JP2018535406A - Rapid detection method of sewage oil by hyperspectral transmission - Google Patents
Rapid detection method of sewage oil by hyperspectral transmission Download PDFInfo
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- 230000005540 biological transmission Effects 0.000 title claims abstract description 45
- 239000010865 sewage Substances 0.000 title claims abstract description 36
- 238000001514 detection method Methods 0.000 title claims abstract description 13
- 239000003921 oil Substances 0.000 claims abstract description 67
- 239000008157 edible vegetable oil Substances 0.000 claims abstract description 21
- 238000011088 calibration curve Methods 0.000 claims abstract description 14
- 238000000034 method Methods 0.000 claims abstract description 12
- 239000008162 cooking oil Substances 0.000 abstract description 3
- 238000007619 statistical method Methods 0.000 abstract description 2
- 235000019198 oils Nutrition 0.000 description 49
- 235000019483 Peanut oil Nutrition 0.000 description 5
- 239000000312 peanut oil Substances 0.000 description 5
- 239000000126 substance Substances 0.000 description 3
- 229930195730 Aflatoxin Natural products 0.000 description 2
- 235000017060 Arachis glabrata Nutrition 0.000 description 2
- 244000105624 Arachis hypogaea Species 0.000 description 2
- 235000010777 Arachis hypogaea Nutrition 0.000 description 2
- 235000018262 Arachis monticola Nutrition 0.000 description 2
- -1 Cyclic aromatic hydrocarbons Chemical class 0.000 description 2
- 239000002253 acid Substances 0.000 description 2
- 239000005409 aflatoxin Substances 0.000 description 2
- HVYWMOMLDIMFJA-DPAQBDIFSA-N cholesterol Chemical compound C1C=C2C[C@@H](O)CC[C@]2(C)[C@@H]2[C@@H]1[C@@H]1CC[C@H]([C@H](C)CCCC(C)C)[C@@]1(C)CC2 HVYWMOMLDIMFJA-DPAQBDIFSA-N 0.000 description 2
- 238000007405 data analysis Methods 0.000 description 2
- 235000014113 dietary fatty acids Nutrition 0.000 description 2
- 229930195729 fatty acid Natural products 0.000 description 2
- 239000000194 fatty acid Substances 0.000 description 2
- 150000004665 fatty acids Chemical class 0.000 description 2
- 235000013305 food Nutrition 0.000 description 2
- 235000011194 food seasoning agent Nutrition 0.000 description 2
- 235000020232 peanut Nutrition 0.000 description 2
- ZCYVEMRRCGMTRW-UHFFFAOYSA-N 7553-56-2 Chemical compound [I] ZCYVEMRRCGMTRW-UHFFFAOYSA-N 0.000 description 1
- XWIYFDMXXLINPU-UHFFFAOYSA-N Aflatoxin G Chemical compound O=C1OCCC2=C1C(=O)OC1=C2C(OC)=CC2=C1C1C=COC1O2 XWIYFDMXXLINPU-UHFFFAOYSA-N 0.000 description 1
- FMMWHPNWAFZXNH-UHFFFAOYSA-N Benz[a]pyrene Chemical compound C1=C2C3=CC=CC=C3C=C(C=C3)C2=C2C3=CC=CC2=C1 FMMWHPNWAFZXNH-UHFFFAOYSA-N 0.000 description 1
- 240000001548 Camellia japonica Species 0.000 description 1
- 239000004278 EU approved seasoning Substances 0.000 description 1
- 238000004566 IR spectroscopy Methods 0.000 description 1
- ISWSIDIOOBJBQZ-UHFFFAOYSA-N Phenol Chemical compound OC1=CC=CC=C1 ISWSIDIOOBJBQZ-UHFFFAOYSA-N 0.000 description 1
- 150000001299 aldehydes Chemical class 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 210000001557 animal structure Anatomy 0.000 description 1
- 229910052785 arsenic Inorganic materials 0.000 description 1
- RQNWIZPPADIBDY-UHFFFAOYSA-N arsenic atom Chemical compound [As] RQNWIZPPADIBDY-UHFFFAOYSA-N 0.000 description 1
- 125000002915 carbonyl group Chemical group [*:2]C([*:1])=O 0.000 description 1
- 238000009614 chemical analysis method Methods 0.000 description 1
- 235000012000 cholesterol Nutrition 0.000 description 1
- 235000018597 common camellia Nutrition 0.000 description 1
- 238000007796 conventional method Methods 0.000 description 1
- 239000002385 cottonseed oil Substances 0.000 description 1
- 235000012343 cottonseed oil Nutrition 0.000 description 1
