CN112946210B - Method for quickly predicting quality of fresh cooked noodles - Google Patents
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Abstract
The invention discloses a method for quickly predicting freshnessA method of cooked noodle quality comprising the steps of: step one, establishing a prediction model I, wherein the prediction model I is as follows: y is 1 ＝109.520‑7.528×10 ‑4 ×S 21 ‑5.333×10 ‑6 ×S 22 ‑6.911×10 ‑4 ×S 2 Wherein Y is 1 The independent variable S is the total score of sensory evaluation of the fresh cooked noodles 21 The area of the strong binding water peak of the fresh cooked noodles, S 22 The area of weakly bound water peak of freshly cooked noodle, S 2 The total water peak area of the fresh cooked noodles is shown; step two, detecting the area S of the strong binding water peak of the fresh cooked surface with the quality to be predicted 21 Area of weak binding water peak S 22 Total water peak area S 2 (ii) a And step three, substituting the detection data obtained in the step two into the prediction model I to calculate the sensory evaluation total score of the fresh and cooked noodles. The method has the beneficial effect of quickly and accurately predicting the quality of the fresh and cooked noodles.
Description
Technical Field
The invention relates to the field of fresh cooked noodle quality prediction. More particularly, the present invention relates to a method for rapidly predicting the quality of fresh cooked noodles.
Background
Fresh cooked noodles are made up of wheat flour, edible salt and water through such technological steps as mixing wheat flour, edible salt and water in dough mixer to obtain dough, rolling to obtain elastic, plastic and extensible noodles, cooking, rolling and cutting to obtain fresh noodles, steaming to modify protein and gelatinize starch, sterilization and packing. The fresh cooked noodles have the advantages of high water content, good taste, non-fried, convenient eating, various eating methods and the like because of not being dehydrated, meet the dietary requirements of modern people, are deeply loved by consumers and have wide market space. At present, the quality of the fresh and cooked noodles is mainly evaluated by methods such as sensory evaluation, texture evaluation and the like, the sensory evaluation needs to culture special sensory evaluators, the texture quality analysis also needs to train special technicians to analyze and measure the product quality and texture by using a texture analyzer, the whole process is time-consuming, the required sample amount is large, and the requirements of rapid and nondestructive detection and monitoring of the quality of the fresh and cooked noodles in modern industrial production and logistics processes cannot be met.
Therefore, how to rapidly predict the sensory quality and the texture quality of the fresh and cooked noodles is a technical problem to be solved in the field. The solution of the problem has important significance for promoting the industrialized and standardized production of the traditional flour staple food in China.
Disclosure of Invention
An object of the present invention is to solve at least the above problems and to provide at least the advantages described later.
It is still another object of the present invention to provide a method for rapidly predicting the quality of fresh cooked noodles, which can rapidly and accurately predict the quality of fresh cooked noodles.
To achieve these objects and other advantages in accordance with the purpose of the invention, there is provided a method for rapidly predicting the quality of fresh cooked noodles, comprising the steps of:
step one, establishing a prediction model I, wherein the prediction model I is as follows:
Y 1 ＝109.520-7.528×10 -4 ×S 21 -5.333×10 -6 ×S 22 -6.911×10 -4 ×S 2
wherein Y is 1 The independent variable S is the total sensory evaluation score of the fresh and cooked noodles 21 The area of the strong binding water peak of the fresh cooked noodle S 22 The area of weak binding water peak of fresh cooked noodle, S 2 The total water peak area of the fresh cooked noodles is shown;
step two, detecting the area S of the strong binding water peak of the fresh cooked surface with the quality to be predicted 21 Area of weak binding water peak S 22 Total water peak area S 2 ；
And step three, substituting the detection data obtained in the step two into the prediction model I to calculate the sensory evaluation total score of the fresh and cooked noodles.
Preferably, the method further comprises the following steps:
if the total sensory evaluation score of the fresh cooked noodles obtained by calculation in the third step is greater than the threshold value,
establishing a prediction model II, wherein the prediction model II is as follows:
Y 2 ＝22.417+1.058×10 -5 ×S 21 -3.355×10 -5 ×S 22 -1.128×10 -4 ×S 2
wherein, Y 2 Scoring the sensory smoothness of the fresh and cooked noodles;
and substituting the detection data obtained in the step two into a prediction model II to calculate the sensory smoothness score of the fresh and cooked noodles.
