Gas chromatography—ion mobility spectrometry (GC-IMS) allows the fast, reliable, and inexpensive chemical composition analysis of volatile mixtures. This sensing technology has been successfully employed in food science to determine food origin, freshness and preventing alimentary fraud. However, GC-IMS data is highly dimensional, complex, and suffers from strong non-linearities, baseline problems, misalignments, peak overlaps, long peak tails, etc., all of which must be corrected to properly extract the relevant features from samples. In this work, a pipeline for signal pre-processing, followed by four different approaches for feature extraction in GC-IMS data, is presented. More precisely, these approaches consist of extracting data features from: (1) the total area of the reactant ion peak chromatogram (RIC); (2) the full RIC response; (3) the unfolded sample matrix; and (4) the ion peak volumes. The resulting pipelines for data processing were applied to a dataset consisting of two different quality class Iberian ham samples, based on their feeding regime. The ability to infer chemical information from samples was tested by comparing the classification results obtained from partial least-squares discriminant analysis (PLS-DA) and the samples’ variable importance for projection (VIP) scores. The choice of a feature extraction strategy is a trade-off between the amount of chemical information that is preserved, and the computational effort required to generate the data models.

Summary: Nuclear magnetic resonance (NMR)-based metabolomics is widely used to obtain metabolic fingerprints of biological systems. While targeted workflows require previous knowledge of metabolites, prior to statistical analysis, untargeted approaches remain a challenge. Computational tools dealing with fully untargeted NMR-based metabolomics are still scarce or not user-friendly. Therefore, we developed AlpsNMR (Automated spectraL Processing System for NMR), an R package that provides automated and efficient signal processing for untargeted NMR metabolomics. AlpsNMR includes spectra loading, metadata handling, automated outlier detection, spectra alignment and peak-picking, integration and normalization. The resulting output can be used for further statistical analysis. AlpsNMR proved effective in detecting metabolite changes in a test case. The tool allows less experienced users to easily implement this workflow from spectra to a ready-to-use dataset in their routines. Availability and implementation: The AlpsNMR R package and tutorial is freely available to download from http://github.com/sipss/AlpsNMR under the MIT license. Supplementary information: Supplementary data are available at Bioinformatics online.

Gas sensor-based electronic noses (e-noses) have gained considerable attention over the past thirty years, leading to the publication of numerous research studies focused on both the development of these instruments and their various applications. Nonetheless, the limited specificity of gas sensors, along with the common requirement for chemical identification, has led to the adaptation and incorporation of analytical chemistry instruments into the e-nose framework. Although instrument-based e-noses exhibit greater specificity to gasses than traditional ones, they still produce data that require correction in order to build reliable predictive models. In this work, we introduce the use of a multivariate signal processing workflow for datasets from a multi-capillary column ion mobility spectrometer-based e-nose. Adhering to the electronic nose philosophy, these workflows prioritized untargeted approaches, avoiding dependence on traditional peak integration techniques. A comprehensive validation process demonstrates that the application of this preprocessing strategy not only mitigates overfitting but also produces parsimonious models, where classification accuracy is maintained with simpler, more interpretable structures. This reduction in model complexity offers significant advantages, providing more efficient and robust models without compromising predictive performance. This strategy was successfully tested on an olive oil dataset, showcasing its capability to improve model parsimony and generalization performance.

Complementary Split Ring Resonators (CSRRs) have been extensively studied as planar sensors in the last two decades. However, their practical use remains limited to controlled environments and classification problems. Their performance reliance on high-end Vector Network Analyzers (VNAs), highly repeatable laboratory conditions, and special sample holders or microfluidic circuits hinders its regular use in chemistry laboratories as analytical tool. Temperature drifts and humidity variations during measuring, uncertainties in the electromagnetic properties of the sample containers, and careles sample handling introduce significant uncertainties in measurements, leading to unreliable results. Therefore, the prediction of target compounds concentration in samples have been out of the research focus up to now. Machine Learning algorithms can help to mitigate these uncertainties and open the applicability of CSRR sensors to quantification problems, where is necessary to determine the amount of a substance in a liquid (or solid) sample. This work presents a novel approach that takles this issue, combining a CSRR sensor with well stablised ML algorithms that enhances its quantification performance. For ilustration purposes, a low-cost, benchtop CSRR-based system is proposed to predict ethanol concentration in water solutions. Ethanol samples from 10% to 96% concentration were prepared in commercial vials, generating 450 randomized measurements. Principal Component Analysis (PCA) was employed for data exploration, while a Partial Least Squares regression model (PLS), tuned with Leave-One-Group-Out Cross-Validation, was trained for ethanol concentration prediction. No feature extraction technique or noise reduction strategy was applied. Although this straightforward workflow is well known in the chemical sensing field, it has not been applied to data acquired with CSRR sensors. The trained model achieved a Root Mean Square Error in Prediction (RMSEP) of 3.7%. Compared with 23.4% RMSEP when using univariate calibration at optimized frequencies, it presentes a prediction performance reduced by a factor of 6. No evidence of underfitting or overfitting was observed during test of the trained model. The low RMSEP achieved by the presented setup demonstrates the potential of CSRR-based sensors when combined with ML techniques for concentration prediction working in realistic, uncontrolled conditions. This pushes forward the applicability of CSRR sensors in the chemical analysis field, which might lead to benchtop, lowcost and relaible analysis devices for many laboratories.

Electronic noses (e-noses) are analytical systems designed to recognize complex volatile patterns through the combined use of sensing elements and data-processing methods

Gas Chromatography–Ion Mobility Spectrometry (GC-IMS) is increasingly used for untargeted metabolomic profiling in biomedical research. However, signal stability can be compromised by technical variability introduced during data acquisition, reducing reliability and affecting downstream analyses such as classification and biomarker discovery. This study presents a robust data correction pipeline to mitigate acquisition-related variability. A dataset of 135 GC-IMS measurements from a pooled urine sample acquired over nine consecutive sessions was analyzed. Two major sources of systematic noise were identified: batch index (measurement session) and elapsed time at room temperature before injection (within-batch acquisition order). Independent linear models fitted to feature intensities indicated that approximately 35% of total variance was attributable to batch effects and 30% to elapsed time, highlighting a strong linear association between acquisition order and signal variability. We present a structured orthogonal correction strategy that removes dominant acquisition-related variability while preserving chemical signal structure. The method reconstructs batch- and acquisition-related signal components, extracts their principal directions via PCA, and performs correction in sample space using orthogonal projection. After correction, the proportion of technically stable features (Relative Standard Deviation, RSD < 20%) increased from 22.6% to 74.2%. The proposed pipeline provides a lightweight, generalizable strategy to enhance the quality and stability of GC-IMS data and improve reliability in metabolomic analysis.