- GVGUFUZHNYFZLC-UHFFFAOYSA-N dodecyl benzenesulfonate;sodium Chemical compound [Na].CCCCCCCCCCCCOS(=O)(=O)C1=CC=CC=C1 GVGUFUZHNYFZLC-UHFFFAOYSA-N 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 238000000605 extraction Methods 0.000 description 1
- 230000005484 gravity Effects 0.000 description 1
- 229910001385 heavy metal Inorganic materials 0.000 description 1
- 229910052740 iodine Inorganic materials 0.000 description 1
- 239000011630 iodine Substances 0.000 description 1
- 150000002576 ketones Chemical class 0.000 description 1
- 239000007788 liquid Substances 0.000 description 1
- 239000010813 municipal solid waste Substances 0.000 description 1
- 239000003129 oil well Substances 0.000 description 1
- 230000003647 oxidation Effects 0.000 description 1
- 238000007254 oxidation reaction Methods 0.000 description 1
- 238000005502 peroxidation Methods 0.000 description 1
- 239000000447 pesticide residue Substances 0.000 description 1
- 238000004940 physical analysis method Methods 0.000 description 1
- 125000005575 polycyclic aromatic hydrocarbon group Chemical group 0.000 description 1
- 229920000642 polymer Polymers 0.000 description 1
- 108090000623 proteins and genes Proteins 0.000 description 1
- 238000007127 saponification reaction Methods 0.000 description 1
- 229940080264 sodium dodecylbenzenesulfonate Drugs 0.000 description 1
- 239000002904 solvent Substances 0.000 description 1
- 230000003595 spectral effect Effects 0.000 description 1
- 238000010998 test method Methods 0.000 description 1
- UFTFJSFQGQCHQW-UHFFFAOYSA-N triformin Chemical compound O=COCC(OC=O)COC=O UFTFJSFQGQCHQW-UHFFFAOYSA-N 0.000 description 1
- 235000015112 vegetable and seed oil Nutrition 0.000 description 1
- 239000008158 vegetable oil Substances 0.000 description 1
- 239000002699 waste material Substances 0.000 description 1
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
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- Biochemistry (AREA)
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- Investigating Or Analysing Materials By Optical Means (AREA)
Abstract
ハイパースペクトル透過による下水油の迅速検出方法である。白色光ハイパースペクトルで、合格の食用油と検出したい油サンプルそれぞれの透過値データの採取を行い、合格の食用油のハイパースペクトル透過値Ｙを用いて、波長Ｘに対して方程式をフィッティングして得られる方程式を、合格の食用油の検量線Ｆ（Ｘ）とし、そして、検出したい油サンプルのハイパースペクトル透過値Ｇを用いて、波長Ｘに対して方程式をフィッティングして得られる方程式を、検出したい油サンプルのハイパースペクトル透過値カーブとして、統計学的方法のＴ検定で、検出したい油サンプルのハイパースペクトル透過値カーブと検量線における各係数の違いを比較し、検出したい油サンプルのハイパースペクトル透過値カーブが検量線から外れる度合いを分析して、検出したい油サンプルが下水油であるかどうかを判断する。本方法は簡単で、効果的であり、ハイパースペクトル走査で透過値データの採取を行うだけで、迅速で効率よく地溝油サンプルを検出するという要求を満たすことができる。
【選択図】なしThis is a rapid detection method of sewage oil by hyperspectral transmission. Obtain the transmission data of the acceptable edible oil and the oil sample to be detected in the white light hyperspectrum, and fit the equation to the wavelength X using the hyperspectral transmission value Y of the acceptable edible oil. We want to detect the equation obtained by fitting the equation to the wavelength X using the hyperspectral transmission value G of the oil sample we want to detect, using the calibration curve F (X) of the acceptable cooking oil As the hyperspectral transmission curve of the oil sample, the statistical method T-test compares the difference in each coefficient between the hyperspectral transmission curve of the oil sample to be detected and the calibration curve, and the hyperspectral transmission value of the oil sample to be detected. Analyze the degree to which the curve deviates from the calibration curve to see if the oil sample you want to detect is sewage oil The judges. The method is simple and effective, and can meet the requirement of detecting gravel oil samples quickly and efficiently by simply collecting transmission value data by hyperspectral scanning.
