Storage period prediction method and freshness discrimination method of badam chips

Abstract

The invention relates to a storage period prediction method and a freshness discrimination method for badam chips, which are characterized in that key indexes are selected, and a storage period prediction model and a freshness discrimination model are established on the basis, so that the storage period and the freshness of the badam chips can be rapidly, efficiently and accurately discriminated. The method can be used for judging the freshness of American almonds and Australian almonds on the basis of a freshness judgment model, that is, the detection can be completed before whether the almonds are fresh or not can be detected by utilizing indexes such as acid value or peroxide value and the like. According to the method, the research on rapid detection of volatile components of the badam chips is facilitated, and a new thought is provided for rapid food detection.

Description

Technical Field

[0001] The present invention relates to the field of rapid food quality detection, and in particular to a method for predicting the storage period of almond chips and a method for determining their freshness. Background Art

[0002] Currently, the main methods for testing the freshness of nuts both domestically and internationally include sensory evaluation and physical and chemical testing. However, these methods often have drawbacks, such as sensory evaluation being time-consuming, highly subjective, and often influenced by the tester's own personal factors. The national standard method, which determines the freshness of nuts by measuring physical and chemical indicators such as peroxide value and acid value, has drawbacks such as cumbersome and time-consuming procedures, the use of large amounts of organic reagents, and low sensitivity.

[0003] The process from almonds to almond chips involves a period of storage. Therefore, there is currently no way to measure the storage period of almonds from harvest to processing into almond chips. In factory applications, measuring the storage period of almond chips and determining their freshness is crucial for subsequent processing, storage, and sales.

[0004] Although the aroma components of almond chips have been studied, the characteristic volatile components associated with the degree of oxidation in almond chips remain unclear. Therefore, given the cumbersome and subjective nature of current methods for testing the freshness of almond chips, there is an urgent need to develop a method that can quickly, accurately, and efficiently predict the storage life and determine the freshness of almond chips. Summary of the Invention

[0005] In view of the problems existing in the prior art, the purpose of the present invention is to provide a method for predicting the storage period of almond chips and a method for judging the freshness, so as to improve the accuracy of detection.

[0006] To achieve the above object, the technical solution adopted by the present invention is as follows: A method for predicting the storage period of almond chips comprises the following steps: S11. Obtain the almond slices to be tested and detect the content of key indicators thereof; S12. Input the content of the key indicator into a storage period prediction model to predict the storage period; the storage period prediction model is:

[0007] in, To predict the storage period, is a constant, - They are the contents of five key indicators in the almond slices to be tested, 、 、 、 、 They are - The coefficient of .

[0008] The method for determining the key indicators is as follows: Almond chips were used as samples, and flavor substances were extracted using headspace solid-phase extraction to obtain fiber heads containing flavor substances. The fiber heads were then decomposed at 250°C for 4 minutes in the inlet of a gas chromatography-mass spectrometer, and a program was started to detect the flavor substances in the almonds. Key indicators were screened from the detected flavor substances; specifically, by analyzing the relationship between the detected flavor substances in the almond chip raw materials during storage and the sensory evaluation, the data of the detected flavor substances were Z-scrore standardized and then subjected to range analysis, and R-type clustering was used to classify the variables, and the variance analysis and correlation coefficient method were combined to determine the key indicators affecting almond chips.

[0009] The almond slices are American almonds, and the key indicators are heptanol, dextrorotatory terpenoids, benzaldehyde, tetradecane, and ethyl octanoate.

[0010] The storage period prediction model is: .

[0011] The almond chips are Australian almonds, and the key indicators are dodecane, naphthalene, heptanol, trans-2-octenal and tetradecane.

[0012] The storage period prediction model is: .

[0013] A method for determining the freshness of almond chips comprises the following steps: S21. Obtain the almond slices to be tested and detect the content of key indicators thereof; S22. Inputting the content of the key indicator into a freshness discrimination model, the freshness discrimination model including two discriminant functions F1 and F2, calculating the function values ​​of the discriminant functions F1 and F2 respectively based on the content of the key indicator, comparing the function values ​​of the discriminant functions F1 and F2, and taking the type corresponding to the discriminant function with the largest function value as the final freshness discrimination result; The discriminant functions F1 and F2 are:

[0014]

[0015] Among them, F1 is the discriminant function value of the fresh group, and F2 is the discriminant function value of the spoiled group is a constant, - They are the contents of key indicators in the almond slices to be tested, 、 、 、 、 Respectively fresh group function F1 - The coefficient of 、 、 、 、 They are respectively the metamorphic group function F2 - The coefficient of .

