scholarly journals PM 9/15 (1)Anoplophora glabripennis: procedures for official control

EPPO Bulletin ◽  
2013 ◽  
Vol 43 (3) ◽  
pp. 510-517 ◽  
Keyword(s):  
Anoplophora Glabripennis ◽  
Official Control

Reports on Recent Activities of European Food Safety Authority

2019 ◽  
Vol 30 (5) ◽  
pp. 217-220
Keyword(s):  
Pesticide Residues ◽  
Food Products ◽  
Sampling Procedure ◽  
Control Programme ◽  
The European Union ◽  
Eu Member States ◽  
Official Control ◽  
National Programmes ◽  
Control Programmes ◽  
Risk Managers

This report provides an overview of the 2017 official control activities on pesticide residues carried out in the European Union (EU) Member States, Iceland and Norway. It summarises the results of both the 2017 EU-coordinated control programme (EUCP) and the national control programmes (NP). While the NPs are mostly risk based (so called enforcement samples) focusing on pesticides or products originating from countries where a number of exceedances have been observed in the past, the EUCP aims to present a statistically representative snapshot of the situation of pesticide residues in food products that are mostly consumed in the EU following a random sampling procedure. The report includes the outcome of a dietary risk assessment based on the results of the overall 2017 control programmes. The comprehensive analysis of the results of all reporting countries provides risk managers with sound-based evidence for designing future monitoring programmes, in particular for taking decisions on which pesticides and food products should be targeted in risk-based national programmes.

scholarly journals Rapid extraction of total lipids and lipophilic POPs from all EU-regulated foods of animal origin: Smedes’ method revisited and enhanced

2020 ◽  
Vol 32 (1) ◽  
Author(s):  
Johannes Haedrich ◽  
Claudia Stumpf ◽  
Michael S. Denison
Keyword(s):  
Milk Fat ◽  
Lipid Extraction ◽  
Poultry Meat ◽  
Animal Origin ◽  
Infant Food ◽  
Total Lipids ◽  
Analytical Results ◽  
Official Control ◽  
Wide Range ◽  
Chicken Eggs

Abstract Background Persistent organic pollutants (POPs) such as dioxins, dioxin-like chemicals and non-dioxin-like PCBs causing adverse effects to human health bio-accumulate through the food web due to their affinity for adipose tissues. Foods of animal origin are therefore the main contributors to human dietary exposure. The European Union’s (EU) food safety policy requires checking of a wide range of samples for compliance with legal limits on a regular basis. Several methods of varying efficiency are applied by official control laboratories for extraction of the different classes of lipids and associated POPs, bound to animal tissue and animal products in varying degrees, sometimes leading to discrepancies especially in fresh weight based analytical results. Results Starting from Smedes’ lipid extraction from marine tissue, we optimized the extraction efficiency for both lipids and lipophilic pollutants, abandoning the time-consuming centrifugation step. The resulting modified Smedes extraction (MSE) method was validated based on multiple analyses of a large number of real-world samples, matrix calibration and performance assessment in proficiency testing utilizing both instrumental and bioanalytical methodologies. Intermediate precision in 12 different foods was below 3% in chicken eggs, egg powder, animal fat, fish, fish oil, poultry, whole milk, milk fat and milk powder, and below 5% in bovine meat, liver, and infant food. In comparison to Twisselmann hot extraction, results presented here show an increased efficiency of MSE by + 25% for bovine liver, + 14% for chicken eggs, + 13% for poultry meat, + 12% for fish, 8% for bovine meat, and 6% for infant food. Conclusions For the first time, a fast and reliable routine method is available that enables the analyst to reproducibly extract "total" lipids from any EU-regulated food sample of animal origin within 6 to 8 min. Increased efficiency translates into a considerable increase in both lipid and wet weight-based analytical results measured for associated POPs, reducing the risk of false non-compliant results. Compared to a 4 h Twisselmann extraction, the extraction of 1000 samples using MSE would result in annual savings of about 250 h or 32 working days. Our MSE procedure contributes to the European Commission's objective of harmonizing analytical results across the EU generated according to Commission Regulation (EU) 2017/644.

scholarly journals Combining WV-2 images and tree physiological factors to detect damage stages of Populus gansuensis by Asian longhorned beetle (Anoplophora glabripennis) at the tree level

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Quan Zhou ◽  
Xudong Zhang ◽  
Linfeng Yu ◽  
Lili Ren ◽  
Youqing Luo
Keyword(s):  
Remote Sensing ◽  
Gansu Province ◽  
Tree Level ◽  
Anoplophora Glabripennis ◽  
Longhorned Beetle ◽  
Asian Longhorned Beetle ◽  
Remote Sensing Images ◽  
Physiological Factors ◽  
Abnormal Phenotype ◽  
Damage Degree

Abstract Background Anoplophora glabripennis (Motschulsky), commonly known as Asian longhorned beetle (ALB), is a wood-boring insect that can cause lethal infestation to multiple borer leaf trees. In Gansu Province, northwest China, ALB has caused a large number of deaths of a local tree species Populus gansuensis. The damaged area belongs to Gobi desert where every single tree is artificially planted and is extremely difficult to cultivate. Therefore, the monitoring of the ALB infestation at the individual tree level in the landscape is necessary. Moreover, the determination of an abnormal phenotype that can be obtained directly from remote-sensing images to predict the damage degree can greatly reduce the cost of field investigation and management. Methods Multispectral WorldView-2 (WV-2) images and 5 tree physiological factors were collected as experimental materials. One-way ANOVA of the tree’s physiological factors helped in determining the phenotype to predict damage degrees. The original bands of WV-2 and derived vegetation indices were used as reference data to construct the dataset of a prediction model. Variance inflation factor and stepwise regression analyses were used to eliminate collinearity and redundancy. Finally, three machine learning algorithms, i.e., Random Forest (RF), Support Vector Machine (SVM), Classification And Regression Tree (CART), were applied and compared to find the best classifier for predicting the damage stage of individual P. gansuensis. Results The confusion matrix of RF achieved the highest overall classification accuracy (86.2%) and the highest Kappa index value (0.804), indicating the potential of using WV-2 imaging to accurately detect damage stages of individual trees. In addition, the canopy color was found to be positively correlated with P. gansuensis’ damage stages. Conclusions A novel method was developed by combining WV-2 and tree physiological index for semi-automatic classification of three damage stages of P. gansuensis infested with ALB. The canopy color was determined as an abnormal phenotype that could be directly assessed using remote-sensing images at the tree level to predict the damage degree. These tools are highly applicable for driving quick and effective measures to reduce damage to pure poplar forests in Gansu Province, China.

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