Nondestructive Detection Method Research Articles

Non-Destructive Pest Detection: Innovations and Challenges in Sensing Airborne Semiochemicals.

Pests, especially invasive ones, pose significant threats to the global ecosystem, crop security, and agriculture economy. Sensing airborne semiochemicals as a nondestructive detection method has been recognized as a promising strategy to detect the presence of these living pests on site. However, sensing airborne semiochemicals in fields is challenging, as they are transmitted in concentrations as low as several nanograms per cubic meter in chemically diverse environments. This low vapor pressure together with similarity in functional groups of pheromones among different species have curtailed the practical deployment of corresponding sensors for real world applications. This review describes the advances in semiochemical detection methods and technologies including traditional analytical instruments, trained animals, and electroantennography with a focus on electronic noses (e-noses). Several key types of volatile organic compound (VOC) sensors used in e-noses are summarized, including their transduction methods, sensing materials, and sensing performance for semiochemical and simulants detection. Notably, it was found that many commercial VOC sensors failed to respond to airborne semiochemicals effectively, leading to a reduced efficiency of e-noses. Future work may focus on developing stable and robust sensing materials with higher sensitivity and selectivity to pheromones and understanding the feasibility of the deployment of the sensors under field conditions.

A Nondestructive Detection Method for the Muti-Quality Attributes of Oats Using Near-Infrared Spectroscopy

This study aimed to achieve a precise and non-destructive quantification of the amounts of total starch, protein, β-glucan, and fat in oats using near-infrared technology in conjunction with chemometrics methods. Eight preprocessing methods (SNV, MSC, Nor, DE, FD, SD, BC, SS) were employed to process the original spectra. Subsequently, the optimal PLS model was obtained by integrating feature wavelength selection algorithms (CARS, SPA, UVE, LAR). After SD-SPA, total starch reached its optimal state (Rp2 = 0.768, RMSEP = 2.057). Protein achieved the best result after MSC-CARS (Rp2 = 0.853, RMSEP = 1.142). β-glucan reached the optimal value after BC-SPA (Rp2 = 0.759, RMSEP = 0.315). Fat achieved the optimal state after SS-SPA (Rp2 = 0.903, RMSEP = 0.692). The research has shown the performance of the portable FT-NIR for a rapid and non-destructive quantification of nutritional components in oats, holding significant importance for quality control and quality assessment within the oat industry.

Detection of mussels contaminated with cadmium by near-infrared reflectance spectroscopy based on RELS-TSVM.

Eating mussels contaminated with cadmium (Cd) can seriously harm health. In this study, a non-destructive and rapid detection method for Cd-contaminated mussels based on near-infrared reflectance spectroscopy was studied. The spectral data of Cd-contaminated and non-contaminated mussels were collected in the range of 950-1700nm. The model based on a robust energy-based least squares twin support vector machine (RELS-TSVM) was established to detect Cd-contaminated mussels. The influence of parameters on the RELS-TSVM model was analyzed, and the most suitable parameters were determined. The average accuracy of the proposed RELS-TSVM model in detecting Cd-contaminated mussels reached 99.92%, which was better than other twin support vector machine-derived models. For test datasets with different kinds of spectral noises (Gaussian noise, baseline shift, stray light, and wavelength shift), the RELS-TSVM model had a high robustness for noise disturbance. The results show that near-infrared spectroscopy combined with the RELS-TSVM model can realize the detection of Cd-contaminated mussels, which can provide technical support for the monitoring of heavy metals in shellfish. PRACTICAL APPLICATION: The method of detecting Cd-contaminated mussels by the NIRS has important practical significance for ensuring the safety of consumers. It provides a new way for the quality assessment and safety detection of shellfish and provides a technical basis for the marine environment assessment and management.

The Study on Nondestructive Detection Methods for Internal Quality of Korla Fragrant Pears Based on Near-Infrared Spectroscopy and Machine Learning.

