Non-contaminated Samples Research Articles

Aflatoxin contamination level estimation in food using reflectance multispectral imaging based system

This study presents a non-invasive, non-destructive, and rapid method of classifying and estimating aflatoxin levels in food samples using a multispectral imaging. A cost effective, mass deployable in-house built multispectral imaging system, suitable for onsite testing and operating in range from 365 nm to 940 nm, is utilized to capture the multispectral images of the aflatoxin contaminated bread samples. Spatial and spectral corrections are performed to mitigate the imperfections of the multispectral imaging system, followed by image preprocessing steps to mitigate the effect of random noise. Linear discriminant analysis is used to reduce the dimensionality of the feature space. Ensemble classification analysis utilizing classical machine learning classifiers yielded an accuracy of 90 % when classifying samples according to aflatoxin contamination levels. A functional relationship between the Bhattacharyya distance of noncontaminated samples to contaminated samples and aflatoxin contamination levels was modeled as a first order polynomial with an R2 value of 0.9723.

Microbiological screening of corneas stored in organ culture medium at Lions Eye Bank of Western Australia from 2011 to 2022.

The purpose of this study was to analyse the contamination rate of corneal samples stored in OCM at Lions Eye Bank of Western Australia over a 12-year period. All OCM samples used to preserve corneas from 2011 to 2022 (inclusive) underwent microbiological testing. Samples were collected into aerobic and anaerobic culture bottles on day 3-5 of corneal preservation and 24h after transfer to thinning medium. Samples were tested for 7days using the BACTEC FX system. Corneas remained in quarantine until clearance was obtained. From 2011 to 2022, 3009 corneas were retrieved and 2756 corneas were stored in OCM. Thirty one (1.1%) positive samples were reported, with 20 growths of bacterial origin and 11 fungal. Microbial contamination was mostly identified on day 1 of culture (77.5%). Donors of contaminated samples had a mean age of 55years, with 17 male and 14 female donors. The highest incidence of contamination came from donors whose cause of death was cancer. Death to enucleation times of contaminated samples ranged from 3.5 to 25.5h (mean = 13.5 ± 7.3) and death to preservation time ranged from 4.1 to 27.5h (mean = 14.8 ± 7.2). These did not significantly differ from the average time from death to enucleation (mean = 13.9 ± 3) and death to preservation (mean = 16.3 ± 4.2) of non-contaminated samples. Microbiological screening of corneas stored in OCM at LEBWA showed a very low rate of positive cultures with no predictive donor characteristics.

Evaluation of point-of-care tests for identification of pathogens to inform clinical mastitis treatment decisions in pasture- and confinement-managed dairy cows in Australia

To support antimicrobial stewardship in livestock production, there is a growing array of point of care diagnostics to guide antimicrobial treatment. The primary objective of this observational study was to evaluate the diagnostic performance of 5 point of care tests currently available in Australia for guiding lactational treatment of non-severe clinical mastitis. A secondary objective was to describe the pathogen profiles of mastitis-causing organisms in cows managed in barns ("intensive") and on pasture ("non-intensive"). Foremilk samples (n = 641) were collected by farm staff in dairy herds in Australia (n = 30) and tested at a university laboratory using a reference test and 5 index tests. The reference test was aerobic culture on Trypticase Soy Agar with 5% sheep blood followed by MALDI-TOF for identification of isolates. The following point of care tests were evaluated as index tests: Accumast®, biplate, Check-Up, Mastatest®, and 3M Petrifilm. We found that 23% of samples were contaminated, with the median herd contamination prevalence being 22%. After excluding contaminated samples, the most common diagnoses (according to the reference test) in intensive herds were no growth (31.7%), Klebsiella spp. (28.1%), E. coli (15.0%), and Strep. uberis (8.4%). The most common diagnoses in non-contaminated samples from cows in non-intensive herds were Strep. uberis (35.0%), no growth (26.9%), and E. coli (13.3%). After 24 h of incubation, all index tests demonstrated limited diagnostic sensitivity for identification of pathogens of interest (range: 0.06 to 0.63). Diagnostic performance was better at the group-level, with sensitivity and specificity for identification of non-contaminated gram-positive growths (i.e., cases that are widely considered to be candidates for antimicrobial treatment) being 0.84 and 0.75 (biplate), 0.76 and 0.90 (Accumast), 0.89 and 0.79 (Check-Up), 0.67 and 0.83 (Petrifilm), and 0.55 and 0.81 (Mastatest). In intensive herds, 22.7 to 40% of cases were classified as antimicrobial treatment candidates by index tests, which was less than for cows in non-intensive herds (41.3 to 61.0%). Despite limited diagnostic reliability at genus and species level, and the need to ensure samples are collected aseptically, our findings indicate that implementation of selective treatment protocols using the tests evaluated in this study would likely reduce antimicrobial usage in Australian herds.

