水果内部品质在线近红外分光检测装置及试验

阐述了近红外分光法检测水果内部品质的基本原理和检测流程 ,分析比较了在线式反射光测定法、不完全遮光型透过光测定法和完全遮光型透过光测定法的特点 ;以柑橘和苹果为检测对象 ,用完全遮光型透过光水果内部品质测定装置进行在线糖度、酸度及内部褐变等检测试验 ,并与常规分析法测定结果进行回归分析。结果表明 ,在线检测的糖度值与实测值的相关系数在 0 .95以上 ,酸度相关系数大于 0 .85 ,且能检测内部缺陷 。

A Comparative Study on the Quality Components Between Self Pollinated Seeds and Naturally Pollinated Seeds in Brassica napus L. in Sichuan Ecological Region

[ Objective] This study was to reveal the differences in crude fat and glucosinolates between self pollinated seeds and naturally pollinated seeds in Brassica napus in sichuan ecological region.. [ Method] Near-infrared spectroscopy method （NIRS） was employed to measure the quality components in self pollinated seeds and naturally pollinated seeds of 861 shares of Brassica napus from Sichuan ecological region. And correlation analysis and regression analysis were conducted based on the experimental data via SPSS （statistics package for social science）. [ Result] The contents of crude fat in the self pollinated seeds were commonly a higher than that in the naturally pollinated seeds at 0.01 significant level; while the contents of glucosinolates in the self pollinated seeds and the naturally pollinated seeds were insignificantly different. Both the correlation relationship and linear regression for the crude fat between the self pollinated seeds and naturally pollinated seeds reached the significant level. The regression equations for the contents of crude fat（y1 ） and glucosinolates（ y2 ） in the naturally pollinated seeds and of crude fat（ x1 ） and glucosinolates（ x2 ） in self pollinated seeds were respectively determined to be y1 = 16.844 ＋0.614x1 and y2 = -0.620 ＋ 1.017 x2. [ Conclusion] In Brassica napus breeding, crude fat in naturally pollinated seeds should be emphatically taken into account, meanwhile concurrently considering that in self pollinated seeds; while glucosinolates in both the self pollinated seeds and the naturally pollinated seeds must be simultaneously concerned.

Determination of Active Components in a Natural Herb with Near Infrared Spectroscopy Based on Artificial Neural Networks

The non-linear relationships between the contents of ginsenoside Rg 1, Rb 1, Rd and Panax notoginseng saponins(PNS) in Panax notoginseng root herb and the near infrared(NIR) diffuse reflectance spectra of the herb were established by means of artificial neural networks(ANNs). Four three-layered perception feed-forward networks were trained with an error back-propagation algorithm. The significant principal components of the NIR spectral data matrix were utilized as the input of the networks. The networks architecture and parameters were selected so as to offer less prediction errors. Relative prediction errors for Rg 1, Rb 1, Rd and PNS obtained with the optimum ANN models were 8.99%, 6.54%, 8.29%, and 5.17%, respectively, which were superior to those obtained with PLSR methods. It is verified that ANN is a suitable approach to model this complex non-linearity. The developed method is fast, non-destructive and accurate and it provides a new efficient approach for determining the active components in the complex system of natural herbs.

Spectroscopic Multicomponent Analysis Using Multi-objective Optimization for Variable Selection

The multiple determination tasks of chemical properties are a classical problem in analytical chemistry. The major problem is concerned in to find the best subset of variables that better represents the compounds. These variables are obtained by a spectrophotometer device. This device measures hundreds of correlated variables related with physicocbemical properties and that can be used to estimate the component of interest. The problem is the selection of a subset of informative and uncorrelated variables that help the minimization of prediction error. Classical algorithms select a subset of variables for each compound considered. In this work we propose the use of the SPEA-II （strength Pareto evolutionary algorithm II）. We would like to show that the variable selection algorithm can selected just one subset used for multiple determinations using multiple linear regressions. For the case study is used wheat data obtained by NIR （near-infrared spectroscopy） spectrometry where the objective is the determination of a variable subgroup with information about E protein content （%）, test weight （Kg/HI）, WKT （wheat kernel texture） （%） and farinograph water absorption （%）. The results of traditional techniques of multivariate calibration as the SPA （successive projections algorithm）, PLS （partial least square） and mono-objective genetic algorithm are presents for comparisons. For NIR spectral analysis of protein concentration on wheat, the number of variables selected from 775 spectral variables was reduced for just 10 in the SPEA-II algorithm. The prediction error decreased from 0.2 in the classical methods to 0.09 in proposed approach, a reduction of 37%. The model using variables selected by SPEA-II had better prediction performance than classical algorithms and full-spectrum partial least-squares.

Development of Three Risk Assessment Models for Deoxynivalenol and Fumonisins B1 ＋ B2 Contents in Maize Kernel

The maximal deoxynivalenol （DON） and fumonisins 131 ＋ B2 （FUM） contents in cereals are dictated by the European regulation 1126/2007. The direct measurement of these mycotoxins is a tedious and expensive process. Our study is based on an alternative tool： near infrared spectroscopy. Different models were developed on 374 maize samples to predict their DON and FUM contents. Several parameters have been determined and used in a multivariate data analysis. Three models were developed： （1） a classification model based on Discriminant Factor Analysis （DFA）, （2） a linear model based on ANalysis of COVAriance （ANCOVA） and （3） a Partial Least Squares Discriminant Analysis model （PLS-DA）. Firstly, the performances of the DFA model for assessing DON and FUM risk were similar： 69 and 72% of the validation samples were respectively well classified. In the second part, the performances of the ANCOVA model for DON were higher than for FUM. The r2 was worth respectively 0.85 and 0.69. In the last part, the performances of the PLS-DA models were better for FUM than for DON. These results show that an evaluation of the mycotoxin risk is possible by analyzing selected kernel parameters measurable by secondary analytical such as near-infrared spectroscopy. Further work is needed to improve the models, adding more samples and using non linear approaches.

A variable differential consensus method for improving the quantitative near-infrared spectroscopic analysis

Consensus methods have presented promising tools for improving the reliability of quantitative models in near-infrared(NIR) spectroscopic analysis.A strategy for improving the performance of consensus methods in multivariate calibration of NIR spectra is proposed.In the approach,a subset of non-collinear variables is generated using successive projections algorithm(SPA) for each variable in the reduced spectra by uninformative variables elimination(UVE).Then sub-models are built using the variable subsets and the calibration subsets determined by Monte Carlo(MC) re-sampling,and the sub-model that produces minimal error in cross validation is selected as a member model.With repetition of the MC re-sampling,a series of member models are built and a consensus model is achieved by averaging all the member models.Since member models are built with the best variable subset and the randomly selected calibration subset,both the quality and the diversity of the member models are insured for the consensus model.Two NIR spectral datasets of tobacco lamina are used to investigate the proposed method.The superiority of the method in both accuracy and reliability is demonstrated.