A method for constructing a quality comprehensive evaluation model of fresh wet noodles

Abstract

This invention discloses a comprehensive quality evaluation model for fresh wet noodles. First, various quality indicators of different flour raw materials and their processed fresh wet noodles are measured and the data is standardized. Second, correlation analysis is used to clarify the relationships between indicators, and principal component analysis is used to reduce the dimensionality of the data and extract core principal components. Finally, based on the principal component loading matrix, the unit eigenvector coefficients are calculated, and a comprehensive quality evaluation model for fresh wet noodles is established accordingly. This method systematically realizes the entire process from data collection and analysis to model construction. The comprehensive quality evaluation model for fresh wet noodles established by this invention is objective, accurate, and easy to operate, providing new ideas and methods for the quality evaluation of fresh wet noodles and contributing to the healthy development of the fresh wet noodle industry.

Description

Technical Field

[0001] This invention relates to the field of noodle quality evaluation technology, specifically to a method for constructing a comprehensive evaluation model for the quality of fresh wet noodles. Background Technology

[0002] Long-life noodles, also known as LL noodles, instant noodles, convenient noodles, and wet-process instant noodles, can be stored at room temperature. They are a type of boiled instant noodles that are not fried but steamed, then packaged and sterilized. They are nutritious, hygienic, do not dehydrate, have a smooth texture, do not require preservatives, and are convenient to eat and carry. With a moisture content as high as 65%, they only need to be reheated briefly before consumption, making them popular among consumers.

[0003] However, despite the popularity of fresh wet noodles in the market, there is relatively little in-depth research on this product. Most of the research focuses on single quality characteristics, lacking comprehensive studies on the relationship between multiple indicators and the quality of fresh wet noodles, and even more so lacking a scientific, reasonable and comprehensive quality evaluation system.

[0004] Furthermore, most existing noodle quality evaluation systems are based on dried noodles. Dried noodles are made through high-temperature drying and dehydration, resulting in a dense noodle structure, low moisture content, and a longer cooking time. In contrast, fresh wet noodles, as a type of ready-to-eat noodle, are made through processes such as steaming, washing, acid soaking, and packaging sterilization, maintaining a high moisture content and a texture similar to fresh noodles. They only require simple heating before consumption, better meeting the demand for convenient, green, and healthy eating habits in today's fast-paced lifestyle. Therefore, directly using the evaluation system for dried noodles cannot accurately reflect the quality characteristics of fresh wet noodles. Establishing a quality evaluation model suitable for fresh wet noodles is not only helpful in scientifically guiding process optimization and quality control, but also of great significance in promoting the development of the ready-to-eat noodle industry towards high quality and health. Summary of the Invention

[0005] Technical issues Existing research on fresh wet noodles mostly focuses on single quality characteristics, lacking comprehensive research on the correlation of multiple indicators and a comprehensive quality evaluation system. Furthermore, the evaluation system for dried noodles cannot be adapted to its processing and quality characteristics. Therefore, there is an urgent need to establish an evaluation model specifically for fresh wet noodles.

[0006] Technical content To address the aforementioned technical problems, this invention provides a method for constructing a comprehensive evaluation model for the quality of fresh-keeping wet noodles, comprising the following steps: Step 1: Take flour raw materials and determine the quality indicators of the flour raw materials; Step 2: Prepare fresh wet noodles from different flour raw materials, and determine the quality indicators of the fresh wet noodles; Step 3: Preprocess the measured quality indicators of the fresh wet noodles; the preprocessing includes data initialization and normalization. Step 4: Reveal the interrelationships between indicators through correlation analysis, and extract principal components that can represent most of the information in the original data through principal component analysis for dimensionality reduction. Step 5: Establish the component loading matrix and calculate the unit eigenvector coefficients based on the component loading matrix; Step 6: Based on the extracted principal components and their corresponding unit eigenvector coefficients, construct a comprehensive evaluation model for the quality of fresh wet noodles.

