528 results about "Flavor" patented technology

The invention relates to the field of food, and provides cheese and a preparation method thereof, and the cheese comprises the following raw materials: micellar casein liquid, single cream, carbohydrates, a leavening agent, chymosin, calcium salt, table salt and water. The cheese is good in stretching softness, easy to shape, good in baking performance, good in flavor and taste and high in stability, and can be stored for at least 9 months under the refrigeration condition.

A system for measuring changes in a fluid stored within a container is disclosed. The system may include a radio-frequency sensing element disposed externally on the container and configured to generate a signal indicative of at least one dielectric property of the fluid, wherein the fluid undergoes composition-based or environment-driven changes due to interactions with the container. In some aspects, a processing component generates an output representative of at least one time-varying fluid characteristic. In some aspects, the container is a wooden barrel with an interior surface adapted to impart flavor or chemical changes to the fluid. In some aspects, the fluid is a beverage including at least one of wine, beer, and distilled spirits, with the time-varying characteristic including a fermentation parameter that changes over time. In some aspects, the at least one time-varying fluid characteristic of the fluid includes at least one of fluid level or fluid volume.

The invention relates to an intelligent lean optimization method and system for a food raw material formula and a medium, and the method comprises the following steps: building a problem according to a user demand, and inputting the problem into a large model; analyzing the large model according to answers output by the questions to obtain food raw material formula knowledge; structuring the food raw material formula knowledge and establishing a knowledge graph; and according to the taste relation in the knowledge graph and the food raw material dosage, calculating to obtain a corresponding product flavor index, and combining a deep reinforcement learning algorithm to dynamically adjust the food raw material dosage through iteration to realize multi-target optimization of the product flavor index, so that the optimized product flavor index is matched with a preset target value. Compared with the prior art, the accuracy and richness of the obtained food raw material formula knowledge are improved, the complex and changeable market requirements can be met, and the efficiency and precision of food raw material formula optimization are remarkably improved.

The disclosure generally relates to an ingredient-based dispensing system for creating customized beverages with ingredients (e.g., using various sauces, syrups, sweeteners, colors and / or flavors added to a base beverage). The ingredient-based dispensing system may include a volume-based dispensing system for automatically dispensing ingredients with a varying viscosity (e.g., sauces) and a time-based dispensing system for automatically dispensing ingredients with a consistent viscosity (e.g., flavors, syrups, and / or colors).

The invention relates to the technical field of white spirit aroma analysis, discloses a method and a device for evaluating aroma contribution of flavor substances in white spirit, and aims to solve the problems of poor consistency and accuracy caused by incapability of realizing quantitative evaluation in an existing aroma contribution evaluation method. According to the scheme, the method mainly comprises the following steps: pre-constructing a function relationship between compound concentration and aroma intensity score; the method comprises the following steps: acquiring the actual concentration of a compound corresponding to a flavor substance in Baijiu, calculating to obtain an aroma intensity score corresponding to the compound according to the actual concentration and based on a pre-constructed function relationship, and judging the aroma contribution degree of the corresponding flavor substance in the Baijiu according to the aroma intensity score corresponding to the compound. The method realizes quantitative analysis of aroma contribution, improves the consistency and accuracy of analysis, and is suitable for various white spirits.

The invention belongs to the technical field of intelligent food manufacturing, and particularly discloses a multi-modal data fused marinated product flavor directional design method and system. S1, constructing an auxiliary material database; s2, mining internal associations of components in the high-quality auxiliary materials by adopting an Apriori algorithm; s3, evaluating the sensory flavor of the marinated product to obtain a product index data set; s4, establishing a multivariate mapping model between the auxiliary material parameters and the sensory quality on the basis of a Python library of Statsmodels; s5, establishing a user score matrix in combination with consumption preference; and S6, optimizing the auxiliary material component model by using a genetic algorithm, and solving by combining the multivariate mapping model obtained in the S4 and the sensory weight obtained in the S5 to obtain the auxiliary material component with the optimal quality. By adopting the multi-modal data fused marinated product flavor directional design method and system disclosed by the invention, the problems of sensory flavor quality consistency control limitation and auxiliary material optimization intelligent management design management of traditional sauce marinated poultry meat are solved, and intelligent control between auxiliary material and flavor sensory is realized.

