A sensory quality evaluation method for buckwheat noodles based on the Stroop effect and its application

Abstract

This invention discloses a sensory quality evaluation method for buckwheat noodles based on the Stroop effect and its application. The method quantifies the interaction effects among sensory attributes such as texture, appearance, and taste in buckwheat noodles by constructing Stroop experimental systems with non-interactive control paradigms, texture-sensory paradigms, appearance-sensory paradigms, and taste-sensory paradigms. Effect size analysis clarifies that when the buckwheat content is ≥50%, texture characteristics have a significant positive promoting effect on bitterness perception (13.76%~17.35%), while appearance characteristics have a significant negative interfering effect on elasticity evaluation (-8.57%~-7.12%). Based on this, this invention establishes a sensory scoring interaction correction model to correct the systematic bias caused by neglecting interaction effects in traditional independent scoring. This method provides a new theoretical basis for the objective evaluation of the sensory quality of buckwheat noodles, the identification of key control dimensions, and product optimization, helping to improve product sensory acceptance and market competitiveness.

Description

Technical Field

[0001] This invention belongs to the field of food sensory science and quality evaluation, specifically relating to a sensory quality evaluation method and application of buckwheat noodles based on the Stroop effect. Background Technology

[0002] Buckwheat products, especially buckwheat noodles, have shown great potential in the healthy staple food market due to their convenient consumption. However, the inherent tough husk and crisp kernel of buckwheat make processing difficult, and the high buckwheat content introduces a noticeable bitter taste, dark color, and rough texture. These undesirable sensory qualities seriously affect consumer acceptance and restrict market promotion.

[0003] In the field of food sensory evaluation, existing technologies mainly focus on the independent evaluation of single sensory attributes or the assessment of simple food systems. For example, CN115097081A discloses a "sensory evaluation method for buckwheat rice based on fuzzy mathematics". This method quantifies sensory scores by establishing a fuzzy evaluation matrix of five factors: color, consistency, appearance, palatability, and aroma. However, its model still assumes that each sensory index is independent and does not consider the interaction between multiple sensory attributes. CN120927379A provides a "rice oral chewing simulation device and intelligent palatability evaluation method". It uses hyperspectral imaging and olfactory sensor arrays to fuse texture and flavor data and uses machine learning to evaluate palatability, representing the development direction of intelligent sensory analysis. However, its model construction is still based on the independent measurement and fusion of various features and does not explore in depth the possible causal or interference relationships between features (such as texture and flavor).

[0004] Currently, sensory evaluations of buckwheat noodles mostly employ traditional descriptive analysis, preference ratings, or instrumental measurements of single attributes (such as texture and color difference). These methods typically assume that each sensory attribute is independent. However, for products like buckwheat noodles with complex sensory characteristics, consumers' overall perception is the result of the integration and interaction of multi-sensory information, including visual, gustatory, and tactile information. Existing technologies neglect this "sensory interaction" effect. For example, a rough appearance may trigger negative expectations regarding texture (such as elasticity), while a superior "chewy" texture may alleviate sensitivity to bitterness to some extent. Simply treating each attribute as an independent variable and summing them up leads to distorted evaluation results, failing to accurately identify the core conflict dimensions affecting consumer acceptance, and thus hindering effective guidance for product formulation optimization and process improvement.

[0005] In the field of cognitive psychology, the Stroop effect is a classic paradigm for studying the conflict and interference between information from different dimensions. Its principles have been attempted to be applied to food sensory research to quantify the subconscious interference between visual, gustatory, and other sensory information. However, no studies have yet systematically applied the Stroop experimental paradigm to grain products, particularly buckwheat noodles, to analyze their multisensory interaction mechanisms, and there are no reports of using this to construct quantitative evaluation models to correct for traditional rating biases.

[0006] Furthermore, existing technologies for improving bitterness mostly focus on the aspect of formulation additives. CN117859863A discloses a "method for reducing the bitterness of buckwheat noodles based on psychophysical principles," which increases the human body's perception threshold for bitterness by adding flavor modifiers such as YE peptides and erythritol, thereby reducing the perception of bitterness at the sensory level. This method "offsets" bitterness by adding external ingredients, and does not "interfere" or "improve" the overall perception of bitterness by optimizing other attributes such as "texture" from the perspective of the interaction of the product's own sensory attributes.