Quality control and grading of Korla fragrant pears significantly impact their commercial value. Rapid and non-destructive detection of soluble solids content (SSC) and firmness is crucial to improving this. This study proposes a method combining near-infrared spectroscopy (NIRS) with machine learning for the rapid, non-destructive detection of SSC and firmness in Korla pears. By analyzing absorbance in the 900-1800 nm range, six preprocessing methods-Savitzky-Golay derivative (SGD), standard normal variate (SNV), multiplicative scatter correction (MSC), Savitzky-Golay smoothing (SGS), vector normalization (VN), and min-max normalization (MMN)-were applied to the raw spectral data. uninformative variable elimination (UVE) and successive projections algorithm (SPA) were then used to extract effective wavelengths. Partial least squares regression (PLSR) models were developed for SSC and firmness based on the extracted data. The results showed that all preprocessing and wavelength-extraction methods improved model accuracy. The optimal SSC prediction model was MSC-SPA-PLSR (R = 0.93, RMSE = 0.195), and the best hardness prediction model was MSC-UVE-PLSR (R = 0.83, RMSE = 0.249). This research aids in establishing a non-destructive testing system, offering producers a rapid and accurate quality assessment tool, and provides the food industry with better production control measures to enhance standardization and market competitiveness of Korla pears.

Non-Invasive Detection of Nitrogen Deficiency in Cannabis sativa Using Hand-Held Raman Spectroscopy

Proper crop management requires rapid detection methods for abiotic and biotic stresses to ensure plant health and yield. Hemp (Cannabis sativa L.) is an emerging economically and environmentally sustainable crop capable of yielding high biomass. Nitrogen deficiency significantly reduces hemp plant growth, affecting photosynthetic capacity and ultimately decreasing yield. When symptoms of nitrogen deficiency are visible to humans, there is often already lost yield. A real-time, non-destructive detection method, such as Raman spectroscopy, is therefore critical to identify nitrogen deficiency in living hemp plant tissue for fast, precise crop remediation. A two-part experiment was conducted to investigate portable Raman spectroscopy as a viable hemp nitrogen deficiency detection method and to compare the technique’s predictive ability against a handheld SPAD (chlorophyll index) meter. Raman spectra and SPAD readings were used to train separate nitrogen deficiency discrimination models. Raman scans displayed characteristic spectral markers indicative of nitrogen deficiency corresponding to vibrational modes of carotenoids, essential pigments for photosynthesis. The Raman-based model consistently predicted nitrogen deficiency in hemp prior to the onset of visible stress symptoms across both experiments, while SPAD only differentiated nitrogen deficiency in the second experiment when the stress was more pronounced. Our findings add to the repertoire of plant stresses that hand-held Raman spectroscopy can detect by demonstrating the ability to provide assessments of nitrogen deficiency. This method can be implemented at the point of cultivation, allowing for timely interventions and efficient resource use.

Dielectric spectroscopy technology combined with machine learning methods for nondestructive detection of protein content in fresh milk.

To quickly achieve nondestructive detection of protein content in fresh milk, this study utilized a network analyzer and an open coaxial probe to analyze the dielectric spectra of milk samples at 100 frequency points within the 2-20GHz range, focusing on the dielectric constant ε' and the dielectric loss factor ε''. Feature variables were extracted from the full dielectric spectra using the successive projections algorithm (SPA), uninformative variables elimination (UVE), and the combined UVE-SPA method. These variables were then used to develop partial least squares regression (PLSR), support vector machine (SVM), decision tree (DT), random forest (RF), and least squares boosting (LSBOOST) models for predicting protein content. The results showed that ε' decreased monotonically with increasing frequency, while ε'' increased monotonically. The UVE-SPA method for feature extraction demonstrated superior performance, with the UVE-SPA-PLSR model being the best for predicting milk protein content, achieving the highest RC 2=0.998 and RP 2=0.989 and the lowest RMSEC=0.019% and RMSEP=0.032%. This study provides a theoretical reference for evaluating milk quality and developing intelligent detection equipment for natural milk.

Vibrational Spectroscopy Can Be Vulnerable to Adversarial Attacks.