Preferably, the method further comprises the following steps:
if the total sensory evaluation score of the fresh cooked noodles obtained by calculation in the third step is greater than the threshold value,
establishing a prediction model III, wherein the prediction model III is as follows:
Y 3 ＝11.782-4.286×10 -4 ×S 21 +1.418×10 -6 ×S 22 -9.786×10 -5 ×S 2
wherein Y is 3 Scoring the sensory firmness of the fresh cooked noodles;
and substituting the detection data obtained in the step two into a prediction model III to calculate the sensory firmness score of the fresh and cooked noodles.
Preferably, the method further comprises the following steps: if the sensory evaluation total score of the fresh cooked noodles calculated in the third step is larger than the threshold value,
establishing a prediction model IV, wherein the prediction model IV is as follows:
Y 4 ＝34.530-6.400×10 -4 ×S 21 +2.227×10 -6 ×S 22 -2.918×10 -4 ×S 2
wherein Y is 4 The sensory elasticity of the fresh and cooked noodles is scored,
substituting the detection data in the second step into a prediction model IV to calculate the sensory elasticity score of the fresh cooked noodles.
Preferably, the method further comprises: if the total sensory evaluation score of the fresh cooked noodles obtained by calculation in the third step is greater than the threshold value,
establishing a prediction model V, wherein the prediction model V is as follows:
Y 5 ＝-748.725+4.753×10 -2 ×S 21 -2.300×10 -3 ×S 22 +3.572×10 -2 ×S 2
wherein Y is 5 The texture hardness of the fresh cooked noodles;
and substituting the detection data obtained in the step two into a prediction model V to calculate the texture hardness of the fresh cooked surface.
Preferably, the method further comprises the following steps: if the total sensory evaluation score of the fresh cooked noodles obtained by calculation in the third step is greater than the threshold value,
establishing a prediction model VI, wherein the prediction model VI is as follows:
Y 6 ＝-209.377+9.841×10 -3 ×S 21 +4.033×10 -3 ×S 22 +1.205×10 -2 ×S 2
wherein Y is 6 The chewiness of the fresh and cooked texture;
and substituting the detection data in the second step into a prediction model VI to calculate the texture chewiness of the fresh cooked noodles.
Preferably, the preparation method of the fresh cooked noodles comprises the following steps:
placing fresh noodles into a stainless steel pot containing 50 times of boiling water, keeping the water slightly boiling, boiling until the white core disappears, taking out, cooling with water, and standing on a filter screen for 1min to obtain fresh cooked noodles.
The invention at least comprises the following beneficial effects: the invention finds that the contents of water in different binding states in the fresh cooked noodles are closely related to the sensory and texture quality characteristics of the product, establishes a prediction model for rapidly predicting the sensory and texture quality of the fresh cooked noodles, can predict the sensory and texture quality of the fresh cooked noodles through the peak areas of the water in different binding states in the fresh cooked noodles, and has the advantages of rapidness and high accuracy. By adopting the method, the operations of sensory evaluation and texture quality determination of the product can be saved. The method has the advantages of rapidness, high accuracy, cost saving and practicability.
Additional advantages, objects, and features of the invention will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following or may be learned from practice of the invention.
Detailed Description
The present invention is further described in detail below with reference to examples so that those skilled in the art can practice the invention with reference to the description.
It is to be noted that the experimental methods described in the following embodiments are all conventional methods unless otherwise specified, and the reagents and materials are commercially available unless otherwise specified.
< preparation of fresh cooked noodle sample >
Weighing 200g of flour, adding a proper amount of distilled water according to the moisture content of the flour, adjusting the final moisture content of the flour wadding to be 34%, adding edible salt accounting for 1% of the weight of the flour, uniformly mixing, pouring into a needle type flour mixing machine, and stirring at a medium speed for 8min to obtain the flour wadding.
Placing the kneaded dough into edible self-sealing bag, and fermenting at room temperature for 30min. Adjusting calender roll interval and being 3.0mm, putting into the calender roll nip with the surface wadding that has kneaded, starting the calender preforming, will preforming fifty percent discount calendering 8 times, arrange in edible self-sealing bag in the surface area and proof 1h. Adjusting the distance between the press rolls to be 2.5mm, and calendering for 1 time; adjusting the distance between the press rolls to be 2.0mm, and calendering for 1 time; adjusting the distance between the press rolls to be 1.5mm, and calendering for 1 time; adjusting the distance between the press rolls, rolling into dough sheet with thickness of 1.5mm, cutting into fresh noodles with width of 3.0mm and length of 100.0mm, and packaging in edible self-sealing bags. Taking a proper amount of fresh noodles, putting into a stainless steel pot containing 50 times of boiling water (distilled water), heating with an electromagnetic oven, keeping the slightly boiling state of water, boiling until the white core of the noodles disappears, taking out, cooling with water for 30s, and standing on a filter screen for 1min for later use.
