Quality evaluation method of fresh corn

Abstract

The invention provides a quality evaluation method of fresh corn. The quality evaluation method comprises the following steps: step 1, treating the fresh corn by adopting different blanching modes; step 2, selecting a plurality of evaluation indexes to respectively measure the fresh corns treated by different blanching modes; step 3, screening a variation coefficient gt; 15% of evaluation indexes are used as key quality indexes; step 4, carrying out data standardization processing on the screened key quality indexes; 5, performing principal component analysis on the key quality indexes by adopting SPSS data analysis software, and calculating the weighted variance contribution rate of each principal component; 6, calculating the coefficient of each key quality index; step 7, obtaining a characteristic equation of each principal component; and step 8, accumulating the product of the feature vector of each principal component in the blanching mode and the contribution rate of the weighted variance of the corresponding principal component to obtain the comprehensive quality score of the fresh corn in the blanching mode.

Description

Technical Field

[0001] The present invention relates to the technical field of agricultural product processing, and particularly relates to a method for evaluating the quality of fresh corn. Background Art

[0002] As an important category of green and healthy foods, fresh corn (especially fresh waxy corn) is highly favored by consumers for its good taste, simple cooking process, and rich nutrition. However, the current processing of fresh waxy corn mainly relies on manual workshop production, and there is no relatively complete quality evaluation method. Especially in the key processing link of blanching, due to the lack of systematic research on the quality change law during the blanching process, it is difficult to ensure the quality consistency of fresh waxy corn after processing, and the nutritional loss is serious. The fresh waxy corn market is uneven, and it fails to create a fair, orderly, and healthy market environment for consumers.

[0003] In addition, although the research on blanching in the field of fruit and vegetable processing has been relatively rich, the industrial processing of fresh waxy corn is still in its infancy, and the systematic comparative research on the impact of fresh waxy corn on nutritional and sensory quality is still insufficient. In addition, traditional quality evaluation methods are often limited to single indicators or a few aspects, and it is difficult to comprehensively and accurately reflect the quality of fresh waxy corn or the comprehensive impact of blanching treatment on fresh waxy corn. Therefore, strengthening the research in this field and constructing a scientific comprehensive evaluation system are of great significance for improving the processing technology level of fresh waxy corn. Summary of the Invention

[0004] The main purpose of the present invention is to provide a method for evaluating the quality of fresh corn to comprehensively and accurately reflect the quality of fresh corn.

[0005] To achieve the above purpose, the present invention provides a method for evaluating the quality of fresh corn, including the following steps:

[0006] Step 1, treating fresh corn with different blanching methods;

[0007] Step 2, selecting multiple evaluation indicators to measure the fresh corn treated by different blanching methods respectively;

[0008] Step 3, screening out the evaluation indicators with a coefficient of variation > 15% as key quality indicators;

[0009] Step 4, performing data standardization processing on the selected key quality indicators;

[0010] Step 5, performing principal component analysis on the key quality indicators using SPSS data analysis software, determining the principal components according to the principle that the cumulative variance contribution rate reaches more than 85% and the eigenvalue > 1, and calculating the weighted variance contribution rate of each principal component;

[0011] Step 6, calculate the coefficients of each key quality index in each principal component;

[0012] Step 7, accumulate the products of the coefficients of the key quality indexes of each principal component and the corresponding key quality index values after the data obtained by one blanching method is standardized, to obtain the characteristic equations of each principal component of this blanching method; and

[0013] Step 8, accumulate the products of the characteristic equations of each principal component of this blanching method and the corresponding weighted variance contribution rates of each principal component, to obtain the comprehensive quality score of the fresh corn of this blanching method.

[0014] In one embodiment, the quality evaluation method of the fresh corn described in the present invention further includes Step 9, comparing the comprehensive quality scores of the fresh corn of different blanching methods.

[0015] In one embodiment, the quality evaluation method of the fresh corn described in the present invention further includes Step 10, calculating the average value of the comprehensive quality scores of the fresh corn of different blanching methods, and when the average value reaches a certain specific value, the fresh corn meets the quality requirements.

