A method and system for optimizing the industrial transition of meat product marinating and grilling processes

Abstract

The application relates to the technical field of food processing, and provides a method and system for optimizing industrial conversion of marinating and roasting processes of meat products, which comprises the following steps: acquiring basic physicochemical index data of raw meat and preset quality characteristics of target meat products, and inputting the data into a pre-constructed multidimensional mapping relationship model; generating an optimal marinating process parameter and roasting process parameter combination according to the output of the multidimensional mapping relationship model; processing the raw meat based on the optimal marinating process parameter and roasting process parameter combination to obtain a meat product to be verified; verifying the quality characteristics of the meat product to be verified; and determining a final process scheme according to the optimal marinating process parameter and roasting process parameter combination when the verification result meets preset conditions. The application solves the industrial pain points in the prior art, such as difficulty in quantifying process parameter coupling effects, long time consumption for obtaining optimal solutions of process parameters, and the like, and realizes a leap from subjective experience to digital intelligence in process design and parameter setting.

Description

Technical Field

[0001] This invention relates to the field of food processing technology, and in particular to a method and system for optimizing the industrial conversion of meat product braising and roasting processes. Background Technology

[0002] In meat processing, braising and roasting are two common cooking techniques. Braising involves heating raw meat in a spice-infused broth, allowing flavor compounds to penetrate the muscle tissue and causing moderate protein denaturation, thus imparting a unique flavor and tenderness. Roasting, on the other hand, uses high temperatures to induce Maillard and caramelization reactions on the meat's surface, resulting in an appealing color and aroma. In recent years, a combined braising and roasting process has gained increasing attention. This technique integrates the advantages of both methods, improving the product's flavor profile while enhancing its water retention and textural properties.

[0003] Currently, in the industrial production of combined braising and roasting processes, operators typically adjust parameters such as braising temperature, braising time, roasting temperature, and roasting time through repeated experiments based on the condition of the raw meat and the quality requirements of the target product, until the product quality meets expectations. This traditional method suffers from two main problems: firstly, the complex interactions between braising and roasting parameters make it difficult for single-factor experiments to fully capture the synergistic effects of multiple parameter changes on the final quality, limiting the efficiency and accuracy of process optimization; secondly, the basic physicochemical properties of raw meat vary between batches, and traditional methods cannot dynamically adjust process parameters according to raw material characteristics, leading to significant fluctuations in product quality and hindering a stable transition from laboratory trials to industrial production.

[0004] Therefore, how to improve the efficiency of process optimization and ensure the stability of product quality is an important issue that the industry urgently needs to address. Summary of the Invention

[0005] This invention provides a method and system for optimizing the industrial conversion of meat product braising and roasting processes, which solves the industry pain points in the prior art, such as the difficulty in quantifying the coupling effect of process parameters, obtaining the optimal solution of process parameters, and long processing time, and realizes the leap from subjective experience to digital intelligence in process design and parameter setting.

[0006] This invention provides a method for optimizing the industrial conversion of meat product braising and roasting processes, comprising the following steps: Acquire basic physicochemical index data of raw meat and preset quality characteristics of target meat products; the basic physicochemical index data are the inherent physicochemical properties of the raw meat itself before processing; The basic physicochemical index data and the preset quality characteristics are input into a pre-constructed multidimensional mapping relationship model, which is used to characterize the correlation between raw material properties, process conditions and finished product quality. Based on the output of the multidimensional mapping relationship model, the optimal combination of braising and roasting process parameters is generated. The raw meat is processed based on the optimal combination of braising and roasting process parameters to obtain the meat product to be verified. The quality characteristics of the meat product to be verified are verified. When the verification results meet the preset conditions, the final process scheme is determined according to the optimal combination of braising process parameters and roasting process parameters.

[0007] According to the present invention, a method for optimizing the industrial conversion of braising and roasting processes for meat products involves obtaining process parameters, including braising temperature, braising time, roasting temperature, and roasting time; using fuzzy mathematical sensory evaluation as an evaluation index, conducting single-factor optimization experiments on each process parameter to determine the suitable level range for each parameter; based on the suitable level range, designing a multi-factor, multi-level experimental scheme using response surface methodology to prepare multiple samples of roasted and braised chicken; constructing a sensory evaluation model based on fuzzy mathematical comprehensive evaluation method, determining the evaluation factor set, comment set, and weight set, and conducting sensory evaluation on each sample of roasted and braised chicken to obtain sensory score data; using the process parameters as independent variables and the sensory score data as dependent variables, fitting a quadratic multinomial regression equation to establish a response surface regression model of the process parameters and sensory score data; and solving and analyzing the response surface regression model to obtain the optimal combination of process parameters that achieves the optimal sensory score data.

[0008] According to the present invention, a method for optimizing the industrial conversion of braising and roasting processes for meat products includes conducting single-factor optimization experiments on the process parameters to determine the suitable level range of each process parameter. Specifically, this includes: processing multiple single-factor test samples of roasted and braised chicken at different braising temperatures, different braising times, different roasting temperatures, and different roasting times; performing sensory evaluation on each single-factor test sample of roasted and braised chicken using a fuzzy mathematical comprehensive evaluation method to obtain single-factor sensory score data corresponding to each process parameter; and determining the suitable level range of each process parameter based on the changing trend of the single-factor sensory score data with each process parameter.

[0009] According to the present invention, a method for optimizing the industrial conversion of meat product braising and roasting processes is provided. The method involves constructing a sensory evaluation model based on fuzzy mathematics comprehensive evaluation, determining an evaluation factor set, a comment set, and a weight set, and conducting sensory evaluations on each sample of roasted and braised chicken to obtain sensory score data. Specifically, this includes: determining color, texture, aroma, and taste as sensory evaluation indicators and constructing an evaluation factor set; assigning weights to each sensory evaluation indicator using a forced decision method and constructing a weight set; determining a comment set containing four evaluation levels (excellent, good, average, and poor) and assigning corresponding scores to each evaluation level; counting the number of votes for each sample of roasted and braised chicken belonging to each evaluation level on each sensory evaluation indicator and constructing a fuzzy relation matrix; performing a synthesis operation between the weight set and the fuzzy relation matrix to obtain a fuzzy comprehensive evaluation result, and calculating sensory score data based on the scores of the comment set.

[0010] According to the present invention, a method for optimizing the industrial conversion of braising and roasting processes for meat products is provided. The method involves designing a multi-factor, multi-level experimental scheme using response surface methodology based on a suitable level range, and preparing multiple sample roasted and braised chickens. Specifically, this includes: constructing a four-factor, three-level response surface experimental design scheme with braising temperature, braising time, roasting temperature, and roasting time as independent variables, and with the upper limit, lower limit, and center value of the suitable level range as three levels; and processing multiple sample roasted and braised chickens for response surface experiments under various combinations of process parameters according to the response surface experimental design scheme.

[0011] According to the present invention, a method for optimizing the industrial conversion of meat product braising and roasting processes, after obtaining the optimal combination of process parameters that achieves the optimal sensory score data, the method further includes: processing a verification sample of roasted and braised chicken according to the optimal combination of process parameters; performing a sensory evaluation on the verification sample of roasted and braised chicken using a fuzzy mathematical comprehensive evaluation method to obtain verification sensory score data; comparing the verification sensory score data with the optimal sensory score predicted by the response surface regression model, and if the error between the two is within a preset range, then confirming that the optimal combination of process parameters is effective.

[0012] According to the present invention, a method for optimizing the industrial conversion of meat product braising and roasting processes, after obtaining the optimal combination of process parameters that achieves the optimal sensory score data, the method further includes: processing the optimized roasted and braised chicken according to the optimal combination of process parameters, and comparing its quality with that of a control roasted chicken processed using traditional roasting techniques; the quality comparison includes: detecting and comparing at least one physicochemical indicator among the yield, moisture content, and color parameters of the two; and detecting and comparing at least one textural characteristic indicator among the hardness, elasticity, and chewiness of the two.

[0013] According to the present invention, a method for optimizing the industrial conversion of meat product braising and roasting processes includes the following quality comparison: detecting and comparing the moisture distribution indicators of the two processes, the moisture distribution indicators including the proportion of bound water, the proportion of non-flowing water, the proportion of free water, and the uniformity of moisture distribution based on low-field nuclear magnetic resonance; detecting and comparing the microstructure indicators of the two processes, the microstructure indicators including the arrangement of muscle fibers and the interfibrillary spaces observed by scanning electron microscopy, and the sarcomere length observed by transmission electron microscopy; and detecting and comparing the flavor characteristic indicators of the two processes, the flavor characteristic indicators including the response characteristics of volatile flavor substances detected by electronic nose, and the content of key aroma compounds determined by gas chromatography-mass spectrometry analysis combined with odor activity value calculation.

[0014] According to the present invention, a method for optimizing the industrial conversion of braising and roasting processes for meat products involves generating an optimal combination of braising and roasting process parameters, then inputting the basic physicochemical index data of the raw meat and the optimal braising and roasting process parameters into a quality prediction model to obtain quality characteristics. The quality prediction model is trained based on a training set, which includes the basic physicochemical index data of the raw meat, the braising and roasting process parameters, and the corresponding quality characteristic labels.

[0015] This invention also provides a system for optimizing the industrial conversion of meat product braising and roasting processes, comprising the following modules: The data acquisition module is used to acquire the basic physicochemical index data of raw meat and the preset quality characteristics of the target meat product; the basic physicochemical index data are the inherent physicochemical properties of the raw meat itself before processing. The data input module is used to input the basic physicochemical index data and the preset quality characteristics into a pre-constructed multidimensional mapping relationship model. The multidimensional mapping relationship model is used to characterize the correlation between raw material properties, process conditions and finished product quality. The parameter output module is used to generate the optimal combination of braising and roasting process parameters based on the output of the multidimensional mapping relationship model. The processing module is used to process the raw meat based on the optimal combination of braising and roasting process parameters to obtain the meat product to be verified. The quality verification module is used to verify the quality characteristics of the meat product to be verified. When the verification result meets the preset conditions, the final process scheme is determined according to the optimal combination of braising process parameters and roasting process parameters.

[0016] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method for optimizing the industrial conversion of meat product braising and roasting processes as described above.

[0017] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method for optimizing the industrial conversion of meat product braising and roasting processes as described above.

[0018] The present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the method for optimizing the industrial conversion of meat product braising and roasting processes as described above.

[0019] This invention provides a method and system for optimizing the industrial conversion of braising and roasting processes for meat products, offering the following advantages: By acquiring basic physicochemical data of raw meat and preset quality characteristics of the target meat product, these are input into a multi-dimensional mapping model. The model comprehensively analyzes the raw material characteristics and quality objectives, automatically generating optimal combinations of braising and roasting process parameters. This transforms the traditional process determination process, which relies on repeated adjustments based on manual experience, into a data-driven intelligent generation process, significantly improving the efficiency and accuracy of process parameter determination. Furthermore, by processing and verifying the generated process parameter combinations, the final process scheme is determined only when the verification results meet preset conditions, forming a closed-loop mechanism of "generation-verification-determination." This ensures both rapid generation of process parameters and the practical feasibility of the process scheme, effectively solving the problem of parameter adaptability during the conversion from laboratory-scale processes to industrial production, and improving the success rate of process conversion and the stability of product quality. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0021] Figure 1 This is one of the flowcharts illustrating the method for optimizing the industrial conversion of meat product braising and roasting processes provided by this invention.

[0022] Figure 2 This is the second flowchart of the method for optimizing the industrial conversion of meat product braising and roasting processes provided by the present invention.

[0023] Figure 3 This is the core logical framework diagram of the "raw materials-process-product" multidimensional mapping relationship model provided by the present invention.

[0024] Figure 4 This is a schematic diagram illustrating the influence of various factors provided by the present invention on the sensory score of roasted and braised chicken.

[0025] Figure 5 These are contour plots and response surface curves illustrating the interaction between brining temperature and brining time, provided by this invention.

[0026] Figure 6 These are contour plots and response surface curves illustrating the interaction between brining temperature and baking temperature, provided by this invention.

[0027] Figure 7 These are contour plots and response surface curves illustrating the interaction between brining temperature and baking time, provided by this invention.

[0028] Figure 8 These are contour plots and response surface curves showing the interaction between braising time and baking temperature provided by this invention.

[0029] Figure 9 These are contour plots and response surface curves showing the interaction between braising time and baking time provided by this invention.

[0030] Figure 10 These are contour plots and response surface curves showing the interaction between baking time and baking temperature provided by this invention.

[0031] Figure 11 This is a sensory radar image of roasted braised chicken and roasted chicken provided by the present invention.

