Portable spectroscopy, digital imaging colorimetry and multivariate statistical tools in contaminant identification: A case study of mint (Mentha) and basil (Ocimum basilicum).

The advent of portable Fourier-Transform Infrared (FTIR) and Raman spectrometers has revolutionized analysis capabilities, presenting the possibility of on-site contaminant identification without the need for specialized laboratory settings. Compared to laboratory instrumentation, portable spectroscopy is more prone to noise, and appropriate spectral processing procedures need to be established. This paper introduces a comprehensive methodology that integrates acquisition techniques, spectral analysis, and mathematical tools necessary for utilizing handheld spectrometers to diagnose plant contamination. It focuses on determining the efficacy of handheld FTIR, Raman spectroscopy, and digital imaging for detecting contaminants in two food plants, Basil (Ocimum basilicum) and Mint (Mentha). The study examines the impact of three pollutants: iron (II) sulphate (FeSO4), zinc (II) sulphate (ZnSO4), and copper (II) sulphate (CuSO4), on these plants, but also the necessary amount of measurements to spot the pollutants' effects. Measurements were conducted at the start, after 24 hours, and after 48 hours of exposure, on both fresh and dried plant leaves, as well as in solution. Spectral effects of each of the pollutants were identified with the use of multivariate statistical process control techniques. With the help of the developed methodologies, researchers can identify in-situ contaminant effects, exposure times and run diagnostics directly on the leaf both in alive and dried plants.

AB-PLS-DA: Pansharpening tailored for scanning electron microscopy and energy-dispersive X-ray spectrometry multimodal fusion.

Pansharpening constitutes a category of data fusion techniques designed to enhance the spatial resolution of multispectral (MS) images by integrating spatial details from a high-resolution panchromatic (PAN) image. This process combines the high-spectral data of MS images with the rich spatial information of the PAN image, resulting in a pansharpened output ideal for more effective image analysis, such as object detection and environmental monitoring. Traditionally developed for satellite data, our paper introduces a novel pansharpening approach customized for the fusion of Scanning Electron Microscopy (SEM) and Energy-Dispersive X-ray Spectrometry (EDS) data. The proposed method, grounded in Partial Least Squares regression with Discriminant Analysis (PLS-DA), significantly boosts the spatial resolution of EDS data while preserving spectral details. A key feature of this approach involves partitioning the PAN image into intensity bins and dynamically adapting this division in cases of overlapping compounds with similar average atomic numbers. We evaluate the method's effectiveness using in-house EDS images obtained from both even and uneven sample surfaces. Comparative analysis against existing benchmarks and state-of-the-art pansharpening techniques demonstrates superior performance in both spectral and spatial quality indicators for our method.

Optimizing energy dispersive X-Ray Spectroscopy (EDS) image fusion to Scanning Electron Microscopy (SEM) images.

Fusion and quality enhancement of the low-resolution Energy Dispersive X-ray Spectroscopy (EDS) maps to Scanning Electron Microscopy (SEM) panchromatic images has been proven effective by various pansharpening algorithms. The present paper aims to target the preprocessing of these maps to enhance the efficiency of the pansharpening process, with as little information loss on the chemical distribution, and as little propagated noise as possible. EDS maps present different noise intensities depending on the flatness of the surface of the analyzed object. The uneven surface maps have limited analytical value due to the noise and have not been resolution-enhanced with pansharpening due to the noise propagation limitation. In this paper, different preprocessing methods are evaluated for enabling uneven-surface particles to pansharpening: background removal, upsampling, and noise filtering. The sequence of applying preprocessing steps is analyzed. The optimal order of preprocessing steps is (i) background removal, (ii) noise filtering, and (iii) interpolation. A methodology for each of these steps is presented in the paper. The best performing pansharpening methodology is chosen to be Affinity for individual map analysis and Wavelet for multi-elemental fusion purposes. Following the methodology results in high-resolution EDS maps, even for uneven-surface particles which are, for the first time in literature, subjected to pansharpening.

