CN111855932B - Method for identifying peanut oil yield and assisting in identifying peanut variety - Google Patents

Method for identifying peanut oil yield and assisting in identifying peanut variety Download PDF

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CN111855932B
CN111855932B CN202010753552.5A CN202010753552A CN111855932B CN 111855932 B CN111855932 B CN 111855932B CN 202010753552 A CN202010753552 A CN 202010753552A CN 111855932 B CN111855932 B CN 111855932B
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CN111855932A (en
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赵海燕
王磊
杨庆利
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Qingdao Agricultural University
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Abstract

The invention discloses a method for identifying the peanut oil yield and assisting in identifying peanut varieties, and belongs to the technical field of peanut oil yield and peanut variety identification. The method for identifying the oil yield of the peanuts and assisting in identifying the peanut varieties comprises the steps of detecting the contents of B, Na, K, Ca, Mn, Cu, Zn, As, Sr, Cs, Ba and Tl in peanut samples, introducing the contents into a discrimination model, and obtaining the oil content of the peanuts and determining several common peanut varieties by calculating and comparing the magnitudes of discrimination values. The method for identifying the peanut oil yield and assisting in identifying the peanut variety can accurately identify the peanuts with high oil yield and the peanuts with low oil yield, has the integral correct identification rate of 92.1 percent, and can be used for identifying the peanut oil yield; in addition, the method assists in identifying a plurality of common peanut varieties, and the identification effect reaches 100%.

Description

Method for identifying peanut oil yield and assisting in identifying peanut variety
Technical Field
The invention belongs to the technical field of peanut oil yield and peanut variety identification, and particularly relates to a method for identifying the peanut oil yield and assisting in identifying peanut varieties based on a mineral element fingerprint analysis technology.
Background
Peanuts are one of the main oil crops in China, and the planting area is second to that of rapes. The peanut mainly comprises fat, protein and carbohydrate, wherein the mass fraction of the fat accounts for 46-52%, and the mass fraction of the unsaturated fatty acid is more than 85%, and has the effects of reducing human serum cholesterol, preventing arteriosclerosis and coronary heart disease, beautifying and moistening skin. 55% of peanuts produced in China are used for preparing oil, and the annual yield of the peanut oil is second to that of the rapeseed oil. However, peanut varieties vary significantly in their fat content (oil content) and protein content. Wherein, the oil content is used as one of the important reference indexes of the oil crop quality, and the edible value and the extraction value of the peanuts are determined.
In the process of preparing oil from peanuts, the peanut seeds are used as extraction raw materials of peanut oil, the oil content is different, the suitable oil pressing mode is different, and the obtained peanut oil is good in smell and taste. The peanut varieties in China are various, the oil content is uneven, the oil plants usually adopt the squeezing method to prepare oil, but the squeezing method is only suitable for the varieties with high oil content, and the solvent leaching method is adopted to extract oil for the varieties with lower oil content. The identification of the peanut variety with high oil content is important for improving the oil production yield.
The physiological requirements of peanut plants of different varieties on nutrient substances and the types and the quantity of absorption and enrichment are also changed when the peanut plants grow, so that the chemical composition and the content of peanut grains are changed, and the oil contents of different varieties of peanuts are different. Common peanut varieties with larger planting areas in China mainly comprise Shandong flower No. 11, Haihua No. 1, Weihua flower No. 10, Fenghua No. 5 and the like.
However, peanuts are used as agricultural products for large-area planting, the production of the peanuts is scattered in each county of each province, and the supervision is difficult. Driven by economic benefits, the phenomenon that peanuts with low oil yield are used as peanuts with high oil yield occurs sometimes, so that the method seriously damages fair trade of peanut markets and rights of consumers, and becomes a difficult problem which puzzles the protection of peanut varieties with high oil yield for a long time. Therefore, at present, an accurate technical method for identifying different varieties of peanuts needs to be established, and the method has important significance and application value for standardizing market order, improving product competitiveness, guaranteeing legal rights and interests of consumers, building an oil crop tracing system and the like.
At present, no related patent for identifying different peanut varieties exists at home and abroad, and related research for identifying peanut varieties by using a mineral element fingerprint analysis technology is not reported.
Disclosure of Invention
Aiming at the defects of the prior art, the invention aims to provide a method for identifying the oil yield of peanuts and assisting in identifying peanut varieties, establishes a model for identifying the peanuts with different oil yields and protects the peanut varieties with high oil yields.