[0032] Figure 12 The present invention provides an electronic nose radar map and principal component analysis diagram of roasted and braised chicken and roasted chicken.

[0033] Figure 13 The present invention provides nuclear magnetic resonance imaging of roasted braised chicken and roasted chicken.

[0034] Figure 14 This is a transverse relaxation time diagram of roasted and braised chicken and roasted chicken provided by the present invention.

[0035] Figure 15 This is a SEM image provided by the present invention, showing the transverse microstructure of muscle tissue and muscle fibers of roasted and braised chicken and roasted chicken.

[0036] Figure 16 This is an ultrastructural image (8.0K×) of the myofibril bundles of roasted and braised chicken and roasted chicken provided by the present invention.

[0037] Figure 17This is a schematic diagram of the system for optimizing the industrial conversion of meat product braising and roasting processes provided by the present invention.

[0038] Figure 18 This is a schematic diagram of the structure of the electronic device provided by the present invention. Detailed Implementation

[0039] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0040] The following is combined Figures 1-18 The embodiments of the present invention are described in detail.

[0041] The method for optimizing the industrial conversion of meat product braising and roasting processes provided in this embodiment of the invention is executed by a system for optimizing the industrial conversion of meat product braising and roasting processes. This system can be configured in a computer, which can be a local computer or a cloud computer. The local computer can be a computer, tablet, etc., and no specific limitation is made here.

[0042] Figure 1 This is one of the flowcharts illustrating the method for optimizing the industrial conversion of meat product braising and roasting processes provided by this invention, such as... Figure 1 As shown, the method includes the following steps: S110. Obtain the basic physicochemical index data of the raw meat and the preset quality characteristics of the target meat product. The basic physicochemical index data refers to the inherent physicochemical properties of the raw meat itself before processing.

[0043] S120. Input the basic physicochemical index data and preset quality characteristics into the pre-constructed multidimensional mapping relationship model. The multidimensional mapping relationship model is used to characterize the correlation between raw material properties, process conditions and finished product quality.

[0044] S130. Based on the output of the multidimensional mapping relationship model, generate the optimal combination of braising and roasting process parameters.

[0045] S140. Based on the optimal combination of braising and roasting process parameters, the raw meat is processed to obtain the meat product to be verified.

[0046] S150. Verify the quality characteristics of the meat product to be verified. When the verification results meet the preset conditions, determine the final process scheme based on the optimal combination of braising and roasting process parameters.

[0047] Specifically, such as Figure 2 The diagram shown is the second flowchart illustrating the method for optimizing the industrial conversion of braising and roasting processes for meat products. This process begins with inputting basic physicochemical indicators of chicken and the target quality of the braised / roasted product. It then invokes a pre-built multi-dimensional mapping model of "raw materials-process-product," intelligently outputting the optimal "braising × roasting" solution based on this model. After obtaining the optimal solution, the process enters the quality verification stage, sequentially judging the textural characteristics, color characteristics, aroma characteristics, and taste characteristics of the sample. When a quality characteristic fails to meet the target requirements, the process returns to the model invocation stage to re-optimize the solution; when all quality characteristics meet the target requirements, the process ends, completing the process optimization. This decision-making process, through a closed-loop mechanism of "generation-verification-feedback," achieves intelligent determination and dynamic adjustment of the process parameters for braised / roasted products.

[0048] It should be noted that the basic physicochemical index data of this invention refer to the inherent physicochemical properties of the muscle tissue of the raw meat before any braising or roasting processing. These inherent physicochemical properties exist independently of subsequent processing techniques and reflect the quality characteristics of the raw meat itself, rather than the quality test results of the processed product.

[0049] Basic physicochemical indicators include, but are not limited to, one or more of the following: moisture content, fat content, protein content, pH value, and textural properties of the raw meat. These indicators are obtained through conventional physicochemical analysis methods after the raw meat has been thawed, trimmed, and before being added to the braising process.

[0050] This invention emphasizes the inherent properties of raw meat before processing because raw meat from different batches and sources exhibits objective differences in its inherent physicochemical properties (for example, chickens from different feeding cycles may have significantly different moisture content and pH values). These inherent differences directly affect the penetration efficiency of flavor substances, the degree of protein denaturation, and moisture retention during subsequent braising and roasting processes, thus determining the quality of the final product. Therefore, only by using the inherent physicochemical properties of the raw meat before processing as model input can truly adaptive process control for different batches of raw materials be achieved.

[0051] Specifically, taking the processing of Xinjiang roasted and braised chicken as an example, the basic physicochemical indicators of the raw meat and the preset quality characteristics of the target meat product are first obtained. The raw meat is selected from 55-day-old Liangfenghua hens, which are tumbled and marinated before being used as prepared chicken. The basic physicochemical indicators of this prepared chicken are obtained, including moisture content, fat content, protein content, and pH value. Simultaneously, based on the quality requirements of the target product, roasted and braised chicken, preset quality characteristics are determined. These preset quality characteristics include color characteristics, texture characteristics, aroma characteristics, and taste characteristics, and the expected level of each quality characteristic is set. Subsequently, the aforementioned basic physicochemical indicators and preset quality characteristics are input into a pre-constructed multidimensional mapping relationship model.

[0052] This multidimensional mapping relationship model is constructed using fuzzy mathematical comprehensive evaluation method and response surface methodology, such as... Figure 3 This diagram showcases the core logical framework for constructing a multi-dimensional mapping model of "raw materials-process-product". The left side of the diagram illustrates the construction of a database of basic physicochemical indicators for raw meat, covering multi-dimensional raw material attributes that affect product quality, including basic indicators such as raw meat variety, fat content, protein content, moisture content, pH value, and textural properties. The middle section shows the setting of "braised × roasted" process parameters, including key process conditions such as braising time, braising temperature, roasting time, and roasting temperature, as well as formula parameters such as spice types and liquid ratios. The right side of the diagram represents the use of color, texture, aroma, and taste as evaluation dimensions to quantify the overall sensory quality of the product.

[0053] Based on the output of the multidimensional mapping model, the optimal combination of braising and roasting process parameters is generated. Specifically, by solving the model, the combination of process parameters that maximizes the fuzzy sensory score is obtained, which is the optimal combination of braising and roasting process parameters. This combination includes braising temperature, braising time, roasting temperature, and roasting time. Then, the prepared chicken is braising according to the generated braising temperature and time. After braising, it is drained and cooled, and then roasted according to the generated roasting temperature and time to obtain the roasted and braised chicken to be verified. Finally, a trained sensory evaluation team evaluates the color, texture, aroma, and taste of the roasted and braised chicken according to preset sensory evaluation standards, obtaining a sensory score. When the sensory score meets the preset threshold condition, the optimal combination of braising and roasting process parameters is confirmed to have passed verification and is determined as the final process scheme.

[0054] This embodiment improves the efficiency of process parameter determination by inputting the basic physicochemical index data of raw meat and the preset quality characteristics of the target meat product into a pre-constructed multi-dimensional mapping model. This is achieved by shifting process parameter adjustment from manual experience to data-driven intelligent generation. Furthermore, after generating the optimal combination of process parameters, this embodiment forms a closed loop through actual processing and sensory verification, ensuring the feasibility of the generated process parameters and the stability of product quality. This effectively solves the problem of difficulty in guaranteeing parameter adaptability during the conversion from laboratory processes to industrial production.

[0055] According to the present invention, a method for optimizing the industrial conversion of braising and roasting processes for meat products is provided. The method involves obtaining process parameters, including braising temperature, braising time, roasting temperature, and roasting time; using fuzzy mathematical sensory evaluation as an evaluation index, conducting single-factor optimization experiments on each process parameter to determine the suitable level range for each parameter; based on the suitable level range, designing a multi-factor, multi-level experimental scheme using response surface methodology to prepare multiple samples of roasted and braised chicken; constructing a sensory evaluation model based on fuzzy mathematical comprehensive evaluation method, determining the evaluation factor set, comment set, and weight set, and conducting sensory evaluation on each sample of roasted and braised chicken to obtain sensory score data; using process parameters as independent variables and sensory score data as dependent variables, fitting a quadratic multinomial regression equation to establish a response surface regression model of process parameters and sensory score data; and solving and analyzing the response surface regression model to obtain the optimal combination of process parameters that achieves the optimal sensory score data.

[0056] Specifically, Liangfenghua hen-based prepared chicken, marinated and tumbled, was selected as the raw material. First, the process parameters to be optimized were determined to be braising temperature, braising time, roasting temperature, and roasting time. In the single-factor optimization experiment stage, the impact of each parameter on the sensory quality of the product was investigated: with other parameters fixed, the braising temperature was set to five levels: 80, 85, 90, 95, and 100℃; with other parameters fixed, the braising time was set to five levels: 15, 20, 25, 30, and 35 minutes; with other parameters fixed, the roasting temperature was set to five levels: 100, 125, 150, 175, and 200℃; with other parameters fixed, the roasting time was set to five levels: 20, 25, 30, 35, and 40 minutes. After the samples for each single-factor test were prepared, color, texture, odor, and taste were used as evaluation indicators. The forced decision method was used to assign weights to each indicator (color 0.15, texture 0.3, odor 0.3, taste 0.25), and an evaluation factor set and weight set were constructed. Four evaluation levels of excellent, good, medium, and poor and their corresponding scores (100, 80, 60, and 40 points) were determined, and a comment set was constructed. Twenty trained professional evaluators were organized to conduct sensory evaluations of each sample. The number of votes for each indicator at each level was counted to construct a fuzzy relation matrix. The weight set and the fuzzy relation matrix were combined to obtain the fuzzy comprehensive evaluation result, and then the fuzzy mathematical sensory score of each sample was calculated.

[0057] Based on the results of the single-factor experiments, sensory scores were plotted as a function of each process parameter to determine the appropriate range for each parameter: braising temperature 85-95℃, braising time 20-30 min, roasting temperature 125-175℃, and roasting time 25-35 min. Based on these appropriate ranges, a four-factor, three-level response surface methodology was designed using Design-Expert software, with braising temperature, braising time, roasting temperature, and roasting time as independent variables, and the upper, lower, and central values ​​of each appropriate range as three levels. A total of 29 combinations of process parameters were obtained, and sample roasted and braised chickens were prepared according to each parameter combination.

[0058] Using the same fuzzy mathematical sensory evaluation model as the single-factor experiment, sensory evaluation was conducted on 29 groups of response interview samples, obtaining sensory score data for each group. With braising temperature, braising time, baking temperature, and baking time as independent variables, and sensory score as the dependent variable, a multiple regression was performed on the experimental data, resulting in a quadratic multinomial regression equation. Solving this regression equation revealed the optimal combination of process parameters for achieving the best sensory score: braising temperature 91.144℃, braising time 25.847 min, baking temperature 153.604℃, and baking time 31.312 min. For ease of production implementation, the parameters were adjusted to braising temperature 90℃, braising time 25 min, baking temperature 150℃, and baking time 30 min. A verification experiment was conducted under these conditions, yielding a sensory score of 82.67 ± 1.76 points, which is basically consistent with the model's predicted value.

[0059] This embodiment systematically examines the coupling effect between multiple process parameters by organically combining single-factor experiments and response surface methodology. It also uses fuzzy mathematics comprehensive evaluation method to transform subjective sensory evaluation into objective quantitative data. Based on this, a response surface regression model between process parameters and sensory scores is established, which can scientifically and accurately solve for the combination of process parameters that achieves the optimal sensory quality. This effectively overcomes the technical limitations of traditional experience-based trial-and-error methods or single-factor optimization methods in taking into account the interaction of multiple parameters, and significantly improves the scientificity and reliability of the optimization of roasted and braised chicken processing technology.

[0060] According to the present invention, a method for optimizing the industrial conversion of meat product braising and roasting processes involves conducting single-factor optimization experiments on process parameters to determine the suitable level range of each process parameter. Specifically, this includes: processing multiple single-factor test samples of roasted and braised chicken at different braising temperatures, different braising times, different roasting temperatures, and different roasting times; conducting sensory evaluations of each single-factor test sample of roasted and braised chicken using a fuzzy mathematical comprehensive evaluation method to obtain single-factor sensory score data corresponding to each process parameter; and determining the suitable level range of each process parameter based on the changing trend of the single-factor sensory score data with each process parameter.

