Data Processing Approach Research Articles

Mass defect filter technique combined with stable isotope tracing for drug metabolite identification using high-resolution mass spectrometry

Metabolism studies are one of the important steps in pharmaceutical research. LC-MS combined with metabolomics data-processing approaches have been developed for rapid screening of drug metabolites. Mass defect filter (MDF) is one of the LC/MS-based metabolomics data processing approaches and has been applied to screen drug metabolites. Although MDF can remove most interference ions from an incubation sample, the true positive rate of the retaining ions is relatively low (approximately 10%). To improve the efficacy of MDF, we developed a two-stage data-processing approach by combining MDF and stable isotope tracing (SIT) for metabolite identification. Pioglitazone (PIO), which is an antidiabetic drug used to treat type 2 diabetes mellitus, was taken as an example drug. Our results demonstrated that this new approach could substantially increase the validated rate from about 10% to 74%. Most of these validated metabolite signals (13/14) could be verified as PIO structure-related metabolites. In addition, we applied this approach to identify uncommon metabolite signals (a mass change beyond the window of 50 Da around its parent drug, MDF1). SIT could remove most interference ions (approximately 98%) identified by MDF1, and four out of five validated metabolite signals could be verified as PIO structure-related metabolites. Interestingly, a lot of the verified metabolites (10/17) were novel PIO metabolites. Among these novel metabolites, nine were thiazolidinedione ring-opening signals that might be related to the toxicity of PIO. Our developed approach could significantly improve the efficacy in drug metabolite identification compared with that of MDF.

Age-related changes in neuronal receptive fields of primary auditory cortex in frequency, amplitude, and temporal domains

Aging populations often experience difficulties hearing. This phenomenon, known as age-related hearing loss, is associated with decreases in hearing sensitivity and pathophysiological changes in the central auditory system, including the primary auditory cortex (AI). To better understand changes in the central auditory system, this study is designed to compare the receptive fields of AI neurons of young and old C57 mice in frequency, amplitude, and temporal domains by using random-delivery (various interval) of multi-frequency gamma-shaped tone pips and a novel data processing approach known as the intensity-stacked spectrotemporal receptive field (iSTRF). We show that the fundamental changes in the response properties of AI neurons in old mice were in line with the literature. Compared to young mice, AI neurons in old mice tuned to lower frequency and higher amplitude of tones with broader frequency tuning, shorter dB (dynamic) range, longer response duration, as well as a higher firing rate. We further compared the entire iSTRF and core iSTRF (above 50% maximal firing rate) between young and old mice; the core iSTRF represented the AI neuronal responses to sound that fall into the central frequency and amplitude ranges from the auditory periphery. It is remarkable that the difference was significant in the entire iSTRF while little difference was observed in the core iSTRFs between young and old mice. However, the lower best frequency and higher best amplitude were obvious in both iSTRFs that were mostly determined by the deficits in the inner ears of old mice. In the frequency domain, the tuning quality factor of entire iSTRFs was significantly greater in old mice, while that of core iSTRFs was not compared to young mice. In the amplitude domain, significantly shorter dB range in old mice were observed in the entire iSTRF, but not in the core iSTRF. In the temporal domain, the response duration of old AI neurons was significantly longer in entire iSTRFs, but not in core iSTRFs, when compared to young AI neurons. Furthermore, the average firing rate of old AI neurons was significantly higher in entire iSTRF, but not in core iSTRF. It is also interesting that the ratio of the firing rate to background firing rate (signal-noise ratio) were similar between young and old AI neurons. Our data suggests that age-related hearing loss has relatively less impact on auditory information processing when sounds fall into the optimal frequency and amplitude ranges of given AI neurons.

Smart Maintenance and Health Monitoring of Buildings and Infrastructure Using High-Resolution Laser Scanners

An integrated structural health monitoring system was proposed for the rapid assessment of damage on large structures such as high-rise buildings, industrial chimneys, long-span bridges, and similar facilities. The system used ground-based high-resolution IR (infrared) laser vibrometers to measure the dynamic response of structures. To utilize these devices as automated scanners in a fast and efficient way, a new targeting and control mechanism was developed. Different aspects of the proposed system, such as targeting precision and scanning efficiency, were discussed by presenting the results of laboratory experiments and outdoor vibration tests. In addition to the enhancements made in the measurement system, a new methodology was introduced to analyze the recorded vibration response. A novel data processing approach, based on a comparison of the mode shapes calculated on the healthy reference and damaged structures, made it possible to determine the location of the flaw. If available, a finite element model of the analyzed structure also enables the degree of the damage to be calculated very accurately. The reliability of the identification algorithm was demonstrated by conducting extensive numerical simulations and vibration tests on scale building models.

