A quality control method for low-temperature short-time baked crisp cookies

Abstract

The application discloses a quality control method for low-temperature short-time baked crisp biscuits, which comprises the following steps: S1, providing a biscuit material slurry, wherein the material slurry comprises oil and water; S2, baking the material slurry by using a microwave device combined with a traditional heat source at a baking temperature of 130 DEG C and a baking time of 20 minutes to obtain a biscuit product; S3, adjusting the proportion of oil and water in the material slurry in S1 to change the porosity of pores in the biscuit product caused by the evaporation of water due to heating in S2; S4, obtaining the porosity parameter of the biscuit product by industrial CT scanning; S5, obtaining the hardness and crispness data of the biscuit product by a texture analyzer; and S6, establishing a mathematical relationship between the porosity parameter and the hardness and crispness. The application realizes accurate prediction and regulation of the taste of biscuits and can be applied to real-time nondestructive quality detection in mass production.

Description

Technical Field

[0001] This invention relates to the field of food processing technology, specifically to a quality control method for low-temperature, short-time baked crispy biscuits. Background Technology

[0002] Cookies are a popular baked good, and their texture (especially hardness and crispness) is a key factor in determining consumer acceptance. Traditional cookie baking processes typically use high baking temperatures (usually above 150°C) and add additives such as shortcrust enzymes to improve the crispness. However, high-temperature baking is energy-intensive, and the introduction of additives increases production costs and recipe complexity.

[0003] With the increasing consumer demand for natural, additive-free foods, developing a low-temperature baking process for cookies that does not rely on additives has significant market value. While low-temperature baking (e.g., below 130°C) can reduce energy consumption and the use of additives, the formation of air pockets (chambers) inside the cookies is extremely sensitive to temperature changes due to the low baking temperature, significantly increasing the difficulty of product quality control. This sensitivity to temperature effects is further amplified when using a combination of microwave and traditional heat sources, making it difficult to guarantee batch-to-batch quality stability.

[0004] Industrial CT scanning technology has been increasingly applied to the analysis of the internal structure of food products. This technology allows for the acquisition of three-dimensional spatial visualization of the pore (chamber) structure and porosity parameters within biscuits. Texture analyzers can accurately measure the hardness and crispness of biscuits. However, no research has yet established a quantitative mathematical relationship between the porosity parameters obtained from CT scans and the hardness and crispness of biscuits, nor has it been applied to the quality control of low-temperature baked biscuits.

[0005] Therefore, there is an urgent need to develop a method that can precisely control the hardness and crispness of biscuits by adjusting the oil-water ratio under low-temperature baking conditions without relying on additives such as crisping enzymes, and to establish a corresponding mathematical model to achieve non-destructive quality monitoring during the production process. Summary of the Invention

[0006] In view of this, the purpose of this invention is to overcome the shortcomings of the prior art and provide a quality control method for low-temperature short-time baking of crispy biscuits. By establishing a mathematical relationship between the internal porosity of the biscuit and its hardness and crispness, the method can achieve accurate prediction and control of the biscuit's texture and can be applied to real-time non-destructive quality inspection in mass production.

[0007] The technical solution adopted is as follows:

[0008] A quality control method for low-temperature, short-time baked crisp biscuits, comprising the following steps:

[0009] S1. Provide a biscuit slurry, the slurry containing oil and water;

[0010] S2. The material paste is baked at a baking temperature of 130°C and a baking time of 20 minutes using a microwave device combined with a traditional heat source to obtain biscuit products;

[0011] S3. Adjust the ratio of oil and water in the slurry described in S1 to change the porosity of the pores inside the biscuit product described in S2 caused by the evaporation of moisture due to heat.

[0012] S4. Obtain the porosity parameters of the biscuit product by industrial CT scanning;

[0013] S5. Obtain the hardness and crispness data of the biscuit product using a texture analyzer;

[0014] S6. Establish a mathematical relationship between the porosity parameter and the hardness and brittleness, so as to predict or control the taste quality of the biscuit product through the mathematical relationship.

[0015] Furthermore, in S6, the mathematical relation is:

[0016] Porosity = 47.013 + 0.00368 × Hardness - 0.00588 × Brittleness.