[0061] Specifically, in the single-factor optimization experiment stage, marinated chicken after tumbling was selected as the raw material, and the effects of four process parameters—braising temperature, braising time, roasting temperature, and roasting time—on the sensory quality of the product were investigated. For the single-factor experiment on braising temperature, the braising time was fixed at 25 min, the roasting temperature at 150℃, and the roasting time at 30 min. The braising temperature was set at five levels: 80, 85, 90, 95, and 100℃. After braising, the chicken was drained and cooled before roasting, resulting in single-factor experimental samples of roasted chicken corresponding to different braising temperatures. For the single-factor experiment on braising time, the braising temperature was fixed at 90℃, the roasting temperature at 150℃, and the roasting time at 30 min. The braising time was set at five levels: 15, 20, 25, 30, and 35 min. Subsequent steps were the same as above, resulting in single-factor experimental samples of roasted chicken corresponding to different braising times. Single-factor experiments were conducted on roasting temperature. With a fixed braising temperature of 90℃, a braising time of 25 min, and a roasting time of 30 min, roasting temperatures were set at five levels: 100, 125, 150, 175, and 200℃. This yielded single-factor experimental samples of roasted braised chicken corresponding to different roasting temperatures. Similarly, single-factor experiments were conducted on roasting time. With a fixed braising temperature of 90℃, a braising time of 25 min, and a roasting temperature of 150℃, roasting times were set at five levels: 20, 25, 30, 35, and 40 min. This yielded single-factor experimental samples of roasted braised chicken corresponding to different roasting times.

[0062] After the preparation of samples for each single-factor experiment, a fuzzy mathematical comprehensive evaluation method was used to conduct sensory evaluation of each sample. Color, texture, odor, and taste were used as evaluation indicators. A forced decision method was used to assign weights to each indicator, constructing an evaluation factor set U={color, texture, odor, taste} and a corresponding weight set Q={0.15, 0.30, 0.30, 0.25}. Four evaluation levels—excellent, good, average, and poor—were determined, and a comment set P={excellent, good, average, poor} was constructed, with corresponding scores P={100, 80, 60, 40} assigned to each level. Twenty trained professional evaluators conducted sensory evaluations of each sample, counting the number of votes for each evaluation level for each indicator, and constructing a fuzzy relation matrix. The weight set and the fuzzy relation matrix were combined to obtain the fuzzy comprehensive evaluation result, which was then combined with the scores from the comment set to calculate the fuzzy mathematical sensory score for each sample. Based on the sensory score data corresponding to different levels of each process parameter, a trend curve of sensory scores changing with each process parameter was plotted. The trend curves show that as the braising temperature increases, the sensory score first rises and then falls, reaching a peak at 90℃; as the braising time increases, the sensory score first rises and then falls, reaching a peak at 25 minutes; as the roasting temperature increases, the sensory score first rises and then falls, reaching a peak at 150℃; and as the roasting time increases, the sensory score first rises and then falls, reaching a peak at 30 minutes. Based on these trends, the suitable ranges for each process parameter are determined as follows: braising temperature 85-95℃, braising time 20-30 minutes, roasting temperature 125-175℃, and roasting time 25-35 minutes.

[0063] This embodiment obtains single-factor test samples by processing them at different levels of brining temperature, brining time, baking temperature, and baking time. Fuzzy mathematical comprehensive evaluation method is used to quantitatively evaluate each sample and obtain the trend of sensory score changes with each process parameter. It can accurately identify the independent influence of each parameter on the sensory quality of the product, thereby scientifically determining the appropriate level range of each process parameter. This provides a reliable basis for parameter selection for subsequent multi-factor response surface optimization experiments and effectively avoids the technical problem of poor optimization effect caused by improper parameter range selection.

[0064] According to the present invention, a method for optimizing the industrial conversion of meat product braising and roasting processes is provided. Based on the fuzzy mathematical comprehensive evaluation method, a sensory evaluation model is constructed, determining the evaluation factor set, the comment set, and the weight set. Sensory evaluation is then performed on each sample of roasted and braised chicken to obtain sensory score data. Specifically, the method includes: determining color, texture, aroma, and taste as sensory evaluation indicators and constructing the evaluation factor set; assigning weights to each sensory evaluation indicator using a forced decision method and constructing the weight set; determining the comment set containing four evaluation levels (excellent, good, average, and poor) and assigning corresponding scores to each evaluation level; counting the number of votes for each sample of roasted and braised chicken belonging to each evaluation level on each sensory evaluation indicator and constructing a fuzzy relation matrix; and performing a synthesis operation between the weight set and the fuzzy relation matrix to obtain the fuzzy comprehensive evaluation result, and calculating the sensory score data based on the scores of the comment set.

[0065] Specifically, when constructing a sensory evaluation model using the fuzzy mathematics comprehensive evaluation method, color, texture, aroma, and taste are first determined as sensory evaluation indicators for roasted and braised chicken, constructing an evaluation factor set U={color, texture, aroma, taste}. Using a forced decision method, 20 trained professional evaluators are organized to assess the importance of each evaluation indicator. Based on the contribution of each indicator to the overall sensory quality, weights are assigned to color, texture, aroma, and taste, constructing a weight set Q={0.15, 0.30, 0.30, 0.25}, where the sum of all weights is 1. Four evaluation levels—excellent, good, average, and poor—are determined, constructing a comment set P={excellent, good, average, poor}, and assigning corresponding scores to each evaluation level: excellent corresponds to 100 points, good to 80 points, average to 60 points, and poor to 40 points, forming a score vector V={100, 80, 60, 40} corresponding to the comment set.

[0066] For each sample of roasted and braised chicken to be evaluated, the aforementioned 20 evaluators independently evaluated the chicken based on four indicators: color, texture, aroma, and taste. For each indicator, they selected one of four grades: excellent, good, average, or poor. The number of votes for each grade on each indicator for each sample was counted. For example, for a given sample, 8 people rated the color as excellent, 7 as good, 5 as average, and 0 as poor; for texture, 9 people rated it as excellent, 6 as good, 5 as average, and 0 as poor; for aroma, 7 people rated it as excellent, 8 as good, 4 as average, and 2 as poor; and for taste, 7 people rated it as excellent, 8 as good, 5 as average, and 0 as poor. The number of votes for each indicator was divided by the total number of evaluators (20) to obtain the membership degree of each indicator at each grade, and a fuzzy relation matrix R was constructed for this sample.

[0067] The weight set Q and the fuzzy relation matrix R are combined, i.e., the fuzzy comprehensive evaluation result vector G=QR is obtained by multiplying the fuzzy relation matrix. Taking this sample as an example, G=[0.3825, 0.3625, 0.2325, 0.0225]. This result vector indicates that, from a comprehensive perspective, the membership degree of this sample belonging to "Excellent" is 0.3825, belonging to "Good" is 0.3625, belonging to "Medium" is 0.2325, and belonging to "Poor" is 0.0225. Finally, the fuzzy comprehensive evaluation result vector G is multiplied by the score vector V of the comment set to calculate the final sensory score data for this sample, i.e., S = G·V^T = 0.3825×100 + 0.3625×80 + 0.2325×60 + 0.0225×40 = 83.05 points, where T is the matrix transpose operator. Following the above method, the sensory score data for all samples of roasted and braised chicken are calculated one by one.

[0068] This embodiment uses color, texture, smell, and taste as evaluation indicators and assigns weights using a forced decision method. It constructs a comment set containing four levels: excellent, good, average, and poor, along with their corresponding scores. Then, it constructs a fuzzy relation matrix by statistically analyzing the number of votes for each sample on each indicator. Finally, it synthesizes the weight set and the fuzzy relation matrix to obtain the fuzzy comprehensive evaluation result. Based on the score of the comment set, it calculates the quantitative sensory score, thus transforming the subjective and vague sensory perceptions of multiple evaluators into objective and accurate scoring data. This effectively eliminates the subjective bias of a single evaluator and significantly improves the scientificity and repeatability of the sensory evaluation results.

[0069] According to the present invention, a method for optimizing the industrial conversion of braising and roasting processes for meat products is provided. Based on a suitable level range, a multi-factor, multi-level experimental scheme is designed using response surface methodology to prepare multiple sample roasted and braised chickens. Specifically, the method includes: constructing a four-factor, three-level response surface experimental design scheme with braising temperature, braising time, roasting temperature, and roasting time as independent variables, and with the upper limit, lower limit, and center value of the suitable level range as three levels; and processing multiple sample roasted and braised chickens for response surface experiments under various combinations of process parameters according to the response surface experimental design scheme.

[0070] Specifically, after completing the single-factor optimization experiment and determining the suitable level range of each process parameter, a response surface methodology was conducted based on the determined suitable level range. According to the results of the single-factor experiment, the suitable level range for brining temperature was determined to be 85-95℃, the suitable level range for brining time was 20-30 min, the suitable level range for baking temperature was 125-175℃, and the suitable level range for baking time was 25-35 min. Using braising temperature, braising time, baking temperature, and baking time as independent variables, and the lower limit, center value, and upper limit of each suitable level range as low, medium, and high levels respectively, factor levels were coded: the three levels of braising temperature were 85℃, 90℃, and 95℃, corresponding to coded values ​​of -1, 0, and 1; the three levels of braising time were 20min, 25min, and 30min, corresponding to coded values ​​of -1, 0, and 1; the three levels of baking temperature were 125℃, 150℃, and 175℃, corresponding to coded values ​​of -1, 0, and 1; and the three levels of baking time were 25min, 30min, and 35min, corresponding to coded values ​​of -1, 0, and 1. Using Design-Expert software, a four-factor, three-level response surface methodology was selected to generate a response surface methodology table containing 29 experimental schemes, including 24 factorials and 5 center point replicates.

[0071] Based on the aforementioned 29 combinations of process parameters, response surface methodology (RSM) test samples of roasted and braised chicken were prepared. Using marinated and tumbled chicken as raw material, for each test scheme, the chicken was braised at the specified temperature and time. After braising, the chicken was hung to drain and allowed to cool naturally to room temperature. Then, it was roasted at the specified temperature and time, resulting in the RSM test sample of that scheme. This process was repeated for all 29 combinations of process parameters, yielding 29 RSM test samples of roasted and braised chicken for subsequent sensory evaluation and model fitting.

[0072] This embodiment constructs a four-factor, three-level response surface methodology by using the upper limit, lower limit, and center value of the appropriate level range determined by single-factor experiments as three levels. Multiple response surface experiment samples of roasted and braised chicken are prepared according to the design scheme. This allows for a systematic examination of the interaction between four process parameters on product quality with fewer experiments, providing a reliable sample data foundation for establishing an accurate response surface regression model. It effectively avoids the problems of long experimental cycles and high costs associated with comprehensive experimental methods, and significantly improves the efficiency of process parameter optimization.

[0073] According to the present invention, a method for optimizing the industrial conversion of meat product braising and roasting processes, after obtaining the optimal combination of process parameters that achieves the optimal sensory score data, further includes: processing a verification sample of roasted and braised chicken according to the optimal combination of process parameters; performing a sensory evaluation on the verification sample of roasted and braised chicken using a fuzzy mathematical comprehensive evaluation method to obtain verification sensory score data; comparing the verification sensory score data with the optimal sensory score predicted by the response surface regression model, and if the error between the two is within a preset range, then confirming that the optimal combination of process parameters is effective.

[0074] Specifically, after solving the response surface regression model to obtain the optimal combination of process parameters, the optimization results were experimentally verified. The optimal combination of process parameters obtained from the response surface model was: braising temperature 91.144℃, braising time 25.847 min, roasting temperature 153.604℃, and roasting time 31.312 min. Considering the convenience of actual production operations, the above parameter combination was modified to: braising temperature 90℃, braising time 25 min, roasting temperature 150℃, and roasting time 30 min. Using the modified optimal combination of process parameters, marinated chicken was used as raw material and braised at 90℃ for 25 min. After braising, the chicken was hung to drain and allowed to cool naturally to room temperature, then roasted at 150℃ for 30 min to obtain the verification sample of roasted and braised chicken.

[0075] The sensory evaluation of the verification sample was conducted using the same fuzzy mathematical comprehensive evaluation method as in previous experiments. Color, texture, odor, and taste were used as evaluation indicators, and a weight set Q = {0.15, 0.30, 0.30, 0.25} was determined using a forced decision method. Evaluation levels were assigned as Excellent, Good, Average, and Poor, with corresponding scores of 100, 80, 60, and 40 points, respectively. Twenty trained professional evaluators conducted the sensory evaluation of the verification sample, counting the number of votes for each indicator at each level, constructing a fuzzy relation matrix, and synthesizing the weight set and the fuzzy relation matrix to obtain the fuzzy comprehensive evaluation result. This result was then combined with the scores from the comment set to calculate the sensory score data for the verification sample. The calculated sensory score for the verification sample was 82.67 points.

[0076] The validation sensory score data was compared with the optimal sensory score predicted by the response surface regression model. The error between the optimal sensory score predicted by the model and the actual sensory score obtained from the validation experiment was small, and the two were basically in agreement. Based on the preset error range, the optimal process parameter combination was confirmed to be effective, that is, the optimal process parameter combination obtained by the response surface method has reliability and repeatability, and can be used to guide actual production.

