Evidence on C-C Coupling to Acetate as Key Reaction Intermediate in Photocatalytic Reduction of CO2 over Pt/TiO2.

Photocatalytic conversion of CO2 with H2O is an attractive application that has the potential to mitigate environmental and energy challenges through the conversion of CO2 to hydrocarbon products such as methane. However, the underlying reaction mechanisms remain poorly understood, limiting real progress in this field. In this work, a mechanistic investigation of the CO2 photocatalytic reduction on Pt/TiO2 is carried out using an operando FTIR approach, combined with chemometric data processing and isotope exchange of (12CO2 + H2O) toward (13CO2 + H2O). Multivariate curve resolution analysis applied to operando spectra across numerous cycles of photoactivation and the CO2 reaction facilitates the identification of principal chemical species involved in the reaction pathways. Moreover, specific probe-molecule-assisted reactions, including CO and CH3COOH, elucidate the capacity of selected molecules to undergo methane production under irradiation conditions. Finally, isotopic exchange reveals conclusive evidence regarding the nature of the identified species during CO2 conversion and points to the significant role of acetates resulting from the C-C coupling reaction as key intermediates in methane production from the CO2 photocatalytic reduction reaction.

Hyperspectral imaging as a non-destructive technique for estimating the nutritional value of food.

Knowledge of the energy and macronutrient content of complex foods is essential for the food industry and to implement population-based dietary guidelines. However, conventional methodologies are time-consuming, require the use of chemical products and the sample cannot be recovered. We hypothesize that the nutritional value of heterogeneous food products can be readily measured instead by using hyperspectral imaging systems (NIR and VIS-NIR) combined with mathematical models previously fitted with spectral profiles.118 samples from different food products were collected for building the predictive models using their hyperspectral imaging data as predictors and their nutritional values as dependent variables. Ten different models were screened (Multivariate Linear regression, Lasso regression, Rigde regression, Elastic Net regression, K-Neighbors regression, Decision trees regression, Partial Least Square, Support Vector Machines, Gradient Boosting regression and Random Forest regression). The best results were obtained with Ridge regression for all parameters. The best performance was for estimating the protein content with a RMSE of 1.02 and a R2 equal to 0.88 in a test set, following by moisture (RMSE of 2.21 and R2 equal to 0.85), energy value (RMSE of 21.84 and R2 equal to 0.76) and total fat (RMSE of 2.17 and R2 equal to 0.72). The performance with carbohydrates (RMSE of 2.12 and R2 equal to 0.61) and ashes (RMSE of 0.25 and R2 equal to 0.38) was worse. This study shows that it is possible to predict the energy and nutrient values of processed complex foods, using hyperspectral imaging systems combined with supervised machine learning methods.

Self-organizing maps for exploration and classification of nuclear magnetic resonance spectra for untargeted metabolomics of breast cancer.

Metabolomics has emerged as a powerful tool for identifying biomarkers of disease, and nuclear magnetic resonance (NMR) spectroscopy allows for the simultaneous detection of a wide range of metabolites. However, due to complex interactions within metabolic networks, metabolites often exhibit high correlation and collinearity. To address this challenge, self-organizing maps (SOMs) of Kohonen maps and counter propagation-artificial neural networks (CP-ANN) were employed in this study to model proton nuclear magnetic resonance spectroscopic (1HNMR) data from control samples and breast cancer (BC) patients. Blood serum samples from a control group (n=24) and BC patients (n=18) were used to extract metabolites using methanol and chloroform solvents in optimum extraction conditions. The 1HNMR data was preprocessed by performing phase, baseline, and shift corrections. Subsequently, the preprocessed data was modeled using Kohonen network as an unsupervised technique and CP-ANN as a supervised technique. In this regard, the model built with CP-ANN successfully distinguished between the two classes with an accuracy of 100 % for both group and sensitivity of 96 % and 100 % for control group and BC patients, respectively. Additionally, CP-ANN algorithm demonstrated predictive capabilities by accurately classifying test samples with 90 % sensitivity, 98 % specificity, and 96 % accuracy for control group and 100 % sensitivity, 90 % specificity, and 96 % accuracy for BC patients. Furthermore, analysis of the resulting topological map revealed 14 significant variables (biomarkers) such as sarcosine, lysine, trehalose, tryptophan, and betaine that effectively differentiated between healthy individuals and BC patients.