[0017] The mathematical relationship shows that porosity is positively correlated with hardness (the coefficient of the hardness term is positive) and negatively correlated with brittleness (the coefficient of the brittleness term is negative). Specifically, the coefficient of the hardness term is 0.00368, while the absolute value of the coefficient of the brittleness term is 0.00588. The negative impact of brittleness on porosity is greater than the positive impact of hardness, indicating that changes in brittleness have a more significant and sensitive effect on porosity regulation.

[0018] Furthermore, in S2, the oil-to-water ratio, by weight, is 0.21-0.29. By adjusting the oil-to-water ratio within this range, the porosity inside the biscuit can be significantly altered, thereby affecting the hardness and crispness of the biscuit.

[0019] Furthermore, in S4, the porosity parameter obtained by the industrial CT scan includes at least the volume fraction, which is the porosity in the mathematical formula in S6.

[0020] Furthermore, in S6, the smaller the numerical difference between hardness and crispness, and the smaller the hardness value, the better the taste of the biscuit product.

[0021] Furthermore, in S4, under the same baking conditions, at least three samples are taken for CT scan analysis, and the data of at least three samples are analyzed for significance. If there is a significant correlation, they are included in the statistics. In S5, the same sample is subjected to hardness and brittleness measurements at both ends and the middle section. The three measurements are analyzed for significance, and samples with significant correlations are included in the statistical analysis. The significant and valid data are used in the porosity analysis of the CT data of the sample by mean regression, with the mean being a single hardness and brittleness parameter.

[0022] Furthermore, the mathematical formula is used for real-time non-destructive testing of biscuit product quality in mass production. Specifically, porosity data is obtained through online CT scanning, and the formula is used to deduce predicted values ​​for hardness and brittleness, thereby enabling rapid determination of product quality.

[0023] Furthermore, the slurry is composed of two types of starch, a type of low-gluten flour, a deep-processed fruit pulp from Rutaceae family fruits, water, palm oil, sugar, and salt.

[0024] Furthermore, in S6, the mathematical relationship also includes a linear model of the oil-to-water ratio variable for comparative verification:

[0025] Porosity = 45.9057 + 0.0038 × hardness − 0.00615 × brittleness + 5.9646 × oil-water ratio;

[0026] or,

[0027] The mathematical relationship also includes a nonlinear regression model containing quadratic and interaction terms for comparative verification:

[0028] Porosity = −12.91 + 0.079 × hardness − 0.049 × brittleness + 0.000021 × brittleness² − 0.000025 × hardness × brittleness;

[0029] or,

[0030] The mathematical relationship also includes a regression model based on a subset of the best-tasting samples as supplementary analysis:

[0031] Porosity = 59.337 + 0.0054 × hardness − 0.0125 × brittleness + 9.028 × oil-water ratio.

[0032] In the above technical solutions,

[0033] This invention employs a low-temperature baking condition of 130℃, significantly lower than the traditional biscuit baking temperature. Under this low-temperature condition, combined with a microwave device to provide uniform heating, the air pockets (chambers) inside the biscuit are mainly formed by the evaporation of moisture in the slurry due to heat, rather than by the traditional puffing process. The introduction of the microwave device makes baking biscuits at low temperatures possible, but it also makes air pocket formation highly sensitive to temperature changes; therefore, precise control of process parameters is crucial.

[0034] The beneficial effects of this invention are as follows:

[0035] 1. This invention bakes biscuits at a low temperature of 130℃ for a short time of 20 minutes without the need for additives such as shortbread enzymes, thus reducing energy consumption while meeting consumers' demand for natural and additive-free foods.

[0036] 2. This invention establishes for the first time a mathematical relationship between the internal porosity of biscuits and their hardness and crispness, quantitatively linking microstructural parameters with macroscopic taste quality, and providing a scientific basis for the precise control of biscuit quality.

[0037] 3. Through the mathematical formula, porosity data can be obtained non-destructively using CT scanning during the production process, and the hardness and brittleness of the product can be predicted in real time. Quality judgment can be completed without destroying the sample, which greatly improves production efficiency and quality control level.

[0038] 4. Since the formation of pores under low-temperature baking conditions is highly sensitive to temperature, this invention establishes a numerical relationship, enabling automated production to automatically adjust parameters such as the oil-water ratio based on real-time detection data, thereby ensuring the stability and consistency of product quality.