To provide an evaluation method of flavor degradation of container-packed milk at the time of being stored at a room temperature.SOLUTION: A milk flavor evaluation method includes a quantification step of quantifying (Z)-6-dodecene-4-olide. The milk may be milk stored at 15 to 40°C. The quantification step may be a step of quantifying a content of (Z)-6-dodecene-4-olide contained in the milk by a GC-MS method.SELECTED DRAWING: None

The invention discloses a white spirit flavor recognition method based on machine learning, and belongs to the technical field of food detection and artificial intelligence. Aiming at the problems of high subjectivity of manual evaluation, low efficiency of mass spectrometry, noise sensitivity of a machine learning model and the like in the prior art, the method provides a solution integrating solvent background deduction and feature weight screening. The method specifically comprises the following steps: diluting a white spirit sample with methanol according to a volume ratio of 1: 10, collecting mass spectrum data through GC-MS, and dynamically deducting a methanol background peak; a BP neural network (GABP) optimized by a genetic algorithm is utilized to automatically analyze a weight coefficient of each molecular peak to flavor classification, and key features are screened; a classification model is constructed based on XGBoost, and parameters are optimized through cross validation, so that automatic judgment of the flavor type, authenticity and quality of the white spirit is realized. According to the method, through data dimension reduction and model collaborative optimization, the overfitting problem caused by high noise and high redundancy of mass spectrum data is solved, the classification accuracy is remarkably improved, and the method can be extensively applied to white spirit brand identification, process optimization and market quality supervision.

The invention discloses a quality mining and evaluation method of a wolfberry characteristic flavor nourishment, and relates to the technical field of food quality analysis and sensory evaluation, and the method comprises the following steps: S1, carrying out non-targeted analysis on the wolfberry characteristic flavor nourishment by adopting headspace solid phase microextraction in combination with comprehensive two-dimensional gas chromatography-time-of-flight mass spectrometry, and obtaining original abundance data of the volatile compound. According to the quality mining and evaluation method for the Chinese wolfberry characteristic flavor nourishment, accurate identification and quantitative evaluation of key flavor substances in the Chinese wolfberry characteristic flavor nourishment are realized by establishing a flavor contribution potential index model and combining a dual verification mechanism. According to the method, the limitation that only a single chemical component is concerned or actual sensory experience is ignored is solved, and the accuracy and reliability of flavor component screening are improved by integrating multi-dimensional parameters such as instrument response intensity, human olfaction threshold and matrix release characteristics and through cross validation of a gas chromatography-olfaction technology and a standard substance addition experiment.

The invention mainly relates to the technical field of wine brewing, and provides a method and a device for predicting the aging years of white spirit and identifying key flavor substances in the white spirit in order to identify the aging years of the white spirit through the flavor substances in the white spirit, which have the core idea that the method comprises the following steps: acquiring flavor substance data and aging year data of a white spirit sample; constructing a flavor substance-aging year data set; establishing an aging year identification model, and training the aging year identification model based on the established flavor substance-aging year data set; embedding a flavor substance characteristic contribution module for analyzing the contribution degree of flavor substances to white spirit aging year recognition into the trained aging year recognition model; and the aging year identification model embedded with the flavor substance characteristic contribution module outputs the aging year type of the white spirit sample to be detected and the key flavor substance with high aging year identification contribution degree based on the flavor substance data of the white spirit sample to be detected.

The invention provides a plant beverage flavor component detection system and method based on artificial intelligence, and the method comprises the steps: analyzing the sensory contribution spectrum of a target plant beverage sample through a multi-dimensional original data matrix of the flavor substance content in the target plant beverage sample; extracting principal component vectors of flavor substances in the target plant beverage sample from the multi-dimensional original data matrix, and determining key flavor markers of flavor components in the target plant beverage sample in combination with a sensory contribution spectrum; extracting key spectrum fingerprint characteristics of flavor components in the target plant beverage sample from a spectrum detection result of the target beverage sample based on the key flavor marker; and constructing a flavor component prediction model of the target plant beverage sample, and further determining a flavor score of the flavor component in the target plant beverage sample in combination with the key spectrum fingerprint characteristics. By adopting the scheme of the invention, the specific contribution of each substance to the overall sensory attribute can be analyzed so as to identify the key flavor substance in the flavor evaluation of the traditional plant beverage.