[0007] As a further improvement of the present invention, the flour raw material mentioned in step one is wheat flour.

[0008] As a further improvement of the present invention, the quality indicators mentioned in step one include moisture content, water absorption rate, total starch content, protein content, wet gluten content, and ash content.

[0009] Preferably, the quality indicator mentioned in step one is the wet gluten content.

[0010] As a further improvement of the present invention, the preparation process of the fresh-keeping wet noodles in step two is as follows: (1) Kneading the dough: Mix flour raw materials with water and salt evenly to obtain dough flakes; (2) Proofing: Place the dough flakes at 20~25℃ and let them stand for 10~15 minutes to mature; (3) Rolling and cutting: Knead the risen dough into a dough, roll the dough to get a sheet, and then cut it into strips to get noodles; the size of the noodles is 15~25 cm×0.1~0.3 cm×0.1~0.3 cm; (4) Pre-cooking and washing: Add the strips to boiling water and cook for 3-5 minutes, then take them out and wash them with cold water for 30-90 seconds; (5) Acid soaking: After draining the surface moisture of the strips after washing, soak the strips in a lactic acid-sodium lactate buffer solution with pH 4~4.5 for 60~120 s; (6) Packaging and sterilization: Finally, the acid-soaked strips are packaged in high-temperature sterilization bags and then sterilized at 90~95℃ for 10~20 min.

[0011] As a further improvement of the present invention, the quality indicators mentioned in step two include hardness, adhesion, elasticity, chewiness, resilience, tensile strength, tensile distance, surface tack, water absorption, cooking loss rate, and sensory score.

[0012] As a further improvement of the present invention, the extraction in step four is based on the scree plot and the variance contribution rate to determine the principal components, preferably with a scree plot > 1 and a cumulative variance contribution rate > 90%.

[0013] As a further improvement of the present invention, in step five, according to the calculation formula "loading amount = square root of principal component eigenvalue × eigenvector", the component loading of each principal component is divided by its variance contribution index λ to obtain the unit eigenvector coefficient corresponding to each eigenvalue.

[0014] As a further improvement to the present invention, the comprehensive evaluation model for the quality of fresh wet noodles in step six is ​​as follows: F = 49.624%F1+21.141%F2+17.674%F3+11.562%F4; Where, F1 = 0.397X1 + 0.325X2 - 0.12X3 + 0.26X4 + 0.402X5 + 0.102X6 + 0.381X7 + 0.37X8 + 0.189X9 - 0.293X10 - 0.274X11 + 0.067X12; F2 =0.125X1-0.248X2+0.432X3+0.443X4+0.04X5 - 0.054X6+0.021X7 +0.067X8 +0.037X9 +0.04X10 +0.405X11 +0.605X12; F3=-0.034X1+0.13X2 -0.433X3 +0.196X4 -0.08X5 -0.493X6-0.251X7 +0.191X8 +0.435X9 +0.44X10 -0.045X11 +0.149X12; F4 =-0.12X1-0.358X2+0.178X3 -0.114X4 -0.125X5 +0.546X6-0.013X7 +0.264X8 +0.525X9 +0.232X10 -0.308X11 +0.022X12; The above parameter X represents the quality indicators of the preserved wet noodles; specifically, X1 is the wet gluten content, X2 is the hardness, X3 is the adhesiveness, X4 is the elasticity, X5 is the chewiness, X6 is the resilience, X7 is the tensile force, X8 is the tensile distance, X9 is the surface stickiness, X10 is the water absorption rate, X11 is the cooking loss rate, and X12 is the sensory score.

[0015] This invention also provides a method for evaluating the quality of preserved wet noodles, the evaluation method being: The quality indicators of fresh wet noodles and the quality indicators of the flour raw materials for fresh wet noodles were measured. The results were then substituted into the above-mentioned comprehensive evaluation model for the quality of fresh wet noodles for calculation. The higher the calculated value, the better the quality of the fresh wet noodles.

