JP7057026B1 - How to quantify sesame lignans - Google Patents
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- JP7057026B1 JP7057026B1 JP2021199134A JP2021199134A JP7057026B1 JP 7057026 B1 JP7057026 B1 JP 7057026B1 JP 2021199134 A JP2021199134 A JP 2021199134A JP 2021199134 A JP2021199134 A JP 2021199134A JP 7057026 B1 JP7057026 B1 JP 7057026B1
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- 241000207961 Sesamum Species 0.000 title claims abstract description 82
- 235000003434 Sesamum indicum Nutrition 0.000 title claims abstract description 82
- 229930013686 lignan Natural products 0.000 title claims abstract description 81
- 150000005692 lignans Chemical class 0.000 title claims abstract description 81
- 235000009408 lignans Nutrition 0.000 title claims abstract description 81
- 235000015112 vegetable and seed oil Nutrition 0.000 claims abstract description 82
- 239000008158 vegetable oil Substances 0.000 claims abstract description 82
- 230000003595 spectral effect Effects 0.000 claims abstract description 53
- 238000001228 spectrum Methods 0.000 claims abstract description 43
- 238000005259 measurement Methods 0.000 claims abstract description 42
- 239000008159 sesame oil Substances 0.000 claims abstract description 31
- 235000011803 sesame oil Nutrition 0.000 claims abstract description 31
- 238000004128 high performance liquid chromatography Methods 0.000 claims abstract description 29
- PEYUIKBAABKQKQ-UHFFFAOYSA-N epiasarinin Natural products C1=C2OCOC2=CC(C2OCC3C2COC3C2=CC=C3OCOC3=C2)=C1 PEYUIKBAABKQKQ-UHFFFAOYSA-N 0.000 claims abstract description 28
- 238000000034 method Methods 0.000 claims abstract description 23
- 238000010606 normalization Methods 0.000 claims abstract description 15
- ZZMNWJVJUKMZJY-AFHBHXEDSA-N Sesamolin Chemical compound C1=C2OCOC2=CC([C@H]2OC[C@H]3[C@@H]2CO[C@@H]3OC2=CC=C3OCOC3=C2)=C1 ZZMNWJVJUKMZJY-AFHBHXEDSA-N 0.000 claims abstract description 14
- ZZMNWJVJUKMZJY-UHFFFAOYSA-N Sesamolin Natural products C1=C2OCOC2=CC(C2OCC3C2COC3OC2=CC=C3OCOC3=C2)=C1 ZZMNWJVJUKMZJY-UHFFFAOYSA-N 0.000 claims abstract description 14
- 238000007781 pre-processing Methods 0.000 claims abstract description 8
- PEYUIKBAABKQKQ-AFHBHXEDSA-N (+)-sesamin Chemical compound C1=C2OCOC2=CC([C@H]2OC[C@H]3[C@@H]2CO[C@@H]3C2=CC=C3OCOC3=C2)=C1 PEYUIKBAABKQKQ-AFHBHXEDSA-N 0.000 claims description 22
- VRMHCMWQHAXTOR-CMOCDZPBSA-N sesamin Natural products C1=C2OCOC2=CC([C@@H]2OC[C@@]3(C)[C@H](C=4C=C5OCOC5=CC=4)OC[C@]32C)=C1 VRMHCMWQHAXTOR-CMOCDZPBSA-N 0.000 claims description 14
- 238000002360 preparation method Methods 0.000 claims description 5
- 238000002329 infrared spectrum Methods 0.000 abstract description 15
- 238000010586 diagram Methods 0.000 abstract 1
- 230000004069 differentiation Effects 0.000 abstract 1
- 238000011002 quantification Methods 0.000 description 11
- 239000003921 oil Substances 0.000 description 9
- 235000019198 oils Nutrition 0.000 description 9
- 238000000862 absorption spectrum Methods 0.000 description 8
- 238000011088 calibration curve Methods 0.000 description 7
- 230000003287 optical effect Effects 0.000 description 7
- 230000000052 comparative effect Effects 0.000 description 5
- 238000012314 multivariate regression analysis Methods 0.000 description 5
- 238000004519 manufacturing process Methods 0.000 description 4
- OKKJLVBELUTLKV-UHFFFAOYSA-N Methanol Chemical compound OC OKKJLVBELUTLKV-UHFFFAOYSA-N 0.000 description 3
- 238000004458 analytical method Methods 0.000 description 3
- 230000003078 antioxidant effect Effects 0.000 description 3
- 235000013305 food Nutrition 0.000 description 3
- 230000003647 oxidation Effects 0.000 description 3
- 238000007254 oxidation reaction Methods 0.000 description 3
- 238000010238 partial least squares regression Methods 0.000 description 3
- UIOFUWFRIANQPC-JKIFEVAISA-N Floxacillin Chemical compound N([C@@H]1C(N2[C@H](C(C)(C)S[C@@H]21)C(O)=O)=O)C(=O)C1=C(C)ON=C1C1=C(F)C=CC=C1Cl UIOFUWFRIANQPC-JKIFEVAISA-N 0.000 description 2
- 240000007594 Oryza sativa Species 0.000 description 2
- 235000007164 Oryza sativa Nutrition 0.000 description 2
- LUSZGTFNYDARNI-UHFFFAOYSA-N Sesamol Natural products OC1=CC=C2OCOC2=C1 LUSZGTFNYDARNI-UHFFFAOYSA-N 0.000 description 2
- 239000003963 antioxidant agent Substances 0.000 description 2
- 239000008162 cooking oil Substances 0.000 description 2
- 201000010099 disease Diseases 0.000 description 2
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 description 2
- 239000003925 fat Substances 0.000 description 2
- 235000009566 rice Nutrition 0.000 description 2
- KQRXQIPRDKVZPW-ISZNXKAUSA-N sesaminol Chemical compound C1=C2OCOC2=CC([C@H]2OC[C@H]3[C@@H]2CO[C@@H]3C2=CC=3OCOC=3C=C2O)=C1 KQRXQIPRDKVZPW-ISZNXKAUSA-N 0.000 description 2
- KQRXQIPRDKVZPW-UHFFFAOYSA-N sesaminol Natural products C1=C2OCOC2=CC(C2OCC3C2COC3C2=CC=3OCOC=3C=C2O)=C1 KQRXQIPRDKVZPW-UHFFFAOYSA-N 0.000 description 2
- 206010028980 Neoplasm Diseases 0.000 description 1
- 238000010521 absorption reaction Methods 0.000 description 1
- 230000001093 anti-cancer Effects 0.000 description 1
- 230000003110 anti-inflammatory effect Effects 0.000 description 1
- QVGXLLKOCUKJST-UHFFFAOYSA-N atomic oxygen Chemical compound [O] QVGXLLKOCUKJST-UHFFFAOYSA-N 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 201000011510 cancer Diseases 0.000 description 1