Identification of chrysanthemum variety via hyperspectral imaging and wavelength selection based on multitask particle swarm optimization.

Chrysanthemum, a widely favored flower tea, contains numerous phytochemicals for health benefits. Due to the different geographical origins and processing technics, its variety has a direct influence on the phytochemical content and pharmacological effect. Accordingly, an accurate identification for chrysanthemum varieties is significant for quality detection and market supervision. In this study, the hyperspectral imaging (HSI) combined with chemometrics methods was exploited to identify the chrysanthemum varieties. First, to alleviate the problem of easily trapping into local optimum in traditional spectral variable selection methods, the multi-tasking particle swarm optimization (MTPSO) was developed to select the key wavelengths by dividing hundreds of variables into low-dimensional subtasks. Second, to enrich the feature information, the spatial texture and color features contained in hyperspectral images were extracted and applied to chrysanthemum identification for the first time. Finally, an ensemble learning model, extreme gradient boosting (XGBoost), was constructed to conduct the chrysanthemum variety classification due to its strong generalization ability. Experimental results showed that the proposed MTPSO achieved the identification accuracy of 96.89%, and increased by 1.11-5.91% than classical spectral feature selection methods. Furthermore, after the involvement of spatial image information, the classification accuracy using spatial-spectral features was improved further, and reached 98.39%. Overall, this study highlights that the feature fusion of key wavelengths and spatial information is more effective for chrysanthemum variety identification, and can also provide technical reference for other HSI-related applications.

Comparing the potential of benchtop and handheld mid-infrared spectrometers for predicting soil phosphorus (P) sorption capacity and evaluating the influence of sample preparation.

Traditional soil phosphorus (P) sorption capacity is examined from a Langmuir isotherm batch technique, which is time-consuming, labour intensive and generates chemical waste. In this work, we provide an efficient and convenient technique with MIR spectroscopy to predict the Langmuir parameter of soil P sorption maximum capacity (Smax, mg·kg-1). Four spectral libraries from benchtop (Bruker) and handheld (Agilent) MIR spectrometers were built with samples in two particle size ranges, <0.100 mm (ball-milled) and <2 mm. respectively. Using an archive of samples with a database of sorption parameters, soils were classified into 'low' and 'high' sorption capacities. Chemometric regression models of partial least squares (PLS), Cubist, support vector machine (SVM) regression and random forest (RF) were evaluated for Smax prediction. Bruker spectral libraries with both soil particle sizes yielded 'excellent models', with SVM predicting Smax values with high accuracy (RPIQV = 4.50 and 4.25 for the spectral libraries of the ball-milled and <2 mm samples, respectively). In comparison, the Agilent handheld spectral libraries contained more noise and less resolution. For Agilent MIR spectroscopy, more homogeneous samples after ball milling resulted in a higher accurate Smax prediction. For Agilent libraries of ball-milled samples, an 'approximate quantitative model' (RPIQV = 2.74) was obtained from the raw spectra using the Cubist algorithm. However, for Agilent spectroscopy of <2 mm samples, the best performing Cubist algorithm can only achieve a 'fair model' (RPIQV=2.23) with the potential to discriminate between 'low' and 'high' Smax values. The results suggest that the benchtop spectrometer can predict the Langmuir Smax value with high accuracy without the need to ball mill samples. However, the handheld spectrometer can only make approximate quantitative predictions of Smax for ball-milled samples. For <2 mm samples, Agilent can only be used to classify 'low' and 'high' sorption capacity soils.

Tiger Milk Mushroom: A Comprehensive Review of Nutritional Composition, Phytochemicals, Health Benefits, and Scientific Advancements with Emphasis on Chemometrics and Multi-Omics.

