94 results about "Organoleptic evaluation" patented technology

The invention discloses an improved food sensory quality evaluation method and system based on a fuzzy mathematical method, and relates to the technical field of food inspection and detection. Performing a single-factor test on the improved food sample, and performing a response surface test on the basis of the single-factor test; performing comprehensive sensory evaluation on each sample in the response surface test by adopting a fuzzy mathematical analysis method; and physical and chemical indexes of the improved food under the optimal process are determined. According to the invention, the technology of the improved food is optimized, and the physicochemical indexes of the improved food with the optimized response surface are measured; a single-factor test and a response surface test are carried out in sequence, sensory scores are taken as evaluation standards, and factors influencing the quality of the improved food are determined; the optimal formula of the improved food is finally determined through a verification experiment; a fuzzy mathematics comprehensive sensory evaluation model is established for the improved food, and the obtained sensory evaluation result is used for response surface optimization evaluation.

The invention provides a Baijiu flavor preference degree analysis method. The Baijiu flavor preference degree analysis method comprises the following steps: (1) collecting preference degree sensory evaluation data and electroencephalogram data after a testee stimulates different Baijiu flavors; (2) preprocessing the electroencephalogram data obtained in the step (1) to obtain different electroencephalogram segment data; (3) taking the different brain wave segment data obtained in the step (2) as input features, combining the preference degree sensory evaluation data collected in the step (1), performing XGBoost model training to obtain a trained XGBoost prediction model, and predicting the preference degree of the testee based on the XGBoost prediction model to obtain a predicted value X; and (4) calculating a preference degree result Y of the testee based on the predicted value X obtained in the step (3) in combination with ratio data between different band powers. According to the method, the XGBoost model is used for carrying out feature learning on the electroencephalogram data, so that the preference degree of common consumers to different white spirit flavors can be explored, and data support is provided for development of new white spirit products.

The invention belongs to the field of food quality evaluation, and provides a delicate flavor synergistic effect evaluation method based on an electroencephalogram monitoring technology. Psychological or physiological factors such as subjective judgment, forced selection or scoring, individual umami cognition and umami sensitivity difference of sensory officers may affect the accuracy of sensory experiment results; the invention discloses methods such as umami sample selection and strength evaluation, electroencephalogram signal acquisition experiment normal form, quantitative evaluation and statistical analysis, feature extraction and response difference analysis, and the like, and the response mechanism of typical umami compounds in the human brain is explored by taking the response of people to umami signals as a research object and taking electroencephalogram signal detection analysis as a main method. A new theoretical basis is provided for quantitative evaluation based on the human palatable taste intensity, and the method is worthy of wide popularization.

The invention relates to the technical field of conditioning meat products, and provides a method for controlling and researching frozen storage quality deterioration of conditioned chicken wings by steady-state EGCG (epigallocatechin gallate), which comprises the following steps: step 1, sample treatment: conditioning unfrozen chicken wings; step 2, measuring the color, the unfreezing loss, the ferrimyoglobin, the sulfydryl, the Shore band, the thiobarbituric acid value TBARS and the volatile basic nitrogen TVB-N of the conditioned chicken wings; 3, analyzing a result; changes of color, unfreezing loss, ferrimyoglobin content, sulfydryl, Shore band, thiobarbituric acid value TBARS and volatile basic nitrogen TVB-N in the chicken wing freezing and thawing process are analyzed and conditioned; and 4, performing sensory evaluation according to the sensory evaluation table. According to the method, the control of the steady-state EGCG on the frozen storage quality deterioration of the conditioned chicken wings can be better studied.

The invention discloses a vehicle-mounted microwave oven smell and VOC release detection and evaluation method and system based on vehicle-mounted high-temperature closed environment simulation. The method comprises the steps that a microwave oven to be detected is placed in a closed test cavity and sealed; the cavity is placed in a heating device to be heated to the preset high temperature and kept for the set time; the VOC sensor is used for monitoring the VOC concentration in the cavity in real time; collecting a gas sample through the sampling interface at the last test stage; performing standard sensory evaluation on the sample by an evaluator; carrying out comprehensive grade evaluation by combining the VOC concentration data and the smell grade score; according to the invention, the special closed cavity is arranged in the oven with accurate temperature control, high-temperature stress is directly and uniformly applied, and the released VOCs are ensured to be gathered in a limited space by utilizing the sealing property of the cavity, so that the VOCs are released in an accelerated manner under the most sensitive temperature and humidity condition of the material of the microwave oven to be detected, and the detection accuracy of the microwave oven to be detected is improved. The obtained release level and odor characteristics are highly related to the experience of the user in the actual vehicle-mounted scene.

