CPA Residue Levels in Consecutive C48 Cases of Oriental Tobacco

Transcription

1 CPA Residue Levels in Consecutive C48 Cases of Oriental Tobacco Heather Westberg and Andrae Spencer Global Laboratory Services, Inc. CORESTA AP 2015

2 Background Crop Protection Agents (CPA) may be applied at various stages of the tobacco growing cycle and if CPA residues are present in the tobacco, outside of product standards, they could be a topic of concern. After tobacco is processed it is normally packed into cases or bales for shipment. The packing stage is a common point where tobacco samples are collected for CPA residue analysis.

3 Objective The objective of the study was to determine the homogeneity of CPA residues throughout the case, as well as across cases. Three consecutive production run cases were sampled at multiple positions within the case and analyzed for CPA residue levels to measure variability within and between cases.

4 Procedure Izmir Oriental tobacco type was chosen for this study. Three consecutively produced cases were supplied by Socotab Yaprak Tutun. The cases were sampled using a Hydraulic Thief sample press. Each case had 10 samples collected from the top, after which the case was inverted and 10 samples were taken from the bottom. Each sample was tested in triplicate for common CPA residues.

5 Sampling Diagram

6 A C48 Case

7 A C48 Case out of the box

8 In the press

9 During Sampling

10 After Sampling

11

12

13

14

15

16

17 Results Case 1 Mean Case 2 Mean Case 3 Mean Thiamethoxam (ppm) Top Bottom Case mean of means Sample Range GRAND MEAN (5.1X) 0.22 Imidacloprid Acetamiprid Top Bottom Case mean of means Top Bottom Case mean of means (3.7X) (15X) 0.13

18 Results Metalaxyl Case 1 Mean Case 2 Mean Case 3 Mean (ppm) Top Bottom Case mean of means Sample Range GRAND MEAN (2.9X) 0.50 Dimethomorph Top Bottom Case mean of means Penconazole Top Bottom Case mean of means (6.9X) (3.4X) 0.35

19 Statistical Analyses Statistical analyses were performed using StatGraphics Centurion XV, StatPoint Technologies, Inc. A One-Way Analysis of Variance (ANOVA) was used to determine differences between groups of means. The ANOVA decomposes the variance of the analyte into two components: between-groups and within-groups. Next, a multiple range test was used to determine which means were significantly different from each other at the 95% confidence level. The Tukey's honest significant difference (HSD) was used to discriminate among the means. With this method, there is a 5.0% risk of calling one or more pairs significantly different when their actual difference equals 0.

20 Results Thiamethoxam Cases Grand Mean Comparison Means and 95.0 Percent Confidence Intervals (internal s) 0.32 Thiamethoxam (ppm) Two groups were identified Case 3 is statistically different Case I Case 2 Case 3

21 Imidacloprid Cases Grand Mean Comparison Means and 95.0 Percent Confidence Intervals (internal s) 0.66 Imidacloprid (ppm) Case I Case 2 Case 3 One group was identified Unable to statistically differentiate between cases

22 Acetamiprid Cases Grand Mean Comparison Means and 95.0 Percent Confidence Intervals (internal s) Acetamiprid (ppm) Two groups were identified Case 3 is statistically Case I Case 2 Case 3

23 Metalaxyl Cases Grand Mean Comparison Means and 95.0 Percent Confidence Intervals (internal s) Metalaxyl (ppm) Two groups were identified Case 2 is statistically different Case I Case 2 Case 3

24 Dimethomorph Cases Grand Mean Comparison Means and 95.0 Percent Confidence Intervals (internal s) 0.35 Dimethomorph (ppm) Case I Case 2 Case 3 One group was identified Unable to statistically differentiate between cases

25 Penconazole Cases Grand Mean Comparison Means and 95.0 Percent Confidence Intervals (internal s) 0.38 Penconazole (ppm) Case I Case 2 Case 3 One group was identified Unable to statistically differentiate between cases

26 Summary All measured residues were well below their respective CORESTA GRL values. The CPA residues results within each case were not homogenous throughout the case based on the 20 locations sampled. Some of the consecutive cases were observed to have statistically different CPA residue levels. The individual sample s measured residue levels, from lowest to highest result for a given CPA, ranged from 2.9X to 15X

27 Conclusions Results based on a single sample location may not be representative of the entire case, which strengthens the importance of collecting multiple residue samples to better determine a tobacco lot s CPA residue content. A better understanding of CPA residue variances within and across cases may lead to more informed decisions in determining a tobacco lot s acceptability.

28 Acknowledgments Emily Moyer and Brandie Garris, Global Laboratory Services, Inc. Christian Rasmussen, Ioannis Kalampoukas, Onur Kuruoglu - Socotab Yaprak Tutun