< determination of Water Peak areas in different binding states in fresh cooked noodle samples >
And placing the prepared fresh and cooked noodle sample on filter paper to adsorb the surface moisture of the fresh and cooked noodle sample, and preparing the fresh and cooked noodle sample into 3 noodle sections with the length of 2cm, wherein each section of noodle is about 0.2-0.3 g. And weighing the surface section, putting the surface section into a glass tube special for a nuclear magnetic resonance spectrometer, sealing the opening of the glass tube by using a non-signal adhesive tape so as to reduce the water loss of the sample, wherein the placement position of the glass tube meets the detection requirement of the nuclear magnetic resonance spectrometer. Scanning by using a Carr-Purcell-Meiboom-Gill (CPMG) pulse sequence to determine the area of the strong binding water peak in the sample (S) 21 ) Area of weak binding water peak (S) 22 ) Total water peak area (S) 2 )。
The CPMG sequence parameters are set as: dominant frequency SF1=21MHz, offset frequency O1=40.18971kHz, number of sampling points TD =10104, sampling frequency SW =100.00kHz, sampling interval Time TW =1000.000ms, number of echoes Echo Count =1000, echo Time =0.101ms, and accumulation number NS =64. The sampling and measuring are repeated for 3 times every time, and the data are saved after the detection is finished. A distributed multi-exponential fit of the CPMG decay curve was performed using the MultiExp Inv analysis software. A multi-exponential fit analysis is performed on the relaxation data using SRIT software algorithms to obtain an improved fit. The peak top time of each process was calculated from the peak position, and the area under each peak (proportion of water molecules corresponding to relaxation time) was determined by cumulative integration. And after the detection is finished, clicking an inversion button to obtain the water distribution state and the proportional spectrum of the sample. By T 21 (0～0.5ms)、T 22 (0.5～40ms)、T 23 (40-1000 ms) respectively corresponding to relaxation times of strongly bound water, weakly bound water and free water, and the peak areas corresponding to the relaxation times are respectively the areas of the strongly bound water peak (S) 21 ) Area of weak binding water peak (S) 22 ) Free water peak area (S) 23 )。
< evaluation of fresh cooked noodle quality >
a. Sensory evaluation
The sensory quality evaluation method is carried out according to the method of national standard GB/T35875-2018. And 7-9 trained sensory evaluators were selected for evaluation. The prepared fresh cooked noodle samples were subjected to sensory quality evaluation. The panelists were asked to evaluate the color, elasticity, smoothness, firmness, surface condition, and taste of the fresh cooked noodles, and each evaluation value was added to obtain a total sensory evaluation score. The sensory evaluation total score (Y) of the fresh cooked noodles is obtained 1 ) Fresh cooked noodle sensory smoothness score (Y) 2 ) And fresh cooked noodle sensory firmness score (Y) 3 ) And the sensory elasticity score (Y) of the fresh and cooked noodles 4 ) Measured value of (a).
TABLE 1 sensory evaluation index and evaluation standard of fresh cooked noodles
b. Evaluation of texture Properties
Analyzing TPA texture characteristic of fresh cooked noodle product with A/LKB-F probe of TA.XTplus type physical property tester of UK SMS company to obtain TPA texture hardness (Y) 5 ) Texture chewiness (Y) 6 ) Measured value of (a).
< creation of prediction model >
And establishing a fresh cooked noodle quality prediction evaluation model.
Excellent 5766, alternate sorting 103, handan wheat 17, medium wheat 175, lu Yuan, xing Mai, dan Xin, ligusticum excellent 5218, shannong 32, liangxing 99, feng De wheat 5, dwarf 58, zheng wheat 369, ligustic 2018, lankou 198, new wheat 26, alternate sorting 988, zheng wheat 136, xinong 325, bainong 418, zhongnong 9302, bainong 207, zheng wheat 366, medium wheat 578, feng De stock 21, xinong 511, jimai 22, 27 small wheat variety grain samples, laboratory milling, preparation of flours with different quality differences, and preparation of 27 fresh cooked flour samples by using the flours.
