Detection of Water Content in Rapeseed Leaves Using Terahertz Spectroscopy

Research on the chemical oxygen demand spectral inversion model in water based on IPLS-GAN-SVM hybrid algorithm

Spectral collinearity and limited spectral datasets are the problems influencing Chemical Oxygen Demand (COD) modeling. To address the first problem and obtain optimal modeling range, the spectra are preprocessed using six methods including Standard Normal Variate, Savitzky-Golay Smoothing Filtering (SG) etc. Subsequently, the 190-350 nm spectral range is divided into 10 subintervals, and Interval Partial Least Squares (IPLS) is used to perform PLS modeling on each interval. The results indicate that it is best modeled in the 7th range (238~253 nm). The values of Mean Square Error (MSE), Mean Absolute Error (MAE) and R2score of the model without pretreatment are 1.6489, 1.0661, and 0.9942. After pretreatment, the SG is better than others, with MSE and MAE decreasing to 1.4727, 1.0318 and R2score improving to 0.9944. Using the optimal model, the predicted COD for three samples are 10.87 mg/L, 14.88 mg/L, and 19.29 mg/L. To address the problem of the small dataset, using Generative Adversarial Networks for data augmentation, three datasets are obtained for Support Vector Machine (SVM) modeling. The results indicate that, compared to the original dataset, the SVM's MSE and MAE have decreased, while its accuracy has improved by 2.88%, 11.53%, and 11.53%, and the R2score has improved by 18.07%, 17.40%, and 18.74%.

Coherent terahertz laser feedback interferometry for hydration sensing in leaves

The response of terahertz to the presence of water content makes it an ideal analytical tool for hydration monitoring in agricultural applications. This study reports on the feasibility of terahertz sensing for monitoring the hydration level of freshly harvested leaves of Celtis sinensis by employing a imaging platform based on quantum cascade lasers and laser feedback interferometry. The imaging platform produces wide angle high resolution terahertz amplitude and phase images of the leaves at high frame rates allowing monitoring of dynamic water transport and other changes across the whole leaf. The complementary information in the resulting images was fed to a machine learning model aiming to predict relative water content from a single frame. The model was used to predict the change in hydration level over time. Results of the study suggest that the technique could have substantial potential in agricultural applications.

Quantitative Measurements of DP in Cellulose Paper Based on Terahertz Spectroscopy

The power transformer is vital to the reliability of the power grid which is most commonly insulated with Kraft paper and immersed in mineral oil, among which the aged state of the paper is mainly correlated to the operating life of the transformer. Degree of polymerization (DP) is a direct parameter to assess the aged condition of insulating paper, but existing DP measurement by viscosity methods are destructive and complicated. In this paper, terahertz time-domain spectroscopy (THz-TDS) was introduced to reach rapid, non-destructive detection of the DP of insulating paper. The absorption spectra of insulating paper show that characteristic peak regions at 1.8 and 2.23 THz both exhibit a log-linear quantitative relationship with DP, and their universalities are confirmed by conducting the above relationship on different types of insulating paper. Fourier transform infrared spectroscopy (FTIR) analysis and molecular dynamics modeling further revealed that 1.8 and 2.23 THz were favorably associated with the growth of water-cellulose hydrogen bond strength and amorphous cellulose, respectively. This paper demonstrates the viability of applying THz-TDS to the non-destructive detection of DP in insulating paper and assigned the vibration modes of the characteristic absorption peaks.

Analysis of the multilayer organization of a sunflower leaf during dehydration with terahertz time-domain spectroscopy

We apply reverse engineering techniques (RET) to analyze the dehydration process of a sunflower leaf with terahertz time-domain spectroscopy. The multilayer structure of the leaf is extracted with accuracy during the entire process. Time variations of thickness and the complex index are emphasized for all leaf layers (2 cuticules, 2 epiderms, and 2 mesophylls). The global thickness of the sunflower leaf is reduced by up to 40% of its initial value.

