Quantitative analysis of solid dosage forms of Atenolol by Raman spectroscopy.

ObjectiveTo develop a Raman spectroscopy-based analytical model for quantification of solid dosage forms of active pharmaceutical ingredient (API) of Atenolol.Significance:For the quantitative analysis of pharmaceutical drugs, Raman Spectroscopy is a reliable and fast detection method. As part of this study, Raman Spectroscopy is explored for the quantitative analysis of different concentrations of Atenolol.MethodsVarious solid-dosage forms of Atenolol were prepared by mixing API with excipients to form different solid-dosage formulations of Atenolol. Multivariate data analysis techniques such as Principal Component Analysis (PCA) and Partial least square regression (PLSR) were used for the qualitative and quantitative analysis, respectively.ResultsAs the concentration of the drug increased in formulation, the peak intensities of the distinctive Raman spectral characteristics associated with the API (Atenolol) gradually increased. Raman spectral data sets were classified using PCA due to their distinctive spectral characteristics. Additionally, a prediction model was built using PLSR analysis to assess the quantitative relationship between various API (Atenolol) concentrations and spectral features. With a goodness of fit value of 0.99, the root mean square errors of calibration (RMSEC) and prediction (RMSEP) were determined to be 1.0036 mg and 2.83 mg, respectively. The API content in the blind/unknown Atenolol formulation was determined as well using the PLSR model.ConclusionBased on these results, Raman spectroscopy may be used to quickly and accurately analyze pharmaceutical samples and for their quantitative determination.

Application of soil magnetometry and geochemical methods to investigate soil contamination with antimony.

The aim of the study was an assessment of the pollution level and identification of the antimony sources in soils in areas subjected to industrial anthropopressure from: transport, metallurgy and electrical waste recycling. The combination of soil magnetometry, chemical analyzes using atomic spectrometry (ICP-OES and ICP-MS), Sb fractionation analysis, statistical analysis (Pearson's correlation matrix, factor analysis) as well as Geoaccumulation Index, Pollution Load Index, and Sb/As factor allowed not only the assessment of soil contamination degree, but also comprehensive identification of different Sb sources. The results indicate that the soil in the vicinity of the studied objects was characterized by high values of magnetic susceptibility and thus, high contents of potentially toxic elements. The most polluted area was in the vicinity of electrical waste processing plants. Research has shown that the impact of road traffic and wearing off brake blocks, i.e. traffic anthropopression in general, has little effect on the surrounding soil in terms of antimony content. Large amounts of Pb, Zn, As and Cd were found in the soil collected in the vicinity of the heap after the processing of zinc-lead ores, the average antimony (11.31 mg kg-1) content was lower in the vicinity of the heap than in the area around the electrical and electronic waste processing plant, but still very high. Antimony in the studied soils was demobilized and associated mainly with the residual fraction.

Multivariate analysis of craniodental morphology in mouse-eared bats (Chiroptera, Vespertilionidae, Myotis) from Vietnam.

This study conducted biostatistical multivariate analyses on 23 craniodental morphological measurements from 209 specimens to study interspecific variations amongst 15 bat species of the genus Myotis in Vietnam. Univariate and multivariate analyses demonstrated that the studied species can be divided into four groups as follows: extra-large-sized species (M.chinensis), large-sized species (M.pilosus, M.indochinensis and M.annectans), medium-sized species (M.altarium, M.hasseltii, M.montivagus, M.horsfieldii, M.ater, M.laniger and M.muricola) and small-sized species (M.annamiticus, M.aff.siligorensis, M.rosseti and M.alticraniatus). Our data revealed that the main craniodental features contributing to the variations in distinguishing Myotis species are the width of the anterior palatal, least height of the coronoid process, length of the upper and lower canine-premolar, zygomatic width and width across the upper canines and lower premolar-molar length. Based on patterns of morphological differences, we conducted comparisons between morphometrically closely resembling species pairs and further discussed additional characteristics that are expected to support the taxonomy and systematics of Vietnamese Myotis bats.

