CN112611830B - A method for distinguishing walnut varieties according to their oxidation characteristics - Google Patents

Abstract

The invention discloses a method for distinguishing walnut varieties according to their oxidation characteristics. The steps are: S1, drying the walnut kernels obtained after walnut shelling for 6 hours at 50° C., and then crushing them for use; S2, using a grease oxidation stability analyzer to measure the data of the change of oxygen pressure with time in the walnut oxidation process; S3, draw the walnut oxidation curve, carry out principal component analysis according to the oxidation curve, obtain a principal component factor score map, and distinguish different varieties of walnuts according to the principal component factor score map. Since the composition of fat and fatty acid in walnut is different, its oxygen consumption is also different. By drawing the oxidation curves of different varieties of walnuts, the principal component analysis method is used to analyze the oxidation curves to obtain the principal component factor score map. According to the principal component factor score map The 95% confidence intervals are disjoint to distinguish different varieties of walnuts. The method is simple in operation, simple in sample processing, does not use various harmful solvents in the detection process, and has good practicability and generalizability.

Description

A method for distinguishing walnut varieties according to their oxidation characteristics

technical field

The present invention belongs to the technical field of testing or analyzing materials by means of determining their chemical or physical properties, and in particular relates to a method for distinguishing walnut varieties according to their oxygen properties.

Background technique

As the most widely distributed nut food in the world, walnuts, together with almonds, cashews and hazelnuts, are known as the "four major dried fruits". my country is rich in walnut germplasm resources, with a wide variety of planting areas and total output ranking first in the world. my country's walnuts have been planted for more than 2,000 years, with a wide range of plantings. They are planted in almost every province in the country, mainly in Yunnan, Shaanxi, Hebei, Shanxi, Xinjiang and other regions.

Walnut kernels are rich in high-quality fats, proteins, carbohydrates, phosphorus, calcium, iron, potassium and other mineral elements and zinc, manganese, chromium and other essential trace elements. According to analysis, the average oil content of walnut is 65.08% to 68.88%, and the highest is 76.3%, which is higher than that of soybean, rapeseed, peanut and sesame. The fatty acids in walnut oil are mainly oleic acid and linoleic acid, which are easy to digest and have a high absorption rate. The protein content of walnut kernels is generally about 15%, and the highest is 29.7%. It is known as high-quality protein because of its high real digestibility and net protein ratio. The vitamin E in walnut kernels can prevent cell aging and memory loss. It also contains vitamin B, C and 18 kinds of amino acids. It has a complete range and reasonable composition. It is an important woody grain and oil product.

ARRANZ et al. (Comparison between free radical scavenging capacity and oxidative stability of nut oils[J]. Food Chemistry, 2008, 110(4): 985-990.) found that the comprehensive antioxidant capacity of walnut oil is better than that of peanut oil and almond oil , but the oxidation induction time of walnut oil (4.7h) was significantly shorter than that of peanut oil (14.6h), almond oil (21.8h), pistachio oil (44.6h) and hazelnut oil (52.7h). Saturated fatty acids account for 88.38% to 95.78%, and walnut oil is easily oxidized during storage. Zhang Liang et al. found that with the increase of oxygen pressure, the oxidation rate of oil also increased; MATE et al. (Peanut and walnutrancidity: effects of oxygen concentration and relative humidity [J]. Journal of Food Science. 1996, 61 ( 2): 465-469.) The study found that the peroxide value of walnut kernels stored under hyperoxic and hypoxic conditions was significantly different.

At present, the methods for identifying vegetable oil varieties mainly include infrared spectroscopy, high performance liquid chromatography fingerprinting, gas chromatography-mass spectrometry, fluorescence spectroscopy, etc. No matter which method is used, the organic solvent is basically used first. The samples are processed, and then the content of various components is detected, and then analyzed according to the detection data to identify oils and fats of different qualities. No matter which method is used, it belongs to destructive testing, which requires complicated sample processing process and expensive instruments, etc., which is contrary to the trend in the field of food testing. In addition, the chemicals used in the testing process also affect the health of operators. threaten.

"Experimental Study on Oxidative Stability of Pecan Oil by PDSC" (Sun Xiaoqin, Zhou Guoyan. Food and Fermentation Industry, 2020, 46(1): 256-261) Determination of Different Pressures by High Pressure Differential Scanning Calorimeter (PDSC) Oxidative stability of pecan oil under the conditions of , oxygen concentration and temperature, based on the single factor experiment, the response surface test was carried out with the oxidation induction time as the response value. The experimental results show that the interaction between oxygen concentration and temperature, temperature and pressure has a very significant effect on the induction time of pecan oil oxidation.

