CN112611830B - Method for distinguishing varieties of walnuts according to oxidation characteristics of walnuts - Google Patents
Method for distinguishing varieties of walnuts according to oxidation characteristics of walnuts Download PDFInfo
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Abstract
The invention discloses a method for distinguishing varieties of walnuts according to the oxidation characteristics of the walnuts. The method comprises the following steps: s1, drying the walnut kernels obtained after the shells of the walnuts are removed at 50 ℃ for 6 hours, and then grinding for later use; s2, determining the data of the change of oxygen pressure along with time in the walnut oxidation process by adopting a grease oxidation stability analyzer; and S3, drawing a walnut oxidation curve, carrying out principal component analysis according to the oxidation curve to obtain a principal component factor score chart, and distinguishing walnuts of different varieties according to the principal component factor score chart. Because the composition of grease and fatty acid in the walnuts is different and the oxygen consumption is also different, the oxidation curves of different varieties of walnuts are drawn, the oxidation curves are analyzed by a principal component analysis method to obtain a principal component factor score chart, and the walnuts of different varieties can be distinguished according to the fact that 95% confidence intervals in the principal component factor score chart are not intersected. The method is simple to operate, simple in sample treatment, free of various harmful solvents in the detection process, and good in practicability and popularization.
Description
Technical Field
The invention belongs to the technical field of testing or analyzing materials by means of determining chemical or physical properties of the materials, and particularly relates to a method for distinguishing varieties of walnuts according to oxygen characteristics of the walnuts.
Background
Walnut is used as the nut food distributed most widely in the world, and is called as 'four dry fruits' together with almond, cashew nut and hazelnut. China has rich walnut germplasm resources and various varieties, and the planting area and the total output are at the top in the world. Chinese walnuts have a planting history of more than 2000 years, have a wide planting range, are almost planted in various provinces in China, and are mainly distributed in Yunnan, Shaanxi, Hebei, Shanxi, Xinjiang and other areas.
Walnut kernel is rich in high-quality fat, protein, carbohydrate, mineral elements such as phosphorus, calcium, iron and potassium and trace elements such as zinc, manganese and chromium which are necessary for human body. According to analysis, the oil content of the walnut is 65.08-68.88% on average and is up to 76.3% higher than that of soybean, rapeseed, peanut and sesame. The fatty acid in the walnut oil is mainly oleic acid and linoleic acid, and the walnut oil is easy to digest and has high absorption rate. The protein content in the walnut kernel is about 15 percent generally, and can reach 29.7 percent at most, and the walnut kernel is praised as high-quality protein due to higher real digestibility and net protein ratio. The vitamin E in the walnut kernel can prevent cell aging and hypomnesis, and simultaneously contains B, C vitamins and 18 amino acids, has complete varieties and reasonable component composition, and is an important woody grain and oil product.
ARRANZ et al (composite between free radial conditioning capacity and oxidative stability of nut oils [ J ]. Food Chemistry 2008, 110 (4): 985-990.) found that the walnut oil has better comprehensive antioxidant capacity than peanut oil and almond oil, but the oxidation induction time (4.7h) of the walnut oil is significantly shorter than that of peanut oil (14.6h), almond oil (21.8h), pistachio oil (44.6h) and hazelnut oil (52.7h), because unsaturated fatty acids in the walnut oil account for 88.38-95.78%, the walnut oil is very easy to oxidize during storage. Zhang et al found that as the oxygen pressure increased, the oxidation rate of the grease increased; MATE et al (Peaut and walnut variety: effects of oxygen concentration and relative humidity [ J ]. Journal of Food science.1996, 61 (2): 465-469.) found that there was a significant difference in the peroxide value of walnut kernels stored under high-oxygen and low-oxygen conditions.
At present, methods for distinguishing plant oil varieties mainly comprise an infrared spectroscopy method, a high performance liquid chromatography fingerprint spectroscopy method, a gas chromatography-mass spectrometry combined determination method, a fluorescence spectroscopy analysis method and the like, and in any method, a sample is basically processed by an organic solvent, then the content of various components is detected, and then analysis is carried out according to detection data so as to distinguish the oils and fats with different qualities. Either method is destructive, requires complicated sample processing procedures and expensive instruments, etc., contrary to the trend in the field of food testing, and the agents used in the testing process pose a threat to the health of the operator.