- 230000032677 cell aging Effects 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 230000006866 deterioration Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 239000000796 flavoring agent Substances 0.000 description 1
- 235000019634 flavors Nutrition 0.000 description 1
- 230000010354 integration Effects 0.000 description 1
- 230000002452 interceptive effect Effects 0.000 description 1
- 235000016709 nutrition Nutrition 0.000 description 1
- 235000014593 oils and fats Nutrition 0.000 description 1
- 229910052760 oxygen Inorganic materials 0.000 description 1
- 239000001301 oxygen Substances 0.000 description 1
- 150000002978 peroxides Chemical class 0.000 description 1
- 238000004611 spectroscopical analysis Methods 0.000 description 1
- 239000000126 substance Substances 0.000 description 1
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 1
Landscapes
- Investigating Or Analysing Materials By Optical Means (AREA)
Abstract
【課題】ごま油を含有する植物油中のゴマリグナンを短時間で高精度に定量する方法を提供する。【解決手段】ゴマリグナンの定量方法は下記の工程１～３を含む。工程１：ＨＰＬＣ分析により、調整用植物油中のセサミン、セサモリン及びエピセサミンから選ばれる１種のゴマリグナンを定量する工程２：調整用植物油の近赤外スペクトルを測定した後に一次微分単独、二次微分単独、及び一次微分とベクトル正規化との組合せから選ばれる１つのスペクトル前処理をして得られるスペクトルデータと、工程１で得られたゴマリグナンの定量値と、を用いて回帰式を得る工程３：フーリエ変換型近赤外光分析装置を用いて測定用植物油のスペクトルを測定した後に工程２で選ばれたスペクトル前処理をして得られるスペクトルデータと、工程２で得られた回帰式と、を用いて測定用植物油中の工程１で選ばれたゴマリグナンを定量する【選択図】なしPROBLEM TO BE SOLVED: To provide a method for quantifying sesame lignan in a vegetable oil containing sesame oil in a short time with high accuracy. SOLUTION: The method for quantifying sesame lignan includes the following steps 1 to 3. Step 1: Quantify one sesame lignan selected from sesamine, sesamolin and episesamine in the adjusting vegetable oil by HPLC analysis Step 2: After measuring the near-infrared spectrum of the adjusting vegetable oil, the first-order differential alone and the second-order differential alone , And the spectral data obtained by one spectral preprocessing selected from the combination of first-order differentiation and vector normalization, and the quantitative value of sesame lignan obtained in step 1 to obtain a regression equation. The spectral data obtained by measuring the spectrum of the vegetable oil for measurement using a Fourier transform type near-infrared light analyzer and then performing the spectral pretreatment selected in step 2 and the regression equation obtained in step 2 are obtained. Quantify the sesame lignan selected in step 1 in the vegetable oil for measurement using [selection diagram] None
Description
本開示は、ゴマリグナンの定量方法に関する。 The present disclosure relates to a method for quantifying sesame lignans.
食用油等の油脂は、一般的に空気や光等の影響によって酸化が進み品質が低下する。また、油脂の酸化により生じる過酸化物は、食品の風味や栄養価の低下を引き起こすのみならず、生体内における癌等の疾病の要因の一つと考えられている。そのため、油脂にとって酸化を防止する抗酸化物質の含量は重要な要素である。 Oils and fats such as cooking oil are generally oxidized by the influence of air, light and the like, and the quality is deteriorated. In addition, peroxides generated by the oxidation of fats and oils are considered to be one of the causes of diseases such as cancer in the living body as well as causing deterioration of the flavor and nutritional value of foods. Therefore, the content of antioxidants that prevent oxidation is an important factor for fats and oils.
食用油の中でもごま油は酸化し難いことが広く知られている。ごま油は、セサミンに代表されるゴマリグナンという特有の微量成分を含んでいる。ゴマリグナンは強い抗酸化活性を有し、ごま油の酸化し難いという性質に寄与しているため、ごま油の品質維持に欠かせない成分である。また、ゴマリグナンを摂取すると、その抗酸化作用により生体内の活性酸素を低減させて酸化による細胞の老化や生活習慣病を予防することや、抗炎症作用および抗癌作用を奏することが知られている。 It is widely known that sesame oil is difficult to oxidize among cooking oils. Sesame oil contains a peculiar trace component called sesame lignan represented by sesamin. Sesame lignan has a strong antioxidant activity and contributes to the property that sesame oil is difficult to oxidize, so it is an indispensable component for maintaining the quality of sesame oil. In addition, it is known that when sesame lignan is ingested, its antioxidant action reduces active oxygen in the body to prevent cell aging and lifestyle-related diseases due to oxidation, and also has anti-inflammatory and anti-cancer effects. There is.
このようにゴマリグナンは優れた機能を有しており、ごま油の製造においてゴマリグナンの含量の把握は非常に重要である。しかし、ゴマリグナンはごま油中に総量で約０．１～１重量％しか含まれていない。そのため、ゴマリグナンの定量には高い精度が必要であり、微量成分の定量精度に優れる高速液体クロマトグラフィー（ＨＰＬＣ）分析が一般に用いられている（非特許文献１）。 As described above, sesame lignan has an excellent function, and it is very important to understand the content of sesame lignan in the production of sesame oil. However, the total amount of sesame lignan contained in sesame oil is only about 0.1 to 1% by weight. Therefore, high accuracy is required for the quantification of sesame lignans, and high performance liquid chromatography (HPLC) analysis having excellent quantification accuracy of trace components is generally used (Non-Patent Document 1).
しかし、ＨＰＬＣ分析は微量成分の定量精度に優れるものの、一検体当たりの分析時間が長いという欠点を有する。そのため、ゴマリグナンの定量を短時間かつ高精度で可能な定量方法が求められている。 However, although HPLC analysis is excellent in quantification accuracy of trace components, it has a drawback that the analysis time per sample is long. Therefore, there is a demand for a quantification method capable of quantifying sesame lignans in a short time and with high accuracy.
そこで本開示は、短時間で試料の近赤外スペクトルの測定が可能なフーリエ変換型近赤外光分析装置を用いて測定対象である測定用植物油中のゴマリグナンを高精度で定量できる方法を提供する。 Therefore, the present disclosure provides a method capable of quantifying sesame lignans in a vegetable oil for measurement to be measured with high accuracy by using a Fourier transform type near-infrared optical analyzer capable of measuring the near-infrared spectrum of a sample in a short time. do.
