Applications of THz Spectral Imaging in the Detection of Agricultural Products

A new horizon for neuroscience: terahertz biotechnology in brain research

Terahertz biotechnology has been increasingly applied in various biomedical fields and has especially shown great potential for application in brain sciences. In this article, we review the development of terahertz biotechnology and its applications in the field of neuropsychiatry. Available evidence indicates promising prospects for the use of terahertz spectroscopy and terahertz imaging techniques in the diagnosis of amyloid disease, cerebrovascular disease, glioma, psychiatric disease, traumatic brain injury, and myelin deficit. In vitro and animal experiments have also demonstrated the potential therapeutic value of terahertz technology in some neuropsychiatric diseases. Although the precise underlying mechanism of the interactions between terahertz electromagnetic waves and the biosystem is not yet fully understood, the research progress in this field shows great potential for biomedical noninvasive diagnostic and therapeutic applications. However, the biosafety of terahertz radiation requires further exploration regarding its two-sided efficacy in practical applications. This review demonstrates that terahertz biotechnology has the potential to be a promising method in the field of neuropsychiatry based on its unique advantages.

Recent advance in nondestructive imaging technology for detecting quality of fruits and vegetables: a review

As an integral part of daily dietary intake, the market demand for fruits and vegetables is continuously growing. However, traditional methods for assessing the quality of fruits and vegetables are prone to subjective influences, destructive to samples, and fail to comprehensively reflect internal quality, thereby resulting in various shortcomings in ensuring food safety and quality control. Over the past few decades, imaging technologies have rapidly evolved and been widely employed in nondestructive detection of fruit and vegetable quality. This paper offers a thorough overview of recent advancements in nondestructive imaging technologies for assessing the quality of fruits and vegetables, including hyperspectral imaging (HSI), fluorescence imaging (FI), magnetic resonance imaging (MRI), thermal imaging (TI), terahertz imaging, X-ray imaging (XRI), ultrasonic imaging, and microwave imaging (MWI). The principles and applications of these imaging techniques in nondestructive testing are summarized. The challenges and future trends of these technologies are discussed.

A Novel Terahertz Metamaterial Microfluidic Sensing Chip for Ultra-Sensitive Detection

A terahertz metamaterial microfluidic sensing chip for ultrasensitive detection is proposed to investigate the response of substances to terahertz radiation in liquid environments and enhance the molecular fingerprinting of trace substances. The structure consists of a cover layer, a metal microstructure, a microfluidic channel, a metal reflective layer, and a buffer layer from top to bottom, respectively. The simulation results show that there are three obvious resonance absorption peaks in the range of 1.5-3.0 THz and the absorption intensities are all above 90%. Among them, the absorption intensity at M1 = 1.971 THz is 99.99%, which is close to the perfect absorption, and its refractive index sensitivity and Q-factor are 859 GHz/RIU and 23, respectively, showing excellent sensing characteristics. In addition, impedance matching and equivalent circuit theory are introduced in this paper to further analyze the physical mechanism of the sensor. Finally, we perform numerical simulations using refractive index data of normal and cancer cells, and the results show that the sensor can distinguish different types of cells well. The chip can reduce the sample pretreatment time as well as enhance the interaction between terahertz waves and matter, which can be used for early disease screening and food quality and safety detection in the future.

Innovations in nondestructive assessment of baked products: Current trends and future prospects

Rising consumer awareness, coupled with advances in sensor technology, is propelling the food manufacturing industry to innovate and employ tools that ensure the production of safe, nutritious, and environmentally sustainable products. Amidst a plethora of nondestructive techniques available for evaluating the quality attributes of both raw and processed foods, the challenge lies in determining the most fitting solution for diverse products, given that each method possesses its unique strengths and limitations. This comprehensive review focuses on baked goods, wherein we delve into recently published literature on cutting-edge nondestructive methods to assess their feasibility for Industry 4.0 implementation. Emphasizing the need for quality control modalities that align with consumer expectations regarding sensory traits such as texture, flavor, appearance, and nutritional content, the review explores an array of advanced methodologies, including hyperspectral imaging, magnetic resonance imaging, terahertz, acoustics, ultrasound, X-ray systems, and infrared spectroscopy. By elucidating the principles, applications, and impacts of these techniques on the quality of baked goods, the review provides a thorough synthesis of the most current published studies and industry practices. It highlights how these methodologies enable defect detection, nutritional content prediction, texture evaluation, shelf-life forecasting, and real-time monitoring of baking processes. Additionally, the review addresses the inherent challenges these nondestructive techniques face, ranging from cost considerations to calibration, standardization, and the industry's overreliance on big data.