In order to solve the technical problems, the technical scheme adopted by the invention is as follows:
a method for identifying the oil yield of peanuts comprises the following steps:
(1) carrying out shelling, cleaning, drying and grinding on a peanut sample to be detected, and then digesting;
(2) detecting the concentration of Na, K, Mn, As, Sr, Cs and Tl in the solution digested in the step (1), calculating to obtain the content of Na, K, Mn, As, Sr, Cs and Tl in the peanut sample to be detected, respectively substituting the content into discrimination models I to II, and calculating to obtain YHigh oil yieldAnd YLow oil yield；
(3) Comparing step (2) meterCalculated YHigh oil yieldAnd YLow oil yieldMagnitude of value, when calculated YHigh oil yield＞YLow oil yieldIf so, the peanut sample to be detected belongs to the peanuts with high oil yield; when calculated YHigh oil yield＜YLow oil yieldIf so, the peanut sample to be detected belongs to the low-oil-yield peanuts;
the discrimination model is as follows:
Yhigh oil yield＝-641.820-1.361×XNa+0.100×XK+0.009×XMn+4.212×XAs+0.047×XSr+2.347×XCs+29.806×XTl ①；
YLow oil yield＝-742.748-1.487×XNa+0.106×XK+0.010×XMn+4.881×XAs+0.051×XSr+2.607×XCs+34.657×XTl ②；
In the above first to second discrimination models, XNa、XK、XMn、XAs、XSr、XCsAnd XTlRespectively representing the content of Na, K, Mn, As, Sr, Cs and Tl in the peanut sample, wherein XNa、XKUnit of (d) is [ mu ] g/g, XMn、XAs、XSr、XCsAnd XTlThe unit of (D) is [ mu ] g/kg.
A method for identifying peanut varieties comprises the following steps:
(1) carrying out shelling, cleaning, drying and grinding on a peanut sample to be detected (the peanut sample to be detected needs to be one of Luhua No. 11, Haishua No. 1, Weihua No. 10 and Fenghua No. 5), and then digesting;
(2) detecting the concentrations of B, K, Ca, Cu, Zn, As, Sr, Cs and Ba in the solution digested in the step (1), calculating to obtain the contents of B, K, Ca, Cu, Zn, As, Sr, Cs and Ba in the peanut sample to be detected, respectively substituting the contents into discrimination models from (c) to (c), and calculating to obtain YLuhua No. 11、YSea flower No. 1、YWeihua No. 10And YFenghua No. 5；
(3) Comparing Y calculated in the step (2)Luhua No. 11、YSea flower No. 1、YWeihua No. 10And YFenghua No. 5The value is large, and the peanut variety corresponding to the maximum value is the peanut variety to which the peanut sample to be detected belongs;
the discrimination model is as follows:
Yluhua No. 11＝-2390.137-8.539×XB+0.426×XK-0.167×XCa+0.102×XCu+0.009×XZn-21.669×XAs+0.094×XSr-1.712×XCs+0.174×XBa ③；
YSea flower No. 1＝-2510.283-9.214×XB+0.439×XK-0.184×XCa+0.106×XCu+0.009×XZn-22.009×XAs+0.094×XSr-2.315×XCs+0.181×XBa ④；
YWeihua No. 10＝-2191.270-19.375×XB+0.405×XK+0.160×XCa+0.092×XCu+0.011×XZn-17.875×XAs+0.092×XSr-2.245×XCs+0.145×XBa ⑤；
YFenghua No. 5＝-2541.563-14.692×XB+0.439×XK+0.030×XCa+0.100×XCu+0.010×XZn-20.405×XAs+0.098×XSr-2.242×XCs+0.168×XBa ⑥；
The above-mentioned discriminant modelB、XK、XCa、XCu、XZn、XAs、XSr、XCsAnd XBaRespectively representing the contents of B, K, Ca, Cu, Zn, As, Sr, Cs and Ba in the peanut sample, wherein XB、XKAnd XCaUnit of (d) is [ mu ] g/g, XCu、XZn、XAs、XSr、XCsAnd XBaThe unit of (D) is [ mu ] g/kg.
A method for identifying the oil yield of peanuts and assisting in identifying peanut varieties comprises the following steps:
(1) carrying out shelling, cleaning, drying and grinding on a peanut sample to be detected, and then digesting;
(2) detecting the concentrations of B, Na, K, Ca, Mn, Cu, Zn, As, Sr, Cs, Ba and Tl in the solution digested in the step (1), and calculating to obtain the contents of B, Na, K, Ca, Mn, Cu, Zn, As, Sr, Cs, Ba and Tl in the peanut sample to be detected;
(3) respectively substituting the contents of Na, K, Mn, As, Sr, Cs and Tl in the peanut sample to be detected into discrimination models I-II, and calculating to obtain YHigh oil yieldAnd YLow oil yield；
(4) Comparing Y calculated in the step (3)High oil yieldAnd YLow oil yieldMagnitude of value, when calculated YHigh oil yield＞YLow oil yieldIf so, the peanut sample to be detected belongs to the peanuts with high oil yield; when calculated YHigh oil yield＜YLow oil yieldIf so, the peanut sample to be detected belongs to the low-oil-yield peanuts;
the first to second discriminant models are:
Yhigh oil yield＝-641.820-1.361×XNa+0.100×XK+0.009×XMn+4.212×XAs+0.047×XSr+2.347×XCs+29.806×XTl ①；
YLow oil yield＝-742.748-1.487×XNa+0.106×XK+0.010×XMn+4.881×XAs+0.051×XSr+2.607×XCs+34.657×XTl ②；
In the above first to second discrimination models, XNa、XK、XMn、XAs、XSr、XCsAnd XTlRespectively representing the content of Na, K, Mn, As, Sr, Cs and Tl in the peanut sample, wherein XNa、XKUnit of (d) is [ mu ] g/g, XMn、XAs、XSr、XCsAnd XTlThe unit of (a) is mu g/kg;
(5) on the basis of judging the oil yield of the peanuts in the step (4), further substituting the contents of B, K, Ca, Cu, Zn, As, Sr, Cs and Ba in the peanut sample to be detected into the judgment models from (c) to (c), and calculating to obtain YLuhua No. 11、YSea flower No. 1、YWeihua No. 10And YFenghua No. 5；
