Use of a quality control approach to assess measurement uncertainty in the comparison of sample processing techniques in the analysis of pesticide residues in fruits and vegetables

2018 ◽  
Vol 410 (22) ◽  
pp. 5465-5479 ◽  
Author(s):  
Steven J. Lehotay ◽  
Lijun Han ◽  
Yelena Sapozhnikova
2016 ◽  
Vol 31 (3-4) ◽  
pp. 89-105 ◽  
Author(s):  
Tijana Djordjevic ◽  
Rada Djurovic-Pejcev

Pesticides are one of the major inputs used for increasing agricultural productivity of crops. However, their inadequate application may produce large quantities of residues in the environment and, once the environment is contaminated with pesticides, they may easily enter into the human food chain through plants, creating a potentially serious health hazard. Nowadays, consumers are becoming more aware of the importance of safe and high quality food products. Thus it is pertinent to explore simple, cost-effective strategies for decontaminating food from pesticides. Various food processing techniques, at industrial and/or domestical level, have been found to significantly reduce the contents of pesticide residues in most food materials. The extent of reduction varies with the nature of pesticides, type of commodity and processing steps. Pesticides, especially those with limited movement and penetration ability, can be removed with reasonable efficiency by washing, and the effectiveness of washing depends on pesticide solubility in water or in different chemical solvents. Peeling of fruit and vegetable skin can dislodge pesticide residues to varying degrees, depending on constitution of a commodity, chemical nature of the pesticide and environmental conditions. Different heat treatments (drying, pasteurization, sterilization, blanching, steaming, boiling, cooking, frying or roasting) during various food preparation and preservation processes can cause losses of pesticide residues through evaporation, co-distillation and/or thermal degradation. Product manufactures, from the simplest grain milling, through oil extraction and processing, juicing/pureeing or canning of fruits and vegetables, to complex bakery and dairy production, malting and brewing, wine making and various fermentation processes, play a role in the reduction of pesticide contents, whereby each operation involved during processing usually adds to a cumulative effect of reduction of pesticides present in the material. There is diversified information available in literature on the effect of food processing on pesticide residues which has been compiled in this article.


2020 ◽  
Vol 16 (3) ◽  
pp. 303-311
Author(s):  
Qi Huang ◽  
Chunsong Cheng ◽  
Lili Li ◽  
Daiyin Peng ◽  
Cun Zhang

Background: Scutellariae Radix (Huangqin) is commonly processed into 3 products for different clinical applications. However, a simple analytical method for quality control has rarely been reported to quickly estimate the degree of processing Huangqin or distinguish differently processed products or unqualified Huangqin products. Objective: To study a new strategy for quality control in the processing practice of Huangqin. Methods: Seven kinds of flavonoids that mainly exist in Huangqin were determined by HPLC-DAD. Chromatographic fingerprints were established to study the variation and discipline of the 3 processed products of Huangqin. PCA and OPLS-DA were used to classify differently processed products of Huangqin. Results: The results showed that baicalin and wogonoside were the main components in the crude and the alcohol Huangqin herb while baicalein and wogonin mainly existed in carbonized Huangqin. The results of mathematical statistics revealed that the processing techniques can make the quality of medicinal materials more uniform. Conclusion: This multivariate monitoring strategy is suitable for quality control in the processing of Huangqin.


2016 ◽  
Vol 99 (2) ◽  
pp. 539-557 ◽  
Author(s):  
Jian Wang ◽  
Wendy Cheung

Abstract This paper presents an ultra HPLC/electrospray ionization-tandem MS method to determine pesticides in wine. We adopted the quick, easy, cheap, effective, rugged, and safe (QuEChERs) method for extraction and used core-shell column to achieve ultra-HPLC to develop and validate a simple and fast method to analyze 187 pesticide residues in red and white wine samples. Pesticide residues were extracted from wine samples using QuEChERS. Ultra HPLC/electrospray ionization-tandem MS quantification was achieved using matrix-matched standard calibration curves with isotopically labeled standards or a chemical analogue as internal standards with an analytical range from 5.0 to 500.0 μg/L. The method performance characteristics that included overall recovery, intermediate precision, and measurement uncertainty were evaluated according to a nested experimental design. Generally, 98.4% (in red wine) and 96.8% (in white wine) of the pesticides had recoveries between 71 and 120%; 98.9% (in red wine) and 99.5% (in white wine) of the pesticides had the intermediate precision ≤20%; and 99.5% (in red wine) and 98.4% (in white wine) of the pesticides had measurement uncertainty ≤50%.


2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Sheng Hu ◽  
Shuanjun Song ◽  
Wenhui Liu

Considering the problem that the process quality state is difficult to analyze and monitor under manufacturing big data, this paper proposed a data cloud model similarity-based quality fluctuation monitoring method in data-driven production process. Firstly, the randomness of state fluctuation is characterized by entropy and hyperentropy features. Then, the cloud pool drive model between quality fluctuation monitoring parameters is built. On this basis, cloud model similarity degree from the perspective of maximum fluctuation border is defined and calculated to realize the process state analysis and monitoring. Finally, the experiment is conducted to verify the adaptability and performance of the cloud model similarity-based quality control approach, and the results indicate that the proposed approach is a feasible and acceptable method to solve the process fluctuation monitoring and quality stability analysis in the production process.


Talanta ◽  
2010 ◽  
Vol 82 (4) ◽  
pp. 1077-1089 ◽  
Author(s):  
Dipakshi Sharma ◽  
Avinash Nagpal ◽  
Yogesh B. Pakade ◽  
Jatinder Kaur Katnoria

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