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Optimization of double-vortex-assisted matrix solid-phase dispersion for the rapid determination of paraben preservative residues in leafy vegetables.

ABSTRACT: The extensive use of preservatives during the growth, transport and storage of vegetables has been a concern because of their known or suspected toxicity that jeopardizes human health. This paper reports the development of a technique that rapidly determines the presence of five paraben preservative residues in leafy vegetables using double-vortex-assisted matrix solid-phase dispersion (DVA-MSPD) and UHPLC-electrospray ionization(-)-quadrupole time-of-flight mass spectrometry detection. We simplified the original MSPD technique by eliminating the use of mortar/pestle and SPE-column procedures. The DVA-MSPD factors were screened by a multilevel categorical design, and then optimized by Box-Behnken Design plus response surface methodology. The limits of quantification were 1.2-1.8 ng g^-1 (dry weight). The satisfactory average recoveries were 85-104% with RSDs less than 10%. The developed method was successfully employed for the rapid determination of selected paraben residues at trace-level in leafy vegetable samples.

SUBMITTER: Yang CJ

PROVIDER: S-EPMC9056883 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature

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Optimization of double-vortex-assisted matrix solid-phase dispersion for the rapid determination of paraben preservative residues in leafy vegetables.

Yang Chun-Ju CJ Chung Wu-Hsun WH Ding Wang-Hsien WH

RSC advances 20200925 58

The extensive use of preservatives during the growth, transport and storage of vegetables has been a concern because of their known or suspected toxicity that jeopardizes human health. This paper reports the development of a technique that rapidly determines the presence of five paraben preservative residues in leafy vegetables using double-vortex-assisted matrix solid-phase dispersion (DVA-MSPD) and UHPLC-electrospray ionization(-)-quadrupole time-of-flight mass spectrometry detection. We simpl ...[more]

PMID: 35515645

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Project description:Cadmium (Cd) is a toxic element that can accumulate in edible plant tissues and negatively impact human health. Traditional Cd quantification methods are time-consuming, expensive, and generate a lot of toxic waste, slowing development of methods to reduce uptake. The objective of this study was to determine whether hyperspectral imaging (HSI) and machine learning (ML) can be used to predict Cd concentrations in plants using kale (Brassica oleracea) and basil (Ocimum basilicum) as model crops. The experiments were conducted in an automated phenotyping facility where all environmental conditions except soil Cd concentration were kept constant. Cd concentrations were determined at harvest using traditional methods and used to train the ML models with data collected from the imaging sensor. Visible/near infrared (VNIR) images were also collected at harvest and processed to calculate reflectance at 473 bands between 400 to 998 nm. All reflectance spectra were subject to the feature selection algorithm ReliefF and Principal Component Analysis (PCA) to generate data and provide input to evaluate three ML classification models: artificial neural network (ANN), ensemble learning (EL), and support vector machine (SVM). Plants were categorized according to Cd concentrations higher or lower than the safety threshold of 0.2 mg kg−1 Cd. Wavelengths with the highest ranks for Cd detection were between 519 and 574, and 692 and 732 nm, indicating that Cd content likely altered the plants’ chlorophyll content and altered leaf internal structure. All models were able to sort the plants into groups, though the model with the best F1 score was the ANN for the validation subset that utilized reflectance from all wavelengths. This study demonstrates that HSI and ML are promising technologies for the fast and precise diagnosis of Cd in leafy green plants, though additional studies are needed to adapt this approach for more complex field environments. Graphical abstract Highlights • Classification modeling can be used to estimate Cd concentrations in leafy greens.• Neural network model was the best algorithm in this study for classifying plants.• Cadmium induced changes in wavelengths linked with chlorophyll and leaf structure. ReliefF; Ensemble learning; Support vector machine; Neural networks basil; Kale.

Dataset Information

Optimization of double-vortex-assisted matrix solid-phase dispersion for the rapid determination of paraben preservative residues in leafy vegetables.

Publications

Optimization of double-vortex-assisted matrix solid-phase dispersion for the rapid determination of paraben preservative residues in leafy vegetables.

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