Experimental study of oxidative stability of pecan oil by PDSC method (sun parsley, national swallow, food and fermentation industry, 2020, 46 (1): 256-261) a response surface test was performed by measuring the oxidative stability of pecan oil under different pressure, oxygen concentration and temperature conditions using a high Pressure Differential Scanning Calorimeter (PDSC) method based on a single factor test with the oxidation induction time as a response value. The experimental result shows that the interaction between the oxygen concentration and the temperature and the interaction between the temperature and the pressure have very significant influence on the oxidation induction time of the pecan oil.
In the method for rapidly detecting adulterated walnut oil (Wangxing and Ling, the academic journal of analytical tests, 2015, 34(7), 789-794), adulterated walnut oil samples are used as low-field nuclear magnetic resonance detection objects, and nuclear magnetic resonance relaxation data of a Cart-Purcell-Meiboom-Gill (CPMG) sequence are analyzed and processed by a Principal Component Analysis (PCA) method and a Partial Least Squares Regression (PLSR) method, so that a novel method capable of rapidly detecting the quality of the walnut oil is sought. Walnut oil samples in several common adulterated forms (soybean oil, corn oil, sunflower oil) and pure walnut oil samples were tested and evaluated. The experimental results show that: pure walnut oil and adulterated walnut oil doped with different types of edible oil can be well distinguished on a main component score chart, and adulterated samples are regularly distributed in the chart along with adulteration proportion; and the PLSR method is adopted to carry out regression on CPMG data and actual adulteration rate, so that accurate quantitative determination of walnut oil adulteration level can be realized.
Although the technology for researching the oxidation stability of the walnuts is disclosed at present, no report is provided for carrying out principal component analysis by utilizing the oxidation characteristics of the walnuts and obtaining a principal component factor score chart so as to distinguish the varieties of the walnuts.
Disclosure of Invention
According to the principle that the contents of oil and fatty acid of different walnut varieties are different and the oxygen consumption is different in the oxidation process, an oil oxidation stability analyzer is used for measuring the data of the change of oxygen pressure of different kinds of walnuts along with time under the condition of accelerated oxidation, a walnut oxidation curve is drawn, a principal component analysis method is adopted to analyze the oxidation curve to obtain principal component factor scores, and the walnuts of different varieties can be distinguished according to the fact that 95% confidence intervals of the principal component factor score graphs do not intersect.
The technical scheme adopted by the method for distinguishing the varieties of walnuts according to the oxidation characteristics of the walnuts comprises the following steps:
s1, walnut sample treatment: drying walnut kernels obtained after walnut shells are removed at 50 ℃ for 6 hours, and then grinding for later use;
s2, determining the change data of oxygen pressure along with time in the walnut oxidation process by adopting a grease oxidation stability analyzer;
and S3, drawing a walnut oxidation curve, analyzing the oxidation curve to obtain a principal component factor score chart, and distinguishing walnuts of different varieties according to the principal component factor score chart.
Preferably, the measuring process of step S2 is specifically:
s21, weighing and paving the crushed walnut kernels in a sample tray of the grease oxidation stability analyzer;
s22, sealing a reaction bin where the oil oxidation stability analyzer works, and opening an oxygen pressure valve to keep the oxygen pressure in the reaction bin between 5 and 8 bar; if the oxygen pressure is too low, the experimental test time is very long, and if the oxygen pressure is too high, the requirement on the sealing performance of the reaction chamber is higher.
S23, starting a heater for operating the oil oxidation stability analyzer to keep the temperature in the reaction bin at 40-110 ℃; the lower the temperature is, the slower the oxidation speed of the sample is, and the experimental testing time is longer; the higher the temperature, the faster the sample oxidation rate, and the shorter the experimental test time.
And S24, recording the data of the change of the oxygen pressure and the temperature in the reaction bin along with the time by a data acquisition system in which the oil oxidation stability analyzer works.
Further preferably, in step S21, the thickness of the walnut kernels laid on the sample tray is less than or equal to 3 mm. When the thickness of the sample tiling is too thick, part of the sample is not easy to be fully oxidized, thereby influencing the accuracy of the measurement result.
Further preferably, the oxygen pressure in the reaction chamber is maintained at 6bar in step S22.
