Peer Review Record

Manuscript ID: JOSTA-202609-020D

Article Title: Surveillance of Pesticide Residues in Marketed Tender Coconut from Southern India

Double-anonymous review Accepted

Review Summary

Item	Details
Submission Date	08 September 2026
Editorial Screening	Completed
External Reviewers	2
Review Rounds	2
Revision Type	Major Revision
Final Decision	Accepted
Acceptance Date	11 October 2026

Item

Details

Submission Date

08 September 2026

Editorial Screening

Completed

External Reviewers

Review Rounds

Revision Type

Major Revision

Final Decision

Accepted

Acceptance Date

11 October 2026

Editorial Assessment

The manuscript reports a residue-monitoring study of marketed tender coconut samples from southern India, using modified QuEChERS extraction with LC-MS/MS and GC-MS/MS. Reviewers considered the topic relevant to food safety, residue monitoring, and sustainable agricultural production, and found the manuscript logically organised, but required substantial clarification and correction before publication. The principal concerns related to analytical validation, including an apparent inconsistency between the stated limit of quantification and the residue concentrations reported in Table 3, and the absence of a full validation dataset and complete analyte list. Reviewers also asked for a clearer account of the sampling design, including the unbalanced distribution of samples across states and the reliance on street vendors, and for the study to acknowledge that market sampling does not establish farm-level source, pesticide application history, or off-label use. Further requests concerned the basis and commodity applicability of the MRL comparisons, consistency of reporting units, a clear distinction between residue occurrence and dietary risk, statistical or descriptive treatment of the results, more detailed instrumental methodology, and a stronger framing of the findings in relation to sustainable agriculture. The authors satisfactorily addressed all comments, after which the manuscript was accepted for publication.

Review Timeline

Submission Received

08 September 2026

Manuscript submitted through the JOSTA submission portal.

Editorial Screening Completed

12 September 2026

Manuscript passed preliminary editorial checks.

Peer Review Initiated

16 September 2026

Two independent reviewers accepted the review assignment.

Reviewer Reports Received

29 September 2026

Reviewers recommended publication following major revision.

Revised Manuscript Received

08 October 2026

Authors submitted a revised version addressing reviewer concerns.

Final Decision

11 October 2026

Accepted for publication.

Published Online

15 October 2026

Article published in Volume 2, Issue 4.

Key Issues Addressed During Revision

Reviewer concerns are summarised by category. Specific comments and author responses are confidential.

Category	Nature of Concern	Status
Analytical Validation – LOD/LOQ Consistency	Apparent inconsistency between the stated LOQ (0.01 mg kg⁻¹ for all insecticides) and lower residue concentrations reported in Table 3 resolved, with analyte- and matrix-specific LOD and LOQ values and the criterion for reporting low concentrations clearly stated	✓ Addressed
Analytical Validation – Method Performance	Full validation data provided in place of broad ranges, including calibration range, regression equation, R², recovery and RSD at each fortification level, number of replicates per sample, and matrix effect, with the basis on which the method was evaluated made explicit	✓ Addressed
Analyte Scope	Complete list of insecticides included in the multi-residue method provided, with the analytical platform for each compound, so that the detection of only two compounds can be interpreted in context, including across the different locations, agroclimatic zones, and states sampled	✓ Addressed
Sampling Design	Sampling strategy explained, including how locations and vendors were selected, how a sample was defined, sampling dates, coconut maturity stage, and the rationale for the total of 85 samples and the unbalanced state-wise distribution (predominantly Kerala), with the limits this places on conclusions for southern India as a whole	✓ Addressed
Sampling Source Limitation	Rationale for sampling from street vendors rather than farms stated, with explicit acknowledgement that market samples do not establish farm of origin, pesticide application history, dosage, pre-harvest interval, or whether residues arose from coconut cultivation	✓ Addressed
Interpretation of Residue Sources	Statements on off-label or non-recommended pesticide use, including the role of root feeding, moderated to cautious wording, since residue detection alone cannot identify application route, crop, or timing	✓ Addressed
MRL Comparison & Applicability	Scientific basis for the Codex, EU, and CDB MRL comparisons justified, with the exact source and commodity definition for each limit tabulated, and positive samples, residue observations, samples exceeding MRL, and matrix-specific exceedances clearly distinguished	✓ Addressed
Units & Reporting Consistency	Concentrations standardised to a single unit (mg kg⁻¹) with appropriate conversion, so that comparisons with MRLs are consistent	✓ Addressed
Occurrence versus Health Risk	Residue detection and regulatory compliance distinguished from dietary exposure and toxicological risk; statements implying consumer health risk removed or qualified, since no dietary risk assessment was performed	✓ Addressed
Statistical Treatment	Appropriate descriptive statistics added (e.g. detection frequency, state- and matrix-wise occurrence, median and maximum concentrations, proportion exceeding MRL), with discussion of whether differences between locations or between coconut water and meat can be statistically evaluated given the monitoring objective	✓ Addressed
Analytical Methodology Detail	Instrument models, chromatographic columns and dimensions, mobile phases, flow rate, injection volume, and ionisation mode added to support reproducibility	✓ Addressed
Discussion & Comparison with Prior Studies	Conclusions about the extent of contamination reworded to reflect the market-sampled, Kerala-dominated dataset, and the relevance of a cited prior study (Kariyannan et al. 2024) to coconut or to another commodity clarified	✓ Addressed
Relevance to Sustainable Agriculture	Findings more clearly linked to sustainable agricultural practice, including reduced inappropriate pesticide use, integrated pest management, residue-conscious crop protection, and compliance with good agricultural practices	✓ Addressed
Terminology & Formatting	Consistent use of "pesticide" and "insecticide" residues, italicised scientific names, corrected spelling and chemical formulae, uniform reference formatting, clear distinction between MRL, LOQ, and LOD, clarified reporting of MRL exceedances in the Abstract, and reconsideration of the phrase calling for standardised limits given existing Codex, EU, and CDB comparisons	✓ Addressed