"Rapid detection of adulterated walnut oil by low-field nuclear magnetic resonance combined with chemometric methods" (Wang Xiaoling et al., Chinese Journal of Analysis and Testing, 2015, 34(7), 789-794), the adulterated walnut oil sample is the low-field nuclear magnetic resonance detection object , using Principal Component Analysis (PCA) and Partial Least Squares Regression (PLSR) to analyze and process the NMR relaxation data of Cart-Purcell-Meiboom-Gill (CPMG) sequences, aiming to explore a method that can quickly detect the quality of walnut oil new method. Walnut oil samples and pure walnut oil samples were tested and evaluated in several common adulterated forms (spiked with soybean oil, corn oil, sunflower oil). The experimental results show that: pure walnut oil and adulterated walnut oil mixed with different kinds of edible oils can be well distinguished on the principal component score graph, and the adulterated samples are regularly distributed in the graph with the proportion of adulteration; using PLSR The CPMG data and the actual adulteration rate were regressed by the method, which could realize the accurate quantitative determination of the adulteration level of walnut oil.

Although the technology of studying the oxidative stability of walnut has been published, there is no report of using walnut oxidation characteristics to conduct principal component analysis and obtain a principal component factor score map to distinguish its varieties.

SUMMARY OF THE INVENTION

According to the principle that different walnut varieties have different oil and fatty acid contents and different oxygen consumption in the oxidation process, the invention uses an oil oxidation stability analyzer to work to measure the data of the oxygen pressure changing with time of different varieties of walnut under the condition of accelerated oxidation, The walnut oxidation curve was drawn, and the principal component analysis method was used to analyze the oxidation curve, and the principal component factor score was obtained.

The technical scheme adopted by the method for distinguishing its varieties according to the oxidation characteristics of walnuts provided by the present invention comprises the following steps:

S1, walnut sample processing: the walnut kernels obtained after shelling the walnuts are dried at 50 ° C for 6 hours, and then crushed for use;

S2, use the oil oxidation stability analyzer to work to measure the data of the change of oxygen pressure with time during the oxidation process of walnut;

S3, draw the walnut oxidation curve, analyze the oxidation curve to obtain a principal component factor score map, and distinguish walnuts of different varieties according to the principal component factor score map.

Preferably, the determination process of the step S2 is specifically:

S21, weigh the crushed described walnut kernel and spread it in the sample tray that described oil oxidation stability analyzer works;

S22. Seal the reaction chamber where the grease oxidation stability analyzer works, open the oxygen pressure valve, and keep the oxygen pressure in the reaction chamber between 5 and 8 bars; if the oxygen pressure is too low, the experimental test time will be very long If the oxygen pressure is too high, the sealing performance of the reaction chamber is required to be higher.

S23. Turn on the working heater of the grease oxidation stability analyzer to keep the temperature in the reaction chamber at 40-110°C; the lower the temperature, the slower the sample oxidation speed and the longer the experimental test time; the higher the temperature, the sample oxidized The faster the speed, the shorter the experimental test time.

S24, the data acquisition system operating the grease oxidation stability analyzer records the data of the oxygen pressure and temperature in the reaction chamber changing with time.

Further preferably, the thickness of the walnut kernels laid on the sample tray in step S21 is less than or equal to 3 mm. When the thickness of the sample is too thick, some samples will not be fully oxidized, thus affecting the accuracy of the measurement results.

Further preferably, the oxygen pressure in the reaction chamber in step S22 is maintained at 6 bar.

Further preferably, the temperature in the reaction chamber in step S23 is kept at 90°C.

Preferably, the oxidation curve is directly derived from the operation of the grease oxidation stability analyzer.

Preferably, SPSS or origin software is used in the step S3, and the data of the oxygen partial pressure changing with time when the walnut kernels are reacted in the reaction chamber for 400-800 min are used as the original analysis data, and then the principal component analysis method is used to analyze the data. The data is analyzed to obtain a principal component factor score map, in which the 95% confidence interval ellipses in the principal component factor score map can be distinguished as different varieties of walnuts.

Further preferably, a number of the principal component factors whose cumulative variance contribution rate reaches more than 75% are used for analysis to obtain the principal component factor score map.

The beneficial effects of the present invention are as follows: the technical scheme of the present invention is to perform principal component analysis according to the oxidation characteristics of walnuts for the first time, and obtain a principal component factor score map, and distinguish different varieties of Walnut. The sample processing process is simple, the detection method is simple to operate, and various harmful solvents are not used to treat the sample in the detection process, which has good practicability and generalizability.