This article presents a comprehensive overview of tiger milk mushroom (TMM), covering its nutritional composition, phytochemicals, health benefits, and related scientific advancements. It describes various potential positive health benefits of TMM, including anticancer, anti-inflammatory, respiratory function enhancement, antioxidant, anti-aging, neuroprotective, photoprotective, antidiabetic, wound-healing, and anti-HIV, among others. This article also underlines the importance of further research into the phytochemicals present in TMM for additional discoveries. It underscores the importance of further research into phytochemicals content of TMM for additional discoveries and emphasizes the potential applications of TMM in nutrition, health, and well-being. Sophisticated techniques, such as chemometrics and multi-omics technologies revealed latest scientific advancements of TMM. This comprehensive overview provides a foundation for future research and development in harnessing TMM's potential for human health.

Estimation of Time-Since-Deposition of bloodstains on different surfaces using ATR-FTIR Spectroscopy and Chemometrics.

Blood is commonly discovered at crime scenes in various forms, including stains, dried residue, pools, and fingerprints on assorted surfaces. Estimating the age of bloodstains is a crucial aspect of reconstructing crime scenes. This research aimed to investigate how the nature of different surfaces affects the estimation of bloodstain age, utilizing a reliable and non-destructive approach. The study employed ATR-FTIR spectroscopy in conjunction with Chemometric techniques such as PCA (Principal Component Analysis) and OPLSR (Orthogonal Signal Correction Partial Least Square Regression Analysis) to analyze spectral data and develop regression models for estimating bloodstain age on cement, metal, and wooden surfaces for up to eleven days. The chemometric models for bloodstains on all three substrates demonstrated strong performance, with predictive Root Mean Square Error (RMSE) values ranging from 1.1 to 1.43 and R2 values from 0.84 to 0.89. Notably, the model developed for metal surfaces was found to be the most accurate with minimal prediction error. The findings of the study showed that the porosity of the substrates upon which bloodstains were found had a discernible influence on the age-related transformations observed in bloodstains; the majority of which occured within the spectral range of 2800 cm- 1 to 3500 cm- 1.

Smartphone-based detection and discrimination of amine vapors by a single dye-adsorbed material.

Smartphone-assisted analysis has become widely utilized for detecting various species in recent years. In such studies, multiple dyes should be employed to ensure selectivity and analyte discrimination. In our research, we have demonstrated the capability of a specially synthesized dye to selectively detect and discriminate liquid amine vapors. The developed material employs meso-toluene-α,ß,α',ß'-tetrabromoBODIPY immobilized on a thin-layer chromatography plate, exhibiting structure-specific color changes in response to amine vapors. The hue values of these colors, observed under both ambient and UV light, enable discrimination even among closely related amine structures. A mobile application has also been developed for the rapid interpretation of test results.

Development of near-infrared spectroscopy (NIRS) for estimating organic matter, total carbon, and total nitrogen in agricultural soil.

The analysis of soil organic matter (OM), total carbon (TC), and total nitrogen (TN) using traditional methods is quite time-consuming and involves the use of hazardous chemical reagents. Absorbance spectroscopy, especially near-infrared (NIR), is becoming more popular for soil analysis. This method requires little sample preparation, no chemicals, and a single spectral analysis to evaluate soil properties. Thus, this research aimed to develop an NIR spectroscopy method for the analysis of OM, TC, and TN in agricultural soils. These findings can provide a good concept of using PLS regression with NIR techniques. The method is as follows:•Topsoil (0-20 cm) samples were collected from various agricultural fields. OM, TC, and TN were analyzed using traditional methods and NIR spectroscopy.•NIR spectra were obtained using an FT-NIR spectrometer, original spectral including with Savitzky-Golay smoothing, standard normal variate (SNV) and multiplicative scatter correction (MSC) preprocessing method were used to create a predicted model through Partial Least Squares (PLS) regression with 65 % calibration, and the rest 35 % for validation.•The results showed significant relationships between measured soil properties (SOM and TC) and NIR absorbance spectra in agricultural soil (R 2 of calibration and validation higher than 0.80).