(6) Comparing Y calculated in the step (5)Luhua No. 11、YSea flower No. 1、YWeihua No. 10And YFenghua No. 5The value is large, and the peanut variety corresponding to the maximum value is the peanut variety to which the peanut sample to be detected belongs;
the third discriminant model comprises the following components:
Yluhua No. 11＝-2390.137-8.539×XB+0.426×XK-0.167×XCa+0.102×XCu+0.009×XZn-21.669×XAs+0.094×XSr-1.712×XCs+0.174×XBa ③；
YSea flower No. 1＝-2510.283-9.214×XB+0.439×XK-0.184×XCa+0.106×XCu+0.009×XZn-22.009×XAs+0.094×XSr-2.315×XCs+0.181×XBa ④；
YWeihua No. 10＝-2191.270-19.375×XB+0.405×XK+0.160×XCa+0.092×XCu+0.011×XZn-17.875×XAs+0.092×XSr-2.245×XCs+0.145×XBa ⑤；
YFenghua No. 5＝-2541.563-14.692×XB+0.439×XK+0.030×XCa+0.100×XCu+0.010×XZn-20.405×XAs+0.098×XSr-2.242×XCs+0.168×XBa ⑥；
The above-mentioned discriminant modelB、XK、XCa、XCu、XZn、XAs、XSr、XCsAnd XBaRespectively representing the contents of B, K, Ca, Cu, Zn, As, Sr, Cs and Ba in the peanut sample, wherein XB、XKAnd XCaUnit of (d) is [ mu ] g/g, XCu、XZn、XAs、XSr、XCsAnd XBaThe unit of (D) is [ mu ] g/kg.
When the peanut variety is identified, the peanut sample to be detected needs to be one of Luhua No. 11, Haishua No. 1, Weihua No. 10 and Fenghua No. 5; the peanut oil yield identification has no requirement on the variety of the peanut sample to be detected.
On the basis of the scheme, the discrimination models are established by the following methods:
A. respectively collecting a large number of peanut samples with high oil yield and low oil yield;
B. respectively shelling, cleaning, drying and grinding the peanut samples, and then digesting;
C. detecting the concentrations of 24 mineral elements B, Na, Mg, P, K, Ca, V, Mn, Co, Ni, Cu, Zn, As, Sr, Mo, Cd, Cs, Ba, La, Ce, Tm, Ir, Pr and Tl in the digested solution, and calculating the average value of the content of each element in each peanut sample; through gradual discriminant analysis and based on the optimal discriminant rate, 7 elements Na, K, Mn, As, Sr, Cs and Tl closely related to the oil yield are screened out; on the basis, a first discrimination model and a second discrimination model are established.
On the basis of the scheme, the discrimination model is established by the following method:
A. respectively collecting a large number of peanut samples of Luhua No. 11, Haichua No. 1, Weihua No. 10 and Fenghua No. 5;
B. respectively shelling, cleaning, drying and grinding the peanut samples, and then digesting;
C. detecting the concentrations of 24 mineral elements B, Na, Mg, P, K, Ca, V, Mn, Co, Ni, Cu, Zn, As, Sr, Mo, Cd, Cs, Ba, La, Ce, Tm, Ir, Pr and Tl in the digested solution, and calculating the average value of the content of each element in each peanut sample; screening 9 elements B, K, Ca, Cu, Zn, As, Sr, Cs and Ba closely related to variety classification through gradual discriminant analysis and based on the optimal discriminant rate; establishing a discrimination model ((C) - (C)) on the basis.
On the basis of the scheme, the detection dosage of the peanut sample is more than or equal to 500 g.
On the basis of the scheme, the drying is carried out at the temperature of 60-80 ℃ until the weight is constant.
On the basis of the scheme, the particle size of the milled peanut powder sample is 0.075mm-0.15 mm.
The technical scheme of the invention has the advantages
The method for identifying the peanut oil yield and assisting in identifying the peanut variety can accurately identify the peanuts with high oil yield and the peanuts with low oil yield, and the overall correct identification rate is 92.1%; can also be used for protecting peanut varieties such as Luhua No. 11 and Haihua No. 1 with high oil yield.
The invention provides a method for identifying the oil yield of peanuts and assisting in identifying peanut varieties, which comprises the steps of screening 7 elements (Na, K, Mn, As, Sr, Cs and Tl) with significant difference in content among peanut samples with different oil yields and 9 elements (B, K, Ca, Cu, Zn, As, Sr, Cs and Ba) with significant difference in content among different varieties of peanuts through early-stage tests, establishing corresponding data models, then respectively shelling, cleaning, drying, grinding and digesting the peanut samples to be identified, detecting the content of the 12 mineral elements in the samples, substituting the mineral elements into the corresponding models to obtain corresponding data, and comparing the corresponding data with each other to identify the oil yield of the peanuts and the varieties of the peanuts, wherein the identification effect reaches 100%.
Detailed Description
Terms used in the present invention have generally meanings as commonly understood by one of ordinary skill in the art, unless otherwise specified.