The invention discloses a potato waxiness evaluation model as well as a construction method and application thereof, and belongs to the technical field of food detection. The model construction method comprises the following steps: firstly, analyzing the waxy quality of potatoes in chewing and swallowing processes through sensory evaluation; then, stress relaxation testing is combined with a Maxwell model, and the mechanical property of the sample in the solid state in the chewing stage is measured; measuring the mechanical properties of the fluid phase sample in the swallowing stage by adopting frequency scanning and amplitude scanning in combination with an Ostwald-de Wavele power law model; and according to the correlation between the mechanical indexes and the taste scores, establishing a mechanical analysis-based potato waxiness evaluation model. According to the method, dynamic phase change and mechanical property analysis are combined for the first time to evaluate the waxy quality, so that waxy evaluation is more accurate and conforms to actual eating experience.

The invention relates to the technical field of sensory evaluation, in particular to a sensory evaluation method of Kokumi taste. The Kokumi taste sense organ evaluation method comprises the following steps: establishing a sense organ group by screening and training product evaluators, and determining optimal sense organ evaluation conditions in the aspects of matrix selection, Kokumi substance addition amount, sample amount, temperature, swallowing mode and whether a nasal splint is used or not. Then selecting a consumer to carry out CATA sensory inspection on the blank matrix and the matrix added with the Kokumi substance under the optimal sensory condition; and carrying out statistics on descriptors with significance in the CATA questionnaire, carrying out time-intensity (TI) analysis, determining sensory attributes as descriptors of Kokumi taste according to a TI result, establishing a reference system, and finally, carrying out scoring quantification on each attribute of the sample by an evaluation group. According to the sensory evaluation method disclosed by the invention, the taste attribute of the Kokumi can be accurately described, the sensory difference between different substances with the Kokumi attribute can be identified, and finally, the Kokumi taste sensory evaluation method is successfully established.

The invention belongs to the technical field of exhaled gas analysis, and particularly relates to an exhaled gas composition for identifying taste sensitivity and a screening method and application thereof. The taste sensitivities of individuals of different age groups are collected by adopting a sensory evaluation method, volatile compound components in exhaled gas are analyzed by adopting high-resolution mass spectrometry, and characteristic exhaled gas markers remarkably related to the taste sensitivities are screened by adopting difference analysis and correlation analysis. Therefore, the exhaled gas marker combination obtained through screening is used for identifying the individual taste sensitivity, and the method has the advantages of being convenient to sample, non-invasive, high in subject acceptability and wide in application range.

The invention provides a sweet substance screening method and application, and relates to the field of sweet substance development and application, and the sweet substance screening method is characterized by comprising the following steps: providing a sweet substance, analyzing the sweet substance by adopting an electronic tongue technology and a molecular docking technology to obtain first information and second information of the sweet substance, and constructing a bimodal model at the same time, obtaining a screening result; according to the technical scheme provided by the invention, taste features of various common sweet substances are analyzed in a mode of combining an electronic tongue technology and a molecular docking technology, so that taste feature differences of the sweet substances and sucrose can be effectively compared; a machine learning prediction model based on taste attribute values of nine electronic tongue sweet substances and four molecular docking characteristic values is established, and sensory evaluation is used for verification; sweet substances similar to cane sugar in taste are rapidly screened, and the screening accuracy is improved. Based on the method, a thought is provided for excavation and development of novel sweet substances.

The invention provides a rheological test-based liquid food suction tube suction difficulty prediction method, which comprises the following steps of: measuring a characteristic flow curve of a liquid food sample through a steady-state flow test, respectively carrying out Carreau-Yasuda model or Casson model (if the liquid food sample is a yield fluid) fitting, calculating to obtain a suction tube predicted flow Qc, then carrying out Herschel-Bulkley model fitting to obtain a consistency index Kh, and calculating the suction tube suction difficulty of the liquid food sample through the consistency index Kh. The power law index is n, and the Herschel-Bulkley yield stress is tau h (if the fluid is yield type fluid). Tau R is calculated according to a pipe flow equation and substituted into a Herschel-Bulkley model, the shearing rate # imgabs0 # and the corresponding apparent viscosity # imgabs1 # are obtained, then the pipe suction difficulty of the liquid food suction pipe is predicted through a constructed prediction model # imgabs2 #, a traditional sensory evaluation method is replaced, and a new thought is provided for research, development and decision making of a food enterprise.