[0007] In summary, existing technologies lack a method to scientifically quantify and analyze the complex interactions among the multiple sensory attributes of buckwheat noodles, such as texture, appearance, and taste. This results in limitations in existing sensory evaluation systems, making it difficult to fully reflect the true sensory quality of the product and failing to provide clear theoretical basis and key technical guidance for precise quality control and product optimization of high-buckwheat-content noodles.

[0008] Therefore, developing a novel evaluation method based on multi-sensory interaction mechanisms, which can scientifically quantify the multi-sensory interaction effects in buckwheat noodles, is not only of significant theoretical value and application value for constructing a more objective sensory evaluation system, identifying key attributes that affect consumer acceptance, and achieving precise control of product quality, but also provides a theoretical basis for sensory optimization and product development of coarse grain staple foods, thus possessing significant theoretical value and urgent industrial application needs. Summary of the Invention

[0009] To address the aforementioned technical problems, this invention proposes a sensory quality evaluation method for buckwheat noodles based on the Stroop effect. This method aims to systematically quantify the mutual interference or promotion effects among multiple sensory attributes of buckwheat noodles, such as texture, appearance, and taste, and accurately identify key conflicting dimensions affecting overall sensory acceptance at different buckwheat contents. Based on this, this invention further establishes a scoring correction model that can effectively correct for sensory interaction effects, significantly improving the objectivity and accuracy of sensory evaluation, thereby providing clear scientific guidance and theoretical basis for the formulation design and process optimization of high-buckwheat-content noodles.

[0010] This invention relates to a sensory quality evaluation method for buckwheat noodles based on the Stroop effect, and the technical solution is as follows:

[0011] 1. Sample Preparation: Using buckwheat flour and wheat flour as the main raw materials, a series of noodle samples with gradient differences in buckwheat content were prepared by blending them in a specific ratio. The buckwheat flour content gradient was set at 20%, 30%, 40%, 50%, and 60% (based on the total mass of the mixed flour), and 10-20 noodle samples were prepared for each gradient. All samples were processed using a uniform process of dough mixing, maturation, rolling (thickness 0.8–1.0 mm), cutting into strips, and drying, and were cooked under the same conditions before use to ensure consistency in the sensory evaluation benchmark.

[0012] 2. Sensory Evaluation Implementation: Selected and trained evaluators were conducted according to the national standard (GB / T 16291.1-2012). Four Stroop paradigm evaluation tasks were designed and implemented for each sample:

[0013] (a) Sensory non-interactive paradigm (control): Evaluators wear blindfolds to isolate visual interference and evaluate texture attributes (firmness, elasticity, smoothness), taste (tasting the crushed sample), and appearance attributes (color, surface condition) in sequence.

[0014] (b) Appearance-Sensory Stroop Paradigm: Evaluators evaluate all sensory properties without wearing blindfolds and with full knowledge of the sample's appearance (color, surface condition). This paradigm examines the interference of visual information on other attributes (texture, taste);

[0015] (c) Taste-Sensory Stroop Paradigm: Evaluators wear blindfolds and first evaluate the taste to establish taste expectations. Then, without removing the blindfolds, they evaluate the texture and finally the appearance. This paradigm examines the real-time interference of preconceived taste information on other attributes.

[0016] (d) Texture-Sensory Stroop Paradigm: Evaluators wear blindfolds and first evaluate texture attributes to establish tactile expectations, then evaluate taste, and finally evaluate appearance; this paradigm examines the real-time interference of texture on flavor perception during chewing.

[0017] Stroop effect size calculation: Based on the sensory ratings obtained in step S2, calculate the Stroop effect size γ among the sensory attributes. The calculation formula is as follows: γ = [(α' - α) / α] × 100% Calculate the effect size; where α is the score under the no-interaction paradigm (benchmark), and α' are the scores of the indicator under the three interaction paradigms of appearance-sensory, taste-sensory, and texture-sensory respectively; γ>0 indicates a facilitating effect, and γ<0 indicates a disruptive effect;

[0018] The interaction or promotion between texture, appearance, and taste attributes can be quantified by classifying the interaction based on the absolute value of the effect: weak interaction: |γ| < 3%; moderate interaction: 3% ≤ |γ| < 10%; strong interaction: |γ| ≥ 10%.