The present invention is described in further detail below with reference to specific examples and with reference to the data. The following examples are intended to illustrate the invention and are not intended to limit the scope of the invention in any way.
Example 1 identification of peanut oil yield
A. Respectively collecting a large number of peanut samples (each of which is at least 500g) with high oil yield and low oil yield, wherein the peanut varieties with high oil yield comprise No. 22 flowering fertile, No. 35 flowering fertile, No. 11 Luhua and No. 1 sea flower, and the like, and the peanut varieties with low oil yield comprise No. 8 Weihua, No. 10 Weihua and No. 5 abundant flower, and the like;
B. respectively shelling, cleaning, drying and grinding the peanut samples, and then digesting;
and the drying in the step B is drying at 60-80 ℃ to constant weight.
And the particle size of the peanut powder sample milled in the step B is 0.075mm-0.15 mm.
C. The concentrations of 24 mineral elements (B, Na, Mg, P, K, Ca, V, Mn, Co, Ni, Cu, Zn, As, Sr, Mo, Cd, Cs, Ba, La, Ce, Tm, Ir, Pr and Tl) in the digested solution were measured, and the average value of the contents of the elements of each species was calculated, with the results shown in Table 1.
TABLE 1 concentration of 24 elements in peanuts with different oil extraction rates
Figure BDA0002610775890000051
Figure BDA0002610775890000061
Note: B. the concentration units of Na, Mg, P, K and Ca are mug/g, and the rest are mug/kg
7 elements (Na, K, Mn, As, Sr, Cs and Tl) closely related to the oil yield are screened out based on the optimal discrimination rate through stepwise discriminant analysis (Fisher function; Wilks' Lambda method; F value: enter 3.84, remove 2.71), and are sequentially marked As XNa、XK、XMn、XAs、XSr、XCsAnd XTlWherein X isNa、XKUnit of (d) is [ mu ] g/g, XMn、XAs、XSr、XCsAnd XTlThe unit of (a) is mu g/kg; on the basis, a discrimination model is established as follows:
Yhigh oil yield＝-641.820-1.361×XNa+0.100×XK+0.009×XMn+4.212×XAs+0.047×XSr+2.347×XCs+29.806×XTl ①；
YLow oil yield＝-742.748-1.487×XNa+0.106×XK+0.010×XMn+4.881×XAs+0.051×XSr+2.607×XCs+34.657×XTl ②；
In the above first to second discrimination models, XNa、XK、XMn、XAs、XSr、XCsAnd XTlRespectively representing the content of Na, K, Mn, As, Sr, Cs and Tl in the peanut sample, wherein XNa、XKUnit of (d) is [ mu ] g/g, XMn、XAs、XSr、XCsAnd XTlThe unit of (D) is [ mu ] g/kg.
Example 2 establishment of discrimination model for discriminating peanut variety
A. Respectively collecting peanut samples of Luhua No. 11, Haichua No. 1, Weihua No. 10 and Fenghua No. 5 in a large amount, wherein each sample is at least 500 g;
B. respectively shelling, cleaning, drying and grinding the peanut samples, and then digesting;
and the drying in the step B is drying at 60-80 ℃ to constant weight.
And the particle size of the peanut powder sample milled in the step B is 0.075mm-0.15 mm.
C. Detecting the concentrations of 24 mineral elements B, Na, Mg, P, K, Ca, V, Mn, Co, Ni, Cu, Zn, As, Sr, Mo, Cd, Cs, Ba, La, Ce, Tm, Ir, Pr and Tl in the digested solution, and calculating the average value of the content of each element in each peanut sample; the results are shown in Table 2.
TABLE 2 concentrations of 24 elements in different peanut varieties
Figure BDA0002610775890000071
Note: B. the concentration units of Na, Mg, P, K and Ca are mug/g, and the rest are mug/kg
Through step-by-step discriminant analysis (Fisher function; Wilks' Lambda method; F value: entering 3.84, removing 2.71), 9 elements B, K, Ca, Cu, Zn, As, Sr, Cs and Ba closely related to variety classification are screened out based on the optimal discriminant rate; are marked sequentially by XB、XK、XCa、XCu、XZn、XAs、XSr、XCsAnd XBa(ii) a Wherein XB、XKAnd XCaUnit of (d) is [ mu ] g/g, XCu、XZn、XAs、XSr、XCsAnd XBaThe unit of (D) is [ mu ] g/kg. Establishing a discrimination model (c) to (c) on the basis of the above formula:
Yluhua No. 11＝-2390.137-8.539×XB+0.426×XK-0.167×XCa+0.102×XCu+0.009×XZn-21.669×XAs+0.094×XSr-1.712×XCs+0.174×XBa ③；
YSea flower No. 1＝-2510.283-9.214×XB+0.439×XK-0.184×XCa+0.106×XCu+0.009×XZn-22.009×XAs+0.094×XSr-2.315×XCs+0.181×XBa ④；
YWeihua No. 10＝-2191.270-19.375×XB+0.405×XK+0.160×XCa+0.092×XCu+0.011×XZn-17.875×XAs+0.092×XSr-2.245×XCs+0.145×XBa ⑤；
YFenghua No. 5＝-2541.563-14.692×XB+0.439×XK+0.030×XCa+0.100×XCu+0.010×XZn-20.405×XAs+0.098×XSr-2.242×XCs+0.168×XBa ⑥；
The above-mentioned discriminant modelB、XK、XCa、XCu、XZn、XAs、XSr、XCsAnd XBaRespectively representing the contents of B, K, Ca, Cu, Zn, As, Sr, Cs and Ba in the peanut sample, wherein XB、XKAnd XCaIn units of μ g/g, XCu、XZn、XAs、XSr、XCsAnd XBaThe unit of (D) is [ mu ] g/kg.