Volatile Compounds for Discrimination between Beef, Pork, and Their Admixture Using Solid-Phase-Microextraction-Gas Chromatography-Mass Spectrometry (SPME-GC-MS) and Chemometrics Analysis.

This study addresses the prevalent issue of meat species authentication and adulteration through a chemometrics-based approach, crucial for upholding public health and ensuring a fair marketplace. Volatile compounds were extracted and analyzed using headspace-solid-phase-microextraction-gas chromatography-mass spectrometry. Adulterated meat samples were effectively identified through principal component analysis (PCA) and partial least square-discriminant analysis (PLS-DA). Through variable importance in projection scores and a Random Forest test, 11 key compounds, including nonanal, octanal, hexadecanal, benzaldehyde, 1-octanol, hexanoic acid, heptanoic acid, octanoic acid, and 2-acetylpyrrole for beef, and hexanal and 1-octen-3-ol for pork, were robustly identified as biomarkers. These compounds exhibited a discernible trend in adulterated samples based on adulteration ratios, evident in a heatmap. Notably, lipid degradation compounds strongly influenced meat discrimination. PCA and PLS-DA yielded significant sample separation, with the first two components capturing 80% and 72.1% of total variance, respectively. This technique could be a reliable method for detecting meat adulteration in cooked meat.

Ideotype based genotype selection in a multivariate dataset of sweet potato (Ipomoea batatas L.).

The dataset extensively examines the factors considered when choosing sweet potato genotypes, considering various characteristics. Notably, Moz1.15 demonstrated the highest marketable root yield at 46.46 t/ha, H5.ej.10 exhibited the highest beta-carotene level at 48.94 mg/100 g, and Moz1.9 recorded the highest vitamin C content at 23.89 mg/100 g. Moreover, there were significant correlations (ranging from 0.21 to 0.84) among the yield and quality traits studied in sweet potatoes. Principal component analysis (PCA) confirmed the connections among these traits, identifying four distinct clusters of genotypes, each characterized by specific significant combinations of traits. Factor analysis using the multi-trait genotype-ideotype index (MGIDI) highlighted the considerable impact of sweet potato traits across two growing seasons (2020-21 and 2021-22), facilitating the selection of genotypes with potential genetic gains ranging from 1.86 % to 75.4 %. Broad-sense heritability (h2) varied from 64.9 % to 99.8 %. The use of the MGIDI index pinpointed several promising genotypes, with BARI Mistialu-12 and H9.7.12 consistently performing well over both years. These genotypes exhibited both strengths and weaknesses.

ToF-SIMS evaluation of PEG-related mass peaks and applications in PEG detection in cosmetic products.

Polyethylene glycols (PEGs) are used in industrial, medical, health care, and personal care applications. The cycling and disposal of synthetic polymers like PEGs pose significant environmental concerns. Detecting and monitoring PEGs in the real world calls for immediate attention. This study unveils the efficacy of time-of-flight secondary ion mass spectrometry (ToF-SIMS) as a reliable approach for precise analysis and identification of reference PEGs and PEGs used in cosmetic products. By comparing SIMS spectra, we show remarkable sensitivity in pinpointing distinctive ion peaks inherent to various PEG compounds. Moreover, the employment of principal component analysis effectively discriminates compositions among different samples. Notably, the application of SIMS two-dimensional image analysis visually portrays the spatial distribution of various PEGs as reference materials. The same is observed in authentic cosmetic products. The application of ToF-SIMS underscores its potential in distinguishing PEGs within intricate environmental context. ToF-SIMS provides an effective solution to studying emerging environmental challenges, offering straightforward sample preparation and superior detection of synthetic organics in mass spectral analysis. These features show that SIMS can serve as a promising alternative for evaluation and assessment of PEGs in terms of the source, emission, and transport of anthropogenic organics.

Research on path planning for clear aligner flexible manufacturing.