[0019] Interaction analysis: The system analyzed the changes in the effect magnitudes of texture, appearance, and taste under different buckwheat contents. The study found that when the buckwheat content was ≥50%, significant interactions occurred: texture had a significant positive effect on taste (13.76% to 17.35%), while appearance had a significant negative effect on elasticity (-8.57% to -7.12%).

[0020] Sensory rating interaction correction model establishment: Based on the significant Stroop effect size calculated in step S3, a sensory rating interaction correction model is established to correct the traditional independent rating. The corrected comprehensive sensory score S is calculated according to the following formula: s i ' = s i × (1 + ∑γ ij / 100), the overall score S = ∑ s i '; where s i Let ∑γ be the original score of indicator i in the non-interaction paradigm. ij The sum of all significant effect sizes (e.g., |γ| > 3%) on index i; this model makes the overall score more accurately reflect the sensory characteristics of the product.

[0021] The above-mentioned sensory quality evaluation method for buckwheat noodles based on the Stroop effect is applied in the product development and quality optimization of buckwheat noodles.

[0022] The above-mentioned sensory quality evaluation method for buckwheat noodles based on the Stroop effect is applied to the identification of key sensory interaction dimensions and to guide the optimization of product formulation and process.

[0023] The above-mentioned sensory quality evaluation method for buckwheat noodles based on the Stroop effect is applied to optimize the texture characteristics of high buckwheat content products in order to positively interfere with the perception of bitterness.

[0024] The above-mentioned sensory quality evaluation method for buckwheat noodles based on the Stroop effect is applied to improving product appearance and reducing its negative visual interference with texture evaluation.

[0025] The present invention has the following advantages and effects compared with the prior art:

[0026] (1) Methodological innovation: This invention introduces the Stroop paradigm system from cognitive psychology into the field of food sensory science for the first time, and designs four logically rigorous evaluation paradigms—sensory non-interaction, appearance-sensory, taste-sensory, and texture-sensory—to separate and quantify the interaction of multiple sensory attributes. This method can not only identify significant sensory conflicts under high buckwheat content (≥50%), but also successfully capture the weak interaction trend (effect size is generally less than 5%) in samples with low buckwheat content (20%~40%), and complete the full dynamic monitoring from "no interaction" to "weak interaction" and then to "strong interaction", providing a systematic and quantifiable innovative methodological framework for multi-sensory interaction research.

[0027] (2) Evaluation accuracy: By calculating the Stroop effect size (γ), this invention achieves objective quantification of the intensity of sensory interaction and constructs a corresponding scoring correction model. This model effectively corrects the positive promoting effect of texture on taste (13.76%–17.35%) and the negative interference effect of appearance on elasticity (-7.12% to -8.57%) in high buckwheat content products, and sets a scientific threshold (|γ|≥3%) to avoid over-correction of low content products, thereby significantly improving the objectivity and reliability of sensory evaluation results;

[0028] (3) Clarity of guidance: It is clear that in products with high buckwheat content (≥50%), "texture" and "appearance" are the two core conflict dimensions for regulating sensory quality; the optimization of texture can effectively interfere with the perception of bitterness, while the improvement of appearance can reduce negative expectations of texture (such as elasticity); this provides a clear and direct optimization path for product development, namely, improving appearance impression and optimizing texture characteristics; for products with low and medium buckwheat content (20%~40%): the sensory interaction effect is weak (effect size <5%), and product optimization can focus more evenly on the improvement of various basic sensory attributes, weakening the focus on sensory conflict issues;

[0029] (4) Wide applicability: The Stroop paradigm evaluation system and interactive correction model constructed in this invention have good scalability. They are not only applicable to buckwheat noodles, but can also provide evaluation methods and quality control references for other foods with similar sensory conflicts or requiring multi-attribute coordination (such as whole grain products, functional foods, etc.), and have broad industry application value. Attached Figure Description

[0030] Figure 1 Pearson's correlation analysis of sensory quality scores of buckwheat noodles by different evaluators;

[0031] Figure 2 Pearson's correlation analysis of sensory quality scores for various indicators of buckwheat noodles by different evaluators;

[0032] Figure 3 To investigate the interaction effects among various sensory elements when evaluating the quality of buckwheat noodles (sample 1) under different Stroop paradigms;

[0033] Figure 4 To investigate the interaction effects among various sensory elements when evaluating the quality of buckwheat noodles (sample 2) under different Stroop paradigms;

[0034] Figure 5 To investigate the interaction effects among various sensory elements when evaluating the quality of buckwheat noodles (sample 3) under different Stroop paradigms;

[0035] Figure 6 To investigate the interaction effects among various sensory elements when evaluating the quality of buckwheat noodles (sample 4) under different Stroop paradigms;

[0036] Figure 7 To investigate the interaction effects among various sensory elements when evaluating the quality of buckwheat noodles (sample 5) under different Stroop paradigms;

[0037] Figure 8 The comprehensive sensory scores of buckwheat noodle samples with different contents under different Stroop paradigms were obtained.

