In this study, a 400-1000nm hyperspectral camera can be used, and FS13, a product of Hangzhou CHNSpec Technology Co., Ltd, LTD., can be used for related research. The spectral range is 400-1000nm, the wavelength resolution is better than 2.5nm, and up to 1200 spectral channels can be reached. The acquisition speed can reach 128FPS in the full spectrum, and the maximum after band selection is 3300Hz (support multi-region band selection).
The main nutrients of the compound feed include water, ash, crude protein, calcium, total phosphorus and so on. The detection of the main nutrients of feed is an indispensable technical link in the production process and an important means to ensure the quality of feed products. The detection and analysis method of feed is the basis of its quality control. At present, the traditional chemical analysis method is generally used to determine the main nutrients of compound feed. The traditional method of determination is often time-consuming and labor-intensive, resulting in time lag, while the determination cost is high, and some even need to destroy the sample itself, which also has higher requirements for operators and laboratories. To explore a method for rapid detection of the main nutrients of compound feed, comprehensively promote and apply it to the actual test and analysis of feed enterprises, which has high social and economic benefits for improving the detection rate and promoting the development of the testing level of compound feed. Hyperspectral image detection is a high-tech set of computer vision and spectral detection, the use of hyperspectral image technology to obtain the sample information contains a large number of spectral information of the three-dimensional image block, it not only has a high spectral resolution, and the spectral information extracted from the image can be used to detect the internal quality of the sample. Therefore, hyperspectral image detection technology is more and more favored by scholars at home and abroad, and has been widely used in the quality detection of agricultural products, but the application research in compound feed is rarely reported. In this study, hyperspectral image technology was used to obtain the visible/near-infrared spectral information of experimental samples of compound feed, and a quantitative analysis model of the main nutrients in compound feed, such as moisture, ash, crude protein, calcium and total phosphorus, was established by using stoichiometric methods, and the model was verified, aiming to explore the feasibility of using hyperspectral imaging technology to detect the main nutrients in compound feed. It also provides a new idea and basis for the rapid detection of compound feed.
In this study, hyperspectral image technology was used to establish quantitative analysis models of crude protein, crude ash, water, total phosphorus and calcium content in compound feed by means of abnormal sample removal, sample set division, optimal spectral pretreatment and characteristic band selection, combined with partial least square stoichiometry. The models were verified. The crude protein sample set divided by SPXY method and crude ash sample set divided by CG method, combined with the combination of AS, FD and SNV, the quantitative analysis model established in the characteristic band has the best effect. The correction set determination coefficient R& of the optimal crude protein model is 0.8373, the root-mean-square error RMSEC is 2.1327%, the relative analysis error RPDc is 2.4851, the validation set RV is 0.7778, RMSEP is 2.6155%, and RPDv is 2.1143. The optimal crude ash R&, RMSEC 1.0107%, RPDc 2.2064, RV 0.7758, RMSEP 1.0611% and RPDv 2.1204 were obtained. The quantitative analysis models of crude protein and crude ash both show good predictive performance and can be used for practical quantitative analysis. Water sample set divided by CG method combined with pretreatment of AS, OSC and Detrend has the best effect in the characteristic band. Its correction set RE is 0.6470, RMSEC is 1.8221%, RPD is 1.6849, validation set Ry is 0.6314, RMSEP is 1.6003%. RPDv is 1.9371, although the model can be used in practical quantitative analysis, its prediction accuracy still needs to be further optimized. The results of the quantitative analysis model obtained from the total phosphorus sample set divided by CG method combined with the pretreatment methods of AS, FD and SNV were optimal. The ratio of RS, RMSEC and RPD of the optimal model was 0.6038, 0.1656% and 1.5700, respectively. Validation sets R9, RMSEP and RPD/ are 0.4672, 0.1916% and 1.3570, respectively. The performance parameters of both the correction model and the validation model are poor, indicating that the model has poor predictive ability and cannot be used in actual quantitative analysis. After pretreatment of calcium sample set divided by CG method and combined with AS, OSC and Detrend method, the quantitative analysis model established in its characteristic band has the best effect, RB of the optimal model is 0.4784, and verification set R≈ is only 0.4406. The prediction effect of the model is poor, and it cannot be applied in practical analysis. The prediction accuracy of the crude protein optimal quantitative analysis model based on hyperspectral image technology is the best, and the prediction performance of the crude ash model is the second, and both can be used accurately in practical detection. The prediction accuracy of the water optimal quantitative analysis model should be improved. However, the optimal quantitative analysis model of total phosphorus and calcium has poor predictive performance and can not be used for practical detection.