Variation of microplastics in the shore sediment of high-altitude lakes of the Indian Himalaya using different pretreatment methods.

Microplastics pollution is a growing environmental concern. However, microplastics studies in high altitude remote lakes are scarce. In this study, microplastics pollution was assessed in the shore sediment of three high altitude lakes in Ladakh of the Indian Himalaya, namely Pangong Lake, Tsomoriri Lake and Tsokar Lake. Sampling of lakes shore sediment was performed in August 2019. Two different pretreatment methods were implemented with sediment samples from same sites, resulting two sets of samples. One set of samples was pretreated utilizing enzymatic degradation together with Fenton reactions. Another set of samples from the same sites were pretreated with 30 % hydrogen peroxide (H2O2) and Fenton reaction. Enzymatically pretreated samples resulted in higher microplastics concentrations than the set of H2O2 pretreated samples, which indicated that microplastics concentrations in sediment samples varies even among samples from the same site and that the pretreatment procedure may impact on the reported microplastics concentrations. Considering both sets of samples, microplastic concentration was 160-1000 MP/kg dw in Pangong Lake, 960-3800 MP/kg dw in Tsomoriri Lake, and 160-1000 MP/kg dw in Tsokar Lake. Blank correction based on the limit of detection and the limit of quantification indicated that microplastics concentrations at some sites of the studied lakes are higher than the limit of detection and the limit of quantification. The findings of this study indicated that the studied lakes in the Indian Himalaya are contaminated with microplastics. In addition, the comparison of microplastics using different pretreatment methods illustrated the importance of harmonization of microplastics studies to enable a reliable comparison among microplastics data. Therefore, this study contributes towards an assessment of microplastics in the high-altitude lakes in Indian Himalaya. The findings attributed towards clearer understanding regarding the need of harmonization of pretreatment methods and demonstrated the importance of reporting complete information in microplastics research.

The assessment of particle selection and blank correction to enhance the analysis of microplastics with Raman microspectroscopy.

Although microplastics research has received enormous attention in the last decade, both the research practices and the quality of produced data should still be improved. In this study, the identification process of microplastics with Raman imaging microscope was improved by decreasing the time needed for the analysis. To do that, new features, including terrain mosaic and automatic particle selection, were utilized and various ways of handling the produced microplastics data were implemented and discussed. Furthermore, blank correction of microplastic concentrations was demonstrated and its effects on the recovery of spiked microplastics was assessed with aqueous and solid samples. Six types of microplastics, including fragments and fibers, were spiked in triplicates of ultra-pure water and reference sediment samples. The spiked samples were pretreated by a modified method of the universal enzymatic purification protocol. Microplastics were analyzed with Raman imaging microscope, using terrain mosaic combined with automatic particle selection. The microplastics data were subjected to different identification steps to estimate the potential overestimation and underestimation of microplastics counts. With the complete correction of Raman-based data, the average recovery rates of fragments (77-80%) were higher than fibers (20-33%). The decrease in recovery rates of spiked microplastics (49-57%) were observed when blank correction was applied (28-47%). The impact of the blank correction depended on the polymer, causing exclusion of PE, PET, and PP from sediment samples. For the completely corrected Raman-based data, the average recovery rates of microplastics were higher for water than sediment samples both with and without blank correction. The results demonstrated the impact of blank correction on the microplastics recovery rates. To our knowledge, this is the first study to explore the use of automatic particle selection of Raman imaging microscope for microplastics analysis. Hence, potential drawbacks and advantages of the new features of Raman imaging microscope were explicitly discussed.

Image based evaluation of textured 3DSEM models.

Photogrammetric methods enable the construction of 3D SEM models from 2D images. Most software for this purpose is designed for photographic images. The software tries to minimize modelling error but some uncertainty usually remains in the model. In such approaches no ground truth measurement for microscopic objects exists for comparison with the finished model. In the proposed method, a textured model surface is compared to the SEM images to map the error locations on the model. The method is illustrated using two datasets.