Nondestructive detection methods based on vibrational spectroscopy have been widely used in many critical applications in a variety of fields such as the chemical industry, pharmacy, national defense, security, and so on. As these methods/applications rely on machine learning models for data analysis, studying the threats associated with adversarial examples in vibrational spectroscopy and defenses against them is of great importance. In this paper, we propose a novel adversarial method to attack vibrational spectroscopy, named SynPat, where synthetic peaks produced by a physical model are placed at key locations to form adversarial perturbations. Our new attack generates perturbations that successfully deceive machine learning models for Raman and infrared spectrum analysis while they blend much better into the spectra and hence are unnoticeable to human operators, unlike the existing state-of-the-art adversarial attacking methods, e.g., images and audio. We verified the superiority of the proposed SynPat by an imperceptibility test conducted by human experts and of defense experiments by an AI detector. To the best of our knowledge, this is a first thorough study on the robustness of vibrational spectroscopic techniques against adversarial samples and defense mechanisms. Our extensive experiments show that machine learning models for vibrational spectroscopy, including conventional and deep models for Raman or infrared classification and regression, are all vulnerable to adversarial perturbations and thus may pose serious security threats to our society.

Combining Dielectric and Hyperspectral Data for Apple Core Browning Detection

Apple core browning not only affects the nutritional quality of apples, but also poses a health risk to consumers. Therefore, there is an urgent need to develop a fast and reliable non-destructive detection method for apple core browning. To deal with the challenges of the long incubation period, strong infectivity, and difficulty in the prevention and control of apple core browning, a novel non-destructive detection method for apple core browning has been developed through combining hyperspectral imaging and dielectric techniques. To reduce the computational complexity of high-dimensional multi-view data, canonical correlation analysis is employed for feature dimensionality reduction. Then, the two low-dimensional vectors extracted from two different sensors are concatenated into one united feature vector; therefore, the information contained in the hyperspectral and dielectric data is fused to improve the detection accuracy of the non-destructive method. At last, five traditional classifiers, such as k-Nearest Neighbors, a support vector machine with radial basis function kernel and polynomial kernel, Decision Tree, and neural network, are trained on the fused feature vectors to discriminate apple core browning. The experimental results on our own constructed dataset have shown that the sensitivity, specificity, and precision of SVM with RBF kernel based on concatenated 70-dimensional feature vectors extracted via canonical correlation analysis reached 99.98%, 99.70%, and 99.70%, respectively, which achieved better results than other models. This study can provide theoretical assurance and technical support for further development of higher accuracy and lower-cost non-destructive detection devices for apple core browning.

Exploring critical quality indicators and developing a non-destructive detection method using near-infrared spectroscopy for sea bass (Lateolabrax japonicus) quality evaluation

In this study, chemometrics were employed to explore the relationship between sensory evaluation and physicochemical indicators of sea bass (Lateolabrax japonicus). Through principal component analysis, cluster analysis, and Pearson correlation analysis, three pivotal indicators were identified: protein content, b* value, and condition factor. Leveraging the grey relational analysis, weights were assigned to these three core quality indicators, resulting in a comprehensive sea bass quality evaluation model: Y = 0.911 × protein (g/100 g) + 0.742 × b* + 0.747 × condition factor. Moreover, near-infrared spectroscopy combined with chemometrics were employed to evaluate the quality of sea bass. The different origins of sea bass were accurately distinguished using orthogonal partial least squares discriminant analysis. The partial least squares regression model was constructed for predicting the critical quality indicator, protein content, with R2P of 0.926. This study offers new insights for developing rapid, economical, and reliable methods for assessing aquatic product quality.

Exploration of Volatileomics and Optical Properties of Fusarium graminearum-Contaminated Maize: An Application Basis for Low-Cost and Non-Destructive Detection.