Further preferably, the temperature in the reaction bin in step S23 is maintained at 90 ℃.
Preferably, the oxidation curve is derived directly from the operation of the grease oxidation stability analyzer.
Preferably, in the step S3, SPSS or origin software is used, data of a change of oxygen partial pressure with time when the walnut kernels react in the reaction bin for 400-800 min is used as original analysis data, and then a principal component analysis method is used to analyze the data to obtain a principal component factor score map, wherein the walnuts of different varieties can be distinguished by non-intersection of 95% confidence interval ellipses in the principal component factor score map.
Further preferably, a plurality of principal component factors with the cumulative variance contribution rate of more than 75% are analyzed to obtain a principal component factor score map.
The invention has the beneficial effects that: the technical scheme includes that principal component analysis is carried out for the first time according to the oxidation characteristics of walnuts to obtain a principal component factor score chart, and different varieties of walnuts are distinguished according to the fact that 95% confidence intervals of the principal component factor score chart are not intersected. The sample treatment process is simple, the detection method is simple to operate, various harmful solvents are not used for treating the sample in the detection process, and the method has better practicability and popularization.
Drawings
FIG. 1 is a schematic diagram of an oxidation stability analyzer for fats and oils used in the embodiment of the present invention;
FIG. 2 shows two different varieties of walnuts of example 1;
FIG. 3 is a graph of the oxidation profiles of different varieties of walnuts of example 1;
FIG. 4 is a graph of the principal component factor scores of walnuts of different varieties according to example 1;
FIG. 5 is two different varieties of walnuts of example 2;
FIG. 6 is a graph of the oxidation curves of different varieties of walnuts of example 2;
FIG. 7 is a graph of the principal component factor scores of walnuts of different varieties according to example 2;
FIG. 8 is a graph of the oxidation profiles of different varieties of walnuts of example 3;
FIG. 9 is a graph of the principal component factor scores of walnuts of different varieties in example 3.
In the figure: 1. a source of oxygen gas; 2. an oxygen pressure valve; 3. a reaction bin; 4. a sample tray; 5. a heater; 6. an oxygen pressure sensor; 7. a temperature sensor; 8. a data acquisition system; 10. a 95% confidence interval of a Wen-185 walnut (HT-1) principal component factor score plot; 11. HT-1-1; 12. HT-1-2; 13. HT-1-3; 20. a 95% confidence interval of a green-ridge walnut (HT-2) principal component factor score map; 21. HT-2-1; 22. HT-2-2; 23. HT-2-3; 30. the yangbi walnuts (HT-3) main component factor score chart has a confidence interval of 95 percent; 31. HT-3-1; 32. HT-3-2; 33. HT-3-3; 40. a 95% confidence interval of a paper skin No. 1 walnut (HT-4) principal component factor score chart; 41. HT-4-1; 42. HT 4-2; 43. HT-4-3.
Detailed Description
The technical solutions of the present invention are described below clearly and completely in conjunction with the embodiments, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present invention without making any creative effort, shall fall within the protection scope of the present invention. Any equivalent changes or substitutions by those skilled in the art according to the following embodiments are within the scope of the present invention.
Example 1
The method for distinguishing the varieties of the walnuts according to the oxidation characteristics comprises the following steps:
(1) walnut sample treatment: drying walnut kernels obtained after walnut shells are removed at 50 ℃ for 6 hours, and then grinding the walnut kernels by using a grinding bowl for later use;
(2) accurately weighing 15.0g of crushed walnut kernels, placing the crushed walnut kernels in a sample tray 4 of a grease oxidation stability analyzer (shown in figure 1) and uniformly spreading the crushed walnut kernels, wherein the spreading thickness is 2mm, and the sample tray cannot be compacted; the oxidation stability analyzer for fats and oils used was Oxitest, and the manufacturer was italian VELP.
(3) The reaction bin 3 is closed, the oxygen pressure valve 2 is opened, the oxygen pressure valve 2 is closed after the oxygen pressure in the reaction bin 3 reaches 6bar, and the oxygen pressure in the reaction bin is kept unchanged;
(4) the heater 5 is started to raise the temperature in the reaction chamber 3 to 90 ℃ and maintain the temperature to be constant;
(5) when the temperature in the reaction bin 3 reaches 90 ℃, the data acquisition system 8 starts to record data, wherein the recorded data comprises pressure, temperature and time;
(6) after the detection is finished, deriving a walnut oxidation curve from the oil oxidation stability analyzer; and analyzing the data by using software such as SPSS (software platform separation system), origin and the like by using a principal component analysis method to obtain a principal component factor score map, and distinguishing different varieties of walnuts according to the non-intersection of ellipses of 95% confidence intervals in the principal component factor score map.
