Quantification of Five Effective Components in Pesticides by Visible Near-Infrared Spectroscopy

Pesticides are chemical compounds that are used to kill pests that damage crops, including insects, rodents, fungi and unwanted plants (weeds). In addition, they are also used in public health to kill vectors of disease, such as mosquitoes. Due to the potential toxicity they pose to other organisms, including humans, pesticides need to be used safely and disposed of properly1.

Quality Control of Pesticide Analysis

Exposure to pesticides could result from contact with them at work, at home or in gardens, such as through contaminated food. WHO reviews evidence and develops internationally-accepted maximum residue limits to protect people from potential health hazards caused by pesticides.2

Reversed-phase high performance liquid chromatography (HPLC) is generally used to estimate the concentration of effective components in pesticides. However, this type of chromatography requires the use of toxic solvents, and is time consuming and well-trained operators, resulting in relatively high costs for routine analysis. The use of visible near-infrared spectroscopy (Vis-NIRS) as an alternative to HPLC could save both time and money.

Table 1. Analyzed compounds and the effect as pesticide. [3]

Compound Effect
Abamectin
  • Control insect and mite pests
Emamectin
  • Controlling lepidopterous pests
Cyhalothrin
  • Control insects in cotton crops
Cypermethrin
  • Fast-acting neurotoxin in insects
  • Moderate toxicity through skin contact or ingestion
Glyphosate
  • Broad-spectrum systemic herbicide and crop desiccant

Experimental Data

To test the validity of the use of Vis-NIRS as alternatives to HPLC, 24–37 pesticide samples with known concentrations of the effective compounds: Abamectin EC, Emamectin EC, Cyhalothrin EC, Cypermethrin and Glyphosate were prepared to evaluate the correlation between changes in spectral data and reference values.

NIRS RapidLiquid Analyzer was used to obtain the spectra over its full wavelength range (400– 2500 nm). The samples were filled into disposable glass vials with 4 mm diameter. Vision Air 2.0 Complete software was used for acquisition and management of data, as well as for the development of the quantification method. A Partial Least Squares (PLS) regression was carried out for each of the analyzed samples and Internal cross-validation (leave-one-out) was applied to confirm the performance of the derived quantitative models during the development of the method.

Table 2. Sample number and concentration range of the five effective compounds of interest.

Ingredient No. of samples Concentration range [wt-%]
Abamectin 18 1.8–3.8
Emamectin 35 1.5–3.5
Cyhalothrin 24 2.3–4.2
Cypermethrin 27 4.0–5.8
Glyphosate 33 21.0–40.5

Table 3. Used equipment and software.

Equipment Metrohm code
NIRS RapidLiquid Analyzer 2.921.1410
NIRS disposable glass vials, 4 mm diameter 6.7402.010
Vision Air 2.0 Complete 6.6072.208

The NIRS XDS RapidLiquid Analyzer was used for spectral data acquisition over the full range from 400 nm to 2500 nm.

Figure 1. The NIRS XDS RapidLiquid Analyzer was used for spectral data acquisition over the full range from 400 nm to 2500 nm.

Results

To build a robust prediction model, the wavelength regions used for each of these compounds were

  1. Abamectin: 1360–1850 nm and 2050– 2500 nm,
  2. Emamectin: 1300-1790 nm,
  3. Cyhalothrin: 400–1080 nm and 1300– 2200 nm,
  4. Cypermethrin: 1300–2200 nm
  5. Glyphosate: 1300–2200 nm.

For the quantification of each of these compounds in pesticide, a model using 2 factors was developed with a Standard Error of Calibration (SEC) of 0.05% and a Standard Error of Cross Validation (SECV) of 0.06% was used. The R2 values between the provided reference values and the calculated values for each of these effective compounds were found to be 0.9946, 0.9911, 0.9952, 0.0052 and 0.9952, respectively.

Raw data spectrum of 18 pesticide samples with Abamectin concentrations ranging from 1.8–3.8%.

Figure 2. Raw data spectrum of 18 pesticide samples with Abamectin concentrations ranging from 1.8–3.8%.

Correlation plot of the predicted Abamectin content by Vis-NIRS versus the reference values evaluated by HPLC.

Figure 3. Correlation plot of the predicted Abamectin content by Vis-NIRS versus the reference values evaluated by HPLC.

Raw data spectrum of 35 pesticide samples with Emamectin concentrations ranging from 1.5–3.5%.

Figure 4. Raw data spectrum of 35 pesticide samples with Emamectin concentrations ranging from 1.5–3.5%.

Correlation plot of the predicted Emamectin content by Vis-NIRS versus the reference values evaluated by HPLC.

Figure 5. Correlation plot of the predicted Emamectin content by Vis-NIRS versus the reference values evaluated by HPLC.

Raw data spectrum of 24 pesticide samples with Cyhalothrin concentrations ranging from 2.3–4.2%.

Figure 6. Raw data spectrum of 24 pesticide samples with Cyhalothrin concentrations ranging from 2.3–4.2%.

Correlation plot of the predicted Cyhalothrin content by Vis-NIRS versus the reference values evaluated by HPLC.

Figure 7. Correlation plot of the predicted Cyhalothrin content by Vis-NIRS versus the reference values evaluated by HPLC.

Raw data spectrum of 27 pesticide samples with Cypermethrin concentrations ranging from 4.0–5.8%.

Figure 8. Raw data spectrum of 27 pesticide samples with Cypermethrin concentrations ranging from 4.0–5.8%.