Applications of THz Spectral Imaging in the Detection of Agricultural Products

Agricultural products need to be inspected for quality and safety, and the issue of safety of agricultural products caused by quality is frequently investigated. Safety testing should be carried out before agricultural products are consumed. The existing technologies for inspecting agricultural products are time-consuming and require complex operation, and there is motivation to develop a rapid, safe, and non-destructive inspection technology. In recent years, with the continuous progress of THz technology, THz spectral imaging, with the advantages of its unique characteristics, such as low energies, superior spatial resolution, and high sensitivity to water, has been recognized as an efficient and feasible identification tool, which has been widely used for the qualitative and quantitative analyses of agricultural production. In this paper, the current main performance achievements of the use of THz images are presented. In addition, recent advances in the application of THz spectral imaging technology for inspection of agricultural products are reviewed, including internal component detection, seed classification, pesticide residues detection, and foreign body and packaging inspection. Furthermore, machine learning methods applied in THz spectral imaging are discussed. Finally, the existing problems of THz spectral imaging technology are analyzed, and future research directions for THz spectral imaging technology are proposed. Recent rapid development of THz spectral imaging has demonstrated the advantages of THz radiation and its potential application in agricultural products. The rapid development of THz spectroscopic imaging combined with deep learning can be expected to have great potential for widespread application in the fields of agriculture and food engineering.

Determination of wheat moisture using terahertz spectroscopy combined with the tabu search algorithm

The detection of the wheat moisture content plays a key role in grain storage and classification. Harvested wheat grains were taken as samples in the current research. A total of 240 reaped wheat samples with different moisture contents were tested by applying terahertz (THz) spectroscopy. The frequency domain spectra and absorption coefficient spectra of wheat were obtained in the band of 0.1-1.2 THz, and the spectra were pretreated by mean centering, Savitzky-Golay (S-G), Multiplicative Scatter Correction (MSC) and Stand Normal Variate (SNV), respectively. Then a special algorithm of Tabu Search (TS) was used to find out the effective variables and remove the useless variables from the terahertz spectrum of the sample. Finally, the partial least squares (PLS) of chemometrics were used for quantitative model building and prediction. The correlation coefficient of calibration (R_c) is 0.9522. The root mean square error of calibration (RMSEC) is 0.4730. The correlation coefficient of prediction (R_p) is 0.9531. The root mean square error of prediction (RMSEP) is 0.5396. The results demonstrated that an accurate quantitative analysis of moisture in wheat samples could be achieved by terahertz time-domain spectroscopy combined with the TS algorithm. In addition, the results show that the model S-G + MSC + TS + PLS can effectively predict wheat moisture, and provide a rapid quantitative detection and analysis method for the detection of wheat moisture.

Recent advances in gold nanoparticle-based lateral flow immunoassay for the detection of bacterial infection

Diagnosis of bacterial infections (BI) is becoming an increasingly difficult task in clinical practice due to their high prevalence and frequency, as well as the growth of antibiotic resistance worldwide. World Health Organization (WHO) reported antibiotic resistance is a major public health problem. BI becomes difficult or impossible to treat when the bacteria acquire immunity against antibiotics. Thus, there is a need for a quick and accurate technique to detect infection. Lateral flow immunoassay (LFIA) is an ideal technique for point-of-care testing of a disease or pathological changes inside the human body. In recent years, several LFIA based strips are being used for the detection of BI by targeting specific analytes which may range from the causative bacterium, whole-cell, DNA, or biomarker. Numerous nanoparticles like lipid-based nanoparticles, polymeric nanoparticles, and inorganic nanoparticles such as quantum dots, magnetic, ceramic, and metallic nanoparticles (copper, silver gold, iron) are widely being used in the advanced treatment of BI. Out of these gold nanoparticle (AuNPs), is being used for detection BI more effectively than other nanoparticles due to their surface functionalization, extraordinary chemical stability, biorecognition, and signal amplification properties and help to improve in conjugation with capture antibodies, and act as a color marker with unique optical properties on LFIA strips. Herein, a review that provides an overview of the principle of LFIA, how LFIA based strip is developed, and how it is helpful to detect a specific biomarker for bedside detection of the BI.