[0039] 5. By adjusting the oil-water ratio to change the porosity, it is possible to precisely produce biscuit products with specific textures and qualities to meet the differentiated needs of different consumer markets for hardness and crispness. Attached Figure Description

[0040] Figure 1 This is a schematic diagram showing the analysis results of the differences in hardness and crispness of different groups of biscuits in the embodiments of the present invention.

[0041] Figure 2 This is a structural diagram of the porosity and hardness / brittleness splitting relationship established based on the decision tree model in an embodiment of the present invention. Detailed Implementation

[0042] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be described in detail below with reference to the accompanying drawings and specific embodiments. However, it should be understood that the following embodiments are for illustrative purposes only and should not be considered as limiting the scope of the invention.

[0043] Example 1

[0044] This embodiment provides a quality control method for low-temperature, short-time baked crisp biscuits, which includes the following steps:

[0045] S1. Providing a biscuit slurry, the slurry comprising oil (such as palm oil) and water;

[0046] Specifically, the biscuit batter includes the following ingredients by weight:

[0047] Two types of starch (potato starch and corn starch, weight ratio 1:1): 30-40 parts

[0048] Cake flour: 40-50 portions

[0049] Deep-processed fruit pulp of Rutaceae family (navel orange pulp): 10-20 parts

[0050] Water: 10-20 parts (adjust the ratio to palm oil within the range of 0.21-0.29)

[0051] Palm oil: 2-6 parts

[0052] Sugar: 5-8 parts

[0053] Salt: 0.5 to 1 part.

[0054] The above ingredients are mixed evenly to form a slurry, which contains no leavening agents or saccharin. This slurry is then used directly for the low-temperature, short-time baking in the subsequent S2 step.

[0055] S2. The material paste is baked at a baking temperature of 130°C and a baking time of 20 minutes using a microwave device combined with a traditional heat source to obtain biscuit products;

[0056] S3. Adjust the ratio of oil and water in the slurry described in S1 to change the porosity of the pores inside the biscuit product described in S2 caused by the evaporation of moisture due to heat.

[0057] S4. Obtain the porosity parameters of the biscuit product by industrial CT scanning;

[0058] S5. Obtain the hardness and crispness data of the biscuit product using a texture analyzer;

[0059] S6. Establish a mathematical relationship between the porosity parameter and the hardness and brittleness, so as to predict or control the taste quality of the biscuit product through the mathematical relationship.

[0060] This invention employs a low-temperature, short-time baking process at 130℃. This avoids damaging the natural aroma components of the Rutaceae fruit pulp with high temperatures, preserving the fruit flavor to the greatest extent possible. Furthermore, without adding leavening agents, the evaporation of water and palm oil upon heating creates a porous (cell-like) structure within the starch and flour raw materials. This porous structure breaks down under external forces such as chewing, resulting in the unique crisp texture of the biscuit. This invention precisely controls the porosity of this porous structure by adjusting the oil-to-water ratio, thereby regulating the crispness.

[0061] Specifically, 13 groups of biscuit slurries (condition groups TA001-TA013) were prepared according to the oil-water ratios shown in Table 1. TA001 was a provisional standard group, while TA002-TA013 were based on the standard groups with variations in oil and water content. Each group of slurries was baked at 130℃ using a microwave device combined with a conventional heat source for 20 minutes to obtain stick-shaped biscuit samples. Three parallel samples were prepared for each group, and one sample was tested at each end and one at the middle.

[0062] Table 1 Oil-water ratio for each condition group

[0063] TA001 25 100 0.25 TA002 25 95 0.26 TA003 25 90 0.28 TA004 25 85 0.29 TA005 25 105 0.24 TA006 25 110 0.23 TA007 25 115 0.22 TA008 23.75 100 0.24 TA009 22.5 100 0.23 TA010 21.25 100 0.21 TA011 26.25 100 0.26 TA012 27.5 100 0.28 TA013 28.75 100 0.29

[0064] Example 2

[0065] The hardness and brittleness of each group of samples were determined using a texture analyzer. The test conditions were as follows: the height (mm), hardness (gf), and brittleness (gf) of each sample were recorded at 5 measuring points.

