Deep chemometrics for retrospective auditing of the water-nutrient nexus in olive oil via 1D-CNN and explainable AI

Systematic Review on Artificial Intelligence‐Enhanced Hyperspectral Imaging for Food Processing: Applications, Technical Bottlenecks, and Future Perspectives

Hyperspectral imaging (HSI) integrates spatial and spectral analysis for nondestructive food quality monitoring. While the integration of artificial intelligence (AI) has significantly advanced HSI analytics, prior reviews have predominantly focused on static, postharvest quality grading. This leaves a critical gap r...

Illusions of Accuracy: Widespread Data Leakage Distorts Machine Learning Results in Biochar Research

To ensure methodological rigour and reliable generalisation, biochar ML research must adopt grouped or study‐level data‐splitting strategies, maintain strict separation of preprocessing steps, and incorporate hierarchical or domain‐aware validation frameworks.

Prior-Attention-Mechanism-Based Spectral Identifier for Revealing Hazardous Chemical Additives in Commercial PVC

Additive components largely determine the environmental and health risks of commercial plastics, but rapid, nondestructive identification of multiple additives in polymer matrices remains challenging. To overcome this challenge, we developed a prior-knowledge-guided triple-scale convolutional neural network (SpecNet)...

Hybrid Machine Learning-Assisted Raman Spectroscopy with Generative Feature Augmentation for Pharmaceutical Identification

Rapid and reliable identification of pharmaceutical residues is important for safeguarding public health, ensuring food safety, and enabling practical Raman-based screening. In this study, we propose HyMLRaman, a hybrid Raman spectroscopy framework that combines deep spectral feature extraction, generative models, and...

Benchmarking vision transformers and convolutional networks for sugarcane leaf disease classification on a consolidated multi-source dataset

Comparative Deep Learning and RISE-Based Explainable AI Framework for Orange Fruit Disease Detection

Orange diseases significantly threaten fruit quality and agricultural productivity, resulting in substantial economic losses. Traditional diagnostic methods are limited by their labor-intensive nature, high costs, and reliance on subjective expert evaluation, increasing the demand for automated solutions. This study sy...