Example 3
(1) Sample collection
Two peanut samples with higher oil yield of No. 22 and No. 35 garden are collected, two varieties with lower oil yield of Weichahua No. 8 and Weicha flower No. 10 are collected, and 3 samples are collected for each variety.
(2) Respectively shelling, cleaning, drying and grinding the sample
The collected peanut samples are washed clean by deionized water after being shelled, and then are put into an oven at 70 ℃ for drying for 12 hours to reach constant weight. The dried sample was ground into a powder with a mortar and a particle size of 0.15 mm.
(3) Sample digestion
Digesting all samples in a closed microwave digestion system, and performing pre-digestion in an accurate temperature control electrothermal digestion device before digestion, wherein the pre-digestion temperature is 85 ℃, the digestion time is 30min, and the sample weight is about 0.250 g.
Sample digestion conditions
Power: 1600W, digestion temperature: 180 ℃, acid system: 8mL HNO3(MOS grade) +2mL H2O2(MOS stage). Temperature rising procedure: the first step is as follows: keeping at 0-120 deg.C (8min) for 2 min; the second step is that: 120 ℃ and 160 ℃ (5min), keeping for 5 min; the third step: 160 ℃ and 180 ℃ (5min), and keeping for 15 min. And (3) cooling procedure: the fourth step: and cooling for 20 min.
After digestion is finished, the microwave digestion tube is taken out of the microwave digestion instrument, the outer plug is unscrewed in a ventilation kitchen, and the digested sample (microwave digestion tube) is placed in an accurate temperature control electric heating digestion device for acid removal.
Acid removing condition
The temperature is 180 deg.C, and the time is 60 min. And (3) driving the acid in the microwave digestion tube to 0.5-1mL according to the constant volume, and carrying out constant volume (determining the optimal constant volume according to the content of elements in the sample and the measurement requirement) by using ultrapure water for measurement.
(4) Determination of mineral elements
ICP-MS (Agilent 7700, Agilent technologies, USA) is used for measuring the concentration of B, Na, K, Ca, Mn, Cu, Zn, As, Sr, Cs, Ba and Tl 12 elements in the sample.
The working conditions of the instrument are as follows: the radio frequency power is 1200W, the auxiliary gas flow is 1.0L/min, the carrier gas flow is 1.0L/min, the cooling gas flow is 1.47L/min, the plasma gas flow is 15L/min, the compensation gas flow is 1.0L/min, the temperature of the atomization chamber is 2 ℃, and the sampling depth is 8 mm.
The assay was repeated 3 times for each sample during the test and was quantified using the external standard method, using an import mix standard (Inorganic vents, Inc) as the standard sample. Internal standards (In, Ge, Bi) (national center for standards research) were used to ensure instrument stability.
The measured concentrations of each element were converted to the content of each element in the peanut sample as shown in table 3.
TABLE 3 content of mineral elements in peanut samples of different varieties
Figure BDA0002610775890000091
Note: B. the unit of Na, K and Ca is mu g/g, the rest is mu g/kg
(5) Oil yield identification of different varieties of peanuts
The contents of 7 elements (Na, K, Mn, As, Sr, Cs and Tl) in the peanut sample are respectively substituted into the discrimination models I-II, for example, the contents of the elements in No. 2 sample of Huayu No. 22 are respectively substituted into the discrimination models I-II, YHigh oil yieldValue greater than YLow oil yieldTherefore, the sample belongs to the variety with high oil yield.
YHigh oil yield＝-641.820-1.361×38.4+0.100×9166+0.009×16645+4.212×16.3+0.047×2192+2.347×32.5+29.806×0.303＝629.5 ①；
YLow oil yield＝-742.748-1.487×38.4+0.106×9166+0.010×16645+4.881×16.3+0.051×2192+2.607×32.5+34.657×0.303＝625.0 ②；
And (3) sequentially substituting the element contents of the rest 2 samples of No. 22 Huayu into discrimination models I and II to perform judgment, wherein the results are all correct, and the overall correct discrimination rate is 100%.
And (3) sequentially substituting the element contents of the 3 samples of Huayu No. 35 into discrimination models I and II to perform discrimination, wherein the results are all correct, and the overall correct discrimination rate is 100%.
And (3) sequentially substituting the element contents of the 3 samples with the Weihua size 8 into a discrimination model I and II to carry out judgment, wherein the results are all correct, and the overall correct discrimination rate is 100%.
The contents of the elements of the 3 samples with the Weihua size 10 are substituted into the discrimination models firstly and secondly in sequence for judgment, the results are all correct, and the overall correct discrimination rate is 100%.