Study of humic-like substances of dissolved organic matter using size exclusion chromatography and chemometrics.

The study of dissolved organic matter (DOM) presents a significant challenge for environmental analyses and the monitoring of wastewater treatment plants (WWTPs). This is particularly true for the tracking of recalcitrant to biodegradation dissolved organic matter (rDOM) compounds, which is generated during the thermal pretreatment of sludge. This study aims to develop analytical and chemometric methods to differentiate melanoidins from humic acids (HAs), two components of rDOM that require monitoring at various stages of wastewater treatment processes due to their distinct biological effects. The developed method implements the separation of macromolecules through ultra-high-performance liquid chromatography size-exclusion chromatography (U-HPLC SEC) followed by online UV and fluorescence detection. UV detection was performed at 210, 254, and 280 nm, and fluorescence detection at six excitation/emission pairs: 230/355 nm, 270/355 nm, 240/440 nm, 270/500 nm, 330/425 nm, and 390/500 nm. Chromatograms obtained for each sample from these nine detection modes were integrated and separated into four molecular fractions: >40 kDa, 20-40 kDa, 10-20 kDa, and <10 kDa. To enhance analytical resolution and normalize the data, ratios were calculated from the areas of chromatographic peaks obtained for each detection mode. The results demonstrate the utility of these ratios in discriminating samples composed of HAs, melanoidins, and their mixtures, through principal component analysis (PCA). Low molecular weight fractions were found to be specific to melanoidins, while high molecular weight fractions were characteristic of HAs. For the detection modes specific to melanoidins, UV absorbance at 210, 254, and 280 nm were predominantly present in the numerators, with tryptophan-like fluorescence emissions in the denominators. Conversely, fluorescence emissions largely represented both numerators and denominators for HAs. This online method also enables the discrimination of pseudo-melanoidins, compounds revealing a nitrogen deficiency in their chemical structures.

Structure-based chemical ontology improves chemometric prediction of antibacterial essential oils.

Plants are valuable resources for drug discovery as they produce diverse bioactive compounds. However, the chemical diversity makes it difficult to predict the biological activity of plant extracts via conventional chemometric methods. In this research, we propose a new computational model that integrates chemical composition data with structure-based chemical ontology. For a model validation, two training datasets were prepared from literature on antibacterial essential oils to classify active/inactive oils. Random forest classifiers constructed from the data showed improved prediction performance in both test datasets. Prior feature selection using hierarchical information criterion further improved the performance. Furthermore, an antibacterial assay using a standard strain of Staphylococcus aureus revealed that the classifier correctly predicted the activity of commercially available oils with an accuracy of 83% (= 10/12). The results of this study indicate that machine learning of chemical composition data integrated with chemical ontology can be a highly efficient approach for exploring bioactive plant extracts.

Discrimination and analytical profiling of colored printed documents using ATR-FTIR spectroscopy coupled with explorative and predictive statistical analysis: Part I.

Printed documents are a common form of evidence in forensic document examination. The integration of spectroscopy with chemometrics have evolved evidential analytical interpretation of printing inks. However, we report the first ever study that explores the examination of both black and colored printed documents combined with explorative Principal Component Analysis (PCA) and supervised techniques viz. Soft independent modelling of class analogy (SIMCA) and Partial Least Square- Discriminant Analysis (PLS-DA). The study investigated 74 (40 Ink-based and 34 Toner- based) colored printed document samples using ATR-FTIR to discriminate and determine the source of origin of an unknown printed document using a non-destructive approach. Qualitative analysis by ATR- FTIR indicated the presence of polystyrene, bisphenol A and acrylates as the common binder polymers in the samples. The study was also able to obtain pigment information like presence of PR 57 and PR 146 in magenta, Carbon black in black, Copper Phthalocyanine and PB 15 in Cyan and PY 74 in yellow colored printed samples. Further, PCA has been used as an explorative technique that showed a variance of 97 % in the dataset and indicating that the color Cyan contributes to the maximum classification accuracy. SIMCA has been used as a supervised method to classify the known and test samples to their respective defined classes. However, SIMCA could only classify Toner-based samples in their respective class and inconclusive results were obtained in case of Ink-based samples. Finally, PLS-DA was also used to classify the two class of samples which resulted in a discrimination accuracy of 98.6 %. The derived model was also used for validation study on blind test samples which provided 100 % classification results.

