Using color histograms and SPA-LDA to classify bacteria

Pesticides in surface water of the Ondas river watershed, western Bahia, Brazil: Spatial-seasonal distribution and risk assessment

This study investigated the occurrence and seasonal distribution of different classes of pesticides in surface waters of the Ondas River Watershed, as well as potential risks to the aquatic health and human water consumption in the western region of Bahia state, Brazil. Two gas chromatography-mass spectrometry analytical methods were applied to monitor 34 pesticides in water samples collected during both the dry and rainy seasons at 17 sites. Upon individual analysis, only γ-HCH, methoxychlor, demeton-S, methyl parathion, fenitrothion, chlorpyrifos, and azoxystrobin exhibited statistically significant differences between seasons. During rainy season, concentration medians of residues were higher for γ-HCH (74.7 ng L-1), methoxychlor (25.1 ng L-1), and azoxystrobin (47.2 ng L-1), potentially linked to historical contamination or illegal use. Conversely, pesticides like methyl parathion, fenitrothion, and chlorpyrifos, belonging to the organophosphate class, showed higher concentration medians in the dry period, measuring 75.1, 5.50, and 10.8 ng L-1, respectively, probably due to region crop activities. The risk quotient (RQ) assessment for aquatic life indicated that 59.0% of the samples in the dry season and 76.0% in the rainy season had RQ values greater than one, signifying a critical scenario for species conservation. Regarding human consumption, elevated risks were observed for heptachlor in both sampling periods and for azoxystrobin during the rainy season, surpassing RQ levels above 1, indicating danger in untreated water ingestion. Additionally, 24.0% and 53.0% of the samples in the dry and rainy seasons, respectively, contained at least one pesticide exceeding the EU resolution limit (100 ng L-1). Therefore, considering this information, implementing mitigation measures to avoid the river's contamination becomes imperative.

Current status and future prospects of using advanced computer-based methods to study bacterial colonial morphology

Despite the advancement of recent molecular technologies, culturing is still considered the gold standard for microbial sample analysis. Here we review three different bacterial colony-based screening modalities that provide significant information beyond the simple shape and color of the colony. The plate imaging technique provides numeration and quantitative spectral reflectance information for each colony, while Raman spectroscopic analysis of bacteria colonies relates the Raman-shifted peaks to specific chemical bonding. Finally, the elastic-light-scatter technique provides a volumetric interaction of the whole colony through laser-bacteria interactions, instantly capturing the morphological traits of the colony and allowing quantitative classifications.

Digital image-based classification of biodiesel

This work proposes a simple, rapid, inexpensive, and non-destructive methodology based on digital images and pattern recognition techniques for classification of biodiesel according to oil type (cottonseed, sunflower, corn, or soybean). For this, differing color histograms in RGB (extracted from digital images), HSI, Grayscale channels, and their combinations were used as analytical information, which was then statistically evaluated using Soft Independent Modeling by Class Analogy (SIMCA), Partial Least Squares Discriminant Analysis (PLS-DA), and variable selection using the Successive Projections Algorithm associated with Linear Discriminant Analysis (SPA-LDA). Despite good performances by the SIMCA and PLS-DA classification models, SPA-LDA provided better results (up to 95% for all approaches) in terms of accuracy, sensitivity, and specificity for both the training and test sets. The variables selected Successive Projections Algorithm clearly contained the information necessary for biodiesel type classification. This is important since a product may exhibit different properties, depending on the feedstock used. Such variations directly influence the quality, and consequently the price. Moreover, intrinsic advantages such as quick analysis, requiring no reagents, and a noteworthy reduction (the avoidance of chemical characterization) of waste generation, all contribute towards the primary objective of green chemistry.