The invention relates to the technical field of food additives, in particular to umami peptide and application thereof. The umami peptide is any one of amino acid sequences as shown in SEQ ID NO: 1-6. Sensory evaluation and electronic tongue analysis show that when the umami peptide is dissolved in a NaCl solution, the umami strength of the peptide is remarkably improved, and the peptide and salt have a remarkable synergistic effect in umami perception; and when the compound is compounded with sodium glutamate, a delicate flavor enhancing effect is shown.

The invention discloses a method for analyzing aroma components of cooking wine based on molecular docking and an odor activity value and application. According to the method, cooking wine aroma is taken as a research object, volatile substances are detected by gas chromatography-mass spectrometry (GC-MS) and headspace gas chromatography-ion mobility spectrometry (HS-GC-IMS), and by combining with odor activity value (OAV) analysis, the combining capacity of aroma molecules and olfactory receptors is simulated through molecular docking, characteristic aroma compounds are screened, and the aroma of the cooking wine is detected. And finally, constructing a recombinant aroma model according to the characteristic aroma compounds, carrying out sensory evaluation, and evaluating the aroma similarity of the recombinant aroma model and the natural cooking wine sample to determine the characteristic aroma components of the cooking wine. The method can be used for analyzing aroma substances of cooking wine and performing standardized flavor management.

The application discloses a kind of based on GC-IMS pure milk flavor sensory digitalization fast evaluation method, belong to food analysis and evaluation technical field.The pure milk sample to be measured is detected by headspace sampling using GC-IMS, and a fingerprint spectrum is obtained.The fingerprint spectrum data or characteristic volatile component data is input into the OPLS-DA model, and the preference degree of pure milk is evaluated according to the clustering result.The construction method of OPLS-DA model is as follows: different pure milk samples are selected, and CATA sensory evaluation method is used for sensory evaluation of pure milk samples.Different pure milk samples are detected by headspace sampling using GC-IMS, and a fingerprint spectrum is obtained.Partial least squares-discriminant analysis method is used for regression modeling using fingerprint spectrum data or characteristic volatile component data to obtain OPLS-DA model.The application uses GC-IMS to digitally and quickly evaluate the flavor of pure milk, which has high sensitivity, does not require sample enrichment and concentration, saves detection time, and avoids tedious pretreatment process.

An information processing apparatus includes a control unit. The control unit acquires target analysis information indicating physical and chemical analysis results corresponding to an inspection target by inputting an inspection image from a nondestructive inspection on the inspection target to a first machine learning model trained by machine learning using a first data set of training data that associates an inspection image from a nondestructive inspection on first food with first analysis information indicating physical and chemical analysis results for the first food. The control unit infers sensory evaluation results corresponding to the inspection target by inputting the acquired target analysis information to a second machine learning model trained by machine learning using a second data set of training data that associates second analysis information indicating physical and chemical analysis results for second food with sensory evaluation results for the second food.

The invention discloses a tobacco leaf modulation process operated by a sensory evaluation method. The tobacco leaf modulation process is characterized by comprising the following steps: sampling at regular time, quickly drying, and carrying out cigarette sensory evaluation; on the basis of a preset modulation process, modulation stages are determined, temperature and humidity parameters and time setting are adjusted, and each modulation stage and the whole modulation process are completed one by one; the preset blending process comprises a yellowing period, a wilting period, a color fixing period and a stem drying period, and the method has the advantages that in the tobacco leaf blending process, the maximum improvement of the quality characteristics, the style characteristics and the usability of the cigarette industry is pursued; the tobacco leaf modulation target goes deep into the internal quality from the external characteristics, and the method has important practical significance and effect on improvement of the modulation quality; compared with a conventional flue-cured tobacco leaf modulation technology, the quality characteristic, the style characteristic and the usability of the cigarette industry are improved by 20%-30%.