Design of a Soft Gripper With Improved Microfluidic Tactile Sensors for Classification of Deformable Objects

Tactile object recognition is vital for robotic handling systems; however, existing technologies that concentrate on tactile sensors with high modulus are not suitable for soft grippers to classify deformable objects. In this letter, we integrated an indenter layer into the traditional microfluidic tactile sensor to increase its sensitivity based on the lensing effect of human skin. For the application of the developed sensor, we built HustGripper, a tendon-driven soft gripper, where the sensor was bonded on the fingertip. Experiments on the tactile classification of the deformable objects were conducted to validate the performance of the sensor, where different indenters, exploratory procedures, and data processing approaches were considered to explore the key factor to determine the classification accuracy.

Remote Sensing Approaches for Meteorological Disaster Monitoring: Recent Achievements and New Challenges.

Meteorological disaster monitoring is an important research direction in remote sensing technology in the field of meteorology, which can serve many meteorological disaster management tasks. The key issues in the remote sensing monitoring of meteorological disasters are monitoring task arrangement and organization, meteorological disaster information extraction, and multi-temporal disaster information change detection. To accurately represent the monitoring tasks, it is necessary to determine the timescale, perform sensor planning, and construct a representation model to monitor information. On this basis, the meteorological disaster information is extracted by remote sensing data-processing approaches. Furthermore, the multi-temporal meteorological disaster information is compared to detect the evolution of meteorological disasters. Due to the highly dynamic nature of meteorological disasters, the process characteristics of meteorological disasters monitoring have attracted more attention. Although many remote sensing approaches were successfully used for meteorological disaster monitoring, there are still gaps in process monitoring. In future, research on sensor planning, information representation models, multi-source data fusion, etc., will provide an important basis and direction to promote meteorological disaster process monitoring. The process monitoring strategy will further promote the discovery of correlations and impact mechanisms in the evolution of meteorological disasters.

A Systematic Literature Review for Distinguishing Tax Terms: Equality, Equity, Justice, and Fairness

The organizational fairness literature is frequently used by fairness scholars to evaluate tax fairness models, conceptions, and measurements, even though tax fairness and tax justice are context-dependent and require further formulation. However, the generalizability of organizational justice studies to other domains and the extent to which context affects justice perceptions remain unknown. There is a dearth of literature that defines the terms tax fairness, tax equity, tax equality, and tax justice accurately. The purpose of this research is to distinguish between the tax phrases "fairness," "equity," "equality," and "justice." Additionally, the author refined the tax words inside the operationalized idea to produce more precise measures for future research. A systematic literature review was conducted as the research method, utilizing the PRISMA research approach and NVivo for qualitative data processing. As a result of the research findings, the phrase "tax fairness," which was previously believed to refer to a lesser degree of tax justice than "tax equity," is now used to refer to tax justice in a broader sense. Tax equity has the most extensive set of characteristics and indicators. Meanwhile, the word "tax justice" refers to a more specialized area of law and procedure, namely tax law. The phrase "tax equity" is included in the indicator "tax fairness," although the term "tax equality" is rarely used in the worldwide tax literature. The more distinct the tax terms, the easier it will be for the tax researcher to use terms like tax fairness, tax justice, tax equity, and tax equality correctly.

Metabolomics in clinical and forensic toxicology, sports anti-doping and veterinary residues.

Metabolomics is a multidisciplinary field providing workflows for complementary approaches to conventional analytical determinations. It allows for the study of metabolically related groups of compounds or even the study of novel pathways within the biological system. The procedural stages of metabolomics; experimental design, sample preparation, analytical determinations, data processing and statistical analysis, compound identification and validation strategies are explored in this review. The selected approach will depend on the type of study being conducted. Experimental design influences the whole metabolomics workflow and thus needs to be properly assessed to ensure sufficient sample size, minimal introduced and biological variation and appropriate statistical power. Sample preparation needs to be simple, yet potentially global in order to detect as many compounds as possible. Analytical determinations need to be optimised either for the list of targeted compounds or a universal approach. Data processing and statistical analysis approaches vary widely and need to be better harmonised for review and interpretation. This includes validation strategies that are currently deficient in many presented workflows. Common compound identification approaches have been explored in this review. Metabolomics applications are discussed for clinical and forensic toxicology, human and equine sports anti‐doping and veterinary residues.