[0016] In one embodiment, the blanching methods in the quality evaluation method of the fresh corn described in the present invention include boiling in boiling water, microwave treatment, and steaming under normal pressure.

[0017] In one embodiment, in the quality evaluation method of the fresh corn described in the present invention, the temperature of boiling in boiling water is 95°C - 105°C, and the time is 20 - 30 min; the power of the microwave treatment is 600W - 800W, the frequency is 2300MHz - 2500MHz, and the time is 5 - 10 min; the pressure of steaming under normal pressure is 0.1MPa, the temperature is 95°C - 105°C, and the time is 20 - 30 min.

[0018] In one embodiment, the evaluation indexes in the quality evaluation method of the fresh corn described in the present invention include several of moisture content, starch content, soluble solid content, soluble sugar content, vitamin C content, chromaticity, and texture.

[0019] In one embodiment, the chromaticity in the quality evaluation method of the fresh corn described in the present invention includes lightness L*, red - green degree a*, yellow - blue degree b*, and total color difference ΔE; the texture includes hardness, cohesiveness, elasticity, gumminess, and chewiness.

[0020] In one embodiment, the data standardization process in the quality evaluation method of the fresh corn described in the present invention is carried out according to the following formula:

[0021] X = (x - μ) / σ

[0022] Among them, x is the measured value of the key quality index;

[0023] μ is the average value of the key quality index of different blanching methods;

[0024] σ is the standard deviation of the key quality index of different blanching methods;

[0025] X is the standardized value of the key quality index.

[0026] In one embodiment of the quality evaluation method of fresh corn described in the present invention, in step 5, the ratio of the variance contribution rate of each principal component to the sum of the variance contribution rates of all principal components is the weighted variance contribution rate of each principal component.

[0027] In one embodiment of the quality evaluation method of fresh corn described in the present invention, before step 1, the fresh corn is subjected to selection, impurity removal, and cleaning pretreatment.

[0028] Advantages of the present invention:

[0029] (1) The present invention provides a quality evaluation method for fresh corn. Based on multiple quality indicators such as nutritional components, chromaticity, and texture, it can not only reflect the appearance characteristics and taste of fresh corn, but also reflect its nutritional value, making the quality evaluation more fair, accurate, and scientific;

[0030] (2) The present invention breaks through the limitations of traditional sensory evaluation, which is easily affected by the subjective preferences of evaluators and requires a high professional level of evaluators. It replaces the sensory evaluation with strong subjectivity and large errors with indicators that can be accurately measured and calculated, and establishes a function model, improving the accuracy, objectivity, and effectiveness of the evaluation results;

[0031] (3) The method of the present invention is convenient and feasible, and the results are accurate, with good operability and practicability, providing technical support and theoretical guidance for the fresh corn processing industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 It is a pretreatment flow chart of fresh waxy corn in an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0033] The technical solutions of the present invention are described in detail below. The following embodiments are implemented on the premise of the technical solutions of the present invention, and the detailed implementation processes are given. However, the protection scope of the present invention is not limited to the following embodiments. For the structures or experimental methods without specific conditions noted in the following embodiments, they are usually in accordance with conventional conditions.

[0034] The present invention provides a quality evaluation method for fresh corn, including the following steps:

[0035] Step 1: Treat fresh corn with different blanching methods.

[0036] Step 2: Select multiple evaluation indexes to measure the fresh corn treated by different blanching methods respectively.

[0037] Step 3: Screen out the evaluation indexes with coefficient of variation > 15% as key quality indexes.

[0038] Step 4: Conduct data standardization processing on the screened key quality indexes.

[0039] Step 5: Conduct principal component analysis on the key quality indexes by using SPSS data analysis software, determine the principal components according to the principle that the cumulative variance contribution rate reaches more than 85% and the eigenvalue > 1, and calculate the weighted variance contribution rate of each principal component.

[0040] Step 6: Calculate the loading coefficient of each key quality index in each principal component. The ratio of the loading coefficient to the square root of the corresponding principal component eigenvalue is the coefficient of the key quality index in the corresponding principal component.

