Recent Advances in THz Detection of Water

Ge, Hongyi; Sun, Zhenyu; Jiang, Yuying; Wu, Xin; Jia, Zhiyuan; Cui, Guodong; Zhang, Yuan

doi:10.3390/ijms241310936

Cited by 10 publications

(6 citation statements)

References 114 publications

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“…These results support this study’s hypotheses, including the hypotheses predicting that a semisupervised FM-detection model is feasible, the performance of the FM-detection model is affected by the spectral heterogeneity between the muscle tissue and fatty tissue, a 1D U-Net discriminator enhances the model’s performance, and the optimal wavelength range exists. The feasibility of semisupervised learning was successfully demonstrated by enhancing the previous supervised sensor fusion technique [ 7 ]. This was achieved by reducing two imaging modalities into a single imaging modality, substituting supervised learning with semisupervised learning, and exploring deep learning.…”

Section: Resultsmentioning

confidence: 99%

“…Thermal imaging can be affected by the temperature of the surrounding environment [ 5 ]. Terahertz spectroscopy’s nonionizing radiation and low photon energies suffer from their limited ability to penetrate water [ 6 , 7 ], which is highly prevalent in poultry meat (~75% water in the muscles) [ 8 ]. Color-based computer vision techniques can also suffer from similarities in color between FM and meat samples [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

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Semisupervised Deep Learning for the Detection of Foreign Materials on Poultry Meat with Near-Infrared Hyperspectral Imaging

Campos

Yoon

Chung

et al. 2023

Sensors

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A novel semisupervised hyperspectral imaging technique was developed to detect foreign materials (FMs) on raw poultry meat. Combining hyperspectral imaging and deep learning has shown promise in identifying food safety and quality attributes. However, the challenge lies in acquiring a large amount of accurately annotated/labeled data for model training. This paper proposes a novel semisupervised hyperspectral deep learning model based on a generative adversarial network, utilizing an improved 1D U-Net as its discriminator, to detect FMs on raw chicken breast fillets. The model was trained by using approximately 879,000 spectral responses from hyperspectral images of clean chicken breast fillets in the near-infrared wavelength range of 1000–1700 nm. Testing involved 30 different types of FMs commonly found in processing plants, prepared in two nominal sizes: 2 × 2 mm2 and 5 × 5 mm2. The FM-detection technique achieved impressive results at both the spectral pixel level and the foreign material object level. At the spectral pixel level, the model achieved a precision of 100%, a recall of over 93%, an F1 score of 96.8%, and a balanced accuracy of 96.9%. When combining the rich 1D spectral data with 2D spatial information, the FM-detection accuracy at the object level reached 96.5%. In summary, the impressive results obtained through this study demonstrate its effectiveness at accurately identifying and localizing FMs. Furthermore, the technique’s potential for generalization and application to other agriculture and food-related domains highlights its broader significance.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Semisupervised Deep Learning for the Detection of Foreign Materials on Poultry Meat with Near-Infrared Hyperspectral Imaging

Campos

Yoon

Chung

et al. 2023

Sensors

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“…This result indicates that our THz spectroscopic images detected an increase in brain tissue water content. This is attributed to the sensitivity of the lower-frequency bands in the THz range, which can detect more changes in water content caused by the higher refractive index of water molecules 25–27 . Based on the results of the individual frequency images, the difference between the two groups in the THz spectroscopic images was derived from the difference in THz reflectance caused by the brain tissue water content.…”

Section: Rerultsmentioning

confidence: 99%

Impact of Cervical Lymphatic Obstruction on Brain Pathophysiology in Cervical Lymphedema Animal Models

Cheon,

Woo,

Cha

et al. 2024

Preprint

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BackgroundInjury to the cervical lymph nodes can lead to cervical lymphedema and subsequent fluid accumulation in the head and neck region, potentially causing pathophysiological alteration in the brain. This condition is thought to be linked with various neurological diseases, although the direct connection between cervical lymphatic obstruction and its effect on the brain has been difficult to establish.MethodsWe produced the disease animal models through lymph node dissection and radiation in fifteen male Sprague–Dawley rats aged 8 weeks and weighing 280–320 g. The models were specifically designed to induce lymphatic obstruction in the cervical region only, with no direct interventions applied to the brain. We evaluated swelling and lymphatic drainage in the head and neck for follow-up. The size of the lateral ventricles was verified through MRI, and changes in water content in brain tissue were directly measured. At 2 and 8 weeks, we observed immune cell infiltration, ventricular enlargement, and pathohistological changes in the harvested brain tissues.ResultsThe experimental animals exhibited lymphatic obstruction in the cervical region, with swelling, abnormal lymphatic drainage, and immune cell infiltration into the brain’s white matter, reminiscent of extremities lymphedema. MRI revealed lateral ventricular enlargement in these animals, indicative of increased cerebrospinal fluid levels compared to the control group. This increase in cerebrospinal fluid was associated with an increase in brain tissue water content, leading to pathophysiological changes akin to those seen in hydrocephalus and cerebral edema.ConclusionThe outcomes in this study underscore a significant link between lymphatic circulatory dysfunction and the onset of neurophysiological diseases. Cervical lymphedema showed pathophysiological changes similar to those seen in extremities lymphedema. However, these changes in the brain could be more critical than in the extremities. Our finding highlights the importance of understanding lymphatic system health in preventing and managing neurological conditions.

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“…Since terahertz radiation excites rotational and roto/vibrational transitions, polar liquids such as water are sensitive to these frequencies, exhibiting both strong absorption and reflection. So, while this allows us to determine the amount of moisture in food or water in biological tissues, including leaves and plants, it also severely limits deep penetration [26].…”

Section: Introductionmentioning

confidence: 99%

THz Data Analysis and Self-Organizing Map (SOM) for the Quality Assessment of Hazelnuts

Greco,

Giarnetti,

Giovenale

et al. 2024

Applied Sciences

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In recent years, the use of techniques based on electromagnetic radiation as an investigative tool in the agri-food industry has grown considerably, and between them, the application of imaging and THz spectroscopy has gained significance in the field of food quality control. This study presents the development of an experimental setup operating in transmission mode within the frequency range of 18 to 40 GHz, which was specifically designed for assessing various quality parameters of hazelnuts. The THz measurements were conducted to distinguish between healthy and rotten hazelnut samples. Two different data analysis techniques were employed and compared: a traditional approach based on data matrix manipulation and curve fitting for parameter extrapolation, and the utilization of a Self-Organizing Map (SOM), for which we use a neural network commonly known as the Kohonen neural network, which is recognized for its efficacy in analyzing THz measurement data. The classification of hazelnuts based on their quality was performed using these techniques. The results obtained from the comparative analysis of coding efforts, analysis times, and outcomes shed light on the potential applications of each method. The findings demonstrate that THz spectroscopy is an effective technique for quality assessment in hazelnuts, and this research serves to clarify the suitability of each analysis technique.

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Recent Advances in THz Detection of Water

Cited by 10 publications

References 114 publications

Semisupervised Deep Learning for the Detection of Foreign Materials on Poultry Meat with Near-Infrared Hyperspectral Imaging

Semisupervised Deep Learning for the Detection of Foreign Materials on Poultry Meat with Near-Infrared Hyperspectral Imaging

Impact of Cervical Lymphatic Obstruction on Brain Pathophysiology in Cervical Lymphedema Animal Models

THz Data Analysis and Self-Organizing Map (SOM) for the Quality Assessment of Hazelnuts

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