[0066] Test results showed that out of all 135 measurement points, 3 points were deemed invalid due to a hardness or brittleness value of 0, while the remaining 132 points were valid. Using the average height of all valid measurement points (7.52 mm) as a reference height, thickness regression correction was performed on the original data to eliminate the influence of thickness differences on the hardness and brittleness data of the same sample. After correction, a set of valid hardness and brittleness data was obtained for each sample.

[0067] The average hardness and brittleness values ​​for each condition group are summarized in Table 2.

[0068] Table 2. Mean values ​​of hardness and brittleness after correction for each condition group (unit: gf)

[0069] TA001 3943.29 3533.42 TA002 3228.13 3094.76 TA003 3838.54 3745.72 TA004 3147.27 3009.28 TA005 3528.32 3364.34 TA006 3529.17 3055.48 TA007 3585.14 3367.63 TA008 3856.85 3379.68 TA009 3396.74 3030.23 TA010 3491.56 3021.17 TA011 3453.87 2900.16 TA012 2994.61 2782.63 TA013 2829.72 2722.54

[0070] Example 3

[0071] The sensory evaluation results of 16 individuals (5 trained evaluators and 11 untrained blind evaluators) were correlated with the hardness and brittleness data measured by the texture analyzer. Using group TA001 as the baseline, the degree of difference between each group and group TA001 was calculated in the following four dimensions:

[0072] (1) The average degree of hardness deviation;

[0073] (2) The average degree of brittleness deviation;

[0074] (3) The absolute degree of the difference between brittleness and hardness;

[0075] (4) The absolute degree of the difference between the hardness and the overall average.

[0076] Analysis results as follows Figure 1 As shown. The calculation method for the comprehensive evaluation index is as follows:

[0077] Combining 1 = Difference values ​​of dimension (1) + dimension (2) + dimension (3) + dimension (4)

[0078] Combining 2 = the difference value of dimension (3) + dimension (4)

[0079] Combine 3 = 0.6 × dimension (3) difference value + 0.4 × dimension (4) difference value

[0080] The overall evaluation ranking shows that groups TA011 and TA009 have the most similar hardness and crispness characteristics to group TA001 (the standard group). This indicates that the oil-water ratio of these two groups can produce a taste quality close to that of the standard group, and the difference between their hardness and crispness values ​​is small, with a relatively low hardness value, avoiding an unpleasant "hard" texture. However, group 011 has a higher oil content, resulting in a darker color than group 001, which is less visually appealing. Furthermore, the increased oil content is detrimental to cost reduction in large-scale industrial production.

[0081] Example 4

[0082] The Royma CT4000 industrial CT scanning equipment is used to perform CT scans (Computed Tomography) on samples.

[0083] Industrial CT scanning is a non-destructive three-dimensional imaging technology that does not damage the workpiece. X-rays penetrate the workpiece, and materials of different densities produce different ray attenuation. The detector simultaneously records a two-dimensional grayscale projection image at each rotation angle. The computer reconstructs a complete three-dimensional internal image, which can clearly show the internal structure of the sample.

[0084] The reconstructed three-dimensional data was analyzed using Dragonfly software to calculate the sample volume, pore volume, porosity, pore distribution map, and corresponding data.

[0085] Among them, the parameters of industrial CT scanning equipment include Volume Fraction (defined as porosity %) and Label Volume (referring to the volume of pores in mm). 3 Mask Volume (in this invention, it refers to the total volume of the crispy biscuits included in the analysis, in mm). 3 ), Label Voxel Count (number of 3D pixels identified as pores), Mask Voxel Count (total number of pixels included in the analysis).

[0086] The porosity measurement results of each group of samples are shown in Table 3.