Microplastics pollution in the Brahmaputra River and the Indus River of the Indian Himalaya.

Rivers act as temporary sinks of microplastics and a key medium allowing microplastics to enter the ocean. In this study, microplastics pollution in river shore sediment of the Indian Himalaya, including the Brahmaputra River and the Indus River was discussed. Sampling campaigns were performed in years 2018 and 2019. Sample pretreatment was performed using Na2WO4·2H2O for density separation and H2O2 for oxidation of organic material. Microplastics analysis was performed by using FTIR microscope. The smaller size of microplastics 20-150 µm were more abundant (531-3485 MP/kg in the Brahmaputra River and 525-1752 MP/kg in the Indus River) than microplastics in size range between 150 µm and 5 mm (20-240 MP/kg in the Brahmaputra River and 60-340 MP/kg in the Indus River). Microplastics were found in sediments of all sampling sites. Fragmented, secondary microplastics were dominant in the river shore sediment of the Indian Himalaya. This study contributes towards filling research gap of microplastics in India's freshwater source and highlights the importance of in-depth complete studies of microplastics in the rivers that act as pathways and sinks for microplastics.

Metal Fractionation in Surface Sediments of the Brahmaputra River and Implications for Their Mobilization.

The Brahmaputra River is the largest tropical river in India that flows along the Himalayan regions and it is the lifeline of millions of people. Metal fractionation in the Brahmaputra River's surface sediments and its correlation with turbidity are assessed in this study. The interaction between metal fractions and the overlying water is studied using multivariate statistical analyses. The strong positive correlation between NH4 of the overlying water and the exchangeable fractions in sediments signifies that the metals in the exchangeable fractions can be substituted by NH4. Subsequently, these metals can be released into the overlying water. The fluctuation in turbidity from 73 to 875 NTU indicates a large variation in the suspended matter concentration, and a higher concentration of suspended matter could provide attachment sites for pollutants such as metals. Significant variation in turbidity manifests a potentially high risk of pollution. In addition, the observation of local people along the Brahmaputra River turning its color to muddy indicates the need for continuous monitoring of water quality and an assessment of pollution is crucial. Although the Brahmaputra River's risk assessment code is at low risk, the exchangeable fractions of Ni and Zn are present at all sites. Thus, the Brahmaputra River requires early preventive measures and management strategies to control metal pollution. This study contributes to an understanding of the fluctuation of turbidity of a tropical river. We provide baseline data for policymakers, and the importance of further intensive studies on metal pollution in the Himalayan Rivers is highlighted.

A new method for extraction of methanol-soluble brown carbon: Implications for investigation of its light absorption ability.

As an important component of organic carbon (OC), brown carbon (BrC) plays a significant role in radiative forcing in the atmosphere. Water-insoluble OC (WIOC) generally has higher light absorption ability than water-soluble OC (WSOC). The mass absorption cross-section (MAC) of WIOC is normally investigated by dissolving OC in methanol. However, all the current methods have shortcomings due to neglecting the methanol insoluble particulate carbon that is detached from the filter and suspended in methanol extracts, which results in MAC uncertainties of the methanol-soluble BrC and its climate warming estimation. In this study, by investigating typical biomass combustion sourced aerosols from the Tibetan Plateau and ambient aerosols from rural and urban areas in China, we evaluated the light absorption of extractable OC fraction for the existing methods. Moreover, a new method was developed to overcome the methanol insoluble particulate carbon detachment problem to achieve more reliable MAC values. We found that OC can be dissolved in methanol in a short time (e.g., 1 h) and ultrasonic treatment and long-term soaking do not significantly increase the extractable OC fraction. Additionally, we proved that methanol insoluble particulate carbon detachment in methanol does exist in previous methods, causing overestimation of the BrC mass extracted by methanol and thus the underestimation of MAC values. We therefore recommend the newly developed extraction method in this study to be utilized in future related studies to quantitatively obtain the light absorption property of methanol-soluble BrC.