Randomly selecting the 2/3 samples, and performing sensory evaluation, texture quality evaluation and water peak area measurement in different binding states. And performing correlation analysis and regression analysis on the moisture state detection data and the comprehensive sensory quality scores by using SPSS software, and establishing a fresh cooked noodle quality prediction evaluation model, namely, entering parameters of the proportion of strong bound water and the proportion of weak bound water into the regression model. Obtaining a prediction model I-a prediction model VI:
the prediction model I: y is 1 ＝109.520-7.528×10 -4 ×S 21 -5.333×10 -6 ×S 22 -6.911×10 -4 ×S 2
And (3) a prediction model II: y is 2 ＝22.417+1.058×10 -5 ×S 21 -3.355×10 -5 ×S 22 -1.128×10 -4 ×S 2
Prediction model iii: y is 3 ＝11.782-4.286×10 -4 ×S 21 +1.418×10 -6 ×S 22 -9.786×10 -5 ×S 2
And (4) a prediction model IV: y is 4 ＝34.530-6.400×10 -4 ×S 21 +2.227×10 -6 ×S 22 -2.918×10 -4 ×S 2
The prediction model V is as follows: y is 5 ＝-748.725+4.753×10 -2 ×S 21 -2.300×10 -3 ×S 22 +3.572×10 -2 ×S 2
Prediction model vi: y is 6 ＝-209.377+9.841×10 -3 ×S 21 +4.033×10 -3 ×S 22 +1.205×10 -2 ×S 2
Wherein the independent variable S 21 The area of the strong binding water peak of the fresh cooked noodles, S 22 The area of weak binding water peak of fresh cooked noodle, S 2 Is the total water peak area of fresh cooked noodles, Y 1 Is the sensory evaluation total score of fresh cooked noodles, Y 2 The score of sensory smoothness of the fresh cooked noodles, Y 3 Is a sensory firmness score of fresh cooked noodles, Y 4 Is a sensory elasticity score of fresh cooked noodles, Y 5 Is the texture hardness of fresh cooked noodles, Y 6 Is the chewiness of fresh cooked flour texture.
Table 2 sensory evaluation and texture evaluation actual values of fresh cooked noodles
< evaluation of accuracy of prediction model >
Selecting the rest 1/3 of the samples, and determining the area of the strong binding water peak (S) in each fresh cooked surface sample 21 ) Area of weak binding water peak (S) 22 ) Total water peak area (S) 2 ) Measured value of (a). And measuring the measured value of the product quality index of the 9 samples of the fresh cooked noodles, including the total sensory evaluation score Y of the fresh cooked noodles 1 Measured value of (1), fresh and cooked noodle feelingFunctionality smoothness score Y 2 Measured value of, sensory firmness score Y of fresh cooked noodles 3 Measured value of (A), sensory elasticity score of fresh cooked noodle Y 4 Measured value of (D), texture hardness Y 5 Measured value of (D), texture chewiness Y 6 The measured value of (d);
the S of the sample 21 、S 22 、S 2 The measured values are substituted into a prediction model I to a prediction model VI, and Y of the fresh cooked noodle sample is respectively calculated 1 、Y 2 、Y 3 、Y 4 、Y 5 、Y 6 The predicted value of (2);
correlation analysis was performed using SPSS statistical analysis software, Y 1 、Y 2 、Y 3 、Y 4 、Y 5 、Y 6 The correlation coefficients of the predicted value and the measured value are high and reach a very significant level (P is less than 0.01), which shows that the product quality of the fresh cooked noodles can be rapidly predicted through the prediction models I to VI based on the water peak areas of different binding states in the fresh cooked noodles, and the method has the advantages of rapidness and high accuracy.
TABLE 3 sensory evaluation and texture evaluation values of fresh cooked noodles
Table 4 sensory evaluation and texture evaluation prediction values of fresh cooked noodles
In practical application, a threshold value can be set according to actual needs, for example, the threshold value is set to 80 minutes, and the sensory evaluation total score (Y) of the fresh and cooked noodles obtained by prediction by the prediction model I is adopted 1 ) When the time is less than 80 minutes, the product is directly judged to be unqualified, other quality prediction is not needed, and the prediction can be improvedAnd (6) measuring the efficiency. If the fresh cooked noodle sensory evaluation total score (Y) is obtained by adopting the prediction model I for prediction 1 ) And when the time is more than 80 minutes, other prediction models are continuously adopted to carry out refined quality prediction, and the prediction is comprehensive, accurate and efficient.
