6 results about "Beef sample" patented technology

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 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 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 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 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 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.