Limits of Detection and Quantification in Comprehensive Multidimensional Separations. 1. A Theoretical Look

Generating realistic data through modeling and parametric probability for the numerical evaluation of data processing algorithms in two-dimensional chromatography

BACKGROUND

Comprehensive two-dimensional chromatography generates complex data sets, and numerous baseline correction and noise removal algorithms have been proposed in the past decade to address this challenge. However, evaluating their performance objectively is currently not possible due to a lack of objective data.

RESULT

To tackle this issue, we introduce a versatile platform that models and reconstructs single-trace two-dimensional chromatography data, preserving peak parameters. This approach balances real experimental data with synthetic data for precise comparisons. We achieve this by employing a Skewed Lorentz-Normal model to represent each peak and creating probability distributions for relevant parameter sampling. The model's performance has been showcased through its application to two-dimensional gas chromatography data where it has created a data set with 458 peaks with an RMSE of 0.0048 or lower and minimal residuals compared to the original data. Additionally, the same process has been shown in liquid chromatography data.

SIGNIFICANCE

Data analysis is an integral component of any analytical method. The development of new data processing strategies is of paramount importance to tackle the complex signals generated by state-of-the-art separation technology. Through the use of probability distributions, quantitative assessment of algorithm performance of new algorithms is now possible. Therefore, creating new opportunities for faster, more accurate, and simpler data analysis development.

Light-Amplified CISS-Based Hybrid QD-DNA Impedimetric Device for DNA Hybridization Detection

We design and build a novel light-amplified electrochemical impedimetric device based on the CISS effect to detect DNA hybridization using a hybrid quantum dot (QD)-DNA monolayer on a ferromagnetic (FM) Ni/Au thin film for the first time. Using spin as a detection tool, the current research considers the chiral-induced spin selectivity (CISS) phenomenon. After injecting a spin current into the QD-DNA system with opposite polarities (up and down), the impedimetric device revealed a large differential change in the charge-transfer resistance (ΔR_ct) of ∼100 ohms for both spins. Nearly, a threefold increase in the ΔR_ct value to ∼270 ohms is observed when light with a wavelength of 532 nm is illuminated on the sample, owing to the amplified CISS effect. The yield of spin polarization as extracted from the Nyquist plot increases by a factor of more than 2 when exposed to light, going from 6% in the dark to 13% in the light. The impact of light on the CISS effect was further corroborated by the observation of the spin-dependent asymmetric quenching of photoluminescence (PL) in the same hybrid system. These observations are absent in the case of a noncomplementary QD-DNA system due to the absence of a helical structure in DNA. Based on this, we develop a spin-based DNA hybridization sensor and achieve a limit of detection of 10 fM. These findings open a practical path for the development of spin-based next-generation impedimetric DNA sensors and point-of-care devices.

Simulating comprehensive two-dimensional gas chromatography mass spectrometry data with realistic run-to-run shifting to evaluate the robustness of tile-based Fisher ratio analysis

Untargeted analysis of comprehensive two-dimensional (2D) gas chromatography time-of-flight mass spectrometry (GC×GC-TOFMS) data has the potential to be hindered by run-to-run retention time shifting. To address this challenge, tile-based Fisher ratio (F-ratio) analysis (FRA) has been developed, which utilizes a supervised, untargeted approach involving a chromatographic segmentation routine termed "tiling" combined with the ANOVA F-ratio statistic to discover class-distinguishing analytes while minimizing false positives arising from shifting. The tiling algorithm is designed to account for retention shifting in both separation dimensions. Although applications of FRA have been reported, there remains a need to thoroughly evaluate the robustness of FRA for different levels of run-to-run retention shifting in order to broaden the scope of its application. To this end, a novel method of simulating GC×GC-TOFMS chromatograms with realistic run-to-run shifting is presented by random generation of low-frequency "shift functions". The dimensionless retention-time precision, <δ_r>, which is four times the standard deviation in retention time normalized to the peak width-at-base is used as a key modeling variable along with the 2D chromatographic saturation, α_e,2D, and within-class relative standard deviation in peak area, RSD_wc. We demonstrate that all three of these variables operate together to impact true positive discovery. To quantify the "success" of true positive discovery, GC×GC-TOFMS datasets for various combinations of <δ_r>, α_e,2D, and RSD_wc were simulated and then analyzed by FRA using a wide range of relative tile areas (RTA), which is a dimensionless measure of tile size. Since each hit in the FRA hit list was known a priori as either a true or false positive based on the simulation inputs, receiver operating characteristic (ROC) curves were readily constructed. Then, the area under the ROC curve (AUROC) was used as a metric for discovery "success" for various combinations of the modeling variables. Based on the results of this study, recommendations for tile size selection and experimental design are provided, and further supported by comparison to previous tile-based FRA applications. For instance, values for <δ_r>, α_e,2D, and RSD_wc obtained from a GC×GC-TOFMS dataset of yeast metabolites suggested an optimum RTA of 6.25, corresponding closely to the RTA of 4.00 employed in the study, implying the simulation results obtained here can be generalized to real datasets.

Variations in phenolic acids and antioxidant activity of navel orange at different growth stages

Ripe navel orange has abundant amounts of phenolic compounds. Few studies monitored changes in these compounds during ripening. In this study, the effects of navel orange maturation on dynamic changes in antioxidant activity, total phenolic content (TPC), total flavonoid content (TFC) and phenolic acids were investigated. Five growth stages of navel orange were studied, and nine phenolic acids were detected via high performance liquid chromatography-triple quadrupole mass spectrometry (HPLC-QQQ-MS). Results showed that antioxidant activity, TFC and TPC decreased gradually with fruit ripening. The concentrations of most phenolic acids also declined during fruit maturation, except for free fractions of sinapic acid and bound fractions of ferulic and caffeic acids. Ferulic acid was the most dominant of all phenolic acids at all growth stages. Partial least-squares showed significant differences among fruits of different maturities. A significant correlation between antioxidant capacity, TPC, TFC and some phenolic acids was found.

Multidimensional Separation of Natural Products Using Liquid Chromatography Coupled to Hadamard Transform Ion Mobility Mass Spectrometry

A high performance liquid chromatograph (HPLC)was interfaced to an atmospheric drift tube ion mobility time of flight mass spectrometry. The power of multidimensional separation was demonstrated using chili pepper extracts. The ambient pressure drift tube ion mobility provided high resolving powers up to 166 for the HPLC eluent. With implementation of Hadamard transform (HT), the duty cycle for the ion mobility drift tube was increased from less than 1% to 50%, and the ion transmission efficiency was improved by over 200 times compared with pulsed mode, improving signal to noise ratio 10 times. HT ion mobility and TOF mass spectrometry provide an additional dimension of separation for complex samples without increasing the analysis time compared with conventional HPLC. Graphical Abstract ᅟ.