Support Vector Machine Classification Model Research Articles

Product Quality Anomaly Recognition and Diagnosis Based on DRSN-SVM-SHAP

Product Quality Anomaly Recognition and Diagnosis Based on DRSN-SVM-SHAP

Altered functional connectivity strength of primary visual cortex in subjects with thyroid-associated ophthalmopathy.

Our objective was to explore the disparities in the intrinsic functional connectivity (FC) patterns of primary visual cortex (V1) between patients with thyroid-associated ophthalmopathy (TAO) and healthy controls (HCs) utilizing resting-state functional MRI. Twenty-one patients with TAO (14 males and 7 females; mean age: 54.17 ± 4.83 years) and 21 well-matched HCs (14 males and 7 females; mean age: 55.17 ± 5.37 years) underwent functional MRI scans in the resting-state. We assessed modifications in the intrinsic FC patterns of the V1 in TAO patients using the FC method. Subsequently, the identified alterations in FC regions in the analysis were selected as classification features to distinguish TAO patients from HCs through the support vector machine (SVM) method. The results indicated that, in comparison to HCs, patients with TAO exhibited notably reduced FC values between the left V1 and the bilateral calcarine (CAL), lingual gyrus (LING) and superior occipital gyrus, as well as between the right V1 and the bilateral CAL/LING and the right cerebellum. Furthermore, the SVM classification model based on FC maps demonstrated effective performance in distinguishing TAO patients from HCs, achieving an accuracy of 61.9% using the FC of the left V1 and 64.29% using the FC of the right V1. Our study revealed that patients with TAO manifested disruptions in FC between the V1 and higher visual regions during rest. This might indicate that TAO patients could present with impaired top-down modulations, visual imagery and vision-motor function. These insights could be valuable in understanding the underlying neurobiological mechanisms of vision impairment in individuals with TAO.

Artificial intelligence-enhanced electrocardiogram analysis for identifying cardiac autonomic neuropathy in patients with diabetes.

To develop and employ machine learning (ML) algorithms to analyse electrocardiograms (ECGs) for the diagnosis of cardiac autonomic neuropathy (CAN). We used motif and discord extraction techniques, alongside long short-term memory networks, to analyse 12-lead, 10-s ECG tracings to detect CAN in patients with diabetes. The performance of these methods with the support vector machine classification model was evaluated using 10-fold cross validation with the following metrics: accuracy, precision, recall, F1 score, and area under the receiver-operating characteristic curve (AUC). Among 205 patients (mean age 54 ± 17 years, 54% female), 100 were diagnosed with CAN, including 38 with definite or severe CAN (dsCAN) and 62 with early CAN (eCAN). The best model performance for dsCAN classification was achieved using both motifs and discords, with an accuracy of 0.92, an F1 score of 0.92, a recall at 0.94, a precision of 0.91, and an excellent AUC of 0.93 (95% confidence interval [CI] 0.91-0.94). For the detection of any stage of CAN, the approach combining motifs and discords yielded the best results, with an accuracy of 0.65, F1 score of 0.68, a recall of 0.75, a precision of 0.68, and an AUC of 0.68 (95% CI 0.54-0.81). Our study highlights the potential of using ML techniques, particularly motifs and discords, to effectively detect dsCAN in patients with diabetes. This approach could be applied in large-scale screening of CAN, particularly to identify definite/severe CAN where cardiovascular risk factor modification may be initiated.

Early detection of infestation by mustard aphid, vegetable thrips and two-spotted spider mite in bok choy with deep neural network (DNN) classification model using hyperspectral imaging data

Although exclusion measures (e.g., air filters, biosecurity practices) can be employed to prevent occurrence of pest outbreaks, indoors vegetable farms in Singapore are still susceptible to various arthropod pests. Due to strong interest from the industry to pursue pesticide-free production, indoors pest management is often focused on early detection for timely containment and eradication, implying the importance of robust and vigorous pest monitoring programs. In recent years, application of machine vision technologies, especially hyperspectral imaging (HSI), has been studied for their capacity to early detect pest infestation. However, there is a lack of studies conducted in actual indoor environments and on multiple arthropod pests. Thus, this study aimed to non-destructively collect hyperspectral data of bok choy Brassica rapa subspecies chinensis which were healthy or infested with either mustard aphids Lipaphis erisymi, vegetable thrips Echinothrips americanus or two-spotted spider mites Tetranichus urticae in indoor environment to build deep neural network (DNN) classification model for early detection. Based on HSI data of control and infested plants collected daily over a period of two weeks, we found that point percentage change (PPC) values associated with leaf reflectance in 420–440 nm, 500–520 nm, 620–637 nm, 720–800 nm, and 850 nm were sensitive to infestation by the mentioned arthropod pests. Deep Neural Network (DNN) classification models trained on collected HSI data were found to outperform Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM) classification models. DNN models achieved 92.8 ± 0.4 % overall classification accuracy across all days. As early as two days after infestation, DNN models could achieved classification precision values of 96.4 %, 96.9 %, 93.9 % and 100 % for control plants and plants infested with either aphids, spider mites or thrips respectively. These results highlight the feasibility of multiclass early detection of different arthropod pests and the potential of HSI system coupled with DNN classification as an autonomous plant health monitoring tool in indoor crop production.