CW Doppler Radar as Occupancy Sensor: A Comparison of Different Detection Strategies

Indoor occupancy sensing is a crucial problem in several application fields that have progressed from intrusion detection systems to automatic control of lighting, heating, air conditioning and many other presence-related loads. Continuous wave Doppler radar is a simple technology to face this problem due to its capability to detect human body movements (e.g., walk, run) and small chest wall vibrations associated to the cardiorespiratory activity. This work deals with a radar prototype operating at 2.4 GHz as a real-time occupancy sensor. The emphasis is on data processing approaches devoted to extract useful information from raw radar signal. Three different strategies, designed to detect human presence in indoor environments, are considered and the main goal is the assessment and comparison of their performance against experimental data collected in controlled conditions. The first strategy is based on the analysis of the standard deviation of the radar signal in time-domain; whereas the second one exploits the histogram of the time-varying signal amplitude. Finally, a third strategy based on an energy measure of the received signal Doppler spectrum is considered. The proposed detection algorithms are optimized through a set of calibration measurements and their performances and robustness are assessed by laboratory trials.

Enhanced CNN Model for Pancreatic Ductal Adenocarcinoma Classification Based on Proteomic Data

Pancreatic ductal adenocarcinoma (PDAC) is one of the deadliest tumors, with just around nine percent of those diagnosed surviving for more than five years after diagnosis. A significant part of the poor result may be attributed to late detection. However, the illness is identified at an initial phase. While growths remain quite tiny and manageable, five-year existence rates can rise to as high as seventy percent. Because of this, there is a huge clinical demand for the creation of a non-invasive examination targeted at the earliest identification of PDAC, which has the ability to recover the current prospects of patients. Considering the grim future for pancreatic cancer, new strategies for early detection and prevention must be developed as rapidly as feasible. Researchers have revealed that proteomics technology is effective in discovering important biomarkers for early-stage pancreatic cancer, according to recent research. One of the most challenging difficulties is recognizing and collecting physiologically relevant information from the huge quantity of data collected when it comes to proteome profiling. Because of the tremendous complexity of proteomics datasets and the fact that they typically have minuscule sample numbers, it is vital to apply non-classical statistical approaches for data processing. Deep learning models are more effective; few efforts have lately made to identify PDAC, but the models are not developed successfully. This paper used an enhanced Convolution neural network (CNN) model to classify pancreatic decease at different stages accurately to clinical correction. The model has effective results compared to existing models.

Magnetic field reconstruction using the pulsed wire method: An accuracy analysis

Learning how to reconstruct the magnetic field profile from the pulsed wire method (PWM) is of fundamental importance to characterize undulators with limited magnetic gap accessibility. This paper presents a new data processing approach to reconstruct undulator magnetic field profiles from the PWM. We study via numerical simulations the accuracy and precision of the novel approach to recover the local undulator parameter and the root-mean-square (RMS) phase error. Due to the wire parameters uncertainties, the PWM can, in the best case, reach an accuracy of approximately 10−3 to reconstruct the local undulator parameter, and a precision (needed to determine the magnetic field quality) of about 10−5. Furthermore, our results show that the RMS phase error can be determined with a precision better than 0.2°. This contribution opens the possibility to apply the PWM to perform magnetic field reconstruction in small-gap short-period long undulators for advanced light sources.

High-performance nontarget analysis of halogenated organic compounds in tap water, fly ash, soil and sediment using ultrahigh resolution mass spectrometry and scripting approaches based on Cl/Br-specific search algorithms