Description of drawings

Fig. 1 is the schematic diagram of the grease oxidation stability analyzer used in the technical scheme of the present invention;

Fig. 2 is the walnut of two different varieties of embodiment 1;

Fig. 3 is the oxidation curve diagram of the different varieties of walnuts of embodiment 1;

Fig. 4 is the principal component factor score diagram of the different varieties of walnuts of Example 1;

Fig. 5 is the walnut of two different varieties of embodiment 2;

Fig. 6 is the oxidation curve diagram of the different varieties of walnuts of embodiment 2;

Fig. 7 is the principal component factor score diagram of the different varieties of walnuts of Example 2;

Fig. 8 is the oxidation curve diagram of the different varieties of walnuts of embodiment 3;

FIG. 9 is a graph of principal component factor scores of different varieties of walnuts in Example 3. FIG.

In the figure: 1. Oxygen gas source; 2. Oxygen pressure valve; 3. Reaction chamber; 4. Sample tray; 5. Heater; 6. Oxygen pressure sensor; 7. Temperature sensor; 8. Data acquisition system; 10. Temperature -185 Walnut (HT-1) principal component factor score map 95% confidence interval; 11, HT-1-1; 12, HT-1-2; 13, HT-1-3; 20, Luling walnut (HT- 2) Principal component factor score map 95% confidence interval; 21, HT-2-1; 22, HT-2-2; 23, HT-2-3; 30, Yangbi walnut (HT-3) principal component factor score map 95% Confidence Interval; 31, HT-3-1; 32, HT-3-2; 33, HT-3-3; 40, Principal Component Factor Score of Paper No. 1 Walnut (HT-4) 95% Confidence Interval ; 41, HT-4-1; 42, HT4-2; 43, HT-4-3.

Detailed ways

The technical solutions of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without creative work fall within the protection scope of the present invention. Any equivalent transformation or substitution made by those skilled in the art according to the following embodiments falls within the protection scope of the present invention.

Example 1

The method for distinguishing its varieties according to the oxidation characteristics of walnuts provided by the present embodiment comprises the following steps:

(1) Processing of walnut samples: the walnut kernels obtained after the walnuts were shelled were dried at 50° C. for 6 hours, and then the walnut kernels were crushed with a grinding bowl for subsequent use;

(2) Accurately weigh 15.0 g of crushed walnut kernels, place them in the sample tray 4 of the oil oxidation stability analyzer (see Figure 1), and spread them evenly and cannot be compacted, and the thickness of the tiling is 2 mm; The oil oxidation stability analyzer model is Oxitest, and the manufacturer is Italian VELP.

(3) airtight reaction chamber 3, open oxygen pressure valve 2, make the oxygen pressure in reaction chamber 3 reach 6bar and close oxygen pressure valve 2, keep the oxygen pressure in the reaction chamber unchanged;

(4) open heater 5 to make the temperature in reaction chamber 3 rise to 90 ℃, and keep this temperature constant;

(5) After the temperature in the reaction chamber 3 reaches 90°C, the data acquisition system 8 starts to record data, and the recorded data includes pressure, temperature and time;

(6) After the detection, derive the walnut oxidation curve from the oil oxidation stability analyzer; use SPSS, origin and other software to analyze the data with the principal component analysis method to obtain the principal component factor score map, according to the principal component factor score map The 95% confidence interval ellipses do not intersect to distinguish different varieties of walnuts.

During testing, at least 3 batches of each walnut were tested in parallel, and the oxidation curve of each batch was output for analysis.

Referring to Figure 2 and Figure 3, select Wen-185 walnut and mark it as HT-1, and test 3 batches in parallel according to the above detection steps, and the obtained oxidation curves are respectively recorded as HT-1-1 (11 in the figure), HT-1 -2 (12 in the figure), HT-1-3 (13 in the figure); choose Luling walnut as HT-2, and test 3 batches in parallel according to the above detection steps, and the obtained oxidation curves are respectively recorded as HT-2 -1 (21 in the figure), HT-2-2 (22 in the figure), HT-2-3 (23 in the figure).

