Citas bibligráficas
Villanueva, E., (2021). Espectroscopía vibracional aplicada a la determinación de residuos de pesticidas en Cacao (Theobroma cacao L.) [Universidad Nacional Agraria La Molina]. https://hdl.handle.net/20.500.12996/4582
Villanueva, E., Espectroscopía vibracional aplicada a la determinación de residuos de pesticidas en Cacao (Theobroma cacao L.) []. PE: Universidad Nacional Agraria La Molina; 2021. https://hdl.handle.net/20.500.12996/4582
@mastersthesis{renati/248994,
title = "Espectroscopía vibracional aplicada a la determinación de residuos de pesticidas en Cacao (Theobroma cacao L.)",
author = "Villanueva López, Eudes",
publisher = "Universidad Nacional Agraria La Molina",
year = "2021"
}
The present research aims to develop a vibrational spectroscopy method for the analysis of pesticide residues in Peruvian cocoa beans. Cocoa beans (34 without pesticides and 41 with pesticides, based on chromatographic reports) were analyzed in different presentations (shell, nib, defatted nib and cocoa butter) by near infrared spectroscopy (NIR), medium infrared (MIR) and Raman. Beans showed residues of 2,4-dichlorofexiacetic acid (majority), carbaryl, cypermethrin and chlorpyrifos. Chemometric analysis of soft independent modeling of classes analogy (SIMCA) determined that the MIR method, FT-IR 4500 portable with triple reflection total attenuated refractive accessory (ATR), produced the best discrimination between nibs (with and without presence pesticide) in the aliphatic spectral region of 3200 - 2800 cm−1 with a distance between classes (ICD) of 3,28. The NIR and Raman methods were not effective under our experimental conditions. Artificial contamination of pesticides in cocoa nibs allowed to verify the functional groups responsible for discrimination in SIMCA, However, when these pesticides were extracted with acetonitrile, the discriminative region varied (1200 – 800 cm−1) evidencing that the liposolubility of pesticides influences their detection. Finally, partial least squares regression analysis (PLSR) showed that the method developed was able to predict the concentration of pesticides in cocoa nibs with a calibration correlation coefficient (Rcal) of 0,957 and validation (Rval) of 0,954, a standard calibration error (SEC) of 14,587 ppb and standard cross validation error (SECV) of 14,873 ppb, using four optimal factors. The detection limit (LOD) was set at 9,788 ppb and the quantification limit (LOQ) at 23,088 ppb. It is concluded that it is possible to implement this rapid technique and use it as screening. Keywords: cocoa, nib, pesticide residues, infrared spectroscopy, Raman
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