[0087] Table 3 Porosity data from CT scans of each condition group

[0088] TA001-1 34.39 6547.78 2252.31 TA001-2 41.73 7955.94 3320.21 TA001-3 34.24 7189.52 2461.66 TA002-1 39.98 6765.83 2704.86 TA002-2 44.42 8065.07 3582.20 TA002-3 44.24 6960.43 3078.95 TA003-1 42.61 6526.79 2780.99 TA003-2 38.60 6631.89 2559.82 TA003-3 39.60 6809.55 2696.84 TA004-1 36.28 6112.26 2217.52 TA004-2 38.23 7143.16 2731.15 TA004-3 37.17 6054.83 2250.78 TA005-1 33.15 7132.96 2364.88 TA005-2 35.44 7010.19 2484.39 TA005-3 35.75 6741.90 2410.19 TA006-1 38.32 7452.57 2855.48 TA006-2 38.25 6342.67 2426.19 TA006-3 41.73 7082.06 2954.98 TA007-1 37.98 7122.91 2705.26 TA007-2 38.09 6767.94 2577.75 TA007-3 45.57 6002.96 2735.18 TA008-01 45.48 7422.98 3398.71 TA008-02 46.79 8351.37 3907.67 TA008-03 46.07 8081.43 3722.76 TA009-01 44.06 7112.40 3133.83 TA009-02 43.97 7371.59 3241.34 TA009-03 43.16 6935.33 2993.56 TA010-01 40.53 6500.07 2634.45 TA010-02 46.47 7398.14 3437.78 TA010-03 45.70 7310.99 3340.85 TA011-01 49.47 6758.11 3343.49 TA011-02 44.37 7663.58 3400.35 TA011-03 49.36 6866.00 3389.30 TA012-01 40.80 6902.74 2816.61 TA012-02 39.82 6279.89 2500.64 TA012-03 44.85 8261.55 3704.99 TA013-01 42.97 7216.54 3100.86 TA013-02 42.01 7955.33 3341.85 TA013-03 40.71 8180.37 3330.48

[0089] Example 5

[0090] The corrected hardness and brittleness data of each sample obtained in Example 2 were compared with the porosity data of each sample obtained in Example 4 using multiple linear regression analysis. With porosity (P) as the dependent variable and hardness (H) and brittleness (F) as independent variables, the regression equations are as follows:

[0091] P = 47.013 + 0.00368 × H − 0.00588 × F (Equation 1)

[0092] The statistical test results of the regression equation show that:

[0093] Porosity is weakly positively correlated with hardness and significantly negatively correlated with brittleness.

[0094] Judging from the absolute value of the coefficients, the coefficient of the brittleness term (0.00588) is greater than that of the coefficient of the hardness term (0.00368), indicating that under the water-oil ratio and baking conditions specified in this invention, the negative regulatory effect of brittleness on porosity is more significant than the positive regulatory effect of hardness on porosity.

[0095] It should be noted that Equation 1, which presents a pattern of "a weak positive correlation between porosity and hardness, and a significant negative correlation with brittleness," reflects the partial correlation after eliminating mutual interference between variables in a multiple regression. This represents the net effect of hardness changes alone on porosity when brittleness remains constant. This is a different statistical concept from the total effect of simultaneous changes in hardness and brittleness due to adjustments in process parameters in actual samples. In actual samples (e.g., comparing group TA011 with group TA001), changes in the oil-water ratio affect both hardness and brittleness simultaneously. High-porosity samples actually show a simultaneous decrease in both hardness and brittleness values, with a more significant decrease in brittleness. The absolute value of the brittleness coefficient in the formula is greater than that of the hardness coefficient, indicating that brittleness is more sensitive to the regulation of porosity than hardness.

[0096] To further verify the effectiveness of this relationship, a decision tree model was used to establish the split relationship structure between porosity and hardness / brittleness, such as... Figure 2 As shown, each node of the decision tree model divides the sample based on a hardness or brittleness threshold, ultimately predicting the sample's porosity range. The model's predictions show good agreement with the linear regression equation, further validating the existence of a quantifiable mathematical relationship between porosity and hardness / brittleness.

[0097] Equation 1 is taken as the main formula.