The data obtained by principal component analysis of the oxidation curves of 6 batches of the above-mentioned two kinds of walnuts are as follows in Table 1:

Table 1 Principal Component Contribution Rate

It can be seen from Table 1 that the cumulative variance contribution rate of the first principal component (PC1) and the second principal component (PC2) reaches 99.91912%, and the first principal component, the second principal component, the third principal component and the fourth principal component The cumulative variance contribution rate of is almost 100%, and further analysis of the scores of PC1 and PC2 yields the principal component factor score map shown in Figure 4. In Figure 4, 10 is the 95% confidence interval of the principal component factor score of Wen-185 walnut (HT-1), and 20 is the 95% confidence interval of the principal component factor score of Luling walnut (HT-2). It can be seen from Figure 4 that the scores of the 3 batches of Wen-185 walnut (HT-1) and the 3 batches of Luling walnut (HT-2) are concentrated respectively. The oval in the figure is the 95% confidence interval. The ellipses did not intersect, which indicated that the analysis of the oxidation curve of walnut by principal component analysis could effectively distinguish the two kinds of walnuts.

Example 2

The measurement and analysis steps of Example 2 and Example 1 are identical, except that, as shown in Figure 5, the walnut samples used are Yangbi walnut (denoted as HT-3) and No. ), the tiling thickness of the walnut kernel sample in step (2) is 3 mm, the oxygen pressure in step (3) is maintained at 8 bar, and the temperature in step (4) is maintained at 110°C. As shown in Figure 6, the oxidation curves obtained by detecting Yangbi walnut (HT-3) were recorded as HT-3-1 (31 in the figure), HT-3-2 (32 in the figure), and HT-3-3 (Figure 32). 33); the oxidation curves obtained by detecting the No. 1 walnut (HT-4) in the paper skin were recorded as HT-4-1 (41 in the figure), HT-4-2 (42 in the figure), HT-4-3 ( 43 in the figure).

The data obtained by principal component analysis of the oxidation curves of 6 batches of the above-mentioned two kinds of walnuts are as follows in Table 2:

Table 2 Principal Component Contribution Rate

It can be seen from Table 2 that the cumulative variance contribution rate of the first principal component (PC1) and the second principal component (PC2) reaches 99.94018%, and the scores of PC1 and PC2 are further analyzed to obtain the principal component factor score diagram as shown in Figure 7 . In Figure 7, 30 is the 95% confidence interval of the principal component factor score of Yangbi walnut (HT-3), and 40 is the 95% confidence interval of the principal component factor score of Zhipi No. 1 walnut (HT-4). It can be seen from Figure 7 that the scores of the 3 batches of Yangbi walnut (HT-3) and the 3 batches of Zhipi No. 1 walnut (HT-4) are concentrated respectively. The ellipse in the figure is the 95% confidence interval. The two ellipses do not intersect, indicating that the analysis of the oxidation curve of walnut by principal component analysis can effectively distinguish two kinds of walnuts.

Example 3

The determination and analysis steps of Example 3 are the same as those of Example 1, the difference is that in this example, three kinds of walnuts are selected to be distinguished. The selected walnuts were Wen-185 walnut (HT-1), Luling walnut (HT-2) and Zhipi No. 1 walnut (HT-4), and the obtained oxidation curves were shown in FIG. 8 . The data obtained by principal component analysis on the data of the oxidation curves of 9 batches of three kinds of walnuts are as follows in Table 3:

Table 3 Principal Component Contribution Rate

It can be seen from Table 3 that the cumulative variance contribution rate of the first principal component (PC1) and the second principal component (PC2) reaches 99.91782%. Further analysis of the scores of PC1 and PC2, the obtained principal component factor score graph is shown in Figure 9. As can be seen from Figure 9, the scores of each of the three batches of Wen-185 walnut (HT-1), Luling walnut (HT-2) and Zhipi No. 1 walnut (HT-4) are concentrated respectively. It is 95% confidence interval, and the three ellipses are not intersected, indicating that the analysis of walnut oxidation curve by principal component analysis method can effectively distinguish three kinds of walnuts.

It should be noted that the thickness of the walnut kernel sample in step (2) is less than or equal to 3mm; the oxygen pressure range in step (3) can be 5-8 bar; the temperature in step (4) can be 40-110°C; step (6) In step (6), the oxidation curve can be directly derived from the oil oxidation stability analyzer, or the data can be derived from the instrument and then the software can be used to draw the oxidation curve; when performing principal component analysis on the oxidation curve data in step (6), the cumulative variance The principal component factors whose contribution rate reaches more than 75% can be used for score analysis.

The above descriptions are only preferred embodiments of the present invention, and are not intended to limit the protection scope of the present invention. Various modifications and variations of the present invention are possible for anyone skilled in the art. Any simple equivalent changes and modifications made according to the protection scope of the application of the present invention and the contents of the description shall 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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