[0041] Step 7: Accumulate the products of the coefficients of the key quality indexes of each principal component and the corresponding standardized key quality index values obtained by treating with a blanching method to obtain the characteristic equation of each principal component of this blanching method; and

[0042] Step 8: Accumulate the products of the characteristic equation of each principal component of this blanching method and the corresponding principal component weighted variance contribution rate to obtain the comprehensive quality score of the fresh corn of this blanching method.

[0043] The quality evaluation method of the fresh corn of the present invention not only overcomes the drawback of strong subjectivity of traditional sensory evaluation, but also takes multiple quality indexes such as nutritional components, chromaticity, texture, etc. as the evaluation basis, which can not only reflect the appearance characteristics and taste of the fresh corn, but also reflect its nutritional value, and the evaluation of the quality is more fair, accurate and scientific.

[0044] In an embodiment, the method of the present invention can be used for the quality evaluation of fresh corn, and is especially suitable for the quality evaluation of fresh waxy corn. The fresh corn is pretreated before step 1. The pretreatment includes, for example, removing impurities from the fresh corn harvested at the milk ripening stage of the harvested grains, removing the husk and silk, cutting off the head and tail, washing, and selecting fresh corn with consistent size, moderate maturity, neat grain arrangement, no deformity and missing grains, no pests and diseases, no rot and mildew for standby. It can also be pretreated according to the prior art method, such as Figure 1 shown, however, the present invention is not limited thereto.

[0045] In the present invention, step 1 is to process fresh corn by different blanching methods. Among them, the blanching methods are, for example, boiling in boiling water, microwave treatment, and steaming with atmospheric pressure steam, so as to comprehensively compare the effects of different blanching methods on the sensory quality and nutritional quality of fresh corn.

[0046] In one embodiment, boiling in boiling water means putting the pre-treated fresh corn into boiling water. The temperature of boiling in boiling water is 95°C - 105°C, for example 100°C, and the time is 20 - 30 min, for example 20 min; microwave treatment means putting the pre-treated fresh corn into a microwave oven. The power of microwave treatment is 600W - 800W, for example 700W, the rated microwave frequency is 2300MHz - 2500 MHz, for example 2450MHz, and the time is 5 - 10 min, for example 6 min; steaming with atmospheric pressure steam means putting the pre-treated fresh corn into a steam environment. The pressure of steaming with atmospheric pressure steam is 0.1MPa, the temperature is 95°C - 105°C, for example 100°C, and the time is 20 - 30 min, for example 20 min.

[0047] In the present invention, step 2 is to select multiple evaluation indexes to measure the fresh corn processed by different blanching methods respectively.

[0048] In one embodiment, the evaluation indexes include one or more of moisture content, starch content, soluble solid content, soluble sugar content, vitamin C content, chromaticity, and texture.

[0049] Among them, moisture is an important component in fresh corn kernels. The loss of moisture will lead to a decrease in the quality and freshness of fresh corn. Within a certain range, the quality of fresh corn is positively correlated with the moisture content. In one embodiment, the determination of moisture content can refer to the national standard GB / T 5009.3 - 2016 "Determination of Moisture in Foods", and the constant weight method is used to determine the moisture content. Specifically, 3 fresh waxy corn ears can be randomly selected for heat treatment, and then a total of 100 corn kernels are peeled from the head and tail of the corn ear in sequence, and three parallel experiments are carried out.

[0050] The starch content is the main factor affecting the sticky and glutinous taste of fresh waxy corn. In one embodiment, the determination of starch content can refer to the national standard GB 5009.9 - 2023 "Determination of Starch in Foods", and the acid hydrolysis method is used for determination.

[0051] Soluble solids are an important index of the taste quality of fresh corn, mainly including sugars, organic acids, tannins, and a small amount of minerals, pigments, and vitamins. In one embodiment, the determination of soluble solid content can refer to GB 12295 - 90 "Determination of Soluble Solids Content in Fruits and Vegetable Products - Refractometer Method", and a handheld refractometer is used to determine the soluble solid content in corn kernels.

[0052] The soluble sugar content is an important indicator of the taste quality of fresh-eating corn, which directly determines the sweetness of fresh-eating corn and affects the quality of the taste of fresh-eating corn cobs. In one embodiment, the anthrone-sulfuric acid method can be used to determine the soluble sugar content in fresh-eating corn kernels.