The invention discloses a GC-IMS-based pure milk flavor sensory digitalization rapid evaluation method, and belongs to the technical field of food analysis and evaluation. The method comprises the following steps: detecting a pure milk sample to be detected by GC-IMS through headspace sampling to obtain a fingerprint spectrum, substituting fingerprint spectrum data or characteristic volatile component data into an OPLS-DA model, and evaluating the preference degree of the pure milk according to a clustering result; the construction method of the OPLS-DA model comprises the following steps: selecting different pure milk samples, and carrying out sensory evaluation on the pure milk samples by adopting a CATA sensory evaluation method; the method comprises the following steps: detecting different pure milk samples through headspace sampling by using GC-IMS to obtain a fingerprint spectrum, and performing regression modeling by using a partial least squares-discriminant analysis method by using fingerprint spectrum data or characteristic volatile component data to construct and obtain an OPLS-DA (Optical Partial Least Squares-Discriminant Analysis) model. According to the invention, GC-IMS is adopted to carry out digital rapid evaluation on the pure milk flavor sense organ, the sensitivity is extremely high, a sample does not need to be enriched and concentrated, the detection time is saved, and a tedious pretreatment process is avoided.

The invention provides a fixation time determination method based on non-volatile compound analysis and a related device, and relates to the technical field of tea processing. The method has the advantages that the non-volatile compound data and the sensory evaluation data under different fixation time are acquired, the core judgment index is extracted, and the optimal fixation time is judged based on the preset condition, so that an objective and scientific fixation time judgment method is provided, and the consistency and the stability of the tea quality are improved.

The application belongs to the field of cigarette quality stability evaluation, and discloses a cigarette quality stability evaluation method and system based on multivariate process statistics-sensory evaluation fusion. 2 T 2 The method takes three cigarette models, ML, XP and YY, as research objects, fuses multivariate process statistics and sensory evaluation, measures 25 chemical indexes such as conventional chemical components and alkaloids, constructs four derived indexes, and realizes multivariate collaborative fluctuation monitoring by means of Hotelling T The result shows that the conventional derived indexes and polyphenol indexes are key quality factors affecting the sensory quality stability of cigarettes, and the conventional components, alkaloids, multivariate acids and high-grade fatty acids are other quality factors; based on the difference in sample size, the application scenarios are divided, the statistical stability, early warning and risk interval of the key quality factors and other quality factors are determined, and quantitative basis and technical support are provided for the fine quality control of the whole process of cigarette production.

This invention discloses a rapid screening method for tobacco leaf raw materials for heated cigarettes, comprising the following steps: (1) pulverizing tobacco leaf raw materials; (2) tobacco core formulation: mixing the pulverized tobacco powder with glycerin, adhesive, and purified water in a certain proportion; (3) mixing evenly; (4) tobacco core forming: selecting an appropriate method for forming according to the tobacco core formulation; (5) tobacco core heat treatment: placing the formed mixture in an oven for baking; (6) tobacco core assembly: preparing heated cigarettes, removing the tobacco core from the smoking section, and then filling it with the formed mixture prepared in step (5), thus completing the tobacco core assembly; (7) using a matching heating device for sensory evaluation to distinguish the quality of different tobacco leaf raw materials. Since the tobacco core preparation process does not involve special instruments and equipment, it can be completed using conventional experimental equipment, making it simple and easy to implement, providing a good smoking experience, with a short process flow, low production costs, and no need for large investments. It is suitable for laboratory sample preparation and sensory evaluation.

The present application relates to cigarette leaf group formula maintenance technical field, especially to a kind of cigarette leaf group formula module division method, device and equipment.The division method includes: obtaining the first aroma style sensory evaluation result of test cigarette sample;According to aroma style sensory evaluation result, determine primary classification index;With primary classification index as the basis, test cigarette sample is compared and clustered, and formula leaf group function module is formed;With tobacco characteristic flavor sensory evaluation index as the basis, according to the second aroma style sensory evaluation result of single-grade raw material trial sample, single-grade raw material trial sample is compared and clustered, and formula leaf group flavor module is formed;With formula leaf group flavor module as reference, the sub-module in formula leaf group function module is integrated, and cigarette leaf group formula module is obtained.The module divided by the method reflects the actual function of formula tobacco leaf, improves the efficiency of leaf group formula maintenance and the stability of product quality.