上記課題を解決するため、本開示の技術は以下の手段をとる。
［１］下記の工程１～３を含み、
前記工程２及び前記工程３で用いられるスペクトルデータは１つの連続した波数領域であり、
前記波数領域が、９４００±１０～５９００±１０ｃｍ
－１
であり、
前記工程１で選ばれるゴマリグナンがセサミンであり、且つ前記工程２で選ばれるスペクトル前処理が一次微分とベクトル正規化との組合せであることを特徴とするごま油を含有する植物油中のゴマリグナンの定量方法。
工程１：ＨＰＬＣ分析により、調整用植物油中のセサミン、セサモリン及びエピセサミンから選ばれる１種のゴマリグナンを定量する工程
工程２：フーリエ変換型近赤外光分析装置を用いて前記調整用植物油のスペクトルを測定した後に一次微分単独、二次微分単独、及び一次微分とベクトル正規化との組合せから選ばれる１つのスペクトル前処理をして得られるスペクトルデータと、前記工程１で得られたゴマリグナンの定量値と、を用いて回帰式を得る工程
工程３：フーリエ変換型近赤外光分析装置を用いて測定用植物油のスペクトルを測定した後に前記工程２で選ばれたスペクトル前処理をして得られるスペクトルデータと、前記工程２で得られた回帰式と、を用いて前記測定用植物油中の前記工程１で選ばれたゴマリグナンを定量する工程
［２］下記の工程１～３を含み、
前記工程２及び前記工程３で用いられるスペクトルデータは１つの連続した波数領域であり、
前記波数領域が、９４００±１０～５９００±１０ｃｍ
－１
であり、
前記工程１で選ばれるゴマリグナンがエピセサミンであり、且つ前記工程２で選ばれるスペクトル前処理が一次微分単独であることを特徴とするごま油を含有する植物油中のゴマリグナンの定量方法。
工程１：ＨＰＬＣ分析により、調整用植物油中のセサミン、セサモリン及びエピセサミンから選ばれる１種のゴマリグナンを定量する工程
工程２：フーリエ変換型近赤外光分析装置を用いて前記調整用植物油のスペクトルを測定した後に一次微分単独、二次微分単独、及び一次微分とベクトル正規化との組合せから選ばれる１つのスペクトル前処理をして得られるスペクトルデータと、前記工程１で得られたゴマリグナンの定量値と、を用いて回帰式を得る工程
工程３：フーリエ変換型近赤外光分析装置を用いて測定用植物油のスペクトルを測定した後に前記工程２で選ばれたスペクトル前処理をして得られるスペクトルデータと、前記工程２で得られた回帰式と、を用いて前記測定用植物油中の前記工程１で選ばれたゴマリグナンを定量する工程
［３］下記の工程１～３を含み、
前記工程２及び前記工程３で用いられるスペクトルデータは１つの連続した波数領域であり、
前記波数領域が、９４００±１０～５９００±１０ｃｍ
－１
であり、
前記工程１で選ばれるゴマリグナンがセサモリンであり、且つ前記工程２で選ばれるスペクトル前処理が二次微分単独であることを特徴とするごま油を含有する植物油中のゴマリグナンの定量方法。
工程１：ＨＰＬＣ分析により、調整用植物油中のセサミン、セサモリン及びエピセサミンから選ばれる１種のゴマリグナンを定量する工程
工程２：フーリエ変換型近赤外光分析装置を用いて前記調整用植物油のスペクトルを測定した後に一次微分単独、二次微分単独、及び一次微分とベクトル正規化との組合せから選ばれる１つのスペクトル前処理をして得られるスペクトルデータと、前記工程１で得られたゴマリグナンの定量値と、を用いて回帰式を得る工程
工程３：フーリエ変換型近赤外光分析装置を用いて測定用植物油のスペクトルを測定した後に前記工程２で選ばれたスペクトル前処理をして得られるスペクトルデータと、前記工程２で得られた回帰式と、を用いて前記測定用植物油中の前記工程１で選ばれたゴマリグナンを定量する工程
In order to solve the above problems, the technique of the present disclosure takes the following means.
[1] Including the following steps 1 to 3 ,
The spectral data used in the step 2 and the step 3 is one continuous wavenumber region.
The wave number region is 9400 ± 10 to 5900 ± 10 cm -1 .
A method for quantifying sesame lignan in a vegetable oil containing sesame oil , wherein the sesame lignan selected in step 1 is sesamin, and the spectral pretreatment selected in step 2 is a combination of first derivative and vector normalization. ..
Step 1: Quantify one type of sesame lignan selected from sesamine, sesamolin and episesamine in the adjusting vegetable oil by HPLC analysis. Step 2: Quantify the spectrum of the adjusting vegetable oil using a Fourier transform type near-infrared light analyzer. Spectral data obtained by performing one spectral preprocessing selected from the first-order differential alone, the second-order differential alone, and the combination of the first-order differential and vector normalization after measurement, and the quantitative value of sesame lignan obtained in the above step 1. Step 3: A spectrum obtained by measuring the spectrum of the vegetable oil for measurement using a Fourier transform type near-infrared light analyzer and then performing the spectrum pretreatment selected in the above step 2. A step of quantifying the sesame lignan selected in the step 1 in the vegetable oil for measurement using the data and the regression equation obtained in the step 2 [2] The following steps 1 to 3 are included.
The spectral data used in the step 2 and the step 3 is one continuous wavenumber region.
The wave number region is 9400 ± 10 to 5900 ± 10 cm -1 .
A method for quantifying sesame lignan in a vegetable oil containing sesame oil, wherein the sesame lignan selected in step 1 is episesamine and the spectral pretreatment selected in step 2 is a first derivative alone.
Step 1: A step of quantifying one kind of sesame lignan selected from sesamin, sesamolin and episesamin in the vegetable oil for preparation by HPLC analysis.
Step 2: One spectrum selected from the first derivative alone, the second derivative alone, and the combination of the first derivative and the vector normalization after measuring the spectrum of the adjusting vegetable oil using a Fourier transform type near-infrared light analyzer. A step of obtaining a regression equation using the spectral data obtained by the pretreatment and the quantitative value of sesame lignan obtained in the above step 1.
Step 3: Spectral data obtained by measuring the spectrum of the vegetable oil for measurement using a Fourier transform type near-infrared light analyzer and then performing the spectrum pretreatment selected in the step 2, and the spectrum data obtained in the step 2. A step of quantifying the sesame lignan selected in the step 1 in the vegetable oil for measurement using a regression equation and
[3] Including the following steps 1 to 3,
The spectral data used in the step 2 and the step 3 is one continuous wavenumber region.
The wave number region is 9400 ± 10 to 5900 ± 10 cm -1 .
A method for quantifying sesame lignan in a vegetable oil containing sesame oil, wherein the sesame lignan selected in the step 1 is sesamolin and the spectral pretreatment selected in the step 2 is a second derivative alone.
Step 1: A step of quantifying one kind of sesame lignan selected from sesamin, sesamolin and episesamin in the vegetable oil for preparation by HPLC analysis.
Step 2: One spectrum selected from the first derivative alone, the second derivative alone, and the combination of the first derivative and the vector normalization after measuring the spectrum of the adjusting vegetable oil using a Fourier transform type near-infrared light analyzer. A step of obtaining a regression equation using the spectral data obtained by the pretreatment and the quantitative value of sesame lignan obtained in the above step 1.
Step 3: Spectral data obtained by measuring the spectrum of the vegetable oil for measurement using a Fourier transform type near-infrared light analyzer and then performing the spectrum pretreatment selected in the step 2, and the spectrum data obtained in the step 2. A step of quantifying the sesame lignan selected in the step 1 in the vegetable oil for measurement using a regression equation and
なお、本明細書において「Ａ～Ｂ」で示される数値範囲は、特段の記載が無い限り、その上限及び下限を含む。つまり、「Ａ～Ｂ」は「Ａ以上、Ｂ以下」を意味する。 Unless otherwise specified, the numerical range indicated by "A to B" in the present specification includes the upper limit and the lower limit thereof. That is, "A to B" means "A or more, B or less".
