Discrete Fourier transform techniques for noise reduction and digital enhancement of analytical signals

Electric Field-Modulated Electrospray Ionization Mass Spectrometry for Quantity Calibration and Mass Tracking

Analyses conducted by electrospray ionization (ESI) mass spectrometry (MS) typically entail performing a number of preparatory steps, which include quantity calibration and mass calibration. Quantity calibration can be affected by signal noise, while mass calibration can be affected by instrumental drift if analyses are performed over an extended period of time. Here, we present two methods for achieving these calibrations using modulation of electrospray plume by alternating electric fields and demodulating the resulting MS ion currents. For this purpose, we use an ESI source fitted with three ring electrodes between the electrospray emitter and the mass spectrometer's inlet. One of these electrodes is supplied with a sine electric signal. Optionally, a nanoESI emitter is also placed between the ring electrodes and the mass spectrometer's orifice to supply calibrant ions. The ion currents, recorded with this setup, present wave-like features. In the first variant, using a triple quadrupole mass analyzer, the ion currents are subjected to data treatment by fast Fourier transform (FFT), and the resulting FFT magnitudes are correlated with analyte concentrations to produce a calibration plot. In the second variant, using a quadrupole time-of-flight mass analyzer, the mass spectra recorded at the analyte ion current maxima are mass-checked using the m/z value of the internal standard (injected via nanoESI emitter), which appears predominantly in the time intervals corresponding to the analyte ion current minima. The setup has been characterized using simulation software and optimized. Overall, the method enables the preparation of quantity calibration plots and monitoring (minor) m/z drifts during prolonged analyses.

Application of Odd-Order Derivatives in Fourier Transform Nuclear Magnetic Resonance Spectroscopy toward Quantitative Deconvolution

When Fourier transform (FT) spectrum peaks are overlapped, primary maxima of odd-order derivatives can be used to evaluate their independent intensities. We studied the feasibility of higher odd-order derivatives on Lorentzian peak shape and magnitude peak shape. Simulation studies for FT nuclear magnetic resonance (NMR) spectroscopy demonstrated good results toward quantitative deconvolution of overlapping FT spectrum peaks. Although it is not so desirable to deconvolute special line shapes such as Gaussian, Voigt, and Tsallis profiles, the odd-order derivatives exhibit a bright future compared to even-order derivatives. An application example of practical NMR spectroscopy with ethylbenzene isomers is presented. White Gaussian noises were added to the simulated spectra at two different signal-to-noise ratios (20 and 40). Kauppinen's denoising and smoothing algorithms can effectively remove interference of the noise and help to have good deconvoluting results using the odd-order derivatives. We compared features of our approach with popular deconvolution sharpening algorithms and conducted a comparison study with Kauppinen's Fourier self-deconvolution. Our approach has a better dynamic range of peak intensities and is not sensitive to the sampling rates. Other common deconvolution methods are also discussed briefly.

Enhancing flow-mediated dilation analysis by optimizing an open-source software with automated edge detection

Flow-mediated dilation (FMD) is a common measure of endothelial function and an indicator of vascular health. Automated software methods exist to improve the speed and accuracy of FMD analysis. Compared with commercial software, open-source software offers similar capabilities at a much lower cost while allowing for increased customization specific to users' needs. We introduced modifications to an existing open-source software, FloWave.us to better meet FMD analysis needs. The purpose of this study was to compare the repeatability and reliability of the modified FloWave.us software to the original software and to manual measurements. To assess these outcomes, duplex ultrasound imaging data from the popliteal artery in older adults were analyzed. The average percent FMD for the modified software was 6.98 ± 3.68% and 7.27 ± 3.81% for observer 1 and 2 respectively, compared with 9.17 ± 4.91% and 10.70 ± 4.47% with manual measurements and 5.07 ± 31.79% with the original software for observer 1. The modified software and manual methods demonstrated higher intraobserver intraclass correlation coefficients (ICCs) for repeated measures for baseline diameter, peak diameter, and percent FMD compared with the original software. For percent FMD, the interobserver ICC was 0.593 for manual measurements and 0.723 for the modified software. With the modified method, an average of 97.7 ± 2.4% of FMD videos frames were read, compared with only 17.9 ± 15.0% frames read with the original method when analyzed by the same observer. Overall, this work further establishes open-source software as a robust and viable tool for FMD analysis and demonstrates improved reliability compared with the original software.NEW & NOTEWORTHY This study improves edge detection capabilities and implements noise reduction strategies to optimize an existing open-source software's suitability for flow-mediated dilation (FMD) analysis. The modified software improves the precision and reliability of FMD analysis compared with the original software algorithm. We demonstrate that this modified open-source software is a robust tool for FMD analysis.

