An XAI-enabled 2D-CNN model for non-destructive detection of natural adulterants in the wonder hot variety of red chilli powder

An AI-based DenseNet-GradCAM framework rapidly detects natural adulterants in red chili powder, ensuring food safety with high accuracy and explainability.

Authors

Dilpreet Singh Brar (ORCID: https://orcid.org/0000-0001-5068-3135)
Vikas Nanda (ORCID: https://orcid.org/0000-0003-0947-9065)
B. M. Singh (ORCID: https://orcid.org/0000-0002-3752-2369)

Institutions

Sant Longowal Institute of Engineering and Technology (IN)

Publication Details

Journal: Sustainable Food Technology
Published: 2025-01-01
DOI: https://doi.org/10.1039/d5fb00118h
Citations: 5
Primary Topic: Spectroscopy and Chemometric Analyses
Type: article
Field-Weighted Citation Impact: 3.76

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article

An XAI-enabled 2D-CNN model for non-destructive detection of natural adulterants in the wonder hot variety of red chilli powder

Dilpreet Singh Brar, Vikas Nanda, B. M. Singh

Jan 1, 2025

5 citations

Sustainable Food Technology

Spectroscopy and Chemometric Analyses

3.76

article

An XAI-enabled 2D-CNN model for non-destructive detection of natural adulterants in the wonder hot variety of red chilli powder

Dilpreet Singh Brar, Vikas Nanda, B. M. Singh

January 1, 2025article en

5 citations

Abstract

An AI-based DenseNet-GradCAM framework rapidly detects natural adulterants in red chili powder, ensuring food safety with high accuracy and explainability.

Sustainable Food TechnologyVol. 3(4)

Sant Longowal Institute of Engineering and Technology (IN)

Openalex Percentile: Top 7%

Spectroscopy and Chemometric Analyses

3.76

DOI Open Access PDF OpenAlex

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An XAI-enabled 2D-CNN model for non-destructive detection of natural adulterants in the wonder hot variety of red chilli powder — Dilpreet Singh Brar, Vikas Nanda, et al. · Sustainable Food Technology (2025) | TGRS Research Map | TGRS