[0016] The method for determining the key indicators is as follows: Almond chips were used as samples, and flavor substances were extracted using headspace solid-phase extraction to obtain fiber heads containing flavor substances. The fiber heads were then decomposed at 250°C for 4 minutes in the inlet of a gas chromatography-mass spectrometer, and a program was started to detect the flavor substances in the almonds. Key indicators were screened from the detected flavor substances; specifically, by analyzing the relationship between the detected flavor substances in the almond chip raw materials during storage and the sensory evaluation, the data of the detected flavor substances were Z-scrore standardized and then subjected to range analysis, and R-type clustering was used to classify the variables, and the variance analysis and correlation coefficient method were combined to determine the key indicators affecting almond chips.

[0017] The almond slices are American almonds, and the key indicators are heptanol, dextrorotatory terpenoids, benzaldehyde, tetradecane, and ethyl octanoate; The discriminant functions F1 and F2 are:

[0018] .

[0019] The almond slices are Australian almonds, and the key indicators are dodecane, naphthalene, heptanol, trans-2-octenal and tetradecane; The discriminant functions F1 and F2 are:

[0020] .

[0021] After adopting the above scheme, the method for judging the freshness of almond chips of the present invention detects the volatile cost in almond chips from the United States and Australia by gas chromatography-mass spectrometry technology, inputs the five key variables of American almond chips and Australian almond chips as the discrimination model, and establishes a freshness discrimination model based on the Bayes discriminant method. The present invention can be used to judge the freshness of American almond chips and Australian almond chips based on the freshness discrimination model, that is, before the acid value or peroxide value and other indicators can be used to detect whether the almond chips are fresh, the detection can be completed. The present invention contributes to the research on rapid detection of almond chips through volatile components, and provides a new idea for rapid detection of food. DETAILED DESCRIPTION

[0022] Example 1 This embodiment discloses a method for predicting the storage period of American almond chips, which comprises the following steps: S11. Obtaining American almond chips to be tested, and detecting the contents of key indicators thereof, including heptanol, dextrorotatory terpenoids, benzaldehyde, tetradecane, and ethyl octanoate; S12. Input the content of the key indicator into a storage period prediction model to predict the storage period; the storage period prediction model is:

[0023] in, To predict the storage period, is a constant, - are the contents of heptanol, dextrorotatory terpenoids, benzaldehyde, tetradecane and ethyl octanoate in the American almond slices to be tested, 、 、 、 、 They are - The coefficient of .

[0024] In the present invention, the key index determination method of American almond chips is as follows: Flavor compounds were extracted using headspace solid-phase extraction. The sample was pulverized into a powder and sieved through a 30-mesh sieve. 1.4 g of the sample was placed in a 20 mL sample extraction vial. 5 mL of saturated sodium chloride solution and 50 µL of a 10 mg / mL 2-octanol internal standard were added. The extraction head was then inserted into the sample vial and equilibrated in a 60°C water bath with a magnetic stirrer for 10 minutes. The fiber head was then removed and headspace extraction was performed for 30 minutes. Finally, the sample was desorbed at 250°C for 4 minutes in the gas chromatography-mass spectrometry inlet, and the program was started.

[0025] GC-MS (gas chromatography-mass spectrometry) parameters were set. Column: DB-5MS (30 m × 0.25 mm × 0.25 μm); Temperature program: Initial temperature: 40°C, hold for 4 min, then increase to 60°C at 2.3°C / min, hold for 2 min; then increase to 80°C at 5°C / min, then to 220°C at 20°C / min, hold for 3 min. Inlet temperature: 250°C; Manual split injection, split ratio: 20:1; Solvent delay time: 3 min; Carrier gas: high-purity He (99.999%).