[0016] Furthermore, the quality indicators of the preserved wet noodles include hardness, adhesion, elasticity, chewiness, resilience, tensile strength, stretching distance, surface stickiness, water absorption rate, cooking loss rate, and sensory score.

[0017] Furthermore, the hardness, adhesion, elasticity, and chewiness of the fresh wet noodles were determined using a texture analyzer. The method was as follows: the fresh wet noodles were reheated for 20-30 seconds, then removed, cooled to 20-25°C, and the surface moisture of the noodles was drained. The measurement was completed within 10-15 minutes. Each sample was tested in parallel 5-10 times, and the maximum and minimum values ​​were removed before calculating the average value.

[0018] Specifically, the texture analyzer used a TPA probe model P / 36R, and the measurement parameters were set as follows: the speed before, during, and after the test were 5.0 mm / s, 1.0 mm / s, and 1.0 mm / s, respectively; the compression degree was 70%; the test force was 5.0 g; and the time interval between the two compressions was 1.0 s.

[0019] Furthermore, in the quality index determination of fresh wet noodles, tensile force and tensile distance were measured using an A / SPR probe. The measurement parameters were as follows: calibration distance of 40 mm, speeds before, during and after the test of 2 mm / s, 2 mm / s and 10 mm / s, respectively; calibration distance of 40 mm, tensile distance of 90 mm, trigger force of 5 g, maximum tensile distance and starting distance of 140 mm and 20 mm, respectively.

[0020] Furthermore, the surface viscosity of the fresh wet noodles was measured using the Cooked Lasagne procedure. Specifically, an HDP / PFS probe was used, and the measurement parameters were as follows: the speeds before, during, and after the measurement were 1.00 mm / s, 0.50 mm / s, and 10.00 mm / s, respectively; the sensing force was 1000 g; the return distance was 20.00 mm; and the contact time was 2.00 s.

[0021] Furthermore, the method for determining the water absorption rate and cooking loss rate in the quality index determination of fresh wet noodles is as follows: Take noodles and put them into boiling water and start timing. Take out a noodle and squeeze it between two glass slides. When the white core disappears, the optimal cooking time has been reached. Then, take the noodles and cook them in boiling water for the optimal cooking time, take them out and soak them in cold water. Then, place the noodles on filter paper to absorb water and weigh them. At the same time, make up the volume of the noodle soup and cooling water, and then dry them to constant weight. Then, calculate the water absorption rate and cooking loss rate according to the formula.

[0022] Furthermore, in the quality index determination of fresh wet noodles, sensory evaluation was conducted using appearance, color, toughness, palatability, stickiness, smoothness, and flavor as indicators.

[0023] Furthermore, the quality indicator of the flour raw material for the preserved wet noodles is the wet gluten content.

[0024] Furthermore, the wet gluten content was determined according to the hand washing method in GB / T 5506.1-2008.

[0025] Beneficial effects This invention, through in-depth analysis of the quality of different wheat flour raw materials and the edible quality of fresh wet noodles made from them, explores the correlation between the quality of wheat flour raw materials and the quality of processed fresh wet noodles, and establishes a method for constructing a comprehensive evaluation model for the quality of fresh wet noodles. This model is objective, accurate, and easy to operate, providing quantitative basis for raw material selection and process optimization. It differs from traditional comprehensive evaluation models for dried noodle quality, helping to enhance the product advantages and market competitiveness of fresh wet noodles, and providing a feasible method for their standardized evaluation and quality control. Attached Figure Description

[0026] Figure 1 Principal component analysis of scree plots for each indicator.

[0027] Figure 2 Sensory characteristics of different types of fresh wet noodles. Detailed Implementation

[0028] The present invention will be further described in detail below with reference to specific embodiments, but the present invention is not limited to these embodiments.

[0029] Comparative Example 1 The reference is made to commercially available regular noodles (Jinshahe Original Flavor Smooth Noodles).