Enhancing Sensitivity and Selectivity in Pesticide Detection: A Review of Cutting-Edge Techniques

The primary goal of our review was to systematically explore and compare the state-of-the-art methodologies employed in the detection of pesticides, a critical component of global food safety initiatives. New approach methods in the fields of luminescent nanosensors, chromatography, terahertz spectroscopy, and Raman spectroscopy are discussed as precise, rapid, and versatile strategies for pesticide detection in food items and agroecological samples. Luminescent nanosensors emerge as powerful tools, noted for their portability and unparalleled sensitivity and real-time monitoring capabilities. Liquid and gas chromatography coupled to spectroscopic detectors, stalwarts in the analytical chemistry field, are lauded for their precision, wide applicability, and validation in diverse regulatory environments. Terahertz spectroscopy offers unique advantages such as noninvasive testing, profound penetration depth, and bulk sample handling. Meanwhile, Raman spectroscopy stands out with its nondestructive nature, its ability to detect even trace amounts of pesticides, and its minimal requirement for sample preparation. While acknowledging the maturity and robustness of these techniques, our review underscores the importance of persistent innovation. These methodologies' significance extends beyond their present functions, highlighting their adaptability to meet ever-evolving challenges. Environ Toxicol Chem 2024;43:1468-1484. © 2024 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

Combining AI Tools with Non-Destructive Technologies for Crop-Based Food Safety: A Comprehensive Review

On a global scale, food safety and security aspects entail consideration throughout the farm-to-fork continuum, considering food's supply chain. Generally, the agrifood system is a multiplex network of interconnected features and processes, with a hard predictive rate, where maintaining the food's safety is an indispensable element and is part of the Sustainable Development Goals (SDGs). It has led the scientific community to develop advanced applied analytical methods, such as machine learning (ML) and deep learning (DL) techniques applied for assessing foodborne diseases. The main objective of this paper is to contribute to the development of the consensus version of ongoing research about the application of Artificial Intelligence (AI) tools in the domain of food-crop safety from an analytical point of view. Writing a comprehensive review for a more specific topic can also be challenging, especially when searching within the literature. To our knowledge, this review is the first to address this issue. This work consisted of conducting a unique and exhaustive study of the literature, using our TriScope Keywords-based Synthesis methodology. All available literature related to our topic was investigated according to our criteria of inclusion and exclusion. The final count of data papers was subject to deep reading and analysis to extract the necessary information to answer our research questions. Although many studies have been conducted, limited attention has been paid to outlining the applications of AI tools combined with analytical strategies for crop-based food safety specifically.

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

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.

Recent Advances in THz Detection of Water

The frequency range of terahertz waves (THz waves) is between 0.1 and 10 THz and they have properties such as low energy, penetration, transients, and spectral fingerprints, which are especially sensitive to water. Terahertz, as a frontier technology, have great potential in interpreting the structure of water molecules and detecting biological water conditions, and the use of terahertz technology for water detection is currently frontier research, which is of great significance. Firstly, this paper introduces the theory of terahertz technology and summarizes the current terahertz systems used for water detection. Secondly, an overview of theoretical approaches, such as the relaxation model and effective medium theory related to water detection, the relationship between water molecular networks and terahertz spectra, and the research progress of the terahertz detection of water content and water distribution visualization, are elaborated. Finally, the challenge and outlook of applications related to the terahertz wave detection of water are discussed. The purpose of this paper is to explore the research domains on water and its related applications using terahertz technology, as well as provide a reference for innovative applications of terahertz technology in moisture detection.

Hidden Dangerous Object Recognition in Terahertz Images Using Deep Learning Methods

As a harmless detection method, terahertz has become a new trend in security detection. However, there are inherent problems such as the low quality of the images collected by terahertz equipment and the insufficient detection accuracy of dangerous goods. This work advances BiFPN at the neck of YOLOv5 of the deep learning model as a mechanism to improve low resolution. We also perform transfer learning, thereby fine-tuning the pre-training weight of the backbone for migration learning in our model. Results from experimental analysis reveal that mAP@0.5 and mAP@0.5:0.95 values witness a percentage increase of 0.2% and 1.7%, respectively, attesting to the superiority of the proposed model to YOLOv5, which is the state-of-the-art model in object detection.

Selection criteria of image reconstruction algorithms for terahertz short-range imaging applications

Terahertz (THz) imaging has been regarded as cutting-edge technology in a wide range of applications due to its ability to penetrate through opaque materials, non-invasive nature, and its increased bandwidth capacity. Recently, THz imaging has been extensively researched in security, driver assistance technology, non-destructive testing, and medical applications. The objective of this review is to summarize the selection criteria for current state-of-the-art THz image reconstruction algorithms developed for short-range imaging applications over the last two decades. Moreover, we summarize the selected algorithms' performance and their implementation process. This study provides an in-depth understanding of the fundamentals of image reconstruction algorithms related to THz short-range imaging and future aspects of algorithm processing and selection.