[0026] Mass spectrometry conditions: ion source temperature: 230 °C; quadrupole temperature: 150 °C; scan mode: full scan; mass scan range: m / z 30-550.

[0027] Qualitative and quantitative analysis of volatile substances: The total ion chromatogram of volatile substances was obtained by GC-MS analysis, and the NIST11 spectral library was searched, combined with the retention index RI, and combined with references to jointly analyze the volatile substances. Key aroma (i.e. key storage indicator) The retention index RI is calculated according to the following formula.

[0028]

[0029] Where: tx is the retention time of the target compound, n and n+1 are the carbon number of the n-alkanes before and after the target elution, tn and tn+1 are the retention times of the corresponding n-alkanes, where tn <tx<tn+1 。

[0030] Semi-quantitative method: Volatile substance content (μg / g) = (component peak area / internal standard peak area) × 0.05.

[0031] By analyzing the relationship between more than 50 variables in the volatile components of almond chips during storage and sensory evaluation, the data of more than 50 variables were Z-scrore standardized and then subjected to range analysis, and the variables were classified using R-type clustering. Combined with variance analysis and correlation coefficient method, the key indicators affecting American almond chips were determined: heptanol, dextrorotatory terpenes, benzaldehyde, tetradecane, and ethyl octanoate.

[0032] The method for establishing the storage period prediction model is as follows: Obtain at least 50 samples of American almond chips and test the content of key indicators in each sample: heptanol (x1), dextrorotatory terpenoids (x2), benzaldehyde (x3), tetradecane (x4), and ethyl octanoate (x5). Then, perform a multivariate linear regression with the storage time (y) to obtain a storage period prediction model. In this embodiment, the storage period prediction model is: .

[0033] Table 1: Statistics of the test of the shelf life prediction model of American almond chips

[0034] The R of the fitting equation reached 0.88, indicating that the storage period prediction model had a good linear relationship.

[0035] Verification of the storage period prediction model: The following table compares the theoretical storage days and actual storage days of American almond chip samples. The data in the table show that the degree of consistency between the predicted time of the almond chip storage model and the actual storage days is high. The present invention can provide a basis for predicting the freshness of almond chips.

[0036] Table 2: Validation data of the storage life prediction model for American almond chips

[0037] The present invention is applied to predicting the storage period: The contents of heptanol (x1=0), dextrorotatory terpenoids (x2=2.2683), benzaldehyde (x3=5.3447), tetradecane (x4=0.212), and ethyl octanoate (x5=0.0791) in an American almond chip sample obtained by GC-MS results are substituted into the regression equation y=284.413+43.214x1+3112.031x2+487.416x3+537.436x4+280.133x5 for predicting the shelf life of American almond chips. The predicted storage time is 123 days, which is highly consistent with the actual number of days of 132 days.

[0038] The present invention also discloses a method for determining the freshness of American almond chips, which comprises the following steps: S21. Obtaining American almond chips to be tested, and detecting the contents of key indicators thereof, including heptanol, dextrorotatory terpenoids, benzaldehyde, tetradecane, and ethyl octanoate; S22. Inputting the content of the key indicator into a freshness discrimination model, the freshness discrimination model including two discriminant functions F1 and F2, calculating the function values ​​of the discriminant functions F1 and F2 respectively based on the content of the key indicator, comparing the function values ​​of the discriminant functions F1 and F2, and taking the type corresponding to the discriminant function with the largest function value as the final freshness discrimination result; The discriminant functions F1 and F2 are:

[0039]

[0040] Among them, F1 is the discriminant function value of the fresh group, and F2 is the discriminant function value of the spoiled group is a constant, - are the contents of heptanol, dextrorotatory terpenoids, benzaldehyde, tetradecane and ethyl octanoate in the American almond slices to be tested, 、 、 、 、 Respectively fresh group function F1 - The coefficient of 、 、 、 、 They are respectively the metamorphic group function F2 - The coefficient of .

[0041] In this embodiment, the method for determining the key indicators of American almonds is the same as the above content and will not be repeated here.