[0053] Fresh-eating corn is rich in vitamin C, which is an essential nutrient for the human body. In one embodiment, the determination of vitamin C content can refer to the national standard GB 5009.86-2016 "Determination of Ascorbic Acid in Foods" and is determined by the 2,6-dichlorophenol indophenol titration method.

[0054] In one embodiment, chromaticity includes lightness L*, red-green degree a*, yellow-blue degree b*, and total color difference ΔE. In another embodiment, the determination of chromaticity can be carried out as follows: Select 5 different positions on the surface of fresh-eating corn kernels for determination, and record the L*, a*, and b* values simultaneously. Measure in parallel multiple times, such as 25 times, and take the average value. The calculation method of the total color difference (ΔE):

[0055]

[0056] Among them, L*0, a*0, and b*0 respectively represent the lightness, red-green degree, and yellow-blue degree of fresh-eating corn after blanching treatment;

[0057] L*, a*, and b* respectively represent the lightness, red-green degree, and yellow-blue degree of fresh-eating corn before blanching treatment.

[0058] Among the quality characteristics of fresh-eating corn, texture is a sensory attribute that affects consumer preference second only to sweetness. In one embodiment, texture includes hardness, cohesiveness, elasticity, gumminess, and chewiness. In another embodiment, the determination of texture can be carried out as follows: Place the peeled corn kernels with the endosperm side up on the texture analyzer base and perform a full-spectrum analysis using a TA4 / 1000 type cylindrical probe, and measure in a compression manner. Use TA-BT-KIT, the trigger loads 7g, the test speed is 0.50mm / s, the compression distance is 1mm, cycle 2 times, measure each sample three times, and take the average value as the final result.

[0059] In the present invention, step 3 is to screen the evaluation indicators with a coefficient of variation > 15% as the key quality indicators.

[0060] The coefficient of variation, also known as the "dispersion coefficient", is a normalized measure of the degree of dispersion of a probability distribution, and its definition is the ratio of the standard deviation to the average value. The present invention calculates the coefficient of variation of each evaluation indicator respectively, screens out the evaluation indicators with a coefficient of variation > 15% as the key quality indicators, the number of key quality indicators is, for example, n, and each key quality indicator is denoted as X1, X2 ··· Xn (Each blanching method yields a set of values X1, X2 ··· X n ). In one embodiment, the key quality indicators screened out by the present invention include vitamin C, a*, b*, ΔE, elasticity, adhesiveness, chewiness, but the present invention is not limited thereto.

[0061] In the present invention, step 4 is to perform data standardization processing on the screened key quality indicators.

[0062] In one embodiment, the data standardization processing is carried out according to the following formula:

[0063] X = (x - μ) / σ

[0064] Wherein, x is the measured value of the key quality indicator;

[0065] μ is the average value of the key quality indicators of different blanching methods;

[0066] σ is the standard deviation of the key quality indicators of different blanching methods;

[0067] X is the standardized value of the key quality indicator.

[0068] In the present invention, step 5 is: performing principal component analysis on the key quality indicators by using SPSS data analysis software, determining the principal components according to the principle that the cumulative variance contribution rate reaches more than 85% and the eigenvalue > 1, and calculating the weighted variance contribution rate of each principal component.

[0069] Performing principal component analysis by using SPSS data analysis software is a common data processing method in the art, and the present invention will not elaborate thereon. In one embodiment, the number of principal components obtained by the present invention is k, and the principal components can be respectively denoted as Z1, Z2... Z k , k is, for example, 3, but the present invention is not limited thereto.

[0070] In the present invention, the weighted variance contribution rate of each principal component is the ratio of the variance contribution rate of each principal component to the sum of the variance contribution rates of all principal components. Among them, the sum of the variance contribution rates of all principal components is also the maximum cumulative variance contribution rate. For example, the variance contribution rate of principal component Z1 is β1, the variance contribution rate of Z2 is β2 ……, Z k 's variance contribution rate is β k , then the weighted variance contribution rate of principal component Z1 is β1' = β1 / (β1 + β2 + …… β k ).