[0030] Examples 1-5 In the above embodiments, five different types of wheat flour were used to determine their moisture content, total starch content, protein content, wet gluten content, ash content, and other indicators. Fresh wet noodles were prepared using the five different types of wheat flour as raw materials to explore the edible quality of the fresh wet noodles. Then, correlation analysis and principal component analysis were used to conduct regression analysis to explore the relationship between wheat flour components and edible quality. Finally, a comprehensive evaluation model for the quality of fresh wet noodles was constructed.

[0031] The specific preparation process of fresh wet noodles is as follows: (1) Mixing the dough: Weigh 100 g of wheat flour into a dough mixer, measure a certain amount of deionized water (65% water absorption rate as measured by mixolab), weigh salt (1.5% of the mass of wheat flour) and dissolve it in the deionized water, and add the well-mixed salt water to the wheat flour in small amounts several times.

[0032] (2) Proofing: Pour the kneaded dough into a stainless steel bowl, cover it with plastic wrap, and place it in a constant temperature and humidity incubator at 25℃ for 15 minutes to mature. The purpose is to allow the water to penetrate the flour evenly, and after the gluten protein fully absorbs the water, it forms a good gluten network, giving the dough good stretchability and extensibility.

[0033] (3) Rolling and cutting: Knead the risen dough into a dough, then place the dough between the rollers of the noodle machine and roll it three times at roller gaps of 3.0 mm and 2.0 mm respectively. Cut the rolled dough into strips with a 20 cm long noodle using a 2 mm spacing cutter. The size of the noodle is 20 cm × 0.20 cm × 0.20 cm.

[0034] (4) Pre-cooking: Add 1000 mL of deionized water to a pot, and add fresh noodles after the water boils. Cook for 3 minutes.

[0035] (5) Washing: The time is controlled at 60 seconds. Washing with cold water can remove starch paste and other adhering substances on the surface and prevent the noodles from sticking together; at the same time, it can cool and shrink the surface of the noodles, enhancing the surface strength.

[0036] (6) Acid soaking: After the noodles are washed and the surface water is roughly drained, they are soaked in a lactic acid-sodium lactate buffer solution with pH 4.2 for 90 seconds. This can inhibit the growth of microorganisms and extend the shelf life of the product while minimizing the acidic taste caused by the noodles after soaking.

[0037] (7) Packaging and sterilization: Pack the noodles in high-temperature sterilization bags and then sterilize them at 95°C for 15 min to obtain fresh wet noodles.

[0038] 1. Determination of basic components of wheat flour The determination of moisture content was performed according to GB 5009.3-2016; the determination of protein content was performed according to GB 5009.5-2016; the determination of wet gluten content was performed according to the hand washing method in GB / T 5506.1-2008; the determination of ash content was performed according to GB 5009.4-2016; and the determination of total starch content was performed according to the kit method (Megazyme Total Starch Detection Kit).

[0039] The basic components of five types of wheat flour were determined, and the results are shown in Table 1. The table shows that Examples 3 and 4 had higher moisture content, with Example 4 exhibiting the highest water absorption rate, making it prone to clumping. The protein content and wet gluten content of the five wheat flours were generally proportional and increased in a gradient, with Example 5 showing the highest wet gluten content. The ash content and total starch content did not differ significantly. Moisture content and water absorption rate are mainly used to determine the optimal amount of water needed for dough preparation. Wet gluten plays a crucial role in protein formation; it is a gelled product of protein. When wheat flour comes into contact with water, glutenin and prolamins absorb water and swell, forming a viscoelastic network structure, i.e., "wet gluten," through disulfide bond cross-linking. Protein content reflects the total protein mass and is the material basis for wet gluten formation. Wet gluten content reflects the functional performance of protein and is more directly related to processing performance. Therefore, further research and analysis of the wet gluten content of wheat flour will be conducted in the future.

[0040] Table 1. Sample numbers and determination results of basic components of different wheat flours.

