17 results about "Beef sample" patented technology

The invention discloses a beef origin tracing method and device based on a Raman spectrum and an adaptive attention residual network, and the method comprises the steps: (1) obtaining a beef sample, and carrying out the preprocessing of the beef sample, and obtaining a beef sample; (2) carrying out Raman spectrum detection on the beef sample to obtain original Raman spectrum data and carrying out pretreatment; (3) inputting the preprocessed Raman spectrum data into a pre-trained Raman spectrum classification model, and outputting the traceability producing area of the beef sample; the Raman spectrum classification model comprises an input convolutional layer module, a feature extraction module, a global feature aggregation module and a classifier module which are connected in sequence. The beef producing area tracing method and device based on the Raman spectrum and the self-adaptive attention residual network have high accuracy and robustness in beef producing area identification.

The application discloses a microbial nano-enzyme probe, a method for detecting salbutamol and application, and comprises a salbutamol monoclonal antibody and a signal carrier, wherein the signal carrier is a yeast cell loaded with iridium oxide nanoparticles. The application firstly constructs a probe by using a yeast cell loaded with iridium oxide nanoparticles in the detection of an immunochromatographic test strip, uses [3, 3', 5, 5' tetramethylbenzidine (TMB) and hydrogen peroxide (H2O2)] as a substrate to trigger a color developing reaction, can realize high-sensitivity target detection, and the detection limit is as low as 0.012 ng / mL, which is 55 times higher than the sensitivity of a traditional gold-labeled test strip. In addition, the application has a good application prospect, can provide a universal platform for sensitive, accurate and rapid detection of food-borne stimulants, and can be successfully applied to the detection of salbutamol in pig liver and beef samples.

The application provides a beef full-chain traceability and quality evaluation method and system based on identification sensing, relates to the technical field of intelligent breeding and food quality traceability, and solves the technical problem that the existing technology cannot realize multi-source data fusion analysis and cross-stage quality mapping in the whole breeding process. The method specifically comprises: acquiring multi-source data of cattle; judging the behavior state of the cattle by calculating the activity intensity feature based on the behavior data; estimating the core temperature of the cattle based on the body surface temperature, activity intensity feature and temperature and humidity; calculating the animal welfare index based on the core temperature, activity intensity feature and environmental data; obtaining the fat content and protein content of the beef sample through Raman spectrum detection, and constructing the mapping relationship between the animal welfare index and the fat content and protein content; obtaining the meat quality evaluation index by weighted summation of the fat content and protein content, generating beef product full-link traceability information and visualizing the information. The application is used for beef quality traceability.

The invention discloses a preparation method of a beef frozen wet powder matrix standard substance, and solves the technical problems of poor authenticity, low fidelity, poor uniformity and high energy consumption when a beef ractopamine residual matrix standard substance is prepared in the prior art. Obtaining a ractopamine positive beef sample; performing acid discharge treatment on the beef sample; pretreating the beef subjected to acid discharge, and cutting the beef into diced meat with the particle size of 5-10mm; preparing a frozen wet powder matrix standard substance by adopting a multi-stage freezing and crushing and pulse screening process; and uniformly mixing the final product at a low temperature, sub-packaging, and storing in an environment of-40 DEG C or-80 DEG C in a sealing manner. The traditional freeze-dried powder process is abandoned, the frozen wet powder is used as a carrier form, and the original physicochemical characteristics of fresh tissues are perfectly reserved by the form.

The application provides a yak meat intramuscular fat content prediction method and system based on near-infrared spectroscopy, relates to the technical field of data analysis, and comprises the following steps: acquiring the original near-infrared spectroscopy curve of multiple-source yak meat samples and the corresponding intramuscular fat content standard value. Secondly, deep feature extraction is performed on the spectrum through a wavelet scattering network to obtain multi-level scattering feature representation with stable deformation. Then, the pasture and part metadata of the samples are fused to construct a hypergraph structure representing the complex high-order correlation between the samples. Then, a heat diffusion model is established on the hypergraph, and the known fat content is used as a heat source for collaborative diffusion to obtain stable state information values fused with topological information. Finally, a prediction model is constructed by combining the multi-level features and the stable state information, so that the intramuscular fat content of new samples can be quickly and non-destructively predicted. The application improves the accuracy and efficiency of the fat content prediction of multiple-source heterogeneous yak meat samples.

The invention provides a yak meat intramuscular fat content prediction method and system based on a near infrared spectrum, and relates to the technical field of data analysis, and the method comprises the steps: obtaining an original near infrared spectrum curve of a multi-source yak meat sample and a corresponding intramuscular fat content standard value; secondly, deep feature extraction is carried out on the spectrum through a wavelet scattering network, and multi-level scattering feature representation with deformation stability is obtained; thirdly, fusing the pasture metadata and the part metadata of the samples, and constructing a hypergraph structure representing complex high-order correlation among the samples; then, a thermal diffusion model is established on the hypergraph, the known fat content is used as a heat source for collaborative diffusion, and a steady-state information value fused with topological information is obtained; and finally, constructing a prediction model in combination with the multi-level features and the steady-state information, and realizing rapid and lossless prediction of the intramuscular fat content of a new sample. According to the method, the accuracy and efficiency of predicting the fat content of the multi-source heterogeneous yak meat sample are improved.

