A Method for the Quantitative Analysis of a Key Component in Complex Mixtures Using Raman Spectroscopy Based on Peak Decomposition.

Raman spectroscopy has been increasingly used in industrial production processes for analytical purposes. However, the quantitative analysis of complex mixtures based on their Raman spectra remains a problem, especially when not all of the spectra of pure components in the mixture are available. In this work, a method for the quantitative analysis of a key component in complex mixtures based on peak decomposition is introduced.

A Method of High Throughput Monitoring Crop Physiology Using Chlorophyll Fluorescence and Multispectral Imaging.

We present a high throughput crop physiology condition monitoring system and corresponding monitoring method. The monitoring system can perform large-area chlorophyll fluorescence imaging and multispectral imaging. The monitoring method can determine the crop current condition continuously and non-destructively.

An Automated Baseline Correction Method Based on Iterative Morphological Operations.

Raman spectra usually suffer from baseline drift caused by fluorescence or other reasons. Therefore, baseline correction is a necessary and crucial step that must be performed before subsequent processing and analysis of Raman spectra. An automated baseline correction method based on iterative morphological operations is proposed in this work.

Detecting crop population growth using chlorophyll fluorescence imaging.

For both field and greenhouse crops, it is challenging to evaluate their growth information on a large area over a long time. In this work, we developed a chlorophyll fluorescence imaging-based system for crop population growth information detection. Modular design was used to make the system provide high-intensity uniform illumination.

[Online Analysis of Dynamic Trend Regression and Endpoint Determination for Chinese Traditional Medicine Extraction Process Based on Ultraviolet spectroscopy].

With the transition of Chinese traditional medicine manufacture industry, modernization has become the inexorable tendency in its future development. However, during the current Chinese traditional medicine producing process, the lack of online monitoring leads to the lagging of quality detection, as well as quality differences between products. In this paper, aiming at realizing online monitoring and end point automatic determination for Chinese traditional medicine (CTM) extraction unit, which is one of the most important units in CTM producing, ultraviolet (UV) spectroscopy is applied to build UV absorbance dynamic model based on the Lambert-Beer law and the CTM extraction kinetic model which presents a new method of UV absorbance dynamic analysis and endpoint determination, including curve regression, robustness analysis and endpoint calculation.

Background Subtraction of Raman Spectra Based on Iterative Polynomial Smoothing.

In this paper, a novel background subtraction algorithm is presented that can automatically recover Raman signal. This algorithm is based on an iterative polynomial smoothing method that highly reduces the need for experience and a priori knowledge. First, a polynomial filter is applied to smooth the input spectrum (the input spectrum is just an original spectrum at the first iteration).

Raman Spectral Analysis of Low-content Benzene Concentration in Gasoline with Partial Least Squares Based on Interference Peak Subtraction.

Raman spectroscopy is adopted to detect the low-content benzene concentrations in gasoline products. Due to the peak overlap of benzene and other species in the gasoline spectrum, the associated statistical regression methods cannot make stable predictions unless there are enough training samples. To extend their extrapolation to small-size training sets, we propose the method of partial least squares based on a spectral pretreatment of interference peak subtraction (IPS-PLS).

Automated decomposition algorithm for Raman spectra based on a Voigt line profile model.

Raman spectra measured by spectrometers usually suffer from band overlap and random noise. In this paper, an automated decomposition algorithm based on a Voigt line profile model for Raman spectra is proposed to solve this problem. To decompose a measured Raman spectrum, a Voigt line profile model is introduced to parameterize the measured spectrum, and a Gaussian function is used as the instrumental broadening function.

[Component analysis of the circulating fluid in an adsorption tower in a P-xylene unit based on raman spectral decomposition].

In order to achieve fast and accurate online analysis of the circulating fluid in an adsorption tower in a p-xylene unit, the Raman spectral analysis method is adopted. However, the Raman spectra of the pure components included in the circulating fluid overlap together, and the concentration of each component varies obviously, the present Raman analysis technology needs a large amount of training samples. Therefore, this paper applies Raman spectral decomposition method in component analysis of the circulating fluid.

[Fast discrimination of edible vegetable oil based on Raman spectroscopy].

A novel method to fast discriminate edible vegetable oils by Raman spectroscopy is presented. The training set is composed of different edible vegetable oils with known classes. Based on their original Raman spectra, baseline correction and normalization were applied to obtain standard spectra.

[Fast spectral modeling based on Voigt peaks].

Indirect hard modeling (IHM) is a recently introduced method for quantitative spectral analysis, which was applied to the analysis of nonlinear relation between mixture spectrum and component concentration. In addition, IHM is an effectual technology for the analysis of components of mixture with molecular interactions and strongly overlapping bands. Before the establishment of regression model, IHM needs to model the measured spectrum as a sum of Voigt peaks.

Modified indirect hard modeling for non-invasive Raman measurements containing surface interference.