[0042] The freshness discrimination model is established as follows: At least 50 samples of American almond chips were obtained. The contents of heptanol, dextrorotatory terpenes, benzaldehyde, tetradecane, and ethyl octanoate in the samples were tested by GC-MS. These samples were used as input to establish a freshness discrimination model based on the Bayesian discriminant method. The almond chip samples were divided into two groups based on sensory scores. Those with a sensory score of 6 or above were classified as the first group (fresh group), and those with a sensory score of less than 6 were classified as the second group (deteriorated group). Therefore, two discriminant functions F1 and F2 were obtained. The discriminant functions F1 and F2 are:

[0043] .

[0044] Verification of the freshness discrimination model: As shown in the table below, the correct classification of the freshness of American almond chips accounted for 89.5% of the total number of original cases, indicating that the freshness prediction performance of the two varieties is good.

[0045] Table 3: Validation data of the freshness discrimination model for American almonds

[0046] **. Misclassified cases Application of freshness discrimination method: The GC-MS results were used to obtain the contents of heptanol (x1=0), dextrorotatory terpenoids (x2=2.2683), benzaldehyde (x3=5.3447), tetradecane (x4=0.212), and ethyl octanoate (x5=0.0791) in the American almond chip sample. The discriminant model (Table 2) was substituted and F1 (fresh group) = 0.982 and F2 (deteriorated group) = 0.018 were obtained. Since F1>F2, the sample was predicted to be "fresh".

[0047] Example 2 This embodiment discloses a method for predicting the storage period of Australian almond chips, which comprises the following steps: S11. Obtaining Australian almond chips to be tested, and detecting the contents of key indicators thereof, including dodecane, naphthalene, heptanol, trans-2-octenal, and tetradecane; S12. Input the content of the key indicator into a storage period prediction model to predict the storage period; the storage period prediction model is:

[0048] in, To predict the storage period, is a constant, - are the contents of dodecane, naphthalene, heptanol, trans-2-octenal and tetradecane in the Australian almond chips to be tested, 、 、 、 、 They are - The coefficient of .

[0049] In the present invention, the key indicators of Australian almond chips are determined as follows: Flavor compounds were extracted using headspace solid-phase extraction. The sample was pulverized into a powder and sieved through a 30-mesh sieve. 1.4 g of the sample was placed in a 20 mL sample extraction vial. 5 mL of saturated sodium chloride solution and 50 µL of a 10 mg / mL 2-octanol internal standard were added. The extraction head was then inserted into the sample vial and equilibrated in a 60°C water bath with a magnetic stirrer for 10 minutes. The fiber head was then removed and headspace extraction was performed for 30 minutes. Finally, the sample was desorbed at 250°C for 4 minutes in the gas chromatography-mass spectrometry inlet, and the program was started.

[0050] GC-MS (gas chromatography-mass spectrometry) parameters were set. Column: DB-5MS (30 m × 0.25 mm × 0.25 μm); Temperature program: Initial temperature: 40°C, hold for 4 min, then increase to 60°C at 2.3°C / min, hold for 2 min; then increase to 80°C at 5°C / min, then to 220°C at 20°C / min, hold for 3 min. Inlet temperature: 250°C; Manual split injection, split ratio: 20:1; Solvent delay time: 3 min; Carrier gas: high-purity He (99.999%).

[0051] Mass spectrometry conditions: ion source temperature: 230 °C; quadrupole temperature: 150 °C; scan mode: full scan; mass scan range: m / z 30-550.

[0052] Qualitative and quantitative analysis of volatile substances: The total ion chromatogram of volatile substances was obtained by GC-MS analysis, and the NIST11 spectral library was searched, combined with the retention index RI, and combined with references to jointly analyze the volatile substances. Key aroma (i.e. key storage indicator) The retention index RI is calculated according to the following formula.

[0053]

[0054] Where: tx is the retention time of the target compound, n and n+1 are the carbon number of the n-alkanes before and after the target elution, tn and tn+1 are the retention times of the corresponding n-alkanes, where tn <tx<tn+1 。

[0055] Semi-quantitative method: Volatile substance content (μg / g) = (component peak area / internal standard peak area) × 0.05.