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.

Non-invasive measurement of leaf water content and pressure-volume curves using terahertz radiation

In this paper we describe a non-invasive method of measuring leaf water content using THz radiation and combine this with psychrometry for determination of leaf pressure–volume relationships. In contrast to prior investigations using THz radiation to measure plant water status, the reported method exploits the differential absorption characteristic of THz radiation at multiple frequencies within plant leaves to determine absolute water content in real-time. By combining the THz system with a psychrometer, pressure–volume curves were generated in a completely automated fashion for the determination of leaf tissue water relations parameters including water potential at turgor loss, osmotic potential at full turgor and the relative water content at the turgor loss point. This novel methodology provides for repeated, non-destructive measurement of leaf water content and greatly increased efficiency in generation of leaf PV curves by reducing user handling time.

Terahertz probing of sunflower leaf multilayer organization

We analyze the multilayer structure of sunflower leaves from Terahertz data measured in the time-domain at a ps scale. Thin film reverse engineering techniques are applied to the Fourier amplitude of the reflected and transmitted signals in the frequency range f < 1.5 Terahertz (THz). Validation is first performed with success on etalon samples. The optimal structure of the leaf is found to be a 8-layer stack, in good agreement with microscopy investigations. Results may open the door to a complementary classification of leaves.

Terahertz fingerprint characterization of 2,4-dichlorophenoxyacetic acid and its enhanced detection in food matrices combined with spectral baseline correction

Rapid and accurate detection of pesticide residues in food matrices are of great significance to food safety. This study aimed to characterize the fingerprint peaks of 2,4-dichlorophenoxyacetic acid (2,4-D) and to enhance its detection accuracy in food matrices by using terahertz (THz) time-domain spectroscopy. Density functional theory was used to simulate molecular dynamics of 2,4-D peaks (1.35, 1.60, 2.37 and 3.00 THz). Four baseline correction methods, including asymmetric least squares smoothing (AsLS), adaptive iteratively reweighted penalized least squares (AirPLS), background correction (Backcor), baseline estimation and denoising with sparsity (BEADS) were compared and used to eliminate spectral baselines of Zizania latifolia (ZIZLA), rice and maize containing 2,4-D residues, from 0.1 to 4 THz. Based on the peak information of 1.35 THz, the detection limit and accuracy of 2,4-D residues in these food matrices were significantly improved after THz spectral baseline correction, providing a new feasibility for food safety and agricultural applications.

Machine learning driven non-invasive approach of water content estimation in living plant leaves using terahertz waves

BACKGROUND

The demand for effective use of water resources has increased because of ongoing global climate transformations in the agriculture science sector. Cost-effective and timely distributions of the appropriate amount of water are vital not only to maintain a healthy status of plants leaves but to drive the productivity of the crops and achieve economic benefits. In this regard, employing a terahertz (THz) technology can be more reliable and progressive technique due to its distinctive features. This paper presents a novel, and non-invasive machine learning (ML) driven approach using terahertz waves with a swissto12 material characterization kit (MCK) in the frequency range of 0.75 to 1.1 THz in real-life digital agriculture interventions, aiming to develop a feasible and viable technique for the precise estimation of water content (WC) in plants leaves for 4 days. For this purpose, using measurements observations data, multi-domain features are extracted from frequency, time, time-frequency domains to incorporate three different machine learning algorithms such as support vector machine (SVM), K-nearest neighbour (KNN) and decision-tree (D-Tree).

