JPWO2014034668A1 - Quantitative determination of γ-oryzanol using near infrared spectroscopy - Google Patents

Abstract

An object of the present invention is to provide a simple and highly accurate method for quantifying γ-oryzanol contained in food, feed, cosmetics, pharmaceuticals, biological samples, and the like, and the following steps (1) to (3): The sample is obtained using the regression equation for quantifying γ-oryzanol in the sample by the method including the obtained regression equation and the near-infrared spectrum data of the sample in the wavelength region used to create the regression equation. Quantitatively γ-oryzanol. (1) Step of measuring the near-infrared light spectrum of the sample (2) Step of quantifying the γ-oryzanol content of the sample (3) In all or part of the wavelength range in which the near-infrared light spectrum was measured A step of analyzing the obtained spectral data and the quantified γ-oryzanol content by a multivariate analysis method and determining a factor related to the γ-oryzanol content

Description

  The present invention relates to a method for quantifying γ-oryzanol using near infrared spectroscopy.

  Rice oil obtained by extracting from rice bran (rice bran oil) is attracting attention as an edible oil that is rich in nutrition and physiological functions, and excellent in taste and texture, and its use is expanding. Major physiological functional components contained in rice oil include γ-oryzanol, vitamin E, and plant sterols. Among them, γ-oryzanol exists specifically in rice oil and has various health functions. It attracts attention. γ-Oryzanol is a compound that was first isolated from rice oil in 1954, and is a generic name for esters of ferulic acid and triterpene alcohol or plant sterols. One of the reasons why rice oil is superior in storage stability compared to other vegetable oils is that it is rich in γ-oryzanol. It is known that γ-oryzanol is also present in corn, barley and the like, but its content is small compared to rice.

  Since γ-oryzanol has an antioxidant action and an ultraviolet protection effect, it is used as an antioxidant and an ultraviolet absorber. Furthermore, it is used as a drug or a raw material for the purpose of improving hyperlipidemia, climacteric disorder, irritable bowel syndrome, physical symptoms in psychosomatic disorders, anxiety, tension, depression and the like. It is also approved for food additives as an antioxidant.

  Since γ-oryzanol is digested in the body and hydrolyzed, most of its physiological functions are considered to be functions of its constituent components ferulic acid, triterpene alcohol, and plant sterol. However, recently, in the form of γ-oryzanol, anti-inflammatory action through the inhibition of activation of transcription factor NF-κB, prevention / improvement effect of diabetes, ulcerative colitis, etc., reduction of type I allergy by action of capturing IgE Many useful functions as health functional foods have been reported, such as the action to prevent and improve ethanolic hepatitis by reducing oxidative stress, and the action of increasing insulin-like growth factor (IGF-1), which has an effect of treating Alzheimer's disease. Yes. From these points, accurately quantifying the content of γ-oryzanol is a very important technique for evaluating the quality of brown rice, rice bran, rice oil and related products containing them.

  As a method for quantifying γ-oryzanol, γ-oryzanol has a strong absorption maximum in the vicinity of a wavelength of 320 nm. Therefore, it is common to dissolve sample oil and fat in a solvent such as n-heptane and perform quantification with a spectrophotometer. is there. Although this method is excellent in rapidity and is a very simple technique, there is a drawback that accurate quantification cannot be performed depending on the sample composition because of low compound selectivity for UV detection. As a more precise analysis method for γ-oryzanol, a method using high performance liquid chromatography (HPLC) is known, and many examples using reverse phase chromatography have been reported (Non-Patent Documents 1 and 2). . However, these HPLC methods require the examination of conditions for each sample, and have the disadvantages that the operation is complicated and lacks in speed.

  Near-infrared spectroscopy is spectroscopy based on absorption of near-infrared light (generally indicating a wavelength range of 800 to 2500 nm) by substance molecules, and in the field of food, measurement and processing of components such as fruits, vegetables, and fish Used for food quality control. The advantage of near-infrared spectroscopy is in particular that the sample is not damaged and that it can be analyzed as it is, so that multicomponent simultaneous quantification is possible very quickly. Near-infrared spectroscopy is a common method for analyzing fruits and vegetables. As non-destructive analysis methods for fruits and vegetables, a saccharide determination method (for example, Patent Document 1), a moisture determination method (for example, Patent Document 2), and a nitrate ion determination method (for example, Patent Document 3) have been reported. Near-infrared spectroscopy has also been put to practical use in the analysis of cereals, and an example of simultaneous analysis of rice starch, protein, moisture, ash, amino acid, and taste value has been reported (Non-patent Document 3). In addition, the authors developed a nondestructive analysis method for brown rice moisture and oil (triacylglycerol) using near-infrared spectroscopy and filed a patent application (Japanese Patent Application No. 2011-212455). Near-infrared spectroscopy is also used for clinical trials, and an example of simultaneous analysis of cholesterol, blood glucose level, triacylglycerol, adiponectin and the like in blood has been reported (Patent Document 4). However, it has not been reported so far that γ-oryzanol has been quantified by nondestructive analysis, and no analysis example using near infrared spectroscopy has been reported.

JP 2009-098033 A JP 2004-020470 A JP 2010-210355 A JP 2007-285922 A

Azrina, A. et al., Extraction and Determination of Oryzanol in Rice Bran of Mixed Herbarium UKMB; AZ 6807: MR 185, AZ 6808: MR 211, AZ6809: MR 29, ASEAN Food Journal, 15 (1), 89-96 (2008) A.G.Gopala Krishna et al., Effect of Refining of Crude Rice Bran Oil on the Retention of Oryzanol in the Refined Oil, JAOCS, 78 (2), 127-131 (2001) Miyuki Kondo, “Chemical Analysis of Foods by Near Infrared Spectroscopy” Bulletin of Nagoya Bunri University, 2007, No. 7, p23-28

  An object of the present invention is to provide a simple and highly accurate method for quantifying γ-oryzanol contained in foods, feeds, cosmetics, pharmaceuticals, biological samples and the like.

The present invention includes the following inventions in order to solve the above problems.
[1] A method for obtaining a regression equation for quantifying γ-oryzanol in a sample, comprising the following steps (1) to (3):
(1) Step of measuring the near-infrared light spectrum of the sample (2) Step of quantifying the γ-oryzanol content of the sample (3) In all or part of the wavelength range in which the near-infrared light spectrum was measured Analyzing the obtained spectral data and the quantified γ-oryzanol content by a multivariate analysis method, and determining a factor related to the γ-oryzanol content [2] a food, feed containing γ-oryzanol, The method according to [1] above, wherein the method is a cosmetic or pharmaceutical product, or a raw material or material thereof.
[3] The method according to [1] or [2], wherein the sample is a grain or a part thereof.
[4] The method according to [3], wherein the grain is brown rice.
[5] The method according to [1] or [2], wherein the sample is oil or fat, a processed product thereof, or a raw material or material thereof.
[6] The method according to [5] above, wherein the fat / oil contains rice oil or rice oil.
[7] The method according to [1] or [2], wherein the sample is derived from a living body.
[8] The method according to [1], further comprising a step of extracting γ-oryzanol in the sample with a solvent before the step (1).
[9] The method according to [8], wherein the solvent is hexane or chloroform.
[10] The method according to any one of [1] to [9], wherein the near-infrared light spectrum is a spectrum of diffusely reflected light or transmitted light.
[11] The method according to any one of [1] to [10], wherein the wavelength region in the step (3) is 800 to 2500 nm or a part thereof.
[12] The method according to [11], wherein the wavelength region in the step (3) includes at least one region of 1440 ± 10 nm, 1938 ± 10 nm, 2030 ± 10 nm, 2105 ± 10 nm.
[13] Using the regression equation obtained by the method according to any one of [1] to [12] and near-infrared light spectrum data of the test sample in the wavelength region used to create the regression equation. A method for quantifying γ-oryzanol characterized by the above.