[Selection figure] None
Description
本発明は、食品検出分野に属し、具体的には、ハイパースペクトル透過による下水油の迅速検出方法に関する。 The present invention belongs to the field of food detection, and specifically relates to a rapid detection method of sewage oil by hyperspectral transmission.
下水油とは、回収された廃食用油、フライを繰り返した食用油、下水道ゴミから精製された劣悪な油、残りカスから精製された油及び劣悪な動物臓物から精製された油である。商業の利益の駆使で、下水油が食用油の産業チェーンに取り込まれ、食品の安全に深刻な影響を与え、そして、関連する社会的問題を引き起こしている。 Sewage oil is recovered waste edible oil, edible oil repeatedly fried, inferior oil refined from sewage trash, oil refined from remaining residue and oil refined from inferior animal organs. By making full use of commercial interests, sewage oil has been incorporated into the edible oil industry chain, seriously affecting food safety and causing related social problems.
下水油を迅速で効率よく検出することは、現在、中国政府部門の要解決の重要な民生問題の一つである。いままで多数の下水油検出方法が開示されたが、一般的には、比重、屈折率、導電率、赤外分光等のような物理的検出指標によるものであり、より多くの場合は、多環芳香族炭化水素、アフラトキシン、アルデヒド・ケトン類、トリグリセリドポリマー、特定の遺伝子、コレステロール、水分、調味料類物質、ドデシルベンゼンスルホン酸ナトリウム、重金属、脂肪酸組成、けん化価、酸価、カルボニル価、過酸化価、ヨウ素価、脂肪酸の相対的な不飽和度等のような植物油規格に準拠した化学的指標によるもの及び透明度や色合い、匂い、味等の外見で判別するものである。 The rapid and efficient detection of sewage oil is now one of the important consumer issues that need to be resolved by the Chinese government sector. Many sewage oil detection methods have been disclosed so far, but generally they are based on physical detection indices such as specific gravity, refractive index, conductivity, infrared spectroscopy, etc. Cyclic aromatic hydrocarbons, aflatoxins, aldehydes and ketones, triglyceride polymers, specific genes, cholesterol, moisture, seasoning substances, sodium dodecylbenzenesulfonate, heavy metals, fatty acid composition, saponification value, acid value, carbonyl value, excess The determination is based on chemical indices based on vegetable oil standards such as oxidation value, iodine value, and relative degree of unsaturation of fatty acids, and on the appearance of transparency, color, smell, taste, and the like.
残念ながら、下水油に人為的な特殊処理をした後、検出したところ、全ての下水油サンプルではないが、多環芳香族炭化水素、調味物質等が除去され得ることが分かる。食用油の物理・化学的指標の検出には、酸価、過酸化価、油の抽出溶媒残留、遊離フェノール（綿実油）、総ヒ素、鉛、アフラトキシン、ベンゾピレン及び農薬残留などの基本指標の検出が含まれており、しかし、下水油でも、これらの指標に合格する可能性があり、下水油を判別することが不可能である。もっと複雑な場合では、精製した下水油と普通の食用油とが一定の比例で混合されると、下水油と普通の食用油とを正確に区別することが一層難しくなり、これが下水油の正確な検出に極大な困難をもたらすことになる。 Unfortunately, after the artificial special treatment of sewage oil, it is detected that polycyclic aromatic hydrocarbons, seasonings and the like can be removed, although not all sewage oil samples. The detection of physical and chemical indicators for edible oils involves detection of basic indicators such as acid value, peroxidation value, oil extraction solvent residue, free phenol (cotton seed oil), total arsenic, lead, aflatoxin, benzopyrene and pesticide residues. However, even sewage oil may pass these indicators, and it is impossible to distinguish sewage oil. In more complex cases, when refined sewage oil and normal edible oil are mixed in a certain proportion, it becomes more difficult to accurately distinguish between sewage oil and normal edible oil, Will lead to extreme difficulties.