In recent years, clear aligner can enhance individual appearance with dental defects, so it used more and more widely. However, in manufacturing process, there are still some problems, such as low degree of automation and high equipment cost. The problem of coordinate system mismatch between gingival curve point cloud and dental CAD model is faced to. The PCA-ICP registration algorithm is proposed, which includes coarse match algorithm and improve-ICP registration algorithm. The principal component analysis (PCA) based method can roughly find the posture relationship between the two point clouds. Using z-level dynamic hierarchical, the ICP registration can accurately find the posture between these two clouds. The final registration maximum distance error is 0.03 mm, which is smaller than robot machining error. Secondly, the clear aligner machining process is conducted to verify the registration effectiveness. Before machining, the path is generated based on the well registered gingival curve. After full registration, the tool path is calculated by establishing a local coordinate system between the workpiece and the tool to avoid interference. This path is calculated and generated as an executable program for ABB industrial robots. Finally, the robot was used for flexible cutting of clear aligners and was able to extract products, ensuring the effectiveness of the proposed research. This method can effectively solve the limitations of traditional milling path planning under such complex conditions.

Relationship between amide ratio assessed by Fourier-transform infrared spectroscopy: A biomarker candidate for polycythemia vera disease.

The study utilized Fourier transform infrared (FTIR) spectroscopy coupled with chemometrics to investigate protein composition and structural changes in the blood serum of patients with polycythemia vera (PV). Principal component analysis (PCA) revealed distinct biochemical properties, highlighting elevated absorbance of phospholipids, amides, and lipids in PV patients compared to healthy controls. Ratios of amide I/amide II and amide I/amide III indicated alterations in protein structures. Support vector machine analysis and receiver operating characteristic curves identified amide I as a crucial predictor of PV, achieving 100% accuracy, sensitivity, and specificity, while amide III showed a lower predictive value (70%). PCA analysis demonstrated effective differentiation between PV patients and controls, with key wavenumbers including amide II, amide I, and CH lipid vibrations. These findings underscore the potential of FTIR spectroscopy for diagnosing and monitoring PV.

IMU Data-Driven and PCA-Based Approach to Establish Quantifiable and Practically Applicable Measures for V2 Technique Elements in Cross-Country Skiing.

Quantifying movement coordination in cross-country (XC) skiing, specifically the technique with its elemental forms, is challenging. Particularly, this applies when trying to establish a bidirectional transfer between scientific theory and practical experts' knowledge as expressed, for example, in ski instruction curricula. The objective of this study was to translate 14 curricula-informed distinct elements of the V2 ski-skating technique (horizontal and vertical posture, lateral tilt, head position, upper body rotation, arm swing, shoulder abduction, elbow flexion, hand and leg distance, plantar flexion, ski set-down, leg push-off, and gliding phase) into plausible, valid and applicable measures to make the technique training process more quantifiable and scientifically grounded. Inertial measurement unit (IMU) data of 10 highly experienced XC skiers who demonstrated the technique elements by two extreme forms each (e.g., anterior versus posterior positioning for the horizontal posture) were recorded. Element-specific principal component analyses (PCAs)-driven by the variance produced by the technique extremes-resulted in movement components that express quantifiable measures of the underlying technique elements. Ten measures were found to be sensitive in distinguishing between the inputted extreme variations using statistical parametric mapping (SPM), whereas for four elements the SPM did not detect differences (lateral tilt, plantar flexion, ski set-down, and leg push-off). Applicability of the established technique measures was determined based on quantifying individual techniques through them. The study introduces a novel approach to quantitatively assess V2 ski-skating technique, which might help to enhance technique feedback and bridge the communication gap that often exists between practitioners and scientists.

Influence of the chemical composition of leachates on the results of ecotoxicity tests for different slag types.