A comprehensive prediction method for drinking water taste based on sensory evaluation and physicochemical parameters is disclosed. The method includes the following steps: S3, sensory evaluation data collection, where the sensory evaluation data includes the following dimensions: odor and taste; and S4, physicochemical parameter detection, including at least the following indicators: pH value, total hardness, and free chlorine concentration. S5, data standardization and preprocessing; S6, construction of a comprehensive taste prediction model: based on the standardized sensory evaluation data and the processed physicochemical parameter data, a multivariate fusion data model is constructed; and S7, inputting each variable according to the pattern of a multiple linear regression model to obtain the final calculation result under the control of multiple variables in the model. This invention's prediction method features a unified evaluation scale, strong comparability of results, significantly improved objectivity of the evaluation, quantitatively correlates taste and physicochemical indicators, and ensures that different evaluators maintain consistency in their evaluation starting point and scale, thereby significantly reducing random fluctuations in the model input data and improving the stability of the prediction model.

The invention belongs to the field of quality evaluation of traditional Chinese medicinal materials, and particularly relates to a method for analyzing and evaluating the quality of medicinal materials and decoction pieces easy to get oil based on an electronic nose, which comprises the following steps: preparing a sample, establishing a sensory evaluation scale, determining a quality index and a determination method, determining optimal measurement parameters of the electronic nose, and establishing an oil leakage early warning model. The method comprises the following steps: constructing an oil leakage deterioration early warning model taking smell evaluation as a core; an intelligent and efficient smell comprehensive evaluation method conforming to traditional Chinese medicine characteristics is established, and rapid guidance is provided for traditional Chinese medicine processing, storage and the like.

The invention discloses a method for establishing an algorithm model for identifying finished milk based on flavor of raw milk, which comprises the following steps: (1) establishing a flavor data set, (2) establishing a model algorithm: taking each element in sensory evaluation data and electronic nose data as an independent dependent variable, and measuring each dependent variable to obtain a flavor data set; the maximum value and the minimum value of different dependent variables are obtained, the interval between the maximum value and the minimum value serves as the evaluation criterion of one dependent variable, and the similarity conditions are calculated through discussing multiple potential conditions in the calculation process. According to the method, UHT milk flavor judgment in the actual production process is effectively simplified, the sensory flavor quality of subsequent finished products is predicted in advance, pre-screening and pre-judgment are achieved, the quality stability and the production efficiency are greatly improved, the satisfaction degree of consumers is improved, and meanwhile the production cost is reduced.

The invention belongs to a sweet orange essential oil peculiar smell identification technology, and particularly relates to an identification and identification technology for sweet orange essential oil peculiar smell substance generation caused by hot processing treatment. The invention discloses a method for determining peculiar smell of sweet orange essential oil by combining a modern molecular sensory technology with molecular docking. The method comprises the steps of sensory evaluation of the sweet orange essential oil, pretreatment of the sweet orange essential oil, GC-O-MS, smelling sensory, preliminary screening of peculiar smell related compounds, further identification of peculiar smell compounds, pretreatment of molecular docking, molecular docking and the like. The method can solve the problem of limitation of identification of the peculiar smell compound of the sweet orange essential oil in the food field and the aroma field at present.

The present application provides a kind of liquor flavor preference analysis method, comprising (1) collecting the preference sensory evaluation data and electroencephalogram data of subjects after different liquor flavor stimulation;(2) the electroencephalogram data obtained in step (1) is preprocessed, and different brain wave band data is obtained;(3) different brain wave band data obtained in step (2) is used as input feature, combined with the preference sensory evaluation data collected in step (1), XGBoost model training is carried out, and the trained XGBoost prediction model is obtained;The preference of the subject is predicted based on the XGBoost prediction model, and the prediction value X is obtained;(4) the prediction value X obtained in step (3) is combined with the ratio data between different wave band powers, and the preference result Y of the subject is calculated.The present application uses XGBoost model to learn the characteristics of electroencephalogram data, so as to explore the preference of ordinary consumers to different liquor flavors, and provide data support for the development of new liquor products.

