The inhalation of a water aerosol from a humidifier containing disinfectants has led to serious lung injuries in Korea. To promote the safe use of products, the Korean government enacted regulations on the chemicals in various consumer products that could have adverse health effects. Given the concern over the potential health risks associated with the hazardous ingredients in deodorizing consumer products, 17 ingredients were analyzed and assessed according to their health risk on 3 groups by the application type in 47 deodorizing products. The risk assessment study followed a stepwise procedure (e.g., collecting toxicological information, hazard identification/exposure assessment, and screening and detailed assessment for inhalation and dermal routes). The worst-case scenario and maximum concentration determined by the product purpose and application type were used as the screening assessment. In a detailed assessment, the 75th exposure factor values were used to estimate the assumed reasonable exposure to ingredients. The exposed concentrations of seven ingredients were calculated. Due to limitation of toxicity information, butylated hydroxyl toluene for a consumer’s exposure via the dermal route only was conducted for a detailed assessment. This study showed that the assessed ingredients have no health risks at their maximum concentrations in deodorizing products. This approach can be used to establish guidelines for ingredients that may pose inhalation and dermal hazards.

Phthalate esters (PAEs) are widely employed as plasticizers to enhance the durability, flexibility, and processability of polymer-based materials. Because PAEs do not form covalent bonds with polymer matrices, they can readily migrate into the environment. This raises significant ecological issues and human health concerns such as endocrine disruption and developmental toxicity. In this study, we developed a surface-enhanced Raman spectroscopy (SERS)-based analytical platform integrated with deep learning algorithms for the rapid and accurate identification and classification of seven representative PAEs. Plasmonic gold nanopillar (AuNP) substrates were engineered to form vertically and horizontally oriented nanogap structures that generate intense localized electromagnetic hotspots, substantially amplifying the Raman signals of adsorbed PAEs. Comprehensive spectral datasets were collected through SERS detection using a portable Raman spectrometer. Deep neural network (DNN) models trained on these data achieved robust classification performance with an accuracy of 99.4 % across all PAE species. Furthermore, SHapley Additive exPlanations (SHAP) analysis provided interpretable insights into the most discriminative Raman spectral features driving the model predictions. The platform was successfully applied to detect PAEs in commercially available consumer products at concentrations near the regulatory threshold of 0.1 % (w/w) established by regulatory bodies such as the European Union's RoHS Directive and the US CPSIA. These findings demonstrate that the integration of nanostructure-enhanced SERS and deep learning constitutes a powerful, high-throughput, and field-deployable approach for reliable PAE detection, with broad applicability in environmental monitoring, consumer product safety evaluation, and regulatory compliance.