従来の方法の検出結果には、経験が必要であり、主観的な要素に大きく影響され、正確度を保証しづらく、また、物理・化学的分析方法では、手間がかかるだけではなく、高価な分析機器と厳しい実験室条件も必要である。そのため、簡単で、迅速的な食用油と下水油の鑑別技術を考え出すことが要望されている。 The detection results of conventional methods require experience, are greatly influenced by subjective factors, and it is difficult to guarantee accuracy. In addition, physical and chemical analysis methods are not only troublesome but also expensive. Analytical instruments and harsh laboratory conditions are also required. Therefore, it is desired to come up with a simple and rapid technique for distinguishing between cooking oil and sewage oil.
本発明の目的は、簡単で、迅速的なハイパースペクトル透過による下水油の迅速検出方法を提供することにあり、該方法は、従来技術では、下水油をうまく効率よく検出して、下水油と普通の食用油とを区別することが困難であるという技術課題を効果的に解決することを趣旨とする。 An object of the present invention is to provide a simple and rapid method for detecting sewage oil by rapid hyperspectral transmission, which in the prior art detects sewage oil well and efficiently, It is intended to effectively solve the technical problem that it is difficult to distinguish from ordinary cooking oil.
ハイパースペクトルは、波長域が多く、解像度が高いという特徴を有する。食用油が透明な液体である場合が多いことから、複数の分光波長域でハイパースペクトル透過分析を行うことによって、オイルの品質を検出することができる。一般的に、ハイパースペクトル透過値の採取データが連続して、透過値カーブを形成しており、さらに、本発明は、データ分析の結果から迅速にオイルの品質を反映できるデータ分析方法を提供して、本発明の目的を達成した。 The hyperspectrum is characterized by a large wavelength range and high resolution. Since edible oil is often a transparent liquid, the quality of the oil can be detected by performing hyperspectral transmission analysis in a plurality of spectral wavelength regions. In general, the collected data of hyperspectral transmission values are continuous to form a transmission curve, and the present invention provides a data analysis method that can quickly reflect the quality of oil from the results of data analysis. Thus, the object of the present invention has been achieved.
本発明のハイパースペクトル透過による下水油の迅速検出方法は、
白色光ハイパースペクトルで、合格の食用油と検出したい油サンプルそれぞれの透過値データの採取を行い、合格の食用油のハイパースペクトル透過値Ｙを用いて、波長Ｘに対して方程式をフィッティングして得られる方程式を、合格の食用油の検量線Ｆ（Ｘ）とするステップと、検出したい油サンプルのハイパースペクトル透過値Ｇを用いて、波長Ｘに対して方程式をフィッティングして得られる方程式を、検出したい油サンプルのハイパースペクトル透過値カーブとして、統計学的方法のＴ検定で、検出したい油サンプルのハイパースペクトル透過値カーブと検量線における各係数の違いを比較し、検出したい油サンプルのハイパースペクトル透過値カーブが検量線から外れる度合いを分析して、検出したい油サンプルが下水油であるかどうかを判断するステップと、を含む。
The rapid detection method of sewage oil by hyperspectral transmission of the present invention is:
Obtain the transmission data of the acceptable edible oil and the oil sample to be detected in the white light hyperspectrum, and fit the equation to the wavelength X using the hyperspectral transmission value Y of the acceptable edible oil. The equation obtained by fitting the equation to the wavelength X is detected by using the calibration curve F (X) of the edible oil to be passed and the hyperspectral transmission value G of the oil sample to be detected. The hyperspectral transmission curve of the oil sample you want to detect is compared with the difference in each coefficient between the hyperspectral transmission curve of the oil sample you want to detect and the calibration curve using the statistical T test. Analyze the degree of deviation of the value curve from the calibration curve, and check whether the oil sample you want to detect is sewage oil Comprising the steps of determining the emergence, the.
