Application Of Contrastive Principal Component Analysis Algorithm In Spectral Analysis Of Food Components

In the last early century,the development of Near-infrared spectroscopy technology(NIR)was in stagnation.Until the emergence of chemometrics,The NIR combined with Near-infrared spectroscopy analysis technology achieved the qualitative and quantitative analysis of related elements in measured substances,which facilitated people's scientific research and life production greatly.However,the NIR have sharp peaks or separated peaks rarely,and the overlap information between the different spectra is more severe.To seize the essential characteristics of the Near-infrared spectrum better,it is necessary to combine various data processing technologies.At present,the data analysis technologies has developed relatively mature,but there still have some problems.Among them,the most important problem is that numbrous different factors work together on the same absorption feature.The irrelevant backgrounds and the limited datasets make the key information extract difficult.Based on the above problems,this study makes the research of the qualitative and quantitative analysis algorithms in Near-infrared spectroscopy analysis technology.The main work are as followed:First of all,based on the traditional qualitative and quantitative methods of Nearinfrared spectroscopy,the study modified the algorithm.Introduce the background constract dataset to restrict the irrelevant features in the target dataset that perform more accurate qualitative and quantitative analysis results;Second,the qualitative experiment of Contrastive Principal Component Analysis(cPCA).The samples were two different types of fruits with and without chlorpyrifos pesticides,and spectra were collected used the Near-infrared spectrometer DLP.After pre-processing,the results show that the cPCA could distinguish the key information whether pesticides are sprayed on the surface of the samples,while the PCA only distinguish irrelevant background information of different fruit types.It shows that the cPCA could analyze the key information features in the Near-infrared spectrum,which is better than the PCA algorithm.Third,the first quantitative experiment of the Contrastive Partial Least Squares(cPLS)to analysis pesticide residues with different gradients in various types of fruits.The experimental materials consist in three different types of fruits and ten different concentrations(0-28mg/kg)chlorpyrifos solutions.The spectra collected used Nearinfrared spectra instrument.After the pre-processing,the correlation coefficient of the traditional PLS is 0.697,while the cPLS improves the correlation coefficient to 0.902,the root mean square error reduced from 0.859 to 0.754,and the ability of algorithm quantitative has been improved further.Fourth,the second quantitative experiment of the Contrastive Partial Least Squares cPLS — the nitrogen element quantitative analysis of different companies.After preprocessing,the PLS and cPLS were analyzed,respectively.The correlation coefficient of the PLS was 0.79,and the root mean square error was 2.4;the correlation coefficient of cPLS was 0.94,and the root mean square error was 0.81.The results of cPLS analysis are better than PLS.The cPCA and cPLS provide new ideas for high-dimension data analysis of Nearinfrared spectroscopy in this study,and have applications in near-infrared spectroscopy widely.