≪ゴマリグナンの定量方法≫
本開示のごま油を含有する植物油中のゴマリグナンの定量方法は、下記の工程１～３を含む。なお、ゴマリグナンの定量方法は工程１～３以外の工程を含んでもよい。
工程１：ＨＰＬＣ分析により、植物油（以下、工程１及び工程２で回帰線を得るために用いる植物油を調整用植物油という）中のセサミン、セサモリン及びエピセサミンから選ばれる１種のゴマリグナンを定量する工程
工程２：フーリエ変換型近赤外光分析装置を用いて前記調整用植物油のスペクトルを測定した後に一次微分単独、二次微分単独、及び一次微分とベクトル正規化との組合せから選ばれる１つのスペクトル前処理をして得られるスペクトルデータと、前記工程１で得られたゴマリグナンの定量値と、を用いて回帰式を得る工程
工程３：フーリエ変換型近赤外光分析装置を用いて測定用植物油のスペクトルを測定した後に前記工程２で選ばれたスペクトル前処理をして得られるスペクトルデータと、前記工程２で得られた回帰式と、を用いて前記測定用植物油中の前記工程１で選ばれたゴマリグナンを定量する工程
≪Calculation method of sesame lignan≫
The method for quantifying sesame lignans in vegetable oil containing sesame oil according to the present disclosure includes the following steps 1 to 3. The method for quantifying sesame lignan may include steps other than steps 1 to 3.
Step 1: A step of quantifying one kind of sesame lignan selected from sesamine, sesamolin and episesamine in the vegetable oil (hereinafter, the vegetable oil used to obtain the regression line in steps 1 and 2 is referred to as the adjusting vegetable oil) by HPLC analysis. 2: After measuring the spectrum of the adjusting vegetable oil using a Fourier transform type near-infrared light analyzer, one spectrum before selected from the first-order differential alone, the second-order differential alone, and the combination of the first-order differential and vector normalization. Step 3: Obtaining a regression equation using the spectral data obtained by the treatment and the quantitative value of sesame lignan obtained in the above step 1. Step 3: Measuring vegetable oil using a Fourier transform type near-infrared light analyzer. It is selected in the step 1 in the vegetable oil for measurement by using the spectrum data obtained by performing the spectrum pretreatment selected in the step 2 after measuring the spectrum and the regression equation obtained in the step 2. The process of quantifying the sesame lignan
＜工程１＞
工程１では、ごま油を含有する植物油中のゴマリグナンをＨＰＬＣ（高速液体クロマトグラフィー）分析により定量する。植物油はごま油を含んでいればよく、特に限定されない。同様にごま油も、ごまの種類や製法等により特に限定されない。ゴマリグナンは、セサミン、セサモリン、及びエピセサミンから選ばれる１種である。ＨＰＬＣ分析の方法は特に限定されないが、例えば以下の工程１－１～工程１－４を含む方法が挙げられる。
工程１－１：セサミン、セサモリン、及びエピセサミンから選ばれる１種の精製ゴマリグナンを各種濃度で含む複数の標品をＨＰＬＣで溶出し、ＵＶ検出器を用いてＵＶの吸収スペクトルを測定する。
工程１－２：工程１－１で測定されたＵＶの吸収スペクトルに基づきゴマリグナンの検量線を作成する。
工程１－３：ごま油を含有する複数の調整用植物油を工程１－１と同じ条件のＨＰＬＣで溶出し、ＵＶの吸収スペクトルを測定する。
工程１－４：工程１－３で測定されたＵＶの吸収スペクトルに工程１－２で作成した検量線を適用して、調整用植物油中のゴマリグナンの量を算出する。
なお、ゴマリグナンの検量線は事前に作成したものを用いることで、工程１－１及び工程１－２を省略してもよい。
<Step 1>
In step 1, sesame lignans in vegetable oil containing sesame oil are quantified by HPLC (High Performance Liquid Chromatography) analysis. The vegetable oil may contain sesame oil and is not particularly limited. Similarly, sesame oil is not particularly limited depending on the type of sesame, the manufacturing method, and the like. Goma lignan is one selected from sesamin, sesamolin, and episesamin. The method of HPLC analysis is not particularly limited, and examples thereof include a method including the following steps 1-1 to 1-4.
Step 1-1: A plurality of preparations containing one purified sesame lignan selected from sesamin, sesamolin, and episesamin at various concentrations are eluted by HPLC, and the UV absorption spectrum is measured using a UV detector.
Step 1-2: A calibration curve of sesame lignan is prepared based on the absorption spectrum of UV measured in step 1-1.
Step 1-3: A plurality of adjusting vegetable oils containing sesame oil are eluted by HPLC under the same conditions as in Step 1-1, and the UV absorption spectrum is measured.
Step 1-4: The amount of sesame lignan in the adjusting vegetable oil is calculated by applying the calibration curve prepared in Step 1-2 to the UV absorption spectrum measured in Step 1-3.
By using a calibration curve prepared in advance for sesame lignan, steps 1-1 and 1-2 may be omitted.
ゴマリグナンの定量は、ＵＶの吸収スペクトルのピーク高さ又はピーク面積のいずれに基づいて行ってもよいが、ピーク面積に基づき定量することが好ましい。なお、ＨＰＬＣ分析に関し、カラムの種類、移動相、検出器の種類、測定波長等は適宜変更可能である。 The sesame lignan may be quantified based on either the peak height or the peak area of the UV absorption spectrum, but it is preferably quantified based on the peak area. Regarding HPLC analysis, the type of column, mobile phase, type of detector, measurement wavelength and the like can be appropriately changed.
＜工程２＞
工程２では、工程１で用いた調整用植物油のスペクトルデータと、工程１で得られたゴマリグナンの定量値と、を用いて回帰式を得る。調整用植物油のスペクトルデータは、調整用植物油の近赤外スペクトルをフーリエ変換型近赤外光分析装置で測定し、得られた原スペクトルデータについてスペクトル前処理をして取得する。
<Step 2>
In step 2, a regression equation is obtained using the spectral data of the adjusting vegetable oil used in step 1 and the quantitative value of sesame lignan obtained in step 1. The spectral data of the adjusting vegetable oil is obtained by measuring the near-infrared spectrum of the adjusting vegetable oil with a Fourier transform type near-infrared optical analyzer and performing spectral preprocessing on the obtained original spectrum data.
