Zooplankton are critical indicators of pressures impacting freshwater ecosystems. We analyzed the response of zooplankton communities across different sub-catchment types-headwaters, natural, urban, urban-agricultural, and agricultural-within the Łyna river-lake system in Northern Poland. Using taxonomic groups and functional traits (body size, feeding strategies), we applied Partial Least Squares Regression (PLS-R) to elucidate the relationships between environmental conditions, land use, and zooplankton metacommunity structure. Two-Way Cluster Analysis (TWCA) identified local subsets with characteristic patterns, while Indicator Species Analysis (ISA) determined area-specific taxa. The natural river zone exhibited significant habitat heterogeneity and feeding niches, whereas urban areas created functional homogenization of zooplankton, dominated by small, broad-diet microphages. Agricultural areas promoted diversity among large filter feeders (Crustacea), active suctors (Rotifera), and amoebae (Protozoa). However, intensified agricultural activities, substantially diminished the zooplankton population, biomass, taxonomic richness, and overall ecosystem functionality. The impact of land cover change is more pronounced at small-scale sub-catchments than at the catchment level as a whole. Therefore, assessing these impacts requires detailed spatial and temporal analysis at the sub-catchment level to identify the most affected areas. This study introduces a new sub-catchment-based perspective on ecosystem health assessment and underscores the zooplankton\'s role as robust indicators of ecological change.

Partial least squares (PLS) regression is a valuable chemometric tool for property prediction when coupled with gas chromatography (GC). Since the separation run time and stationary phase selection are crucial for effective PLS modeling, we study these GC parameters on the prediction of viscosity, density and hydrogen content for 50 aerospace fuels. Due to the diversity of compounds in the fuels (primarily alkanes, cycloalkanes, and aromatics), we explore both polar and non-polar stationary phase columns. The robustness for the PLS models was evaluated by their normalized root mean square error of cross-validation (NRMSECV). PLS models built for viscosity across 1-min, 3-min, 7-min, and 10-min time window (TW) high-speed GC separations produced nearly the same NRMSECV with the polar column data with an average (standard deviation) of 4.41 % (0.34 %) versus the non-polar column data of 4.69 % (0.15 %). In contrast, while the NRMSECV of density modeling with the polar column data varied more than the viscosity models, averaging 7.54 % (0.67 %), the non-polar column data produced a significantly higher average NRMSECV of 10.06 % (0.35 %). Similarly, for hydrogen content, the NRMSECV with the polar column data averaged 9.50 % (0.87 %), which was significantly lower than the NRMSECV with the non-polar column data averaging 12.10 % (0.88 %). We also investigated the impact of smoothing the GC data on the corresponding PLS models. By applying varying degrees of smoothing, we can effectively obtain similar chromatographic peak patterns in a shorter TW. For example, a 10-min smoothed chromatogram appears like the 1-min separation with no smoothing but resulted in nearly the same NRMSECV. Overall, the fast separation with a 1-min TW produced robust PLS models for viscosity with either stationary phase column, whereas for density and hydrogen content the polar stationary phase column produced superior PLS models, thus with proper stationary phase selection, a fast separation run time could be readily applied with optimal PLS property modeling results.

In the manufacturing process of electrical devices, ensuring the cleanliness of technical surfaces, such as direct bonded copper substrates, is crucial. An in-line monitoring system for quality checking must provide sufficiently resolved lateral data in a short time. UV hyperspectral imaging is a promising in-line method for rapid, contactless, and large-scale detection of contamination; thus, UV hyperspectral imaging (225-400 nm) was utilized to characterize the cleanliness of direct bonded copper in a non-destructive way. In total, 11 levels of cleanliness were prepared, and a total of 44 samples were measured to develop multivariate models for characterizing and predicting the cleanliness levels. The setup included a pushbroom imager, a deuterium lamp, and a conveyor belt for laterally resolved measurements of copper surfaces. A principal component analysis (PCA) model effectively differentiated among the sample types based on the first two principal components with approximately 100.0% explained variance. A partial least squares regression (PLS-R) model to determine the optimal sonication time showed reliable performance, with R2cv = 0.928 and RMSECV = 0.849. This model was able to predict the cleanliness of each pixel in a testing sample set, exemplifying a step in the manufacturing process of direct bonded copper substrates. Combined with multivariate data modeling, the in-line UV prototype system demonstrates a significant potential for further advancement towards its application in real-world, large-scale processes.

