Study On Multi-Component Determination And Pattern Recogition Analysis Using Near Infrared Spectra Technique

This thesis focuses on in-depth investigation into the analytic method of using a combination of near infrared spectroscopy (NIRS) with chemometric techniques to achieve rapid determination and pattern recognition analysis of multi-component content in foods, feedstuff and compound fertilizer. Methods established in the thesis have been successfully applied to product analysis and quality control, fully utilizing the rapid, accurate and practical advantages of near infrared spectroscopy. Through specific problem solving, chemometrics methods have also been further developed. Due to complicated product composition and serious interference between product components, absorbance difference used during near infrared spectroscopy was usually small, the signal was weak, and absorbance peak were each overlapped and wide. No un-interfered feature peak could be identified for the testing component, which made it very difficult to conduct multi-component quantitative analysis and pattern recognition analysis in product. Therefore, this paper proposed using several methods including PLS, KPLS, PLS-BP, GRNN and Elman methods to establish linear and nonlinear multi-component models and build pattern recognition model using PCA-Mahalanobis Distance. Main contributions made by this thesis are summarized below.1?Investigation of multi-component quantitative analysis and its applications using PLS near infrared spectroscopyThis paper established a method of applying NIRS with Partial Least Squares (PLS) to analyze concentration of 7 fatty acids found in pork. Before building a PLS model, collected raw spectrum need to be processed first, using mathematical techniques such as noise filtering and increasing signal-to-noise. Experiments indicated that light scattering was the main factor influencing NIRS. When using the PLS method to extract main components, main components can be represented using variable matrix and argument matrix simultaneously. This can effectively reduce dimensionality, avoid possible overlap relationship between independent variables, and thus improve the reliability and accuracy of results. Kernel function was introduced to establish KPLS (Kernel Partial Least Square) nonlinear multi-component model for N, P2O5, K 2O in compound fertilizer, KPLS model was able to extract nonlinear information hidden in the spectroscopy via reflection of inner nonlinear algorithms to high dimensionality space. Comparing to PLS linear system, KPLS was able to improve forecasting relevance and accuracy when analyzing for multi-component, especially for P2O5 and K 2O.2?Investigation of combined use of PLS and BP neural network in NIRS quantitative analysis of multi-component substancesIn order to solve problems such as slow learning, and network over-fitting when implementing BP neural network, a new method PLS-BP method was established by introducing PLS to compress spectroscopy data being entered into BP neural network. This PLS-BP method was then applied to simultaneously determine moisture, ash, protein and phosphorus content, as well as the four types of amino acid content in feedstuff. Comparing to the BP method, PLS-BP method greatly enhanced operation speed and reduced training time by fewer input data. In addition, the prediction results based on PLS-BP model would also be more precise than those based on BP model. This study also initiated the method of extracting main factors and weight of spectroscopy X and component Y by PLS. This method is able to solve the problem that the number of implied layers, weight initialization of input layer and output layer were selected only by experience. In this thesis, a PLS-BP prediction model was also established based on determination of fibre, starch and protein, the three main nutrients found in potato. The combined use of PLS and BP networks in the PLS-BP model was able to improve training effect, increase operation speed, enhance precision. This research has theoretical contribution as well as practical implications to the establishment of NIRS BP network3?Using partial least squares and general regression neural network for NIRS multi-component quantitative analysisIn this part of the thesis, the GRNN method was innovatively introduced into NIRS multi-component analysis. PLS was used to compress NIRS before taken as inputs of GRNN, this established the method for determination of chlorine, fibre, fat content in feedstuff. Comparing PLS-GRNN with BP and GRNN network, training steps and time for PLS-GRNN and GRNN network was significantly fewer than BP network. PLS-GRNN had better prediction precision and better fitting than GRNN and BP network. Applications of this method have successfully predicted contents of total sugar and acid in Nanfeng Orange. This research provided a new way for NIRS multi-component quantitative analysis.4?Investigation of use of Elman neural network in NIRS multi-component quantitative analysisThis thesis proposed a combination of Elman neural network with NIRS techniques by introducing Elman neural network, which have dynamic information processing ability, into NIRS multi-component analysis. After PLS compression of original spectra data and addition of internal simple feedback signal, the ability of Elman network to process dynamic information have been enhanced. This simplifies nodal structure of Elman network and thus increased model building and forecasting speed. During determination of phenylalanine (Phe), lysine (Lys), tyrosine (Tyr), cystine (Cys) content in feedstuff, although BP and Elman model had the same of training speed, MRE of Elman neural network worse than BP network, but MRE had high prediction precision. This result showed that Elman neural network has accommodating ability in response to dynamic system. The applications of this method have also successfully predicted content of fat, protein, lactose and total solid in fresh milk, which showed the Elman neural network was a novel and reliable prediction method, which suggested Elman neural network is a new way for advanced determination of multi-component with overlapping NIRS in a dynamic nonlinear system.5?Identification of adulterated milk by using PCA-Mahalanobis Distance and NIRSPattern recognition analysis models were built for pasteurized milk and reconstituted milk, fresh milk and adulterated milk for vegetable cream, and whey powder based on PCA-Mahalanobis Distance and NIRS. The optimal conditions were defined for these models, and accurate discrimination for adulterated reconstituted milk, vegetable cream,, and whey powder with concentrations between 0.50%-100%,0.50%-10%,0.20%-3.3%, respectively achieved 100%. This brings a new method for discrimination of adulterated milk that has been mixed with whey powder and vegetable cream.