(6) Auxiliary identification of peanut varieties
Substituting the contents of 9 elements B, K, Ca, Cu, Zn, As, Sr, Cs and Ba in Weihua No. 1 sample No. 10 into a discriminant model for judgment, and comparing YLuhua No. 11、YSea flower No. 1、YWeihua No. 10And YFenghua No. 5Magnitude of value, result YWeihua No. 10The value is the maximum, which indicates that the variety is successfully identified, the other results are all correct, and the overall correct discrimination rate is 100%.
YLuhua No. 11＝-2390.137-8.539×17.4+0.426×6293-0.167×712+0.102×6906+0.009×29269-21.669×14.6+0.094×9239-1.712×14.5+0.174×3795＝2178.6 ③；
YSea flower No. 1＝-2510.283-9.214×17.4+0.439×6293-0.184×712+0.106×6906+0.009×29269-22.009×14.6+0.094×9239-2.315×14.5+0.181×3795＝2158.5 ④；
YWeihua No. 10＝-2191.270-19.375×17.4+0.405×6293+0.160×712+0.092×6906+0.011×29269-17.875×14.6+0.092×239-2.245×14.5+0.145×3795＝2198.2 ⑤；
YFenghua No. 5＝-2541.563-14.692×17.4+0.439×6293+0.030×712+0.100×6906+0.010×29269-20.405×14.6+0.098×239-2.242×14.5+0.168×3795＝2159.9 ⑥。
Example 4
(1) Sample collection
2 peanut samples with high oil yield of Luhua No. 11 and Haihua No. 1 are collected, No. 5 peanut samples with low oil yield of Fenghua No. 5 are collected, and 3 samples are collected for each variety.
(2) Respectively shelling, cleaning, drying and grinding the sample
The collected peanut samples are washed clean by deionized water after being shelled, and then are put into an oven at 70 ℃ for drying for 12 hours to reach constant weight. The dried sample was ground into a powder with a mortar and a particle size of 0.15 mm.
(3) Sample digestion
Digesting all samples in a closed microwave digestion system, and performing pre-digestion in an accurate temperature control electrothermal digestion device before digestion, wherein the pre-digestion temperature is 85 ℃, the digestion time is 30min, and the sample weight is about 0.250 g.
Sample digestion conditions
Power: 1600W, digestion temperature: 180 ℃, acid system: 8mL HNO3(MOS grade) +2mL H2O2(MOS stage). Temperature rising procedure: the first step is as follows: keeping at 0-120 deg.C (8min) for 2 min; the second step is that: 120 ℃ and 160 ℃ (5min), keeping for 5 min; the third step: 160 ℃ and 180 ℃ (5min), and keeping for 15 min. And (3) cooling procedure: the fourth step: and cooling for 20 min.
After digestion is finished, the microwave digestion tube is taken out of the microwave digestion instrument, the outer plug is unscrewed in a ventilation kitchen, and the digested sample (microwave digestion tube) is placed in an accurate temperature control electric heating digestion device for acid removal.
Acid removing condition
The temperature is 180 deg.C, and the time is 60 min. And (3) driving the acid in the microwave digestion tube to 0.5-1mL according to the constant volume, and carrying out constant volume (determining the optimal constant volume according to the content of elements in the sample and the measurement requirement) by using ultrapure water for measurement.
(4) Determination of mineral elements
ICP-MS (Agilent 7700, Agilent technologies, USA) is used to measure the concentration of 12 elements, such As B, Na, K, Ca, Mn, Cu, Zn, As, Sr, Cs, Ba and Tl.
The working conditions of the instrument are as follows: the radio frequency power is 1200W, the auxiliary gas flow is 1.0L/min, the carrier gas flow is 1.0L/min, the cooling gas flow is 1.47L/min, the plasma gas flow is 15L/min, the compensation gas flow is 1.0L/min, the temperature of the atomization chamber is 2 ℃, and the sampling depth is 8 mm.
The assay was repeated 3 times for each sample during the test and quantitative analysis was performed by external standard method using an inlet mixing standard (Inorganic venture, Inc). Internal standards (In, Ge, Bi) (national center for standards research) were used to ensure instrument stability.
The measured concentrations of each element were converted to the content of each element in the peanut sample as shown in table 4.
TABLE 4 content of mineral elements in peanut samples of different varieties
Figure BDA0002610775890000111
Figure BDA0002610775890000121
Note: B. the unit of Na, K and Ca is mu g/g, the rest is mu g/kg
(5) Oil yield identification of different varieties of peanuts
The contents of 7 elements (Na, K, Mn, As, Sr, Cs and Tl) in peanut sample are respectively substituted into discriminating models (I) to (II), for example, the contents of the elements in sample No. 2 of Luhua No. 11 are respectively substituted into discriminating models, Y isHigh oil yieldValue greater than YLow oil yieldTherefore, the sample belongs to the variety with high oil yield.
YHigh oil yield＝-641.820-1.361×30.8+0.100×8785+0.009×18958+4.212×6.04+0.047×2525+2.347×53.0+29.806×0.402＝645.8 ①；
YLow oil yield＝-742.748-1.487×30.8+0.106×8785+0.010×18958+4.881×6.04+0.051×2525+2.607×53.0+34.657×0.402＝642.5 ②；
And (3) sequentially substituting the element contents of the rest 2 samples of Luhua No. 11 into discrimination models I and II to perform judgment, wherein the results are all correct, and the overall correct discrimination rate is 100%.