New solutions to old problems: A practical approach to identify samples with intravenous fluid contamination in clinical laboratories

ObjectivesContamination with intravenous (IV) fluids is a common cause of specimen rejection or erroneous results in hospitalized patients. Identification of contaminated samples can be difficult. Common measures such as failed delta checks may not be adequately sensitive nor specific. This study aimed to determine detection criteria using commonly ordered tests to identify IV fluid contamination and validate the use of these criteria. MethodsConfirmed contaminated and non-contaminated samples were used to identify patterns in laboratory results to develop criteria to detect IV fluid contamination. The proposed criteria were implemented at a tertiary care hospital laboratory to assess performance prospectively for 6 months, and applied to retrospective chemistry results from 3 hospitals and 1 community lab to determine feasibility and flagging rates. The algorithm was also tested at an external institution for transferability. ResultsThe proposed algorithm had a positive predictive value of 92 %, negative predictive value of 91 % and overall agreement of 92 % when two or more criteria are met (n = 214). The flagging rates were 0.03 % to 0.07 % for hospital and 0.003 % for community laboratories. ConclusionsThe proposed algorithm identified true contamination with low false flagging rates in tertiary care urban hospital laboratories. Retrospective and prospective analysis suggest the algorithm is suitable for implementation in clinical laboratories to identify samples with possible IV fluid contamination for further investigation.

Aflatoxins detection in almonds via fluorescence imaging and deep neural network approach

The detection of aflatoxin B in raw food materials represents a topic of great interest worldwide because of the huge health and economic impact of aflatoxin contamination. In this paper, we present an original approach to aflatoxin detection, using a portable instrument to acquire fluorescence images, among other spectral responses. The acquired images are processed by combining a color space conversion from the RGB scale to Y′CbCr, and a neural network approach to encode a vector of features. After a feature reduction using a Receiving Operating Curve method, two-class and three-class classification tasks of contaminated vs non-contaminated samples are accomplished. This procedure has been applied to artificially contaminated grained almond samples in the range of 0–320.2 ng/g, achieving an overall classification accuracy between 84.7% and 93.0%, depending on the number of classes. Thus, in this setting, we show that good classification performance can be achieved using only an image acquisition and analysis approach. The proposed procedure can represent a cheap, rapid, non-destructive yet sensitive method for the assessment of aflatoxin B contamination in food matrices, and its monitoring and tracing throughout the food chain.

Scaling-up RADseq methods for large datasets of non-invasive samples: Lessons for library construction and data preprocessing.

Genetic non-invasive sampling (gNIS) is a critical tool for population genetics studies, supporting conservation efforts while imposing minimal impacts on wildlife. However, gNIS often presents variable levels of DNA degradation and non-endogenous contamination, which can incur considerable processing costs. Furthermore, the use of restriction-site-associated DNA sequencing methods (RADseq) for assessing thousands of genetic markers introduces the challenge of obtaining large sets of shared loci with similar coverage across multiple individuals. Here, we present an approach to handling large-scale gNIS-based datasets using data from the spotted hyena population inhabiting the Ngorongoro Crater in Tanzania. We generated 3RADseq data for more than a thousand individuals, mostly from faecal mucus samples collected non-invasively and varying in DNA degradation and contamination level. Using small-scale sequencing, we screened samples for endogenous DNA content, removed highly contaminated samples, confirmed overlap fragment length between libraries, and balanced individual representation in a sequencing pool. We evaluated the impact of (1) DNA degradation and contamination of non-invasive samples, (2) PCR duplicates and (3) different SNP filters on genotype accuracy based on Mendelian error estimated for parent-offspring trio datasets. Our results showed that when balanced for sequencing depth, contaminated samples presented similar genotype error rates to those of non-contaminated samples. We also showed that PCR duplicates and different SNP filters impact genotype accuracy. In summary, we showed the potential of using gNIS for large-scale genetic monitoring based on SNPs and demonstrated how to improve control over library preparation by using a weighted re-pooling strategy that considers the endogenous DNA content.