[0071] In the present invention, step 6 is: calculating the coefficients of each key quality indicator in each principal component, and the ratio of the loading coefficient of each key quality indicator in each principal component to the square root of the corresponding principal component eigenvalue is the coefficient of the key quality indicator in the corresponding principal component.

[0072] For example, the loading coefficients of the key quality indicators of the principal component Z1 are A 1X1 ’ , A 1x2 ’ ···A 2Xn ’ , and the coefficients of the key quality indicators of the principal component Z1 are correspondingly A 1X1 , A 1x2 ···A 2Xn , then A 1X1 = A 1X1 ’ / square root of the eigenvalue of the principal component Z1, A 1X2 = A 1X2 ’ / square root of the eigenvalue of the principal component Z1... A 1Xn = A 1Xn ’ / square root of the eigenvalue of the principal component Z1.

[0073] The calculation of the loading coefficients / coefficients of the key quality indicators in the principal component analysis is a conventional technical means in the art, and the present invention will not elaborate here. For example, it is calculated based on the covariance matrix of the data.

[0074] In the present invention, step 7 is to accumulate the products of the coefficients of the key quality indicators of each principal component and the corresponding key quality indicator values after standardizing the data processed by a blanching method, to obtain the characteristic equations of each principal component of the blanching method.

[0075] For example, for the boiling water blanching method, the characteristic equations of the principal components are Z1 = A 1X1 X1 + A 1x2 X2 + ··· A 1xn X n , Z2 = A 2x1 X1 + A 2x2 X2 + ··· A 2xn X n , …… Z k = A kx1 X1 + A kx2 X2 + ··· A kxn X n . The calculation methods of the characteristic equations of the principal components for other blanching methods are similar and will not be elaborated here.

[0076] In the present invention, step 8 is: accumulating the products of the characteristic equations of each principal component of the blanching method and the corresponding principal component weighted variance contribution rate, to obtain the comprehensive quality score of the fresh corn of the blanching method.

[0077] Specifically, for the boiling blanching method, the comprehensive quality score F of fresh corn is F = β1Z1 + β2Z2 + ··· + β k Z k . The calculation method of the comprehensive quality score for other blanching methods is similar and will not be elaborated here. The calculation method of the principal component weighted variance contribution rate has been described in the previous text and will not be elaborated here.

[0078] In the present invention, the higher the comprehensive quality score of fresh corn indicates the better the quality of fresh corn. In one embodiment, the quality evaluation method of the fresh corn of the present invention further includes step 9: comparing the comprehensive quality scores of fresh corn under different blanching methods, that is, processing the same kind of corn by different blanching methods. The higher the score of a certain blanching method, the better the quality of the fresh corn obtained by this blanching method, and this blanching method is more suitable for the processing of this kind of fresh corn.

[0079] In another embodiment, the quality evaluation method of the fresh corn of the present invention further includes step 10: calculating the average value of the comprehensive quality scores of fresh corn under different blanching methods. When the average value reaches a certain specific value, the fresh corn meets the quality requirements. Specifically, corns from different sources are processed by various blanching methods respectively, and the average value of the comprehensive quality scores of the fresh corn obtained by different blanching methods for the same source of corn is calculated. If the average value reaches a certain specific value, the corn of this source meets the quality requirements. The present invention does not make a special limitation on a certain specific value and can be selected according to needs.

[0080] Thus, the present invention provides a quality evaluation method for fresh corn. Taking evaluation indexes such as nutritional components, chromaticity, and texture as the evaluation basis, combined with its coefficient of variation, the key quality indexes for principal component analysis are determined, and a comprehensive evaluation system for the quality of fresh waxy corn during the blanching process is established. The evaluation method of the present invention solves the problems of poor quality consistency and lack of fixed evaluation criteria in the current fresh corn processing process. By systematically studying the influence of the blanching process on the sensory quality and nutritional quality of fresh waxy corn and constructing a scientific comprehensive evaluation system, it can promote the upgrading and development of the fresh corn processing industry.

[0081] The technical solution of the present invention will be further described in detail below through a specific embodiment.