[0077] This embodiment obtains a verification sample of roasted and braised chicken by processing it according to the optimal combination of process parameters, and conducts a sensory evaluation on it using the same fuzzy mathematical comprehensive evaluation method as when building the model. The obtained verification sensory scores are compared with the model prediction values ​​to verify the reliability and accuracy of the response surface optimization results, confirm the effectiveness of the optimal combination of process parameters, and thus provide a reliable basis for subsequent industrial production. This effectively avoids the technical risk of unstable product quality that may result from directly applying unverified optimization results to production.

[0078] According to the present invention, a method for optimizing the industrial conversion of meat product braising and roasting processes, after obtaining the optimal combination of process parameters that achieves the optimal sensory score data, further includes: processing optimized roasted and braised chicken according to the optimal combination of process parameters, and comparing its quality with that of control roasted chicken processed using traditional roasting techniques; the quality comparison includes: detecting and comparing at least one physicochemical indicator among the yield, moisture content, and color parameters of the two; detecting and comparing at least one textural characteristic indicator among the hardness, elasticity, and chewiness of the two.

[0079] Specifically, the same batch of marinated chicken was divided into two groups: the first group was marinated and roasted according to the optimized combination of process parameters (marinating temperature 90℃, marinating time 25min, roasting temperature 150℃, roasting time 30min) to obtain the optimized roasted and marinated chicken sample; the second group was roasted according to the traditional roasting process (preheating the oven to 200℃ and roasting at 220℃ for 35min) to obtain the control roasted chicken sample.

[0080] Physicochemical indicators were tested on the two groups of samples. Yield determination: The weight of the pre-treated chicken and the weight after roasting were weighed separately, and the yield was calculated. Moisture content determination: Following the direct drying method in the national food safety standard, the moisture content of chicken breast samples was determined. Colorimetric parameters determination: A colorimeter was used to measure the cross-section of the chicken breast muscle, recording the brightness, redness, and yellowness values. Multiple measurements were taken from different parts of each sample, and the average value was calculated. pH determination: Following the national food safety standard, the crushed sample was weighed, added to potassium chloride solution, and measured using a pH meter.

[0081] Texture properties were tested on two groups of samples. Chicken breast tissue was cut into 1cm × 1cm × 1cm cubes and analyzed using a texture analyzer. The testing conditions were as follows: cylindrical flat-bottom probe; pre-test speed, test speed, and post-test speed set to 1.00mm / s, 5.00mm / s, and 1.00mm / s, respectively; compression distance 10.00mm; test time 5.00s; and deformation 50%. The samples were cut perpendicular to the fiber direction, and texture properties such as hardness, elasticity, and chewiness were recorded. Multiple measurements were taken for each sample, and the average value was calculated.

[0082] Through the above tests, the differences between optimized roasted and braised chicken and traditional roasted chicken in terms of yield, moisture content, color parameters, hardness, elasticity, and chewiness were compared, and the effect of the optimized process on improving product quality was evaluated.

[0083] This embodiment obtains optimized roasted and braised chicken by processing according to the optimal combination of process parameters, and compares and tests the yield, moisture content, color parameters and other physicochemical indicators, as well as hardness, elasticity and chewiness characteristics of the control roasted chicken processed by traditional roasting process. It can objectively and quantitatively evaluate the actual improvement effect of the optimized process on product quality, verify the superiority of the optimized process parameter combination, and provide reliable quality data support for the industrial application of the optimized process.

[0084] According to the present invention, a method for optimizing the industrial conversion of meat product braising and roasting processes includes quality comparison further comprising: detecting and comparing the moisture distribution indicators of the two processes, including the proportion of bound water, the proportion of non-flowing water, the proportion of free water, and the uniformity of moisture distribution based on low-field nuclear magnetic resonance; detecting and comparing the microstructure indicators of the two processes, including the arrangement of muscle fibers and the intermuscular spaces observed by scanning electron microscopy, and the length of sarcomeres observed by transmission electron microscopy; and detecting and comparing the flavor characteristic indicators of the two processes, including the response characteristics of volatile flavor substances detected by electronic nose, and the content of key aroma compounds determined by gas chromatography-mass spectrometry analysis combined with odor activity value calculation.

[0085] Specifically, after comparing the physicochemical indicators and textural properties of the optimized roasted and braised chicken with those of the traditional roasted chicken, a further in-depth quality comparison of the two was conducted on their moisture distribution, microstructure, and flavor characteristics.

[0086] To compare moisture distribution indicators, low-field nuclear magnetic resonance (NMR) technology was used to detect moisture in two groups of samples. Chicken samples were cut into small cubes, placed in NMR-specific sample tubes, and then placed in the sample cell of a low-field NMR analyzer. The relaxation characteristics of the samples were measured using a Carr-Purcell-Meiboom-Gill pulse sequence. Three NMR peaks appeared in the acquired transverse relaxation time spectrum, corresponding to bound water, immobile water, and free water, respectively. Through inversion processing, the proportions of bound water, immobile water, and free water in both groups of samples were obtained. Proton density-weighted images of the samples were then acquired using NMR imaging, and the uniformity of moisture distribution was compared between the two groups by examining the distribution uniformity of the red areas in the images.

[0087] To compare the microstructural parameters, scanning electron microscopy (SEM) and transmission electron microscopy (TEM) were used to observe the two groups of samples. For SEM observation, the samples were cut into small cuboid pieces, fixed with glutaraldehyde solution, rinsed with phosphate buffer, dehydrated with a gradient of ethanol, and critically dried. The samples were then mounted on the sample stage with the observation surface facing upwards, ion-sputter coated, and placed under an SEM to observe the cross-sectional morphology of the muscle fibers. The arrangement and interfibrillary spaces of the muscle fibers in the two groups of samples were compared. For TEM observation, the samples were divided into strips along the muscle fiber direction, fixed with glutaraldehyde and osmium tetroxide, dehydrated with acetone, and ultrathin sections were cut. These sections were stained with lead-citrate and placed under a TEM to observe the ultrastructure of myofibrils. The sarcomere length, as well as the clarity and integrity of the I, A, and Z bands, were compared between the two groups of samples.

[0088] To compare flavor characteristics, electronic nose and gas chromatography-mass spectrometry (GC-MS) were used to detect two groups of samples. For electronic nose detection, the crushed sample was weighed, placed in a headspace vial, sealed, and headspace sampling was performed using the electronic nose. The response intensity values ​​of volatile flavor compounds for each sample were obtained from 10 sensors, and radar charts were plotted for comparative analysis. For GC-MS analysis, the crushed chicken sample was accurately weighed, placed in a headspace vial, and the internal standard o-dichlorobenzene was added. After sealing, the vial was placed on a solid-phase microextraction platform for equilibration, and the extraction head was inserted for adsorption. Then, the sample was desorbed at the injection port and separated and identified by GC-MS. The mass spectrometry data were matched with a standard spectral library, and the volatile substances were qualitatively identified by combining the retention index of n-alkanes. The content of each volatile substance was calculated based on the concentration of the internal standard and the peak area. Furthermore, the sensory threshold of each aroma compound was examined, and its odor activity value was calculated. Key aroma compounds with an odor activity value greater than 1 were screened, and the differences in the content of key aroma compounds between the two groups of samples were compared.

[0089] This embodiment uses low-field nuclear magnetic resonance to detect and compare the moisture distribution indicators of the two, scanning electron microscopy and transmission electron microscopy to detect and compare the microstructure indicators of the two, and electronic nose and gas chromatography-mass spectrometry combined with odor activity value calculation to detect and compare the flavor characteristics of the two. It can comprehensively and objectively reveal the quality differences between optimized roasted and braised chicken and traditional roasted chicken from multiple deep dimensions such as moisture state, muscle tissue structure, and volatile flavor composition. It provides multi-dimensional scientific evidence support for the superiority of the optimized process from macro to micro and from physical properties to chemical composition.

[0090] According to the present invention, a method for optimizing the industrial conversion of braising and roasting processes for meat products is provided. After generating the optimal combination of braising and roasting process parameters, the basic physicochemical index data of the raw meat and the optimal braising and roasting process parameters are input into a quality prediction model to obtain quality characteristics. The quality prediction model is trained based on a training set, which includes the basic physicochemical index data of the raw meat, the braising and roasting process parameters, and the corresponding quality characteristic labels.

[0091] Specifically, taking the quality characteristic prediction of Xinjiang roasted and braised chicken as an example, a training set for training the quality prediction model is first constructed. This training set contains multiple sample data, each including basic physicochemical indicators of the raw meat, braising and roasting process parameters, and corresponding quality characteristic labels. The basic physicochemical indicators of the raw meat include fat content, protein content, moisture content, and textural properties, while also recording the variety of the raw meat. The braising process parameters include braising temperature, braising time, braising spice ratio, and braising liquid ratio; the roasting process parameters include roasting temperature and roasting time. The quality characteristic labels include color characteristics, texture characteristics, aroma characteristics, and taste characteristics. These labels are obtained by a trained sensory evaluation team using fuzzy mathematics sensory evaluation methods to assess the actual processed roasted and braised chicken samples. After constructing the training set, the basic physicochemical indicators of the raw meat, braising process parameters, and roasting process parameters are used as input features, and the corresponding quality characteristic labels are used as output targets to train the initial neural network model. After training, the quality prediction model is obtained. In practical applications, the basic physicochemical index data of the raw meat to be predicted, as well as the optimal braising and roasting process parameters obtained through the aforementioned process optimization methods, are input into the quality prediction model. The model outputs the corresponding quality characteristics, thereby achieving a preliminary assessment of product quality.

[0092] This embodiment constructs a training set containing raw material physicochemical indicators, braising and roasting process parameters, and quality characteristic labels. Based on this training set, a quality prediction model is trained, enabling rapid prediction of product quality under given raw material and process conditions. This quality prediction model can estimate the product quality corresponding to the optimal process scheme before actual processing, providing data support for the selection and verification of process schemes, reducing the number of physical trials, and improving the overall efficiency of process optimization.

[0093] The method of this application is further illustrated below through complete embodiments.

[0094] This embodiment takes Xinjiang roasted and braised chicken as the research object, constructs a fuzzy mathematical sensory evaluation model, and combines it with response surface methodology (RSM) to optimize its braising and roasting processes. The optimal combination of roasting-braising process parameters is obtained, and the quality of the optimized roasted and braised chicken is compared with that of traditional roasted chicken. Physicochemical analysis, texture determination, low-field nuclear magnetic resonance (LF-NMR), microstructure observation, and gas chromatography-mass spectrometry (GC-MS) are used to analyze the quality differences between the optimized roasted and braised chicken and traditional roasted chicken, verifying the effectiveness of this method in optimizing the roasted and braised chicken process. This provides a feasible technological path and theoretical support for the standardized processing and large-scale distribution of Xinjiang specialty roasted and braised chicken, and has guiding significance for promoting the industrial production of roasted and braised chicken.

[0095] 1. Materials and Methods 1.1 Materials and Instruments The materials include: prepared chicken products; compound spice packet; o-dichlorobenzene and n-alkanes (C7–C40) are of chromatographic grade.

[0096] The instruments included: SQP electronic analytical balance; KQ5200B ultrasonic cleaner; FW-80 high-speed universal grinder; CR-400 colorimeter; TA-XT Plus texture analyzer; DH-101-3BS electric thermostatic drying oven; oven; PEN3 portable electronic nose; 20mL headspace vials; FE-20 pH meter; Agilent 1200 high performance liquid chromatograph; Trace 1310GC / Q-Exactive gas chromatograph-mass spectrometer.

[0097] 1.2 Experimental Methods 1.2.1 Process flow of traditional roasted chicken and Xinjiang roasted and braised chicken (1) Prepared Chicken (full name, C) product: 55-day-old Liangfenghua hens are selected, and their heads, claws and internal organs are removed. After being tumbled and marinated according to the formula, the chickens are vacuum frozen and transported to the laboratory, where they are frozen at -18°C for later use. After the prepared chickens are thawed at 4°C, excess fat is removed from the chickens and they are split open along the breast.

[0098] (2) Traditional roasted chicken (full name, RC) The oven was preheated to 200℃, and then the seasoned chicken was roasted at 220℃ for 35 minutes. This was then used to determine various experimental indicators.

[0099] (3) Xinjiang Roasted Chicken (full name: BRC) Braising: Add a compound spice packet to the pot based on the weight of the meat, with a meat:water:compound spice ratio of 1:1.74:0.0512. Braise at 90℃ for 25 minutes, turning twice to ensure even flavor absorption.