A Study on Prevalence, Profile, and Risk Factors of Developing Fungal Infection in Patients With Diabetic Foot Ulcer

Background: Many chronic nonhealing diabetic foot ulcers (DFUs) with increased rates of amputation are frequently associated with fungal infections. Purpose: To evaluate the prevalence, profile, and risk factors of developing a fungal infection in patients with DFU. Methods: This prospective observational study was carried out from October 2018 to July 2020. All adult patients with DFUs admitted to the surgery ward were recruited. Patients on antifungal therapy or who received such therapy within 6 weeks prior to admission were excluded. Three deep tissue samples were sent for bacterial culture, fungal culture, and histopathological examination of fungal elements. Results: A total of 251 patients were enrolled in the study. Of the 23.3% of patients with positive fungal growth (n = 47/202), 2% (n = 4/202) had pure fungal growth and 21.3% (n = 43/202) had mixed growth with bacteria in their ulcers (ie, non-contaminated samples). A significant association was found between wound grade (P = .027), ulcer duration (P = .028), and positive fungal growth in DFUs. Conclusions: In this study, the prevalence of fungal infection in DFUs was 23.3%; Candida tropicalis (27.08%) was the most common isolate, followed by C. albicans (20.83%). The rate of fungal infections was high in patients with mild diabetic foot infection or DFU of 7 to 14 days’ duration.

Evaluating new simplified assays for harm reduction from methanol poisoning using chromotropic acid kits: An analytical study on Indian and Iranian alcoholic beverages.

Ingestion of methanol can result in severe irreversible morbidity, and death. Simple and easy methods to detect methanol and other hazardous additives prior to consumption can prevent fatalities. This form of harm reduction is analogous to the widely practiced "pill testing" of recreational drugs in various countries. We aimed to evaluate the performance of two qualitative and quantitative kits to simultaneously identify the presence of methanol and formaldehyde in alcoholic beverages, and compare this to the standard gas chromatographic (GC) method. Two-hundred samples of Indian and Iranian alcoholic drinks were examined by two new qualitative and quantitative chemical kits designed based on a modified chromotropic acid (CA) method, as well as a gold standard GC method. Methanol levels were similar when evaluated by GC and quantitative method (Z = - 0.328, p = 0.743). The 75th percentile of methanol level detection was 4,290 mg L-1 (range; 0-83,132) using GC compared to that of 4,671 mg L-1 (range; 0-84,960) using the qualitative kit (predefined color intensity reflecting the methanol/ethanol ratio). The quantitative kit was able to detect all methanol-contaminated and non-contaminated samples (110 and 60 cases, respectively: 100% sensitivity). In 25 samples, GC analysis showed no methanol; but the qualitative kit detected possible toxic substances. Formaldehyde measurement by UV/Vis analysis showed the presence of formaldehyde in 23 samples (92%) with a median 912 [IQR 249, 2,109; range 112-2,742] mg L-1. Methanol and formaldehyde can be easily detected using these simple CA chemical kits. Qualitative positive results may indicate the risk of poisoning if the beverage is consumed. CA kits can be used in community setting by public health units and community organizations to monitor for methanol contamination and inform a public health response to reduce methanol-related harms to the public.

Microbial diversity and geochemistry of groundwater impacted by steel slag leachates

To understand long-term impacts of steel slag material on aquifer geochemistry and microbial communities, we conducted four sampling campaigns in the Gier alluvial groundwater (Loire, France). In its northern part, the aquifer flows under a 200,000 m3 steel slag exhibiting high levels of chromium and molybdenum. Geochemical analyses of the water table revealed the existence of water masses with different chemical signatures. They allowed us to identify an area particularly contaminated by leachates from the slag heap, whatever the sampling period. Water samples from this area were compared to non-contaminated samples, with geochemical characteristics similar to the river samples. To follow changes in microbial communities, the V3-V4 region of 16 s rRNA gene was sequenced. Overall, we observed lower diversity indices in contaminated areas, with higher relative abundances of Verrucomicrobiota and Myxococcota phyla, while several Proteobacteria orders exhibited lower relative abundances. In particular, one single genus among the Verrucomicrobiota, Candidatus Omnitrophus, represented up to 36 % of total taxon abundance in areas affected by steel slag leachates. A large proportion of taxa identified in groundwater were also detected in the upstream river, indicating strong river-groundwater interactions. Our findings pave the way for future research work on C. Omnitrophus remediation capacities.

