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.

Application of Extended Multiplicative Signal Correction to Short-Wavelength near Infrared Spectra of Moisture in Marzipan

Short-wavelength near infrared spectroscopy (SW-NIR) is a very rapid, versatile and precise technique, which can be used in many different situations and for very types of products and chemical compounds. Extended multiplicative signal correction (EMSC) is a modification of the standard MSC pre-processing method that allows the separation of physical light scattering effects from chemical (vibrational) light absorbance effects in spectra. In this paper, the EMSC is applied and compared with first derivate, second derivate, MSC and SNV in combination of PLSR to obtain robust models in terms of accuracy and predict ability with a reduced calibration data set using SW-NIR spectra of moisture in marzipan. The Extended Multiplicative Signal Correction—EMSC and combination methods provide the best results in terms of prediction ability and calibration SW-NIR spectra of moisture in marzipan. The best classification results were obtained by Extended Multiplicative Signal Correction followed by second derivates.

A time domain correction method for Doppler-distortion signal from acoustic moving source

Doppler effect widely exists in the signal from the moving acoustic source. In order to solve such problems as frequency shift and frequency band expansion, a time domain cor- rection method is presented in this paper. First, the discrete time vector for interpolation and the amplitude restoration formula is derived based on the moving relationship and the Morse acoustic theory, then the amplitude weights are corrected and the distortion signal is interpolated. Every point of the discrete signal is operated separately in time domain. Compared with the existing frequency domain methods, this method does not need to know the characteristic frequency beforehand and would not be influenced by the blending of the frequency band. Hence, this method can be employed to correct multiple frequency signals and it is also a simple and effective Doppler effect reduction method.

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.