[0082] 1. Selection and pretreatment of raw materials: The harvested fresh waxy corn is cleaned of impurities, husked, tasseled, trimmed, and washed. Fresh waxy corn with the same size, moderate maturity, neatly arranged grains, no deformity and missing grains, no pests and diseases, no rot and mildew is reserved for use.

[0083] 2. Process the fresh waxy corn by different heat treatment methods

[0084] (1) Boiling in boiling water: Put the pre-treated fresh waxy corn into boiling water, control the blanching temperature at about 100 °C, and set the time to 20 min;

[0085] (2) Microwave treatment: Put the pre-treated fresh waxy corn into the microwave oven, with a processing power of 700 W, a rated microwave frequency of 2450 MHz, for 6 min;

[0086] (3) Steaming under normal pressure: Put the pre-treated fresh waxy corn into a steam environment of 0.1 MPa and about 100 °C, and the blanching time is 20 min.

[0087] 3. Determination of quality indicators of fresh-eating waxy corn

[0088] (1) Determination of moisture content: Refer to the national standard GB / T 5009.3-2016 "Determination of moisture in foods", and use the constant weight method to determine the moisture content. Randomly select 3 fresh-eating waxy corn ears for heat treatment. For each treatment, a total of 100 corn kernels are peeled from the head and tail of the corn ear in sequence, and three parallel experiments are conducted.

[0089] (2) Determination of starch content: Refer to the national standard GB 5009.9-2023 "Determination of starch in foods", and use the acid hydrolysis method for determination.

[0090] (3) Determination of soluble solids content: Refer to GB 12295-90 "Determination of soluble solids content in fruits and vegetables - Refractometer method", and use a handheld refractometer to determine the soluble solids content in corn kernels.

[0091] (4) Determination of soluble sugar content: Use the anthrone-sulfuric acid method to determine the soluble sugar content in fresh-eating waxy corn kernels.

[0092] (5) Determination of vitamin C content: Refer to the national standard GB 5009.86-2016 "Determination of ascorbic acid in foods", and use the 2,6-dichloroindophenol titration method for determination.

[0093] (6) Determination of chromaticity: Select 5 different positions on the surface of fresh-eating waxy corn kernels for determination, and record the L*, a*, and b* values simultaneously. Perform parallel determination 25 times, and take the average of the results. Calculation method of total color difference (ΔE):

[0094]

[0095] (7) Determination of texture: The peeled corn kernels were placed on the texture analyzer base with the endosperm side facing up, and a TA4 / 1000 cylindrical probe was used for full-spectrum analysis, and the measurement was carried out in a compression mode. The TA-BT-KIT was used, the trigger load was 7 g, the test speed was 0.50 mm / s, the compression distance was 1 mm, and the cycle was 2 times. Each sample was measured three times, and the average value was taken as the final result.

[0096] The measurement results of each index of fresh waxy corn after different blanching methods are shown in Table 1.

[0097] Table 1

[0098]

[0099]

[0100] Note: The values in the table are the mean ± standard deviation; the significant differences are represented by English letters. The means in the same row with the same letter superscript indicate no significant difference (p>0.05); the means in the same row with completely different letter superscripts indicate significant difference (p<0.05).

[0101] 4. Establishment of the comprehensive quality evaluation model for fresh waxy corn

[0102] (1) Screening of key quality indicators for fresh waxy corn: According to the measured evaluation index data of fresh waxy corn, the evaluation indicators with a coefficient of variation > 15% were selected as key quality indicators, including the indicators vitamin C, a*, b*, ΔE, elasticity, adhesiveness, and chewiness.

[0103] A coefficient of variation > 15 can be considered as a sign of a relatively high degree of data dispersion, indicating that there are significant differences in this evaluation index among different treatment groups. By screening the indicators with a coefficient of variation > 15%, the complexity of the evaluation system can be reduced, and at the same time, the key indicators with the greatest impact on quality can be retained, so as to construct a more scientific and efficient comprehensive evaluation model.