Smartphone-based magneto-immunosensor on carbon black modified screen-printed electrodes for point-of-need detection of aflatoxin B1 in cereals

Considering the complexities and speed of modern food chains, there is an increasing demand for point-of-need detection of food contaminants, particularly highly regulated chemicals and carcinogens such as aflatoxin B1. We report a user-friendly smartphone-based magneto-immunosensor on carbon black modified electrodes for point-of-need detection of aflatoxin B1 in cereals. For buffered analyte solutions and a corn extract sample, the assay demonstrated a low limit of detection of 13 and 24 pg/mL, respectively. The assay was also highly reproducible, exhibiting mean relative standard deviations of 3.7% and 4.0% for the buffered analyte and corn extract samples. The applicability of the assay was validated on the basis of EU guidelines and the detection capability was lower than or equal to 2 μg/kg, which is the EU maximum residue limit for aflatoxin B1 in cereals. False-positive and false-negative rates were less than 5%. Additionally, an open-source android application, AflaESense, was designed to provide a simple interface that displays the result in a traffic-light-type format, thus minimizing user training and time for data analysis. AflaESense was used for smartphone-based screening of spiked corn samples containing aflatoxin B1 (0.1, 2, and 10 ng/mL), and naturally contaminated corn containing 0.15 ng aflatoxin B1/mL. The measured values were in close agreement with spiked concentrations (r2 = 0.99), with recovery values ranging between 80 and 120%. Finally, contaminated samples correctly triggered a red alert while the non-contaminated samples led to the display of a green color of AflaESense. To the best of our knowledge, this is the first smartphone-based electrochemical system effective for screening samples for contamination with aflatoxin B1.

Ion mobility spectrometry as an emerging tool for characterization of the volatile profile and identification of microbial growth in pomegranate juice

Headspace - gas chromatography (HS-GC) coupled to ion mobility spectrometry (IMS) is proposed as an alternative to plate counting to detect and quantify the microbial contamination in pomegranate juice. Thus, contaminated samples by the yeast Saccharomyces cerevisiae were monitored during 6 non-consecutive days over two weeks using two types of preservatives (a sorbate/benzoate mixture and a natural preservative from vegetable extracts). IMS is an emerging technique with high potential for volatile organic compounds (VOCs) monitoring because its high sensitivity and separation power of ions. The fingerprint of the samples allowed to establish difference in the volatile composition of contaminated and non-contaminated samples. Ethyl acetate, ethyl butyrate and limonene were characterized obtaining limits of detection and quantification below 0.029 and 0.097 µg g−1, respectively. Furthermore, chemometric models were performed to detect contaminated pomegranate juice and to assess the concentration of yeast, obtaining a validation success of 100 and 90.91%, respectively.

Evaluation of the Quick Wee method of inducing faster clean catch urine collection in pre-continent infants: a randomized controlled trial

IntroductionThe acquisition of non-contaminated urine samples in pre-continent infants remains a challenge. The Quick Wee method uses bladder stimulation to induce voiding. A previous randomized trial showed a higher rate of voiding within 5 minutes using this method. We evaluated this method in an Irish hospital providing secondary care.MethodsA non-blinded, randomized, controlled trial was carried out. Eligible infants were between 1 and 12 months of age, who required urine sampling as part of clinical care. Participants were randomly allocated to receive the intervention (Quick Wee Method—supra-pubic stimulation with cold saline) or the control (usual care—clean catch with no bladder stimulation) for 5 min. Primary outcome was voiding of urine within 5 min.ResultsA total of 140 infants were included in this study (73 in intervention group; 67 in control group). Baseline characteristics were similar. 25% in the intervention group passed urine in the 5-min trial period compared with 18% in the control group [P = 0.4, absolute difference 7% (95% confidence interval: − 7% to + 20%)].ConclusionThe Quick Wee method is a simple and inexpensive intervention that did not show a statistically significant increase in urine samples obtained in pre-continent infants.