Fusarium graminearum (F. graminearum) in maize poses a threat to grain security. Current non-destructive detection methods face limited practical applications in grain quality detection. This study aims to understand the optical properties and volatileomics of F. graminearum-contaminated maize. Specifically, the transmission and reflection spectra (wavelength range of 200-1100 nm) were used to explore the optical properties of F. graminearum-contaminated maize. Volatile organic compounds (VOCs) of F. graminearum-contaminated maize were determined by headspace solid phase micro-extraction with gas chromatography-tandem mass spectrometry. The VOCs of normal maize were mainly alcohols and ketones, while the VOCs of severely contaminated maize became organic acids and alcohols. The ultraviolet excitation spectrum of maize showed a peak redshift as fungi grew, and the intensity decreased in the 400-600 nm band. Peak redshift and intensity changes were observed in the visible/near-infrared reflectance and transmission spectra of F. graminearum-contaminated maize. Remarkably, optical imaging platforms based on optical properties were developed to ensure high-throughput detection for single-kernel maize. The developed imaging platform could achieve more than 80% classification accuracy, whereas asymmetric polarization imaging achieved more than 93% prediction accuracy. Overall, these results can provide theoretical support for the cost-effective preparation of low-cost gas sensors and high-prediction sorting equipment for maize quality detection.

Research on wheat impurity identification method based on terahertz imaging technology

The traditional detection of impurities in wheat has difficulties such as low precision, time-consuming, and cumbersome, therefore, it is important to study the method of rapid and accurate detection of impurities in wheat for correctly assessing the quality grade of wheat. Terahertz (THz) technology has many superior properties such as transient, broadband, low-energy, and penetrating, which can realize rapid and nondestructive detection of wheat quality. In this study, a classification and recognition algorithm AHA-RetinaNet-X for wheat impurity terahertz images based on RetinaNet and Artificial hummingbird algorithm (AHA) is proposed.A THz three-dimensional tomography imaging system is used to image wheat and its impurities, and two THz image datasets, respectively the wheat and impurity dataset for verifying the classification effect of wheat and impurities and the impurity dataset for verifying the classification effect of impurities. The experimental results show that the AHA-RetinaNet-X model outperforms other detection and classification models in terms of accuracy, F1-score, precision, recall, and specificity, and is able to achieve 96.1%, 94.9%, 95.2%, 95.8%, 95.5%, 95.3%, and 93.3% for the wheat and impurity dataset and the impurity dataset, respectively, 95.6%, 96.3%, and 95.2%, and the mAP value of AHA-RetinaNet-X is also higher than the other models and can reach 92.1%. The combination of THz imaging technology and AHA-RetinaNet-X can realize the classification and identification of wheat and impurities, which provides a new method for the non-contact rapid nondestructive detection and identification of wheat and impurities, and also provides a reference for the research of the identification and detection methods of other substances.

Diffraction Separation for the Ground Penetrating Radar Data by Masking Filters

Ground penetrating radar is a high-resolution, efficient, non-destructive geophysical detection method. It is widely used in various application scenarios such as tunnel geological prediction and road maintenance. Ground penetrating radar data contains a variety of valid signals as well as noise. The diffracted waves of ground penetrating radar contain high-resolution small target imaging information. A critical challenge in GPR applications is how to extract diffracted waves from the wave fields. We provide a strategy to achieve this goal by applying the masking filters. Considering the complexity of the ground penetrating radar wave field and the weak energy of the diffracted waves, the median filter is first employed to suppress the linear reflections and then the f-k filter and filter are implemented to further increase the proportion of diffractions in the wave fields. Three numerical experiments are employed to test the diffraction-separation method.

Corrosion and Protection of Chinese Bronze Relics: A Review

The corrosion problem affecting ancient Chinese bronze relics and the protective measures required post-excavation are crucial for the study of historical cultural heritage and for ensuring heritage revitalization and sustainable development. This work includes a statistical analysis, clusters information, and thoroughly examines international research on bronze relic corrosion and protection. It delves into the timeline and trends of research, the main countries leading the research efforts, the research content, and the relationships between these factors. A comprehensive review is provided on the corrosion principles, materials, detection methods, and protection techniques for bronze. The study explores the corrosion principles and processes of bronze from a materials science perspective both before and after excavation. It summarizes non-destructive detection methods and examines specific factors that influence corrosion. Furthermore, the article reviews current corrosion protection methods for bronze and related protection materials, including commonly used strategies such as surface corrosion inhibitors and organic resin coatings for protection. It also discusses the potential application of advanced corrosion protection methods in the realm of metal materials in recent years to safeguard bronze. Proposing innovative solutions, the study suggests the possibility of constructing biomimetic superhydrophobic surfaces to create a barrier isolating humid air from contacting bronze materials, thereby reducing the adhesion of corrosive media to the substrate and significantly diminishing the likelihood of corrosion. In conclusion, the article looks towards the future, considering the challenges and potential development directions for the corrosion protection of bronze and related protection materials.

