multi-component quantitative analysis without complicated adjustment of the reference substance and without crosstalk.
【図１】本発明の実施例１の検出装置のブロック図。FIG. 1 is a block diagram of a detection device according to a first embodiment of the present invention.
【図２】本発明の実施例１の検出装置で測定したグルコ
ース水溶液の赤外吸収スペクトル特性図。FIG. 2 is an infrared absorption spectrum characteristic diagram of an aqueous glucose solution measured by the detection device of Example 1 of the present invention.
【図３】本発明の実施例１の検出装置で測定したグルコ
ース，クレアチニン，アルブミン，尿素の４成分混合水
溶液の赤外吸収スペクトル特性図。FIG. 3 is an infrared absorption spectrum characteristic diagram of a 4-component mixed aqueous solution of glucose, creatinine, albumin, and urea measured by the detection device of Example 1 of the present invention.
【図４】本発明の実施例１の定量方法のフローチャー
ト。FIG. 4 is a flowchart of the quantification method according to the first embodiment of the present invention.
【図５】本発明の実施例２の定量方法のフローチャー
ト。FIG. 5 is a flowchart of a quantification method according to Example 2 of the present invention.
【図６】本発明の実施例２の対象成分であるグルコース
と尿素のクロストークに対する効果を表す説明図。FIG. 6 is an explanatory diagram showing an effect on crosstalk between glucose and urea, which are target components of Example 2 of the present invention.
【図７】本発明の実施例２のグルコースの測定結果と基
準法との相関を表した説明図。FIG. 7 is an explanatory diagram showing the correlation between the measurement result of glucose and the reference method in Example 2 of the present invention.
１…赤外光源、２…赤外光、３…干渉計、４…試料室、
５…減衰全反射プリズムセル、６…試料、７…検出器、
８…増幅器、９…ＡＤ変換器、１０…コンピュータ。1 ... Infrared light source, 2 ... Infrared light, 3 ... Interferometer, 4 ... Sample chamber,
5 ... Attenuated total reflection prism cell, 6 ... Sample, 7 ... Detector,
8 ... Amplifier, 9 ... AD converter, 10 ... Computer.
Claims (6)
する検出装置を用い、前記応答によって対象成分の濃度
を推定する測定法で、その校正段階で複数成分からなる
混合物の応答と前記混合物中の各成分の濃度から構成さ
れる参照データを用い、前記参照データの成分濃度と応
答の回帰によって検量線を得る定量分析法において、演
算処理によって得た単成分の推定応答の組合せ演算によ
って混合物の応答を模倣し参照データとすることを特徴
とする多成分物質定量分析法。1. A measuring method for estimating the concentration of a target component from the response by using a detection device which responds to a change in the concentration of the target component in an analyte, and a response of a mixture composed of a plurality of components at the calibration stage and the response. Using the reference data composed of the concentration of each component in the mixture, in the quantitative analysis method to obtain the calibration curve by regression of the component concentration of the reference data and the response, by the combined calculation of the estimated response of the single component obtained by the calculation process. A quantitative analysis method for multi-component substances, which is characterized by imitating the response of a mixture as reference data.
である多成分物質定量分析法。2. A multi-component substance quantitative analysis method, wherein the detection device according to claim 1 is a spectrometer.
分の応答と該成分の濃度の回帰式による、希望濃度に対
する成分応答の推定を対象成分全てに対して行い、各対
象成分の推定応答を足し合わせることによって得られた
ものである多成分物質定量分析法。3. The reference data according to claim 1, wherein the response of a single component and the concentration of the component are regression equations are used to estimate the component response to the desired concentration for all the target components. Quantitative analysis method for multi-component substances, which is obtained by adding up estimated responses.
対象物の応答に請求項３に記載した成分応答を加減する
ことで得られた多成分物質定量分析法。4. The multi-component substance quantitative analysis method according to claim 1, wherein the reference data is obtained by adjusting the response of the analyte to the component response described in claim 3.
法は、重回帰分析，主成分分析，部分最小二乗法である
多成分物質定量分析法。5. The multi-component substance quantitative analysis method according to claim 1, wherein the regression method of the reference data is multiple regression analysis, principal component analysis, or partial least squares method.
法を利用した血液生化学成分分析装置。6. A blood biochemical component analyzer utilizing the method for quantitatively analyzing a multi-component substance according to claim 1.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| JP16885693A JPH0727703A (en) | 1993-07-08 | 1993-07-08 | Quantitative analysis of multiple component substance |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| JP16885693A JPH0727703A (en) | 1993-07-08 | 1993-07-08 | Quantitative analysis of multiple component substance |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| JPH0727703A true JPH0727703A (en) | 1995-01-31 |
Family
ID=15875831
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| JP16885693A Pending JPH0727703A (en) | 1993-07-08 | 1993-07-08 | Quantitative analysis of multiple component substance |
Country Status (1)
| Country | Link |
|---|---|
| JP (1) | JPH0727703A (en) |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2000506594A (en) * | 1995-09-12 | 2000-05-30 | パルプ アンド ペーパー リサーチ インスチチュート オブ カナダ | Measurement of sodium sulfide and sulfidity in green liquor and smelt solution |
| JP2004156912A (en) * | 2002-11-01 | 2004-06-03 | Jasco Corp | Method and apparatus for measuring bod and method and apparatus for treating waste water |
| US8337125B2 (en) | 2006-06-28 | 2012-12-25 | Teeness Asa | Container adapted to be inserted in a tool holder, a tool holder and a system |
| CN109520941A (en) * | 2018-11-20 | 2019-03-26 | 天津大学 | The receptance function bearing calibration of online spectrum measurement instruments |
| EP1463937B1 (en) * | 2001-11-30 | 2020-02-26 | ExxonMobil Research and Engineering Company | Method for analyzing an unknown material as a blend of known materials calculated so as to match certain analytical data and predicting properties of the unknown based on the calculated blend |
-
1993
- 1993-07-08 JP JP16885693A patent/JPH0727703A/en active Pending
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2000506594A (en) * | 1995-09-12 | 2000-05-30 | パルプ アンド ペーパー リサーチ インスチチュート オブ カナダ | Measurement of sodium sulfide and sulfidity in green liquor and smelt solution |
| EP1463937B1 (en) * | 2001-11-30 | 2020-02-26 | ExxonMobil Research and Engineering Company | Method for analyzing an unknown material as a blend of known materials calculated so as to match certain analytical data and predicting properties of the unknown based on the calculated blend |
| JP2004156912A (en) * | 2002-11-01 | 2004-06-03 | Jasco Corp | Method and apparatus for measuring bod and method and apparatus for treating waste water |
| US8337125B2 (en) | 2006-06-28 | 2012-12-25 | Teeness Asa | Container adapted to be inserted in a tool holder, a tool holder and a system |
| CN109520941A (en) * | 2018-11-20 | 2019-03-26 | 天津大学 | The receptance function bearing calibration of online spectrum measurement instruments |
| CN109520941B (en) * | 2018-11-20 | 2021-02-09 | 天津大学 | Response function correction method of on-line spectral measuring instrument |
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