And (3) sequentially substituting the element contents of the 3 samples of the sea flower No. 1 into a first discrimination model and a second discrimination model to carry out discrimination, wherein the results are all correct, and the overall correct discrimination rate is 100%.
And (3) sequentially substituting the element contents of the 3 samples of the Fenghua No. 5 into a first judgment model and a second judgment model to judge, wherein the results are all correct, and the overall correct judgment rate is 100%.
(6) Auxiliary identification of peanut varieties
Substituting the contents of 9 elements B, K, Ca, Cu, Zn, As, Sr, Cs and Ba in sample No. 1 of Luhua No. 11 into a discrimination model to judge, and comparing YLuhua No. 11、YSea flower No. 1、YWeihua No. 10And YFenghua No. 5Magnitude of value, result YLuhua No. 11The value is the maximum, which indicates that the variety is successfully identified, the other results are all correct, and the overall correct discrimination rate is 100%.
YLuhua No. 11＝-2390.137-8.539×17.4+0.426×8939-0.167×626+0.102×7379+0.009×25266-21.669×6.07+0.094×2665-1.712×54.5+0.174×1644＝2456.5 ③；
YSea flower No. 1＝-2510.283-9.214×17.4+0.439×8939-0.184×626+0.106×7379+0.009×25266-22.009×6.07+0.094×2665-2.315×54.5+0.181×1644＝2437.9 ④；
YWeihua No. 10＝-2191.270-19.375×17.4+0.405×8939+0.160×626+0.092×7379+0.011×25266-17.875×6.07+0.092×2665-2.245×54.5+0.145×1644＝2401.6 ⑤；
YFenghua No. 5＝-2541.563-14.692×17.4+0.439×8939+0.030×626+0.100×7379+0.010×25266-20.405×6.07+0.098×2665-2.242×54.5+0.168×1644＝2417.8 ⑥。
And (4) sequentially substituting the contents of all elements of the 3 samples of the sea flower No. 1 into discrimination models from (c) to (c), and judging, wherein the results are all correct, and the overall correct discrimination rate is 100%.
And substituting the contents of all elements of 3 samples of Fenghua No. 5 into a discrimination model to judge, wherein the results are all correct, and the integral correct discrimination rate is 100%.
The foregoing is directed to preferred embodiments of the present invention, other and further embodiments of the invention may be devised without departing from the basic scope thereof, and the scope thereof is determined by the claims that follow. However, any simple modification, equivalent change and modification of the above embodiments according to the technical essence of the present invention are within the protection scope of the technical solution of the present invention.

Claims (7)

1. The method for identifying the oil yield of the peanuts is characterized by comprising the following steps:
(1) carrying out shelling, cleaning, drying and grinding on a peanut sample to be detected, and then digesting;
(2) detecting the concentration of Na, K, Mn, As, Sr, Cs and Tl in the solution digested in the step (1), calculating to obtain the content of Na, K, Mn, As, Sr, Cs and Tl in the peanut sample to be detected, respectively substituting the content into discrimination models I to II, and calculating to obtain YHigh oil yieldAnd YLow oil yield；
(3) Comparing Y calculated in the step (2)High oil yieldAnd YLow oil yieldMagnitude of value, when calculated YHigh oil yield＞YLow oil yieldIf so, the peanut sample to be detected belongs to the peanuts with high oil yield; when calculated YHigh oil yield＜YLow oil yieldIf so, the peanut sample to be detected belongs to the low-oil-yield peanuts;
the discrimination model is as follows:
Yhigh oil yield＝-641.820-1.361×XNa+0.100×XK+0.009×XMn+4.212×XAs+0.047×XSr+2.347×XCs+29.806×XTl ①；
YLow oil yield＝-742.748-1.487×XNa+0.106×XK+0.010×XMn+4.881×XAs+0.051×XSr+2.607×XCs+34.657×XTl ②；
In the above first to second discrimination models, XNa、XK、XMn、XAs、XSr、XCsAnd XTlRespectively representing the content of Na, K, Mn, As, Sr, Cs and Tl in the peanut sample, wherein XNa、XKUnit of (d) is [ mu ] g/g, XMn、XAs、XSr、XCsAnd XTlThe unit of (D) is [ mu ] g/kg.