[0041] 2. Methods for determining the quality of fresh wet noodles 2.1 Determination of textural properties The hardness, adhesiveness, elasticity, and chewiness of noodles were determined using a texture analyzer. The noodles were reheated for 30 seconds, then removed, cooled to room temperature, and drained. Measurements were completed within 15 minutes. Each sample was tested in parallel 10 times, and the maximum and minimum values ​​were discarded before calculating the average. A TPA probe (model P / 36R) was used, with the following parameters set: pre-measurement speed of 5.0 mm / s, during-measurement speed of 1.0 mm / s, and post-measurement speed of 1.0 mm / s; pressure level of 70%; test force of 5.0 g; and time interval of 1.0 s between two compressions.

[0042] The tensile properties of noodles were measured using an A / SPR probe. The calibration distance was 40 mm, and the speeds before, during, and after the measurement were 2 mm / s, 2 mm / s, and 10 mm / s, respectively. The calibration distance was 40 mm, the tensile distance was 90 mm, and the trigger force was 5 g. The maximum tensile distance and the initial distance were 140 mm and 20 mm, respectively.

[0043] The surface viscosity of noodles was measured using the Cooked Lasagne program with an HDP / PFS probe. The specific experimental parameters were as follows: the speeds before, during, and after the measurement were 1.00 mm / s, 0.50 mm / s, and 10.00 mm / s, respectively; the sensing force was 1000 g; the return distance was 20.00 mm; and the contact time was 2.00 s.

[0044] 2.2 Determination of cooking characteristics Place 20 noodles into boiling water and start timing. Every 10 seconds, remove one noodle and press it between two glass slides. The optimal cooking time is reached when the white core disappears.

[0045] Take approximately 10 g of noodles and record the exact amount. Cook the noodles in 500 ml of boiling water for the optimal cooking time, then remove and soak in cold water for 30 seconds. Place the noodles on filter paper to absorb water for 5 minutes, then weigh them. Pour the cooking water and cooling water into a 500 mL volumetric flask and bring to a final volume. Transfer 50 mL of this volume to a constant-weight aluminum box, and finally dry the aluminum box in a 105℃ oven until a constant weight is achieved. The formulas for calculating the water absorption rate and cooking loss rate of the noodles are as follows:

[0046]

[0047] In the formula: m1 is the mass of noodles after cooking, g; m0 is the mass of noodles before cooking, g; m is the dry matter mass of 50 mL of noodle soup, g; w is the moisture content of noodles before cooking, %.

[0048] 2.3 Evaluation of sensory characteristics A quantitative descriptive analysis method was used, and sensory evaluations were conducted by 10 trained sensory evaluators. According to Table 2, the re-cooked noodles were divided into 30 mL transparent sensory evaluation cups, and the evaluators were required to evaluate the appearance, color, palatability, toughness, stickiness, smoothness, and flavor of the cooked noodles within 10 minutes.

[0049] Table 2 Scoring criteria for sensory evaluation of noodles

[0050] 2.4 Principal Component Analysis Principal Component Analysis (PCA) is a widely used data dimensionality reduction and feature extraction method in multivariate statistical analysis. First, a dataset of noodle texture, tensile properties, cooking properties, sensory properties, and surface viscosity is collected, and the original data is standardized to eliminate the influence of different dimensions and magnitudes on the results. Then, the covariance matrix is ​​calculated and eigenvalue decomposition is performed to extract principal components. The top few principal components are selected based on the scree plot and variance contribution rate. By selecting principal components with larger variance contribution rates, the representativeness of the selected principal components is ensured. These principal components are then used to reduce the dimensionality of the data, simplifying the complexity of the original data.

[0051] 2.5 Evaluation Model Construction To further evaluate the overall quality of fresh wet noodles, a comprehensive evaluation model was constructed based on the results of PCA. The top principal components with the largest variance contribution rates were selected, and their variance contribution rates and cumulative contribution rates were combined to establish a mathematical model reflecting the edible quality of fresh wet noodles. The model's predictive accuracy and reliability were verified by comparing it with experimental data. This evaluation model can provide a scientific basis for subsequent process optimization, helping to improve the quality of fresh wet noodles.