Analysis of spatiotemporal changes mechanism of cell wall biopolymers and monosaccharide components in kiwifruit during Botryosphaeria dothidea infection.

To further reveal the interaction mechanism between plants and pathogens, this study used confocal Raman microscopy spectroscopy (CRM) combined with chemometrics to visualize the biopolymers distribution of kiwifruit cell walls at different infection stages at the cellular micro level. Simultaneously, the changes in the content of various monosaccharides in fruit were studied at the molecular level using high-performance liquid chromatography (HPLC). There were significant differences in the composition of various nutrient components in the cell wall structure of kiwifruit at different infection times after infection by Botryosphaeria dothidea. PCA could cluster samples with infection time of 0-9 d into different infection stages, and SVM was used to predict the PCA classification results, the accuracy >96 %. Multivariate curve resolution-alternating least squares (MCR-ALS) helped to identify single substance spectra and concentration signals from mixed spectral signals. The pure substance chemical imaging maps of low methylated pectin (LMP), high methylated pectin (HMP), cellulose, hemicellulose, and lignin were obtained by analyzing the resolved concentration data. The imaging results showed that the lignin content in the kiwifruit cell wall increased significantly to resist pathogens infection after the infection of B. dothidea. With the development of infection, B. dothidea decomposed various substances in the host cell walls, allowing them to penetrate the interior of fruit cells. This caused significant changes in the form, structure, and distribution of various chemicals on the fruit cell walls in time and space. HPLC showed that glucose was the main carbon source and energy substance obtained by pathogens from kiwifruit during infection. The contents of galactose and arabinose, which maintained the structure and function of the fruit cell walls, decreased significantly and the cell wall structure was destroyed in the late stage of pathogens infection. This study provided a new perspective on the cellular structure changes caused by pathogenic infection of fruit and the defense response process of fruit and provided effective references for further research on the mechanisms of host-pathogen interactions in fruit infected by pathogens.

Elemental Profiling for the Detection of Food Mixtures: A Proof of Principle Study on the Detection of Mixed Walnut Origins Using Measured and Calculated Data.

Element profiling is a powerful tool for detecting fraud related to claims of geographical origin. However, these methods must be continuously developed, as mixtures of different origins in particular offer great potential for adulteration. This study is a proof of principle to determine whether elemental profiling is suitable for detecting mixtures of the same food but from different origins and whether calculated data from walnut mixtures could help to reduce the measurement burden. The calculated data used in this study were generated based on measurements of authentic, unadulterated samples. Five different classification models and three regression models were applied in five different evaluation approaches to detect adulteration or even distinguish between adulteration levels (10% to 90%). To validate the method, 270 mixtures of walnuts from different origins were analyzed using inductively coupled plasma mass spectrometry (ICP-MS). Depending on the evaluation approach, different characteristics were observed in mixtures when comparing the calculated and measured data. Based on the measured data, it was possible to detect admixtures with an accuracy of 100%, even at low levels of adulteration (20%), depending on the country. However, calculated data can only contribute to the detection of adulterated walnut samples in exceptional cases.

Characterization of flavor volatiles in raw and cooked pigmented onion (Allium cepa L) bulbs: A comparative HS-GC-IMS fingerprinting study.