In this study, four ecotoxicological tests on Vibrio fischeri bacteria, Sinapis alba L. (white mustard), Daphnia magna S. (daphnia's) and earthworms were performed for three types of aqueous slag (ladle, blast furnace and converter) leachates with two-grain sizes (<4 mm, <10 mm). Concentrations of toxic elements and concentrations of Cr(VI), Ca, Na, Al, and other ions were determined. The raw slags were analyzed using X-ray fluorescence spectroscopy (XRFS), and major substances were determined by X-ray powder diffraction (XRD). The aqueous slag leachates passed ecotoxicological tests and met the required criteria, showing no toxicity to Vibrio fischeri and complying with white mustard test criteria. According to the results of the ecotoxicity tests with daphnia, the blast furnace slag samples were not ecotoxic, while two other slag samples were found to be entirely compliant. Characterization of the slags showed that the effect of element/ion leachability and slag grain size is essential. Biplot principal component analysis (PCA) showed that grain size does not significantly affect the separation of individuals on the plane. A positive correlation on toxicity was found with pH, conductivity, calcium content, dissolved content, salinity and fluoride concentration, whereas a negative correlation was found with magnesium concentration, dissolved organic carbon and potassium concentration. The effective concentration at 50% inhibition (EC50) value for Vibrio fischeri correlated with the first dimension of bivariate assessment. In summary, it was found that the investigated slags can be effectively reused as they comply with regulations and do not endanger the environment.

Data driven surrogate signal extraction for dynamic PET using selective PCA: time windows versus the combination of components.

Respiratory motion correction is beneficial in PET, as it can reduce artefacts caused by motion and improve quantitative accuracy. Methods of motion correction are commonly based on a respiratory trace obtained through an external device (like the Real Time Position Management System) or a data driven method, such as those based on dimensionality reduction techniques (for instance PCA). PCA itself being a linear transformation to the axis of greatest variation. Data driven methods have the advantage of being non-invasive, and can be performed post-acquisition. However, their main downside being that they are adversely affected by the tracer kinetics of the dynamic PET acquisition. Therefore, they are mostly limited to static PET acquisitions. This work seeks to extend on existing PCA-based data-driven motion correction methods, to allow for their applicability to dynamic PET imaging. The methods explored in this work include; a moving window approach (similar to the Kinetic Respiratory Gating method from Schleyer et al.), extrapolation of the principal component from later time points to earlier time points, and a method to score, select, and combine multiple respiratory components. The resulting respiratory traces were evaluated on 22 data sets from a dynamic 18FFDG study on patients with Idiopathic Pulmonary Fibrosis. This was achieved by calculating their correlation with a surrogate signal acquired using a Real Time Position Management System. The results indicate that all methods produce better surrogate signals than when applying conventional PCA to dynamic data (for instance, a higher correlation with a gold standard respiratory trace). Extrapolating a late time point principal component produced more promising results than using a moving window. Scoring, selecting, and combining components held benefits over all other methods. This work allows for the extraction of a surrogate signal from dynamic PET data earlier in the acquisition and with a greater accuracy than previous work. This potentially allows for numerous other methods (for instance, respiratory motion correction) to be applied to this data (when they otherwise could not be previously used).

A bayesian spatio-temporal dynamic analysis of food security in Africa.

Exploring the factors influencing Food Security and Nutrition (FSN) and understanding its dynamics is crucial for planning and management. This understanding plays a pivotal role in supporting Africa's food security efforts to achieve various Sustainable Development Goals (SDGs). Utilizing Principal Component Analysis (PCA) on data from the FAO website, spanning from 2000 to 2019, informative components are derived for dynamic spatio-temporal modeling of Africa's FSN Given the dynamic and evolving nature of the factors impacting FSN, despite numerous efforts to understand and mitigate food insecurity, existing models often fail to capture this dynamic nature. This study employs a Bayesian dynamic spatio-temporal approach to explore the interconnected dynamics of food security and its components in Africa. The results reveal a consistent pattern of elevated FSN levels, showcasing notable stability in the initial and middle-to-late stages, followed by a significant acceleration in the late stage of the study period. The Democratic Republic of Congo and Ethiopia exhibited particularly noteworthy high levels of FSN dynamicity. In particular, child care factors and undernourishment factors showed significant dynamicity on FSN. This insight suggests establishing regional task forces or forums for coordinated responses to FSN challenges based on dynamicity patterns to prevent or mitigate the impact of potential food security crises.

