The mean concentrations of selected heavy metals in fruits and vegetables analyzed were: Lead (0.17 ± 0.38 mg/kg) > Mercury (0.06 ± 0.09 mg/kg) > Cadmium (0.02 ± 0.007 mg/kg) > Arsenic (0.002 ± 0.003 mg/kg). Among them, garlic showed the highest heavy metal accumulation followed by potato. The concentration of some heavy metals Fe, Mn, Cu, Zn, Pb, Cd and Hg in various vegetables (roots, stems, leafy, fruits, cereals and legumes) grown in four major industrial and urban cities .Heavy metals can have serious consequences for human health when vegetables polluted with these metals are ingested.

There are few methods used to detect heavy metal contamination such as

1. Atomic Absorption Analysis (AAS),

2. Neutron Activation Analysis (NAA),

3. X-ray Fluorescence Spectroscopy (XRF) and

4. Inductively Coupled Plasma Spectrometry (ICPMS)..

ANOVA, which stands for Analysis of Variance, is a statistical test used to analyze the difference between the means of more than two groups.

https://www.researchgate.net/figure/Results-of-ANOVA-comparisons-for-five-heavy-metals-concentrations-in-vegetable-fruit_tbl4_263426051

https://www.researchgate.net/figure/of-two-way-ANOVA-test-for-heavy-metal-levels-in-vegetables-harvested-from_tbl3_317804437

https://www.als-journal.com/1026-23-2/

When heavy metal results in vegetables are below the limit of detection (LOD), it is generally not recommended to take the LOD as the actual value. Instead, these results are typically reported as "below the limit of detection" or with a value of zero, as assuming the LOD could overestimate the actual concentration. This approach aligns with best practices in analytical chemistry to avoid misrepresentation of data.

I think that in your study, additional samples are necessary for the reconcentration process, or a review of the sample preparation procedures, including digestion or extraction, should be conducted.

Depende del área de procedencia del vegetal, si existe en el área contaminación o no; como se encuentran los metales en el suelo, con lo que descartamos problemas de la técnica de digestión o extracción.

When handling non-detects (ND) or results below the limit of detection (LOD) in your dataset, particularly when conducting statistical analyses like ANOVA, it's essential to choose a method that maintains the integrity of your data while providing valid and reliable results. Substituting non-detects with half the LOD is a common and often reasonable approach for conducting ANOVA, provided that the proportion of non-detects is not too large. However, for more robust and accurate analysis, especially with significant non-detects, consider using statistical methods like MLE or ROS. Ensure that you validate the assumptions of your chosen statistical tests and consider consulting a statistician to confirm the best approach for your specific dataset and research objectives.