[0056] By analyzing the relationship between more than 50 variables in the volatile components of almond chips during storage and sensory evaluation, the data of more than 50 variables were Z-scrore standardized and then subjected to range analysis, and the variables were classified using R-type clustering. Combined with variance analysis and correlation coefficient method, the key indicators affecting Australian almond chips were determined: dodecane, naphthalene, heptanol, trans-2-octenal and tetradecane.

[0057] The method for establishing the storage period prediction model is as follows: At least 50 samples of Australian almond chips were obtained, and the key indicators of each sample were tested: dodecane (x1), naphthalene (x2), heptanol (x3), trans-2-octenal (x4), and tetradecane (x5). Multiple linear regression was then performed with the storage time (y) to obtain a storage period prediction model. In this embodiment, the storage period prediction model is: .

[0058] Table 4: Statistics of the test of the storage life prediction model of Australian almond chips

[0059] The R of the fitting equation reached 0.88, indicating that the storage period prediction model had a good linear relationship.

[0060] Verification of the storage period prediction model: The following table compares the theoretical storage days and actual storage days of Australian almond chip samples. The data in the table show that the degree of consistency between the predicted time of the almond chip storage model and the actual storage days is high. The present invention can provide a basis for predicting the freshness of almond chips.

[0061] Table 5: Validation data of the storage life prediction model for Australian almond chips

[0062] The present invention also discloses a method for determining the freshness of Australian almond chips, which comprises the following steps: S21. Obtain Australian almond chips to be tested, and detect the contents of key indicators thereof, including dodecane, naphthalene, heptanol, trans-2-octenal, and tetradecane; S22. Inputting the content of the key indicator into a freshness discrimination model, the freshness discrimination model including two discriminant functions F1 and F2, calculating the function values ​​of the discriminant functions F1 and F2 respectively based on the content of the key indicator, comparing the function values ​​of the discriminant functions F1 and F2, and taking the type corresponding to the discriminant function with the largest function value as the final freshness discrimination result; The discriminant functions F1 and F2 are:

[0063]

[0064] Among them, F1 is the discriminant function value of the fresh group, and F2 is the discriminant function value of the spoiled group is a constant, - are the contents of dodecane, naphthalene, heptanol, trans-2-octenal and tetradecane in the Australian almond chips to be tested, 、 、 、 、 Respectively fresh group function F1 - The coefficient of 、 、 、 、 They are respectively the metamorphic group function F2 - The coefficient of .

[0065] In this embodiment, the method for determining the key indicators of Australian almonds is the same as the above content and will not be repeated here.

[0066] The freshness discrimination model is established as follows: At least 50 samples of Australian almond chips were obtained. The contents of heptanol, dextrorotatory terpenes, benzaldehyde, tetradecane, and ethyl octanoate in the samples were tested by GC-MS. The samples were used as input to establish a freshness discrimination model based on the Bayesian discriminant method. The almond chip samples were divided into two groups based on the sensory score. The samples with a sensory score of 6 or above were classified as the first group (fresh group), and the samples with a sensory score of less than 6 were classified as the second group (deteriorated group). Therefore, two discriminant functions F1 and F2 were obtained. The discriminant functions F1 and F2 are:

[0067] .

[0068] Verification of the freshness discrimination model: As shown in the table below, the correct classification of the freshness of Australian almond chips accounted for 84.2% of the total number of original cases, indicating that the freshness prediction performance of the two varieties is good.

[0069] Table 6: Validation data of the freshness discrimination model for Australian almonds

[0070] **. Misclassified cases In summary, the method for distinguishing the freshness of almond chips of the present invention detects the volatile components in almond chips from the United States and Australia by using gas chromatography-mass spectrometry technology, inputs the five key variables of American almond chips and Australian almond chips as the discrimination model, and establishes a freshness discrimination model based on the Bayesian discriminant method. The present invention can be used to judge the freshness of American almond chips and Australian almond chips based on the freshness discrimination model, that is, before the acid value or peroxide value and other indicators can be used to detect whether the almond chips are fresh, the detection can be completed. The present invention contributes to the research on rapid detection of almond chips through volatile components, and provides a new idea for rapid food detection.