前記の白色光ハイパースペクトルは、波長４５０〜９５０ｎｍの白色光ハイパースペクトルであり、さらに好ましくは、波長４５０〜６５０ｎｍの白色光ハイパースペクトルである。 The white light hyperspectrum is a white light hyperspectrum having a wavelength of 450 to 950 nm, and more preferably a white light hyperspectrum having a wavelength of 450 to 650 nm.
上記白色光ハイパースペクトル透過値の採取は、関連するハイパースペクトロメータ等で行われる。 The white light hyperspectral transmission value is collected by a related hyperspectrometer or the like.
前記ハイパースペクトルの波長は４５０〜９５０ｎｍであり、または透過値の違いによって選ばれる所定の波長域であり、例えば４５０〜６５０ｎｍである。 The wavelength of the hyperspectrum is 450 to 950 nm, or a predetermined wavelength range selected according to the difference in transmission value, for example, 450 to 650 nm.
前記カーブフィッティングの方法は、最小二乗法であり、ＭＡＴＬＡＢ等の数学ソフトウェアで行われてもよい。 The curve fitting method is a least square method, and may be performed by mathematical software such as MATLAB.
前記Ｔ検定は統計学的な平均の差の検定方法であり、有意水準αが０.０５であるが、必要に応じて変更されてもよく、統計分析をＳＰＳＳ等の統計ソフトウェアで行ってもよく、Ｐ＜０.０５であると、差が大きく、検出したい油サンプルが下水油であると考えられる。 The T test is a statistical average difference test method, and the significance level α is 0.05. However, it may be changed as necessary, and the statistical analysis may be performed by statistical software such as SPSS. Well, if P <0.05, the difference is large and the oil sample to be detected is considered to be sewage oil.
大量の実験により、異なるオイルの透過値は、ある波長域で有意差を有し、得られる透過値カーブも異なることが検証される。本発明の方法によれば、合格の食用油サンプルのハイパースペクトル透過値の検量線を用いて、全てのサンプルのデータベースを作成し、検出したい油サンプルを対比することにより、検出したい油サンプルが合格の食用油であるかどうかを判別することができる。そのため、この特徴では、数理統計の方法で確認を行い、当業者がハイパースペクトル走査を行い、カーブ方程式の違いによってオイルの品質を対比すればよく、操作が簡単で、信頼性が高く、判別しやすい。本発明で用いる方法は、簡単で、効果的であり、ハイパースペクトル走査で透過値データの採取を行うだけで、迅速で効率よく下水油サンプルを検出するという要求を満たすことができる。 A large amount of experiment verifies that the transmission values of different oils have a significant difference in a certain wavelength range, and the transmission value curves obtained are also different. According to the method of the present invention, a calibration curve of hyperspectral transmission values of edible edible oil samples is used to create a database of all samples, and by comparing the oil samples to be detected, the oil samples to be detected pass. It is possible to determine whether or not the edible oil. Therefore, this feature can be confirmed by mathematical statistics, a person skilled in the art can perform a hyperspectral scan, compare the oil quality by the difference in the curve equation, easy operation, high reliability, and discrimination. Cheap. The method used in the present invention is simple and effective, and can satisfy the requirement of detecting a sewage oil sample quickly and efficiently by simply collecting transmission value data by hyperspectral scanning.
以下の実施例が本発明の更なる説明であり、本発明を制限するものではない。 The following examples are further illustrations of the invention and do not limit the invention.
（実施例１）
魯花落花生油と下水油の透過値カーブの違いを比較する。
(Example 1)
Compare the difference in permeation curve between peanut oil and sewage oil.