  According to the present invention, it is possible to provide a method for quantifying γ-oryzanol contained in foods, feeds, cosmetics, pharmaceuticals, biological samples and the like easily and with high accuracy. In particular, the present invention is very useful in that the γ-oryzanol content in fats and oils and the γ-oryzanol content in one grain of brown rice can be quantified nondestructively and rapidly.

It is a figure which shows the correlation of NIR prediction value (quantitative value) and chemical analysis value (actual measurement value) about (gamma)-oryzanol content in a hexane solution. It is a figure which shows the correlation of NIR predicted value (quantitative value) and chemical analysis value (actual measurement value) about (gamma)-oryzanol content in rice oil. It is a figure which shows the UV chromatogram obtained by analyzing (gamma)-oryzanol sample (98%, Tsukino foodstuff) by HPLC, and detecting with a ultraviolet visible spectroscopic detector (320 nm). It is a figure which shows the correlation of NIR prediction value (quantitative value) and chemical analysis value (actual measurement value) about (gamma)-oryzanol content in one grain of brown rice.

[Method for obtaining regression equation for quantifying γ-oryzanol in sample]
The present invention provides a method for obtaining a regression equation for quantifying γ-oryzanol in a sample (hereinafter referred to as “method for obtaining the regression equation of the present invention”). The method of obtaining the regression formula of this invention should just contain the process of the following (1)-(3).
(1) A step of measuring a near-infrared light (hereinafter referred to as “NIR”) spectrum of a sample for obtaining a regression equation (2) A step of quantifying the γ-oryzanol content of the sample for obtaining a regression equation (3 ) The spectrum data obtained in all or part of the wavelength range where the NIR spectrum was measured and the measured value of the γ-oryzanol content were analyzed by a multivariate analysis method, and factors related to the γ-oryzanol content were determined. Step of determining The method for obtaining the regression equation of the present invention may include steps other than (1) to (3), and the contents thereof are not limited.

  In step (1), the NIR spectrum of the sample is measured. The measuring method of the NIR spectrum is not particularly limited, but the diffuse reflection method or the transmission method is preferable, and it is more preferable to use these properly depending on the sample. If the sample is a solid, it is preferable to use the diffuse reflection method. If the sample is a liquid, the measurement is preferably performed using the transmission method. In this case, it is preferable to measure diffuse reflection light. When the sample is a solution, it is preferable to measure the transmitted light by appropriately adjusting the concentration. The measurement cell is not particularly limited, but it is preferable to use properly depending on the measurement sample and the measurement method. NIR spectra are measured not only in off-line analysis but also in at-line, on-line, in-line, non-invasive analysis, etc. Also good. The NIR spectrum can be measured by taking a sample and measuring with a near-infrared analyzer in the analysis room, or by bringing a near-infrared analyzer into a production site or warehouse and measuring it on the spot. It is also possible to continuously measure the NIR spectrum of a sample by incorporating a near-infrared analyzer in the production line.

  For measurement of the NIR spectrum, a commercially available near-infrared spectrometer can be used, but those capable of measuring transmitted light and diffuse reflected light are preferred. Moreover, what was equipped with the solid measurement module, the liquid measurement module, the optical fiber module, and the solid permeation | transmission measurement module according to a sample can be utilized. Moreover, what provided the temperature control function of the measurement cell is more preferable. Specifically, MPA (Bruker Optics), TANGO (Bruker Optics), NIRFlex N-500 (Buch), NIRFlex N-400 (Buch), NIRLab N-200 (Buch), NIRMaster (Buch), NIRS6500 (Nireco) ), XDS / NIR series (Nireko), SpectraStar Series (BLTEC), optical quality checker HOS-F series (SAIKA), optical quality checker portable HQC-F30 (SAIKA), IRAffinity-1 (Shimadzu Corporation), LAMBDA 750/950 / 1050 and Frontier NIR (Perkin Elmer), FT-IR4000 series and FT-IR6000 series (JASCO), FT-NIR MB36 0 (ABB Bomem) and the like, with preference given to MPA, preferably TANGO, MPA is particularly preferred. The obtained spectrum is preferably a Fourier transform spectrum.

  The γ-oryzanol determined in the present invention refers to an ester of ferulic acid and triterpene alcohol or an ester of ferulic acid and plant sterol. Examples of the triterpene alcohol include cycloartenol, 24-methylenecycloartanol, cyclobranol, cycloartanol, cyclosador, cyclolaudenol, butyrosperimol, parqueol, and isocyclosador. Plant sterols include α-sitosterol, β-sitosterol, stigmasterol, campesterol, α-sitostanol, β-sitostanol, stigmasteranol, campestanol, brassicasterol, fucosterol, isofucosterol, spinasterol, avenasterol, etc. Is mentioned. The molecular species composition of γ-oryzanol is not particularly limited, and ferulic acid and the ester of the above-mentioned triterpene alcohol or plant sterol may be present in any ratio.

  The sample is not particularly limited, but a sample that may contain γ-oryzanol is preferable. Specific examples include foods, feeds, cosmetics, pharmaceuticals, and biological samples. Especially, the thing in which the plant or the part containing (gamma) -oryzanol is used as a raw material or a raw material, or the thing to which (gamma) -oryzanol is added as a food additive or an antioxidant is more preferable. Examples of plants containing γ-oryzanol include rice, barley, and corn. Part of the plant includes these seeds, or grains. The grain may be in any form such as the seed itself, a part of the seed, or a powder obtained by pulverizing these. Rice is preferred as the plant, and rice seeds from which rice husks have been removed, rice bran obtained from brown rice, brown rice in the middle of whitening, white rice after whitening, processed rice bran, defatted rice bran, oil residue of rice bran, rice bran in the middle of degreasing , And a pulverized product thereof.