Candida Spp. in Lower Respiratory Tract Secretions - A Ten Years Retrospective Study.

IntroductionLower respiratory tract secretions (LRTS) like sputum and tracheal aspirates are frequently sent to the microbiology laboratory from patients with various respiratory pathologies. Improper collection techniques can lead to false-positive results, resulting in improper therapy.Aim of the studyTo determine the percentage of contaminated samples sent to the microbiology laboratory, to establish the prevalence of Candida spp. in non-contaminated samples and therefore, the presence of Candida spp. originating in lower respiratory tract infections. Material andMethodsA 10-year data survey was conducted to assess the differences in Candida prevalence from contaminated versus non-contaminated samples, assessed and categorised by Bartlett grading system, and to emphasise the importance of quality control for potentially contaminated samples. The data were analysed according to gender, age, referring departments, and the species of Candida. For the statistical analysis, Kruskal-Wallis and Fisher tests were used, and the alpha value was set for 0.5.ResultsThe prevalence of Candida spp. in all analysed samples was 31.60%. After excluding the contaminated samples, the actual prevalence was 27.66%. Of all sputum samples, 31.6% were contaminated. Patients aged more than 40 years old were more prone to provide contaminated sputum samples. C. albicans is more prevalent in non-contaminated sputum samples. In both sputum and tracheal aspirates, the chances of identifying a single species are higher than the chances of identifying multiple species.ConclusionsThe study emphasises the importance of assessing the quality of sputum samples because of the high number of improperly collected samples sent to the microbiology laboratory.

Hyperspectral monitor of soil chromium contaminant based on deep learning network model in the Eastern Junggar coalfield

In China, over 10% of cultivated land is polluted by heavy metals, which can affect crop growth, food safety and human health. Therefore, how to effectively and quickly detect soil heavy metal pollution has become a critical issue. This study provides a novel data preprocessing method that can extract vital information from soil hyperspectra and uses different classification algorithms to detect levels of heavy metal contamination in soil. In this experiment, 160 soil samples from the Eastern Junggar Coalfield in Xinjiang were employed for verification, including 143 noncontaminated samples and 17 contaminated soil samples. Because the concentration of chromium in the soil exists in trace amounts, combined with the fact that spectral characteristics are easily influenced by other types of impurity in the soil, the evaluation of chromium concentrations in the soil through hyperspectral analysis is not satisfactory. To avoid this phenomenon, the pretreatment method of this experiment includes a combination of second derivative and data enhancement (DA) approaches. Then, support vector machine (SVM), k-nearest neighbour (KNN) and deep neural network (DNN) algorithms are used to create the discriminant models. The accuracies of the DA-SVM, DA-KNN and DA-DNN models were 95.61%, 95.62% and 96.25%, respectively. The results of this experiment demonstrate that soil hyperspectral technology combined with deep learning can be used to instantly monitor soil chromium pollution levels on a large scale. This research can be used for the management of polluted areas and agricultural insurance applications.

How the Soil Microbial Communities and Activities Respond to Long-Term Heavy Metal Contamination in Electroplating Contaminated Site.

The effects of long-term heavy metal contamination on the soil biological processes and soil microbial communities were investigated in a typical electroplating site in Zhangjiakou, China. It was found that the soil of the electroplating plant at Zhangjiakou were heavily polluted by Cr, Cr (VI), Ni, Cu, and Zn, with concentrations ranged from 112.8 to 9727.2, 0 to 1083.3, 15.6 to 58.4, 10.8 to 510.0 and 69.6 to 631.6 mg/kg, respectively. Soil urease and phosphatase activities were significantly inhibited by the heavy metal contamination, while the microbial biomass carbon content and the bacterial community richness were much lower compared to noncontaminated samples, suggesting that the long-term heavy metal contamination had a severe negative effect on soil microorganisms. Differently, soil dehydrogenase was promoted in the presence of Chromate compared to noncontaminated samples. This might be due to the enrichment of Sphingomonadaceae, which have been proven to be able to secrete dehydrogenase. The high-throughput sequencing of the 16S rRNA gene documented that Proteobacteria, Actinobacteria, and Chloroflexi were the dominant bacterial phyla in the contaminated soil. The Spearman correlation analysis showed the Methylobacillus, Muribaculaceae, and Sphingomonadaceae were able to tolerate high concentrations of Cr, Cr (VI), Cu, and Zn, indicating their potential in soil remediation.