調整用植物油の近赤外スペクトルは、フーリエ変換型近赤外光分析装置を用いて測定される。フーリエ変換型近赤外光分析装置は、試料の近赤外光（一般に波数１２５００～４０００ｃｍ－１）の吸収に基づく分光を行う装置であり、試料に近赤外光を照射し、透過光または反射光を干渉計により干渉させ、その信号強度をフーリエ変換することで試料の近赤外スペクトルを測定する。フーリエ変換型近赤外光分析装置を用いることにより、分散型の近赤外光分析装置を用いた場合と比べて、短時間で調整用植物油の近赤外スペクトルを測定することができる。 The near-infrared spectrum of the adjusting vegetable oil is measured using a Fourier transform type near-infrared light analyzer. The Fourier conversion type near-infrared light analyzer is a device that performs spectroscopy based on the absorption of near-infrared light (generally, wave number 12500 to 4000 cm -1 ) of the sample, and irradiates the sample with near-infrared light to transmit or transmit light. The near-infrared spectrum of the sample is measured by interfering the reflected light with an interferometer and performing Fourier transform on the signal intensity. By using the Fourier transform type near-infrared light analyzer, the near-infrared spectrum of the vegetable oil for adjustment can be measured in a shorter time than when the distributed near-infrared light analyzer is used.
調整用植物油の近赤外スペクトルを測定して得られた原スペクトルデータは、スペクトル前処理をすることにより、回帰式を得る際に用いるスペクトルデータに変換される。スペクトル前処理は、一次微分単独、二次微分単独、及び一次微分とベクトル正規化との組合せから選ばれる１つを用いることができるが、本発明においては、ゴマリグナンがセサミンの場合は一次微分とベクトル正規化との組み合わせであり、ゴマリグナンがエピセサミンである場合は一次微分単独であり、ゴマリグナンがセサモリンである場合は二次微分単独である。 The original spectrum data obtained by measuring the near-infrared spectrum of the adjusting vegetable oil is converted into the spectrum data used when obtaining the regression equation by performing spectral preprocessing. For the spectrum preprocessing, one selected from the first derivative alone, the second derivative alone, and the combination of the first derivative and the vector normalization can be used, but in the present invention, when the sesame lignan is sesamin, the first derivative and the first derivative can be used. In combination with vector normalization, if sesame lignan is episesamin, it is the first derivative alone , and if sesame lignan is sesamolin, it is the second derivative alone .
回帰式を作成する際に用いられるスペクトルデータの波数領域は近赤外光の範囲内で適宜選択可能であり、複数の連続する波数領域であってもよいが、本発明では、１つの連続した波数領域であり、具体的には９４００±１０～５９００±１０ｃｍ－１ の波数領域が用いられる。この範囲の波数領域を用いて回帰式を作成することにより、定量精度をより向上することができる。なお、フーリエ変換型近赤外光分析装置による近赤外スペクトルの測定は、測定対象に応じて波数分解能（以下、分解能という）を適宜選択して測定される。そのため、本開示における「連続した波数領域」とは、当該波数領域全体にわたって所定の分解能で示された領域を意味する。工程２における分解能は特に限定されないが、測定精度と測定時間のバランスの観点から１６ｃｍ－１又は８ｃｍ－１であることが好ましく、８ｃｍ－１であることが更に好ましい。また、回帰式を作成する際に用いられるスペクトルデータの波数領域は、測定した波数領域の全体であってもよいし、その一部であってもよい。 The wavenumber region of the spectral data used when creating the regression equation can be appropriately selected within the range of near-infrared light, and may be a plurality of continuous wavenumber regions, but in the present invention, one continuous wavenumber region is used. This is the wavenumber region, and specifically, the wavenumber region of 9400 ± 10 to 5900 ± 10 cm -1 is used . By creating a regression equation using the wavenumber region in this range, the quantification accuracy can be further improved. The near-infrared spectrum is measured by the Fourier transform type near-infrared optical analyzer by appropriately selecting the wavenumber resolution (hereinafter referred to as resolution) according to the measurement target. Therefore, the “continuous wavenumber region” in the present disclosure means a region indicated by a predetermined resolution over the entire wavenumber region. The resolution in the step 2 is not particularly limited, but is preferably 16 cm -1 or 8 cm -1 from the viewpoint of the balance between the measurement accuracy and the measurement time, and more preferably 8 cm -1 . Further, the wavenumber region of the spectral data used when creating the regression equation may be the entire measured wavenumber region or a part thereof.
回帰式の作成は多変量回帰分析により行われる。多変量回帰分析の方法は特に限定されないが、部分的最小二乗回帰（ＰＬＳ）法が特に好ましい。多変量回帰分析は市販のソフトウェアを使用することができる。例えば、ＯＰＵＳ（Ｂｒｕｋｅｒ社製）、Ｔｈｅ Ｕｎｓｃｒａｍｂｌｅｒ Ｘ（株式会社エス・ティ・ジャパン社製）、Ｐｉｒｏｕｅｔｔｅ（ジーエルサイエンス株式会社製）等が使用可能である。 Regression equations are created by multivariate regression analysis. The method of multivariate regression analysis is not particularly limited, but the partial least squares regression (PLS) method is particularly preferable. Commercially available software can be used for multivariate regression analysis. For example, OPUS (manufactured by Bruker), The Unscrambler X (manufactured by ST Japan Co., Ltd.), Pirouette (manufactured by GL Sciences Co., Ltd.) and the like can be used.
スペクトル前処理されたスペクトルデータと、工程１で得られたゴマリグナンの定量値とを多変量回帰分析に供することにより、スペクトルデータからゴマリグナンの含量を予測する回帰式を得ることができる。その際に、スペクトルデータを説明変数に設定し、ゴマリグナンの定量値を目的変数に設定することにより、スペクトルデータの中に内在するゴマリグナンの含量の変化と相関の高い因子を決定し、この因子の回帰係数を用いて回帰式を作成する。なお、作成される回帰式の精度を向上し、ゴマリグナンの定量方法の誤差を低減するために、調整用植物油の検体数は多数、例えば１００個以上であることが好ましい。 By subjecting the spectral data preprocessed to the spectrum and the quantitative value of sesame lignan obtained in step 1 to multivariate regression analysis, a regression equation for predicting the content of sesame lignan can be obtained from the spectral data. At that time, by setting the spectral data as an explanatory variable and setting the quantitative value of sesame lignan as the objective variable, a factor having a high correlation with the change in the content of sesame lignan contained in the spectral data was determined, and the factor of this factor was determined. Create a regression equation using the regression coefficient. In order to improve the accuracy of the prepared regression equation and reduce the error in the method for quantifying sesame lignans, the number of samples of the adjusting vegetable oil is preferably large, for example, 100 or more.
＜工程３＞
工程３では、フーリエ変換型近赤外光分析装置を用いて測定対象である植物油（以下、測定用植物油という）の近赤外スペクトルを測定した後に工程２と同じスペクトル前処理をして得られるスペクトルデータと、工程２で作成した回帰式と、を用いて測定用植物油中のゴマリグナンを定量する。工程３ではフーリエ変換型近赤外光分析装置を用いて測定された近赤外スペクトルに基づき測定用植物油中のゴマリグナンを定量するため、測定用植物油を損なうことなく短時間での定量が可能である。
<Process 3>
In step 3, the near-infrared spectrum of the vegetable oil to be measured (hereinafter referred to as measurement vegetable oil) is measured using a Fourier transform type near-infrared light analyzer, and then the same spectral pretreatment as in step 2 is performed. The sesame lignan in the vegetable oil for measurement is quantified using the spectral data and the regression equation created in step 2. In step 3, sesame lignans in the vegetable oil for measurement are quantified based on the near-infrared spectrum measured using the Fourier transform type near-infrared light analyzer, so that the quantification can be performed in a short time without damaging the vegetable oil for measurement. be.