[0038] Figure 9 Samples of buckwheat noodles with different buckwheat contents are displayed. Detailed Implementation

[0039] To enable those skilled in the art to better understand the present invention, the present invention will now be further described in conjunction with specific embodiments.

[0040] Example 1:

[0041] Consistency analysis of sensory evaluation panel member ratings

[0042] 1.1 Sample Preparation

[0043] In this embodiment, taking a noodle sample as an example, the following steps are followed: using buckwheat flour and wheat flour as the main raw materials, and adding food colloids and quality improvers, the noodles are processed through processes such as kneading, maturing, rolling, cutting, and drying.

[0044] The samples were cooked and drained before being used for sensory evaluation. To establish an evaluation reference system, Jiujiangtang buckwheat and highland barley noodles and commercially available Jinshahe buckwheat noodles were selected as reference samples for sensory characteristics.

[0045] All samples were placed in transparent bowls with random numbers and evaluated in a standard sensory evaluation chamber according to the quantitative descriptive testing requirements in GB / T 10220 "General Methodology of Sensory Analysis".

[0046] 1.2 Sensory Evaluation Methods and Procedures

[0047] In this embodiment, the sensory quality evaluation is based on the national standard GB / T 35875-2018 "Grain and Oil Inspection: Evaluation of Processing Quality of Wheat Flour and Noodles". The weights and descriptions of some indicators are adjusted according to the characteristics of buckwheat noodles to form an evaluation standard applicable to this invention (see Table 1 for details).

[0048] The specific evaluation process is as follows: First, smell the sample to evaluate its flavor characteristics; then observe the appearance and color of 2-3 cooked noodles; next, gently suck the noodles into your lips to feel the smoothness of their surface; then bite the noodles with your incisors to assess their hardness; finally, chew thoroughly to comprehensively evaluate their elasticity and overall taste quality.

[0049] Table 1 Sensory Evaluation Items and Evaluation Criteria for Buckwheat Noodles Solidity 10 Medium firmness (8-10) Slightly soft or slightly firm (7) Very soft or very firm (4-6) elasticity 25 Good elasticity (21-25) Average elasticity (16-20) Poor elasticity (10-15) Smoothness 20 Smooth and refreshing (17-20) Slightly smooth (13-16) Not refreshing (9-12) Taste 25 Buckwheat has a light aroma and moderate bitterness (21-25). It is quite bitter but acceptable (16-20). It is very bitter and difficult to accept (10-15). Surface condition 10 Fine and smooth surface structure (8-10) Medium (7) Rough surface, expansion, and severe deformation (4-6) Color 10 The noodles have a buckwheat brownish-yellow color, bright (8-10), moderate brightness (7), and grayish-brown color with poor brightness (4-6).

[0050] 1.3 Establishment of the Sensory Quality Evaluation Team

[0051] In this embodiment, the sensory evaluation team was formed in strict accordance with GB / T 16291.1-2012 "General Guidelines for the Selection, Training and Management of Sensory Evaluation Evaluators Part 1: Selection of Evaluators".

[0052] Through general selection, preliminary selection, systematic training and final screening of laboratory members, an optimal evaluation group was formed, consisting of 15 evaluators (7 men and 8 women, aged 20-25).

[0053] Sensory evaluation experiments were conducted in the standard sensory evaluation laboratory of Wuhan University of Light Industry. The experimental protocol and testing procedures strictly followed GB / T 10220 "General Methodology of Sensory Analysis" and adopted the quantitative descriptive testing methods specified therein.

