Background correction in near-infrared spectra of plant extracts by orthogonal signal correction

In near-infrared (NIR) analysis of plant extracts, excessive background often exists in near-infrared spectra. The detection of active constituents is difficult because of excessive background, and correction of this problem remains difficult. In this work, the orthogonal signal correction (OSC) method was used to correct excessive background. The method was also compared with several classical background correction methods, such as offset correction, multiplicative scatter correction (MSC), standard normal variate (SNV) transformation, de-trending (DT), first derivative, second derivative and wavelet methods. A simulated dataset and a real NIR spectral dataset were used to test the efficiency of different background correction methods. The results showed that OSC is the only effective method for correcting excessive background.

Distinguishing transgenic from non-transgenic Arabidopsis plants by ~1H NMR-based metabolic fingerprinting

We have recently reported the construction of an nuclear magnetic resonance （NMR）-based metabonomics study platform, Automics. To examine the application of Automics in transgenic plants, we performed metabolic fingerprinting analysis, i.e., 1H NMR spectroscopy and multivariate analysis, on wild-type and transgenic Arabidopsis. We found that it was possible to distinguish wild-type from four transgenic plants by PLS-DA following application of orthogonal signal correction （OSC）. Scores plot following OSC clearly demonstrates significant variation between the transgenic and non-transgenic groups, suggesting that the metabolic changes among wild-type and transgenic lines are possibly associated with transgenic event, We also found that the major contributing metabolites were some specific amino acids （i.e., threonine and alanine）, which could correspond to the insertion of the selective marker BAR gene in the transgenic plants. Our data suggests that NMR-based metabonomics is an efficient method to distinguish fingerprinting difference between wild-type and transgenic plants, and can potentially be applied in the bio-safety assessment of transgenic plants.

Property Prediction Using Hierarchical Regression Model Based on Calibration

Redundant information and inaccurate model will greatly affect the precision of quality prediction.A multiphase orthogonal signal correction modeling and hierarchical statistical analysis strategy are developed for the improvement of process understanding and quality prediction.Bidirectional orthogonal signal correction is used to remove the structured noise in both X and Y,which does not contribute to prediction model.The corresponding loading vectors provide good interpretation of the covariant part in X and Y.According to background,hierarchical PLS（Hi-PLS）is used to build regression model of process variables and property variables.This blocking leads to two model levels：the lower level shows the relationship of variables in each annealing furnace using hierarchical PLS based on bidirectional orthogonal signal correction,and the upper level reflects the relationship of annealing furnaces.With analysis of continuous annealing line data,the production precisions of hardness and elongation are improved by comparison of previous method.Result demonstrates the efficiency of the proposed algorithm for better process understanding X and property interpretation Y.