Modelling highly co-eluted peaks of analytes with high spectral similarity

Tensor methods in data analysis of chromatography/mass spectroscopy-based plant metabolomics

Plant metabolomics is an important research area in plant science. Chemometrics is a useful tool for plant metabolomic data analysis and processing. Among them, high-order chemometrics represented by tensor modeling provides a new and promising technical method for the analysis of complex multi-way plant metabolomics data. This paper systematically reviews different tensor methods widely applied to the analysis of complex plant metabolomic data. The advantages and disadvantages as well as the latest methodological advances of tensor models are reviewed and summarized. At the same time, application of different tensor methods in solving plant science problems are also reviewed and discussed. The reviewed applications of tensor methods in plant metabolomics cover a wide range of important plant science topics including plant gene mutation and phenotype, plant disease and resistance, plant pharmacology and nutrition analysis, and plant products ingredient characterization and quality evaluation. It is evident from the review that tensor methods significantly promote the automated and intelligent process of plant metabolomics analysis and profoundly affect the paradigm of plant science research. To the best of our knowledge, this is the first review to systematically summarize the tensor analysis methods in plant metabolomic data analysis.

A compounds annotation strategy using targeted molecular networking for offline two-dimensional liquid chromatography-mass spectrometry analysis: Yupingfeng as a case study

Component overlapping and long-time consumption hinder the data processing of offline two-dimensional liquid chromatography mass spectrometry (offline 2D-LC MS) system. Although molecular networking has been commonly employed in data processing of liquid chromatography mass spectrometry (LC-MS), its application in offline 2D-LC MS is challenged by voluminous and redundant data. In light of this, for the first time, a data deduplication and visualization strategy combining hand-in-hand alignment with targeted molecular networking (TMN) for compounds annotation of offline 2D-LC MS data was developed and applied to the chemical profile of Yupingfeng (YPF), a classical traditional Chinese medicine (TCM) prescription, as a case study. Firstly, an offline 2D-LC MS system was constructed for the separation and data acquisition of YPF extract. Then the data of 12 fractions derived from YPF were deconvoluted and aligned as a whole data file by hand-in-hand alignment, resulting in a 49.2% reduction in component overlapping (from 17951 to 9112 ions) and an improvement in the MS² spectrum quality of precursor ions. Subsequently, the MS²-similarity adjacency matrix of focused parent ions was computed by a self-building Python script, which realized the construction of an innovative TMN. Interestingly, the TMN was found to be able to efficiently distinguish and visualize the co-elution, in-source fragmentations and multi-type adduct ions in a clustering network. Consequently, a total of 497 compounds were successfully identified depending on only seven TMN analysis guided by product ions filtering (PIF) and neutral loss filtering (NLF) for the targeted compounds in YPF. This integrated strategy improved the efficiency of targeted compound discovery in offline 2D-LC MS data, also shown a huge scalability in accurate compound annotation of complex samples. In conclusion, our study developed available concepts and tools while providing a research paradigm for efficient and rapid compound annotation in complex samples such as TCM prescriptions, with YPF as an example.

Obtaining clean and informative mass spectra from complex chromatographic and high-resolution all-ions-fragmentation data by nonnegative parallel factor analysis 2

A major challenge in processing of complex data obtained from chromatography hyphenated to mass spectrometry is to resolve chromatographically co-eluting compounds. In this study, we present a workflow for the resolution of ultra-high pressure liquid chromatography high-resolution mass spectrometry data obtained by the broadband data-independent acquisition MS^E operation (UHPLCHRMS^E). The workflow is based on a recently introduced algorithm for Parallel Factor Analysis 2 (PARAFAC2) that allows to enforce non-negativity on all the model coefficients. The workflow was tested on three sets of UHPLC-HRMS^E measurements from a Lupinus angustifolius L. crop field study, which included plant tissue samples, soil samples and samples from drainage water as well as stream water close to the field. The three datasets included 93, 59, and 75 chromatographic runs in total for the plant, soil and water batches, respectively. Nonnegative-PARAFAC2 models were fitted on the summed high and low energy (HE and LE) traces on chromatographic intervals corresponding to spiked standard for the three sample sets independently. In soil and plant samples, 13 out of 14 spiked standards were resolved by NN-PARAFAC2 even in presence of chromatographic co-elution, and their mass spectral loadings could be matched to a reference spectrum. In contrast, only seven spiked standards were correctly resolved and matched for the water samples because a higher chromatographic baseline rendered the data noisier. The results show that the workflow we present can provide improved mass spectral selectivity for data-independent acquisition compared to using the raw mass spectra and can be used to match fragment ions from the HE trace, and precursor and adduct ions from the LE trace even in presence of co-eluting compounds.

Identification of systematic absence of cross-peaks (SACPs) in a two-dimensional asynchronous Spectrum using an auxiliary 2D quotient Spectrum and a statistical test

Systematic Absence of Cross Peaks (SACPs) in a two-dimensional (2D) asynchronous spectrum, a sensitive indicator of the signal purity, is very important in analyzing bilinear data. However, identification of SACPs in practice remains a challenge because of noise in the corresponding 2D asynchronous spectrum. We firstly show that SACP can be identified via a statistical test using a large amount of 2D asynchronous spectra. To meet the practical demand that SACPs must be identified based on a single 2D asynchronous spectrum in many cases, we use a 2D quotient spectrum (Q (x, y)) as an effective auxiliary tool to recognize SACPs. The expectation of Q(x, y) is zero when (x, y) is within SACP or background regions in the corresponding 2D asynchronous spectrum. When (x, y) is in a cross-peak region, the expectation of the absolute value of Q(x, y) is a constant regardless of whether the cross-peak in a 2D asynchronous spectrum is strong or weak. Thus, a unified threshold can be set up to differentiate the SACP region from cross-peak region via the auxiliary 2D quotient spectrum. We have applied this approach on two real-world examples and satisfactory results have been obtained. This result demonstrates that the statistical test with a 2D quotient spectrum is applicable in real-world systems.

Supervised neural computing solutions for fluorescence identification of benzimidazole fungicides. Data and decision fusion strategies

Benzimidazole fungicides (BFs) are a type of pesticide of high environmental interest characterized by a heavy fluorescence spectral overlap which complicates its detection in mixtures. In this paper, we present a computational study based on supervised neural networks for a multi-label classification problem. Specifically, backpropagation networks (BPNs) with data fusion and ensemble schemes are used for the simultaneous resolution of difficult multi-fungicide mixtures. We designed, optimized and compared simple BPNs, BPNs with data fusion and BPNs ensembles. The information environment used is made up of synchronous and conventional BF fluorescence spectra. The mixture spectra are not used in the training nor the validation stage. This study allows us to determine the convenience of fusioning the labels of carbendazim and benomyl for the identification of BFs in complex multi-fungicide mixtures.