Variations in volatile flavor components in pigmented onion bulbs (purple, white, and yellow) before and after cooking were characterized by headspace gas chromatography-ion migration spectrometry (HS-GC-IMS) to investigate their odor traits. Results showed that 39 and 45 volatile flavor compounds were identified from pigmented onion bulbs before and after cooking via the HS-GC-IMS fingerprinting, respectively. Sulfurs (accounting for 50.65%-63.42%), aldehydes (13.36%-22.11%), and alcohols (11.32%-17.94%) ranked the top three prevailing compound categories in all pigmented onions (both raw and cooked). Compared to the raw colored onion bulbs, the relative proportion of sulfurs in cooked onions decreased, whereas the relative proportion of alcohols, esters, pyrazines, and furans increased. Two reliable prediction models were established through orthogonal partial least squares-discriminant analysis (OPLS-DA), and 8 and 22 distinctive odor compounds were sieved out by variable importance in projection (VIP>1.0) as volatile labels, respectively. Both principal component analysis (PCA) and clustering heatmap exhibited favorable distinguishing effects for various pigmented onion bulbs before and after cooking. These results might offer insights into understanding the odor characteristics of different pigmented onions.

Discrimination of bottled mineral water from tap water using a Dip-Type colorimetric paper-based sensor array and chemometrics.

Mineral water is a natural water that originated from an underground water table, a well, or a natural spring which is considered microbiologically intact. The revenue from the bottled mineral water industry will be USD 342.40 billion in 2023, and it is expected to grow at a compound annual growth rate (CAGR) of 5.24 %. Consequently, the discrimination of original bottled mineral water from tap water is an important issue that requires designing sensors for simple and portable identification of these two types of water. In this work, we have developed a Dip-Type colorimetric paper-based sensor array with three organic dyes (Bromothymol Blue, Bromophenol Blue, and Methyl Red) followed by chemometrics' pattern recognition methods (PCA and LDA) for discrimination of original bottled mineral waters from tap waters based on differences in ion variety and ion quantity. Forty brands of mineral water and twenty-six Tap water samples from different regions of Shiraz and other Iranian cities were analyzed by this sensor array. Moreover, these experiments were performed in two consecutive years to check the versatility of the sensor with seasonal changes in waters. This sensor array was able to discriminate these two water types from each other with an accuracy of > 95 % based on the analysis of 85 water samples.

Unveiling the Contamination Patterns of Neonicotinoid Insecticides: Detection, Distribution, and Risk Assessment in Panax notoginseng across Plant Parts.

This study analyzed neonicotinoid insecticides (NEOs) and metabolite (m-NEOs) residues in 136 Panax notoginseng samples via ultra-performance liquid chromatography-tandem mass spectrometry. Imidacloprid was the most detected NEO (88.24% of samples), ranging from 1.50 to 2850 µg/kg. To the best of our knowledge, some novel NEOs were detected in P. notoginseng for the first time. NEO clustering patterns varied among plant parts, with higher contamination in leaves and flowers. Fourteen NEO/m-NEOs, including cycloxaprid and acetamiprid, showed site-specific behavior, indicating the possibility of using multiple NEOs simultaneously during planting, resulting in formation of distinct metabolites in different plant parts. Transfer rates in decoction and infusion ranged from 10.06 to 32.33%, reducing residues postprocessing. Dietary risk assessment showed low hazard quotients (HQa: 7.05 × 10-7 to 2.09 × 10-2; HQc: 3.74 × 10-7 to 2.38 × 10-3), but risk-ranking scores indicated potential hazards with imidacloprid and acetamiprid in flowers and leaves. The findings are expected to promote safety assessment and distribution research of NEOs in plants.

Chemical analysis of different parts from agarwood columns by artificially agarwood-inducing method based on GC-MS and UPLC-TOF-MS.