ＨＥＡＤＷＡＬＬシステムによって、波長４５０ｎｍから６５０ｎｍの白色光ハイパースペクトルで魯花落花生油の透過値データを採取し、５回繰り返した後、ＭＡＴＬＡＢソフトウェアで、魯花落花生油のハイパースペクトル透過値（Ｙ）を用いて、波長（Ｘ）に対して方程式をフィッティングして、得られた方程式を魯花落花生油の検量線Ｆ（Ｘ）とする。 By using the HEADWALL system, the transmission value data of peanut peanut oil was collected with a white light hyperspectrum at a wavelength of 450 nm to 650 nm. Then, the equation is fitted to the wavelength (X), and the obtained equation is taken as a calibration curve F (X) for peanut oil.
５回繰り返して得られたデータフィッティング方程式が、
Ｆ１（ｘ）＝−０.００６４ｘ４＋０.３０３８ｘ３−４.３１７４ｘ２＋１０.６８ｘ＋７０３.２８ （Ｒ２＝０.９８３１）
Ｆ２（ｘ）＝−０.００４１ｘ４＋０.２０５２ｘ３−３.４１９２ｘ２＋１９.７５ｘ＋６３８.３ （Ｒ２＝０.６９１３）
Ｆ３（ｘ）＝−０.００７５ｘ４＋０.３４４ｘ３−４.８３４５ｘ２＋１４.４９２ｘ＋６９２.５３ （Ｒ２＝０.９７５１）
Ｆ４（ｘ）＝−０.００３３ｘ４＋０.１７５２ｘ３−３.０８０３ｘ２＋１８.９６６ｘ＋６３６.４９ （Ｒ２＝０.５７）
Ｆ５（ｘ）＝０.００１５ｘ４−０.０７４８ｘ３＋１.４２７７ｘ２−１４.５６６ｘ＋７１８.８５ （Ｒ２＝０.７２７７）
である。
The data fitting equation obtained by repeating 5 times is
F 1 (x) = − 0.0063x4 + 0.3038x3−4.3174x2 + 10.68x + 703.28 (R2 = 0.9831)
F 2 (x) = − 0.40041x4 + 0.205x3−3.4192x2 + 19.75x + 638.3 (R2 = 0.6913)
F 3 (x) = − 0.0075x4 + 0.344x3−4.8345x2 + 14.492x + 692.53 (R2 = 0.9751)
F 4 (x) = − 0.000033x4 + 0.1852x3-3.8033x2 + 18.966x + 636.49 (R2 = 0.57)
F 5 (x) = 0.0015x4-0.0748x3 + 1.277x2-14.566x + 718.85 (R2 = 0.277)
It is.
下水油についても、上記の魯花落花生油と同様にしてハイパースペクトル透過値の採取を行い、５回繰り返した後、ＭＡＴＬＡＢソフトウェアで、下水油のハイパースペクトル透過値（Ｇ）を用いて、波長（Ｘ）に対して方程式をフィッティングして、得られた方程式を下水油のカーブＧ（Ｘ）とする。 For the sewage oil, the hyperspectral transmission value was collected in the same manner as the above peanut peanut oil, and after repeating 5 times, using the hyperspectral transmission value (G) of sewage oil, the wavelength ( The equation is fitted to X), and the obtained equation is defined as a sewage oil curve G (X).
５回繰り返して得られたデータフィッティング方程式が、
Ｇ１（ｘ）＝−０.０４１６ｘ４＋２.０７２１ｘ３−３１.５８２ｘ２＋９２.３５３ｘ＋１６８１.５ （Ｒ２＝０.９６５３）
Ｇ２（ｘ）＝−０.０３７９ｘ４＋１.９０６６ｘ３−２９.１７９ｘ２＋８３.１４７ｘ＋１６６１.３ （Ｒ２＝０.９６９９）
Ｇ３（ｘ）＝−０.０３９８ｘ４＋１.９９６２ｘ３−３０.７４８ｘ２＋９４.９１７ｘ＋１６２７.１ （Ｒ２＝０.９６９２）
Ｇ４（ｘ）＝−０.０３７８ｘ４＋１.８８４８ｘ３−２８.６１ｘ２＋７８.４７ｘ＋１６５３.２ （Ｒ２＝０.９６５１）
Ｇ５（ｘ）＝−０.０３７３ｘ４＋１.８８３７ｘ３−２９.０５１ｘ２＋８５.９８３ｘ＋１６３７.２ （Ｒ２＝０.９６９）
である。
The data fitting equation obtained by repeating 5 times is
G 1 (x) = − 0.0416x4 + 2.0721x3−31.582x2 + 92.353x + 1681.5 (R2 = 0.96553)
G 2 (x) = - 0.0379x4 + 1.9066x3-29.179x2 + 83.147x + 1661.3 (R2 = 0.9699)
G 3 (x) = - 0.0398x4 + 1.9962x3-30.748x2 + 94.917x + 1627.1 (R2 = 0.9692)
G 4 (x) = − 0.0378x4 + 1.848x3−28.61x2 + 78.47x + 1653.2 (R2 = 0.9651)
G 5 (x) = - 0.0373x4 + 1.8837x3-29.051x2 + 85.983x + 1637.2 (R2 = 0.969)
It is.