The invention provides a buccal product and a production process thereof. The production process comprises the following steps: acquiring a base material; sequentially laminating a plurality of flavor layers and a slow release layer on the base material to obtain the buccal product, wherein the flavor layers and the slow-release layers are alternately arranged, and the flavor layers comprise active substances. By constructing a plurality of groups of alternately arranged flavor layers and slow-release layers, the total release time can be prolonged by the physical barrier of the multiple slow-release layers, so that the problem that the release rate is too high is solved; the active substances of the multiple flavor layers are slowly released in a layered manner, so that the purpose of clear release layers can be achieved, and in addition, each flavor layer can contain different or same active substances, so that the buccal product is rich in taste, and the taste experience is improved.

The invention provides a cheese product and a preparation method thereof. According to the cheese product, the flavor of the cheese product is improved through a combined enzymolysis technology of protease and lipase, so that essence is prevented from being added; meanwhile, under the condition that the fat content of the cheese product is low, the taste of the product is improved through the synergistic effect of the inulin and the konjaku flour.

Provided is a technique for improving the flavor of food. In the present invention, Bacillus bacteria are used to produce components such as 2-methylbutyric acid, diacetyl and acetoin that are effective for improving the flavor of food.

The invention discloses a low-sodium myofibrillar protein flavor prediction method and a low-sodium myofibrillar protein flavor prediction device. The method comprises the following steps: firstly, constructing a protein-flavor interaction system containing myofibrillar protein, potassium chloride, kappa-carrageenan and representative aldehyde flavor compounds; then, by means of spectroscopy, rheometer combination, an electronic tongue and the like, protein conformational change, typical aldehyde molecule binding rate and taste features are represented in a multi-dimensional mode. And establishing a random forest prediction model, taking protein conformation parameters as input, and taking a flavor binding rate and effective taste intensity as output. The model can reveal cross correlation between protein conformations and functions and identify key conformation parameters that affect flavor. And in combination with quantification and prediction capabilities of the model, a conformation-flavor relationship can be systematically analyzed, a basis is provided for optimization of a low-sodium formula, flavor is enhanced while salty taste is increased and bitter taste is inhibited, and healthy and sustainable development of low-sodium meat products is promoted.

A method of training a machine learning model for evaluating flavor profiles of an alcoholic beverage includes a spectral sensor collecting optical properties of the beverage or flavoring agent suspended in the beverage within a container. Temperature and pressure sensors collect environment data of the container during a flavoring session. A control apparatus displays a user interface requesting a taste tester to select labels for the session. A control apparatus memory stores selected labels responsive to the taste tester's selections. A feature extraction module extracts features from the optical properties and environment data based on predetermined criteria. A machine learning engine trains a machine learning model to continuously learn flavor characteristics under a plurality of flavoring sessions using the extracted features and selected labels. The trained machine learning model is deployed for quality control and for determining the difference between a good batch and poor batch of the alcoholic beverage.

The invention discloses a method for improving the flavor of mulberry wine by screening aroma-producing yeast from mulberries. The method relates to the technical field of microorganisms and solves the problem that the existing mulberry wine preparation process cannot fully reflect the nutritional value and unique flavor of mulberries. Low-temperature treatment is used to increase the relative abundance of target bacterial genera in a sample, and a sample with a high relative abundance of desired bacterial species and a low fungal count is selected. Then, a specific screening method is adopted to separate and molecularly identify the aroma-producing yeast. Yeast with good tolerance and the ability to produce a strong aroma is selected from the sample through tolerance experiments and electronic nose analysis, and the yeast is compounded into a starter. The alcohol content of the obtained mulberry wine is increased by more than 2.2% vol to 13.5% vol, the total ester is increased by 25%, and the relative content of aromatic compounds such as aldehydes and ketones is increased. The aroma-producing yeast species are obtained through directional separation, which can avoid blindness, improve screening efficiency, and enhance the flavor of the fruit wine.

The invention provides a flavor analysis method, device and equipment, a storage medium and a program product, and relates to the technical field of food analysis, the flavor analysis method comprises the following steps: in response to a received flavor improvement instruction for a to-be-analyzed object, obtaining flavor collection data of the to-be-analyzed object and a to-be-improved flavor, the flavor acquisition data comprises attribute data, metabolome data and sensory evaluation data; according to the flavor acquisition data, determining a causal relationship among a basic node corresponding to the attribute data, a metabolome node corresponding to the metabolome data and a flavor node corresponding to the sensory evaluation data, and node values of non-observation influence nodes corresponding to the metabolome node and the flavor node respectively; and based on the to-be-improved flavor, the causal relationship and the node value, determining a key reason node influencing the to-be-improved flavor. According to the method, the key reason node influencing the to-be-improved flavor can be more accurately determined, so that the flavor of the to-be-analyzed object can be improved according to the key reason node.