Non-invasive Raman spectroscopy has been used in an increasing number of applications in recent years. However, in situations where surface signal is excessive, the acquired spectrum of probed sample suffers from surface interference in either conventional backscattering Raman or specially designed Raman methods. A computational method for Raman spectral recovery is required.

[A fast classification method for petroleum products based on the Raman spectroscopy].

A fast and effective method for classification of petroleum products based on Raman spectroscopy is proposed. A knowledge base composed by Raman spectra of training samples, intra-class feature spectra and intra-class thresholds of all classes was firstly established. Then, correlation coefficients between the test sample and the intra-class feature spectra were calculated.

An improved algorithm to remove cosmic spikes in Raman spectra for online monitoring.

Raman spectral analysis integrated with multivariate calibration is a fast and effective solution to monitor chemical product properties. However, Raman instruments utilizing charge-coupled device (CCD) detectors suffer from occasional spikes caused by cosmic rays. Cosmic spikes can disturb or even destroy the meaningful chemical information expressed by normal Raman spectra.

[The application of piecewise direct standardization with SNV in calibration transfer of Raman spectra].

To implement calibration transfer between Raman spectrometers, an improved piecewise direct standardization (PDS) is proposed in the present paper. Standard normal variate (SNV) is firstly introduced to reduce the influence of spectral background and intensity corresponding to the master spectrometer and the slave spectrometer; then PDS algorithm is used to eliminate the differences between Raman spectra for a specific sample. Moreover, a new quantitative criterion, called transfer error rate, is proposed to evaluate the performance of calibration model transfer.

[Fast analysis of benzene concentration in gasoline based on low-resolution dispersive Raman spectroscopy].

In order to fast analyze the benzene concentration in gasoline, a new measure method based on low-resolution dispersive Raman spectroscopy is proposed. There exist strong measurement noise and fluorescence background in dispersive Raman spectra, so the present paper applies the Savitzky-Golay smoothing filter to remove the measurement noise and uses iterative polynomial curve-fitting to reduce the fluorescence background. Based on ridge regression, principal component regression and partial least squares algorithm, three calibration models of the benzene concentration in gasoline are built and validated by a set of gasoline samples from a refinery.

[Fast recognition of gasoline brands based on the Raman spectroscopy].

A novel method for fast recognition of gasoline brands based on the Raman spectroscopy is presented. A classification model on the basis of product gasoline samples with known brands was established. The detailed modeling process includes measurement and pretreatment of Raman spectra of these samples, principal component analysis (PCA) to obtain loading vectors and score vectors of all known samples, and calculating each average score vector for all of the samples with the same brand.

[Hydrocarbon group analysis of gasoline by Raman spectroscopy with outlier detection].

Hydrocarbon group of gasoline not only determines the quality of gasoline, but also directly relates to the impact of exhaust air on the environment. The present paper successfully applied Raman spectroscopy to the quantitative analysis of hydrocarbon group in gasoline. Contaminated samples were removed from calibration set by outlier detection, which effectively improved the partial least squares (PLS) prediction accuracy.

[Application of Raman spectra feature extraction in chemical fiber component qualitative identification].

According to the characteristics of the textile fibers Raman spectra, a qualitative identification method based on Raman feature extraction is proposed. This fast method consists of spectrum measurement and spectral data processing algorithm, including spectrum preprocessing, feature extraction and matching recognition. It can be used to identify the components of fibers or fabrics, especially chemical fibers, which is an inspective difficulty in daily analytic work for its remarkable Raman feature.

[Spectral wavelength selection based on PLS projection analysis].

In order to enhance the prediction accuracy of spectral analysis models and reduce their input number, this paper presents a simple and rapid wavelength selection method based on PLS projection correlation coefficients. These correlation coefficients are decided by both the changes in spectra data and the PLS regression coefficients between spectra matrix and concentration vector. Compared with the traditional wavelength selection method based on correlation analysis, the novel proposed method obviously improves the robustness of spectral analysis models and reduces their input number sharply.

[Near-infrared spectral quantitative analysis by combining classification with local PLS].

As a rapid analytical technology, near-infrared (NIR) spectroscopy has been developed fast in recent years. To improve the accuracy of near-infrared spectral quantitative analysis, the present paper first classifies a testing sample by a support vector machine classifier and selects some similar training samples of the same type to build the calibration model, than predicts the property of the testing sample. To avoid the negative influence of classification failure, a new hybrid algorithm (called H_PLS) was proposed.

[A simple and convenient standardization algorithm of NIR spectra].

Due to the limitation of current algorithms for NIR spectral analysis model transfer, a simple and convenient algorithm to standardize the spectra was proposed, and a new performance index called spectra standard error (SSE) was also constructed to evaluate the validity of model transfer algorithms. SSE expresses the ratio of J2 to J1, where J2 describes the distances between the spectra of the same sample using different instruments, and J2 describes the average distance between the spectra of different samples using the original instrument for their central spectrum. In the present paper we first used Savitzky-Golay smoothing to realize baseline correction for different spectra, and then applied standard normal variate method to standardize spectra and polynomial filtering to avoid noise.