[0104] (2) Data standardization processing: The selected key quality indicators vitamin C, a*, b*, ΔE, elasticity, adhesiveness, and chewiness were standardized according to the formula X = (x - μ) / σ, where x is the measured value of the key quality indicator, μ is the average value of the key quality indicators of different blanching methods, σ is the standard deviation of the key quality indicators of different blanching methods, and X is the standardized value of the key quality indicator. After standardization, each key quality indicator was denoted as X1, X2... X7, where X1 represents the indicator vitamin C, X2 represents the indicator a*, X3 represents the indicator b*, X4 represents the indicator ΔE, X5 represents the indicator elasticity, X6 represents the indicator adhesiveness, and X7 represents the indicator chewiness. The results of data standardization processing of the key quality indicators of different treatment methods are shown in Table 2.

[0105] Table 2

[0106]

[0107]

[0108] (3) Determine the principal components: Use the SPSS data analysis software to perform principal component analysis on the key quality indicators. Determine the principal components based on the principle that the cumulative variance contribution rate reaches more than 85% and the eigenvalue > 1. The principal components are denoted as Z1, Z2, and Z3 respectively. The specific results are shown in Table 3.

[0109] Table 3

[0110] Eigenvalue Variance contribution rate % Cumulative variance contribution rate % <![CDATA[Principal component Z1]]> 2.985 42.646 42.646 <![CDATA[Principal component Z2]]> 2.247 32.100 74.745 <![CDATA[Principal component Z3]]> 1.149 16.408 91.153

[0111] The weighted variance contribution rate of the principal component Z1 is β1’ = 42.646 / 91.153 ≈ 0.468;

[0112] The weighted variance contribution rate of the principal component Z2 is β2’ = 32.100 / 91.153 ≈ 0.352;

[0113] The weighted variance contribution rate of the principal component Z1 is β1’ = 16.408 / 91.153 ≈ 0.180.

[0114] (4) Calculate the characteristic equation: Calculate the coefficients of each key quality indicator in each principal component. The ratio of the loading coefficient of each key quality indicator in each principal component to the square root of the corresponding principal component eigenvalue is the coefficient of the key quality indicator in the corresponding principal component. Among them, the loading coefficients are shown in Table 4, and the coefficients of each key quality indicator are shown in Table 5.

[0115] Multiply and sum the coefficients of each key quality indicator of each principal component with the corresponding key quality indicator values after standardization processed by a blanching method to obtain the characteristic equation of each principal component of this blanching method.

[0116] Table 4

[0117]

[0118]

[0119] Table 5

[0120]

[0121] The characteristic equations of each principal component are as follows:

[0122] Z1 = (0.562)X1 + (-0.059)X2 + (-0.319)X3 + (-0.502)X4 + (0.393)X5 + (0.410)X6 + (0.0

[0123] 65)X7

[0124] Z2 = (-0.114)X1+(0.062)X2+(-0.485)X3+(0.174)X4+(-0.471)X5+(0.356)X6+(0.6

[0125] 07)X7

[0126] Z3 = (-0.010)X1+(0.915)X2+(-0.237)X3+(-0.119)X4+(-0.087)X5+(-0.057)X6+(-0

[0127] .285)X7

[0128] (5) Comprehensive quality score of fresh - eating corn: The sum of the products of the characteristic equations of the principal components of this blanching method and the weighted variance contribution rates of the corresponding principal components is used to obtain the comprehensive quality score of the fresh - eating corn of this blanching method. The eigenvector values of the principal components of different blanching methods are shown in Table 6.

[0129] Table 6

[0130]

[0131] The comprehensive quality score F of fresh - eating corn for different blanching methods is F = 0.468Z1+0.352Z2+0.180Z3.

[0132] Thus, the comprehensive quality score of fresh - eating waxy corn obtained by boiling treatment is F = 0.468×(-1.325)+0.352×(-1.496)+0.180×0.525≈ - 1.052

[0133] The comprehensive quality score of fresh - eating waxy corn obtained by steam blanching treatment is F = 0.468×2.083+0.352×(-0.088)+0.180×0.682≈1.067;

[0134] The comprehensive quality score of fresh - eating waxy corn obtained by microwave treatment is F = 0.468×(-0.757)+0.352×1.584+0.180×(-1.206)≈ - 0.014.