[0052] 3. Results and Discussion When evaluating the quality of noodles, numerous indicators are required, and these indicators often overlap and are highly correlated, making it difficult to accurately assess their quality. To minimize information loss and ensure the comprehensive evaluation closely approximates the original state, this invention employs principal component analysis. The main idea of ​​this method is to recombine numerous correlated indicators into a new set of independent comprehensive indicators to replace the original numerous indicators, thereby establishing an evaluation model to accurately reflect the edible quality of fresh wet noodles. For principal component analysis, let X1 be the wet gluten content (%), X2 be the hardness (g), X3 be the adhesiveness (g·s), X4 be the elasticity, X5 be the chewiness, X6 be the resilience, X7 be the tensile force (g), X8 be the tensile distance (mm), X9 be the surface stickiness (g), X10 be the water absorption rate (%), X11 be the cooking loss rate (%), and X12 be the sensory score (points).

[0053] Table 3 shows the evaluation indicators for each type of noodle quality. It can be seen that the water absorption rate, cooking loss rate, hardness, and chewiness of the indicators in Comparative Example 1 differ significantly from those of the fresh wet noodles in the Example, and there are also dimensional differences among the different evaluation indicators. First, the data for each evaluation indicator were standardized using Min-Max normalization, and the results are shown in Table 4, to eliminate the problems caused by the dimensional differences between the evaluation indicators. Table 5 shows the correlation analysis results between the standardized indicators, revealing varying degrees of correlation between different indicators, providing feasibility for simplifying the number of parameters in principal component analysis.

[0054] Table 3 Evaluation Indicators for Various Noodle Quality

[0055] Table 4 Standardization of data for each indicator

[0056] Table 5 Correlation Analysis of Various Indicators

[0057] First, the number of principal components extracted is determined based on the scree plot. The scree plot lists the eigenvalues ​​of each principal component factor on a single graph and connects them with line segments. See the attached graph for details. Figure 1 As can be seen intuitively from the graph, a steeper curve indicates that the principal component factors on the curve contain more original data information, while a flatter curve indicates that the principal component factors on the curve contain less original data information. Furthermore, the eigenvalues ​​of the first four factors are all greater than 1. After the fourth principal component factor, the scree plot curve gradually flattens out. Therefore, extracting the first four components can effectively reflect the overall information and has extremely high representativeness.

[0058] The eigenvalues, contribution rates, and cumulative contribution rates of these four principal components are shown in Table 6. It can be seen that the variance contribution rates of the first four principal component factors are 49.624%, 21.141%, 17.674%, and 11.562%, respectively, and the cumulative variance contribution rate reaches 100%.

[0059] Table 6. Eigenvalues, contribution rates, and cumulative contribution rates of each principal component

[0060] Component loadings reflect the correlation between the four extracted principal components and the original evaluation indicators. The sign of the loading indicates the direction of correlation between the indicator and the principal component, while the magnitude represents the contribution rate of the sub-indicator to the principal component. A larger absolute value indicates a greater contribution rate, meaning the principal component better reflects the indicator. As shown in Table 7, X1, X5, X7, and X8 have large loadings on principal component 1, indicating that principal component 1 mainly represents the influence of wet gluten content in wheat flour on noodle hardness, chewiness, and stretchability. Principal component 2 mainly reflects the influence on noodle adhesion, elasticity, cooking loss rate, and sensory score. Principal component 3 mainly reflects the influence on surface stickiness, water absorption, and resilience. Principal component 4 mainly reflects the influence of noodle resilience and surface stickiness.

[0061] Table 7 Component loading matrix of principal components

[0062] Based on the formula "loading = square root of principal component eigenvalue × eigenvector", dividing the component loading of each principal component in Table 7 by its variance contribution index λ yields the unit eigenvector coefficients corresponding to each eigenvalue, as shown in Table 8. Using these unit eigenvector coefficients, combined with the variance contribution rates of the first four principal components, a comprehensive evaluation model F for the quality of fresh-keeping wet noodles can be constructed.