Halogenated organic compounds (HOCs) are a huge group of pollutants, which have caused severe environmental pollution worldwide. This study developed a nontarget analysis method for characterization of known and unknown HOCs in tap water, fly ash, soil and sediment using Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR-MS) with aid of scripting data-processing approaches. The FT-ICR-MS was equipped with an electrospray ionization source operated in negative ion mode, and full scan at a mass resolution around 200,000 was applied. Data screening scripts were developed based on exact mass differences and abundance ratios between neighboring chlorine/bromine isotopologues. Cosine similarity analysis was applied to evaluating similarity between measured and simulated isotopologue distributions which were constituted by abundance ratios between neighboring isotopologues for identification of polychlorinated and polybrominated compounds. From huge amount of peaks in raw mass spectra, thousands of MS features of HOCs were screened out. In total 824 HOC formulae were finally identified, including 702 organochlorines, 108 organobromines, and 14 mix-chlorine/bromine-containing organic compounds (OClBrs), mainly being monochlorinated/monobrominated compounds (∼90%) whose abundances were also the most prominent. Dozens of HOCs were tentatively structurally elucidated, including halogenated phenols, carboxylic acids, benzenesulfonic acids, etc. Three novel OClBrs were structurally identified, i.e., bromochlorobenzoic acid, bromodichlorophenol and bromotrichlorophenol. The data-processing scripts and strategies can facilitate accurate and high-throughput nontarget analysis of both common and novel/emerging halogenated organic pollutants in complex matrices. The research results provide informative insights into pollution profiles of HOCs in the environment, suggesting that halogenated organic pollutants remain to be seriously concerned.

A data processing approach with built-in spatial resolution reduction methods to construct energy system models.

Introduction: Data processing is a crucial step in energy system modelling which prepares input data from various sources into a format needed to formulate a model. Multiple open-source web-hosted databases offer pre-processed input data within the European context. However, the number of documented open-source data processing workflows that allow for the construction of energy system models with specified spatial resolution reduction methods is still limited. Methods:The first step of the data-processing method builds a dataset using web-hosted pre-processed data and open-source software. The second step aggregates the dataset using a specified spatial aggregation method. The spatially aggregated dataset is used as input data to construct sector-coupled energy system models. Results:To demonstrate the application of the data processing process, three power and heat optimisation models of Germany were constructed using the proposed data processing approach. Significant variation in generation, transmission and storage capacity of electricity were observed between the optimisation results of the energy system models. Conclusions: This paper presents a novel data processing approach to construct sector-coupled energy system models with integrated spatial aggregations methods.

Breakage Monitoring of Executive Body Cutters in Continuous Miner According to Mechanical Vibration Parameters. Part 1. Research Methodology

Vibromonitoring is the most methodologically developed and technically equipped with recording tools and data processing approach. However, in the sources of scientific and technical information about mining it is presented much less than other monitoring methods. One of the promising ways to solve this problem, in particular monitoring the breakage of cutting disc cutters of an executive body, is to use parameters of natural oscillations of the parts of gearboxes that occur during impulse loading due to such breakages. It should be noted that in the technically complex equipment of the mining industry, in particular in mining machines, there are many sources of vibration, the vibtaions of which are superimposed on each other. And it is not always possible to isolate and identify the required informative signal with the source. With this in mind the paper presents a research methodology on the possibility of monitoring the breakage of the executive body cutters on the example of the continuous miner “Universal 600” according to the parameters of natural vibrations of the executive body cutting discs arising from the breakage of the cutters. The place of registration of these vibrations is determined – the gearbox handles, on the output shafts of which cutting discs are placed. The frequency of such vibrations is set equal to ~5 Hz. The possibility of using the AGAT-M vibration analyzer with M/AC102-1A vibration sensors as a means of further research has been substantiated in the paper. The paper considers an option of placing vibration sensors on non-rotating gear housings of the executive body drive. An algorithm for recording vibration parameters has been developed, which makes it possible to separate its sources.

Active and Passive Seismic Imaging of the Central Abitibi Greenstone Belt, Larder Lake, Ontario

AbstractPassive seismic methods are considered cost‐effective and environmentally friendly alternatives to active (reflection) seismic methods. We have acquired colocated active and passive seismic surveys over a metal‐endowed Archean granite‐greenstone terrane in the Larder Lake area to investigate the reliability of estimated elastic properties using passive seismic methods. The passive seismic data were processed using two different data processing approaches, ambient noise surface‐wave tomography (ANSWT) and receiver function analysis, to generate shear‐wave velocity and P to S wave (P‐S) convertibility profiles of the subsurface, respectively. The Cadillac‐Larder Lake Fault (CLLF) was imaged as a south‐dipping subvertical zone of weak reflectivity in the reflection seismic profile. To the north of the CLLF, a package of north‐dipping reflections in the upper crust (at depths of 5–10 km) resides on the boundary of high (on the top) and low (on the bottom) shear‐wave velocity zones estimated using the ANSWT method. This package of reflections is most likely caused by overlaying mafic volcanic and underlying felsic intrusive rocks. The P‐S convertibility profile imaged the Moho boundary at ∼40‐km depth as well as a south‐dipping feature that penetrates the mantle, which is interpreted to be either caused by the delamination of the lower crust or a possible deeper extension of the Porcupine‐Destor Fault. Overall, the reflectivity, shear‐wave velocity, and P‐S convertibility profiles exhibit a good correlation and provided a detailed image of the subsurface lithological structure to a depth of 10 km.