  Examples of foods containing γ-oryzanol-containing plants or parts thereof as raw materials and materials include confectionery (rice cake, steamed confectionery, rock cake, crepe, rice cake, hot cake, jelly, gum, candy, gummi, Muffins, steamed buns, cupcakes, baked goods, grilled sweets, dumplings, cookies, castella, madeleine, waffles, etc.), breads (bread, scones, naan etc.), noodles (udon, ramen, soba, pasta, macaroni, etc.) , Kneaded products (kamaboko, chikuwa, hanpen, sweet potato, sausage, etc.), cooking in general (white rice, rice balls, rice porridge, tofu, cereal, gnocchi soup, okonomiyaki, curry, stew, gratin, tempura, pasta soup, dango soup, pizza, Hamburger, fried rice, soup, etc.), flour (rice flour, brown rice flour, Starch, bread crumbs, potato starch, tempura, etc.), drinks (tea, coffee, cocoa, soft drinks, fruit drinks, milky drinks, alcoholic beverages, energy drinks, etc.), seasonings (soy sauce, sauce, cooked liquor, mirin, dashi) And miso), tablet confectionery, and supplements.

  Although the content of γ-oryzanol is lower than that of plants, foods using animal-derived materials or materials are also preferable as samples. In particular, livestock bred with feed containing γ-oryzanol, foods using cultured fish as raw materials or materials, food additives and foods added with γ-oryzanol as an antioxidant are suitable. Examples of foods using animal-derived ingredients or materials include beef, beef tallow, milk, pork, lard, horse meat, lamb, chicken, chicken eggs, liver, fish, fish oil, fish eggs, fish meal and processed products thereof (for example, For example, egg yolk, egg white, sometimes egg, boiled egg, fried egg, meringue, powdered milk, skimmed milk powder, pudding, yogurt and the like.

Examples of feed containing such raw materials and materials include feed for livestock such as cattle, horses and pigs, feed for poultry such as chickens, pet feed for dogs and cats, feed for cultured fish, and the like.
Cosmetics containing such raw materials and materials include, for example, creams, lotions, gels, mists, masks, packs, shampoos, rinses, baths containing plant extracts containing γ-oryzanol such as rice, barley and corn. Agents and the like.
Examples of pharmaceuticals containing such raw materials and materials include tablets, powders, capsules and the like using excipients containing plant-derived components containing γ-oryzanol such as rice, barley, and corn. In addition, pharmaceuticals of all dosage forms containing γ-oryzanol as an active ingredient, such as hyperlipidemia therapeutic agent, climacteric disorder therapeutic agent, skin external preparation, hair restorer, toothpaste, oral medicine, eye drops, nasal drops, etc. Can be mentioned.

  When the sample is food, feed, cosmetics, pharmaceuticals, biological sample, etc., or these raw materials and materials, and the purpose is non-destructive analysis, the sample may be subjected to NIR spectrum measurement in step (1) as it is, In order to improve the measurement accuracy, it is preferable to prepare a sample. For example, when the sample is a solid, it is preferable that foreign matters have been removed. If there is no problem, it is preferable to remove the moisture by performing a drying treatment. The drying method is not particularly limited, but it is preferable to use a freeze drying method, a vacuum drying method, or the like in order to avoid damage or alteration of the sample. It is preferable that the solid sample is subjected to these pretreatments, and an appropriate measurement cell is used depending on its size and shape. For example, when brown rice is used as a sample, several grains of brown rice may be packed in a grain cell or the like, but when measuring with one grain, it is preferable to use a single cell.

  When non-destructive analysis is not intended, it is preferable to perform a pulverization process for homogenizing the sample, and it is preferable to perform a drying process to remove moisture. The drying method in this case is not particularly limited, but a method that can avoid denaturation of the sample, such as a freeze drying method or a vacuum drying method, is preferable. The pulverization method is not particularly limited. For example, when the sample is solid and the sample is too large to be crushed as it is, it is preferable to cut and break it to an appropriate size before applying the pulverization treatment. In this case, it is preferable to first perform freeze-drying treatment or vacuum drying treatment before crushing. For example, when the sample is a liquid, it is preferable that precipitates and foreign matters have been removed. When there is no problem even when non-destructive analysis is not intended or when non-destructive analysis is intended, it is preferable to perform homogenization by stirring treatment or water removal by drying treatment. The drying method is not particularly limited, and a freeze drying method, a vacuum drying method, or the like can be used.

  In the case where the main component is protein regardless of whether the sample is solid or liquid, and non-destructive analysis is not intended, it is preferable to perform protein removal treatment. The method of protein removal treatment is not particularly limited, and examples thereof include precipitation method by adding acid or organic solvent, ultrafiltration method, dialysis method, column method and the like. When the sample is solid, the sample is preferably broken and pulverized and then suspended in a suitable solvent for protein removal treatment. The concentration of γ-oryzanol in the sample is preferably at least 10 ppm or more. As an example, when brown rice is directly analyzed, 50 ppm or more is preferable, and 100 ppm or more is more preferable.

  Oils and fats or processed products thereof can also be suitably used as samples. Although it does not specifically limit about the raw material, composition, manufacturing method, etc. of fats and oils or its processed goods, The thing containing (gamma) -oryzanol is preferable. Examples of fats and oils containing γ-oryzanol include rice oil, rice germ oil, γ-oryzanol-rich oil, corn oil, and barley oil. Also, blended oils of these with other fats and oils are suitable as fats and oils containing γ-oryzanol. Other fats and oils include, for example, sesame oil, safflower oil, soybean oil, rapeseed oil, sunflower oil, olive oil, grape seed oil, coconut oil, palm oil, palm kernel oil, cottonseed oil, coconut oil, linseed oil, castor oil, pearl oil , Wheat germ oil, perilla oil, sesame oil, sacha inchi oil, walnut oil, kiwi seed oil, salvia seed oil, macadamia nut oil, hazelnut oil, pumpkin seed oil, coconut oil, tea seed oil, borage oil, cacao oil, sal fat, Examples thereof include vegetable oils and fats such as shea fat and algae oil, or animal oils such as beef tallow, lard and fish oil, or oils and fats such as transesterified oil, hydrogenated oil and fractionated oil. Among these, rice oil or fats and oils containing rice oil are preferable. The purity of fats and oils is not particularly limited, and may be any of refined fats and oils, crude oil before refining, and fats and oils during refining. The refined fats and oils may be before use or after use (waste oil). Further, by-products such as deodorized scum, soap stock, free fatty acid, wax (wax), gum and the like produced during the essential oil process can be suitably used as a sample.

  Examples of processed oils and fats include tempura, tempura, deep-fried, fried, margarine, butter, cheese, french fries, deep-fried, deep-fried, fresh cream, coffee milk (powder), whipped cream, dressing, mayonnaise, tartar Foods such as sauce, oyster sauce, white sauce, potato chips, chocolate, biscuits, corn snacks, popcorn, crackers, fried rice crackers, rice crackers, short cakes, eclairs, cream puffs, wafers, bavaroa, donuts, karinto, ice cream It is done. Moreover, for example, cosmetics mainly composed of fats and oils such as soaps, detergents, foundations, cleansing oils, aroma oils, lip balms, and nail polishes can be used.