[0100] Cooling: After braising, hang to drain for 30 minutes and allow to cool naturally to room temperature (ambient temperature ≤25℃).

[0101] Baking: Place in an oven at 150℃ and bake for 30 minutes, then remove and cool.

[0102] In this embodiment, edible quality assessments, such as fuzzy mathematical sensory evaluation and descriptive sensory evaluation, were performed using whole chicken samples that had just been roasted. Basic physicochemical indicators and textural quality determinations, such as moisture content, yield, color difference, pH, texture, low-field NMR imaging, moisture distribution, and histological analysis, were all performed on chicken breast sections of freshly roasted samples. For odor determinations, such as electronic nose and GC-MS, the test samples were rapidly pulverized after roasting, flash-frozen in liquid nitrogen, and stored at -80°C for later use.

[0103] 1.2.2 Fuzzy Mathematics Sensory Evaluation Method Referring to GB / T22210-2008 "Sensory Evaluation Standard for Meat and Meat Products", fuzzy mathematical sensory evaluation was conducted on single-factor and response surface evaluation indicators. The sensory evaluation standards for roasted and braised chicken were formulated as shown in Table 1. Twenty graduate students engaged in meat product research were invited to conduct flavor sensory evaluation. Roasted chicken and roasted and braised chicken meat were placed on white porcelain plates and observed in bright light. The color and shape of the chicken meat were observed, and then its aroma, texture and taste were evaluated.

[0104] Table 1 Sensory Evaluation Table for Roasted and Braised Chicken

[0105] Establishment of a Fuzzy Mathematics Sensory Evaluation Model: Referring to existing methods, a fuzzy mathematical sensory evaluation of roasted and braised chicken is conducted. First, the evaluation factor set, comment set, and weight set are established: The evaluation factor set U = {U1, U2, U3, U4}, where U1 to U4 represent the four evaluation factors of color, texture, aroma, and taste, respectively; the comment set P = {P1, P2, P3, P4}, where P1 to P4 represent the four evaluation levels of "Excellent," "Good," "Average," and "Poor," respectively. Each level is assigned a corresponding score: 100 points (P1), 80 points (P2), 60 points (P3), and 40 points (P4), i.e., P={100,80,60,40}; the weight set Q={Q1,Q2,Q3,Q4}={0.15,0.3,0.3,0.25} is determined by the forced decision method. The weights here reflect the importance of each evaluation factor in the overall evaluation, where Q1 to Q4 correspond to the weight allocation of U1 to U4, respectively.

[0106] The number of votes given by 20 evaluators for each factor at each level was counted, and the fuzzy relation matrix R of sensory evaluation of roasted and braised chicken was obtained. j .

[0107] Here, the sample numbers are defined as j=1,2,3,...,n, where n is the number of rows in the matrix and m is the number of columns. Each row (i.e., S) j1 ......S jm The evaluation result for each factor is represented by Z, where Z = Q × R. j .

[0108] 1.2.3 Single-factor optimization experimental design To identify the key processing techniques that significantly affect the quality of roasted and braised chicken, single-factor experiments were conducted on four factors: braising temperature, braising time, roasting temperature, and roasting time. The effects of these factors on the fuzzy mathematical sensory evaluation of roasted and braised chicken were discussed. The experimental factors and levels are shown in Table 2.

[0109] Table 2 Single-factor level table of processing technology

[0110] 1.2.4 Response Surface Optimization Experimental Design Based on the results of the single-factor experiment, and using fuzzy mathematical sensory scores as response values, a four-factor, three-level response surface experimental design was adopted for the four factors of braising temperature, braising time, baking temperature, and baking time. The factor levels are shown in Table 3, and the response surface experimental design is shown in Table 4.

[0111] Table 3 Response Surface Factor Level Coding Table

[0112] Table 4 Response Surface Design

[0113] 1.2.5 Descriptive Sensory Evaluation To explore the quality differences between traditional roasted chicken and roasted braised chicken, a descriptive sensory evaluation was conducted on both according to the GB / T22210-2008 standard. A sensory evaluation panel of 20 members was formed to evaluate the aroma characteristics of the samples. The sensory descriptive terms for the aroma characteristics of the samples are shown in Table 5. Flavor sensory evaluations were also conducted on the optimized roasted braised chicken and traditional roasted chicken. Sensory descriptive terms were categorized as: herbal aroma, spicy aroma, roasted aroma, fatty aroma, meaty aroma, and off-odor (chicken smell). Sensory scores ranged from 0 to 3 points, with intervals of 0.5 points. (0 points indicates the odor is undetectable, and 3 points indicates the odor is extremely strong).

[0114] Table 5. Corresponding references for sensory descriptive terms

[0115] 1.2.6 Electronic nose analysis Following existing methods with slight modifications, 2g of sample was weighed and sealed in a gas chromatography-mass spectrometry vial. The odor in the sample was detected using a PEN3 electronic nose, which incorporates 10 highly sensitive compound detection sensors. The sample was detected via headspace sampling while sealed, using air as the carrier gas at a flow rate of 300 mL / min. The instrument's automatic cleaning time was 180 s, and the sample measurement time was 60 s. For data processing, the response values ​​from 56 s to 59 s were selected for analysis because the sensor signal is relatively stable during this period and can better represent the characteristics of the odor.

[0116] The electronic nose analysis uses the PEN3 electronic nose, which includes W1C, W5S, W3C, W6S, W5C, W1S, WIW, W2S, W2W, and W3S metal oxide gas sensors to detect olfactory sensitivity information. The sensitive substances corresponding to each sensor are shown in Table 6.

[0117] Table 6 Standard Sensor Array and Performance Description for Electronic Nose

[0118] 1.2.7 Qualitative and quantitative analysis of aroma substances and calculation of odor activity value (OAV) SPME conditions: The 50 μm PDMS / DVB extraction head was aged at 250 °C for 30 min. 2.0 ± 0.1 g of minced chicken was accurately weighed into a 20 mL headspace vial, and 1 μL of o-dichlorobenzene (0.1 μg / μL) was added as an internal standard. The vial was sealed and placed on a solid-phase microextraction platform, equilibrated at 60 °C for 10 min, then a 2 cm DVB / CAR / PDMS extraction head was inserted. Extraction and adsorption were performed at 60 °C for 30 min, followed by elution at 250 °C for 5 min, and then separation and identification were performed by GC-MS.

[0119] GC conditions: DB-WAX capillary column (30m×0.25mm×0.25μm), carrier gas: helium (99.999%); flow rate: 1.0mL / min; injection port temperature: 250℃; temperature program: initial temperature 40℃ held for 4min, increase to 100℃ at 4℃ / min, hold for 2min, increase to 200℃ at 6℃ / min, hold for 2min, and finally increase to 230℃ at 8℃ / min, hold for 2min.

[0120] MS conditions: Ionization mode: EI; Electron energy: 70 eV; Filament emission current: 0.25 mA; Inlet temperature: 230 °C; Ion source temperature: 230 °C; Quadrupole temperature: 150 °C; Scan mode: Full scan; Mass scan range: 30–500 m / z.

[0121] Qualitative method: Mass spectrometry data were matched with the NIST11 standard spectral library, retaining only components with both positive and negative match scores greater than 800 for qualitative analysis, and compared with C7~C under the same detection conditions. 30 A comparison of the retention index (RI) of n-alkanes. The formula for calculating the retention index is as follows: In the formula: t n t n＋1 The samples and n-alkane C are respectively. n n-Alkanes C n＋1 The retention time is denoted as n / min, where n is the number of carbon atoms.

[0122] Quantitative method: The content of the analyte aroma compound relative to the internal standard is calculated based on the known concentration and peak area of ​​the internal standard o-dichlorobenzene. The calculation formula is as follows: In the formula: L is the content of the substance being measured (μg / kg); A x The value is for determining the peak area of ​​the substance; C0 is the internal standard concentration (μg / μL); V is the internal standard injection volume (μL); A0 is the peak area of ​​the added internal standard; m is the mass of the sample being measured (g); 1000 is the conversion factor.

[0123] Odor activity value (OAV) is the ratio of the mass concentration of volatile flavor compounds to its threshold value. When OAV > 1, it indicates that volatile compounds contribute to the overall flavor. The calculation formula is as follows: In the formula: OAV i —Odor activity value of aroma substances; C i —Concentration of volatile flavor compounds (μg / kg); OT i—Odor threshold of volatile flavor compounds in water.

[0124] 1.2.8 Determination of basic physicochemical properties of traditional roasted chicken and roasted braised chicken 1.2.8.1 Determination of Moisture Content Refer to the direct drying method in GB 5009.3—2016 "National Food Safety Standard - Determination of Moisture in Food".

[0125] 1.2.8.2 Determination of Yield Weigh the pretreated Xinjiang Liangfenghua hen (m1, g), and weigh it again after roasting (m2, g). Measure each sample three times and take the average value. The calculation formula is as follows: 1.2.8.3 Measurement of color difference The color of roasted chicken and braised chicken was measured using a colorimeter. The cross-section of the muscle of the sample was placed vertically on the colorimetric correction lens, ensuring the lens opening was tightly against the meat surface to prevent light leakage. Five measurements were taken at different locations, and the brightness value L was recorded. Redness value a Yellowness value b The colorimeter must be calibrated before use.

[0126] 1.2.8.4 pH determination The pH value of food was determined according to GB 5009.237—2016, "National Food Safety Standard: Determination of pH Value of Food". 3 g of crushed roasted and braised chicken sample was weighed and added to 30 ml of 0.1 mol / L KCl solution. The electrode was inserted into the solution, and the temperature of the sample was kept within the range of 20±2℃. After the reading stabilized, the reading was taken directly. Each sample was measured three times and the average value was taken.

[0127] 1.2.9 Analysis of the differences in texture and quality between traditional roasted chicken and roasted / braised chicken 1.2.9.1 Determination of textural properties Chicken legs were cut into 1 cm × 1 cm × 1 cm cubes to determine their hardness, elasticity, adhesiveness, and chewiness. The testing conditions were as follows: probe model P / 36 R cylindrical flat-bottom probe; pre-test speed, test speed, and post-test speed were 1.00, 5.00, and 1.00 mm / s, respectively; compression distance was 10.00 mm; test time was 5.00 s; deformation was 50%; the test environment was room temperature; the sample was cut along a direction perpendicular to the fiber; and each sample was measured three times.

[0128] 1.2.9.2 Low-field MRI Using a variety of roasted and braised chickens with optimized processing techniques and traditional Xinjiang roasted chicken as subjects, samples were taken from the central part, cut into small cubes (1 cm × 2 cm × 2 cm), and placed in a dedicated NMR sample tube. The tube opening was then sealed with plastic film. The samples were then placed in the sample cell of an NMI20040H-I low-field NMR analyzer for calibration. Simultaneously, proton density-weighted imaging of the samples was performed using an MRI system, with the repetition time (TR) set to 600 ms and the number of slices set to 2.

[0129] 1.2.9.3 Determination of moisture distribution and migration patterns The moisture state of chicken samples was determined and analyzed using low-field nuclear magnetic resonance (LF-NMR). Chicken samples were cut into small cubes (1 cm × 2 cm × 2 cm) and placed in a 25 cm diameter cylindrical glass tube at the center of a magnetic field. The relaxation characteristics of the samples were measured using a Carr-Purcell Meiboom-Gill (CPMG) pulse. Experimental parameters: 125,030 sampling points, 16 repeated scans, 2,000 echoes, and the experiment was repeated three times with the average value taken. After scanning, the T2 value distribution and relaxation integral area were inverted using T2Fit software.

[0130] 1.2.9.4 Histological analysis Microstructure observation was performed using SEM. The sample was cut into cuboid pieces (2 mm × 2 mm × 5 mm), fixed with 2.5% glutaraldehyde solution for 24 h, rinsed with 0.1 mol / L phosphate buffer (pH=7.3), and incubated at room temperature for 2 h. The sample was then rinsed with distilled water and dehydrated twice using a gradient of different volume fractions of ethanol, 1 h each time. After dehydration, the sample was frozen in liquid nitrogen to break up and placed in a supercritical CO2 desiccator. The sample was then placed on the SEM stage with the observation side facing up, sputtered with gold using an Eiko IB5 ion sputtering system, and observed and photographed using a Quanta 200 FEG field emission environmental scanning electron microscope (accelerating voltage 20 kV) at 500x magnification.