[0135] Thus, the comprehensive quality score of fresh - eating waxy corn obtained by steam blanching treatment is the highest, followed by that obtained by microwave treatment, and the lowest is that obtained by boiling treatment, that is, the comprehensive quality of fresh - eating waxy corn obtained by steam blanching treatment is better.

[0136] Of course, it is also possible to calculate the average value of the comprehensive quality scores of the fresh-eating corn obtained by the three processing methods, which is 0.0002, and determine whether it reaches a certain specific value, so as to evaluate the quality of the fresh-eating corn.

[0137] Of course, the present invention may also have many other embodiments. Without departing from the spirit and essence of the present invention, those skilled in the art can make various corresponding changes and modifications according to the present invention, but these corresponding changes and modifications should all fall within the protection scope of the claims of the present invention.

Claims

1. A method for evaluating the quality of fresh corn, characterized in that, It includes the following steps: Step 1: Treat fresh corn with different blanching methods; Step 2: Select multiple evaluation indicators and measure the fresh corn treated by different blanching methods respectively; Step 3: Screen out the evaluation indicators with a coefficient of variation > 15% as the key quality indicators; Step 4: Conduct data standardization processing on the selected key quality indicators; Step 5: Use SPSS data analysis software to conduct principal component analysis on the key quality indicators, determine the principal components according to the principle that the cumulative variance contribution rate reaches more than 85% and the eigenvalue > 1, and calculate the weighted variance contribution rate of each principal component; Step 6: Calculate the coefficients of each key quality indicator in each principal component; Step 7: Accumulate the products of the coefficients of each key quality indicator in each principal component and the corresponding key quality indicator values after data standardization processing obtained by a blanching method to obtain the characteristic equation of each principal component of this blanching method; And Step 8: Accumulate the products of the characteristic equation of each principal component of this blanching method and the corresponding principal component weighted variance contribution rate to obtain the comprehensive quality score of the fresh corn of this blanching method.

2. The quality evaluation method of fresh corn according to claim 1, characterized in that It also includes Step 9: Compare the comprehensive quality scores of the fresh corn with different blanching methods.

3. The quality evaluation method of fresh corn according to claim 1, characterized in that It also includes Step 10: Calculate the average value of the comprehensive quality scores of the fresh corn with different blanching methods. When the average value reaches a certain specific value, the fresh corn meets the quality requirements.

4. The quality evaluation method of fresh corn according to claim 1, characterized in that, The blanching methods include boiling in boiling water, microwave treatment, and steaming with atmospheric pressure steam.

5. The quality evaluation method of fresh corn according to claim 4, characterized in that, The temperature for boiling in boiling water is 95°C - 105°C, and the time is 20 - 30 min; the power for microwave treatment is 600W - 800W, the frequency is 2300MHz - 2500MHz, and the time is 5 - 10 min; the pressure for steaming with atmospheric pressure steam is 0.1MPa, the temperature is 95°C - 105°C, and the time is 20 - 30 min.

6. The quality evaluation method of fresh corn according to claim 1, characterized in that The evaluation indicators include several of moisture content, starch content, soluble solid content, soluble sugar content, vitamin C content, chromaticity, and texture.

7. The quality evaluation method of fresh-eating corn according to claim 6, wherein The chromaticity includes lightness L*, red - green degree a*, yellow - blue degree b*, and total color difference ΔE; the texture includes hardness, cohesiveness, elasticity, adhesiveness, and chewiness.

8. The quality evaluation method of fresh-eating corn according to claim 1, wherein, The data standardization processing is carried out according to the following formula: X = (x - μ) / σ where x is the measured value of the key quality indicator; μ is the average value of the key quality indicators of different blanching methods; σ is the standard deviation of the key quality indicators of different blanching methods; X is the standardized value of the key quality indicator.

9. The quality evaluation method of fresh corn according to claim 1, characterized in that In Step 5, the ratio of the variance contribution rate of each principal component to the sum of the variance contribution rates of all principal components is the weighted variance contribution rate of each principal component.

10. The quality evaluation method of fresh corn according to claim 1, wherein Before Step 1, the fresh corn is subjected to pre - treatment of selection, impurity removal, and cleaning.

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