A gold nanogap substrate was used to measure the thiram and carbaryl residues in various fruit juices using surface-enhanced Raman scattering (SERS). The gold nanogap substrates can detect carbaryl and thiram with limits of detection of 0.13 ppb (0.13 μgkg-1) and 0.22 ppb (0.22 μgkg-1). Raw SERS data were first preprocessed to reduce noise and undesirable effects and, were later used for model creation, implementing classification, and regression analysis techniques. The partial least-squares regression models achieved the highest prediction correlation coefficient (R2) of 0.99 and the lowest root mean square of prediction value below 0.62 ppb for both pesticide-infected juice samples. Furthermore, to differentiate between juice samples contaminated by both pesticides and control (pesticide-free), logistic-regression classification models were produced and achieved the highest classification accuracies of 100% and 99% for contaminated juice containing thiram and 100% accurate results for contaminated juice containing carbaryl. This indicates that the gold nanogap surface has significant potential for achieving high sensitivity in detecting trace contaminants in food samples.

BACKGROUND: Pulse ingredients often replace grains in grain-free dog diets owing to their high-protein content. However, research to ascertain the benefit of this modification is limited.
OBJECTIVE: This study aimed to correlate food compounds in 1 corn-inclusive control diet and 3 grain-free diets with increasing inclusions of whole pulses (≤45%; Pulse15, Pulse30, and Pulse45), formulated to meet similar macronutrient and micronutrient targets with postprandial amino acids (AAs) in healthy dogs >20 wk.
METHODS: Diets were analyzed for biochemical compounds using tandem mass spectrometry. Twenty-eight outdoor-housed, healthy, adult Siberian Huskies were allocated to diet, and meal responses were analyzed at baseline and weeks 2, 4, 8, 16, and 20 with samples collected at fasted and 15, 30, 60, 90, 120, and 180 min after meal presentation. Blood AAs were analyzed by ultra performance liquid chromatography and differences across week, treatment, and time postmeal were analyzed in SAS Studio. Partial least squares regression was performed in SAS Studio using biochemical compounds in the diet as predictor variables and blood AAs as response variables.
RESULTS: In plasma, Pulse45 had ∼32% greater postprandial Asn than Pulse15, and the control diet had ∼34% greater postprandial Leu and ∼35% greater Pro than Pulse15 (P < 0.05). In whole blood, Pulse30 had ∼23% greater postprandial Lys than the control diet, and the control diet had ∼21% greater postprandial Met and ∼18% greater Pro than Pulse45 and Pulse30, respectively (P < 0.05). Several phospholipids were correlated with postprandial AAs. Compounds in the urea cycle and glycine and serine metabolism were more enriched (P < 0.05) in plasma and whole blood, respectively.
CONCLUSIONS: In macronutrient-balanced and micronutrient-balanced canine diets that differ in their inclusion of corn-derived compared with pulse-derived ingredients, postprandial changes in circulating AAs are largely indicative of the dietary AAs. This helps further our understanding of AA metabolism in healthy dogs fed grain-free diets.

The color potato has the function of both a food and vegetable. The color potato not only contains various amino acids and trace elements needed by the human body but also contains anthocyanins. Anthocyanins have many functions, such as antioxidation, inflammation inhibition, vision improvement, and cancer prevention, so colored potatoes are deeply loved by consumers and have good market prospects. However, at present, the detection of anthocyanin content in color potatoes mainly depends on chemical methods, which are time-consuming and laborious, so it is necessary to study a fast and accurate detection method. In this study, microscopic hyperspectral equipment was used to collect the spectral information of the outer skin and inner skin of potatoes. The original spectrum, pretreatment spectrum, and characteristic spectrum variables of the outer skin and inner skin were predicted by the convolution neural network (CNN) algorithm and partial least squares regression (PLS) algorithm, respectively, and the performance of the model was evaluated by the prediction set correlation coefficient (Rp), prediction set root mean square error (RMSEP), correction set correlation coefficient (Rc), correction set root mean square error (RMSEC), and residual prediction deviation (RPD). The results revealed that the inner skin Raw + CNN model constructed under raw spectral data is optimal with Rc = 0.9508, RMSEC = 0.0374%, Rp = 0.9461, RMSEP = 0.2361% and RPD = 4.4933. The inner skin Savitzky-Golay (SG) + Detrend (DET) + CNN model constructed from pre-processed spectral data is optimal with Rc = 0.9499, RMSEC = 0.0359%, Rp = 0.9439, RMSEP = 0.2384%, RPD = 4.6516. The inner skin DET + competitive adaptive reweighted sampling (CARS) +CNN model constructed from the feature-based spectral data was optimal with Rc = 0.9527, RMSEC = 0.0708%, Rp = 0.9457, RMSEP = 0.2711%, and RPD = 4.1623. It can be seen that the Rp, RMSEP, Rc, RMSEC, and RPD values for modeling the spectral information of the inner skin were higher than those of the outer skin under the three different spectral data. The prediction accuracy of the model built by the CNN algorithm was better than the conventional algorithm PLS, the application of the CNN algorithm in inner skin can achieve accurate prediction of anthocyanin content in potato.