  In the case of fats and oils or processed fats and oils whose main component is fats and oils, the sample may be subjected to the NIR spectrum measurement in step (1) as it is, but it is preferable to prepare the sample in order to improve measurement accuracy. For example, in the case where the sample is a solid fat or oil processed product, it is preferable that foreign matters are removed and the sample is in a homogeneous state. The NIR spectrum may be measured by the diffuse reflection method while it is in a solid state, but if dissolution by heating is possible, the NIR spectrum is preferably measured by heating to a liquid state and measuring by the transmission method. For example, when the sample is a liquid oil or processed oil product, it is preferable to homogenize by performing a stirring process, and it is preferable that precipitates and foreign substances are removed. When the viscosity of the sample is high, it is preferable to lower the sample viscosity by heating the measurement cell containing the sample. When the sample is opaque and can be clarified by heating, it is preferable to measure in a state where the measurement cell is heated and clarified. The concentration of γ-oryzanol contained in these fats and oils is preferably 10 ppm or more, more preferably 100 ppm or more, and further preferably 1000 ppm or more. The case where the main component is a processed fat or oil other than fat is the same as the case of the above-mentioned food, feed, cosmetics, pharmaceuticals, etc., or these raw materials and materials.

  The regression equation obtained by the present invention can also be used for quantification of γ-oryzanol in a sample derived from a living body for the purpose of clinical trials or γ-oryzanol metabolism studies. Although the sample derived from a living body is not particularly limited, for example, a biological component of an animal is preferable. Examples of animal-derived samples include blood, serum, plasma, tissue fluid, lymph fluid, cerebrospinal fluid, breast milk, pus, mucus, runny nose, sputum, urine, feces, ascites, semen from humans, laboratory animals, livestock, farmed fish, etc. And body fluids such as skin, mucous membrane, various organs, tissues such as bone, hair, body hair and the like.

When the sample is a sample derived from a living body and is intended for nondestructive analysis, the sample may be directly used for the NIR spectrum measurement in the step (1), but the sample is prepared in order to improve the measurement accuracy. It is preferable. When the sample is a solid, it is preferable to remove foreign substances, and when the sample is a liquid, it is preferable to perform homogenization by removing the foreign substances and precipitation.
When non-destructive analysis is not aimed, it is preferable to perform homogenization or protein removal treatment by crushing or stirring. However, since γ-oryzanol contained in the sample is predicted to be very low in concentration, there is a possibility that sufficient measurement sensitivity cannot be obtained by direct analysis. Therefore, it is a condition that a stable recovery rate can be obtained, but it is more preferable to concentrate a sample or prepare a crude lipid extracted using a solvent as a measurement sample than to directly analyze the sample. The extraction method is not particularly limited, but it is preferable to perform the protein removal treatment first in order to improve extraction efficiency. The method of protein removal treatment is not particularly limited, and is the same as the case of the above-mentioned food, feed, cosmetics, pharmaceuticals, etc. or these raw materials and materials. When the sample is directly analyzed, the concentration of γ-oryzanol in the measurement sample is preferably 10 ppm or more.

  You may extract (gamma)-oryzanol in a sample with a solvent, and use the obtained extract for process (1) or process (2). When using such an extract for measurement, it is preferable to have a step of extracting γ-oryzanol in the sample with a solvent before step (1) of the method for obtaining the regression equation of the present invention. The solvent used for extraction is preferably a solvent in which γ-oryzanol can be dissolved. Examples of the solvent in which γ-oryzanol can be dissolved include ethers such as diethyl ether, dimethyl ether, methyl ethyl ether, diisopropyl ether, 2-methoxyethanol, and tetrahydrofuran (THF), methanol, ethanol, propanol, and isopropanol (2-propanol). ), Alcohols having 3 to 11 carbon atoms such as butanol, t-butanol, glycerin and ethylene glycol, alkanes having 5 to 17 carbon atoms such as pentane and hexane, and alkenes having 5 to 20 carbon atoms such as pentene and hexene. , Acetone, acetonitrile, chloroform, hexane, cyclohexane, heptane, ethyl acetate, dioxane, tetrahydrofuran (THF), trifluoroacetic acid, benzene, dimethyl sulfoxide (DMSO), stone Ether, toluene, dimethylformamide, dichloromethane, carbon tetrachloride, etc. t- butyl acetoacetate are exemplified. These solvents may be used alone, or may be used in an arbitrary ratio as long as they can be mixed uniformly. The extraction method is not particularly limited, and a known lipid extraction method can be used. For example, a Soxhlet extraction method, a chloroform-methanol extraction method, an acid decomposition method and the like can be mentioned. Among them, the Soxhlet extraction method is preferable in terms of extraction efficiency. The sample may be solvent-extracted as it is, but when the sample is a solid, it is preferable to remove foreign matter, and it is preferable to perform a pulverization treatment in order to homogenize the sample and improve extraction efficiency. When the sample is a liquid, it is preferable to remove foreign matters and precipitates, and it is preferable to homogenize by performing a stirring process. In the case of removing moisture, it is preferable to use a freeze drying method, a vacuum drying method, or the like.

  The extract may be in the state of crude lipid from which the solvent has been removed or in the form of an extract dissolved in the solvent. In the former case, it is preferable to prepare the sample by the same method as for the oil and fat sample. In the latter case, the removal of the solvent may be performed and the preparation similar to that of the oil and fat sample may be performed, or the extract may be subjected to NIR spectrum measurement as it is or after performing solvent exchange, dilution or the like as necessary. Also good. In the case of an extract, the dissolution solvent can be the same as the above-mentioned extraction solvent, but hexane or chloroform is preferably used, and hexane is more preferable for highly accurate measurement. When the extraction solvent is not one of these solvents, it is preferable to perform solvent exchange with hexane or chloroform. The method for removing the solvent is not particularly limited, but it is preferably performed under low temperature and vacuum conditions in order to avoid denaturation of the measurement sample. Specifically, a method similar to the above-described drying (moisture removal) method can be suitably used. The γ-oryzanol concentration in the measurement sample is the same as that of the above-described oil and fat sample when the sample is a crude lipid, but is preferably 10 ppm or more and more preferably 50 ppm or more when the sample is a solution.

  The sample for obtaining the regression equation is preferably prepared in a form similar to the test sample used for quantification of the γ-oryzanol content and having a different γ-oryzanol content. A method for preparing such a sample is not particularly limited. For example, when the sample is a grain such as brown rice, a method for using various varieties and strains having different γ-oryzanol contents can be used as a sample for obtaining a regression equation. When the sample is a solid such as a food or processed oil product, a method of adjusting the concentration by mixing a plant containing γ-oryzanol or a part thereof as a raw material or material can be considered. When the sample is a homogeneous liquid such as fats and oils, the concentration of the sample for obtaining the regression equation may be adjusted by mixing a high and low γ-oryzanol content at an arbitrary ratio. When the sample is a solution, it can be used as it is. Moreover, although it can melt | dissolve in a solvent, it does not specifically limit about the form before melt | dissolving in a solvent. As a sample for obtaining the regression equation, a solution obtained by dissolving a sample of γ-oryzanol in a solvent may be used, but it is more preferable to prepare and use a solution having a composition close to that of the test sample. In addition, if there is one solution sample having a known γ-oryzanol concentration, a solution sample for obtaining a regression equation is obtained by adding γ-oryzanol to this solution or diluting this solution with a solvent to obtain γ-oryzanol. The concentration can be adjusted as needed. The sample for obtaining the regression equation is preferably prepared so that all the test samples are placed between the sample having the minimum concentration and the maximum concentration of γ-oryzanol. The larger the number of samples for obtaining the regression equation, the better. In particular, in the case of a solid sample such as brown rice, 30 samples or more are preferable and 50 samples or more are more preferable in order to obtain sufficient prediction accuracy.