The present invention discloses a flavor regulation and quality improvement process method for beef deep processing, and relates to a meat product processing technology, and the method comprises the following steps: S1, a beef raw material is pretreated, beef is washed to remove impurities, and the washed beef is cut into 2-3 cm square blocks; s2, flavor regulation and control treatment: the beef cubes are soaked in seasoning liquid, the seasoning liquid is composed of table salt, sugar, soy sauce, natural spices and water, the soaking process lasts for 2-4 hours in the environment of 4-8 DEG C, and it is ensured that flavor substances evenly permeate; s3, quality improvement treatment, wherein an enzyme tenderization technology is adopted. According to the flavor regulation and control and quality improvement process method for beef deep processing, the quality and flavor of beef products are effectively improved through the links of scientific pretreatment, flavor regulation and control, quality improvement, heat treatment and the like, the quality of raw materials is strictly controlled in the pretreatment stage, and food safety is ensured; in the flavor regulation and control link, flavor substances uniformly permeate through a specific seasoning liquid formula and a low-temperature soaking technology.

The invention discloses a Baijiu blending big data analysis decision system and method, and belongs to the technical field of intelligent food manufacturing and flavor modeling. Physicochemical indexes, gas chromatography-mass spectrometry, sensory evaluation and historical proportioning schemes of multiple batches of raw wine are collected, and a heterogeneous flavor original data set is constructed; performing unified feature extraction and vectorization processing on the data to generate a flavor multi-modal feature matrix; constructing a flavor mapping map based on an attention mechanism, and extracting key flavor contribution features; using the target flavor label to guide a neural generation network to generate a wine base combination candidate set, and outputting a matching suggestion; performing subjective consistency scoring through a flavor simulation model and an expert scoring discrimination model, and screening an optimal matching scheme; the method has the advantages of being high in modeling precision, high in sensory consistency, high in blending efficiency and the like, and is suitable for intelligent matching design and personalized seasoning optimization of complex flavor products.

The utility model relates to the technical field of production of flavors and fragrances, and discloses a storage box for production of flavors and fragrances, which comprises a box body, a cover body is rotatably mounted on the outer side of the box body, and a tank body for storing flavors is arranged in the box body; pressure is applied to the limiting rods through the springs, the tank bodies are tightly attached to the interiors of the clamping grooves, the phenomenon that the tank bodies are unstable due to the fact that gaps exist between the clamping grooves and the tank bodies is avoided, then the reeds are clamped into the corresponding inserting grooves according to the gaps between the adjacent tank bodies, and when the first protection plate and the second protection plate are subjected to pressure, the tank bodies are prevented from falling off. The rotating rods are driven to rotate and move on the connecting rods at the same time, the reeds are compressed when the rotating rods rotate, the pressure between the first protection plates and the second protection plates is relieved through contraction of the reeds, and therefore the situation that adjacent tank bodies collide and are damaged is avoided; and the essence is prevented from deteriorating due to light irradiation during storage.

The invention relates to a selenium-rich condiment and a preparation method thereof. According to the seasoning provided by the invention, various nutritional ingredients are added, the taste regulating composition contains vegetables, meat and the like, so that the nutritional ingredients are enriched, the flavor is unique, organic selenium is provided for the seasoning by the added selenium-enriched vegetables, selenium-enriched yeast and the like, the selenium element daily required by a human body can be provided, and the health of the human body is ensured. According to the technology, effective components of the seasoning are increased, the functionality is higher, the market requirement is met, and application and popularization are convenient.

The invention discloses a fish hydrolyzed collagen peptide-based flavor beverage and a preparation method thereof, and relates to the technical field of food beverages. The flavored beverage comprises the following raw materials: fish hydrolyzed collagen peptide, a compound flavor modification stabilizer, a compound sweetening agent, concentrated fruit and vegetable juice, casein phosphopeptides, VC magnesium phosphate, zinc lactate, a low-nucleic-acid yeast extract, sodium hyaluronate, an acidity regulator and purified water. The compound flavor modification stabilizer is matched with the low nucleic acid yeast extract to form synergistic flavor regulation and control, the hydrophobic bitter peptide fragment of the fish hydrolyzed collagen peptide and the fishy smell characteristic group are wrapped from the molecular level, and meanwhile, the soft modification on the sensory level is realized through the gamma-aminobutyric acid; the effect of debitterizing and deodorizing without adding artificial essence and a large amount of cane sugar is achieved, the product flavor is natural and harmonious, the sour and sweet taste is palatable, the beverage completely conforms to the current healthy consumption trend of low sugar and no artificial addition, and the problem that the beverage has flavor defects is solved.