The utility model relates to the technical field of beef sample detection, and discloses a yak meat sample detection device which comprises detection equipment, a display screen is arranged on the outer wall of the detection equipment, an adjusting knob is movably connected to the outer wall of the detection equipment, a lifting button is arranged on the outer wall of the detection equipment, and the lifting button is movably connected with the display screen. The top of the detection equipment is movably connected with a sealing cover, an electric telescopic machine is installed in an inner cavity of the detection equipment, a power output shaft of the electric telescopic machine is connected with a detection table, a loader is placed at the top of the detection table, a transparent cover is placed on the outer wall of the loader, and a temperature cavity is formed in the inner cavity of the detection equipment. According to the device for detecting the yak meat sample, the heating pipe on the device is matched with an adjusting knob, a display screen and a camera module for use, so that the device can completely record the change of the sample and the freshness of the meat at different temperatures and time.

The invention relates to the technical field of food detection, is used for solving the problems that existing manual grading is limited by subjective factors, evaluation standards are inconsistent, repeatability is poor and efficiency is low, and particularly discloses a beef pattern grade prediction analysis method based on texture map extraction. The method comprises the following steps: acquiring surface texture spectral information of a beef sample through hyperspectral imaging, introducing a multi-angle scanning and gray-level co-occurrence matrix construction technology, identifying the uniformity and contrast ratio of the texture of a muscle region from multiple angles, constructing texture morphological characteristics with higher discrimination, and determining the texture morphological characteristics of the beef sample. According to the method, automatic identification of pattern grades and grade interval delimitation are realized, and cross verification is completed through a standard nutritional quality index and edible quality index two-dimensional detection result. And the precision and efficiency of beef pattern grade prediction are obviously improved.

The invention relates to the technical field of processing of fresh meat and animal products, in particular to a finite element-based ultrasonic rolling, kneading and pickling beef brine mass transfer modeling method and application, which comprises the following steps: measuring the content change of salt and moisture in a beef sample under different ultrasonic power conditions, and combining with a Fick second law diffusion model, so as to determine the salt content and moisture content of the beef sample; corresponding salt and water mass transfer coefficients are obtained; establishing a three-dimensional physical field model and a beef sample geometric model by utilizing finite element analysis software, and establishing a simulation model of salt and moisture diffusion; establishing a function relationship between different ultrasonic powers and mass transfer coefficients of salt and moisture; and finally, the two models are combined for use, so that the spatial and temporal distribution conditions of the salinity and the moisture under any ultrasonic power condition can be visually presented. The model method has the characteristics of convenience in operation and dynamic visualization, the permeation condition of the pickling liquid in the beef in the pickling process can be observed, and the development of an ultrasonic synergistic tumbling pickling technology is facilitated.

The invention discloses a preparation method of a pig-derived component integrated rapid detection chip based on RPA-CRISPR / Cas12a-LFS. The preparation method comprises the following steps: mixing pork into beef according to different proportions to prepare an adulterated beef sample, and extracting DNA of the meat sample by using a microneedle rapid one-step method; designing and screening an RPA primer, and carrying out an RPA amplification reaction; crRNA is designed on the basis of the RPA product, and CRISPR / Cas12a reaction is carried out; the method comprises the following steps: designing a quality control and detection probe on the NAHLFS, preparing red gold nanoparticles, connecting the gold nanoparticles with the probe, and assembling the NAHLFS; the method comprises the following steps: designing and manufacturing a micro-fluidic chip, and integrating three processes of RPA, CRISPR and NAHLFS on the chip; and a constant-temperature heating device is additionally arranged on the micro-fluidic chip. The RPA-CRISPR / Cas12a-LFS micro-fluidic chip prepared by the preparation method disclosed by the invention can be used for specifically detecting the pig-derived components in the meat product; in the detection process, the operation is simple, the cost is low, professional laboratories and expensive equipment instruments are not needed, and the method is very suitable for on-site rapid detection.

The invention discloses a beef component rapid detection method based on near infrared spectroscopy and multivariable modeling, and the method comprises the following steps: collecting spectral data of a beef sample by using a near infrared spectrometer, and determining the content of each component in the beef sample; carrying out abnormal value testing on the acquired near infrared spectrum data and the content of each component, and removing abnormal points; preprocessing the original spectral data, and dividing residual samples into a training set and a test set; on the basis of the training set, screening characteristic variables related to a target component from a full wave band; establishing a beef component content prediction model by using the training set and the corresponding characteristic variables by using a partial least square regression method, and testing the model by using the test set; and inputting to-be-detected spectral data into the corrected prediction model to obtain the content of the components in the to-be-detected beef sample. According to the method, the generalization ability and robustness of the model are remarkably improved, and wide applicability and accurate and stable rapid detection covering various mainstream beef cattle varieties are realized.