  In step (2), the γ-oryzanol content of the sample whose NIR spectrum was measured in step (1) is measured. What is necessary is just to measure (gamma)-oryzanol content by the sample unit provided to the process (1). The method for quantifying the γ-oryzanol content in the sample is not particularly limited, and a known method can be used. For example, γ-oryzanol can be easily quantified by an absorbance method using a spectrophotometer or a Folin-Denis method which is a detection method of polyphenols. When higher analysis accuracy is required, precise measurement using gas chromatography (GC), high performance liquid chromatography (HPLC), or the like can be performed. In order to improve the prediction accuracy of the calibration curve, it is preferable to perform precise measurement, and it is more preferable to use HPLC in terms of accuracy. When the sample is a solid, for example, a crude lipid is extracted by a known method, and a solution obtained by solvent substitution, concentration, dilution, purification, etc., if necessary, can be subjected to quantification. When the sample is a liquid, it may be subjected to quantification as it is, and solvent substitution, concentration, dilution, purification, etc. may be performed as necessary.

  When HPLC is used, the method is not particularly limited, but reverse phase chromatography, normal phase chromatography, gel permeation chromatography, ion chromatography and the like are preferable, and reverse phase chromatography is more preferable. The mobile phase used in reverse phase chromatography is preferably a solvent that can dissolve γ-oryzanol and is suitable for reverse phase chromatography. Suitable solvents include lower alcohols such as methanol, ethanol, propanol, isopropanol and n-butanol, ethers such as tetrahydrofuran (THF), acetonitrile, acetic acid, formic acid, acetone, dimethyl sulfoxide (DMSO), hexane and ethyl acetate. , Dioxane, acetone, chloroform, benzene, toluene, dichloromethane, ammonium acetate, ammonium formate, water, trifluoroacetic acid, and a homogeneous mixture thereof. The organic solvent used for the mobile phase is preferably higher than HPLC grade. Examples of the column used for reverse phase chromatography include polymer-based, silica-based and hybrid columns, and of these, silica-based columns are preferred. Examples of the reverse phase column include C30, C18 (ODS), C8, C4, TMS, Ph, PFP, and CN columns having different stationary phases. Among these, the C18 (ODS) column is particularly preferable. The shape of the column is not particularly limited. The particle size of the column filler is not particularly limited, but the average particle size is preferably 5 μm or less, more preferably 3 μm or less. The column temperature is usually used in the range of about 10-60 ° C, preferably in the range of about 20-50 ° C, more preferably in the range of about 30-50 ° C. The flow rate is not particularly limited as long as it does not cause a problem in column performance. The detector is not particularly limited as long as it can detect γ-oryzanol. Specifically, a fluorescence (FL) detector, an ultraviolet-visible spectroscopy (UV-Vis) detector, a photodiode array (PDA) detector, an evaporative light scattering (ELSD) detector, a mass (MS) detector, an electrochemical Examples include (EC) detectors, differential refractive index (RI) detectors, electrical conductivity (CD) detectors, and corona CAD detectors. Among them, in view of the specificity of γ-oryzanol, it is preferable to use an ultraviolet-visible spectroscopic detector, a photodiode array detector, a mass detector, or a fluorescence detector.

  γ-oryzanol is composed of a plurality of molecular species. Depending on the separation conditions, the peak of γ-oryzanol may be single or may be divided into a plurality of peaks. This separation condition is not particularly limited, but a condition in which a peak is separated for each molecular species is preferable in terms of easy identification. If a standard for each molecular species is available, the γ-oryzanol quantification method can be quantified for each molecular species, and the total of the quantitative values may be used as the total amount of γ-oryzanol. However, a standard for each molecular species is available. If this is not possible, the mixture may be quantified using the total peak area value or height as a standard. For quantification, the peak height or the area may be used, but when peak separation for each molecular species cannot be obtained, it is better to use the peak area. As long as the purity of γ-oryzanol is clear, the composition of the sample is not particularly limited, and it may be a mixture of molecular species or a single molecular species. Examples of γ-oryzanol (mixture) preparations include “TSUNO γ-oryzanol” (Tsukino Foods), “γ-oryzanol” (Oryza Oily), etc., and examples of single molecular species include “ferulic acid cyclohexane”. Artenyl ”(Wako Pure Chemical Industries) and the like.

  In step (3), the spectrum data obtained in all or a part of the wavelength range in which the NIR spectrum is measured and the quantified γ-oryzanol content are analyzed by a multivariate analysis method, and the γ-oryzanol content is determined. Determine the factors involved. The part of the wavelength range in which the NIR spectrum is measured is an arbitrary partial region included in the wavelength range in which the NIR spectrum is measured. Although a part of wavelength range which measured the NIR spectrum is not specifically limited, It is preferable that it is 800-2500 nm or its part, and it is more preferable that it is 1333-2175 nm or its part. Further, those containing 1440 ± 10 nm, 1938 ± 10 nm, 2030 ± 10 nm, 2105 ± 10 nm, preferably 1 or more, more preferably 2 or more regions related to γ-oryzanol are preferable. Further, this one wavelength region is preferably a continuous region where the difference between the maximum wavelength and the minimum wavelength is 20 nm or more, more preferably a continuous region of 100 nm or more, and a continuous region of 200 nm or more. Is more preferable. Only one wavelength region or a plurality of wavelength regions may be set. However, when diffuse reflected light is used, the sum of the differences between the maximum wavelength and the minimum wavelength in the wavelength region used for analysis (if there is only one wavelength region used for analysis) is preferably 250 nm or more, and 500 nm or more. More preferably.

  The multivariate analysis method used in the step (3) includes PLS (partial least squares) regression analysis method, multiple linear regression analysis (MLR) method, principal component regression analysis (PCR, principal component regression) method, There are CLS (classical least squares) regression analysis methods, among which the PLS regression analysis method is preferably used. PLS regression analysis can be performed using commercially available software. Examples of the software to be used include The Unscrambler (Camo Software Japan Co., Ltd.), OPUS (Bruker Optics Co., Ltd.), NIRCal (Nippon Büch Co., Ltd.), and Pirouette (GL Sciences Inc.). Using these software, the NIR diffuse reflected light spectrum is analyzed, and the result is used in the quantification method of the present invention. The spectrum data or the Fourier transform spectrum data to be used may be original spectrum data, but it is preferable to use data obtained by processing the original spectrum data. As a method of data processing, for example, first-order differentiation, second-order differentiation, third-order differentiation such as third-order differentiation (Derivative), smoothing (Smoothing), spectrum subtraction (Normalization), normalization (Normalize), MSC correction (MultiplicativeScatter) Correction), SNV correction (Standard Normal Variate Correction), and the like.