Chemical profiling and quantification of multiple components in Jin-Gu-Lian capsule using a multivariate data processing approach based on UHPLC-Orbitrap Exploris 240 MS and UHPLC-MS/MS.

The Jin-Gu-Lian capsule, a Chinese Miao herbal compound, is widely used to treat rheumatoid arthritis. In this study, a rapid, selective, and sensitive UHPLC-Orbitrap Exploris 240 MS method was developed to analyze the chemical composition of Jin-Gu-Lian capsules. A total of 88 compounds were identified, including 23 flavonoids, 23 organic acids, 14 phenylpropanoids, 12 phenols, eight alkaloids, four terpenes, three quinones, and one ketone. Among these, 21 compounds were clearly detected based on a comparison with reference standards and selected as quality control markers. Thereafter, these compounds were simultaneously determined in the Jin-Gu-Lian capsules. The established method was successfully validated and applied for the simultaneous determination of 21 biologically active compounds in Jin-Gu-Lian capsules of 27 sample batches. Quantitative data of the analytes were analyzed using multivariate statistical analysis to determine the quality of the Jin-Gu-Lian capsules. Four compounds (JGLC6 [salidroside], JGLC8 [chlorogenic acid], JGLC12 [liriodendrin], JGLC19 [quercetin]) were identified as chemical markers for quality control of Jin-Gu-Lian capsules. Altogether, the established method was validated as a novel and efficient tool, that can be used for rapid analysis of Jin-Gu-Lian capsules. Accordingly, this study serves as a reference for scientific research on traditional Chinese and ethnic medicine.

Expansion of the composition library for chemodiversity of hardwood extractives at molecular level by ultrahigh-resolution mass spectrometry.

To enhance the characterization of wood extractives at molecular level, a detailed ultrahigh-resolution mass spectrometry (UHRMS)-based analytical methodology was developed in this work. The analytical strategies, including selection of compatible solvent for extraction, evaluation of ionization solvent for effective electrospray ionization, and multi-dimensional data analysis, were established to ensure a comprehensive characterization of complex compositions in wood extractives. Extraction capability of seven solvents with varied polarities was examined by a standard reference material of hardwood biomass and evaluated based on thousands of compounds which were much more than those discovered before. With a variety of data-processing approaches, including compound type distribution, double bond equivalent versus carbon number plot, and van Krevelen diagram, the chemodiversity of the extractives was fully explored from different perspectives. This work greatly expanded the compound library of wood extractives and could also provide guidance for the integrated composition analysis of other biomass materials.

BIG EARTH DATA PROCESSING USING MACHINE LEARNING FOR INTEGRATED MAPPING OF THE DEAD SEA FAULT, JORDAN

In this research, an integrated framework on the big Earth data analysis has been developed in the context of the geomorphology of Jordan. The research explores the correlation between several thematic datasets, including machine learning and multidisciplinary geospatial data. GIS mapping is widely used in geological mapping as the most adequate technical tool for data visualization and analysis. GIS applications encourage geological prospective modeling by visualizing data aimed at the prognosis of mineral resources. However, automatization using machine learning for big Earth data processing provides the speed and accurate processing of multisource massive datasets. This is enabled by the application of scripting and programming in cartographic techniques. This study presents the combined machine learning methods of cartographic analysis and big Earth data modeling. The objective is to analyze a correlation between the factors affecting the geomorphological shape of Jordan with respects to the Dead Sea Fault and geological evolution. The technical methodology includes the following three independent tools: 1) Generic Mapping Tools (GMT); 2) Selected libraries of R programming language; 3) QGIS. Specifically, the GMT scripting program was used for topographic, seismic and geophysical mapping, while QGIS was used for geologic mapping and R language for geomorphometric modeling. Accordingly, the workflow is logically structured through these three technical tools, representing different cartographic approaches for data processing. Data and materials include multisource datasets of the various resolution, spatial extent, origin and formats. The results presented cartographic layouts of qualitative and quantitative maps with statistical summaries (histograms). The novelty of this approach is explained by the need to close a technical gap between the traditional GIS and scripting mapping, which is wider for big data mapping and where the crucial factors are speed and precision of data handling, as well as effective visualization achieved by the machine graphics. The paper analyzes the underlying geologic processes affecting the formation of geomorphological landforms in Jordan with a 3D visualization of the selected fragment of the Dead Sea Fault zone. The research presents an extended description in methodology, including the explanations of code snippets from the GMT modules and examples of the use of R libraries ‘raster’ and ‘tmap’. The results revealed strong correlation between the geological and geophysical settings which affect geomorphological patterns. Integrated study of the geomorphology of Jordan was based on multisource datasets processed by scripting. A thorough analysis presented regional correlations between the geomorphological, geological and tectonic settings in Jordan. The paper contributed both to the development of cartographic engineering by introducing scripting techniques and to the regional studies of Jordan including the Dead Sea Fault as a special region of Jordan. The results include 12 new thematic maps including a 3D model.