Beneath the surface: Exploring microplastic intricacies in Anadara granosa.

Anadara granosa or blood cockles have been reported to be a candidate for biomonitoring agents due to their sedimentary nature and their nutrient uptake mechanisms. Yet, this bivalve is still regarded as a delicacy in Asian cuisine. Malaysia is the largest exporter of this sea product that contaminated cockles may also be experienced by the importing countries. However, the bioaccumulation of microplastics in A. granosa cultivated in Malaysia has not been extensively studied. It is crucial to comprehend the risk posed to humans by consuming A. granosa in their diet. Therefore, the purpose of this research is to investigate the levels of microplastic accumulation in A. granosa from major exporters in Peninsular Malaysia, to evaluate the associated risk of microplastics on the species, and to estimate daily human consumption of microplastics through the consumption of A. granosa. The abundance of microplastics was quantified through the use of a stereo microscope, and the polymer type was determined using FTIR and micro-FTIR. Findings from this investigation revealed that all samples of A. granosa were contaminated with microplastics, with the highest levels of accumulation found in bivalves collected from the west coast (0.26 ± 0.15 particles/g) of Peninsular Malaysia. Fragment and fiber microplastics, measuring between 0.05 and 0.1 mm in size, were found to be the most prevalent in A. granosa, with blue being the dominant identified colour and rayon being the most common polymer type. Microplastic risk assessment due to the presence of polyacrylate, polycarbonate (PC), and polymethyl methacrylate (PMMA) resulted in a high risk of contamination for A. granosa. It was further determined that the current estimated dietary intake (EDI) suggests that consumers of A. granosa uptake approximately 21.8-93.5 particles/person/year of microplastics. This study highlights that A. granosa accumulates microplastics, which could potentially result in bioaccumulation and biomagnification in humans through consumption.

A highly sensitive SERS substrate of porous membrane-Ag NPs for kidney cancer detection.

BACKGROUND: The substrate employed in surface-enhanced Raman spectroscopy (SERS) constitutes an essential element in the cancer detection methodology. In this research, we introduce a three-dimensional (3D) structured SERS substrate that integrates a porous membrane with silver nanoparticles to enhance SERS spectral signals through the utilization of the aggregation effect of silver nanoparticles. This enhancement is crucial because accurate detection results strongly depend on the intensity of specific peaks in Raman spectroscopy. A highly sensitive SERS substrate can significantly improve the accuracy of detection results. RESULTS: We collected 66 plasma samples from individuals with kidney cancer and control individuals, including both bladder cancer patients and healthy individuals. Then, we utilized substrates with and without porous membranes to acquire the SERS spectra of the samples, enabling us to evaluate the enhancement effect of our SERS substrate. The spectral analysis demonstrated enhanced peak intensities in the experimental group (with porous substrate) compared to the control group (without porous substrate). The uniformity and reproducibility of the SERS substrate are also significantly enhanced, which is very helpful for improving the accuracy of detection results. Additionally, the Principal Component Analysis-Linear Discriminant Analysis algorithm (PCA-LDA) was employed to classify the SERS spectra of both groups. In the experimental group, the classification accuracy was 98.5 % for kidney cancer, and 83.3 % for kidney and bladder cancer. Compared to the control group, it improved by 3 % and 12.6 % respectively. SIGNIFICANT: This indicates that our 3D structured SERS substrate combined with multivariate statistical algorithms PCA-LDA can not only improve the accuracy of SERS detection technology in single cancer detection, but also has great potential in multiple cancer detection. This 3D structured SERS substrate is expected to become a new auxiliary means for cancer detection.