While embodiments of the invention have been described above, it is not limited to the applications set forth in the description and the embodiments, which are fully applicable to various fields of endeavor for which the invention may be embodied with additional modifications as would be readily apparent to those skilled in the art, and the invention is therefore not limited to the details given herein and to the embodiments shown and described without departing from the generic concept as defined by the claims and their equivalents.
Claims (6)
1. The method for rapidly predicting the quality of the fresh cooked noodles is characterized by comprising the following steps of:
step one, establishing a prediction model I, wherein the prediction model I is as follows:
Y 1 ＝109.520-7.528×10 -4 ×S 21 -5.333×10 -6 ×S 22 -6.911×10 -4 ×S 2
wherein, Y 1 The independent variable S is the total score of sensory evaluation of the fresh cooked noodles 21 The area of the strong binding water peak of the fresh cooked noodles, S 22 The area of weakly bound water peak of freshly cooked noodle, S 2 The total water peak area of the fresh and cooked noodles is shown;
step two, detecting the area S of the strong binding water peak of the fresh cooked surface with the quality to be predicted 21 Area of weak binding water peak S 22 Total water peak area S 2 ；
Step three, substituting the detection data of the step two into a prediction model I to calculate to obtain a fresh cooked noodle sensory evaluation total score;
further comprising:
if the total sensory evaluation score of the fresh cooked noodles obtained by calculation in the third step is greater than the threshold value,
establishing a prediction model II, wherein the prediction model II is as follows:
Y 2 ＝22.417+1.058×10 -5 ×S 21 -3.355×10 -5 ×S 22 -1.128×10 -4 ×S 2
wherein Y is 2 Scoring the sensory smoothness of the fresh and cooked noodles;
and substituting the detection data obtained in the step two into a prediction model II to calculate the sensory smoothness score of the fresh and cooked noodles.
2. The method of claim 1, further comprising:
if the total sensory evaluation score of the fresh cooked noodles obtained by calculation in the third step is greater than the threshold value,
establishing a prediction model III, wherein the prediction model III is as follows:
Y 3 ＝11.782-4.286×10 -4 ×S 21 +1.418×10 -6 ×S 22 -9.786×10 -5 ×S 2
wherein Y is 3 Scoring the sensory firmness of the fresh cooked noodles;
and substituting the detection data in the step two into the prediction model III to calculate to obtain the sensory firmness score of the fresh and cooked noodles.
3. The method of claim 1, further comprising:
if the total sensory evaluation score of the fresh cooked noodles obtained by calculation in the third step is greater than the threshold value,
establishing a prediction model IV, wherein the prediction model IV is as follows:
Y 4 ＝34.530-6.400×10 -4 ×S 21 +2.227×10 -6 ×S 22 -2.918×10 -4 ×S 2
wherein, Y 4 Scoring the sensory elasticity of the fresh cooked noodles;
and substituting the detection data obtained in the step two into a prediction model IV to calculate the sensory elasticity score of the fresh cooked noodles.
4. The method of claim 1, further comprising:
if the total sensory evaluation score of the fresh cooked noodles obtained by calculation in the third step is greater than the threshold value,
establishing a prediction model V, wherein the prediction model V is as follows:
Y 5 ＝-748.725+4.753×10 -2 ×S 21 -2.300×10 -3 ×S 22 +3.572×10 -2 ×S 2
wherein, Y 5 Texture hardness of the fresh cooked noodles;
and substituting the detection data obtained in the step two into a prediction model V to calculate the texture hardness of the fresh cooked surface.
5. The method of claim 1, further comprising:
if the total sensory evaluation score of the fresh cooked noodles obtained by calculation in the third step is greater than the threshold value,
establishing a prediction model VI, wherein the prediction model VI is as follows:
Y 6 ＝-209.377+9.841×10 -3 ×S 21 +4.033×10 -3 ×S 22 +1.205×10 -2 ×S 2
wherein, Y 6 The chewiness of the fresh and cooked texture;
and substituting the detection data in the second step into a prediction model VI to calculate the texture chewiness of the fresh cooked noodles.
6. The method for rapidly predicting the quality of fresh cooked noodles as claimed in claim 1, wherein the fresh cooked noodles are prepared by the steps of:
placing fresh noodles into a stainless steel pot containing 50 times of boiling water, keeping the water slightly boiling, boiling until the white core disappears, taking out, cooling with water, and standing on a filter screen for 1min to obtain fresh cooked noodles.
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