Agarwood is resin-containing wood produced by plants that have been injured. It is widely used in herbal medicine, incense, decorative items, and so on. In this study, we conducted resin area statistical analysis, determined starch particle and reducing sugar contents, and performed multivariate statistical analysis of chemical composition by GC-MS and UPLC-Q-TOF-MS to explore the different components in sections cut from an agarwood column, designated as A1-A4. The results showed that after stimulation by Agar-Bit inducer, the internal phloem parenchyma cells of the column started to form agarwood, and then starch granules were converted into soluble reducing sugars and agarwood resin. Section A1 showed rapid loss of starch granules, resulting in higher contents of reducing sugars and resin. The resin areas of agarwood in the respective sections were different, gradually decreasing on going from A1 to A4. Total numbers of metabolites of 87 and 63 were identified by GC-MS and UPLC-Q-TOF-MS, respectively. Of these, 10 and 16 metabolites with significant differences (variable importance projection >1) were selected through multivariate statistical analysis. These metabolites included chromones, sesquiterpenes, alkanes, and fatty acids. Among them, 6-methoxy-2-(2-phenylethyl)chromone and 6,7-dimethoxy-2-(2-phenylethyl)chromone were significant markers detected by both GC-MS and UPLC-Q-TOF-MS, which may be essential substances responsible for differences in the agarwood-forming capacities of the cut sections. In conclusion, there has been limited research on the different agarwood-forming capacities of agarwood columns. Here, we explored the differences in various sections of agarwood through chemical analysis to provide a more comprehensive and in-depth understanding of its constitution.

Blend uniformity monitoring in a continuous manufacturing mixing process for a low-dosage formulation using a stream sampler and near infrared spectroscopy.

Continuous manufacturing has the potential to offer several benefits for the production of oral solid dosage forms, including reduced costs, low-scale equipment, and the application of process analytical technology (PAT) for real-time process control. This study focuses on the implementation of a stream sampler to develop a near infrared (NIR) calibration model for blend uniformity monitoring in a continuous manufacturing mixing process. Feeding and mixing characterizations were performed for three loss-in-weight feeders and a commercial continuous mixer to prepare powder blends of 2.5-7.5 % w/w ibuprofen DC 85 W with a total throughput of 33 kg/h. The NIR spectral acquisition was performed after the mixing stage using a stream sampler for flowing powders. A continuous mixer shaft speed of 250 RPM was selected to operate the mixing process based on a variability analysis developed with in-line spectral data acquired using the stream sampler at 6 RPM. A partial least squares regression (PLS-R) model was performed and evaluated, yielding a root-mean-square error of prediction (RMSEP) of 0.39 % w/w and a bias of 0.05 % w/w. An independent experimental run conducted two days later revealed that the continuous mixing process and the NIR calibration model presented low day-to-day variation. The minimum practical error (MPE) and sill values through variographic analysis showed low variance associated with the sampling process using the stream sampler. Results demonstrated the promising capacity of the stream sampler coupled to an NIR probe to be implemented within continuous manufacturing processes for the real-time determination of API concentration.

Non-invasive exploration of malting barley (Hordeum vulgare) in vitro germination and varietal effects using short wave infrared spectral imaging and ANOVA simultaneous component analysis.

ANOVA-simultaneous component analysis (ASCA) was applied to short-wave infrared spectral fingerprints of 5 malting barley varieties collected using a hyperspectral imaging system to determine the effect of germination, the influence of time and the influence of barley by means of a full factorial experimental design. ASCA indicated that there was a significant (p < 0.0001) effect of the germination status, the germination time and interaction on the spectral data for all varieties. The biochemical and physiological modification of the samples were characterised by visualisation of the longitudinal scores obtained from simultaneous component analysis for the germination time factor. This resulted in the visualisation and explanation of biochemical change over the course of barley germination as a factor of time. The relevant loadings indicated a significant change to the proteome, lipid and starch structure as driven by the uptake of water over time. The ASCA model were extrapolated to include the effect of barley variety to the already mentioned germination status and germination time factors, resulting once again in all the effects being significant (p < 0.0001). Here it was shown that all the barley varieties are significantly different from one another pre- and post-modification, based on the molecular vibrations observed in the short wave-infrared (SWIR) spectra, suggesting that the detection of biotic stress factors, such as pre-harvest germination, also differ for each variety, by indicating that the germination profile of each barley variety varies as a function of germination time. Thus, also the malting performance, germinative energy and chemical profile of each barley variety tested will vary before, during and after imbibition and germination - indicating the importance of malting commercial barley malt true to variety. These results indicate that (SWIR) spectral imaging instrumentation can possibly be used to monitor controlled germination of barley grain. Due to the shown ability of SWIR spectral imaging to detect small biochemical changes over time of barley grain during germination.