Solving Advanced Task-Specific Problems in Measurement Sciences with Generative AI

The Generative Pre-Trained Transformer known as ChatGPT-4 has undergone extensive pretraining on a diverse data set, enabling it to generate coherent and contextually relevant text based on the input it receives. This capability allows it to perform tasks from answering questions and has attracted significant interest in material sciences, synthetic chemistry, and drug discovery. In this work, we posed four advanced task-specific problems to ChatGPT, which were recently published in leading journals for topics in analytical chemistry, spectroscopy, bioimage super-resolution, and electrochemistry. ChatGPT-4 successfully implemented the four ideas after assigning the "persona" to the AI and posing targeted questions. We show two cases where "unguided" ChatGPT could complete the assignments with minimal human direction. The construction of a microwave spectrum from a free induction curve and super-resolution of bioimages was accomplished using this approach. Two other specific tasks, correcting a complex baseline with morphological operations of set theory and estimating the diffusion current, required additional input, e.g., equations and specific directions from the user. In each case, the MATLAB code was eventually generated by ChatGPT-4 even when the original authors did not provide any code themselves. We show that a validation test must be implemented to detect and correct mistakes made by ChatGPT-4, followed by feedback correction. These approaches will pave the way for open and transparent science and eliminate the black boxes in measurement science when it comes to advanced data processing.

Assessing the Influence of Sensor-Induced Noise on Machine-Learning-Based Changeover Detection in CNC Machines

The noise in sensor data has a substantial impact on the reliability and accuracy of (ML) algorithms. A comprehensive framework is proposed to analyze the effects of diverse noise inputs in sensor data on the accuracy of ML models. Through extensive experimentation and evaluation, this research examines the resilience of a LightGBM ML model to ten different noise models, namely, Flicker, Impulse, Gaussian, Brown, Periodic, and others. A thorough analytical approach with various statistical metrics in a Monte Carlo simulation setting was followed. It was found that the Gaussian and Colored noise were detrimental when compared to Flicker and Brown, which are identified as safe noise categories. It was interesting to find a safe threshold limit of noise intensity for the case of Gaussian noise, which was missing in other noise types. This research work employed the use case of changeover detection in (CNC) manufacturing machines and the corresponding data from the publicly funded research project (OBerA).

Enhancing spatial resolution in Fourier transform infrared spectral image via machine learning algorithms

Owing to the intrinsic signal noise in the characterization of chemical structures through Fourier transform infrared (FT-IR) spectroscopy, the determination of the signal-to-noise ratio (SNR) depends on the level of the concentration of the chemical structures. In situations characterized by limited concentrations of chemical structures, the traditional approach involves mitigating the resulting low SNR by superimposing repetitive measurements. In this study, we achieved comparable high-quality results to data scanned 64 times and superimposed by employing machine learning algorithms such as the principal component analysis and non-negative matrix factorization, which perform the dimensionality reduction, on FT-IR spectral image data that was only scanned once. Furthermore, the spatial resolution of the mapping images correlated to each chemical structure was enhanced by applying both the machine learning algorithms and the Gaussian fitting simultaneously. Significantly, our investigation demonstrated that the spatial resolution of the mapping images acquired through relative intensity is further improved by employing dimensionality reduction techniques. Collectively, our findings imply that by optimizing research data through noise reduction enhancing spatial resolution using the machine learning algorithms, research processes can be more efficient, for instance by reducing redundant physical measurements.