Non-invasive, pre-symptomatic detection of potato cyst nematode infection in tomato using chlorophyll fluorescence analysis.

Potato cyst nematodes (PCN) are notorious pathogens in all major potato production areas worldwide. Mainly due to the low mobility of this soil pathogen, PCN infestations are mostly observed as patches ('foci') that only cover a fraction of the acreage. In-field pre-symptomatic localization of this pathogen is valuable as it would allow for the localized application of control measures. Although the mapping of foci is technically feasible, it is unpractical as it would take the analysis of numerous soil samples. We investigated whether chlorophyll fluorescence (Chl-F) could be suitable as a rapid, non-destructive method for early PCN detection. To this end, the impact of four Globodera pallida densities on the Chl-F of tomato was investigated in a phenotyping greenhouse for 26 days. Furthermore, classical plant performance indicators biomass and root surface area were compared with Chl-F. Thermal dissipation ('NPQ_Lss') and actual photosynthetic rate ('QY_Lss') responded at 1 DPI, while QY_Lss was most sensitive to low PCN infection levels. Chl-F parameters responded more readily to PCN infection than biomass and root surface area. The efficiency of photosystem II (QY_max) and the potential activity of photosystem II (Fv/Fo) initially increased at low PCN infection levels, whereas a sharp decrease was observed at higher infestation levels. Hence, our data suggest that low PCN levels promoted plant performance before becoming detrimental at higher levels. While Chl-F allowed for early and sensitive PCN detection, it remains to be investigated whether these signals can be distinguished from those produced by other below-ground stressors in the field.

Decision Tree Clusters: Non-destructive detection of overheating defects in porcelain insulators using quantitative thermal imaging techniques

Porcelain insulators are subject to performance deterioration during operation under the influence of complex environments, thus increasing the occurrence of flashover accidents. Regular inspection of insulator condition is of great significance for the stable operation of power grids. Therefore, we propose a non-destructive detection method of insulator overheating defects using quantitative thermography. It can be used for defect detection of porcelain insulators with internal overheating defects. In this study, the relationship between the insulating properties of porcelain insulators and the heating anomalies was first analyzed. Then, Decision Tree Clusters (DTC) algorithm is used to extract the spatial temperature feature information to achieve the defective insulator location capture. Finally, Submodular-pick Local Interpretable Model-agnostic Explanations (SLIME) is used for model decision visualization to verify the feasibility of the technique. The experimental results show that DTC not only ensures the accuracy of insulator long-distance detection, but also realizes the quantitative detection of insulators.

Research on Silage Corn Forage Quality Grading Based on Hyperspectroscopy

Corn silage is the main feed in the diet of dairy cows and other ruminant livestock. Silage corn feed is very susceptible to spoilage and corruption due to the influence of aerobic secondary fermentation during the silage process. At present, silage quality testing of corn feed mainly relies on the combination of sensory evaluation and laboratory measurement. The sensory review method is difficult to achieve precision and objectivity, while the laboratory determination method has problems such as cumbersome testing procedures, time-consuming, high cost, and damage to samples. In this study, the external sensory quality grading model for different qualities of silage corn feed was established using hyperspectral data. To explore the feasibility of using hyperspectral data for external sensory quality grading of corn silage, a hyperspectral system was used to collect spectral data of 200 corn silage samples in the 380–1004 nm band, and the samples were classified into four grades: excellent, fair, medium, and spoiled according to the German Agricultural Association (DLG) standard for sensory evaluation of silage samples. Three algorithms were used to preprocess the fodder hyperspectral data, including multiplicative scatter correction (MSC), standard normal variate (SNV), and S–G convolutional smoothing. To reduce the redundancy of the spectral data, variable combination population analysis (VCPA) and competitive adaptive reweighted sampling (CARS) were used for feature wavelength selection, and linear discriminant analysis (LDA) algorithm was used for data dimensionality reduction, constructing random forest classification (RFC), convolutional neural networks (CNN) and support vector machines (SVM) models. The best classification model was derived based on the comparison of the model results. The results show that SNV-LDA-SVM is the optimal algorithm combination, where the accuracy of the calibration set is 99.375% and the accuracy of the prediction set is 100%. In summary, combined with hyperspectral technology, the constructed model can realize the accurate discrimination of the external sensory quality of silage corn feed, which provides a reliable and effective new non-destructive testing method for silage corn feed quality detection.