RESULTS

The results demonstrated SVM outperformed other classifiers using tenfold and leave-one-observations-out cross-validation for different days classification with an overall accuracy of 98.8%, 97.15%, and 96.82% for Coffee, pea shoot, and baby spinach leaves respectively. In addition, using SFS technique, coffee leaf showed a significant improvement of 15%, 11.9%, 6.5% in computational time for SVM, KNN and D-tree. For pea-shoot, 21.28%, 10.01%, and 8.53% of improvement was noticed in operating time for SVM, KNN and D-Tree classifiers, respectively. Lastly, baby spinach leaf exhibited a further improvement of 21.28% in SVM, 10.01% in KNN, and 8.53% in D-tree in overall operating time for classifiers. These improvements in classifiers produced significant advancements in classification accuracy, indicating a more precise quantification of WC in leaves.

CONCLUSION

Thus, the proposed method incorporating ML using terahertz waves can be beneficial for precise estimation of WC in leaves and can provide prolific recommendations and insights for growers to take proactive actions in relations to plants health monitoring.

THz Water Transmittance and Leaf Surface Area: An Effective Nondestructive Method for Determining Leaf Water Content

Water availability is a major limiting factor in plant productivity and plays a key role in plant species distribution over a given area. New technologies, such as terahertz quantum cascade lasers (THz-QCLs) have proven to be non-invasive, effective, and accurate tools for measuring and monitoring leaf water content. This study explores the feasibility of using an advanced THz-QCL device for measuring the absolute leaf water content in Corylus avellana L., Laurus nobilis L., Ostrya carpinifolia Scop., Quercus ilex L., Quercus suber L., and Vitis vinifera L. (cv. Sangiovese). A recently proposed, simple spectroscopic technique was used, consisting in determining the transmission of the THz light beam through the leaf combined with a photographic measurement of the leaf area. A significant correlation was found between the product of the leaf optical depth (τ) and the leaf surface area (LA) with the leaf water mass (Mw) for all the studied species (Pearson's r test, p ≤ 0.05). In all cases, the best fit regression line, in the graphs of τLA as a function of Mw, displayed R2 values always greater than 0.85. The method proposed can be combined with water stress indices of plants in order to gain a better understanding of the leaf water management processes or to indirectly monitor the kinetics of leaf invasion by pathogenic bacteria, possibly leading to the development of specific models to study and fight them.

A Comprehensive Review on Food Applications of Terahertz Spectroscopy and Imaging

Food product safety is a public health concern. Most of the food safety analytical and detection methods are expensive, labor intensive, and time consuming. A safe, rapid, reliable, and nondestructive detection method is needed to assure consumers that food products are safe to consume. Terahertz (THz) radiation, which has properties of both microwave and infrared, can penetrate and interact with many commonly used materials. Owing to the technological developments in sources and detectors, THz spectroscopic imaging has transitioned from a laboratory-scale technique into a versatile imaging tool with many practical applications. In recent years, THz imaging has been shown to have great potential as an emerging nondestructive tool for food inspection. THz spectroscopy provides qualitative and quantitative information about food samples. The main applications of THz in food industries include detection of moisture, foreign bodies, inspection, and quality control. Other applications of THz technology in the food industry include detection of harmful compounds, antibiotics, and microorganisms. THz spectroscopy is a great tool for characterization of carbohydrates, amino acids, fatty acids, and vitamins. Despite its potential applications, THz technology has some limitations, such as limited penetration, scattering effect, limited sensitivity, and low limit of detection. THz technology is still expensive, and there is no available THz database library for food compounds. The scanning speed needs to be improved in the future generations of THz systems. Although many technological aspects need to be improved, THz technology has already been established in the food industry as a powerful tool with great detection and quantification ability. This paper reviews various applications of THz spectroscopy and imaging in the food industry.

