Spectroscopic Methods for Pesticide Detection

Pesticide prediction is crucial for several reasons impacting food safety, environmental health, and agricultural practices. Here is why it is important:

Ensuring Food Safety:

• Reduced Risk of Exposure: Accurate prediction helps identify fruits, vegetables, and grains with pesticide residues exceeding safe limits. This protects consumers from potential health risks associated with pesticide ingestion.
• Improved Food Quality: Predicting pesticide presence allows for better control over food production and distribution, promoting safer and higher-quality food products.
• Traceability and Transparency: Vis-NIR based prediction can be used for traceability purposes, allowing authorities to identify the source of contamination in case of incidents.

Environmental Protection:

• Monitoring Pesticide Levels: Prediction tools can be used to track pesticide levels in soil and water, helping to identify potential contamination and prevent ecological damage.
• Promoting Sustainable Practices: By monitoring pesticide use, farmers can optimize application and minimize environmental impact.

Vis-NIR spectroscopy for Pesticide Prediction

Pesticide prediction using Vis-NIR spectroscopy is a developing field with exciting potential for the food industry and environmental monitoring. Here is a deeper dive into this technique:

Advantages of Vis-NIR for Pesticide Prediction:

• Non-destructive: Samples do not need to be chemically treated, preserving their integrity.
• Rapid: Analysis can be done quickly compared to traditional methods.
• Portable: Portable spectrometers allow for on-site measurements.
• Cost-effective: Vis-NIR spectrometers are less expensive than some traditional methods.
• Multi-analyte potential: The technique can potentially detect and quantify multiple pesticides in a single scan.

Applications:

• Food safety monitoring: Rapid screening of fruits, vegetables, and grains for pesticide residues.
• Environmental monitoring: Detection of pesticides in soil and water samples.
• Precision agriculture: Monitoring pesticide application and optimizing use in fields.

METHOD

The principle behind Vis-NIR spectroscopy for pesticide prediction on food relies on the interaction between light and the chemical composition of the sample. Here is a breakdown of the key steps:

• Light Absorption by Molecules: Pesticides and other chemicals have unique molecular structures that cause them to absorb specific wavelengths of light within the visible (Vis, 400-750 nm) and near-infrared (NIR, 750-1000 nm) regions. Think of it like sunlight hitting a stained-glass window - certain colors (wavelengths) pass through, while others are absorbed by the colored glass.
• Spectral Characterize: When Vis-NIR light shines on a food sample, these chemicals selectively absorb specific wavelengths according to their structure. The remaining light intensity is then measured at various wavelengths by the Vis-NIR spectrometer. This data creates a unique "spectrum" for the sample. This spectrum reflects the overall chemical makeup of the sample.
• Pesticide Detection through Spectral Analysis: By comparing the obtained spectrum of the food sample to a library of known pesticide spectra, scientists can identify the presence of specific pesticides. Each pesticide has a characteristic spectral fingerprint due to its unique light absorption pattern. The higher the concentration of a pesticide in the sample, the greater the impact it will have on the overall absorbance at its specific wavelengths. This difference in absorbance at key wavelengths allows for the detection and potential quantification of the pesticide.

Measurement setup

Vis-NIR spectroscopy offers a promising non-destructive approach for the crucial task of predicting pesticide residue levels. Traditionally, the USB2000 spectrometer and LS-1 light source have been used for pesticide prediction. The USB2000 spectrometer measures light across a specific range to identify a sample's spectral fingerprint. However, limitations exist, such as a potentially restricted wavelength range and a less stable light source.

Figure 1: USB2000+ spectrometer.

We suggest a new system with the USB2000+ spectrometer and HL-2000 light source. The USB2000+ is a compact, modular spectrometer renowned for its high performance and flexibility. It is equipped with a sophisticated 2048-element CCD array that captures detailed spectral information across a wide range of wavelengths. This feature makes it particularly effective for the detection of pesticides, which often requires precise spectral analysis to identify the unique signatures of various chemical compounds.

Figure 2: HL-2000 light source.

On the other hand, the HL-2000 Tungsten Halogen Light Source is designed to complement the USB2000+ in applications requiring a stable and broad-spectrum light source. Its continuous output from the visible to near-infrared regions ensures that it can illuminate a wide variety of samples, which is crucial for detecting the diverse range of pesticides that may be present in a sample.

The HL-2000's design includes a built-in shutter, filter holder, and an adjustable attenuator, providing users with complete control over the intensity and quality of the light reaching the sample. This level of control is necessary to achieve the high degree of sensitivity required for detecting low concentrations of pesticides.

Together, the USB2000+ spectrometer and HL-2000 light source offer a robust solution for the detection of pesticides, ensuring that analyses are both accurate and efficient. Their combined capabilities facilitate the identification and quantification of pesticide residues, contributing to the safety and quality control of agricultural products.

Result

Figure 3: The spectral of cucumber with absence of pesticide (AP) and presence of pesticide (PP).

Figure 3 depicts the absorbance spectral (Log(1/R)) of two cucumber samples: one free of pesticides and one treated with diazinon pesticide. The presence of the pesticide caused spectral differences in both the visible and NIR regions.

In the visible region, both samples displayed a peak around 680 nm, due to chlorophyll absorption, a natural pigment in cucumbers. However, the presence of the pesticide resulted in a decrease in this peak's absorbance compared to the pesticide-free sample.

Moving to the NIR region (wavelengths exceeding 900 nm), both samples exhibited an absorbance increase. This could be attributed to the second overtone of O-H bonds or the third overtone of C-H bonds within the cucumber. Notably, the absorbance increase in this NIR region was more pronounced in the pesticide-contaminated sample compared to the one free of pesticides. This enhanced NIR absorbance is C-H bond vibrations, potentially indicating an interaction between the pesticide and the cucumber's chemical makeup.

Conclusion

Spectroscopic techniques, particularly Vis-NIR (Visible-Near Infrared) spectroscopy, are becoming a valuable tool for detecting pesticide residues on food due to their ability to analyze the interaction between light and a sample's chemical composition. Pesticides have unique spectral fingerprints, absorbing specific wavelengths of Vis-NIR light. By measuring the remaining light intensity (spectral analysis), a profile is created that reflects the food's overall chemistry. This profile can then be compared to a library of known pesticide spectra to identify and potentially quantify their presence. Differences in absorbance patterns, like a decrease in chlorophyll absorption (visible region) and a stronger NIR absorbance increase (potentially C-H bond related) in pesticide-contaminated samples, provide crucial information. While limitations exist due to complex food matrices and reference database quality, spectroscopic techniques offer a promising solution for rapid, non-destructive pesticide screening on food. As technology advances and databases improve, the accuracy and reliability of Vis-NIR spectroscopy in pesticide prediction are expected to continue to rise, significantly contributing to food safety and consumer protection.