[0054] 1.4 Consistency Analysis of Sensory Evaluation Panel Members' Scores

[0055] To verify the consistency of the evaluation panel's scores, Pearson correlation analysis was used to analyze the sensory quality scores of the buckwheat noodles given by each evaluator. The results are as follows: Figure 1 As shown in the figure, the overall sensory scores of the 15 evaluators for the buckwheat noodles were significantly correlated at the 0.01 level (two-tailed) (P<0.01), indicating a high degree of consistency among the groups in their overall quality evaluation. Further correlation analysis was conducted on the scores of individual indicators (color, surface condition, firmness, elasticity, smoothness, and taste), and the results are shown in the figure. Figure 2As shown, all individual indicator scores were significantly correlated at the 0.05 level (two-tailed) (p<0.05), demonstrating that the group possessed basic attribute discrimination ability; however, at the 0.01 level (p<0.01), some raters' scores did not show highly significant correlations, suggesting perceptual differences among individuals.

[0056] In the sensory evaluation process, there are complex interactions among various attribute indicators, mainly due to the inherent interconnectedness of the human sensory system itself. A prominent sensory feature may inhibit or interfere with the objective judgment of other attributes. For example, visual features (such as color and surface condition) may affect the perception of oral tactile and gustatory attributes; the perception of elasticity may also trigger a prejudgment of the overall impression, thus interfering with the evaluation of other indicators. Therefore, this invention pays particular attention to the interactions between different sensory attributes.

[0057] Example 2:

[0058] Sensory Interaction Evaluation of Buckwheat Noodles Based on the Stroop Effect

[0059] Four Stroop paradigms were implemented for evaluation: In a standard sensory evaluation room, each evaluator sequentially evaluated each sample (S1-S5) using one of the following paradigms: (a) sensory non-interaction paradigm; (b) appearance-sensory Stroop paradigm; (c) taste-sensory Stroop paradigm; and (d) texture-sensory Stroop paradigm. The evaluation order of each sample under different paradigms was randomized, with sufficient time intervals to avoid fatigue effects.

[0060] 2.1 Stroop effect analysis among various sensory components during quality evaluation of buckwheat noodle sample 1 (20% buckwheat content)

[0061] like Figure 3 As shown, the sensory evaluation results of noodle samples with a 20% buckwheat content under different Stroop paradigms indicate that the scores for firmness, elasticity, smoothness, taste, surface condition, and color are basically consistent across the four evaluation modes: no interaction, texture-sensory, appearance-sensory, and taste-sensory. Among these, elasticity and taste scores are slightly higher in the texture-sensory and appearance-sensory paradigms than in the other modes, but the differences are not statistically significant.

[0062] The taste scores under the four modes were 22.04, 22.47, 22.27, and 22.07, respectively, all falling within the "buckwheat aroma, moderate bitterness" scoring range (21-25 points). The Stroop effect sizes of the texture, appearance, and taste of Sample 1 on other sensory indicators are shown in Table 2; the absolute values ​​of all effect sizes were below 2.5%. This result indicates that when the amount of buckwheat added is 20%, the interaction between various sensory attributes is relatively weak.

[0063] Table 2. Stroop effect of texture, appearance, and taste indicators of buckwheat noodle sample 1 on other indicators.

[0064] 2.2 Stroop effect analysis among various sensory components during quality evaluation of buckwheat noodle sample 2 (30% buckwheat content)

[0065] Figure 4 The sensory scores of samples with a buckwheat content of 30% are shown under different Stroop paradigms. Under the four evaluation modes of no interaction, texture-sensory, appearance-sensory, and taste-sensory, the scores for firmness, surface condition, and color of the samples were consistent, with no significant differences (p < 0.05).

[0066] Elasticity, palatability, and smoothness scored slightly higher than other modes in the texture-sensory and appearance-sensory paradigm, with elasticity scores of 20.95 and 20.62, palatability scores of 22.22 and 21.91, and smoothness scores of 16.89 and 17.13, respectively. The score ranges for each indicator are as follows: elasticity 20.04~20.62, smoothness 16.56~17.89, palatability 21.09~22.22.

[0067] The Stroop effect sizes of texture, appearance, and taste on other sensory indicators for Sample 2 are shown in Table 3. Texture had a 5.37% enhancing effect on taste, and appearance had a 3.90% enhancing effect. The effect sizes among the remaining indicators were low, with absolute values ​​all less than 3%.

[0068] Table 3. Stroop effect of texture, appearance, and taste indicators of buckwheat noodle sample 2 on other indicators.