2. A method for identifying the oil yield of peanuts and assisting in identifying peanut varieties is characterized by comprising the following steps:
(1) carrying out shelling, cleaning, drying and grinding on a peanut sample to be detected, and then digesting;
(2) detecting the concentrations of B, Na, K, Ca, Mn, Cu, Zn, As, Sr, Cs, Ba and Tl in the solution digested in the step (1), and calculating to obtain the contents of B, Na, K, Ca, Mn, Cu, Zn, As, Sr, Cs, Ba and Tl in the peanut sample to be detected;
(3) respectively substituting the contents of Na, K, Mn, As, Sr, Cs and Tl in the peanut sample to be detected into discrimination models I-II, and calculating to obtain YHigh oil yieldAnd YLow oil yield；
(4) Comparing Y calculated in the step (3)High oil yieldAnd YLow oil yieldMagnitude of value, when calculated YHigh oil yield＞YLow oil yieldIf so, the peanut sample to be detected belongs to the peanuts with high oil yield; when calculated YHigh oil yield＜YLow oil yieldIf so, the peanut sample to be detected belongs to the low-oil-yield peanuts;
the first to second discriminant models are:
Yhigh oil yield＝-641.820-1.361×XNa+0.100×XK+0.009×XMn+4.212×XAs+0.047×XSr+2.347×XCs+29.806×XTl ①；
YLow oil yield＝-742.748-1.487×XNa+0.106×XK+0.010×XMn+4.881×XAs+0.051×XSr+2.607×XCs+34.657×XTl ②；
In the above first to second discrimination models, XNa、XK、XMn、XAs、XSr、XCsAnd XTlRespectively representing the content of Na, K, Mn, As, Sr, Cs and Tl in the peanut sample, wherein XNa、XKUnit of (d) is [ mu ] g/g, XMn、XAs、XSr、XCsAnd XTlThe unit of (a) is mu g/kg;
(5) based on the oil yield of the peanuts judged in the step (4), the method further comprises the step ofSubstituting the contents of B, K, Ca, Cu, Zn, As, Sr, Cs and Ba in the peanut sample to be tested into the discrimination models, and calculating to obtain YLuhua No. 11、YSea flower No. 1、YWeihua No. 10And YFenghua No. 5；
(6) Comparing Y calculated in the step (5)Luhua No. 11、YSea flower No. 1、YWeihua No. 10And YFenghua No. 5The value is large, and the peanut variety corresponding to the maximum value is the peanut variety to which the peanut sample to be detected belongs;
the third discriminant model comprises the following components:
Yluhua No. 11＝-2390.137-8.539×XB+0.426×XK-0.167×XCa+0.102×XCu+0.009×XZn-21.669×XAs+0.094×XSr-1.712×XCs+0.174×XBa ③；
YSea flower No. 1＝-2510.283-9.214×XB+0.439×XK-0.184×XCa+0.106×XCu+0.009×XZn-22.009×XAs+0.094×XSr-2.315×XCs+0.181×XBa ④；
YWeihua No. 10＝-2191.270-19.375×XB+0.405×XK+0.160×XCa+0.092×XCu+0.011×XZn-17.875×XAs+0.092×XSr-2.245×XCs+0.145×XBa ⑤；
YFenghua No. 5＝-2541.563-14.692×XB+0.439×XK+0.030×XCa+0.100×XCu+0.010×XZn-20.405×XAs+0.098×XSr-2.242×XCs+0.168×XBa ⑥；
The above-mentioned discriminant modelB、XK、XCa、XCu、XZn、XAs、XSr、XCsAnd XBaRespectively representing the contents of B, K, Ca, Cu, Zn, As, Sr, Cs and Ba in the peanut sample, wherein XB、XKAnd XCaUnit of (d) is [ mu ] g/g, XCu、XZn、XAs、XSr、XCsAnd XBaThe unit of (D) is [ mu ] g/kg.
3. The method for identifying the oil yield of peanuts and assisting in identifying the peanut varieties according to claim 2, wherein the discrimination models are established by the following methods:
A. respectively collecting a large number of peanut samples with high oil yield and low oil yield;
B. respectively carrying out shelling, cleaning, drying and grinding on the peanut samples, and then digesting;
C. detecting the concentrations of 24 mineral elements B, Na, Mg, P, K, Ca, V, Mn, Co, Ni, Cu, Zn, As, Sr, Mo, Cd, Cs, Ba, La, Ce, Tm, Ir, Pr and Tl in the digested solution, and calculating the average value of the content of each element in each peanut sample; 7 elements Na, K, Mn, As, Sr, Cs and Tl which are closely related to the oil yield are screened out through gradual discriminant analysis and based on the optimal discriminant rate; on the basis, a first discrimination model and a second discrimination model are established.
4. The method for identifying the oil yield of the peanuts and assisting in identifying the peanut varieties as claimed in claim 2, wherein the identification model (c) - (c) is established by the following method:
A. respectively collecting a large number of peanut samples of Luhua No. 11, Haichua No. 1, Weihua No. 10 and Fenghua No. 5;
B. respectively shelling, cleaning, drying and grinding the peanut samples, and then digesting;
C. detecting the concentrations of 24 mineral elements B, Na, Mg, P, K, Ca, V, Mn, Co, Ni, Cu, Zn, As, Sr, Mo, Cd, Cs, Ba, La, Ce, Tm, Ir, Pr and Tl in the digested solution, and calculating the average value of the content of each element in each peanut sample; screening 9 elements B, K, Ca, Cu, Zn, As, Sr, Cs and Ba closely related to variety classification through gradual discriminant analysis and based on the optimal discriminant rate; establishing a discrimination model ((C) - (C)) on the basis.
5. The method for identifying the oil yield of the peanuts and assisting in identifying the peanut varieties according to any one of claims 2 to 4, wherein the detection dosage of the peanut sample is not less than 500 g.
6. The method for identifying the oil yield of the peanuts and assisting in identifying the peanut varieties according to any one of claims 2 to 4, wherein the drying is carried out at a temperature of 60 ℃ to 80 ℃ until the weight is constant.
7. The method for identifying the oil yield of peanuts and assisting in identifying the peanut varieties according to any one of claims 2 to 4, wherein the particle size of the milled peanut powder sample is 0.075mm-0.15 mm.
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