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Full-scan gas chromatography-mass spectrometry (GC-MS) provides rapid untargeted screening and detection for trace and coeluting components. Even though modern mass spectrometers have sufficient sensitivity to detect signals of trace components, this does not translate to the ability of the data-processing approach to isolate interference signals and extract pure component spectra. The raw band-target entropy minimization (rBTEM) approach based on band-target entropy minimization was previously reported to enable accurate spectra reconstruction from severely coeluting and trace GC-MS components. In this study, we evaluate the limit of detection for the approach. This study is systematically compared to classical spectral deconvolution for measurements of chlorpyrifos pesticide standards sampled with solid-phase microextraction at various concentration levels in both distilled and seawater media. The rBTEM approach was able to detect chlorpyrifos at 0.5 ppb in distilled water and 1.0 ppb in seawater while the classical approach provided numerous false negative results. In addition, rBTEM consistently provided higher match scores for chlorpyrifos. Attaining accurate identification of trace and coeluting components from full-scan GC-MS analysis has immense potential impact in biomedical, agricultural, and environmental work.

Gas chromatography-mass spectrometry (GC-MS) is a versatile analytical method but its data is usually complicated by the presence of severely co-eluting and trace-level components. In this work, we introduce an optimized band-target entropy minimization approach for the analysis of complex mass spectral data. This new approach enables an automated mass spectral analysis which does not require any user-dependent inputs. Moreover, the approach provides improved sensitivity and accuracy for mass spectral reconstruction of severely co-eluting and trace-level components. The accuracy of our approach is compared to the automatic mass spectral deconvolution and identification system (AMDIS) with two controlled mixtures and a sample of Eucalyptus essential oil. Our approach was able to putatively identify 130 compounds in Eucalyptus essential oil, which was 46% in excess of that identified by AMDIS. This new approach is expected to benefit GC-MS analysis of complex mixtures such as biological samples and essential oils, in which the data are often complicated by co-eluting and trace-level components. Graphical abstract ᅟ.

Gas chromatography-mass spectrometry (GC-MS) is an important analytical technique for the analysis of organophosphorus pesticides in food products. Because of the complex matrices of food products, multiple preprocessing steps are required prior to performing the GC-MS analysis. Despite that, it is difficult to totally eliminate the interference of complex matrix background. In this work, we introduce an entropy minimization technique that can eliminate the need for comprehensive preprocessing steps to detect organophosphorus pesticides in a fortified orange juice sample. The pure mass spectra and extracted-ion chromatograms of the pesticides were extracted and reconstructed. The results achieved higher National Institute of Standard and Technology (NIST) match scores in comparison to the conventional background subtraction technique. Taken together, the entropy minimization technique is capable of providing rapid qualitative and quantitative analyses of complex GC-MS data. This technique is expected to have great potential for natural products and food analysis applications.

A major challenge for accurate GC-MS analysis is to achieve excellent chromatographic separation and a high signal-to-noise ratio. In fact, real-world samples are often contaminated with impurities that manifest as intense background noise in GC-MS data. Herein, a dynamic background noise removal system is developed based on an entropy minimization algorithm to extract pure mass spectra of straight-chain n-alkane components in a sample of hydrogenated jet fuel. This system is capable of extracting pure component spectra from overlapping peaks that are heavily superimposed by background noise without a priori information. The performance of the system surpasses that of the background subtraction method applied in Agilent ChemStation software. The robustness of the system to accurately analyse real-world samples is expected to simplify and revolutionise the application of GC-MS for chemical analysis.

Liquid chromatography-mass spectrometry (LC/MS) is a major technique for the analysis of complex samples. Data processing for LC/MS untargeted analysis is very challenging due to the presence of asymmetric, co-eluted at trace-level peaks. We report a multivariate curve resolution approach based on entropy minimization to resolve LC/MS data. Our liquid chromatography band-target entropy minimization (lc-BTEM) approach enables automated resolution of complex LC/MS peaks without the requirement of any parameter inputs. We demonstrated the performance of our approach on a co-eluted and trace-level peaks in an American ginseng extract. Consequently, we were able to putatively annotate the presence of more than 50 ginsenosides. The extracted pure mass spectra and in-source fragments information further enabled the annotation of [M+H]+, [M+H-Neu]+ and [M-H]- ions. This approach is expected to benefit LC/MS untargeted analysis and data independent analysis of complex samples.

Chemical profiling with gas chromatography/mass spectrometry (GC/MS) full-spectrum acquisition mode often leads to the discovery of known-unknown components. These are non-identified components which arise from the limitation of the data processing method or limited breadth of mass spectral libraries. The recent introduction of the NIST Hybrid Search sought to relieve the latter limitation by providing an improved class annotation technique. Herein, we demonstrate the importance of using a precise mass spectral reconstruction technique to increase the confidence of detecting the presence of known-unknowns and subsequently identifying them successfully with NIST Hybrid Search function. We compared the AMDIS algorithm against the rBTEM algorithm and found that the latter could more accurately reconstruct the mass spectra of co-eluting known-unknown components. This has a far-reaching implication to increase the number of identified compounds in GC/MS scan data.