Knowledge Management System Of Institute of Semiconductors,CAS
|Thesis Advisor||李卫军 ; 覃鸿|
|Place of Conferral||北京|
|Keyword||近红外光谱 定性分析 非均匀固体颗粒 深度学习 鲁棒性|
With the advantages of fast and nondestructive and pollution-free, near infrared spectroscopy (NIR) analysis technology has been widely used in the fields of food, pharmacy,agriculture, petroleum, petrochemical, tobacco, biomedicine, textile and so on. At present, more efforts mainly focus on NIR spectroscopy quantitative analysis researches, but few researches concentrated on NIR qualitative analysis method. Researchers mainly concentrated on application of NIR qualitative analysis in worldwide. In addition, solid irregular object such as corn seeds with different sizes and shapes is not studied in-depth, the spectrum of irregular object contains category information, and it also contains individual information need to be eliminated.so the qualitative analysis of irregular solid grain compared with ordinary uniform sample is more difficult, the research of NIR spectroscopy of non-uniform solid grain object is not researched abundantly. The shortage of poor robustness for qualitative analysis is also not researched in-depth, which have hindered the popularization and application of NIR qualitative analysis technique. In order to solve above problems, following key problems of NIR spectroscopy qualitative analysis methods are researched in this paper by taking the maize seeds and wheat seeds on solid and irregular state as the research object .
In order to solving the problem of multi-species identification for irregular particles with different sizes and shapes, the difficulties of spectral collection and analysis of irregular solid particle are analyzed firstly in this thesis The NIR diffuse reflectance model and diffuse transmission model for corn seeds identification were established by using several classification methods, and the performance differences of the two spectral acquisition methods were compared and analyzed by designed experiments. The experiments showed that diffuse transmission is more suitable than the diffuse reflectance in NIR identification of corn seeds and the recognition rate can reach higher than 90% with diffuse transmission method.
A SVDD based spectroscopy quality detection method of the NIR qualitative analysis is put forward in the thesis. A comparative study of several spectroscopy quality detection method was carried out by doping the artificial anomalies spectra in the normal spectra under the mode of diffuse transmission of maize single seed. The experimental results show the SVDD method can eliminate the abnormal spectroscopy effectively.The correct rejection rate of abnormal samples is higher than 90%.
From the aspects of reducing instrument cost and manufacturing difficulty, and aiming at haploid identification task, genetic algorithm (GA) to select characteristic wave points is studied. It is proved that the absorbance of 10 characteristic wave points can achieve full spectroscopy identification effect in the maize haploid identification task by the experimental results.
Aiming at the problem of combinatorial optimization problem of feature extraction, a method based on particle swarm optimization algorithm (PSO) is was proposed. The optimal parameter combination of preprocessing and feature extraction in the qualitative analysis model was designed to finding by using particle swarm algorithm. Then, the model was tested by using substitution optimal parameter combination into the qualitative analysis model. The results not only show that the PSO method can search the combination of preprocessing and feature extraction parameters which satisfy the optimal performance of the model efficiently, but also the qualitative analysis model with the optimal parameter combination has better generalization ability.
A method based on Stack Auto Encoder(SAE) neural network is was proposed. The corn spectrum without coat was used as training set, and the network SAE analysis model was established by using the Stack Auto-Encoding which is unsupervised learning algorithm and Softmax classifier. Then, the corn seed with coat was classified and identified by using the established model. The experimental results indicates SAE-based method can effectively reduce the influence of seed coat, the influence of seed coat can be reduced under 3% with SAE-based method.
The haploid discrimination method based on Deep belief network (DBN) was put forward aiming at the problem of rapid sorting of haploid. Firstly, a multilayer belief network model of multiple haploid grain spectra was obtained by using multilayer Restricted Boltzmann Machine and BP neural network method. Then, the haploid and diploid of each corn species were classified. It was proved that the multiple species haploid identification model by using DBN method has higher classification performance, and the haploid identification rate of 10 varieties can be higher than 90% in the experiment.
Aiming at the problem of poor robustness of NIR qualitative identification model, methods based on sample diversity and multi-day joint modeling are studied in this paper. Firstly, the partial least squares (PLS) space is constructed based on the method of sample diversity by using the diverse history data and modeling data. Then, the modeling data was projected into the PLS space, and the feature was extracted and classified. The experimental results show that the proposed method can improve the robustness of model effectively, and recognition rate can be kept at more than 90% while increasing the number of varieties with model parameters unchanged. Besides, multi-day joint modeling set was expanded by increasing the number of single-day data firstly, and then the multi-day Joint model was established, and model performance is tested with test data of several days. The experimental results show that the recognition rate can be higher than 90% for at least 15 days with seven-day joint model.
The object of this paper is irregular solid particles from the agricultural field with a variety of shapes and sizes, while the method and the conclusions proposed in this paper can be extended to other areas and other forms of object.
|李浩光. 近红外光谱定性分析方法的研究[D]. 北京. 中国科学院研究生院,2016.|
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