During detection, each walnut is tested in at least 3 batches in parallel, and the oxidation curve of each batch is output for analysis.
Referring to FIG. 2 and FIG. 3, selecting a-185 walnut as HT-1, testing 3 batches in parallel according to the above detection steps, and obtaining oxidation curves as HT-1-1 (11 in the figure), HT-1-2 (12 in the figure) and HT-1-3 (13 in the figure); selecting green walnut as HT-2, testing 3 batches in parallel according to the above detection steps, and respectively marking the obtained oxidation curves as HT-2-1 (21 in the figure), HT-2-2 (22 in the figure) and HT-2-3 (23 in the figure).
The data obtained by analyzing the main components of the data of the oxidation curves of the two walnut batches of 6 walnut are shown in the following table 1:
TABLE 1 principal Components contribution ratio
As can be seen from table 1, the cumulative variance contribution ratio of the 1 st principal component (PC1) and the 2 nd principal component (PC2) reached 99.91912%, and the cumulative variance contribution ratio of the 1 st principal component, the 2 nd principal component, the 3 rd principal component, and the 4 th principal component almost reached 100%, and further, the scores of PC1 and PC2 were analyzed to obtain the principal component factor score chart shown in fig. 4. In FIG. 4, 10 is the 95% confidence interval of the Warm-185 walnut (HT-1) principal component factor score map, and 20 is the 95% confidence interval of the Green walnut (HT-2) principal component factor score map. As can be seen from FIG. 4, the scores of 3 batches of warm-185 walnuts (HT-1) and 3 batches of green-green walnuts (HT-2) are respectively concentrated, the ellipses in the graph are 95% confidence intervals, and the two ellipses do not intersect, which shows that the two walnuts can be effectively distinguished by analyzing the walnut oxidation curve by the principal component analysis method.
Example 2
Example 2 the same measurement and analysis steps as in example 1, with the difference that, as shown in fig. 5, the walnut samples used were the walnuts with yangbi (identified as HT-3) and the walnuts with leathern No. 1 (identified as HT-4), the tiled thickness of the walnut kernel sample in step (2) was 3mm, the oxygen pressure in step (3) was maintained at 8bar, and the temperature in step (4) was maintained at 110 ℃. As shown in fig. 6, oxidation curves obtained by detecting the walnuts with yangbiens (HT-3) are respectively labeled as HT-3-1 (31 in the figure), HT-3-2 (32 in the figure), and HT-3-3 (33 in the figure); the oxidation curves obtained by detecting the walnut (HT-4) No. 1 in the leatheroid are respectively marked as HT-4-1 (41 in the figure), HT-4-2 (42 in the figure) and HT-4-3 (43 in the figure).
The data obtained by performing principal component analysis on the data of the oxidation curves of 6 batches of the two walnuts are shown in the following table 2:
TABLE 2 principal Components contribution ratio
It can be seen from table 2 that the cumulative variance contribution ratio of the 1 st principal component (PC1) and the 2 nd principal component (PC2) reached 99.94018%, and further analysis of the PC1 and PC2 scores resulted in the principal component factor score map shown in fig. 7. In fig. 7, 30 is the 95% confidence interval of the main component factor score of walnuts with yangbi (HT-3), and 40 is the 95% confidence interval of the main component factor score of walnuts with leatheroid number 1 (HT-4). As can be seen from fig. 7, the scores of 3 batches of walnuts with yangbi (HT-3) and 3 batches of walnuts with leatheroid No. 1 (HT-4) are respectively collected, the ellipses in the figure are 95% confidence intervals, and the two ellipses are not intersected, which shows that the two walnuts can be effectively distinguished by analyzing the walnut oxidation curve by the principal component analysis method.