[0131] Changes in the microstructure of chicken meat were observed using TEM. Chicken meat samples were cut into strips (2 mm × 2 mm × 1 mm) along the muscle fiber direction and immersed in 2.5% glutaraldehyde electron microscopy fixative (pH=7.4) for 48 h at 4 °C. Fixation was then performed in 1% osmium tetroxide solution, followed by dehydration via a series of fractionated acetone washes. After pretreatment, 100 nm ultrathin sections were cut from the treated tissue and stained with lead-citric acid for 4 min. These stained sections were stored at room temperature until used for TEM examination. The stained ultrathin sections were observed under a transmission electron microscope. The sarcomere length of the original tissue was measured on TEM images (8000 magnification) using ImageJ software version 1.53a (National Institute of Health, Bethesda, MD, USA). Three images were examined for each sample, and the sarcomere length of 10 muscle fibers was measured in each image.

[0132] 1.3 Data Processing SPSS 21.0 and Origin 2021 software were used for research data analysis and processing. Independent samples t-tests were used to compare differences, with P < 0.05 indicating statistical significance. Each experiment was performed in triplicate, and the mean of the measurements was used as the final result to enhance the reliability of the experimental results. Design-Expert 8.0.6 software was used to analyze the response surface methodology data to explore the interactions between various factors and the optimization conditions.

[0133] 2 Results Analysis 2.1 Results and Analysis of Roasted and Braised Chicken Process Optimization 2.1.1 Optimization Results and Analysis of Single-Factor Experiments Using fuzzy sensory evaluation scores as indicators, single-factor optimization was performed on braising temperature, braising time, roasting temperature, and roasting time. The results are as follows: Figure 4 As shown.

[0134] 2.1.1.1 Effect of braising temperature on fuzzy sensory scores like Figure 4As shown in (a), with the increase of braising temperature, the fuzzy sensory score of the roasted and braised chicken showed a trend of first rising and then falling, reaching the highest value (80.40±0.51 points) at 90℃. This is presumably because, under lower braising temperatures, although it is beneficial for moisture retention and meat tenderness, the penetration of the braising sauce is limited, resulting in insufficient flavor formation and thus a lower sensory score. When braising at around 90℃, moisture is effectively retained, and the meat is firm and tender, reaching its optimal texture. When the braising temperature is too high, the muscle fibers contract excessively, resulting in significant juice loss, decreased taste, and skin damage, further affecting the overall product quality and leading to a lower sensory score. Based on the influence of braising temperature on fuzzy sensory scores, this embodiment selected three levels—85℃, 90℃, and 95℃—for subsequent response surface methodology experiments.

[0135] 2.1.1.2 Effect of braising time on fuzzy sensory scores like Figure 4 As shown in (b), with the extension of braising time, the fuzzy sensory score of the roasted and braised chicken showed a trend of first increasing and then decreasing, reaching a peak at 25 min. This is mainly because during the braising process, the spice components gradually penetrate from the surface of the chicken to the interior, promoting the continuous accumulation of flavor substances, thus the sensory score shows an upward trend in the 20-25 min stage. However, when the braising time is further extended, the hardness and chewiness decrease, thereby affecting the overall taste of the roasted and braised chicken. At the same time, excessively long braising time may cause excessive penetration of spices, resulting in an overly strong flavor, which in turn reduces sensory acceptance. Based on the influence of braising time on fuzzy sensory score, this embodiment selects three levels of 20, 25, and 30 min for subsequent response surface methodology experiments.

[0136] 2.1.1.3 Effect of baking temperature on vague sensory scores like Figure 4 As shown in (c), the fuzzy sensory score of roasted chicken initially increases and then decreases with increasing roasting temperature. At lower temperatures (100–120°C), flavor compounds are insufficiently generated, resulting in a low sensory score. As the roasting temperature rises to 140–60°C, the surface and internal proteins of the chicken undergo moderate thermal denaturation, forming an ideal texture. Simultaneously, moderate oxidation of some fats and an accelerated Maillard reaction rate promote the generation of flavor compounds, thereby improving the color, aroma, and texture of the meat, significantly increasing the sensory score. When the temperature further increases to 180–200°C, significant moisture loss occurs, and the meat gradually becomes dry and hard. Excessive reactions at high temperatures can easily produce undesirable flavors (such as bitterness and burnt taste), leading to a decrease in the sensory score. Based on the above-mentioned influence of roasting temperature on fuzzy sensory scores, this embodiment ultimately selected three levels—125°C, 150°C, and 175°C—for subsequent response surface methodology experiments.

[0137] 2.1.1.4 Effect of baking time on vague sensory scores like Figure 4 As shown in (d), with the extension of roasting time, the fuzzy sensory score of the roasted braised chicken showed a trend of first increasing and then decreasing, reaching a peak at 30 min. During the short roasting stage (20-30 min), the chicken meat retained moisture well, and the flavor compounds were released evenly, exhibiting superior sensory quality. When the roasting time exceeded 30 min, the sensory score gradually decreased, as prolonged heating led to a large loss of moisture, resulting in dry and tough meat with reduced tenderness. At the same time, excessive Maillard reaction and lipid oxidation may generate burnt, bitter, or fatty flavor compounds, disrupting the overall flavor harmony. Based on the influence of roasting time on the fuzzy sensory score, this embodiment selected three levels—25, 30, and 35 min—for subsequent response surface methodology experiments.

[0138] 2.1.2 Response Surface Methodology Experiment: Fuzzy Mathematics-Based Sensory Evaluation Results Twenty trained professional students were selected to conduct sensory evaluations of the roasted and braised chicken. The results of the evaluation indicators for the roasted and braised chicken, categorized as excellent, good, average, and poor, are shown in Table 7.

[0139] 2.1.3 Establishment of the Fuzzy Relation Matrix The evaluation indicators of color, texture, smell, and taste were assigned weights and scores. The evaluation results of 20 sensory evaluation participants were normalized, and the fuzzy weight set Q={Q1,Q2,Q3,Q4}={0.15,0.3,0.3,0.25} was used. The 29 groups of sensory evaluation results in Table 4 were graded according to the sensory evaluation indicators. Taking the first group of experiments as an example, a fuzzy relation matrix was established: Calculation of the fuzzy mathematics comprehensive evaluation result Z: Z i =Q×R i , where i represents the i-th sample (or the i-th group of tests).

[0140] The fuzzy mathematical comprehensive evaluation results were calculated, and scores of 100, 80, 60, and 40 were assigned to Excellent, Good, Average, and Poor, respectively. Each assigned score was multiplied by the evaluation result Z1 = (0.200, 0.450, 0.300, 0.050), and then summed to obtain the sensory score of 83.05 for test number 1. 2.1.4 Optimization Results of Response Surface Methodology Experiment Based on the results of the single-factor experiments, a four-factor, three-level response surface optimization design was conducted using sensory scores as the response values. The results are shown in Table 7. Design-Expert 12 was used to perform multivariate regression fitting on the experimental data in Table 7, resulting in a quadratic multivariate regression model of fuzzy sensory scores (Y) on the four factors of braising temperature (A), braising time (B), baking temperature (C), and baking time (D): Y = 81.60 + 4.44A + 1.97B + 2.61C + 3.00D - 0.025AB - 1.87AC - 1.22AD - 3.07BC + 1.01BD - 2.48CD - 8.40A 2 -5.34B 2 -3.52C 2 -4.86D 2 .

[0141] Table 7 Sensory Evaluation Voting Results for Roasted and Braised Chicken

[0142] 2.1.5 Model Variance Analysis and Response Surface Analysis Table 8 shows that the model is highly significant (P < 0.0001), indicating a good model fit; the lack-of-fit term (P = 0.5930) is not significant, indicating a small error in the model's fit to the experimental data; the coefficient of determination R0... 2 The corrected coefficient of determination R is 0.9293. Adj 2 The value is 0.8586, indicating that the model fits the actual experimental situation well and can reflect the changes in the relationship between experimental factors and response values. The linear terms A, C, and D, and the quadratic term A... 2 B 2 C 2 D 2 The effect on the fuzzy sensory rating was extremely significant (P<0.01), and the linear term B and the interaction term BC had significant effects on the sensory rating (P<0.05). The F-value is an important statistical indicator for assessing the significance of the influence of each variable on the response value; the larger the value, the more significant the influence of the corresponding variable on the result. Based on the F-value, the order of influence of each factor on the product sensory rating is: A>D>C>B. Braising temperature > Baking time > Baking temperature > Braising time.

[0143] Table 8 Results of Analysis of Variance for Response Surface Model

[0144] Note: This indicates a significant impact (P<0.05); This indicates a highly significant effect (P < 0.01).

[0145] 2.1.6 Interaction Analysis Among Factors The 3D surface plot and contour plot of the response of various factors to the fuzzy sensory rating of roasted and braised chicken are shown below. Figures 5-10 As shown. By observing the inclination of the 3D surface, the degree of influence of the interaction between the two factors on the comprehensive score response value can be determined. The steeper the response surface, the more sensitive the response value is to the fuzzy sensory score of the roasted chicken; the closer the contour lines are to an ellipse, the stronger the interaction. Figure 5 , Figure 6 , Figure 7 , Figure 8 , Figure 9 , Figure 10 It can be seen that when the levels of two factors are zero, the other two factors change simultaneously. As the levels of the two factor variables increase, the sensory evaluation shows a trend of first increasing and then decreasing. The contour lines of the interaction between braising time (B) and baking temperature (C), and between baking temperature (C) and baking time (D) are elliptical, indicating that the interaction is significant. Furthermore, the contour line of braising time (B) and baking temperature (C) is more elliptical than that of baking temperature (C) and baking time (D), indicating a stronger significance.

[0146] 2.1.7 Model Validation The optimal process, determined through response surface methodology, was a braising temperature of 91.144℃ and a braising time of 25.847 min, followed by a roasting temperature of 153.604℃ and a roasting time of 31.312 min. To better reflect actual production conditions, slight modifications were made, resulting in the optimal process conditions of a braising temperature of 90℃ and a braising time of 25 min, and a roasting temperature of 150℃ and a roasting time of 30 min. Three parallel experiments were then conducted, yielding a fuzzy sensory score of 82.67 ± 1.76 points for the roasted and braised chicken, which generally matched the predicted value, demonstrating the model's rationality.

[0147] 2.2 Analysis of the quality differences between traditional roasted chicken and roasted / braised chicken 2.2.1 Fuzzy Mathematics Sensory Evaluation Results Table 9 shows the fuzzy sensory evaluation results for the optimized roasted and braised chicken and roasted chicken. Based on the voting results, a fuzzy evaluation matrix was constructed, and then fuzzy sensory scores were obtained after fuzzy synthesis. The calculation process is the same as in 2.1.3. The fuzzy mathematical sensory scores of the roasted and braised chicken group and the roasted chicken group were 84.65 and 79.10, respectively. The overall score of the roasted and braised chicken group was higher than that of the roasted chicken group, and its advantages in color, texture, aroma and taste were more balanced, resulting in better overall sensory quality.

[0148] Table 9. Sensory evaluation voting results for roasted braised chicken and roasted chicken

[0149] 2.2.2 Descriptive sensory evaluation results Six sensory descriptive terms for aroma characteristics were selected to evaluate the sensory characteristics of both the optimized roasted and braised chicken and the traditional roasted chicken. The sensory descriptive terms were categorized as: herbal aroma, spicy aroma, roasted aroma, fatty aroma, meaty aroma, and off-odor. Sensory scores ranged from 0 to 3 points, with intervals of 0.5 points. The sensory evaluation results are as follows: Figure 11 As shown, the results indicate that, compared to traditional roasted chicken, roasted braised chicken scored higher in herbal and spicy aroma attributes, exceeding 2.5 and 2.0 points respectively, while traditional roasted chicken scored less than 1.5 and slightly more than 1.5 points respectively. This may be due to the introduction of natural spices in the process optimization, which enhanced the release of these aromas. In terms of roasted aroma, roasted braised chicken scored the same as traditional roasted chicken. Regarding fat and meat aroma, the scores were similar, with roasted braised chicken scoring approximately 2.2 and 2.8 points respectively, while traditional roasted chicken scored approximately 2.7 and 2.6 points, indicating that the optimized process maintained the basic flavor profile well. In terms of chicken odor, roasted braised chicken scored significantly lower than traditional roasted chicken by 0.5 points, indicating that the process optimization effectively suppressed the generation of chicken odor and improved the overall flavor. The sensory evaluation results show that the process optimization made roasted braised chicken more layered and acceptable in terms of aroma sensory characteristics, which corresponds to the fuzzy mathematical sensory evaluation results.