[0069] 2.3 Stroop effect analysis among various sensory components during quality evaluation of buckwheat noodle sample 3 (40% buckwheat content)

[0070] Figure 5 The sensory scores of a sample with 40% buckwheat content under different Stroop paradigms are presented. Compared with samples with lower buckwheat content, the overall sensory score of this sample is lower. Regarding texture characteristics, the firmness score ranges from 6.69 to 6.87, elasticity from 17.13 to 17.51, and smoothness from 14.29 to 15.11. Among appearance attributes, the surface condition score ranges from 6.98 to 7.02, and the color score ranges from 6.98 to 7.22. The taste score indicates that the sample has a noticeable bitterness, but it is still within an acceptable range. Among the four evaluation modes, smoothness scored the highest (15.11) under the appearance-sensory paradigm, while taste scored the highest (20.34) under the texture-sensory paradigm.

[0071] The Stroop effect sizes of texture, appearance, and taste on other sensory indicators for Sample 3 are shown in Table 4. The promoting effect size of appearance on smoothness was 5.10%, and the promoting effect size of texture on taste was 4.70%. The absolute values ​​of the effect sizes among the remaining indicators were all less than 2%.

[0072] Table 4. Stroop effect of texture, appearance, and taste indicators of buckwheat noodle sample 3 on other indicators.

[0073] 2.4 Stroop effect analysis among various sensory components during quality evaluation of buckwheat noodle sample 4 (50% buckwheat content)

[0074] Figure 6 The sensory evaluation results of noodle samples with 50% buckwheat content under different Stroop paradigms are presented. Sample 4 showed relatively consistent scores for firmness, smoothness, surface condition, and color across the four evaluation modes. The taste score was 18.64 in the texture-sensory mode, significantly higher than the non-interaction mode (15.89), appearance-sensory mode (15.93), and flavor-sensory mode (15.69) (p < 0.05). Other sensory attributes (firmness, elasticity, smoothness, surface condition, and color) showed no significant differences across the different evaluation modes.

[0075] The Stroop effect of texture, appearance, and taste on other sensory indicators of Sample 4 is shown in Table 5. Texture had a 17.35% enhancing effect on taste. Appearance had a -7.12% and -5.27% interfering effect on elasticity and smoothness, respectively. The absolute values ​​of the remaining effect values ​​were all below 4%.

[0076] Table 5. Stroop effect of texture, appearance, and taste indicators of buckwheat noodle sample 4 on other indicators.

[0077] 2.5 Stroop effect analysis among various sensory components during quality evaluation of buckwheat noodle sample 5 (60% buckwheat content)

[0078] Figure 7 The sensory evaluation results of a noodle sample with 60% buckwheat content under different Stroop paradigms are presented. The firmness, surface condition, and color scores of sample 5 were relatively consistent across the four evaluation modes, with no significant differences. The surface condition scores were 5.13, 4.98, 5.13, and 5.04, respectively, and the color scores were 5.15, 5.04, 5.13, and 5.18, respectively. The elasticity score was significantly lower in the appearance-sensory mode than in the non-interaction mode. The taste score was significantly higher in the texture-sensory mode than in the other modes.

[0079] The Stroop effect of texture, appearance, and taste on other sensory indicators of sample 5 is shown in Table 6. Among them, the promoting effect of texture on taste is 13.76%, and the interfering effect of appearance on elasticity is -8.57%. The absolute values ​​of the effect sizes among the remaining indicators are all less than 4%.

[0080] Table 6. Stroop effect of texture, appearance, and taste indicators of buckwheat noodle sample 5 on other indicators.

[0081] Example 3

[0082] Overall Characterization Analysis of the Stroop Effect in Multisensory Interaction

[0083] Depend on Figure 8 It can be seen that the overall sensory score of buckwheat noodles generally declines with increasing buckwheat content. Samples with 20% and 30% buckwheat content scored the highest under all evaluation methods, demonstrating that these products not only present the flavor characteristics of buckwheat but also maintain the excellent sensory quality of traditional noodles, resulting in the best acceptability. When the buckwheat content reaches 40%, the score drops significantly (to around 72 points), indicating that this content may be a critical point, where negative sensory characteristics such as darker color, increased bitterness, or coarser texture caused by buckwheat begin to significantly affect the overall evaluation. Samples with 50% and 60% buckwheat content scored the lowest (mostly below 60 points), indicating that excessive buckwheat addition has a significant negative impact on the sensory quality of the noodles, greatly reducing their overall acceptability.