Example 3
Example 3 the same procedure as in example 1 was followed except that three walnuts were selected for differentiation. The selected walnuts are respectively warm-185 walnut (HT-1), green walnut (HT-2) and leatheroid No. 1 walnut (HT-4), and the obtained oxidation curve graph is shown in FIG. 8. The data obtained by performing principal component analysis on the data of the oxidation curves of 9 batches of three walnuts are shown in the following table 3:
TABLE 3 principal Components contribution ratio
It can be seen from table 3 that the cumulative variance contribution ratio of the 1 st principal component (PC1) and the 2 nd principal component (PC2) reached 99.91782%, and further analysis of the scores of PC1 and PC2 resulted in a principal component factor score graph as shown in fig. 9. As can be seen from FIG. 9, the scores of 3 batches of each of the warm-185 walnut (HT-1), the green walnut (HT-2) and the skin No. 1 walnut (HT-4) are respectively concentrated, the ellipses in the graph are 95% confidence intervals, and the three ellipses are not intersected, which shows that the three walnuts can be effectively distinguished by analyzing the walnut oxidation curve by adopting a principal component analysis method.
In the step (2), the thickness of the flat spread of the walnut kernel sample is less than or equal to 3 mm; the oxygen pressure range in the step (3) can be 5-8 bar; the temperature in the step (4) can be 40-110 ℃; in the step (6), an oxidation curve can be directly derived from the oil oxidation stability analyzer, or the oxidation curve can be drawn by software after data is derived from the oil oxidation stability analyzer; when principal component analysis is performed on the data of the oxidation curve in the step (6), the principal component factors with the accumulated variance contribution rate of more than 75% can be used for score analysis.
The above description is only a preferred embodiment of the present invention, and is not intended to limit the scope of the present invention. The present invention may be subject to various modifications and changes by any person skilled in the art. Any simple equivalent changes and modifications made in accordance with the protection scope of the present application and the content of the specification are intended to be included within the protection scope of the present invention.
Claims (7)
1. A method for distinguishing varieties of walnuts according to oxidation characteristics of the walnuts, which is characterized by comprising the following steps:
s1, walnut sample treatment: drying walnut kernels obtained after walnut shells are removed at 50 ℃ for 6 hours, and then grinding for later use;
s2, determining the change data of oxygen pressure along with time in the walnut oxidation process by adopting a grease oxidation stability analyzer; the specific process is as follows: s21, weighing and paving the crushed walnut kernels in a sample tray of the grease oxidation stability analyzer; s22, sealing a reaction bin where the oil oxidation stability analyzer works, and opening an oxygen pressure valve to keep the oxygen pressure in the reaction bin between 5 and 8 bar; s23, starting a heater for operating the oil oxidation stability analyzer to keep the temperature in the reaction bin at 40-110 ℃; s24, recording the data of the change of the oxygen pressure and the temperature in the reaction bin along with the time by a data acquisition system in which the oil oxidation stability analyzer works;
and S3, drawing a walnut oxidation curve, analyzing the oxidation curve to obtain a principal component factor score chart, and distinguishing walnuts of different varieties according to the principal component factor score chart.
2. The method as claimed in claim 1, wherein the walnut kernels are spread on the sample tray to a thickness of 3mm or less in step S21.
3. The method for distinguishing walnut varieties according to the oxidation characteristics of walnuts as claimed in claim 1, wherein the oxygen pressure in the reaction chamber is maintained at 6bar in step S22.
4. The method for differentiating walnut varieties according to walnut oxidation characteristics of claim 1, wherein the temperature in the reaction chamber is maintained at 90 ℃ in step S23.
5. The method for distinguishing varieties of walnuts according to oxidation characteristics of claim 1, wherein the oxidation curve is directly derived from the operation of the oil oxidation stability analyzer.
6. The method for distinguishing walnut varieties according to the oxidation characteristics of walnuts as claimed in claim 1, wherein in step S3, SPSS or origin software is used, data of the change of oxygen partial pressure with time when walnut kernels react in the reaction bin for 400-800 min is used as original analysis data, and then a principal component analysis method is used to analyze the data to obtain a principal component factor score map, wherein the walnut varieties can be distinguished by the disjoint 95% confidence intervals in the principal component factor score map.
7. The method for distinguishing varieties of walnuts according to oxidation characteristics of claim 6, wherein a principal component factor score chart is obtained by analyzing a plurality of principal component factors with an accumulated variance contribution rate of more than 75%.
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