Rapid identification of moxa wool storage period based on hyperspectral imaging technology and machine learning

Moxa wool (MW), derived from the dried leaves of Artemisiaargyi, plays a significant role in traditional Chinese medicine. However, the quality of MW varies with its storage period, impacting its therapeutic efficacy. Traditional methods for quality detection are limited and destructive. To address this, we propose a non-destructive detection method using hyperspectral imaging technology and machine learning algorithms to accurately identify the storage period of MW. Nevertheless, hyperspectral data poses challenges due to its high dimensionality and redundancy, leading to increased computational complexity. To overcome this, we employed principal component analysis (PCA), competitive adaptive reweighted sampling (CARS), and successive projection algorithm (SPA) for data dimensionality reduction and wavelength selection. The results demonstrate that these techniques significantly enhance the accuracy of MW storage year identification. For Nanyang MW, the CARS+SVM model achieved the highest accuracy rates of 99.8% in the visible-near-infrared (VNIR) range and 99.55% in the shortwave infrared (SWIR) range. Similarly, for Qichun MW, the SPA+SVM model achieved identification accuracies of 99.78% and 99.47% in the VNIR and SWIR ranges, respectively. This research provides valuable insights into the rapid detection of MW quality by indication of storage years and presents a novel approach for quality control of MW in the field of traditional Chinese medicine. The combination of hyperspectral imaging and machine learning offers a promising solution for efficient and accurate MW identification, contributing to the advancement of traditional medicine practices.

Monitoring of parasite Orobanche cumana using Vis-NIR hyperspectral imaging combining with physio-biochemical parameterson host crop Helianthus annuus.

This study provided a non-destructive detection method with Vis-NIR hyperspectral imaging combining with physio-biochemical parameters in Helianthus annuus in response to Orobanche cumana infection that took insights into the monitoring of sunflower weed. Sunflower broomrape (Orobanche cumana Wallr.) is an obligate weed that attaches to the host roots of sunflower (Helianthus annuus L.) leading to a significant reduction in yield worldwide. The emergence of O. cumana shoots after its underground life-cycle causes irreversible damage to the crop. In this study, a fast visual, non-invasive and precise method for monitoring changes in spectral characteristics using visible and near-infrared (Vis-NIR) hyperspectral imaging (HSI) was developed. By combining the bands sensitive to antioxidant enzymes (SOD, GR), non-antioxidant enzymes (GSH, GSH + GSSG), MDA, ROS (O2-, OH-), PAL, and PPO activities obtained from the host leaves, we sought to establish an accurate means of assessing these changes and conducted imaging acquisition using hyperspectral cameras from both infested and non-infested sunflower cultivars, followed by physio-biochemical parameters measurement as well as analyzed the expression of defense related genes. Extreme learning machine (ELM) and convolutional neural network (CNN) models using 3-band images were built to classify infected or non-infected plants in three sunflower cultivars, achieving accuracies of 95.83% and 95.83% for the discrimination of infestation as well as 97.92% and 95.83% of varieties, respectively, indicating the potential of multi-spectral imaging systems for early detection of O. cumana in weed management.