Visual tool for real-time monitoring of membrane fouling via Raman spectroscopy and process model based on principal component analysis.

Membrane fouling, i.e. accumulation of unwanted material on the surface of the membrane is a significant problem in filtration processes since it commonly degrades membrane performance and increases operating costs. Therefore, the advantages of early stage monitoring and control of fouling are widely recognized. In this work, the potential of using Raman spectroscopy coupled to chemometrics in order to quantify degree of membrane fouling in real-time was investigated. The Raman data set collected from adsorption experiments with varying pHs and concentrations of model compound vanillin was used to develop a predictive model based on principal component analysis (PCA) for the quantification of the vanillin adsorbed on the membrane. The correspondence between the predicted concentrations based on the PCA model and actual measured concentrations of adsorbed vanillin was moderately good. The model developed was successful in monitoring both adsorption and desorption processes. Furthermore, the model was able to detect abnormally proceeding experiment based on differentiating PCA score and loading values. The results indicated that the presented approach of using Raman spectroscopy combined with a PCA model has potential for use in monitoring and control of fouling and cleaning in membrane processes.

Measurement uncertainty of lactase-containing tablets analyzed with FTIR.

Uncertainty is one of the most critical aspects in determination of measurement reliability. In order to ensure accurate measurements, results need to be traceable and uncertainty measurable. In this study, homogeneity of FTIR samples is determined with a combination of variographic and multivariate approach. An approach for estimation of uncertainty within individual sample, as well as, within repeated samples is introduced. FTIR samples containing two commercial pharmaceutical lactase products (LactaNON and Lactrase) are applied as an example of the procedure. The results showed that the approach is suitable for the purpose. The sample pellets were quite homogeneous, since the total uncertainty of each pellet varied between 1.5% and 2.5%. The heterogeneity within a tablet strip was found to be dominant, as 15-20 tablets has to be analyzed in order to achieve <5.0% expanded uncertainty level. Uncertainty arising from the FTIR instrument was <1.0%. The uncertainty estimates are computed directly from FTIR spectra without any concentration information of the analyte.

Uncertainty and periodic behavior of process derived from online NIR.

Past years have shown that near infra-red (NIR) can be successfully applied in online process control. The NIR measurements are commonly utilized because they are fast, versatile and relatively cost-effective. The online instruments produce an enormous amount of data, which need to be analyzed for, e.g., reliability, like any other online data. Instrumental data containing huge amount of simultaneously determined variables is multivariate in nature, and it has to be taken into account when the data is analyzed. The aim of this study was to show that variographic analysis gives a novel insight to online NIR data and the total uncertainty including variation arising from process itself can be estimated. It will be shown, that variographic analysis can be utilized in monitoring the process dynamics, as well as, in optimization of sampling interval. The periodic behavior was identified with autocorrelation and fast Fourier transformation (FFT) as well as with the variographic analysis. However, the variographic analysis gave a more detailed insight to the process dynamics and enabled estimation of uncertainty as a function of sampling interval. These approaches are illustrated with real industrial data originating from a petrochemical plant. Similar periodic behavior could be detected by applying any of the three mathematical methods to the online variable sets containing either NIR or other process control variables. The total uncertainty of the NIR data was estimated by applying variographic analysis with an assumption that the different principal components (PC) are individual "error sources" causing uncertainty.

Predicting the drug concentration in starch acetate matrix tablets from ATR-FTIR spectra using multi-way methods.

The amounts of drug and excipient were predicted from ATR-FTIR spectra using two multi-way modelling techniques, parallel factor analysis (PARAFAC) and multi-linear partial least squares (N-PLS). Data matrices consisted of dissolved and undissolved parallel samples having different drug content and spectra, which were collected at axially cut surface of the flat-faced matrix tablets. Spectra were recorded comprehensively at different points on the axially cut surface of the tablet. The sample drug concentrations varied between 2 and 16% v/v. The multi-way methods together with ATR-FTIR spectra seemed to represent an applicable method for the determination of drug and excipient distribution in a tablet during the release process. The N-PLS calibration method was more robust for accurate quantification of the amount of components in the sample whereas the PARAFAC model provided approximate relative amounts of components.