測定用植物油のスペクトルデータは、工程２における調整用植物油のスペクトルデータを取得する場合と同様にして取得することができる。つまり、フーリエ変換型近赤外光分析装置を用いて測定用植物油の近赤外スペクトルを測定し、得られた原スペクトルデータに対して工程２と同じスペクトル前処理をしてスペクトルデータを得る。フーリエ変換型近赤外光分析装置を用いることにより、分散型の近赤外光分析装置を用いた場合と比べて短時間で測定用植物油の近赤外スペクトルを測定することができる。また、工程３におけるスペクトルの測定条件やスペクトルデータの波数領域等は、ゴマリグナンの定量精度を向上するため、工程２における測定条件や波数領域と同一であることが好ましい。 The spectral data of the vegetable oil for measurement can be acquired in the same manner as in the case of acquiring the spectral data of the vegetable oil for adjustment in step 2. That is, the near-infrared spectrum of the vegetable oil for measurement is measured using a Fourier transform type near-infrared light analyzer, and the obtained original spectrum data is subjected to the same spectral pretreatment as in step 2 to obtain spectral data. By using the Fourier transform type near-infrared light analyzer, the near-infrared spectrum of the vegetable oil for measurement can be measured in a shorter time than when the distributed near-infrared light analyzer is used. Further, it is preferable that the measurement conditions of the spectrum in step 3 and the wave number region of the spectrum data are the same as the measurement conditions and wave number region in step 2 in order to improve the quantification accuracy of sesame lignan.
測定用植物油中のゴマリグナンを定量は、測定用植物油のスペクトルデータに工程２で作成した回帰式を適用することにより行う。このスペクトルデータと回帰式からゴマリグナンを定量する工程には、市販のソフトウェアを使用することができる。なお、工程３で使用するソフトウェアは、ゴマリグナンの定量精度を向上するため、工程２で用いたソフトウェアと同一であることが好ましい。 The quantification of sesame lignans in the vegetable oil for measurement is performed by applying the regression equation created in step 2 to the spectral data of the vegetable oil for measurement. Commercially available software can be used in the step of quantifying sesame lignans from this spectral data and the regression equation. The software used in step 3 is preferably the same as the software used in step 2 in order to improve the accuracy of quantifying sesame lignans.
以下、本開示の具体的な構成及び効果を実施例及び比較例に基づき説明する。 Hereinafter, specific configurations and effects of the present disclosure will be described based on Examples and Comparative Examples.
＜ゴマリグナンの検量線の作成＞
先ず、各種濃度の精製ゴマリグナン（セサミン、エピセサミン、セサモリン、セサミノール、又はセサモール）を含有する複数の標品を用いて、以下の条件でＨＰＬＣ分析を行った。そして、得られたＵＶの吸収スペクトルのピーク面積から各ゴマリグナンの検量線を作成した。
カラム：Ｄｅｖｅｌｉｓｉｌ ＯＳＤ－５（４．６×１５０ｍｍ）（野村化学株式会社製）
移動相：水：メタノール＝３０：７０（容積比）
流量：０．８ｍＬ／ｍｉｎ
検出器：ＵＶ検出器（ＳＰＤ－２０Ａ Ｐｒｏｍｉｎｅｎｃｅ）（株式会社島津製作所）
測定波長：２９０ｎｍ
<Creation of calibration curve for sesame lignan>
First, HPLC analysis was performed under the following conditions using a plurality of specimens containing various concentrations of purified sesame lignan (sesamin, episesamin, sesamolin, sesaminol, or sesamol). Then, a calibration curve of each sesame lignan was prepared from the peak area of the obtained UV absorption spectrum.
Column: Develisil OSD-5 (4.6 x 150 mm) (manufactured by Nomura Chemical Co., Ltd.)
Mobile phase: Water: Methanol = 30:70 (volume ratio)
Flow rate: 0.8 mL / min
Detector: UV detector (SPD-20A Prominence) (Shimadzu Corporation)
Measurement wavelength: 290 nm
実施例１
＜調整用植物油中のゴマリグナンの定量＞
製造日が異なる複数（１００個）の調整用植物油（竹本油脂株式会社製 太白胡麻油（Ａ－１））について、標品のＨＰＬＣ分析と同じ条件でＨＰＬＣ分析を行った。得られたＵＶの吸収スペクトルのピーク面積と、予め作成したセサミンの検量線とを対比して、各調整用植物油（Ａ－１）中のセサミンを定量した。
Example 1
<Quantification of sesame lignans in vegetable oil for adjustment>
Multiple (100 pieces) adjusting vegetable oils (Taishaku sesame oil (A-1) manufactured by Takemoto Oil & Fat Co., Ltd.) with different production dates were subjected to HPLC analysis under the same conditions as the HPLC analysis of the standard. The peak area of the obtained UV absorption spectrum was compared with the calibration curve of sesamin prepared in advance, and sesamin in each adjusting vegetable oil (A-1) was quantified.
＜回帰式の作成＞
フーリエ変換型近赤外分光計ＭＰＡ（Ｂｒｕｋｅｒ社製）を用いて、ＨＰＬＣ分析に用いた各調整用植物油（Ａ－１）の近赤外スペクトルを以下の条件で測定し、原スペクトルデータを得た。
測定波数：１２５００～４０００ｃｍ－１
積算回数：１２８回
分解能：８ｃｍ－１
<Creation of regression equation>
Using a Fourier transform near-infrared spectrometer MPA (manufactured by Bruker), the near-infrared spectrum of each adjusting vegetable oil (A-1) used for HPLC analysis was measured under the following conditions, and the original spectrum data was obtained. rice field.
Wavenumber measured: 12500-4000 cm -1
Number of integrations: 128 times Resolution: 8 cm -1
解析ソフトＯｐｕｓ（Ｂｒｕｋｅｒ社製）を用いて各調整用植物油の原スペクトルデータについてスペクトル前処理（一次微分及びベクトル正規化）を行った後、波数領域を９４００～５９００ｃｍ－１に限定してスペクトルデータを得た。各調整用植物油について、スペクトルデータを説明変数に、ＨＰＬＣ分析で求めたセサミンの定量値を目的変数にそれぞれ設定し、ＰＬＳ法で多変量回帰分析することにより回帰式を作成した。 After performing spectral preprocessing (first derivative and vector normalization) on the raw spectral data of each vegetable oil for adjustment using the analysis software Opus (manufactured by Bruker), the wave number region is limited to 9400 to 5900 cm -1 and the spectral data. Got For each adjusting vegetable oil, the spectral data was set as the explanatory variable and the quantitative value of sesamine obtained by HPLC analysis was set as the objective variable, and a regression equation was created by performing multivariate regression analysis by the PLS method.