Tracing sources of dissolved organic matter along the terrestrial-aquatic continuum in the Ore Mountains, Germany.

There is growing concern about the rising levels of dissolved organic matter (DOM) in surface waters across the Northern hemisphere. However, only limited research has been conducted to unveil its precise origin. Compositional changes along terrestrial-aquatic pathways can help determine the terrestrial sources of DOM in streams. Stream water, soil water and soil horizons were sampled at four sites representing typical settings within a forested catchment in the Ore Mountains (Erzgebirge, Germany) from winter 2020 to spring 2022. The samples were analyzed using pyrolysis-gas chromatography/mass spectrometry (Py-GC/MS). The resulting data were successfully subjected to semi-automatic processing of the molecular composition of DOM, reaching a percentage of identified peaks up to 98 %. Principal component analysis (PCA) and cluster analyses were carried out to identify distinct differences between DOM from the potential sources and in the streams. According to the PCA, organic soil horizons, soil water, and stream water samples could be clearly distinguished. Cluster analysis revealed that soil water DOM at all depths of Peats and deeper horizons of the Peaty Gleysols contributed the most to DOM in the stream section dominated by organic soils. In areas dominated by mineral soils, stream DOM resembled the DOM from the deeper mineral horizons of Cambisols and Podzols. Overall, our results suggested that most of the DOM exported from the catchment was derived from deeper mineral soil horizons, with little contribution of DOM derived from organic soils. Therefore, DOM fingerprint analysis of in-situ soil water proved to be a promising approach for tracing back the main sources of stream water DOM.

Association between Dietary Patterns and All-Cause Mortality in the Chinese Old: Analysis of the Chinese Longitudinal Healthy Longevity Survey Cohort.

Diet is one of the most important ways to intervene and promote the health of older adults and reduce all-cause mortality. This study aimed to investigate the association between dietary patterns and all-cause mortality in the Chinese old. This study involved 11,958 subjects aged 65-116 years in the Chinese Longitudinal Healthy Longevity Survey (CLHLS) from 2008 to 2018. Dietary patterns were derived from principal component analysis (PCA) with varimax rotation. Four dietary patterns were derived: the 'milk-egg-sugar pattern', 'carnivorous pattern', 'healthy pattern', and 'northeastern pattern'. Cox proportional hazard models were built for males and females separately to estimate the relationship between different dietary patterns and all-cause mortality. After adjusting for all covariates, the milk-egg-sugar pattern played a reverse role in mortality risk in males and females in different quartiles. In the carnivorous pattern, only males in the fourth quartile were observed to have a significantly reduced mortality risk (HR = 0.84 (95% CI: 0.77-0.93)). Both genders benefited from the healthy pattern, which consistently lowered mortality risk across all quartiles (males: HR = 0.87 (95% CI: 0.84-0.89); females: HR = 0.95 (95% CI: 0.92-0.97)). The northeastern pattern also showed an inverse association with all-cause mortality in males (HR = 0.94 (95% CI: 0.92-0.97)) and females (HR = 0.96 (95% CI: 0.93-0.98)). This study showed the association between dietary patterns and all-cause mortality in the Chinese old, which is significant for further quantitative studies.

Comparison of HPLC, HPTLC, and In Silico Lipophilicity Parameters Determined for 5-Heterocyclic 2-(2,4-Dihydroxyphenyl)-1,3,4-thiadiazoles.

The 5-heterocyclic 2-(2,4-dihydroxyphenyl)-1,3,4-thiadiazoles were obtained as potential biologically active compounds. Lipophilicity is one of the most important physicochemical properties of compounds and was already taken into account during the drug candidates design and development. The lipophilicity of compounds was determined using the computational (log P) and chromatography (log kw, RMw) methods. The experimental ones included the reverse-phase column high performance liquid chromatography RP (HPLC) with C8, C18, phosphatidylcholine (IAM), and cholesterol stationary phases and the thin layer chromatography (RP-HPTLC) with C8 and C18 stationary phases and various organic modifiers under the isocratic conditions. Descriptive statistics, correlation, and PCA analyses were used to compare the obtained results. For lipophilicity estimation of the tested compounds by HPTLC, dioxane and MeOH seem to be particularly beneficial as organic modifiers. The chromatographic lipophilicity parameters log kw (RMw) were well correlated and highly redundant (85%) compared with those calculated. Most compounds possess lipophilicity parameters within the recommended range for drug candidates.