Identification of new eligible indicator organisms for combined sewer overflow via 16S rRNA gene amplicon sequencing in Kanda River, Tokyo

Fecal indicator bacteria (FIB) are commonly used to evaluate the pollution impact of combined sewer overflows (CSOs) in urban rivers. Although water quality assessment with FIB has a long tradition, recent studies demonstrated that FIB have a low correlation with pathogens and therefore are not accurate enough for the assessment of potential human hazards in water. Consequently, new eligible and more specific indicators have to be identified, which was done in this study via sequencing of genetic markers from total community DNA. To identify potential microbiome-based indicators, microbial communities in samples from an urban river in Tokyo under different climatic conditions (dry and rainy) were compared with the influent and effluent of three domestic wastewater treatment plants (WWTPs) by analyzing 16 S rRNA gene amplicon libraries. In the first part of this study, physicochemical parameters and FIB quantification with selective culture techniques facilitated the identification of samples contaminated with CSO, sewage, or both. This allowed the grouping of samples into CSO-contaminated and non-contaminated samples, an essential step prior to the microbiome comparison between samples. Increased turbidity, ammonia concentrations, and E. coli [up to (9.37 ± 0.95) × 102 CFU/mL after 11.5 mm of rainfall] were observed in CSO-contaminated river samples. Comparison of dry weather (including WWTP samples) and rainy weather samples showed a reduction in microbial diversity in CSO-contaminated samples. Furthermore, the results of this study suggest Bacteroides spp. as a novel indicator of sewage pollution in surface waters.

Analysis of the physical and chemical properties of soil contaminated with oily (petroleum) hydrocarbons.

This paper is focused on analyzing the engineering properties and geotechnical changes of the soil contaminated by petroleum hydrocarbons, compared to non-contaminated soil. Also, identifying the physical and chemical behaviors and the interaction mechanisms between soil and contaminants at the time of contamination is far-reaching. This study aims to investigate the effects of petroleum contaminants on Atterberg limits, cation-exchange capacity, electrical conductivity and acidity, maximum dry density, and optimum moisture content of clay soil compared to non-contaminated samples with similar geological conditions in Gachsaran petroleum refinery region. The significant effects of contaminants on clay soil include an increase in cation-exchange capacity and electrical conductivity, but a decrease in acidity compared to non-contaminated samples. The results of the Atterberg limit as a physical index of the soil showed an increase in the petroleum hydrocarbon concentration, which enhanced the plastic and liquid limits, but decreased the plasticity index. The results of compaction tests using water and oil fluids indicated that the concentration of petroleum hydrocarbons in sandy soil increased maximum dry density and decreased optimum moisture content. Moreover, in a soil sample with stable pollution levels, maximum dry density and optimum moisture content were higher and lower in the compaction test using water fluid compared to those in compaction tests using oil fluid, respectively. Finally, increasing the soil pollution level reduced the difference between maximum dry density and the optimum moisture content in the compaction test using water fluid compared to the same test using oil fluid.

Machine learning algorithms for the automated classification of contaminated maize at regulatory limits via infrared attenuated total reflection spectroscopy