  By using the spectral data of the wavelength region used for the analysis and the quantified γ-oryzanol content (actual value of γ-oryzanol) for PLS regression analysis, a factor related to γ-oryzanol content is determined to obtain a regression equation Can do. The factor related to the γ-oryzanol content refers to a hypothetical variable highly correlated with the change in the γ-oryzanol content inherent in the spectral data. Using the regression coefficient of this factor, a regression equation for predicting the γ-oryzanol content is created from the spectrum data. In near-infrared spectroscopy, after creating a regression equation, the measurement accuracy of the created regression equation is evaluated using a sample other than the sample for obtaining the regression equation (evaluation sample). The measurement accuracy of the regression equation is evaluated by the standard deviation or average value of the residual (measurement error) between the actual measurement value of the evaluation sample and the analysis value based on the predicted value calculated by the created regression equation. Scatter charts of the predicted value (quantitative value) and the actual measurement value are shown in FIGS. The calibration curve regression equation is a regression equation derived so that the correlation between the actual measurement value and the predicted value (quantitative value) is maximized.

[Method of quantifying γ-oryzanol using NIR spectroscopy]
The quantification method of γ-oryzanol using the NIR spectroscopy of the present invention (hereinafter referred to as “the quantification method of the present invention”) includes a regression equation obtained by the method of obtaining the regression equation of the present invention, and the regression equation. Is a quantification method using NIR spectrum data of a test sample in the wavelength region used for preparation of the above. Specifically, first, the NIR spectrum of the test sample is measured. The wavelength range for measuring the NIR spectrum is not particularly limited as long as it includes the wavelength region used for preparing the regression equation, and is preferably 800 to 2500 nm.

  The NIR spectrum data of the test sample can be measured in the same manner as in the case of measuring the NIR spectrum data in the method of obtaining the regression equation of the present invention, but the NIR spectrum measuring device, measurement method, and analysis method are regression equations. It is preferable to use the same sample as that for obtaining the above. That is, a NIR spectrum is measured using a commercially available near-infrared spectrometer, and spectrum data, preferably Fourier transform spectrum data, is measured. The spectrum data or the Fourier transform spectrum data may be the original spectrum data, but it is preferable to use the original spectrum data. Examples of data processing methods include, for example, first-order differentiation, second-order differentiation, third-order differentiation and the like (Derivative), smoothing (Smoothing), spectrum subtraction (Normalization), normalization (Normalization), MSC correction (Multiplicative). Scatter correction), SNV correction (standard normal variant correction), and the like.

  The predicted value of the γ-oryzanol content of the test sample is calculated by analyzing the NIR spectrum data (preferably subjected to the above processing) of the test sample and applying the previously obtained regression equation. This predicted value becomes the quantitative value of γ-oryzanol of the test sample by NIR. The analysis of the spectrum data here and the calculation of the predicted value (quantitative value) can be performed using commercially available software. Examples of the software to be used include The Unscrambler (Camo Software Japan Co., Ltd.), OPUS (Bruker Optics), NIRCal (Nippon Büch), Pirouette (GL Science).

  In the quantification method of the present invention, it is assumed that the test sample contains a large amount of water, and in that case, it may be quantified per dry matter amount. In such a case, it is necessary to obtain and quantify NIR spectrum data after performing moisture removal treatment on the measurement sample, or to quantify the moisture content by some method and correct the predicted value (quantitative value). Examples of the method for determining the water content include a method in which a part of a sample is collected and measured by a loss on drying method or a Karl Fischer method. For the purpose of nondestructive analysis, a method for predicting the water content using NIR spectrum data can be suitably used.

  EXAMPLES Hereinafter, although an Example demonstrates this invention in detail, this invention is not limited to these.

[Example 1: Calibration curve regression equation of γ-oryzanol sample]
1-1 Examination of hexane solvent
(1) 1 g of commercially available palm oil is dissolved in 100 mL of hexane to make a 1% (w / v) solution, and γ-oryzanol (98%, manufactured by Tsukino Foods) is added to add γ-oryzanol content of 5 to 280 ppm. A solution sample was prepared. The γ-oryzanol content in each sample was quantified using a spectrophotometer Nano Drop2000c (Thermo Fisher Scientific) based on the absorbance at 320 nm using a γ-oryzanol / hexane solution as a standard.
(2) The NIR transmitted light of the sample prepared in (1) was measured in the range of 800 nm to 2500 nm (12500 to 4000 cm ⁻¹ ) using an FT type near infrared analyzer MPA (Bruker Optics).
(3) Pre-processing (second-order differentiation) of the obtained original spectrum, wavelength data of the pre-processed spectrum (1333-1470 nm, 1835-2175 nm) as explanatory variables, and PLS with γ-oryzanol content as an objective variable A calibration model of a hexane solvent sample was created by regression analysis. The correlation between the predicted NIR value (quantitative value) and the chemical analysis value (actual measurement value) is shown in FIG. The NIR predicted value (ppm, quantitative value) here is a calculated value derived by a measuring device from a calibration curve regression equation of γ-oryzanol content and NIR spectrum data. From the PLS regression analysis results, four regions of 1440 ± 10 nm, 1938 ± 10 nm, 2030 ± 10 nm, and 2105 ± 10 nm were listed as important wavelength data related to the γ-oryzanol content.

1-2 Examination of chloroform solvent In Example 1, hexane was changed to chloroform, and a calibration curve regression equation was similarly prepared.
1-3 Examination of methanol solvent In Example 1, hexane was changed to methanol, and a calibration curve regression equation was similarly prepared.
1-4 Examination of t-Butanol Solvent In Example 1, hexane was changed to t-butanol, and a calibration curve regression equation was similarly prepared.

  Table 1 shows the correlation coefficient between the NIR predicted value (ppm, quantitative value) of γ-oryzanol in different solvents and the actually measured value. A very high correlation coefficient was obtained in hexane and chloroform, but sufficient correlation was not obtained in the case of t-butanol or methanol.

[Example 2: Examination of optimum wavelength range of hexane solvent sample]
For the hexane solvent sample of Example 1, a γ-oryzanol calibration model was created by changing the wavelength range of the wavelength data of the pretreatment spectrum in various ways. The coefficient of determination R ² in all conditions of the obtained calibration models are shown in Table 2. The wavelength indicates the wavelength range used, and analysis was performed using data of one section (conditions No. 7 to 15) or two sections (conditions No. 1 to 6) divided by the minimum wavelength and the maximum wavelength. . As can be seen from Table 2, condition no. From 1 to 15, a high correlation was found between the predicted NIR value (quantitative value) and the chemical analysis value (actual measurement value). These highly correlated conditions include one or more of the 1440 ± 10 nm, 1938 ± 10 nm, 2030 ± 10 nm, and 2105 ± 10 nm wavelength regions related to γ-oryzanol shown in Example 1. was there.