[0098] To further verify the robustness of the master formula and explore higher-precision prediction models, the present invention also conducted the following comparative verifications and supplementary analyses:

[0099] (a) Linear model of oil-water ratio variable (comparative verification 1)

[0100] Based on the main formula, the oil-to-water ratio (O / W) is included as the third independent variable in the regression equation:

[0101] P = 45.9057 + 0.0038 × H − 0.00615 × F + 5.9646 × (O / W) (Equation 2)

[0102] After adding the oil-water ratio variable, all three independent variables—hardness, brittleness, and oil-water ratio—showed statistical significance, and the overall goodness of fit of the model improved compared to the main formula. The oil-water ratio coefficient was positive (+5.9646), indicating that within the oil-water ratio range of 0.25 to 0.29 defined in this invention, increasing the oil-water ratio increases porosity. This comparative verification demonstrates that the conclusions of the main formula (positive correlation of hardness and negative correlation of brittleness) still hold after introducing the new variable, proving the robustness of the main formula.

[0103] (II) Nonlinear Regression Model (Comparative Validation 2)

[0104] To further verify whether the linear assumption of the master formula is sufficient, a nonlinear regression model including quadratic terms and interaction terms was established:

[0105] P=−12.91+0.079×H−0.049×F+0.000021×F 2 −0.000025×H×F (Equation 3)

[0106] This model achieves the highest fitting accuracy across the entire dataset. The signs of the coefficients for the hardness linear term (+0.079) and brittleness linear term (-0.049) are completely consistent with the main formula, and the brittleness square term (+0.000021×F)... 2 The addition of the hardness-brittleness interaction term (-0.000025×H×F) further optimized the model's fit to the high-brittleness region and the hard-brittleness interaction region. This comparative verification shows that even under the nonlinear model, the fundamental law that hardness is positively correlated with porosity and brittleness is negatively correlated with porosity remains unchanged.

[0107] (III) Optimal Taste Subset Model (Supplementary Analysis)

[0108] The optimal taste sample group (TA001, TA009, TA010, TA011) confirmed by sensory evaluation was used as the analysis subset, and a regression equation was established:

[0109] P = 59.337 + 0.0054 × H − 0.0125 × F + 9.028 × (O / W) (Equation 4)

[0110] Compared with Equation 2 (the oil-to-water ratio model for the entire dataset), the absolute values ​​of the crispness coefficient (-0.0125) and the oil-to-water ratio coefficient (+9.028) in the optimal taste subset model are significantly increased, indicating that within the optimal taste range, crispness and oil-to-water ratio are more sensitive to the regulation of porosity. This supplementary analysis provides further optimization directions for the application of the main formula under the optimal taste objective.

[0111] Consistency verification of the four formulas:

[0112] To verify that there is no logical contradiction among the four formulas above, the same set of values ​​for hardness (H=3500 gf), brittleness (F=2800 gf), and oil-water ratio (O / W=0.27) were substituted into the four formulas respectively:

[0113] Equation 1 (Main Formula): P = 43.43%

[0114] Equation 2 (Linear model of oil-water ratio): P = 43.60%

[0115] Equation 3 (Nonlinear Model): P = 46.03%

[0116] Equation 4 (Optimal Taste Subset Model): P = 45.68%

[0117] The porosity calculated by the four formulas ranges from 43% to 46%, and the results are basically consistent. More importantly, in all four formulas, the hardness coefficient is positive and the brittleness coefficient is negative, showing complete consistency in directionality and no logical contradiction. The differences mainly stem from the different model structures (linear / nonlinear) and applicable datasets, which proves that the law revealed by the master formula has robustness across models and datasets.

[0118] In practical applications, this invention preferably uses Equation 1 (the main formula) for biscuit quality prediction and control because it is simple in structure, easy to calculate, and has clear physical meaning, making it suitable for real-time non-destructive testing on production lines. When the oil-water ratio needs to be included in the overall control, Equation 2 or Equation 4 can be used for supplementary analysis; when pursuing the highest fitting accuracy, Equation 3 can be used for fine prediction.

[0119] Example 6

[0120] The master formula of the mathematical relation in Example 5 is applied to quality control in the actual production line. The specific steps are as follows:

[0121] (1) Install an online industrial CT scanning device in the production line to scan the baked biscuit products in real time and obtain porosity data;

[0122] (2) Substitute the measured porosity into the relationship P=47.013+ 0.00368×H−0.00588×F, and combine it with the ideal difference range between hardness and brittleness (the smaller the difference, the better), to reverse the predicted values ​​of hardness and brittleness of the current product.