[0084] Table 7. Comparison of the Stroop effect of different proportions of buckwheat noodles on other indicators in terms of texture and appearance.

[0085] Furthermore, a systematic comparison of the sensory quality of buckwheat noodles under four evaluation modes, as shown in Table 7, revealed significant interactions among sensory attributes. Texture characteristics had a significant promoting effect on taste perception, with the highest Stroop effect reaching 17.35%. Appearance attributes, however, interfered with texture evaluation, with a negative effect of -8.57%. The sensory interaction effect exhibited a non-linear characteristic with varying buckwheat content. At a buckwheat content of 60%, the promoting effect of texture-taste was weaker than in the 50% sample, indicating the existence of an "optimal content window." Beyond this window, the interaction weakened due to excessive bitterness or texture deterioration. Sensory evaluation of high-buckwheat noodles involves multi-sensory integration. Excessive bitterness can produce a "signal-dominant effect," masking the moderating role of texture. Appearance, through the "visual expectation effect," interferes with the objective evaluation of texture. Sensory interactions are influenced by both the physicochemical properties of the sample and the cognitive factors of the evaluator, presenting a complex non-linear relationship.

[0086] Example 4

[0087] Establishment of a sensory rating interaction correction model

[0088] In this embodiment, the interaction effects of sensory indicators in noodle samples with different buckwheat contents were systematically analyzed, and the Stroop effect was used to characterize them as a whole. It was found that traditional sensory scoring methods usually treat each indicator as independent and use simple summation as the comprehensive score. This method cannot reflect the mutual interference between indicators (such as the promotion of taste by texture or the inhibition of elasticity by appearance), resulting in systematic errors in the evaluation results.

[0089] To correct the systematic bias caused by neglecting interaction in traditional rating methods, this embodiment establishes a sensory rating interaction correction model based on the Stroop effect size. The correction formula is as follows:

[0090]

[0091]

[0092] Note:

[0093] s´ i As an indicator Adjusted score;

[0094] s i As an indicator The raw score under the non-interactive rating method;

[0095] As an indicator right Stroop effect size (only summaries for effect sizes exceeding 3%).

[0096] S represents the optimized overall sensory score.

[0097] The method of this invention, taking buckwheat noodles prepared primarily from buckwheat flour and wheat flour as an example, elaborates in detail its sensory quality evaluation method based on the Stroop effect. This method is also applicable to the evaluation of whole grain products or functional foods, and is not limited to the evaluation of noodles.

[0098] The accuracy of the evaluation is reflected in the fact that this invention uses Stroop effect size to quantitatively correct traditional independent scores, effectively eliminating the systematic bias caused by sensory interaction. Taking noodle samples with different buckwheat contents as examples, based on the original scores in the non-interaction mode, significant interaction items with an absolute value exceeding 3% are selected for correction. Samples with low buckwheat content (20%-40%) show weaker interaction and smaller correction: the score for 20% samples remains unchanged, while the scores for 30% and 40% samples increase by 2.40% and 2.28%, respectively. Samples with high buckwheat content (50%-60%) are affected by the combined positive promotion of texture-taste and the negative interference of appearance-elasticity, resulting in score increases of 1.17% and 1.98%, respectively. The corrected scores are closer to the actual experience of consumers under the combined influence of multiple sensory information. Specific data can be found in the comparison of samples 4 and 5 before and after correction in Example 4 (for example, the original taste score of sample 4 was 15.89, which was corrected to 18.65 after a texture-taste effect of 17.35%; the original elasticity score of sample 5 was correspondingly reduced after an appearance-elasticity effect of -8.57%). The above results show that the method of the present invention can accurately quantify the intensity of sensory interaction and significantly improve the objectivity and reliability of sensory evaluation results.

