Development of non-destructive testing techniques for the detection of glyphosate residue in selected pulses

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University of Guelph

Canada is one of the leading producers and exporters of pulses worldwide. Dry beans, dry peas, lentils, and chickpeas are the most common types of pulses cultivated in Canada. The cultivation of pulses relies on the application of several types of pesticides to enhance the productivity by safeguarding pulse crops from pests, weeds, and insects. Among numerous pesticides, glyphosate is one of the most extensively used broad-spectrum systemic herbicides used as a weed control spray or a desiccant. The excessive application of glyphosate on pulses could result in a negative impact on trade, leading to commodity rejection. As a result, the purpose of the study was to investigate non-destructive testing techniques for the detection of glyphosate residues in Canadian-grown pulses at five concentration levels (5 mg/kg, 10 mg/kg, 15 mg/kg and 20 mg/kg). In order to perform non-destructive testing of glyphosate residues in pulses, a standard protocol was developed for the artificial spiking of glyphosate in selected pulses at desired levels. The study involved six pulse types (chickpea, yellow pea, red lentil, large green lentil, French green lentil, and black beluga lentil), four different concentrations (5 mg/kg, 10 mg/kg, 15 mg/kg and 20 mg/kg) and two different solvents (water and water + ethanol (50:50)). The study demonstrated that the highest glyphosate absorption as determined by enzyme-linked immunosorbent assay (ELISA) was observed when water was used as solvent in all pulses and at all concentration levels. The glyphosate residues in the six selected intact pulses were determined using Fourier transform infrared (FTIR) spectroscopy technique. The optimum model was developed using partial least squares regression (PLSR) technique and variable importance in projection (VIP) and selectivity ratio (sRatio) based variable selection method with a correlation coefficient for prediction (R2p) of 0.93, 0.92, 0.96, 0.91, 0.96, and 0.92 whereas a root mean square error of prediction (RMSEP) value of 1.293, 1.402, 0.982, 0.912, 0.984 and 1.302 for yellow pea, chickpea, large green lentil, red lentil, black beluga, and French green lentil, respectively. Similarly, the detection of glyphosate residues in red lentil flour and large green lentil flour was carried out employing FTIR spectroscopy. The VIP-PLSR model worked optimum for both red lentil flour and large green lentil flour, leading to a R2p of 0.931 and 0.985, and RMSEP of 1.385 and 0.757 respectively. The effectiveness of surface-enhanced Raman spectroscopy (SERS) was studied to determine the glyphosate residue levels in intact chickpeas and yellow peas. The PLSR model along with spectral pre-processing showed the maximum accuracy in chickpea and yellow pea with a R2p of 0.95 and 0.99, and RMSEP values of 1.105 and 1.709, respectively. The feasibility of using near-infrared (NIR) hyperspectral imaging (HSI) system in the 900 - 2500 nm wavelength range was studied to detect glyphosate residue levels in intact black beluga lentil, red lentil, large green lentil, and French green lentil. The VIP-PLSR method showed highest performance in the selected lentils with a R2p and RMSEP 0.933 and 1.915 for black beluga lentils, a R2p and RMSEP of 0.925 and 2.066 for red lentils. Whereas, in large green lentil and French green lentil the R2p and RMSEP were 0.940 and 1.741 and 0.941 and 1.726 respectively. Overall, the findings highlight the potential of FTIR, SERS, and NIR HSI techniques for rapid and non-destructive detection of glyphosate content in both intact pulses and pulse flours. The integration of these approaches into commercial milling units could enhance the effective prediction of glyphosate levels in pulse samples. Additionally, these monitoring techniques could play a vital role in ensuring the consistent quality and rigorous adherence to safety standards.

Pulses, Non destructive techniques, glyphosate residues, Chemometrics, Surface enhanced Raman spectroscopy, Hyperspectral imaging, Fourier transform infrared spectroscopy, Machine learning, Artificial intelligence, Chickpeas, Yellow peas, Lentils, Pesticides
Sindhu, S., & Manickavasagan, A. (2023). Nondestructive testing methods for pesticide residue in food commodities: A review. Comprehensive Reviews in Food Science and Food Safety, 22(2), 1226-1256.
Sindhu, S., Manickavasagan, A., & Ali, A. (2023). Effect of soaking conditions on glyphosate absorption in selected pulses: understanding solvent behaviour and morphological changes. International Journal of Environmental Analytical Chemistry, 1-16.
Sindhu, S., Sharma, S., & Manickavasagan, A. (2023). Evaluating chemometric techniques for non-destructive detection of glyphosate residues in single pulse grains by using FTIR spectroscopy. Journal of Consumer Protection and Food Safety, 1-18.