Critical Evaluation of Analytical Methods for the Determination of Anthropogenic Organic Contaminants in Edible Oils: An Overview of the Last Five Years

López-Ruiz, Rosalía; Marín-Sáez, Jesús; Prestes, Osmar D.; Romero-González, Roberto; Garrido Frenich, Antonia

doi:10.1080/10408347.2022.2040352

Cited by 1 publication

(1 citation statement)

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“…By 2021, the total consumption of vegetable oil in the country reached 42.545 million tons, an increase of 4.5% year-on-year, and the overall consumption scale reached a new high. During the planting process of oil crops, they are often attacked by pests and diseases, making pesticide control essential [ 6 ]. However, due to the high fat content in edible oil, fat-soluble pesticides can easily accumulate during the processing of edible oil and enter the human body through dietary consumption, posing a threat to consumers’ health.…”

Section: Introductionmentioning

confidence: 99%

Monitoring of Chlorpyrifos Residues in Corn Oil Based on Raman Spectral Deep-Learning Model

Xue

Jiang

2023

Foods

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This study presents a novel method for the quantitative detection of residual chlorpyrifos in corn oil through Raman spectroscopy using a combined long short-term memory network (LSTM) and convolutional neural network (CNN) architecture. The QE Pro Raman+ spectrometer was employed to collect Raman spectra of corn oil samples with varying concentrations of chlorpyrifos residues. A deep-learning model based on LSTM combined with a CNN structure was designed to realize feature self-learning and model training of Raman spectra of corn oil samples. In the study, it was discovered that the LSTM-CNN model has superior generalization performance compared to both the LSTM and CNN models. The root-mean-square error of prediction (RMSEP) of the LSTM-CNN model is 12.3 mg·kg−1, the coefficient of determination (RP2) is 0.90, and the calculation of the relative prediction deviation (RPD) results in a value of 3.2. The study demonstrates that the deep-learning network based on an LSTM-CNN structure can achieve feature self-learning and multivariate model calibration for Raman spectra without preprocessing. The results of this study present an innovative approach for chemometric analysis using Raman spectroscopy.

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Section: Introductionmentioning

confidence: 99%