The invention relates to the technical field of food flavor analysis and intelligent perception. The aroma prediction method and system based on olfactory receptors and dynamic simulation are provided, and the method comprises the following steps: based on a public olfactory receptor database, carrying out integration processing on ligand specificity data of various human olfactory receptors and combination experiment data of a target aroma substance to generate a combination characteristic database; performing simulation processing on the environmental parameters of the target scene through the bionic reactor to obtain dynamic release data; carrying out machine learning model training processing based on the combination characteristic database and the dynamic release data, and generating an aroma contribution degree prediction model; performing aroma reconstruction experiment and sensory evaluation processing based on key aroma substance sorting output by the aroma contribution degree prediction model, and verifying prediction accuracy; the technical effects that the goodness of fit between aroma prediction and real olfactory experience is improved, the correlation deviation between the dynamic release process and the sensory intensity is reduced, the analysis efficiency is improved, and the industrial high-throughput requirement is met are achieved.

The invention provides a method for identifying the producing area of radix asparagi, which is characterized in that hexanal, 1-octylene-3-ketone, 2-isopropyl-3-methoxypyrazine and (E, E)-2, 4-nonadienal are used as key odor markers of radix asparagi, and the radix asparagi of different producing areas is identified by measuring the odor activity values (OAV) of the odor markers. According to the present invention, the volatile components of the asparagus cochinchinensis produced in Yunnan, Guizhou, Sichuan and Guangxi are systematically represented and quantified through the HS-SPME / GC-QQQ-MS / MS technology, the characteristic substances with the odor contribution are screened through the odor activity value (OAV), the asparagus cochinchinensis odor intensity characteristic spectrum is established, and the chemical component-flavor characteristic-origin identification method is constructed; the subjective limitation of traditional sensory evaluation is broken through, objectiveness and standardization of origin identification are realized, and the method has important application value in origin traceability of asparagus cochinchinensis.

The invention relates to a spice isomer distinguishing method and device based on EEG electroencephalogram signals, and the method comprises the steps: obtaining brain wave signals of a testee under the stimulation of different spice isomers, and carrying out the preprocessing of the brain wave signals; calculating the power spectrum density of the preprocessed brain wave signal, and extracting power values of different frequency bands; dividing the brain wave signal into a plurality of brain regions according to the arrangement position of the collection electrode, and identifying spectrum features of different brain regions; respectively marking the brain wave signals corresponding to the isomers of each spice as a group, and performing inter-group comparison on the brain wave signals of different isomers of the same spice; power spectrum density values of all brain regions of the testee in all frequency bands are collected in real time, and the types of the spices and the isomers of the spices smelled by the testee are recognized based on the inter-group comparison result. Compared with the prior art, the method has the advantages that the spice isomers can be accurately distinguished, and an objective and real-time technical means is provided for sensory evaluation of spices.

The invention discloses a cigarette aroma style evaluation method based on a relative odor activity value of a rear nasal cavity smoke aroma type. The method comprises the following steps: step (1), capturing rear nasal cavity smoke; (2) carrying out GC-MS (Gas Chromatography-Mass Spectrometer) analysis by using a gas chromatograph-mass spectrometer; and step (3), performing relative score on the aroma types. According to the method disclosed by the invention, smoke components which really generate flavor in a real smoking process are obtained through rear nasal cavity smoke trapping, and the trapping is more accurate than that of a conventional Cambridge filter; according to the method, the contribution and influence of different aroma types on the whole aroma style are quantitatively evaluated by classifying the same aroma types and calculating the ROAV value, the subjectivity of relative sensory evaluation is high, the reproducibility is poor, and the method is more objective and good in reproducibility.

The invention relates to an evaluation method for characterizing cigarette smoking physiological irritation and application of the evaluation method. The evaluation method comprises the following steps: (1) determining the activity of alpha-salivary amylase in saliva of a smoker at three time points before and after smoking a to-be-detected cigarette; (2) calculating the variations delta U1, delta U2 and delta U3 of the activity of the alpha-salivary amylase after three time points after the to-be-detected cigarette is sucked and before the to-be-detected cigarette is sucked; (3) determining the content of main irritant components in the cigarette smoke; the main irritant components comprise total particulate matters, tar, formic acid, acrolein and crotonaldehyde; and (4) substituting the change quantity of the alpha-salivary amylase activity and the sensory evaluation score into a physiological irritation evaluation formula to obtain a physiological irritation score.