＜測定用植物油中のゴマリグナンの定量＞
調整用植物油とは製造日の異なる測定用植物油（竹本油脂株式会社製 太白胡麻油（Ａ－１））について、調整用植物油と同じ条件で近赤外スペクトルの測定及び加工を行い、スペクトルデータを得た。測定用植物油のスペクトルデータに作成した回帰式を適用し、測定用植物油（Ａ－１）中のセサミンの定量値（以下、ＮＩＲ定量値という）を算出した。
<Quantification of sesame lignans in vegetable oil for measurement>
The near-infrared spectrum of the vegetable oil for measurement (Taishaku sesame oil (A-1) manufactured by Takemoto Oil & Fat Co., Ltd.), which has a different manufacturing date from the vegetable oil for adjustment, is measured and processed under the same conditions as the vegetable oil for adjustment, and spectral data is obtained. rice field. The regression equation created for the spectral data of the vegetable oil for measurement was applied to calculate the quantitative value of sesamin (hereinafter referred to as NIR quantitative value) in the vegetable oil for measurement (A-1).
＜定量精度の検討＞
近赤外スペクトルの測定に用いた測定用植物油（Ａ－１）について上記条件でＨＰＬＣ分析を行い、ＵＶの吸収スペクトルのピーク面積と予め作成した検量線とを対比して測定用植物油（Ａ－１）中のセサミンの定量値（以下、ＨＰＬＣ定量値という）を算出した。そして、測定用植物油（Ａ－１）のＮＩＲ定量値とＨＰＬＣ定量値との誤差を下記式により算出し、以下の基準で評価した。実施例１の誤差は２％未満であった。
誤差（％）＝｜ＨＰＬＣ定量値－ＮＩＲ定量値｜÷ＨＰＬＣ定量値×１００
◎：２％未満
〇：２～３％
×：３％超
<Examination of quantitative accuracy>
The measurement vegetable oil (A-1) used for the measurement of the near-infrared spectrum was subjected to HPLC analysis under the above conditions, and the peak area of the UV absorption spectrum was compared with the calibration curve prepared in advance to measure the vegetable oil (A-). 1) The quantitative value of sesamine in 1) (hereinafter referred to as HPLC quantitative value) was calculated. Then, the error between the NIR quantitative value and the HPLC quantitative value of the vegetable oil for measurement (A-1) was calculated by the following formula and evaluated according to the following criteria. The error in Example 1 was less than 2%.
Error (%) = | HPLC quantified value-NIR quantified value | ÷ HPLC quantified value x 100
◎: Less than 2% 〇: 2-3%
×: Over 3%
実施例２～１５、参考例１６～５１、比較例１～２５
実施例１と同様にして、表１～表３に示す実施例２～１５、参考例１６～５１及び比較例１～２５についても同様に測定および評価を行った。
Examples 2 to 15, Reference Examples 16 to 51, Comparative Examples 1 to 25
In the same manner as in Example 1, Examples 2 to 15, Reference Examples 16 to 51 and Comparative Examples 1 to 25 shown in Tables 1 to 3 were also measured and evaluated in the same manner.
表１～表３中の植物油は以下の通りである。
Ａ－１：竹本油脂株式会社製 太白胡麻油
Ａ－２：竹本油脂株式会社製 太白胡麻油と焙煎ごま油の１：９の混合油
Ａ－３：竹本油脂株式会社製 太白胡麻油と焙煎ごま油の５：５の混合油
Ａ－４：竹本油脂株式会社製 太白胡麻油と焙煎ごま油の９：１の混合油
Ａ－５：かどや製油株式会社製 純白ごま油
Ａ－６：九鬼産業株式会社製 九鬼太白純正胡麻油
Ａ－７：竹本油脂株式会社製 純正胡麻油
Ａ－８：かどや製油株式会社製 金印純正ごま油
The vegetable oils in Tables 1 to 3 are as follows.
A-1: Taishiro sesame oil manufactured by Takemoto Yushi Co., Ltd. A-2: 1: 9 mixed oil of Taishiro sesame oil and roasted sesame oil manufactured by Takemoto Yushi Co., Ltd. A-3: Taishiro sesame oil and roasted sesame oil manufactured by Takemoto Yushi Co., Ltd. : 5 mixed oil A-4: 9: 1 mixed oil of thick white sesame oil and roasted sesame oil manufactured by Takemoto Oil & Fat Co., Ltd. A-5: Pure white sesame oil manufactured by Kadoya Oil Co., Ltd. Sesame oil A-7: Genuine sesame oil manufactured by Takemoto Oil & Fat Co., Ltd. A-8: Genuine sesame oil manufactured by Kadoya Oil Co., Ltd.
実施例１から実施例１５、及び参考例１６から５１は、ＨＰＬＣ定量値に対するＮＩＲ定量値の誤差が小さく、フーリエ変換型近赤外光分析装置を用いた高精度の定量方法を実現できた。また、フーリエ変換型近赤外光分析装置を用いているため、測定用植物油中のゴマリグナンを短時間で定量できた。一方、比較例１から比較例１８は、ゴマリグナンがセサミノール又はセサモールであるため、一次微分単独、二次微分単独、及び一次微分とベクトル正規化との組合せのいずれの前処理をしてもＨＰＬＣ定量値に対するＮＩＲ定量値の誤差が大きく、フーリエ変換型近赤外光分析装置を用いた定量精度が低かった。比較例１９から比較例２５は、前処理が一次微分単独、二次微分単独、及び一次微分とベクトル正規化との組合せのいずれでもないため、ＨＰＬＣ定量値に対するＮＩＲ定量値の誤差が大きく、フーリエ変換型近赤外光分析装置を用いた定量精度が低かった。
In Examples 1 to 15 and Reference Examples 16 to 51 , the error of the NIR quantified value with respect to the HPLC quantified value was small, and a highly accurate quantification method using a Fourier transform type near-infrared optical analyzer could be realized. In addition, because a Fourier transform type near-infrared optical analyzer was used, sesame lignans in the vegetable oil for measurement could be quantified in a short time. On the other hand, in Comparative Examples 1 to 18, since the sesame lignan is sesaminol or sesamol, HPLC is performed regardless of the pretreatment of the first derivative alone, the second derivative alone, or the combination of the first derivative and the vector normalization. The error of the NIR quantitative value with respect to the quantitative value was large, and the quantitative accuracy using the Fourier transform type near-infrared optical analyzer was low. In Comparative Examples 19 to 25, since the preprocessing is neither the first derivative alone, the second derivative alone, nor the combination of the first derivative and the vector normalization, the error of the NIR quantitative value with respect to the HPLC quantitative value is large, and Fourier The quantitative accuracy using the transform-type near-infrared optical analyzer was low.
