Zhaozhou Lin

Rapid screening and identification of target constituents using full scan-parent ions list-dynamic exclusion acquisition coupled to diagnostic product ions analysis on a hybrid LTQ-Orbitrap mass spectrometer

A highly sensitive and effective strategy for rapid screening and identification of target constituents has been developed using full scan-parent ions list-dynamic exclusion (FS-PIL-DE) acquisition coupled to diagnostic product ions (DPIs) analysis on a hybrid LTQ-Orbitrap mass spectrometer. The FS-PIL-DE was adopted as a survey scan to trigger the MS/MS acquisition of all the predictable constituents contained in traditional Chinese medicines. Additionally, DPIs analysis can provide a criterion to judge the target constituents detected into certain chemical families. Results from analyzing polymethoxylated flavonoids (PMFs) in the leaves of Citrus reticulata Blanco demonstrated that FS-PIL-DE was capable of targeting a greater number of constituents than FS, FS-PIL and FS-DE, thereby increasing the coverage of constituent screening. As a result, 135 PMFs including 81 polymethoxyflavones, 54 polymethoxyflavanones or polymethoxychalcones were identified preliminarily. And this was the first time to systematically report the presence of PMFs in the leaves of Citrus reticulata Blanco, especially for polymethoxylated flavanones and chalcones, most of which were new compounds. The results indicated that the developed FS-PIL-DE coupled to DPIs analysis methodology could be employed as a rapid, effective technique to screen and identify target constituents from TCMs extracts and other organic matter mixtures whose compounds contained can also be classified into families based on the common carbon skeletons.

Application of orthogonal space regression to calibration transfer without standards

To transfer a calibration model in cases where the standardization samples are rare or unstable, a method based on orthogonal space regression (OSR) is proposed. It uses virtual standardization spectra to account for response changes between instruments or batches. A comparative study of the proposed OSR, piecewise direct standardization, finite impulse response, orthogonal signal correction, and model updating (MU) was conducted on both pharmaceutical tablet data and chlorogenic acid data. The results of these studies suggest that both the OSR and the MU are superior to the other transfer techniques in terms of root‐mean‐squared error of prediction and ratio of performance to interquartile distance. Moreover, OSR requires no identical standard samples, and it avoids re‐optimizing the transfer models. In conclusion, both the differences among spectra measured on different spectrometers and the differences between different batches can be corrected successfully using the OSR method. Copyright

Characterization of rational biomarkers accompanying fever in yeast-induced pyrexia rats using urine metabolic footprint analysis

Fever is a prominent feature of diseases and is an ongoing process that is always accompanied by metabolic changes in the body system. Despite the success of temperature regulation theory, the underlying biological process remains unclear. To truly understand the nature of the febrile response, it is crucial to confirm the biomarkers during the entire biological process. In the current study, a 73-h metabolic footprint analysis of the urine from yeast-induced pyrexia rats was performed using ultra-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry. Potential biomarkers were selected using orthogonal partial least squares-discriminate analysis (OPLS-DA), the rational biomarkers were verified by Pearson correlation analysis, and the predictive power was evaluated using receiver operator characteristic (ROC) curves. A metabolic network constructed using traditional Chinese medicine (TCM) grammar systems was used to validate the rationality of the verified biomarkers. Finally, five biomarkers, including indoleacrylic acid, 3-methyluridine, tryptophan, nicotinuric acid and PI (37:3), were confirmed as rational biomarkers because their correlation coefficients were all greater than 0.87 and because all of the correlation coefficients between any pair of these biomarkers were higher than 0.75. The areas under the ROC curves were all greater than 0.84, and their combined predictive power was considered reliable because the greatest area under the ROC curve was 0.968. A metabolic network also demonstrated the rationality of these five biomarkers. Therefore, these five metabolites can be adopted as rational biomarkers to reflect the process of the febrile response in inflammation-induced pyrexia.

Near Infrared Spectroscopy Model Development and Variable Importance in Projection Assignment of Particle Size and Lobetyolin Content of Codonopsis Radix