Insight into Rheological Properties and Structure of Native Waxy Starches: Cluster Analysis Grouping.

Recent interest in the use of waxy starches in food production is due to the possibility of replacing chemically modified starches as texture-forming agents with native starch analogues. However, there is a lack of a coherent research comparing different varieties of commercially available waxy starches with respect to their molecular and functional properties. Therefore, the objective of this study was to compare native waxy starches from potatoes, corn, and rice, with particular attention to rheological characteristics in relation to molecular structure. The investigated potato, corn, and rice starch preparations were characterized by significantly different molecular properties due to both botanical origin of starch and variety. The molecular weights of waxy starches were significantly higher than those of their normal counterparts. This phenomenon was accompanied by a more loose conformation of the waxy starch macromolecule in solution. The presence of amylose confers the ability to coagulate starch sol into gel, resulting in substantial changes in the rheological properties of starch paste, and waxy starch pastes being characterized by more viscous flow and smoother texture. Hierarchical cluster analysis indicated that differences between functional properties are more notable for normal than for waxy preparations, in which potato starch, regardless of its variety, was characterized by the most unique characteristics.

Advanced Image Analytics for Mobile Robot-Based Condition Monitoring in Hazardous Environments: A Comprehensive Thermal Defect Processing Framework.

In hazardous environments like mining sites, mobile inspection robots play a crucial role in condition monitoring (CM) tasks, particularly by collecting various kinds of data, such as images. However, the sheer volume of collected image samples and existing noise pose challenges in processing and visualizing thermal anomalies. Recognizing these challenges, our study addresses the limitations of industrial big data analytics for mobile robot-generated image data. We present a novel, fully integrated approach involving a dimension reduction procedure. This includes a semantic segmentation technique utilizing the pre-trained VGG16 CNN architecture for feature selection, followed by random forest (RF) and extreme gradient boosting (XGBoost) classifiers for the prediction of the pixel class labels. We also explore unsupervised learning using the PCA-K-means method for dimension reduction and classification of unlabeled thermal defects based on anomaly severity. Our comprehensive methodology aims to efficiently handle image-based CM tasks in hazardous environments. To validate its practicality, we applied our approach in a real-world scenario, and the results confirm its robust performance in processing and visualizing thermal data collected by mobile inspection robots. This affirms the effectiveness of our methodology in enhancing the overall performance of CM processes.

Methods for Estimating the Detection and Quantification Limits of Key Substances in Beer Maturation with Electronic Noses.

To evaluate the suitability of an analytical instrument, essential figures of merit such as the limit of detection (LOD) and the limit of quantification (LOQ) can be employed. However, as the definitions k nown in the literature are mostly applicable to one signal per sample, estimating the LOD for substances with instruments yielding multidimensional results like electronic noses (eNoses) is still challenging. In this paper, we will compare and present different approaches to estimate the LOD for eNoses by employing commonly used multivariate data analysis and regression techniques, including principal component analysis (PCA), principal component regression (PCR), as well as partial least squares regression (PLSR). These methods could subsequently be used to assess the suitability of eNoses to help control and steer processes where volatiles are key process parameters. As a use case, we determined the LODs for key compounds involved in beer maturation, namely acetaldehyde, diacetyl, dimethyl sulfide, ethyl acetate, isobutanol, and 2-phenylethanol, and discussed the suitability of our eNose for that dertermination process. The results of the methods performed demonstrated differences of up to a factor of eight. For diacetyl, the LOD and the LOQ were sufficiently low to suggest potential for monitoring via eNose.