To provide a fermentation composition which is reduced in acid taste by post acid formation during storage, and a method for producing the same; and to provide a fermentation composition which is simply reduced in amino acid odor generated when a milk raw material is fermented using viable bacteria of bacteria belonging to Lactococcus and bacteria belonging to Leuconostoc that is lactic acid bacteria together with viable bacterial of lactic acid bacteria and bifidobacteria in production of a fermentation composition without blending flavor and a masking material, and a method for producing the same.SOLUTION: A method for producing a fermentation composition that is reduced in acid taste by post acid formation during storage includes the steps of: fermenting a milk raw material by viable bacteria of one or more kinds of bacteria selected from the group consisting of lactic acid bacteria and bacteria belonging to Bifidobacterium; and adding killed bacteria of the lactic acid bacteria. A method for producing a fermentation composition that is reduced in amino acid odor includes the steps of: fermenting a milk raw material by viable bacteria of one or more bacteria selected from the group consisting of lactic acid bacterial other than bacteria belonging to Lactococcus and bacteria belonging to Leuconostoc, and bacteria belonging to Bifidobacterium; and adding killed bacteria of one or more lactic acid bacteria selected from the group consisting of bacteria belonging to Lactococcus and bacteria belonging to Leuconostoc without using flavor.SELECTED DRAWING: None

The invention discloses an olfactory function detection kit and an olfactory scoring method, and the olfactory function detection kit is characterized in that six flavors of garlic, bromhidrosis, vinegar, lemon, fennel and mint are prepared into solid olfactory elements, and the solid olfactory elements are small in volatility, easy to store, strong in durability and simple to prepare. On the basis of clinical research on normal people and people with abnormal olfaction, a set of adaptive calculation formula is integrated, operation and calculation are more convenient, specificity and sensitivity are both improved, and compared with a conventional olfaction detection method, clinical working efficiency is improved, preparation is more convenient, storage is convenient, cost is low, and the method is more suitable for evaluation of olfaction functions of Chinese people. The detection kit provided by the invention has good detection performance and clinical applicability, and can effectively improve the detection accuracy.

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 invention relates to the technical field of model prediction, and discloses a model and method capable of rapidly evaluating the shelf life of hotpot condiment, the model comprises a main prediction model and an auxiliary prediction model, the main prediction model takes sensory indexes as measurement indexes, and the auxiliary prediction model takes quality indexes as measurement indexes. The weight ratio of the evaluation result of the main prediction model to the evaluation result of the auxiliary prediction model is (0.7-0.9): (0.1-0.3). The increase of the acid value means that the degree of oxidative rancidity of grease is aggravated, which directly affects the flavor and quality of the hotpot condiment; the sensory score comprehensively considers the spice fragrance, numb feeling, grease fragrance and other visual feeling indexes of consumers, the spice fragrance, grease fragrance and other indexes change obviously, and deterioration of the flavor of the product can be visually reflected. The selection of the two closely surrounds a product deterioration core factor, and lays a foundation for the model to accurately predict the shelf life.

The invention provides a Chinese and western medicine taste prediction method based on deep learning and a chemical language, and the method comprises the steps: S1, initializing a chemical language pre-training model ChemBERTa based on a RoBERTa architecture, and enabling the model to serve as a semantic encoder for simplifying a molecular linear input specification expression; s2, constructing a standardized medicine taste data set covering the taste information of the traditional Chinese medicine and the western medicine; s3, on the basis of the model framework in the S1, building a basic pre-training model for medicine taste prediction; s4, adopting a transfer learning strategy, and utilizing the medicine taste data set constructed in the S2 to carry out retraining and domain adaptation on the basic pre-training model in the S3 to obtain an intelligent medicine taste prediction model; and S5, predicting the drug taste of the drug molecules by using the model in S4. According to the method, deep semantic modeling of the medicine flavor is achieved, the method is a universal, explainable and extensible medicine flavor intelligent identification technology for Chinese and western medicines, and analysis of compatibility rules of the Chinese and western medicines and clinical precise medication are facilitated.