Application of a Target-Guided Data Processing Approach in Saturated Peak Correction of GC×GC Analysis.

Detector and column saturations are problematic in comprehensive two-dimensional gas chromatography (GC×GC) data analysis. This limits the application of GC×GC in metabolomics research. To address the problems caused by detector and column saturations, we propose a two-stage data processing strategy that will incorporate a targeted data processing and cleaning approach upstream of the “standard” untargeted analysis. By using the retention time and mass spectrometry (MS) data stored in a library, the annotation and quantification of the targeted saturated peaks have been significantly improved. After subtracting the nonperfected signals caused by saturation, peaks of coelutes can be annotated more accurately. Our research shows that the target-guided method has broad application prospects in the data analysis of GC×GC chromatograms of complex samples.

Simultaneous tailoring of pulsed thermography experimental and processing parameters for enhanced defect detection and sizing in adhesive bonds in carbon fibre composites

Thermographic inspection provides opportunity to tailor non-destructive evaluation to specific applications. The paper discusses the opportunities this presents through consideration of adhesive bonds between composites, such as those joining structural members and outer skins, where access is restricted to a single side. To date, literature focusses on the development of either an experimental procedure or data processing approach. This research aims to demonstrate the importance of tailoring both of these aspects to an application to obtain improved defect detection and robust quantification. Firstly, the heating stimulus is optimised to maximise the thermal contrast created between defect and non-defect regions using a development panel. Traditional flash heating is compared to longer square pulse heating, using a developed shutter system, compromising between experimental duration and heat input. A pulse duration of 4 seconds using two 130 W halogen bulbs was found double the detection depth from 1 mm to 2 mm, revealing all defects in the development panel. Temporal processing was maintained for all data using thermal signal reconstruction. Spatial defect detection routines were then implemented to provide robust defect/feature detection. Spatial defect detection encompassed a combination of image enhancement and edge detection algorithms. A two-stage kernel filter/binary enhancement method followed by the use of Canny edge detection was found most robust, providing a sizing error of 1.8% on the development panel data. This process was then implemented on adhesive bonds with simulated bond line defects. The simulated defects are based on target detection threshold of 10 mm diameter void found at 1–2 mm depth. All simulated void defects were detected in the representative bonded joint down to the minimum diameter tested of 5 mm. By considering the tailoring of multiple aspects of the inspection routine independently, an overall optimised approach for the application of interest has been defined.

Photoluminescence Imaging for the In-Line Quality Control of Thin-Film Solar Cells

Renewable energy sources such as photovoltaic (PV) technologies are considered to be key drivers towards climate neutrality. Thin-film PVs, and particularly copper indium gallium selenide (CIGS) technologies, will play a crucial role in the turnaround in energy policy due to their high efficiencies, high product flexibility, light weight, easy installation, lower labour-intensiveness, and lower carbon footprint when compared to silicon solar cells. Nonetheless, challenges regarding the CIGS fabrication process such as moderate reproducibility and process tolerance are still hindering a broad market penetration. Therefore, cost-efficient and easily implementable in-line process control methods are demanded that allow for identification and elimination of non-conformal cells at an early production step. As part of this work, a practical approach towards industrial in-line photoluminescence (PL) imaging as a contact-free quality inspection tool is presented. Performance parameters of 10 CIGS samples with 32 individually contacted cells each were correlated with results from PL imaging using green and red excitation light sources. The data analysis was fully automated using Python-based image processing, object detection, and non-linear regression modelling. Using the red excitation light source, the presented PL imaging and data processing approach allows for a quantitative assessment of the cell performance.