[0123] (3) Compare the predicted value with the target value (such as the hardness and brittleness characteristics of group TA001). If the deviation exceeds the allowable range, automatically adjust the oil-water ratio of the material slurry in S1 (preferably fine-tune within the range of 0.21-0.29) so that the porosity of the next batch of products returns to the target range.

[0124] (4) Through the above closed-loop control, the hardness and crispness of the biscuits are automatically regulated, ensuring the consistency and stability of product quality in mass production.

[0125] For further refined control, Equation 4 (Optimal Taste Subset Model) can be used to optimize the oil-water ratio.

[0126] The detailed descriptions listed above are merely specific illustrations of feasible embodiments of the present invention and are not intended to limit the scope of protection of the present invention. All equivalent embodiments or modifications made without departing from the spirit of the present invention should be included within the scope of protection of the present invention.

Claims

1. A quality control method for low-temperature, short-time baked crisp biscuits, characterized in that, Includes the following steps: S1. Provide a biscuit slurry, the slurry containing oil and water; S2. The material paste is baked at a baking temperature of 130°C and a baking time of 20 minutes using a microwave device combined with a traditional heat source to obtain biscuit products; S3. Adjust the ratio of oil and water in the slurry described in S1 to change the porosity of the pores inside the biscuit product described in S2 caused by the evaporation of moisture due to heat. S4. Obtain the porosity parameters of the biscuit product by industrial CT scanning; S5. Obtain the hardness and crispness data of the biscuit product using a texture analyzer; S6. Establish a mathematical relationship between the porosity parameter and the hardness and brittleness, so as to predict or control the taste quality of the biscuit product through the mathematical relationship.

2. The quality control method for low-temperature short-time baking of crisp biscuits according to claim 1, characterized in that, In S6, the mathematical relation is: Porosity = 47.013 + 0.00368 × Hardness - 0.00588 × Brittleness.

3. The quality control method for low-temperature short-time baking of crisp biscuits according to claim 2, characterized in that, In S2, the ratio of oil to water by weight is 0.21-0.

29.

4. The quality control method for low-temperature short-time baking of crisp biscuits according to claim 2, characterized in that, In S4, the porosity parameter obtained by the industrial CT scan includes at least a volume fraction, which is the porosity in the mathematical formula in S6.

5. The quality control method for low-temperature short-time baking of crisp biscuits according to claim 1, characterized in that, In S6, the smaller the numerical difference between hardness and crispness, and the smaller the hardness value, the better the taste of the biscuit product.

6. The quality control method for low-temperature short-time baking of crisp biscuits according to claim 1, characterized in that, In S4, under the same baking conditions, at least three samples were taken for CT scan analysis. The data of at least three samples were analyzed for significance. If there was a significant correlation, the data were included in the statistics. In S5, the hardness and brittleness of the same sample were measured once at each end and once in the middle section. The three measurements were analyzed for significance. The samples with significant correlations were included in the statistical analysis. The significant and valid data were used for porosity analysis of the CT data of the sample by mean regression, with the mean being a single hardness and brittleness parameter.

7. The quality control method for low-temperature short-time baking of crisp biscuits according to claim 1, characterized in that, The mathematical formula is used for real-time non-destructive testing of biscuit products in mass production.

8. The quality control method for low-temperature short-time baking of crisp biscuits according to claim 1, characterized in that, The slurry consists of two types of starch, one type of low-gluten flour, deep-processed fruit pulp from Rutaceae family fruits, water, palm oil, sugar, and salt.

9. The quality control method for low-temperature short-time baked biscuits according to claim 2, characterized in that, In S6, the mathematical relationship also includes a linear model of the oil-water ratio variable for comparative verification: Porosity = 45.9057 + 0.0038 × Hardness − 0.00615 × Brittleness + 5.9646 × Oil-water ratio; or, The mathematical relationship also includes a nonlinear regression model containing quadratic and interaction terms for comparative verification: Porosity = −12.91 + 0.079 × hardness − 0.049 × brittleness + 0.000021 × brittleness² − 0.000025 × hardness × brittleness; or, The mathematical relationship also includes a regression model based on a subset of the best-tasting samples as supplementary analysis: Porosity = 59.337 + 0.0054 × hardness − 0.0125 × brittleness + 9.028 × oil-water ratio.

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