7 results about "Kjeldahl method" patented technology

The invention discloses a method for detecting the nitrogen content of organ tissues of different varieties of oilseed rapes based on visible near-infrared spectroscopy, which comprises the following steps: firstly, collecting oilseed rape leaf, shell, stalk and root system samples under the conditions of multiple varieties and multiple nitrogen fertilizer levels, and acquiring spectral data within the range of 430-2500nm by using a visible near-infrared spectroscopy; and a Kjeldahl method is synchronously adopted to measure the real nitrogen content as a reference value. Preprocessing the spectral data, including de-noising, standard normal variable transformation, multivariate scatter correction and derivative processing, so as to weaken the influence of scattering and baseline drift; characteristic wavelengths related to the nitrogen content are screened through stepwise regression, variable projection importance, competitive self-adaptive reweighted sampling, a continuous projection algorithm and other methods, and a random forest model, a support vector machine model, a partial least squares discriminant analysis model, a partial least squares regression model, a support vector regression model, an XGBoost model and other models are combined. And respectively constructing a classification identification model and a regression prediction model.

This invention discloses a rapid detection method for protein in feed based on wet chemistry. The method includes the following steps: (1) mixing the feed sample with the sample extraction buffer, shaking to extract, centrifuging, and taking the supernatant; (2) adding the working solution of the protein standard and the supernatant to the diquinoline formic acid working solution, incubating, and measuring the absorbance value at a wavelength of 562 nm using an ELISA reader to obtain the absorbance values ​​of the protein standard and the supernatant; plotting a standard curve with the concentration of the protein standard as the abscissa and the absorbance value as the ordinate, and obtaining a linear regression equation; substituting the absorbance value of the supernatant into the linear regression equation to calculate the protein concentration in the supernatant, and then calculating the protein content in the feed sample based on the extraction buffer volume in step (1). This invention achieves a high degree of consistency between the detection results and the Kjeldahl nitrogen determination method, while significantly shortening the detection time of a single sample to less than 1 hour.

The embodiment of the invention discloses an oyster mushroom protein accumulation prediction method based on matrix composition and a temperature and humidity coupling effect. The method comprises the following steps: acquiring original spectral data of training oyster mushroom samples under different matrix proportions and temperature and humidity coupling conditions; determining the content of reference protein by using a Kjeldahl method; performing smoothing, first-order derivative and multivariate scatter correction preprocessing on the original spectral data under each coupling condition by using an adaptive fusion network in the generative adversarial network to obtain preprocessed spectral data after denoising; extracting features through a principal component analysis method to obtain training key spectral features; aiming at each coupling condition, constructing a support vector regression model, a random forest model and a partial least square regression model by using the training key spectral features and the reference protein content; screening an optimal model under each coupling condition; and performing same pretreatment and principal component analysis on a to-be-detected oyster mushroom sample to obtain to-be-detected key spectral characteristics, and predicting the protein content based on the optimal model corresponding to the target coupling condition.

The invention provides a rice product raw material quality prediction method and device based on deep feature fusion, and relates to the technical field of food quality detection and control. The method. The method comprises the following steps: equally dividing raw materials of the rice product into a plurality of groups according to the weight, extracting the raw materials with the same weight from each group as samples to be detected, detecting cracks, yellowing and insect pests in the samples by using a Japan bamboo particle evaluation instrument, and weighing; meanwhile, the ground sample is equally divided into three parts, the content of protein, moisture and amylose in the sample is measured through a Kjeldahl method, a drying oven drying method and an iodine colorimetric method respectively, the absorbance of the sample in a specific wave band is recorded through an infrared spectrum imaging technology, a prediction model of the content of protein, moisture and amylose is established based on a partial least square method, and the prediction model is used for predicting the content of protein, moisture and amylose. And constructing a quality evaluation system of the rice product raw materials with the proportions of cracks, yellowing and insect pests, and dividing the quality of the rice product raw materials into different grades of excellent, good, qualified and poor according to different evaluation standards.

The application provides a rice product raw material quality prediction method and device based on a partial least squares method, and relates to the technical field of food quality detection and control. The method first divides the rice product raw material into several groups according to weight, extracts equal-weight raw materials from each group as a to-be-detected sample, and uses a Japanese Sakata grain evaluator to detect cracks, yellowing and insect-infested grains in the sample and weigh it. At the same time, the sample is ground into three equal parts, and the Kjeldahl method, oven drying method and iodine colorimetric method are used to respectively determine the content of protein, moisture and amylose in the sample. The infrared spectrum imaging technology is used to record the absorbance of the sample at a specific waveband, and a prediction model of the content of protein, moisture and amylose is established based on the partial least squares method. The crack, yellowing and insect-infested grain proportion are used to construct a rice product raw material quality evaluation system, and the rice product raw material quality is divided into different grades such as excellent, good, qualified and poor according to different evaluation standards.

The invention belongs to the technical field of cationic starch detection methods, and particularly relates to a pretreatment method of a dry-process cationic starch substitution degree detection sample, which comprises the following steps: S1, weighing 25g of sample, adding 200ml of ice water (below 4 DEG C), stirring, pouring into a dehydrator with 400-mesh filter cloth, and spin-drying at a high speed for 30 seconds; s2, putting the filter cloth into 200ml of ice water to wash out the sample, then putting the filter cloth into the dehydrator, pouring the sample, and spin-drying at a high speed for 4 minutes; s3, 1 g of the spin-dried sample is weighed, the substitution degree of the spin-dried sample is detected through a Kjeldahl method, the moisture of the sample is detected synchronously so that the substitution degree can be calculated, and through the steps of the method, the process steps are simplified, and pollution to the environment is reduced.

The present invention belongs to the technical field of agricultural engineering, and particularly relates to a method and system for screening remote sensing monitoring indicators of crop growth parameters, including: obtaining unmanned aerial vehicle (UAV) remote sensing images of a research area by using a UAV multispectral sensor, and preprocessing the UAV remote sensing images to obtain orthoimage data, including multi-band reflectance; extracting multi-category vegetation indices based on the orthoimage by using a grid calculation method; calculating the texture features of multiple bands based on the orthoimage by using the gray-level co-occurrence matrix method; measuring the plant nitrogen content by using the Kjeldahl method to obtain the measured value of the plant nitrogen content; screening sensitive feature variables from the multi-band reflectance, vegetation indices and texture features by using a sensitive feature screening method for plant nitrogen content based on variable shrinkage search; and constructing a plant nitrogen content prediction model by using the Adaboost method based on the screened sensitive feature variables. The present invention simplifies variables, compresses the model, and improves the operation efficiency of the model.