Automated Composition Assessment of Natural Extracts: Untargeted Mass Spectrometry-Based Metabolite Profiling Integrating Semiquantitative Detection

Recent developments in mass spectrometry-based metabolite profiling allow unprecedented qualitative coverage of complex biological extract composition. However, the electrospray ionization used in metabolite profiling generates multiple artifactual signals for a single analyte. This leads to thousands of signals per analysis without satisfactory means of filtering those corresponding to abundant constituents. Generic approaches are therefore needed for the qualitative and quantitative annotation of a broad range of relevant constituents. For this, we used an analytical platform combining liquid chromatography-mass spectrometry (LC-MS) with Charged Aerosol Detection (CAD). We established a generic metabolite profiling for the concomitant recording of qualitative MS data and semiquantitative CAD profiles. The MS features (recorded in high-resolution tandem MS) are grouped and annotated using state-of-the-art tools. To efficiently attribute features to their corresponding extracted and integrated CAD peaks, a custom signal pretreatment and peak-shape comparison workflow is built. This strategy allows us to automatically contextualize features at both major and minor metabolome levels, together with a detailed reporting of their annotation including relevant orthogonal information (taxonomy, retention time). Signals not attributed to CAD peaks are considered minor metabolites. Results are illustrated on an ethanolic extract of Swertia chirayita (Roxb.) H. Karst., a bitter plant of industrial interest, exhibiting the typical complexity of plant extracts as a proof of concept. This generic qualitative and quantitative approach paves the way to automatically assess the composition of single natural extracts of interest or broader collections, thus facilitating new ingredient registrations or natural-extracts-based drug discovery campaigns.

Ultra-short columns for the chromatographic analysis of large molecules

Today, reverse phase liquid chromatography (RPLC) analysis of proteins is almost exclusively performed on conventional columns (100-150 mm) in gradient elution mode. However, it was shown many years ago that large molecules present an on/off retention mechanism, and that only a very short inlet segment of the chromatographic column retains effectively the large molecules. Much shorter columns - like only a few centimetres or even a few millimetres - can therefore be used to efficiently analyse such macromolecules. The aim of this review is to summarise the historical and more recent works related to the use of very short columns for the analysis of model and therapeutic proteins. To this end, we have outlined the theoretical concepts behind the use of short columns, as well as the instrumental limitations and potential applications. Finally, we have shown that these very short columns were also possibly interesting for other chromatographic modes, such as ion exchange chromatography (IEX), hydrophilic interaction chromatography (HILIC) or hydrophobic interaction chromatography (HIC), as analyses in these chromatographic modes are performed in gradient elution mode.

Detection of the 5-hydroxymethylfurfural content in roasted coffee using machine learning based on near-infrared spectroscopy

Since 5-hydroxymethylfurfural (5-HMF) is carcinogenic to humans, its detection in foods is essential. This study performed near-infrared (NIR) spectroscopy (11998-4000 cm^-1) to determine the 5-HMF content in roasted coffee. The random forest (RF) was used to extract important wavenumbers, after which three machine learning models (ordinary least square (OLS), support vector machine (SVM), and RF) were established for the prediction. RF obtained the best prediction results (R_c² = 0.98 and R_p² = 0.92) compared with OLS and SVM and effectively extracted the important wavenumbers (11667 cm^-1, 11666 cm^-1, 10905 cm^-1, 7096 cm^-1, 7095 cm^-1, 7094 cm^-1, 7093 cm^-1, 7092 cm^-1, 5054 cm^-1, 5026 cm^-1, 5025 cm^-1, and 5024 cm^-1). The results demonstrated that machine learning models based on NIR spectroscopy could provide a non-destructive approach for determining 5-HMF content in roasted coffee.

Searching for Alternatives to the Savitzky-Golay Filter in the Spectral Processing Domain

An elegant, well-established effective data filter concept, proposed originally by Abraham Savitzky and Marcel J.E. Golay, is undoubtedly a very effective tool, however not free from limitations and drawbacks. Despite the latter, over the years it has become a "monopolist" in many fields of spectra processing, claiming a "commercial" superiority over alternative approaches, which would potentially allow to obtain equivalent or in some cases even more reliable results. In order to show that basic operations performed on spectral datasets, like smoothing or differentiation, do not have to be equated to the application of the one particular single algorithm, several of such alternatives are briefly presented within this paper and discussed with regard to their practical realization. A special emphasis is put on the fast Fourier methodology (FFT), being widespread in the general domain of signal processing. Finally, a user-friendly Matlab routine, in which the outlined algorithms are implemented, is shared, so that one can select and apply the technique of spectral data processing more adequate for their individual requirements without the need to code it prior to use.

