Study of 2,4-D Spectral Characteristics and Its Detection in Zizania Latifolia Using Terahertz Time-Domain Spectroscopy

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.

Discrimination of Pericarpium Citri Reticulatae in different years using Terahertz Time-Domain spectroscopy combined with convolutional neural network

Pericarpium Citri Reticulatae (PCR) in longer storage years possess higher medicinal values, but their differentiation is difficult due to similar morphological characteristics. Therefore, this study investigated the feasibility of using terahertz time-domain spectroscopy (THz-TDS) combined with a convolutional neural network (CNN) to identify PCR samples stored from 1 to 20 years. The absorption coefficient and refractive index spectra in the range of 0.2-1.5 THz were acquired. Partial least squares discriminant analysis, random forest, least squares support vector machines, and CNN were used to establish discriminant models, showing better performance of the CNN model than the others. In addition, the output data points of the CNN intermediate layer were visualized, illustrating gradual changes in these points from overlapping to clear separation. Overall, THz-TDS combined with CNN models could realize rapid identification of different year PCRs, thus providing an efficient alternative method for PCR quality inspection.

Moxa Wool in Different Purities and Different Growing Years Measured by Terahertz Spectroscopy

Moxa wool is a traditional Chinese herbal medicine, which can warm channels to dispel coldness. At present, there is no unified index to evaluate the purity and growing years of moxa wool in the market. Terpineol is one of the effective substances in the volatile oil of moxa wool. Here, we characterize the purity and growing years of moxa wool by studying terpineol. Gas chromatography-mass spectrometry (GC-MS) and high-performance liquid chromatography (HPLC) are the methods for monitoring terpineol at present, all of which have defects of complicated procedures. We established linear fitting to distinguish the different purities of moxa wool through the intensities (areas) of terpineol, the characteristic peaks, and the consequence presented; the coefficient of determination (R2) was higher than 0.90. Furthermore, based on the characteristic peak position of standard terpineol, the correlation model with the purity and growing year of moxa wool was set up, thereby differentiating the quality of moxa wool. We have built the partial least squares (PLS) model of the growing years of moxa wool with high accuracy, and the determination coefficient is greater than 0.98. In addition, we compare the quantitative accuracy of Raman spectroscopy with terahertz technology. Finally, a new method of terahertz spectroscopy to evaluate quality of moxa wool was found. It provides a new idea for the identification of inferior moxa wool in the market and a new method for identifying the quality of moxa wool in traditional Chinese medicine.

Analyzing cadmium-phytochelatin2 complexes in plant using terahertz and circular dichroism information

Phytochelatins are plants' small metal-binding peptides which chelate internal heavy metals to form nontoxic complexes. Detecting the complexes in plants would simplify identification of cultivars with both high tolerance and enrichment capabilities for heavy metals which represent phytoextraction performance. Thus, a terahertz spectroscopy combined with density functional theory, chemometrics and circular dichroism was used for characterization of phytochelatin2 (PC₂), Cd-PC₂ mixture standards, and pak choi (Brassica chinensis) leaves as a plant model. Results showed PC₂ chelates Cd²⁺ in a 2:1 ratio to form Cd(PC₂)₂ complex; Cd connected to thoils of PC₂ and changed β-turn and random coil of PC₂ peptide chain to β-Sheet which presented as terahertz vibrations of PC₂ around 1.03 and 1.71 THz being suppressed; the best models for detecting the complex in pak choi were obtained by partial least squares regression modeling combined with successive projections algorithm selection; the models used PC₂ as a natural probe for visualizing and quantifying chelated Cd in pak choi leaf and achieved a limit of detection up to 1.151 ppm. This study suggested that terahertz information of the heavy metal-PCs complexes is qualified for representing a simpler alternative to classical index for evaluating phytoextraction performance of plant; it provided a general protocol for structure analysis and detection of heavy metal-PCs complexes in plant by terahertz absorbance.

Past and Future of Plant Stress Detection: An Overview From Remote Sensing to Positron Emission Tomography

Plant stress detection is considered one of the most critical areas for the improvement of crop yield in the compelling worldwide scenario, dictated by both the climate change and the geopolitical consequences of the Covid-19 epidemics. A complicated interconnection of biotic and abiotic stressors affect plant growth, including water, salt, temperature, light exposure, nutrients availability, agrochemicals, air and soil pollutants, pests and diseases. In facing this extended panorama, the technology choice is manifold. On the one hand, quantitative methods, such as metabolomics, provide very sensitive indicators of most of the stressors, with the drawback of a disruptive approach, which prevents follow up and dynamical studies. On the other hand qualitative methods, such as fluorescence, thermography and VIS/NIR reflectance, provide a non-disruptive view of the action of the stressors in plants, even across large fields, with the drawback of a poor accuracy. When looking at the spatial scale, the effect of stress may imply modifications from DNA level (nanometers) up to cell (micrometers), full plant (millimeters to meters), and entire field (kilometers). While quantitative techniques are sensitive to the smallest scales, only qualitative approaches can be used for the larger ones. Emerging technologies from nuclear and medical physics, such as computed tomography, magnetic resonance imaging and positron emission tomography, are expected to bridge the gap of quantitative non-disruptive morphologic and functional measurements at larger scale. In this review we analyze the landscape of the different technologies nowadays available, showing the benefits of each approach in plant stress detection, with a particular focus on the gaps, which will be filled in the nearby future by the emerging nuclear physics approaches to agriculture.