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Keywords = black olm

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20 pages, 7370 KiB  
Article
Explainable Deep Learning to Predict Kelp Geographical Origin from Volatile Organic Compound Analysis
by Xuming Kang, Zhijun Tan, Yanfang Zhao, Lin Yao, Xiaofeng Sheng and Yingying Guo
Foods 2025, 14(7), 1269; https://doi.org/10.3390/foods14071269 - 4 Apr 2025
Viewed by 501
Abstract
In addition to its flavor and nutritional value, the origin of kelp has become a crucial factor influencing consumer choices. Nevertheless, research on kelp’s origin traceability by volatile organic compound (VOC) analysis is lacking, and the application of deep learning in this field [...] Read more.
In addition to its flavor and nutritional value, the origin of kelp has become a crucial factor influencing consumer choices. Nevertheless, research on kelp’s origin traceability by volatile organic compound (VOC) analysis is lacking, and the application of deep learning in this field remains scarce due to its black-box nature. To address this gap, we attempted to identify the origin of kelp by analyzing its VOCs in conjunction with explainable deep learning. In this work, we identified 115 distinct VOCs in kelp samples using gas chromatography coupled with ion mobility spectroscopy (GC-IMS), of which 68 categories were discernible. Consequently, we developed a comprehensible one-dimensional convolutional neural network (1D-CNN) model that incorporated 107 VOCs exhibiting significant regional disparities (p < 0.05). The model successfully discerns the origin of kelp, achieving perfect metrics across accuracy (100%), precision (100%), recall (100%), F1 score (100%), and AUC (1.0). SHapley Additive exPlanations (SHAP) analysis highlighted the impact of features such as 1-Octen-3-ol-M, (+)-limonene, allyl sulfide-D, 1-hydroxy-2-propanone-D, and (E)-2-hexen-1-al-M on the model output. This research provides deeper insights into how critical product features correlate with specific geographic information, which in turn boosts consumer trust and promotes practical utilization in actual settings. Full article
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26 pages, 9214 KiB  
Article
Evaluation of Agricultural Measures to Safeguard the Vulnerable Karst Groundwater Habitat of the Black Olm (Proteus anguinus parkelj) from Nitrate Pollution
by Matjaž Glavan and Rozalija Cvejić
Sustainability 2024, 16(24), 11309; https://doi.org/10.3390/su162411309 - 23 Dec 2024
Viewed by 982
Abstract
The black olm (Proteus anguinus parkelj Sket & Arntzen) is an endemic species found exclusively in the Dobličica River subterranean water systems of the Dinaric karst in southern Slovenia. These unique habitats are vulnerable to contamination due to rapid water flow, primarily [...] Read more.
The black olm (Proteus anguinus parkelj Sket & Arntzen) is an endemic species found exclusively in the Dobličica River subterranean water systems of the Dinaric karst in southern Slovenia. These unique habitats are vulnerable to contamination due to rapid water flow, primarily from nitrates from agricultural fertilisers and untreated urban wastewater. The safe limit of nitrate concentration for olms is 9.2 mg NO3/L, yet measurements in karst springs have shown levels ranging from 3 mg to over 20 mg NO3/L. The SWAT modelling tool assessed agri-environmental and land use scenarios for their impact on nitrate leaching. Using the model, we identified hotspots with high nitrogen leaching potential that require immediate attention and implementation of better agricultural practices for fertiliser use. For these hotspots, the most effective approach combines scenarios of cover crops (R2), reduced fertilisation (R3), crop rotation (R4), and conversion of cropland to grassland (E2, E4, E5), potentially decreasing nitrate leaching by up to 60%. Implementing the best scenarios is expected to reduce nitrogen levels below the limit value of 9.2 mg NO3/L, essential for maintaining the black olm habitat. Full article
(This article belongs to the Topic Human Impact on Groundwater Environment)
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