Ｔ検定で各係数の違いを比較すると、有意水準αが０.０５であり、有意性検定から明らかなように、カーブの方程式では有意差（Ｐ＜）０.０５を有しているから、下水油と魯花落花生油の間に大きな違いがあると考えられる。そのため、地溝油サンプルが食用油ではなく、下水油であると判定する。 When the difference between each coefficient is compared by the T test, the significance level α is 0.05, and as is clear from the significance test, the curve equation has a significant difference (P <) 0.05. There seems to be a big difference between sewage oil and camellia peanut oil. Therefore, it is determined that the graben oil sample is not edible oil but sewage oil.
（付記）
（付記１）
白色光ハイパースペクトルで、合格の食用油と検出したい油サンプルそれぞれの透過値データの採取を行い、合格の食用油のハイパースペクトル透過値Ｙを用いて、波長Ｘに対して方程式をフィッティングして得られる方程式を、合格の食用油の検量線Ｆ（Ｘ）とするステップと、検出したい油サンプルのハイパースペクトル透過値Ｇを用いて、波長Ｘに対して方程式をフィッティングして得られる方程式を、検出したい油サンプルのハイパースペクトル透過値カーブとして、統計学的方法のＴ検定で、検出したい油サンプルのハイパースペクトル透過値カーブと検量線における各係数の違いを比較し、検出したい油サンプルのハイパースペクトル透過値カーブが検量線から外れる度合いを分析して、検出したい油サンプルが下水油であるかどうかを判断するステップと、を含むことを特徴とする、ハイパースペクトル透過による下水油の迅速検出方法。
(Appendix)
(Appendix 1)
Obtain the transmission data of the acceptable edible oil and the oil sample to be detected in the white light hyperspectrum, and fit the equation to the wavelength X using the hyperspectral transmission value Y of the acceptable edible oil. The equation obtained by fitting the equation to the wavelength X is detected by using the calibration curve F (X) of the edible oil to be passed and the hyperspectral transmission value G of the oil sample to be detected. The hyperspectral transmission curve of the oil sample you want to detect is compared with the difference in each coefficient between the hyperspectral transmission curve of the oil sample you want to detect and the calibration curve using the statistical T test. Analyze the degree of deviation of the value curve from the calibration curve, and check whether the oil sample you want to detect is sewage oil Characterized in that it comprises the steps of: determining the emergence, rapid detection method of the sewage oil by hyperspectral transmission.
（付記２）
前記白色光ハイパースペクトルが波長４５０〜９５０ｎｍの白色光ハイパースペクトルであることを特徴とする、付記１に記載のハイパースペクトル透過による下水油の迅速検出方法。
(Appendix 2)
2. The method for quickly detecting sewage oil by hyperspectral transmission according to appendix 1, wherein the white light hyperspectrum is a white light hyperspectrum having a wavelength of 450 to 950 nm.
（付記３）
前記白色光ハイパースペクトルが波長４５０〜６５０ｎｍの白色光ハイパースペクトルであることを特徴とする、付記２に記載のハイパースペクトル透過による下水油の迅速検出方法。
(Appendix 3)
The method for quickly detecting sewage oil by hyperspectral transmission according to appendix 2, wherein the white light hyperspectrum is a white light hyperspectrum having a wavelength of 450 to 650 nm.
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