Adaptive Savitzky–Golay Filters for Analysis of Copy Number Variation Peaks from Whole-Exome Sequencing Data

Copy number variation (CNV) is a form of structural variation in the human genome that provides medical insight into complex human diseases; while whole-genome sequencing is becoming more affordable, whole-exome sequencing (WES) remains an important tool in clinical diagnostics. Because of its discontinuous nature and unique characteristics of sparse target-enrichment-based WES data, the analysis and detection of CNV peaks remain difficult tasks. The Savitzky–Golay (SG) smoothing is well known as a fast and efficient smoothing method. However, no study has documented the use of this technique for CNV peak detection. It is well known that the effectiveness of the classical SG filter depends on the proper selection of the window length and polynomial degree, which should correspond with the scale of the peak because, in the case of peaks with a high rate of change, the effectiveness of the filter could be restricted. Based on the Savitzky–Golay algorithm, this paper introduces a novel adaptive method to smooth irregular peak distributions. The proposed method ensures high-precision noise reduction by dynamically modifying the results of the prior smoothing to automatically adjust parameters. Our method offers an additional feature extraction technique based on density and Euclidean distance. In comparison to classical Savitzky–Golay filtering and other peer filtering methods, the performance evaluation demonstrates that adaptive Savitzky–Golay filtering performs better. According to experimental results, our method effectively detects CNV peaks across all genomic segments for both short and long tags, with minimal peak height fidelity values (i.e., low estimation bias). As a result, we clearly demonstrate how well the adaptive Savitzky–Golay filtering method works and how its use in the detection of CNV peaks can complement the existing techniques used in CNV peak analysis.

Prediction of exam scores using a multi-sensor approach for wearable exam stress dataset with uniform preprocessing

BACKGROUND

Physiological signals, such as skin conductance, heart rate, and temperature, provide valuable insight into the physiological responses of students to stress during examination sessions.

OBJECTIVE

The primary objective of this research is to explore the effectiveness of physiological signals in predicting grades and to assess the impact of different models and feature selection techniques on predictive performance.

METHODS

We extracted a comprehensive feature vector comprising 301 distinct features from seven signals and implemented a uniform preprocessing technique for all signals. In addition, we analyzed different algorithmic selection features to design relevant features for robust and accurate predictions.

RESULTS

The study reveals promising results, with the highest scores achieved using 100 and 150 features. The corresponding values for accuracy, AUROC, and F1-Score are 0.9, 0.89, and 0.87, respectively, indicating the potential of physiological signals for accurate grade prediction.

CONCLUSION

The findings of this study suggest practical applications in the field of education, where the use of physiological signals can help students cope with exam stress and improve their academic performance. The importance of feature selection and the use of appropriate models highlight the importance of engineering relevant features for precise and reliable predictions.

Ultra-short ion-exchange columns for fast charge variants analysis of therapeutic proteins

The purpose of this work was to study the potential of recently developed ultra-short column hardware for ion exchange chromatography (IEX). Various prototype and commercial columns having lengths of 5, 10, 15, 20 and 50 mm and packed with non-porous 3 µm particles were systematically compared. Both pH and salt gradient modes of elution were evaluated. Similarly, what has been previously reported for reversed phase liquid chromatography (RPLC) mode, an "on-off" retention mechanism was observed in IEX for therapeutic proteins and their fragments (25-150 kDa range). Because of the non-porous nature of the IEX packing material, the column porosity was relatively low (ε = 0.42) and therefore the volumes of ultra-short columns were very small. Based on this observation, it was important to reduce as much as possible all the sources of extra-column volumes (i.e. injection volume, extra-bed volume, detector cell volume and connector tubing volume), to limit peak broadening. With a fully optimized UHPLC system, very fast separations of intact and IdeS digested mAb products were successfully performed in about 1 min using an IEX column with dimensions of 15 × 2.1 mm. This column was selected for high-throughput separations, since it probably offers the best compromise between efficiency and analysis time. For such ultra-fast separations, PEEK tubing was applied to bypass the column oven (column directly connected) to the optical detector via a zero dead volume connection.