[0071] The above description is merely an embodiment of the present invention and does not limit the technical scope of the present invention. Therefore, any minor modifications, equivalent changes and modifications made to the above embodiments based on the technical essence of the present invention are still within the scope of the technical solution of the present invention.

Claims

1. A method for predicting the storage period of almond chips, characterized in that: The following steps are involved: S11. Obtain the almond slices to be tested and detect the content of key indicators thereof; S12. Input the content of the key indicator into a storage period prediction model to predict the storage period; the storage period prediction model is: in, To predict the storage period, is a constant, - They are the contents of five key indicators in the almond slices to be tested, 、 、 、 、 They are - The coefficient of .

2. The method for predicting the storage period of almond chips according to claim 1, wherein: The key indicators are determined as follows: Almond chips were used as samples, and flavor substances were extracted using headspace solid-phase extraction to obtain fiber heads containing flavor substances. The fiber heads were then decomposed at 250°C for 4 minutes in the inlet of a gas chromatography-mass spectrometer, and a program was started to detect the flavor substances in the almonds. Key indicators were screened from the detected flavor substances; specifically, by analyzing the relationship between the detected flavor substances in the almond chip raw materials during storage and the sensory evaluation, the data of the detected flavor substances were Z-scrore standardized and then subjected to range analysis, and R-type clustering was used to classify the variables, and the variance analysis and correlation coefficient method were combined to determine the key indicators affecting almond chips.

3. The method for predicting the storage period of almond chips according to claim 1 or 2, wherein: The almond slices are American almonds, and the key indicators are heptanol, dextrorotatory terpenoids, benzaldehyde, tetradecane, and ethyl octanoate.

4. The method for predicting the storage period of almond chips according to claim 3, wherein: The storage period prediction model is: 。 5. The method for predicting the storage period of almond chips according to claim 1 or 2, wherein: The almond chips are Australian almonds, and the key indicators are dodecane, naphthalene, heptanol, trans-2-octenal and tetradecane.

6. The method for predicting the storage period of almond chips according to claim 5, wherein: The storage period prediction model is: 。 7. A method for determining the freshness of almond chips, characterized in that: The following steps are involved: S21. Obtain the almond slices to be tested and detect the content of key indicators thereof; S22. Inputting the content of the key indicator into a freshness discrimination model, the freshness discrimination model including two discriminant functions F1 and F2, calculating the function values ​​of the discriminant functions F1 and F2 respectively based on the content of the key indicator, comparing the function values ​​of the discriminant functions F1 and F2, and taking the type corresponding to the discriminant function with the largest function value as the final freshness discrimination result; The discriminant functions F1 and F2 are: Among them, F1 is the discriminant function value of the fresh group, and F2 is the discriminant function value of the spoiled group is a constant, - They are the contents of key indicators in the almond slices to be tested, 、 、 、 、 Respectively fresh group function F1 - The coefficient of 、 、 、 、 They are respectively the metamorphic group function F2 - The coefficient of .

8. The method for determining the freshness of almond chips according to claim 7, wherein: The key indicators are determined as follows: Almond chips were used as samples, and flavor substances were extracted using headspace solid-phase extraction to obtain fiber heads containing flavor substances. The fiber heads were then decomposed at 250°C for 4 minutes in the inlet of a gas chromatography-mass spectrometer, and a program was started to detect the flavor substances in the almonds. Key indicators were screened from the detected flavor substances; specifically, by analyzing the relationship between the detected flavor substances in the almond chip raw materials during storage and the sensory evaluation, the data of the detected flavor substances were Z-scrore standardized and then subjected to range analysis, and R-type clustering was used to classify the variables, and the variance analysis and correlation coefficient method were combined to determine the key indicators affecting almond chips.

9. The method for determining the freshness of almond chips according to claim 7, wherein: The almond slices are American almonds, and the key indicators are heptanol, dextrorotatory terpenoids, benzaldehyde, tetradecane, and ethyl octanoate; The discriminant functions F1 and F2 are: 。 10. The method for determining the freshness of almond chips according to claim 7, wherein: The almond slices are Australian almonds, and the key indicators are dodecane, naphthalene, heptanol, trans-2-octenal and tetradecane; The discriminant functions F1 and F2 are: 。

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