Characterization and Water Content Estimation Method of Living Plant Leaves Using Terahertz Waves

An increasing global aridification due to climate change has made the health monitoring of vegetation indispensable to maintaining the food supply chain. Cost-effective and smart irrigation systems are required not only to ensure the efficient distribution of water, but also to track the moisture of plant leaves, which is an important marker of the overall health of the plant. This paper presents a novel electromagnetic method to monitor the water content (WC) and characterisation in plant leaves using the absorption spectra of water molecules in the terahertz (THz) frequency for four consecutive days. We extracted the material properties of leaves of eight types of pot herbs from the scattering parameters, measured using a material characterisation kit in the frequency range of 0.75 to 1.1 THz. From the computed permittivity, it is deduced that the leaf specimens increasingly become transparent to the THz waves as they dry out with the passage of days. Moreover, the loss in weight and thickness of leaves were observed due to the natural evaporation of leaf moisture cells and change occurred in the morphology of fresh and water-stressed leaves. It is also illustrated that loss observed in WC on day 1 was in the range of 5% to 22%, and increased from 83.12% to 99.33% on day 4. Furthermore, we observed an exponential decaying trend in the peaks of the real part of the permittivity from day 1 to 4, which was reminiscent of the trend observed in the weight of all leaves. Thus, results in paper demonstrated that timely detection of water stress in leaves can help to take proactive action in relation to plants health monitoring, and for precision agriculture applications, which is of high importance to improve the overall productivity.

Terahertz Time Domain Spectroscopy of Transformer Insulation Paper after Thermal Aging Intervals

An accelerated thermal aging process was used to simulate the condition of paper insulation in transformer oil-paper systems. Optical parameters of the insulation paper after various aging intervals were analyzed with terahertz time-domain spectroscopy (THz-TDS) over the range 0.1~1.8 THz. The result shows that the paper had seven absorption peaks at 0.19, 0.49, 0.82, 1.19, 1.43, 1.53, and 1.74 THz, and density functional theory of B3LYP/6-311G+ (d, p) was used to simulate the molecular dynamics of the repeating component (cellobiose) of the cellulose paper. Theoretical spectra were consistent with experiment, which had absorption peaks at 0.18, 0.82, 1.47, and 1.53 THz in the same frequency range. At the same time, the paper samples after various aging intervals had different refractive indexes, and least squares fitting revealed a linear relationship between the degree of polymerization and the refractive index of the paper. Hence, this paper demonstrates that THz-TDS could be used to analyze the aging condition of transformer insulation paper and provides the theoretical and experimental basis for detection.

Spectral Characterization and Molecular Dynamics Simulation of Pesticides Based on Terahertz Time-Domain Spectra Analyses and Density Functional Theory (DFT) Calculations

This work provides the experimental and theoretical fundamentals for detecting the molecular fingerprints of six kinds of pesticides by using terahertz (THz) time-domain spectroscopy (THz-TDS). The spectra of absorption coefficient and refractive index of the pesticides, chlorpyrifos, fipronil, carbofuran, dimethoate, methomyl, and thidiazuron are obtained in frequencies of 0.1–3.5 THz. To accurately describe the THz spectral characteristics of pesticides, the wavelet threshold de-noising (WTD) method with db 5 wavelet fucntion, 5-layer decomposition, and soft-threshold de-noising was used to eliminate the spectral noise. The spectral baseline correction (SBC) method based on asymmetric least squares smoothing was used to remove the baseline drift. Spectral results show that chlorpyrifo had three characteristic absorption peaks at 1.47, 1.93, and 2.73 THz. Fipronil showed three peaks at 0.76, 1.23, and 2.31 THz. Carbofuran showed two peaks at 2.72 and 3.06 THz. Dimethoate showed three peaks at 1.05, 1.89, and 2.92 THz. Methomyl showed five peaks at 1.01, 1.65, 1.91, 2.72, and 3.20 THz. Thidiazuron showed four peaks at 0.99, 1.57, 2.17, and 2.66 THz. The density functional theory (DFT) of B3LYP/6-31G+(d,p) was applied to simulate the molecular dynamics for peak analyzing of the pesticides based on isolated molecules. The theoretical spectra are in good agreement with the experimental spectra processed by WTD + SBC, which implies the validity of WTD + SBC spectral processing methods and the accuracy of DFT spectral peak analysis. These results support that the combination of THz-TDS and DFT is an effective tool for pesticide fingerprint analysis and the molecular dynamics simulations.