The application discloses a kind of based on olfactory visualization chip beef freshness rapid detection method, comprising the following steps: selecting tetramethoxy phenyl porphyrin iron (III) chloride and phenol red as gas-sensitive material, and preparation is obtained with two sensors olfactory visualization chip;Different storage time under the representative beef sample is set, freshness index data is combined with the response characteristic matrix obtained corresponding storage time, to establish the back propagation neural network quantitative model of beef freshness.The response characteristic matrix of the olfactory visualization chip of the beef sample to be measured is input into the quantitative model established in beef, to obtain the predicted value of TVB-N content and the predicted value of TVC content of the beef sample to be measured.The application can realize rapid, simple, accurate quantitative detection of beef freshness;To solve the shortcomings of the prior art detection process, high detection cost, easy to be disturbed by environment and unable to detect visualized etc.

The invention relates to the technical field of quality detection in the processing process of fresh meat and animal products, in particular to an ultrasonic synergistic tumbling and pickling beef salinity and moisture content imaging detection method. The method comprises the following steps: disclosing a correlation between the salt and moisture content and spectral data of the novel ultrasonic synergistic tumbled and pickled beef, and determining a quantitative prediction model of the salt and moisture in a beef sample; a contrast experiment verifies that the detection performance is good; and a dynamic permeation map of salt and moisture in the pickling process is successfully drawn by using a hyperspectral visualization technology. Experiments show that the method can realize rapid and nondestructive detection and visual characterization of salt and moisture content in the ultrasonic synergistic tumbling and pickling process, provides technical support for quality monitoring and process optimization in the ultrasonic synergistic tumbling and pickling process, and is suitable for online detection and intelligent control of the ultrasonic synergistic tumbling and pickling process.

The application discloses a method for identifying grazing beef, and relates to the technical field of livestock product quality identification, and comprises the following steps: detecting three core indexes of a beef sample, i.e., a yellowness value, an n-6 / n-3 polyunsaturated fatty acid ratio and a shear force, and quickly and accurately determining whether the beef is raised by grazing or feeding in a shed. The application is based on the key quality differences between grazing beef and feeding beef, and establishes a scientific identification method and threshold standard. The method is simple to operate and has high accuracy, can be used for grassland beef quality certification and brand protection, and has important economic value and market application prospect.

The invention provides a geographical indication beef product identification method, and relates to the technical field of beef traceability identification. Collecting a geographically indicated beef sample and a non-geographically indicated beef sample; carrying out sample pretreatment on all the collected samples under the same condition to obtain a metabolite extracting solution; carrying out non-targeted metabonomics determination on the metabolite extracting solution through high performance liquid chromatography-tandem high-resolution mass spectrometry to obtain original data; performing a series of data preprocessing operations such as peak alignment, retention time correction and peak area extraction on the original data to obtain a metabolome data array; through multivariate statistical analysis and univariate statistical analysis, difference analysis is carried out on the geographically indicated beef and the non-geographically indicated beef, and a classification identification model is constructed, so that the identification of the geographically indicated beef product is realized, the production place of the geographically indicated beef product can be accurately identified, and a technical guarantee is provided for beef traceability identification.

The invention comprises a meat freshness detection method and system based on Raman spectrum and electronic nose. The method comprises the following steps: A, carrying out Raman spectrum acquisition, volatile basic nitrogen determination and electronic nose sampling on a meat sample; b, obtaining a Raman feature vector based on a Raman spectrum acquisition result, obtaining an electronic nose feature vector based on an electronic nose sampling result, splicing the Raman feature vector and the electronic nose feature vector to obtain fused beef sample features, and establishing a partial least square discriminant analysis model based on volatile basic nitrogen determination and the fused beef sample features; c, performing Raman spectrum collection and electronic nose sampling on a meat material to be detected, and obtaining fused beef sample characteristics according to the step B; and judging the freshness of the to-be-detected meat material by utilizing the obtained fused beef sample characteristics and the partial least square discriminant analysis model. Complementary fusion of molecular structure information and volatile gas information is realized, so that the detection efficiency and accuracy can be considered.

The application discloses a kind of based on dynamic metabolic excitation and smell fingerprint pollution beef identification method and device.The method includes the following steps: S1 sample preparation and inoculation;S2 dynamic metabolic excitation and signal acquisition: the sample after inoculation is placed in the environment of program temperature control, and the high-resolution time series data aligned with temperature-time curve is obtained by executing the preset dynamic temperature cycle program;S3 dynamic feature extraction and processing: the time series data collected is preprocessed and feature extracted;High-dimensional dynamic feature vector is constructed;S4 pollution identification: the high-dimensional dynamic feature vector is input into the machine learning classification model trained in advance, and the microbial contamination type identification result of beef sample is output by the model.The method and device of the application can actively excite, capture and analyze the new method and device of specific metabolic signal in complex microbial contamination system, to realize the rapid and accurate identification of various foodborne pathogens in beef.