Facile E-nose based on single antenna and graphene oxide for sensing volatile organic compound gases with ultrahigh selectivity and accuracy

Volatile organic compounds (VOCs) gases exist as important indicators in various processes such as human breath, food spoilage, plant disease, and industrial production, which makes VOC sensing a promising nondestructive detection method. Nanomaterial enabled electronic noses (e-noses) are gaining increasing attention for their exceptional ability to distinguish between multiple VOCs. To achieve this, multiple sensors with distinct nanomaterials are crucial for providing enough diversity for successful discrimination. Apparently if e-nose can be implemented solely on single sensor with single common nanomaterial operating at room temperature, the complexity and power consumption can be greatly decreased. In this study, a significant milestone is achieved as, for the first time, a single antenna sensor coated with commercial graphene oxide, demonstrates comparable performance to array-based e-noses, and even outperforms them in terms of the ultimate ambition of selectivity—isomeric VOC classification. An impressive classification accuracy of 96.7 % is attained for multiple VOC gases, including isomers. Additionally, the concentration of each component in VOC isomer mixtures is accurately determined. Unlike conventional antenna sensors, the sensor maintains stable communication during sensing operations. Finally, the practical feasibility of the antenna e-nose (Ant-nose) is successfully demonstrated with a series of food quality assessments.

Using terahertz spectroscopy to quantify bioactive flavonoids in Moxa Wool as predictor of rheumatoid arthritis treatment outcomes

BackgroundMoxibustion, a traditional Chinese medicine practice, employs Moxa Wool, derived from Artemisia argyi. Flavonoids, the key pharmacological constituents in Moxa Wool, are known for their anti-inflammatory and analgesic properties. The purity of Moxa Wool, particularly its flavonoid content, directly influences the efficacy of moxibustion treatments. However, quantifying these bioactive flavonoids accurately and non-destructively has been a challenge. PurposeThis study introduces terahertz spectroscopy as a non-destructive optical detection method for qualitative detection and quantitative analysis of flavonoids in Moxa Wool. By establishing a mathematical model between spectral signals and clinical efficacy, a reliable correlation between flavonoid concentration and the therapeutic effect of moxibustion can be established, providing a potential predictive model for the treatment outcomes of rheumatoid arthritis. Study DesignWe adopted terahertz spectroscopy technology and combined it with terahertz metamaterial biosensors to achieve rapid, efficient, and non-destructive testing of the quality of Moxa Wool. This method reduces the detection time from hours to minutes while lowering the sample detection limit, overcoming the limitations of traditional detection methods in pharmacological research. MethodsThrough terahertz metamaterial biosensors, rapid detection of the purity of Moxa Wool has been achieved. A combination of molecular simulation and terahertz spectroscopy was used to quantitatively analyze the flavonoid content in different purities of Moxa Wool. To ensure accuracy, the quantitative results of flavonoids obtained by terahertz spectroscopy were validated using high-performance liquid chromatography (HPLC). In addition, moxibustion treatment was performed on rats with rheumatoid arthritis using Moxa Wool, and medical indicator information was recorded. A mathematical analysis model was established to evaluate the correlation between flavonoid content and analgesic and anti-inflammatory effects. ResultsTerahertz spectroscopy analysis shows that there is a direct correlation between the flavonoid content in moxibustion and the absorption peak intensity. The maximum R2 in the model analysis is 0.98, indicating a high accuracy in predicting the purity of Moxa Wool. These results were also validated by HPLC. In a rat model, the purity of 30:1 Moxa Wool samples showed a 50 % decrease in TNF-α, IL-1β, and IL-6 levels during treatment compared to low-purity samples, significantly reducing inflammation markers and pain symptoms. Meanwhile, The PLS prediction model established a correlation between terahertz-detected flavonoid levels and treatment outcomes (PWL and IL-1β). The maximum R2 in the model is 0.91, indicating a high correlation between flavonoid levels and the anti-inflammatory and analgesic effects of moxibustion treatment. ConclusionThis study not only demonstrates the effectiveness of terahertz spectroscopy in the pharmacological quantification of bioactive compounds but also establishes a novel predictive model for the efficacy of moxibustion in rheumatoid arthritis treatment. It underscores the potential of integrating traditional medicine insights with advanced technology to enhance therapeutic strategies in pharmacology.