[0099] Table 8 Comparison of overall sensory scores for buckwheat noodles before and after correction 20 86.65 86.65 0.00 30 81.55 83.51 2.40 40 72.24 73.89 2.28 50 59.13 59.82 1.17 60 54.98 56.07 1.98

Claims

1. A sensory quality evaluation method for buckwheat noodles based on the Stroop effect, characterized in that, Includes the following steps: S1: Design noodle samples with a gradient of buckwheat content, cook them and set them aside; S2: The organization has trained evaluators to conduct multi-context sensory evaluations and score the noodle samples for each grade. S3: Stroop Effect Size Calculation: Based on the ratings in S2, calculate the Stroop effect size γ using the following formula: γ = [(α' - α) / α] × 100% α represents the score under the sensory non-interaction paradigm for a certain single indicator, and α' represents the scores under different interaction paradigms for the same indicator. S4: Interaction analysis: Compare the effect sizes among samples with different buckwheat contents, and identify and quantify the mutual interference or promotion effects between taste, texture and appearance attributes. S5: Sensory Rating Interaction Correction: The ratings are corrected, and the corrected overall sensory score S is calculated using the following formula: s i ' = s i × (1 + ∑γ ij / 100) S = ∑ s i ' Among them, s i Let ∑γ be the original score of indicator i in the non-interaction paradigm. ij s is the sum of the Stroop effect sizes γ values ​​for all effect sizes under index i whose absolute value exceeds 3%. i ' is the score after correction for sensory evaluation index i.

2. The sensory quality evaluation method for buckwheat noodles based on the Stroop effect according to claim 1, characterized in that, In S1, the buckwheat content gradient was designed to be 20%, 30%, 40%, 50%, and 60%, and 10 to 20 noodle samples were prepared for each gradient.

3. The sensory quality evaluation method for buckwheat noodles based on the Stroop effect according to claim 1, characterized in that, In S2, 10-20 trained, top-performing evaluators conduct multi-contextual sensory evaluations, including: (a) Sensory non-interactive paradigm; Sensory non-interactive paradigm: Evaluators wear blindfolds, first evaluate the texture attributes, then evaluate the taste of the crushed sample, and finally remove the blindfolds to evaluate the appearance attributes. (b) Appearance-Sensory Stroop Paradigm: The evaluator evaluates all sensory attributes, including texture, taste, and appearance, under conditions where the appearance of the sample is fully visible. (c) Taste-Sensory Stroop Paradigm: Evaluators wear blindfolds and first evaluate the taste, then the texture attributes, and finally the appearance attributes. (d) Texture-Sensory Stroop Paradigm: Evaluators wear blindfolds and first evaluate texture attributes, then taste, and finally appearance attributes. Sensory evaluation indicators are designated as indicator i, including: texture attributes, taste, and appearance attributes; texture attributes include: firmness 10 points, elasticity 25 points, smoothness 20 points; taste 25 points; appearance attributes include: surface condition 10 points, color 10 points; the total score for each indicator is 100 points.

4. The sensory quality evaluation method for buckwheat noodles based on the Stroop effect according to claim 1, characterized in that, In S4, when quantifying the mutual interference or promotion between texture, appearance, and taste attributes, γ>0 indicates a promoting effect, and γ<0 indicates a interference effect. Based on the magnitude of the absolute value of the effect, the interaction is classified as follows: weak interaction: |γ| < 3%; moderate interaction: 3% ≤ |γ| < 10%; strong interaction: |γ| ≥ 10%.

5. The sensory quality evaluation method for buckwheat noodles based on the Stroop effect according to claim 1, characterized in that, In S5, when applied to noodle samples with a buckwheat content ≥50%, the significant Stroop effect γ includes at least: the positive promoting effect of texture characteristics on taste perception, with an effect size between 13.76% and 17.35%, and the negative interference effect of appearance characteristics on elasticity evaluation, with an effect size between -8.57% and -7.12%. When applied to noodle samples with a buckwheat content of 30% to 40%, there is a partial moderate Stroop effect, and the interaction correction model is selected according to the product accuracy requirements. When applied to noodle samples with a buckwheat content ≤20%, the absolute values ​​of each effect size are all less than 3%, and the interaction can be ignored, so the original score under the non-interaction paradigm is used.

6. The application of the sensory quality evaluation method for buckwheat noodles based on the Stroop effect according to any one of claims 1-5 in the product development and quality optimization of buckwheat noodles.

7. The application of the Stroop effect-based sensory quality evaluation method for buckwheat noodles according to any one of claims 1-5 in identifying key sensory interaction dimensions and guiding product formulation and process optimization.

8. The application of the Stroop effect-based sensory quality evaluation method for buckwheat noodles according to any one of claims 1-5 in optimizing the texture characteristics of high buckwheat content products to positively interfere with bitterness perception.

9. The application of the Stroop effect-based sensory quality evaluation method for buckwheat noodles according to any one of claims 1-5 in improving product appearance to reduce its negative visual interference with texture evaluation.

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