[Example 3: Regression equation of calibration curve of oil and fat sample]
(1) 1 g of commercially available rice oil, “germ oil gamma” (γ-oryzanol content of about 30%, Tsukino Food), or rice germ oil (γ-oryzanol content of about 5%, (Tsukino Food) was added at an arbitrary ratio to prepare an oil and fat sample having a γ-oryzanol content of 0.3 to 15% by weight. Samples of γ-oryzanol 5-15% were prepared by mixing rice oil and germ oil gamma, and samples of γ-oryzanol 0.3-5% were prepared by mixing rice oil and rice germ oil. The γ-oryzanol content in each sample was determined using a spectrophotometer Nano Drop2000c (Thermo Fisher Scientific) based on the absorbance at 320 nm using γ-oryzanol (98%, Tsukino Food) / hexane solution as a standard. Quantified.
(2) The NIR transmitted light of the sample prepared in (1) was measured in the range of 800 nm to 2500 nm (12500 to 4000 cm ⁻¹ ) using an FT type near infrared analyzer MPA (Bruker Optics). In addition, in order to homogenize the fat and oil sample, the measurement cell was heated to 75 ° C. and the temperature stability was confirmed, and then the NIR spectrum was acquired.
(3) Pre-processing (second-order differentiation) of the obtained original spectrum, wavelength data of the pre-processed spectrum (1333-1470 nm, 1835-2175 nm) as explanatory variables, and PLS with γ-oryzanol content as an objective variable A calibration model of oil and fat samples was created by regression analysis. The correlation between the predicted NIR value (quantitative value) and the chemical analysis value (actual measurement value) is shown in FIG. The predicted NIR value (% by weight, quantitative value) here is a calculated value derived from a calibration curve regression equation of γ-oryzanol content and NIR spectrum data by a measuring device. The quantitative value and the actually measured value showed a very high correlation (R ² = 0.99), and it became clear that the prediction accuracy was as high as that of the hexane solution of Example 1.

[Example 4: Calibration curve regression formula of brown rice sample]
4-1 Extraction of crude lipid
(1) One grain of brown rice was weighed, freeze-dried, wrapped in filter paper and crushed with a spoon.
(2) Put the crushed sample together with the filter paper into a cylindrical glass filter for extraction, and with a semi-micro fat extractor set, use 25 mL of ether, set the heating machine to 100 ° C., and perform Soxhlet extraction for 6 hours .
(3) The extract was transferred to a pear-shaped flask and dried with a rotary evaporator.
(4) The crude lipid of the pear-shaped flask was dissolved in 1.2 mL of chloroform, and the chloroform solution of the lipid was transferred to a 2 mL vial, and concentrated and dried by a centrifugal evaporator to obtain one grain of crude rice.

4-2 Measurement of γ-oryzanol content
(1) Ethanol was added to the crude lipid obtained in 4-1 (4) above to make a 1 mg / mL solution.
(2) The γ-oryzanol content was measured by the HPLC method with a sample injection amount of 10 μL (corresponding to 10 μg of crude lipid). A Prominence HPLC system (LC-20AD, Shimadzu Corporation) was used as the HPLC apparatus. An ultraviolet-visible absorbance detector (wavelength: 320 nm, Shimadzu Corporation) was used as the detector, and “STR-ODSII” (2.0 × 150 mm, particle diameter: 5 μm) manufactured by Shinwa Kako was used as the column. The mobile phase was methanol: acetonitrile = 1: 1 (v / v), the column temperature was 40 ° C., and the flow rate was 0.20 mL / min.
(3) The γ-oryzanol sample (98%, Tsukino Food) was analyzed under the same conditions as in (2) above, and the four γ-oryzanol peaks (ferulic acid) were determined from the UV chromatogram obtained in (2) above. Cycloartenyl, ferulic acid 24-methylenecycloartanyl, ferulic acid campesteryl, ferulic acid β-sitosteryl). The UV chromatogram of the γ-oryzanol sample is shown in FIG.
(4) Using cycloartenyl ferulate (99%, Wako Pure Chemical Industries) as a sample, the quantitative value of each γ-oryzanol molecular species was calculated in terms of cycloartenyl ferulate.

4-3 Preparation of γ-oryzanol content calibration curve regression formula
(1) Three kinds of 12 rice varieties of brown rice were prepared.
(2) The NIR diffuse reflected light of brown rice was measured in the range of 800 nm to 2500 nm (12500 to 4000 cm ⁻¹ ) using an FT type near infrared analyzer MPA (Bruker Optics) as an analyzer.
(3) About the brown rice which acquired the spectrum, the crude lipid was extracted by the method of said 4-1 and 4-2, and the content of (gamma)-oryzanol in one grain of each brown rice was quantified.
(4) Pre-processing (second-order differential processing) of the obtained original spectrum, PLS regression analysis using the wavelength data of the pre-processed spectrum (13333 to 2355 nm) as an explanatory variable and the measured γ-oryzanol content as an objective variable Thus, a γ-oryzanol calibration model for 1 grain of brown rice was prepared. The correlation between the NIR predicted value (quantitative value) and the chemical analysis value (actually measured value) is shown in FIG. The NIR predicted value (ppm, quantitative value) here is a calculated value derived by a measuring device from a calibration curve regression equation of γ-oryzanol content and NIR spectrum data.

  As shown in FIG. 4, the NIR predicted value (quantitative value) and the chemical analysis value (actually measured value) are highly correlated, and the prediction accuracy is not as good as when the oil or its solution is used as a sample. It was shown that this method can also be applied to brown rice samples.

[Example 5: Examination of optimum wavelength range of brown rice sample]
A γ-oryzanol calibration model was prepared for the brown rice sample by changing the wavelength range of the wavelength data of the pretreatment spectrum in various ways. The coefficient of determination R ² in all conditions of the obtained calibration models are shown in Table 3. The wavelength indicates the wavelength range used, and analysis is performed using data from one section (other than condition Nos. 2 and 5) or two sections (conditions No. 2 and 5) divided by the minimum and maximum wavelengths. It was. As can be seen from Table 3, condition no. In 1 to 10, a high correlation was found between the predicted NIR value (quantitative value) and the chemical analysis value (actual measurement value). These highly correlated conditions include one or more of the 1440 ± 10 nm, 1938 ± 10 nm, 2030 ± 10 nm, and 2105 ± 10 nm wavelength regions associated with γ-oryzanol shown in Example 1, and the minimum There was a common point that the sum of the wavelength regions of one section (other than conditions No. 2 and 5) or two sections (conditions No. 2 and 5) divided by the wavelength and the maximum wavelength was 500 nm or more.

[Test Example 1: Determination of γ-oryzanol contained in rice germ oil and rice crude oil]
Quantification of γ-oryzanol was performed on rice germ oil and rice crude oil by the following methods 1 to 4 and the results were compared. Each method was measured three times, and the average value was calculated.

1: Quantification by NIR spectrum
(1) Using an FT-type near-infrared analyzer MPA (Bruker Optics), put rice germ oil or rice crude oil directly in a measuring cell heated to 75 ° C, wait for the temperature to stabilize, and transmit NIR transmitted light. It measured in the range of 800 nm to 2500 nm (12500-4000 cm < ^-1 >).
(2) The γ-oryzanol content was predicted by applying the NIR spectral data measured in (1) to the γ-oryzanol calibration model prepared in Example 3.