Effect of missing values in estimation of mean of auto-correlated measurement series.

Sampling and uncertainty of sampling are important tasks, when industrial processes are monitored. Missing values and unequal sources can cause problems in almost all industrial fields. One major problem is that during weekends samples may not be collected. On the other hand a composite sample may be collected during weekend. These systematically occurring missing values (gaps) will have an effect on the uncertainties of the measurements. Another type of missing values is random missing values. These random gaps are caused, for example, by instrument failures. Pierre Gy's sampling theory includes tools to evaluate all error components that are involved in sampling of heterogeneous materials. Variograms, introduced by Gy's sampling theory, have been developed to estimate the uncertainty of auto-correlated process measurements. Variographic experiments are utilized for estimating the variance for different sample selection strategies. The different sample selection strategies are random sampling, stratified random sampling and systematic sampling. In this paper both systematic and random gaps were estimated by using simulations and real process data. These process data were taken from bark boilers of pulp and paper mills (combustion processes). When systematic gaps were examined a linear interpolation was utilized. Also cases introducing composite sampling were studied. Aims of this paper are: (1) how reliable the variogram is to estimate the process variogram calculated from data with systematic gaps, (2) how the uncertainty of missing gap can be estimated in reporting time-averages of auto-correlated time series measurements. The results show that when systematic gaps were filled by linear interpolation only minor changes in the values of variogram were observed. The differences between the variograms were constantly smallest with composite samples. While estimating the effect of random gaps, the results show that for the non-periodic processes the stratified random sampling strategy gives more reliable results than systematic sampling strategy. Therefore stratified random sampling should be used while estimating the uncertainty of random gaps in reporting time-averages of auto-correlated time series measurements.

Multivariate statistical analysis of a multi-step industrial processes.

Monitoring and quality control of industrial processes often produce information on how the data have been obtained. In batch processes, for instance, the process is carried out in stages; some process or control parameters are set at each stage. However, the obtained data might not be utilized efficiently, even if this information may reveal significant knowledge about process dynamics or ongoing phenomena. When studying the process data, it may be important to analyse the data in the light of the physical or time-wise development of each process step. In this paper, a unified approach to analyse multivariate multi-step processes, where results from each step are used to evaluate future results, is presented. The methods presented are based on Priority PLS Regression. The basic idea is to compute the weights in the regression analysis for given steps, but adjust all data by the resulting score vectors. This approach will show how the process develops from a data point of view. The procedure is illustrated on a relatively simple industrial batch process, but it is also applicable in a general context, where knowledge about the variables is available.

IR spectroscopy together with multivariate data analysis as a process analytical tool for in-line monitoring of crystallization process and solid-state analysis of crystalline product.

Crystalline product should exist in optimal polymorphic form. Robust and reliable method for polymorph characterization is of great importance. In this work, infra red (IR) spectroscopy is applied for monitoring of crystallization process in situ. The results show that attenuated total reflection Fourier transform infra red (ATR-FTIR) spectroscopy provides valuable information on process, which can be utilized for more controlled crystallization processes. Diffuse reflectance Fourier transform infra red (DRIFT-IR) is applied for polymorphic characterization of crystalline product using X-ray powder diffraction (XRPD) as a reference technique. In order to fully utilize DRIFT, the application of multivariate techniques are needed, e.g., multivariate statistical process control (MSPC), principal component analysis (PCA) and partial least squares (PLS). The results demonstrate that multivariate techniques provide the powerful tool for rapid evaluation of spectral data and also enable more reliable quantification of polymorphic composition of samples being mixtures of two or more polymorphs. This opens new perspectives for understanding crystallization processes and increases the level of safety within the manufacture of pharmaceutics.