Category

Nature of Concern

Status

Analytical Validation – LOD/LOQ Consistency

Apparent inconsistency between the stated LOQ (0.01 mg kg⁻¹ for all insecticides) and lower residue concentrations reported in Table 3 resolved, with analyte- and matrix-specific LOD and LOQ values and the criterion for reporting low concentrations clearly stated

✓ Addressed

Analytical Validation – Method Performance

Full validation data provided in place of broad ranges, including calibration range, regression equation, R², recovery and RSD at each fortification level, number of replicates per sample, and matrix effect, with the basis on which the method was evaluated made explicit

✓ Addressed

Analyte Scope

Complete list of insecticides included in the multi-residue method provided, with the analytical platform for each compound, so that the detection of only two compounds can be interpreted in context, including across the different locations, agroclimatic zones, and states sampled

✓ Addressed

Sampling Design

Sampling strategy explained, including how locations and vendors were selected, how a sample was defined, sampling dates, coconut maturity stage, and the rationale for the total of 85 samples and the unbalanced state-wise distribution (predominantly Kerala), with the limits this places on conclusions for southern India as a whole

✓ Addressed

Sampling Source Limitation

Rationale for sampling from street vendors rather than farms stated, with explicit acknowledgement that market samples do not establish farm of origin, pesticide application history, dosage, pre-harvest interval, or whether residues arose from coconut cultivation

✓ Addressed

Interpretation of Residue Sources

Statements on off-label or non-recommended pesticide use, including the role of root feeding, moderated to cautious wording, since residue detection alone cannot identify application route, crop, or timing

✓ Addressed

MRL Comparison & Applicability

Scientific basis for the Codex, EU, and CDB MRL comparisons justified, with the exact source and commodity definition for each limit tabulated, and positive samples, residue observations, samples exceeding MRL, and matrix-specific exceedances clearly distinguished

✓ Addressed

Units & Reporting Consistency

Concentrations standardised to a single unit (mg kg⁻¹) with appropriate conversion, so that comparisons with MRLs are consistent

✓ Addressed

Occurrence versus Health Risk

Residue detection and regulatory compliance distinguished from dietary exposure and toxicological risk; statements implying consumer health risk removed or qualified, since no dietary risk assessment was performed

✓ Addressed

Statistical Treatment

Appropriate descriptive statistics added (e.g. detection frequency, state- and matrix-wise occurrence, median and maximum concentrations, proportion exceeding MRL), with discussion of whether differences between locations or between coconut water and meat can be statistically evaluated given the monitoring objective

✓ Addressed

Analytical Methodology Detail

Instrument models, chromatographic columns and dimensions, mobile phases, flow rate, injection volume, and ionisation mode added to support reproducibility

✓ Addressed

Discussion & Comparison with Prior Studies

Conclusions about the extent of contamination reworded to reflect the market-sampled, Kerala-dominated dataset, and the relevance of a cited prior study (Kariyannan et al. 2024) to coconut or to another commodity clarified

✓ Addressed

Relevance to Sustainable Agriculture

Findings more clearly linked to sustainable agricultural practice, including reduced inappropriate pesticide use, integrated pest management, residue-conscious crop protection, and compliance with good agricultural practices

✓ Addressed

Terminology & Formatting

Consistent use of "pesticide" and "insecticide" residues, italicised scientific names, corrected spelling and chemical formulae, uniform reference formatting, clear distinction between MRL, LOQ, and LOD, clarified reporting of MRL exceedances in the Abstract, and reconsideration of the phrase calling for standardised limits given existing Codex, EU, and CDB comparisons

✓ Addressed

Peer Review Statement

Journal of Sustainable Technology in Agriculture (JOSTA) employs a double-anonymous peer review process. The identities of authors and reviewers remain mutually confidential throughout the review and publication process. Reviewer reports and correspondence are not published.

Voluntary reviewer disclosure. JOSTA respects reviewers' autonomy regarding attribution. A reviewer who wishes to be identified in the published article may notify the editorial office at any point before final acceptance. Upon written consent, the reviewer's name and affiliation will be acknowledged in the Publication & Reviewer Details section of the article. Reviewers who do not provide consent remain anonymous and are listed as Anonymous. This policy is strictly voluntary; no reviewer is under any obligation to disclose their identity.

This page provides a transparent summary of the editorial and peer-review process in accordance with JOSTA's open-science commitments. It does not constitute a full disclosure of reviewer comments or author responses.