Near infrared (NIR) diffuse reflectance spectroscopy was investigated to simultaneously determine the particle size (physical attribute) and active ingredient lobetyolin (chemical attribute) of Codonopsis radix. Laser diffraction and high-performance liquid chromatography were used as reference methods to determine particle size and lobetyolin content, respectively. Several spectral pretreatment methods were compared, with first derivative combined with nine-point Savitzky–Golay smoothing filter as the best method for establishing the partial least-squares models of particle size and lobetyolin. Then, synergy interval partial least squares (SiPLS) and backward interval partial least squares (BiPLS) were compared. The results showed that BiPLS was the appropriate method for establishing the particle size model; the root mean square error of calibration (RMSEC), root mean square error of cross-validation (RMSECV) and root mean square error of prediction (RMSEP) values were 34.3 μm, 36.1 μm and 29.2 μm, respectively, and the values of Rcal2, Rcv2 and rpre2 were 0.92, 0.91 and 0.94, respectively. The ratio of performance to deviation (RPD) was 4.1. Meanwhile, SiPLS was the optimal method for establishing the lobetyolin model; the RMSEC, RMSECV, RMSEP values were 0.052 mg g−1, 0.059 mg g−1 and 0.054 mg g−1, respectively, and the values of Rcal2, Rcv2 and rpre2 were 0.87, 0.84 and 0.83, respectively. The RPD was 2.5. According to the variable importance in projection (VIP) scores and the variable selection method of SiPLS, 1210–1296 nm was the second overtone of C–H; 2070–2156 nm and 2242–2328 nm were the combination of C–O, O–H and C–H. Therefore, the results showed that NIR could be used to determine physical and chemical properties simultaneously.

Improving the creditability and reproducibility of variables selected from near infrared spectra

A method based on an assembly of two metrics, including the variable importance in projection (VIP) and the PLS regression coefficients B, was developed for wavelength selection in multivariate calibration of spectral data. The proposed algorithm termed VIP-CARS combined the two metrics in a sequential and iterative manner, rather than directly introducing VIP into CARS-PLS. This approach is particularly attractive for quantification due to its relatively higher reproducibility and robustness compared to the CARS procedure. The method was tested on datasets taken from the corn and Rukuaixiao Tablets. It was shown that a small number of well-defined relevant spectral variables were identified with the proposed approach, providing easy spectral interpretation and high creditability. Moreover, with the implementation of the VIP-CARS algorithm, the prediction performance of the final model and the reproducibility of the selected wavelengths were also improved.

Evaluating the reliability of spectral variables selected by subsampling methods

It is imperfect to evaluate a subsampling variable selection method using only its prediction performance. To further assess the reliability of subsampling variable selection methods, dummy noise variables of different amplitudes were augmented to the original spectral data, and the false variable selection number was recorded. The reliabilities of three subsampling variable selection methods including Monte Carlo uninformative variable elimination (MC‐UVE), competitive adaptive reweighted sampling (CARS), and stability CARS (SCARS) were evaluated using this dummy noise strategy. The evaluation results indicated that both CARS and SCARS produced more parsimonious variable sets, but the reliabilities of their final variable sets were weaker than those of MC‐UVE. On the contrary, only marginal improvement on the prediction performance was obtained using MC‐UVE. Further experiments showed that removing white noise‐like variables beforehand would improve the reliability of variables extracted by CARS and SCARS. Copyright

Metabolomics data fusion between near infrared spectroscopy and high-resolution mass spectrometry: A synergetic approach to boost performance or induce confusion

In general, data fusion can improve the classification performance of the model, but little attention is paid to the influence of the data fusion on the spatial distribution of the modeling samples. In this paper, the effect of data fusion on sample spatial distribution was studied through integrating NIR data and UHPLC-HRMS data for sulfur-fumigated Chinese herb medicine. Twelve samples collected from four different geographical origins were sulfur fumigated in the lab, and then metabolomics analysis was conducted using NIR and UHPLC-LTQ-Orbitrap mass spectrometer. First of all, the discriminating power of each technique was respectively examined based on PCA analysis. Secondly, combining NIR and UHPLC-HRMS data sets together with or without variable selection was parallelly compared. The results demonstrated that the discriminable ability was remarkably improved after data fusion, indicating data fusion could visualize variable selection and enhance group separation. Samples in the margin between two classes of samples may increase the experience error but has positive effect on the separation direction. Besides, an interesting feature extraction could obtain better discriminable effect than common data fusion. This study firstly provided a new path to employ a comprehensive analytical approach for discriminating SF Chinese herb medicines to simultaneously benefit from the advantages of several technologies.

Dealing with heterogeneous classification problem in the framework of multi-instance learning

To deal with heterogeneous classification problem efficiently, each heterogeneous object was represented by a set of measurements obtained on different part of it, and the heterogeneous classification problem was reformulated in the framework of multi-instance learning (MIL). Based on a variant of count-based MIL assumption, a maximum count least squares support vector machine (maxc-LS-SVM) learning algorithm was developed. The algorithm was tested on a set of toy datasets. It was found that maxc-LS-SVM inherits all the sound characters of both LS-SVM and MIL framework. A comparison study between the proposed approach and the other two MIL approaches (i.e., mi-SVM and MI-SVM) was performed on a real wolfberry fruit spectral dataset. The results demonstrate that by formulating the heterogeneous classification problem as a MIL one, the heterogeneous classification problem can be solved by the proposed maxc-LS-SVM algorithm effectively.