Mould infested maize poses a severe problem for farmers, food producers and for consumers worldwide. Mycotoxins are secondary metabolites produced by certain mould species that contaminate food and feed. The consumption of these toxins may cause serious health problems for humans and animals. The trichothecene deoxynivalenol (DON) constitutes one of the most commonly occurring Fusarium toxins encountered in maize and requires improved methods to limit its entrance into the food and feed system. While a variety of chromatographic and mass spectrometry methods for the identification of such toxins have been established, these are considered time-consuming, cost-intensive and require highly qualified personnel. Alternatively, optical techniques, such as mid-infrared spectroscopy offer rapid detection of fungal infections in cereals and other commodities with minimised sample preparation and analysis time. The present study demonstrates a rapid fungal contamination detection strategy in maize taking advantage of IR-spectroscopy combined with advanced machine learning algorithms. The developed method represents an advancement for the analysis of differences in protein and carbohydrate content revealed in the associated IR-spectra related to the amount of toxin contamination at the European Union (EU) regulatory limits for DON in maize (i.e. 1,250 μg/kg). The employed maize varieties are naturally infected samples or have been infected with Fusarium verticillioides, Fusarium graminearum or Fusarium culmorum. Sieved maize samples at the EU regulatory limit were correctly classified using machine learning approaches, therefore enabling the differentiation between DON-contaminated and non-contaminated maize samples. Specifically, a variety of machine learning methods, including Adaptive Boosting (AdaBoost), Random Forests, Support Vector Machine (SVM) and Multilayer Perceptron (MLP) demonstrated excellent classification and validation performance using the obtained IR-spectra. As a result, 183 maize samples of different varieties and infection levels were accurately classified. 94% of the non-contaminated samples and 91% of the contaminated samples were correctly classified using an MLP classification approach.

Heavy metals concentrations in bee products collected from contaminated and non-contaminated areas from Upper Egypt Governorates

Twenty fresh clover honey, ten beeswax and ten bee bread samples represented contaminated and non-contaminated areas were collected directly from the apiaries during 2015 .The aim of this study was to evaluate the presence of toxic metals ( Lead (Pb), Cadmium (Cd), Iron (Fe), Copper (Cu) and Zinc (Zn)) in honey, beeswax and bee bread stored inside honey bee colonies. The highest lead contents (0.5488 mg/kg) was estimated in honey samples collected from industrialized area The lowest Pb content were estimated in honey samples collected from rural area (0.5096 mg/kg).The lowest Cd concentration (0.0961 mg/kg). However, the highest content of Cd (0.1042 mg/kg) was recorded in honey samples collected from urbanized areas. High concentration of (Cu) was estimated in honey samples collected from apiaries located in industrialized area (0.0757 mg/kg) while the lowest were recorded in rural area (0.0432 mg/kg) . Zn occurred in low concentration in honey samples The highest value was recorded in honey samples from rural area (0.241) mg/kg and the lowest in honey samples from apiaries located in Reclaimed soils (0.185) mg/kg. Heavy metalconcentrations of Pb, Cd, Fe, Cu and Zn in beeswax samples collected from contaminated and non-contaminated areas were 1.388, 0.194, 16.696, 0.619 and 4.606 mg/kg. While the averages of heavy metal concentrations in non contaminated area decreased to 1.175; 0.160; 15.466; 0.391 and 2.520 mg/kg, respectively. Contamination in bee bread samples showed that lead concentration (1.094 mg/kg to 1.338 mg/kg) was detected in bee bread samples collected from honey bee colonies located in non-contaminated areas and samples collected from( industrialized and urban areas).

Long Term Ageing Effect on Physical and Shear Strength Characteristics of Oil Contaminated Sandy Soils of Al-Ahmadi Field in Kuwait

Serious environmental damage was caused to Kuwait when more than 600 oil wells were set alight following the 1991 Gulf War, leading to hydrocarbon contamination and a series of ‘oil lakes’ being created in the desert. The effects of these highly damaging actions are still detectable today, 27 years later. The main contribution of this paper is that the effects on the behavior of physical and shear strength properties of the Al-Ahmadi field sand brought about via the long-term ageing influence of hydrocarbon contamination will be explored, using the methodology of comparing contaminated and non-contaminated soil samples. For the purpose of this paper, nine samples, from more than 28 years’ oil contaminated site, were subject to both physical and shear strength tests alongside hydrocarbon analysis using Gas Chromatograph Mass Spectrometer (GC-MS) testing. The results of these tests showed a notable increase in both the mean values of fines particles (from 7.8% to 11.8%) and of gravel particles (from 5.8% to 27.8%). These results clearly illustrate that the overall effect of the change in soil gradation was a poorer one. A reduction in strength was also observed in these tests, which possibly related to accumulation of particles within the soil layer leading to poorer grain size and to ageing effect that increase asphalting lead to weak bonding between particles.