[0150] 2.2.3 Analysis of Electronic Nose Response Values An electronic nose works by mimicking various olfactory receptor cells in mammals and using its stored odor fingerprint data to quickly identify corresponding odor characteristics in a sample. The radar chart data of the electronic nose's response values ​​can visually reflect the differences between different types of volatile flavor compounds. For example... Figure 12 As shown, the electronic nose detection results for roasted and braised chicken and roasted chicken differed significantly. Roasted and braised chicken showed higher response intensities to sensors W5S (sensitive to nitrogen oxides), W2S (sensitive to alcohols, aldehydes, and ketones), W1W (sensitive to inorganic sulfides), and W1S (sensitive to methyl compounds) than roasted chicken, indicating that roasted and braised chicken contains more volatile flavor compounds and has a stronger meaty aroma. PCA analysis of the electronic nose data from roasted and braised chicken showed that a higher cumulative contribution rate indicates a better reflection of the original indicators by the principal components. The contribution rate of the first principal component (PC1) was 99.4%, with a clear separation between roasted and braised chicken in PC1, indicating a significant difference in volatile flavor compounds between the two. The contribution rate of the second principal component (PC2) was extremely low (0.5%), and the sum of the contribution rates was 99.9%, indicating that the results reflected almost all the information in the sample.

[0151] 2.2.4 Analysis of GC-MS detection combined with OAV calculation results Volatile substances (VVIS) in roasted chicken and roasted braised chicken samples were detected using GC-MS, and the results are shown in Table 10. A total of 52 VVIS were detected in the roasted chicken, including 19 terpenes, 6 alcohols, 1 phenol, 1 ether, 7 aldehydes, 5 ketones, 12 heterocyclic compounds, and 1 ester. A total of 55 VVIS were detected in the roasted braised chicken, including 34 terpenes, 6 alcohols, 3 phenols, 6 aldehydes, 1 ketone, 4 heterocyclic compounds, and 1 ester. Table 11 shows the OAV values ​​of VVIS in the roasted and roasted braised chicken samples. 22 compounds in the roasted chicken had an OAV greater than 1, and 18 compounds in the roasted braised chicken had an OAV greater than 1.

[0152] The flavor of roasted chicken primarily relies on endogenous reaction pathways. Its high-temperature roasting process (220℃) significantly promotes the Maillard reaction, producing abundant pyrazine compounds, such as 2,5-dimethylpyrazine (OAV: 171) and 2,5-dimethyl-3-ethylpyrazine (OAV: 35), which together constitute its signature rich roasted aroma. Aldehydes and alcohols mainly originate from fat oxidation and are the main components that produce the characteristic flavor of chicken fat after heating. Intense fat oxidation generates key aldehydes such as hexanal (OAV: 23) and octanal (OAV: 111), as well as 1-octen-3-ol (OAV: 47) and n-hexanol (OAV: 5), giving roasted chicken its typical fatty aroma. The flavor characteristics of roasted and braised chicken mainly originate from the migration of exogenous flavor compounds. The spices introduced during the braising process introduce a large number of terpenoids, such as D-limonene (OAV: 149), anethole (OAV: 150), and α-terpineol (OAV: 109), which constitute its prominent herbal and sweet aroma base. In addition, the extremely high OAV value of phenolic substances, such as eugenol (OAV: 370), is a core contributor to its spicy properties. The high water activity and relatively mild heat treatment conditions during the braising process inhibit the Maillard reaction, resulting in almost no detectable pyrazine roasting compounds in the braised chicken; at the same time, the degree of fat oxidation is significantly lower than that of roasted chicken (P < 0.05), and its fat aroma intensity is weaker. Alcohols are mainly produced through the oxidative degradation of unsaturated fatty acids. 1-Octen-3-ol, produced by lipid oxidation, has a mushroom aroma and is a key volatile flavor compound in roasted chicken. Compared to roasted chicken, roasted braised chicken has a lower content of 1-octen-3-ol. This may be due to two reasons: firstly, the antioxidant effect of spices during the braising process inhibits excessive fat degradation; secondly, the compound spice packet imparts a spicy and herbal aroma to the roasted braised chicken during the braising process, masking or reducing the fatty taste. Linalool, artemisinin, L-carvone, guaiacol, and (E)-cinnamaldehyde, commonly found in roasted braised chicken, are aroma compounds derived from the compound spices added during braising and significantly contribute to the overall flavor of the roasted braised chicken. This indicates that roasted braised chicken has a richer herbal aroma compared to roasted chicken. Ketones with an OAV greater than 1, 3-hydroxy-2-butanone and 2-nonanone, were detected in roasted chicken but not in roasted braised chicken, suggesting more vigorous fat oxidation and Maillard reactions in roasted chicken. In summary, the volatile flavor profiles of roasted chicken and roasted braised chicken follow distinctly different formation pathways. The flavor of roasted chicken is primarily dominated by endogenous Maillard reactions and fat oxidation at high temperatures, exhibiting a rich "roasted aroma" and "fatty aroma." In contrast, the flavor of roasted braised chicken is mainly shaped by the migration of exogenous spices during the braising process, exhibiting a distinct "herbal aroma" and "spicy aroma," while its off-flavors (chicken odor, rancidity) are effectively suppressed. This explains the differences in sensory attributes between roasted chicken and roasted braised chicken, consistent with the results of electronic nose and descriptive sensory evaluations.

[0153] Table 10 Changes in the types and contents of aroma compounds in roasted and braised chicken and roasted chicken

[0154] Note: Different lowercase letters for the same indicator indicate significant differences between different samples; "-" indicates that the same as in Tables 11, 12, 13, and 14 was not detected.

[0155] Table 11. Volatile Acid (OAV) Values ​​of Roasted and Braised Chicken and Roasted Chicken

[0156] 2.2.5 Determination of basic physicochemical properties Table 12 reveals the impact of braising treatment on the edible quality of roasted and braised chicken. Regarding water retention, the yield and moisture content of the roasted and braised chicken were significantly increased (P<0.05), mainly due to the heat-induced effect during braising and the penetration of braising liquid components. Regarding color, the brightness (L) of the roasted and braised chicken samples increased significantly. Significantly decreased redness (a) ) and yellowness (b) The color of the chicken increased significantly (P<0.05), which was due to the synergistic effect of the braising and roasting processes. Specifically, the natural pigments in the braising liquid directly penetrated into the chicken meat, forming the basis for the color of the roasted and braised chicken samples. At the same time, the reducing sugars and amino acids in the braising liquid underwent a vigorous Maillard reaction during the roasting stage, resulting in the unique deep red and glossy appearance of the roasted and braised chicken.

[0157] Table 12 Physicochemical properties of roasted and braised chicken and roasted chicken

[0158] 2.2.6 Analysis of the differences in texture and quality between traditional roasted chicken and roasted / braised chicken 2.2.6.1 Determination of textural properties Texture is one of the most important quality attributes of meat products. Cohesion reflects the density of the meat's structure; a higher value indicates better texture and mouthfeel. Chewability is a comprehensive reflection of hardness, elasticity, and cohesion; a higher value indicates better "bite" in terms of mouthfeel. Table 13 reveals the effect of braising on the texture of roasted and braised chicken. There were significant differences in hardness, adhesiveness, and chewability between roasted and braised chicken and roasted chicken (P<0.05). The hardness of roasted and braised chicken (12.38±0.34 N) was significantly lower than that of traditional roasted chicken (15.61±0.35 N). The braising process may enhance protein hydration by promoting denaturation, dissolution, and gelation of myofibril proteins, allowing more water to be retained in the protein network structure, thereby improving the water-holding capacity of the meat and giving roasted and braised chicken a more tender mouthfeel. There were no significant differences between the two products in terms of cohesion and elasticity (P>0.05). Both showed a cohesiveness of 0.47, indicating that their internal structures had similar resistance to damage and the ability to maintain integrity. The elasticity values ​​were also similar, suggesting that the ability of the samples to recover their original shape after the first compression deformation did not change significantly after different processing methods (P>0.05). The adhesiveness (5.85±0.17 N) and chewiness (5.85±0.17 N) of the roasted braised chicken were significantly lower than those of traditional roasted chicken (7.57±0.21 N), consistent with the trend in hardness changes, jointly confirming that the roasted braised chicken has a softer and more tender texture, resulting in better eating quality. In conclusion, process optimization significantly improved the product's textural properties. Compared to traditional roasted chicken, the roasted braised chicken, while maintaining its original structural cohesiveness and elasticity, achieved lower hardness, adhesiveness, and chewiness, resulting in a softer overall texture, which helps improve its acceptability.

[0159] Table 13 Texture characteristics of roasted braised chicken and roasted chicken

[0160] 2.2.6.2 Results of low-field nuclear magnetic resonance imaging MRI imaging technology was used to obtain proton density-weighted images of the interior of roasted and braised chicken and roasted chicken samples. These weighted images can reflect the distribution of hydrogen protons in the samples. The density and mobility of hydrogen protons, i.e., moisture content and moisture distribution information, can be converted into intuitive and visible images, so as to intuitively observe the moisture distribution status. Figure 13(Note: Different column numbers indicate three repetitions (n=3), BRC: roasted chicken, RC: roasted chicken) The legend on the right shows the hydrogen proton density from high to low, with the color transitioning from red to green. Generally, denser red areas indicate higher hydrogen proton density and water content, while denser green areas indicate more dispersed hydrogen proton density and lower water content. As can be seen from the figure, the roasted chicken exhibits a uniform red-orange hue in the MRI image, indicating a relatively high and uniform moisture content and distribution. The roasted chicken sample appears bluish-green, with a significantly lower hydrogen proton density than the roasted chicken, which is consistent with the water retention results of this embodiment.

[0161] 2.2.6.3 Results of the determination of moisture distribution and migration patterns Depend on Figure 14 As can be seen, the relaxation time spectrum shows three NMR peaks from left to right. The first peak, appearing between 0.1 and 10 ms, represents bound water. This water exists within the cell and is tightly bound to proteins via hydrogen bonds, exhibiting stable properties. The second peak, appearing between 10 and 100 ms, represents non-flowing water. This water is mainly distributed between myofibrils and membranes. The non-flowing water peak has the largest area and signal intensity, accounting for over 80% of the total, indicating that the water in chicken leg meat mainly exists in a non-flowing state. The hydrativism of muscle is primarily measured by this water. The third peak, with a relaxation time between 100 and 1000 ms, represents free water. Compared to roasted chicken, roasted and braised chicken exhibits a higher distribution range of bound and free water, while the distribution range of non-flowing water is similar. This may be because the braising process allows external moisture to enter the meat sample, relatively increasing the free water content.

[0162] 2.2.6.4 Histological analysis (1) SEM analysis The microstructure of muscle fibers determines the fluidity of water and the mechanical strength of meat. Figure 15 The images visually illustrate the histological cross-sections of untreated raw chicken and roasted / braised chicken processed using two different methods. It can be seen that the myofibrils in both roasted / braised and roasted chicken exhibit varying degrees of shrinkage, which significantly impacts moisture loss and may contribute to the difference in tenderness between the two. Compared to the roasted / braised chicken sample, the roasted chicken sample shows clearly visible large gaps, causing the fixed water within the myofibril network to be squeezed into these large voids, resulting in reduced water retention and yield.

[0163] (2) TEM analysis The sarcomere is the basic unit of the myofibril, composed of the dark band (A band) and the light band (I band). The dark, fine line in the center of the I band is called the Z line. Figure 16As can be seen, the muscle fibers of the untreated raw chicken (C) are relatively well-organized, with the I, A, and Z bands clearly visible in the sarcomeres. Significant differences exist in the microstructure of myofibrils between the roasted and braised chicken and the roasted chicken, specifically in the varying degrees of shortening of the sarcomere length (Table 14). Table 14 shows that compared to the raw chicken (C group, 1.92±0.08 μm), the sarcomere length of the roasted and braised chicken (BRC group) was shortened to 1.35±0.02 μm, with a contraction rate as high as 29.69%; while the sarcomere length of the roasted chicken (RC group) was 1.60±0.02 μm, with a contraction rate of only 16.84%. This indicates that the roasted and braised chicken with optimized processing underwent more intense myofibril contraction. During the braising process, heat can be rapidly and evenly conducted to the deep muscle layers, triggering strong sarcomere contraction.

[0164] Table 14. Myosalplast length (μm) and contraction rate (%) of roasted and braised chicken and roasted chicken.

[0165] 3. Conclusion This embodiment optimizes the processing of traditional Xinjiang roasted chicken using a fuzzy mathematical evaluation system and response surface methodology. The optimized roasted chicken processing conditions are braising at 90℃ for 25 min and roasting at 150℃ for 30 min. This process significantly improves the overall quality of the product. Compared to traditional roasted chicken, the addition of braising results in a higher yield, a brighter red color, reduced hardness and chewiness, and increased tenderness. LF-NMR shows a more uniform moisture distribution and a higher proportion of bound water. SEM / TEM observations show a more compact muscle fiber arrangement and a more complete structure, indicating that the addition of braising is more conducive to retaining moisture and improving texture. Simultaneously, the roasted chicken is richer in volatile flavor compounds, with a significant increase in the content of key aroma substances such as terpenes and phenols, resulting in a more prominent natural and spicy aroma, a significant reduction in off-flavors, and a more harmonious overall flavor. In summary, this embodiment constructs a standardized roasted chicken processing technology that can be used for industrial production, providing a reference for the industrial production of pre-prepared roasted chicken in Xinjiang.