[0063] Table 8. Eigenvectors of each principal component

[0064] Based on the eigenvectors of the principal components in Table 8, linear relationships between the principal components and various quality indicators of noodles can be constructed. The linear relationships are as follows: F1=0.397X1+0.325X2-0.12X3+0.26X4+0.402X5+0.102X6+0.381X7+0.37X8+0.189X9 -0.293 X10 -0.274X11 +0.067X12 F2 =0.125X1-0.248X2+0.432X3+0.443X4+0.04X5 - 0.054X6+0.021X7 +0.067X8 +0.037X9 +0.04X10 +0.405X11 +0.605X12 F3=-0.034X1+0.13X2 -0.433X3 +0.196X4 -0.08X5 -0.493X6-0.251X7 +0.191X8 +0.435X9 +0.44X10 -0.045X11 +0.149X12 F4 =-0.12X1-0.358X2+0.178X3 -0.114X4 -0.125X5 +0.546X6-0.013X7 +0.264X8 +0.525X9 +0.232X10 -0.308X11 +0.022X12 A comprehensive evaluation model F for the quality of fresh wet noodles was constructed using four principal components F1, F2, F3, and F4 and their variance contribution rates. F is a linear combination of the principal components F1, F2, F3, and F4, i.e.: F = 49.624%F1+21.141%F2+17.674%F3+11.562%F4 The mathematical model was used to comprehensively evaluate the quality of the noodles, and the evaluation results are shown in Table 9. The quality of the fresh wet noodles can be compared based on the absolute value of the F value. The larger the absolute value, the better the quality. Therefore, it can be concluded that the fresh wet noodles prepared in Example 5 have the highest comprehensive score and the best quality.

[0065] Table 9 Factor scores and overall scores for different types of noodles

[0066] The sensory characteristics of different types of freshly preserved wet noodles were investigated, and the results are shown in [the table below]. Figure 2Sensory characteristics refer to the comprehensive performance of noodles in terms of appearance, color, flavor, and resilience. They reflect the overall quality of the noodles and consumer acceptance, directly influencing consumers' purchasing intentions and eating experience. From the sensory results, the five types of fresh wet noodles exhibited uniform color and intact shape in appearance, a chewy and smooth texture that didn't stick to the teeth, and a natural wheat aroma, demonstrating good edible quality. However, they differed in flavor, color, and smoothness. The fresh wet noodles corresponding to Example 5 showed the best sensory characteristics, which were basically consistent with the comprehensive score results obtained in Table 9, further verifying the accuracy and feasibility of the above model. Therefore, this invention constructs an effective comprehensive evaluation model for the quality of fresh wet noodles based on the selected core indicators and their corresponding unit eigenvector coefficients.

[0067] The embodiments provided above are not intended to limit the scope of the invention, nor are the described steps intended to limit the order of execution. Any obvious modifications made to the invention by those skilled in the art based on existing common knowledge also fall within the scope of protection defined by the claims.

Claims

1. A method for evaluating the quality of freshly preserved wet noodles, characterized in that, The evaluation method is as follows: The quality indicators of fresh wet noodles and the quality indicators of the flour raw materials for fresh wet noodles are measured. Then, the measurement results are substituted into the above-mentioned comprehensive evaluation model of fresh wet noodle quality for calculation. The higher the calculated result value, the better the quality of fresh wet noodles. The comprehensive evaluation model for the quality of fresh wet noodles is as follows: F = 49.624%F1+21.141%F2+17.674%F3+11.562%F4; Where, F1 = 0.397X1 + 0.325X2 - 0.12X3 + 0.26X4 + 0.402X5 + 0.102X6 + 0.381X7 + 0.37X8 + 0.189X9 - 0.293X10 - 0.274X11 + 0.067X12; F2 =0.125X1-0.248X2+0.432X3+0.443X4+0.04X5 - 0.054X6+0.021X7 + 0.067X8 +0.037X9 +0.04X10 +0.405X11 +0.605X12; F3=-0.034X1+0.13X2 -0.433X3 +0.196X4 -0.08X5 -0.493X6-0.251X7 +0.191 X8 +0.435X9 +0.44X10 -0.045X11 +0.149X12; F4 =-0.12X1-0.358X2+0.178X3 -0.114X4 -0.125X5 +0.546X6-0.013X7 + 0.264X8+0.525X9 +0.232X10 -0.308X11 +0.022X12; X1 is wet gluten content, X2 is hardness, X3 is adhesiveness, X4 is elasticity, X5 is chewiness, X6 is resilience, X7 is tensile strength, X8 is tensile distance, X9 is surface tackiness, X10 is water absorption rate, X11 is cooking loss rate, and X12 is sensory score.