Claims (3)
前記工程２及び前記工程３で用いられるスペクトルデータは１つの連続した波数領域であり、
前記波数領域が、９４００±１０～５９００±１０ｃｍ －１ であり、
前記工程１で選ばれるゴマリグナンがセサミンであり、且つ前記工程２で選ばれるスペクトル前処理が一次微分とベクトル正規化との組合せであることを特徴とするごま油を含有する植物油中のゴマリグナンの定量方法。
工程１：ＨＰＬＣ分析により、調整用植物油中のセサミン、セサモリン及びエピセサミンから選ばれる１種のゴマリグナンを定量する工程
工程２：フーリエ変換型近赤外光分析装置を用いて前記調整用植物油のスペクトルを測定した後に一次微分単独、二次微分単独、及び一次微分とベクトル正規化との組合せから選ばれる１つのスペクトル前処理をして得られるスペクトルデータと、前記工程１で得られたゴマリグナンの定量値と、を用いて回帰式を得る工程
工程３：フーリエ変換型近赤外光分析装置を用いて測定用植物油のスペクトルを測定した後に前記工程２で選ばれたスペクトル前処理をして得られるスペクトルデータと、前記工程２で得られた回帰式と、を用いて前記測定用植物油中の前記工程１で選ばれたゴマリグナンを定量する工程 Including steps 1 to 3 below
The spectral data used in the step 2 and the step 3 is one continuous wavenumber region.
The wave number region is 9400 ± 10 to 5900 ± 10 cm -1 .
A method for quantifying sesame lignan in a vegetable oil containing sesame oil , wherein the sesame lignan selected in step 1 is sesamin, and the spectral pretreatment selected in step 2 is a combination of first derivative and vector normalization. ..
Step 1: Quantify one type of sesame lignan selected from sesamine, sesamolin and episesamine in the adjusting vegetable oil by HPLC analysis. Step 2: Quantify the spectrum of the adjusting vegetable oil using a Fourier transform type near-infrared light analyzer. Spectral data obtained by performing one spectral preprocessing selected from the first-order differential alone, the second-order differential alone, and the combination of the first-order differential and vector normalization after measurement, and the quantitative value of sesame lignan obtained in the above step 1. Step 3: A spectrum obtained by measuring the spectrum of the vegetable oil for measurement using a Fourier transform type near-infrared light analyzer and then performing the spectrum pretreatment selected in the above step 2. A step of quantifying the sesame lignan selected in the step 1 in the vegetable oil for measurement using the data and the regression equation obtained in the step 2.
前記工程２及び前記工程３で用いられるスペクトルデータは１つの連続した波数領域であり、 The spectral data used in the step 2 and the step 3 is one continuous wavenumber region.
前記波数領域が、９４００±１０～５９００±１０ｃｍ The wave number region is 9400 ± 10 to 5900 ± 10 cm. －１-1 であり、And
前記工程１で選ばれるゴマリグナンがエピセサミンであり、且つ前記工程２で選ばれるスペクトル前処理が一次微分単独であることを特徴とするごま油を含有する植物油中のゴマリグナンの定量方法。 A method for quantifying sesame lignan in a vegetable oil containing sesame oil, wherein the sesame lignan selected in step 1 is episesamine and the spectral pretreatment selected in step 2 is a first derivative alone.
工程１：ＨＰＬＣ分析により、調整用植物油中のセサミン、セサモリン及びエピセサミンから選ばれる１種のゴマリグナンを定量する工程 Step 1: A step of quantifying one kind of sesame lignan selected from sesamin, sesamolin and episesamin in the vegetable oil for preparation by HPLC analysis.
工程２：フーリエ変換型近赤外光分析装置を用いて前記調整用植物油のスペクトルを測定した後に一次微分単独、二次微分単独、及び一次微分とベクトル正規化との組合せから選ばれる１つのスペクトル前処理をして得られるスペクトルデータと、前記工程１で得られたゴマリグナンの定量値と、を用いて回帰式を得る工程 Step 2: One spectrum selected from the first derivative alone, the second derivative alone, and the combination of the first derivative and the vector normalization after measuring the spectrum of the adjusting vegetable oil using a Fourier transform type near-infrared light analyzer. A step of obtaining a regression equation using the spectral data obtained by the pretreatment and the quantitative value of sesame lignan obtained in the above step 1.
工程３：フーリエ変換型近赤外光分析装置を用いて測定用植物油のスペクトルを測定した後に前記工程２で選ばれたスペクトル前処理をして得られるスペクトルデータと、前記工程２で得られた回帰式と、を用いて前記測定用植物油中の前記工程１で選ばれたゴマリグナンを定量する工程 Step 3: Spectral data obtained by measuring the spectrum of the vegetable oil for measurement using a Fourier transform type near-infrared light analyzer and then performing the spectrum pretreatment selected in the step 2, and the spectrum data obtained in the step 2. A step of quantifying the sesame lignan selected in the step 1 in the vegetable oil for measurement using a regression equation and
前記工程２及び前記工程３で用いられるスペクトルデータは１つの連続した波数領域であり、 The spectral data used in the step 2 and the step 3 is one continuous wavenumber region.
前記波数領域が、９４００±１０～５９００±１０ｃｍ The wave number region is 9400 ± 10 to 5900 ± 10 cm. －１-1 であり、And
前記工程１で選ばれるゴマリグナンがセサモリンであり、且つ前記工程２で選ばれるスペクトル前処理が二次微分単独であることを特徴とするごま油を含有する植物油中のゴマリグナンの定量方法。 A method for quantifying sesame lignan in a vegetable oil containing sesame oil, wherein the sesame lignan selected in the step 1 is sesamolin and the spectral pretreatment selected in the step 2 is a second derivative alone.
工程１：ＨＰＬＣ分析により、調整用植物油中のセサミン、セサモリン及びエピセサミンから選ばれる１種のゴマリグナンを定量する工程 Step 1: A step of quantifying one kind of sesame lignan selected from sesamin, sesamolin and episesamin in the vegetable oil for preparation by HPLC analysis.
工程２：フーリエ変換型近赤外光分析装置を用いて前記調整用植物油のスペクトルを測定した後に一次微分単独、二次微分単独、及び一次微分とベクトル正規化との組合せから選ばれる１つのスペクトル前処理をして得られるスペクトルデータと、前記工程１で得られたゴマリグナンの定量値と、を用いて回帰式を得る工程 Step 2: One spectrum selected from the first derivative alone, the second derivative alone, and the combination of the first derivative and the vector normalization after measuring the spectrum of the adjusting vegetable oil using a Fourier transform type near-infrared light analyzer. A step of obtaining a regression equation using the spectral data obtained by the pretreatment and the quantitative value of sesame lignan obtained in the above step 1.
工程３：フーリエ変換型近赤外光分析装置を用いて測定用植物油のスペクトルを測定した後に前記工程２で選ばれたスペクトル前処理をして得られるスペクトルデータと、前記工程２で得られた回帰式と、を用いて前記測定用植物油中の前記工程１で選ばれたゴマリグナンを定量する工程 Step 3: Spectral data obtained by measuring the spectrum of the vegetable oil for measurement using a Fourier transform type near-infrared light analyzer and then performing the spectrum pretreatment selected in the step 2, and the spectrum data obtained in the step 2. A step of quantifying the sesame lignan selected in the step 1 in the vegetable oil for measurement using a regression equation and
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