[0166] The following describes the system for optimizing the industrial conversion of meat product braising and roasting processes provided by the present invention. The system for optimizing the industrial conversion of meat product braising and roasting processes described below and the method for optimizing the industrial conversion of meat product braising and roasting processes described above can be referred to in correspondence with each other.

[0167] like Figure 17 The figure shown is a system for optimizing the industrial conversion of meat product braising and roasting processes according to the present invention, comprising: The data acquisition module 1710 is used to acquire the basic physicochemical index data of raw meat and the preset quality characteristics of the target meat product; the basic physicochemical index data are the inherent physicochemical properties of the raw meat itself before processing. The data input module 1720 is used to input basic physicochemical index data and preset quality characteristics into a pre-constructed multidimensional mapping relationship model. The multidimensional mapping relationship model is used to characterize the relationship between raw material properties, process conditions and finished product quality. The parameter output module 1730 is used to generate the optimal combination of braising and roasting process parameters based on the output of the multidimensional mapping relationship model. Processing module 1740 is used to process raw meat based on the optimal combination of braising and roasting process parameters to obtain meat products to be verified. The quality verification module 1750 is used to verify the quality characteristics of the meat products to be verified. When the verification results meet the preset conditions, the final process scheme is determined based on the optimal combination of braising and roasting process parameters.

[0168] Specifically, the functions of each module in the system for optimizing the industrial conversion of meat product braising and roasting processes provided in this embodiment of the invention correspond one-to-one with the operation flow of each step in the above-mentioned method-like embodiments, and the achieved effects are also the same. For details, please refer to the above embodiments, and this will not be repeated in this embodiment of the invention.

[0169] Figure 18 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 18 As shown, the electronic device may include: a processor 1810, a communications interface 1820, a memory 1830, and a communications bus 1840, wherein the processor 1810, the communications interface 1820, and the memory 1830 communicate with each other through the communications bus 1840. The processor 1810 can call logic instructions in the memory 1830 to execute a method for optimizing the industrial conversion of meat product braising and roasting processes. This method includes: acquiring basic physicochemical index data of the raw meat and preset quality characteristics of the target meat product; the basic physicochemical index data refers to the inherent physicochemical properties of the raw meat before processing; inputting the basic physicochemical index data and preset quality characteristics into a pre-constructed multidimensional mapping relationship model, which characterizes the correlation between raw material properties, process conditions, and finished product quality; generating an optimal combination of braising and roasting process parameters based on the output of the multidimensional mapping relationship model; processing the raw meat based on the optimal combination of braising and roasting process parameters to obtain the meat product to be verified; verifying the quality characteristics of the meat product to be verified; and when the verification results meet preset conditions, determining the final process scheme data to achieve the optimal combination of process parameters based on the optimal combination of braising and roasting process parameters.

[0170] Furthermore, the logical instructions in the aforementioned memory 1830 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, essentially, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0171] On the other hand, the present invention also provides a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the method for optimizing the industrial conversion of meat product braising and roasting processes provided by the above methods. The method includes: acquiring basic physicochemical index data of raw meat and preset quality characteristics of target meat products; the basic physicochemical index data are the inherent physicochemical properties of the raw meat itself before processing; inputting the basic physicochemical index data and preset quality characteristics into a pre-constructed multidimensional mapping relationship model, which is used to characterize the correlation between raw material properties, process conditions and finished product quality; generating the optimal combination of braising process parameters and roasting process parameters according to the output of the multidimensional mapping relationship model; processing the raw meat based on the optimal combination of braising process parameters and roasting process parameters to obtain the meat product to be verified; verifying the quality characteristics of the meat product to be verified, and when the verification result meets the preset conditions, determining the final process scheme data to achieve the optimal combination of process parameters according to the optimal combination of braising process parameters and roasting process parameters.

[0172] In another aspect, the present invention also provides a non-transitory computer-readable storage medium storing a computer program thereon. When executed by a processor, the computer program implements a method for optimizing the industrial conversion of braising and roasting processes for meat products, as provided by the methods described above. This method includes: acquiring basic physicochemical index data of raw meat and preset quality characteristics of the target meat product; the basic physicochemical index data being the inherent physicochemical properties of the raw meat before processing; inputting the basic physicochemical index data and preset quality characteristics into a pre-constructed multidimensional mapping relationship model, which characterizes the correlation between raw material properties, process conditions, and finished product quality; generating an optimal combination of braising and roasting process parameters based on the output of the multidimensional mapping relationship model; processing the raw meat based on the optimal combination of braising and roasting process parameters to obtain the meat product to be verified; verifying the quality characteristics of the meat product to be verified; and when the verification results meet preset conditions, determining the final process scheme data to achieve the optimal combination of process parameters based on the optimal combination of braising and roasting process parameters.

[0173] The system embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0174] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of various embodiments or some parts of embodiments.

[0175] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for optimizing the industrial conversion of meat product braising and roasting processes, characterized in that, include: Obtain basic physicochemical index data of raw meat and preset quality characteristics of target meat products; The basic physicochemical index data are the inherent physicochemical properties of the raw meat itself before processing; The basic physicochemical index data and the preset quality characteristics are input into a pre-constructed multidimensional mapping relationship model, which is used to characterize the correlation between raw material properties, process conditions and finished product quality. Based on the output of the multidimensional mapping relationship model, the optimal combination of braising and roasting process parameters is generated. The raw meat is processed based on the optimal combination of braising and roasting process parameters to obtain the meat product to be verified. The quality characteristics of the meat product to be verified are verified. When the verification results meet the preset conditions, the final process scheme is determined according to the optimal combination of braising process parameters and roasting process parameters.

2. The method for optimizing the industrial conversion of meat product braising and roasting processes according to claim 1, characterized in that, The multidimensional mapping relationship model is constructed based on the fuzzy mathematics comprehensive evaluation method and the response surface methodology, and specifically includes: Obtain process parameters, including braising temperature, braising time, roasting temperature, and roasting time; Using fuzzy mathematical sensory rating as the evaluation index, single-factor optimization experiments were conducted on the process parameters to determine the appropriate level range for each process parameter. Based on the aforementioned suitable level range, a multi-factor, multi-level experimental scheme was designed using response surface methodology, and multiple samples of roasted and braised chicken were prepared. A sensory evaluation model was constructed based on the fuzzy mathematics comprehensive evaluation method. The evaluation factor set, comment set and weight set were determined, and sensory evaluation was carried out on each sample of roasted and braised chicken to obtain sensory score data. Using process parameters as independent variables and sensory score data as dependent variables, a quadratic multinomial regression equation is fitted to establish a response surface regression model between process parameters and sensory score data. The response surface regression model is solved and analyzed to obtain the optimal combination of process parameters that achieves the sensory score data.

3. The method for optimizing the industrial conversion of meat product braising and roasting processes according to claim 2, characterized in that, The step of conducting single-factor optimization experiments on the process parameters to determine the suitable level range for each process parameter specifically includes: Multiple single-factor experimental samples of roasted and braised chicken were obtained by processing chickens at different braising temperatures, braising times, roasting temperatures, and roasting times. The sensory evaluation of each single-factor experimental sample of roasted and braised chicken was carried out by the fuzzy mathematics comprehensive evaluation method to obtain the single-factor sensory score data corresponding to each process parameter. Based on the changing trends of the single-factor sensory score data with each process parameter, the appropriate level range of each process parameter is determined.

4. The method for optimizing the industrial conversion of meat product braising and roasting processes according to claim 2, characterized in that, The sensory evaluation model constructed based on the fuzzy mathematics comprehensive evaluation method determines the evaluation factor set, comment set, and weight set, and conducts sensory evaluation on each sample of roasted and braised chicken to obtain sensory score data, specifically including: Color, texture, odor, and taste were identified as sensory evaluation indicators, and a set of evaluation factors was constructed. Weights are assigned to each sensory evaluation indicator using a forced decision method, and a weight set is constructed. Determine a set of comments that includes four evaluation levels: excellent, good, average, and poor, and assign corresponding scores to each evaluation level; The number of votes for each sample of roasted and braised chicken belonging to each evaluation level on each sensory evaluation index was counted, and a fuzzy relation matrix was constructed. The weight set and the fuzzy relation matrix are combined to obtain the fuzzy comprehensive evaluation result, and the sensory score data is calculated based on the score of the comment set.

5. The method for optimizing the industrial conversion of meat product braising and roasting processes according to claim 2, characterized in that, Based on the aforementioned suitable level range, a multi-factor, multi-level experimental scheme was designed using response surface methodology to prepare multiple samples of roasted and braised chicken, specifically including: Using brining temperature, brining time, roasting temperature, and roasting time as independent variables, and with the upper limit, lower limit, and center value of the suitable level range as three levels, a four-factor, three-level response surface experimental design scheme is constructed. According to the aforementioned response surface methodology, multiple response surface methodology samples of roasted and braised chicken were obtained by processing under various combinations of process parameters.

6. The method for optimizing the industrial conversion of meat product braising and roasting processes according to claim 2, characterized in that, After obtaining the optimal combination of process parameters that yields the sensory evaluation data, the process further includes: The verification sample roasted and braised chicken was obtained by processing according to the optimal combination of process parameters. The sensory evaluation of the verification sample roasted and braised chicken was carried out using the fuzzy mathematics comprehensive evaluation method to obtain the verification sensory score data. The sensory evaluation data is compared with the optimal sensory evaluation predicted by the response surface regression model. If the error between the two is within a preset range, the optimal combination of process parameters is confirmed to be effective.

7. The method for optimizing the industrial conversion of meat product braising and roasting processes according to claim 2, characterized in that, After obtaining the optimal combination of process parameters that yields the sensory evaluation data, the process further includes: The optimized roasted and braised chicken was processed according to the optimal combination of process parameters, and its quality was compared with that of the control roasted chicken processed using the traditional roasting process. The quality comparison includes: detecting and comparing at least one physicochemical indicator among the yield, moisture content, and color parameters of the two; and detecting and comparing at least one textural property indicator among the hardness, elasticity, and chewiness of the two.

8. The method for optimizing the industrial conversion of meat product braising and roasting processes according to claim 7, characterized in that, The quality comparison also includes: The moisture distribution indicators of the two were detected and compared, including the proportion of bound water, the proportion of non-flowing water, the proportion of free water, and the uniformity of moisture distribution, as determined by low-field nuclear magnetic resonance. The microstructural indices of the two were detected and compared, including the arrangement of myofibrils and the interfibrillary spaces observed by scanning electron microscopy, and the length of sarcomeres observed by transmission electron microscopy. The flavor characteristic indicators of the two were detected and compared. The flavor characteristic indicators include the response characteristics of volatile flavor substances based on electronic nose detection, and the content of key aroma compounds determined by gas chromatography-mass spectrometry analysis combined with odor activity value calculation.

9. The method for optimizing the industrial conversion of meat product braising and roasting processes according to claim 2, characterized in that, After generating the optimal combination of braising and roasting process parameters, the method further includes: The basic physicochemical index data of the raw meat and the optimal braising and roasting process parameters are input into the quality prediction model to obtain the quality characteristics. The quality prediction model is trained based on a training set, which includes basic physicochemical index data of raw meat, braising process parameters and roasting process parameters, as well as corresponding quality feature labels.

10. A system for optimizing the industrial conversion of meat product braising and roasting processes, characterized in that, include: The data acquisition module is used to acquire basic physicochemical index data of raw meat and preset quality characteristics of target meat products; The basic physicochemical index data are the inherent physicochemical properties of the raw meat itself before processing; The data input module is used to input the basic physicochemical index data and the preset quality characteristics into a pre-constructed multidimensional mapping relationship model. The multidimensional mapping relationship model is used to characterize the correlation between raw material properties, process conditions and finished product quality. The parameter output module is used to generate the optimal combination of braising and roasting process parameters based on the output of the multidimensional mapping relationship model. The processing module is used to process the raw meat based on the optimal combination of braising and roasting process parameters to obtain the meat product to be verified. The quality verification module is used to verify the quality characteristics of the meat product to be verified. When the verification result meets the preset conditions, the final process scheme is determined according to the optimal combination of braising process parameters and roasting process parameters.

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