Correlation plot of the predicted Cypermethrin content by Vis-NIRS versus the reference values evaluated by HPLC.

Figure 9. Correlation plot of the predicted Cypermethrin content by Vis-NIRS versus the reference values evaluated by HPLC.

Raw data spectrum of 33 pesticide samples with Glyphosate concentrations ranging from 21.0–40.5%.

Figure 10. Raw data spectrum of 33 pesticide samples with Glyphosate concentrations ranging from 21.0–40.5%.

Correlation plot of the predicted Glyphosate content by Vis-NIRS versus the reference values evaluated by HPLC.

Figure 11. Correlation plot of the predicted Glyphosate content by Vis-NIRS versus the reference values evaluated by HPLC.

Table 4. Results of the quantitative method development for Abamectin content.

. .
Regression model PLS with 2 factors
Pre-treatment Raw data
Wavelength range 1360 – 1850 nm
2050 – 2500 nm
R2 0.9946
SEC 0.05%
SECV 0.06%

Table 5. Results of the quantitative method development for Emamectin content.

. .
Regression model PLS with 1 factor
Pre-treatment Raw data
Wavelength range 1300 – 1790 nm
R2 0.9911
SEC 0.61%
SECV 0.62%

Table 6.  Results of the quantitative method development for Cyhalothrin content.

. .
Regression model PLS with 2 factors
Pre-treatment Raw data
Wavelength range 400–1080 nm
1300–2200 nm
R2 0.9952
SEC 0.05%
SECV 0.05%

Table 7.  Results of the quantitative method development for Cypermethrin content.

. .
Regression model PLS with 2 factors
Pre-treatment Raw data
Wavelength range 1300-2200 nm
R2 0.9286
SEC 0.16%
SECV 0.16%

Table 8.  Results of the quantitative method development for Glyphosate content.

. .
Regression model PLS with 2 factors
Pre-treatment Raw data
Wavelength range 1300–2170 nm
R2 0.9987
SEC 0.03%
SECV 0.03%

Table 9.  Comparison of predicted Vis-NIR values with the HPLC reference values.

Compound Vis-NIR [%] HPLC [%] Residual RSD
Abamectin 2.63 2.74 -0.11 -3.87
Abamectin 2.58 2.60 -0.02 -0.91
Abamectin 2.70 2.64 0.06 2.34
Abamectin 2.59 2.57 0.02 0.82
Abamectin 2.51 2.61 -0.10 -3.81
Abamectin 2.48 2.58 -0.10 -3.96
Abamectin 2.53 2.58 -0.05 -2.10
Abamectin 2.58 2.62 -0.04 -1.39
Abamectin 2.57 2.56 0.01 0.29
Abamectin 2.55 2.63 -0.08 -2.92
Abamectin 2.54 2.63 -0.09 -3.45
Abamectin 2.57 2.69 -0.12 -4.35
Emamectin 2.47 2.36 0.11 4.58
Emamectin 2.44 2.45 -0.01 -0.52
Emamectin 2.31 2.39 -0.08 -3.54
Emamectin 2.47 2.36 0.11 4.58
Cyhalothrin 3.14 3.14 0.00 0.14
Cyhalothrin 3.27 3.16 0.11 3.47
Cyhalothrin 3.19 3.13 0.06 1.97
Cyhalothrin 3.13 3.17 -0.04 -1.22
Cyhalothrin 3.17 3.16 0.01 0.37
Cyhalothrin 3.24 3.16 0.08 2.68
Cyhalothrin 3.26 3.15 0.11 3.37
Cyhalothrin 3.20 3.32 -0.12 -3.69
Cyhalothrin 3.30 3.17 0.13 4.01
Cyhalothrin 3.10 3.08 0.02 0.77
Cypermethrin 4.93 4.84 0.09 1.91
Cypermethrin 5.03 4.98 0.05 1.04
Cypermethrin 4.88 5.02 -0.14 -2.73
Cypermethrin 5.05 4.97 0.08 1.51
Cypermethrin 5.11 5.10 0.01 0.16
Cypermethrin 5.08 4.92 0.16 3.17
Cypermethrin 5.12 5.07 0.05 1.04
Cypermethrin 5.09 5.03 0.06 1.15
Cypermethrin 5.01 4.95 0.06 1.28
Cypermethrin 4.97 4.83 0.14 2.98
Cypermethrin 4.96 4.97 -0.01 -0.20
Cypermethrin 5.03 5.10 -0.07 -1.36
Glyphosate 39.61 39.16 0.45 1.14
Glyphosate 39.19 39.86 -0.67 -1.69
Glyphosate 39.16 39.08 0.08 0.19
Glyphosate 39.56 39.46 0.10 0.25
Glyphosate 39.74 39.35 0.39 0.99
Glyphosate 39.09 39.14 -0.05 -0.13
Glyphosate 39.83 39.35 0.48 1.21

Conclusion

These high correlation values between the reference values and the calculated values using the Vis-NIRS indicate that it is a highly reliable and much faster quality control method for pesticide as compared to the traditionally used HPLC method. Therefore, Vis-NIRS can be used as excellent alternatives to HPLC for the routine analysis of pesticides, and it could save both time and money.

References

[1] http://www.who.int/topics/pesticides/en/

[2] http://www.who.int/

[3] https://en.wikipedia.org/

This information has been sourced, reviewed and adapted from materials provided by Metrohm AG.

For more information on this source, please visit Metrohm AG.

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