Principal component analysis (PCA) and partial least squares regression (PLS) were combined in this study to identify key material descriptors determining tabletability in direct compression and roller compaction. An extensive material library including 119 material descriptors and tablet tensile strengths of 44 powders and roller compacted materials with varying drug loads was generated to systematically elucidate the impact of different material descriptors, raw API and filler properties as well as process route on tabletability. A PCA model was created which highlighted correlations between different powder descriptors and respective characterization methods and, thus, can enable reduction of analyses to save resources to a certain extent. Subsequently, PLS models were established to identify key material attributes for tabletability such as density and particle size but also surface energy, work of cohesion and wall friction, which were for the first time demonstrated by PLS as highly relevant for tabletability in roller compaction and direct compression. Further, PLS based on extensive material characterization enabled the prediction of tabletability of materials unknown to the model. Thus, this study highlighted how PCA and PLS are useful tools to elucidate the correlations between powder and tabletability, which will enable more robust prediction of manufacturability in formulation development.

Virus-like particles (VLPs) are a promising class of biopharmaceuticals for vaccines and targeted delivery. Starting from clarified lysate, VLPs are typically captured by selective precipitation. While VLP precipitation is induced by step-wise or continuous precipitant addition, current monitoring approaches do not support the direct product quantification, and analytical methods usually require various, time-consuming processing and sample preparation steps. Here, the application of Raman spectroscopy combined with chemometric methods may allow the simultaneous quantification of the precipitated VLPs and precipitant owing to its demonstrated advantages in analyzing crude, complex mixtures. In this study, we present a Raman spectroscopy-based Process Analytical Technology (PAT) tool developed on batch and fed-batch precipitation experiments of Hepatitis B core Antigen VLPs. We conducted small-scale precipitation experiments providing a diversified data set with varying precipitation dynamics and backgrounds induced by initial dilution or spiking of clarified Escherichia coli-derived lysates. For the Raman spectroscopy data, various preprocessing operations were systematically combined allowing the identification of a preprocessing pipeline, which proved to effectively eliminate initial lysate composition variations as well as most interferences attributed to precipitates and the precipitant present in solution. The calibrated partial least squares models seamlessly predicted the precipitant concentration with R 2 of 0.98 and 0.97 in batch and fed-batch experiments, respectively, and captured the observed precipitation trends with R 2 of 0.74 and 0.64. Although the resolution of fine differences between experiments was limited due to the observed non-linear relationship between spectral data and the VLP concentration, this study provides a foundation for employing Raman spectroscopy as a PAT sensor for monitoring VLP precipitation processes with the potential to extend its applicability to other phase-behavior dependent processes or molecules.

So far, compliance with ISO 3632 standard specifications for top-quality saffron guarantees good agricultural and post-harvest production practices. Tracking early-stage oxidation remains challenging. Our study aims to address this issue by exploring the visible, fluorescence, and near-infrared spectra of category I saffron. Using a multi-spectral sensor, we tested fresh and artificially aged saffron in powder form. High autofluorescence intensities at 600-700 nm allowed calibration for the \'content of aged saffron\'. Samples with minimum coloring strength (200-220 units) were classified as 70% aged, while those exceeding maximum aroma strength (50 units) as 100% aged. Consistent patterns across origin, age, and processing history indicated potential for objectively assessing early-oxidation markers. Further analyses uncovered multiple contributing fluorophores, including cis-apocarotenoids, correlated with FTIR-based aging markers. Our findings underscore that sensing autofluorescence of traded saffron presents an innovative quality diagnostic approach, paving new research pathways for assessing the remaining shelf-life along its supply chain.

Understanding the evolution of aroma profiles in stored sesame paste (SP) is essential for maintaining its quality. This study investigated the storage quality of SP and potential aroma markers indicative of sensory degradation. The descriptive sensory analysis demonstrated changes in aroma attributes during storage, transitioning from roasted sesame and nutty aromas to fermented and green aromas. Physicochemical analysis showed deepening color, intensified lipid oxidation, decreased levels of bioactive components, increased particle aggregation, and deteriorated flowability over 63 days at 40 °C. Gas chromatography-olfactometry-mass spectrometry identified 37 aroma-active compounds, with pyrazines, aldehydes, and phenols identified as the major constituents. Partial least squares regression analysis revealed 2-ethyl-3-methyl-pyrazine, 2-methoxy-4-vinylphenol, and benzaldehyde as key aroma-active compounds contributing significantly to the distinctive aromas \"roasted nut and roasted sesame\" found in SP. Conversely, hexanal and dimethyl disulfide emerged as potential markers of undesirable aromas in SP, including \"rancid, green, and fermented\". These findings provide insights into SP changes during storage, which is vital for preservation and quality enhancement strategies.