2. The evaluation method according to claim 1, characterized in that, The quality indicators of the fresh wet noodles are hardness, adhesion, elasticity, chewiness, resilience, tensile strength, stretching distance, surface stickiness, water absorption rate, cooking loss rate, and sensory score.

3. The evaluation method according to claim 2, characterized in that, In the quality index determination of fresh wet noodles, hardness, adhesion, elasticity and chewiness were measured using a texture analyzer. The test method was as follows: the fresh wet noodles were re-cooked for 20-30 seconds, then removed, cooled to 20-25℃, the surface moisture of the noodles was drained, and the measurement was completed within 10-15 minutes. Each sample was tested in parallel 5-10 times, and the maximum and minimum values ​​were removed and the average value was calculated.

4. The evaluation method according to claim 3, characterized in that, The texture analyzer used a TPA probe model P / 36R. The measurement parameters were set as follows: the speed before, during, and after the test were 5.0 mm / s, 1.0 mm / s, and 1.0 mm / s, respectively; the compression level was 70%; the test force was 5.0 g; and the time interval between two compressions was 1.0 s.

5. The evaluation method according to claim 2, characterized in that, In the quality index determination of fresh wet noodles, tensile force and tensile distance were measured using an A / SPR probe. The measurement parameters were as follows: calibration distance 40 mm, speeds before, during and after measurement 2 mm / s, 2 mm / s and 10 mm / s respectively, calibration distance 40 mm, tensile distance 90 mm, trigger force 5 g, maximum tensile distance and starting distance 140 mm and 20 mm respectively.

6. The evaluation method according to claim 2, characterized in that, In the quality index determination of fresh wet noodles, surface viscosity was measured using the Cooked Lasagne program. Specifically, an HDP / PFS probe was used, and the measurement parameters were as follows: the speeds before, during, and after the measurement were 1.00 mm / s, 0.50 mm / s, and 10.00 mm / s, respectively; the sensing force was 1000 g; the return distance was 20.00 mm; and the contact time was 2.00 s.

7. The evaluation method according to claim 2, characterized in that, The method for determining the water absorption rate and cooking loss rate in the quality index determination of fresh wet noodles is as follows: Take noodles and put them into boiling water and start timing. Take out a noodle and squeeze it between two glass slides. When the white core disappears, the optimal cooking time has been reached. Then take the noodles and cook them in boiling water for the optimal cooking time. Take them out and soak them in cold water. Then place the noodles on filter paper to absorb water and weigh them. At the same time, make up the volume of the cooking water and cooling water, and then dry them to constant weight. Then calculate the water absorption rate and cooking loss rate according to the formula.

8. The evaluation method according to claim 2, characterized in that, In the quality index determination of fresh wet noodles, sensory evaluation is conducted using appearance, color, toughness, palatability, stickiness, smoothness, and flavor as indicators.

9. The evaluation method according to claim 1, characterized in that, The quality indicator of the flour raw material for the preserved wet noodles is the wet gluten content.

10. The evaluation method according to claim 9, characterized in that, The wet gluten content was determined according to the hand washing method in GB / T5506.1-2008.

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