2: Determination of hexane solution by NIR spectrum
(1) 1 g of rice germ oil or rice crude oil was dissolved in 100 mL of hexane to prepare a 1% (w / v) solution, which was used as a sample.
(2) The NIR transmitted light of the sample prepared in (1) was measured in the range of 800 nm to 2500 nm (12500 to 4000 cm ⁻¹ ) using an FT type near infrared analyzer MPA (Bruker Optics).
(3) The NIR spectral data measured in (1) was applied to the γ-oryzanol calibration model prepared in Example 1 to quantify the γ-oryzanol content.

3: Chemical analysis (absorbance method)
Spectrophotometer Nano Drop2000c (Thermo Fisher Scientific Co., Ltd.) based on the absorbance at 320 nm using the hexane solution of rice germ oil or rice crude oil of 2 above and the hexane solution of γ-oryzanol (98%, Tsukino Food Industries) as the standard. ).

4: Chemical analysis (HPLC method)
The γ-oryzanol content in rice germ oil or rice crude oil was quantified using the same method as the method for measuring the γ-oryzanol content in the brown rice crude lipid in Example 4-2.

  Table 4 shows the predicted value or quantitative value of γ-oryzanol in rice germ oil and rice crude oil predicted or quantified by the methods 1 to 4. As can be seen from Table 4, in rice germ oil, the predicted value by NIR and the quantitative value by chemical analysis were almost the same, indicating the validity of the NIR method. On the other hand, in US crude oil, the predicted values of the two NIR methods and the quantitative values of the HPLC method are almost the same, but the quantitative values of the absorbance method are slightly higher. This is presumably because rice crude oil has more impurities than rice germ oil, and there are substances other than γ-oryzanol that have an absorption maximum at 320 nm, and the NIR method is more versatile than the absorbance method. (Accurate prediction close to the HPLC method is possible even for a sample with many impurities).

[Test Example 2: Determination of model samples using various edible oils]
(1) A model sample in which γ-oryzanol content is about 0.5% by weight by adding γ-oryzanol (98%, Tsukino Food) to commercially available palm oil, rapeseed oil, soybean oil, and fish oil and dissolving them by heating. Prepared.
(2) For the model sample of (1), the predicted value and quantitative value of the γ-oryzanol content predicted or quantified by the four methods as in Test Example 1 were calculated. Measurements were performed three times by each method, and the calculated average values are shown in Table 5. As is clear from Table 5, the predicted value by NIR and the quantitative value by chemical analysis almost coincided with each model sample. Therefore, it was found that this method can be applied to fats and oils other than rice oil.

[Test Example 3: Determination of γ-oryzanol in rice bran]
(1) 100 mL of diethyl ether was added to about 10 g of rice bran of different rice varieties A, B and C, and Soxhlet extraction was performed in the same manner as in Example 4 to remove the solvent and obtain crude lipid.
(2) NIR predicted values and quantitative values were derived from the crude lipid of (1) in the same manner as in Test Example 1, and converted into the content (% by weight) per rice bran. Table 6 shows the average values obtained by measuring three times by each method. As is clear from Table 6, the NIR predicted value and the chemical analysis quantitative value almost coincided with each rice bran, but the absorbance method quantitative value tends to be slightly higher, which is the same as in Test Example 1. Is possible.

[Test Example 4: Analysis of feed]
A similar test was conducted by changing the rice bran of Test Example 3 to a chicken feed containing brown rice flour. The feed was ground in a mortar before extraction, and after homogenization, crude lipids were extracted. As a result, the predicted NIR value and the chemical analysis value almost coincided, indicating the validity of this method.

[Test Example 5: Analysis of soap]
A similar test was conducted by changing the rice bran of Test Example 3 to a soap made from a mixed oil of rice oil and palm oil. About 1 g of soap was cut out and crude lipid was extracted. As a result, the predicted NIR value and the chemical analysis value almost coincided, indicating the validity of this method.

[Test Example 6: Analysis of drug containing γ-oryzanol]
A similar test was conducted by changing the rice bran of Test Example 3 to a fine granule containing about 20% of γ-oryzanol. As a result, the predicted NIR value and the chemical analysis value almost coincided, indicating the validity of this method.

[Test Example 7: Blood analysis]
Six 4-week-old Wistar male rats (Nippon Claire Co., Ltd.) raised for 3 weeks in a diet containing about 10% rice germ oil (γ-oryzanol content: about 1.0%). The same test was conducted in place of the serum. Serum was subjected to ether anesthesia on the last day of breeding, followed by laparotomy, and blood was collected by sampling the abdominal aorta. Serum was separated from the collected blood according to a conventional method. The separated serum was subjected to protein removal treatment by ultrafiltration treatment and addition of ethanol, and then a lipid containing γ-oryzanol was extracted with hexane to obtain an analysis sample. As a result, the average values of NIR predicted values and chemical analysis values almost coincided, indicating the validity of this method.

  The present invention is not limited to the above-described embodiments and examples, and various modifications are possible within the scope shown in the claims, and technical means disclosed in different embodiments are appropriately combined. The obtained embodiment is also included in the technical scope of the present invention. Moreover, all the academic literatures and patent literatures described in this specification are incorporated herein by reference.

Claims (13)

A method for obtaining a regression equation for quantifying γ-oryzanol in a sample, comprising the following steps (1) to (3).
(1) Step of measuring the near-infrared light spectrum of the sample (2) Step of quantifying the γ-oryzanol content of the sample (3) In all or part of the wavelength range in which the near-infrared light spectrum was measured A step of analyzing the obtained spectral data and the quantified γ-oryzanol content by a multivariate analysis method and determining a factor related to the γ-oryzanol content   The method according to claim 1, wherein the sample is a food, feed, cosmetic or pharmaceutical product containing γ-oryzanol, or a raw material or material thereof.   The method according to claim 1 or 2, wherein the sample is a grain or a part thereof.   The method according to claim 3, wherein the cereal is brown rice.   The method according to claim 1 or 2, wherein the sample is oil or fat, a processed product thereof, or a raw material or material thereof.   The method according to claim 5, wherein the fats and oils contain rice oil or rice oil.   The method according to claim 1 or 2, wherein the sample is derived from a living body.   The method according to claim 1, further comprising a step of extracting γ-oryzanol in the sample with a solvent before the step (1).   The method according to claim 8, wherein the solvent is hexane or chloroform.   The method according to claim 1, wherein the near-infrared light spectrum is a spectrum of diffusely reflected light or transmitted light.   The method according to claim 1, wherein the wavelength region in the step (3) is 800 to 2500 nm or a part thereof.   12. The method according to claim 11, wherein the wavelength region in the step (3) includes at least one region of 1440 ± 10 nm, 1938 ± 10 nm, 2030 ± 10 nm, and 2105 ± 10 nm.   A regression equation obtained by the method according to any one of claims 1 to 12, and near-infrared light spectrum data of a test sample in a wavelength region used for the creation of the regression equation, γ- Method for quantifying oryzanol.