Plastic-Optical-Fiber-Enabled Smart Glove for Machine-Learning-Based Gesture Recognition

Gesture recognition has always been an important research direction in the field of human-computer interaction (HCI). In this paper, a wearable gesture recognition system based on D-shaped plastic optical fiber (POF) curvature sensor was proposed and experimentally studied. A highly bend sensitive D-shaped POF curvature sensor was made and integrated into a five-channel signal acquisition system on a PCB board (8×4.5 cm), which was embedded into an elastic glove to collect fingers' movement data. Thirteen gestures and eleven grasping actions were defined, and the gesture data, the grasping action data and the gesture data mixed with grasping action data were normalized, calibrated and imported into a support vector machine (SVM) classifier based on Gaussian kernel function and feedforward neural networks (FNN) respectively. The recognition accuracy based on SVM of 13 gestures and 11 grasping actions reached 99.8% and 97.7% respectively. The recognition accuracy of 13 kinds of gesture data mixed with 11 kinds of grasping action data based on Gaussian kernel function in SVM classification model and FNN were 98.9% and 99.4% respectively.

New approach for satellite DEM accuracy enhancement by combing machine learning, fuzzy majority voting, and weighted interpolation techniques

The digital elevation model (DEM) is crucial in many global and regional scientific studies in civilian and military applications. The aim of this research is to develop and test a new DEM approach for correcting the various errors in the Shuttle Radar Topography Mission (SRTM) digital elevation model. Firstly, the DEMs with the feature attributes from Sentinel-2 multispectral imagery are generated. Secondly, SRTM DEM with one band and attributes of a sentinel-2 image with eight bands are used as input data in supervised max-like hood, an artificial neural network (ANN), and support vector machine (SVM) classification models. Thirdly, ANN, supervised max-like hood, and SVM classification models, which have various properties, are fused by fuzzy majority voting (probability fusion). Finally, the fused probability is assigned for each pixel of the image, which has 12 fixed ground control points (GCPs), which is considered new input data for the inverse probability weighted interpolation (IPWI) approach to create the corrected SRTM elevations. The results were contrasted with a reference DEM (RD) created by image matching with Worldview-1 stereo satellite images, which had a 1-m vertical accuracy. The results of this study demonstrated that the RMSE of the original SRTM DEM was 5.95. On the other hand, the RMSE of the estimated elevations by the IPWI approach has been improved to 1.98 compared with that of the MLR method (3.01). The study shows a series of significant improvements in the SRTM when assessed with the reference DEM, with an RMSE reduction of (66.72%) when compared to the widely utilized multiple linear regression (MLR) method. It can be concluded that the elevation error of the original SRTM DEM is clearly reduced by the suggested approach.

Deoxynivalenol Detection beyond the Limit in Wheat Flour Based on the Fluorescence Hyperspectral Imaging Technique.

Deoxynivalenol (DON) is a harmful fungal toxin, and its contamination in wheat flour poses a food safety concern globally. This study proposes the combination of fluorescence hyperspectral imaging (FHSI) and qualitative discrimination methods for the detection of excessive DON content in wheat flour. Wheat flour samples were prepared with varying DON concentrations through the addition of trace amounts of DON using the wet mixing method for fluorescence hyperspectral image collection. SG smoothing and normalization algorithms were applied for original spectra preprocessing. Feature band selection was carried out by applying the successive projection algorithm (SPA), uninformative variable elimination (UVE), competitive adaptive reweighted sampling (CARS), and the random frog algorithm on the fluorescence spectrum. Random forest (RF) and support vector machine (SVM) classification models were utilized to identify wheat flour samples with DON concentrations higher than 1 mg/kg. The results indicate that the SG-CARS-RF and SG-CARS-SVM models showed better performance than other models, achieving the highest recall rate of 98.95% and the highest accuracy of 97.78%, respectively. Additionally, the ROC curves demonstrated higher robustness on the RF algorithm. Deep learning algorithms were also applied to identify the samples that exceeded safety standards, and the convolutional neural network (CNN) model achieved a recognition accuracy rate of 97.78% for the test set. In conclusion, this study demonstrates the feasibility and potential of the FHSI technique in detecting DON infection in wheat flour.

Site classification methodology using support vector machine: A study

The site effect is a crucial factor when analyzing seismic risk and establishing ground motion attenuation relationships. A number of countries have introduced building site classification into earthquake-resistant design codes to account for local site effects on ground motion. However, most of the site classification indicators rely on drilling data, which is often expensive and requires considerable manpower. As a result, the less detailed drilling data may lead to an undetermined site category of numerous stations. In this study, a Support Vector Machine (SVM) algorithm-based site classification model was trained to address this issue using strong ground motion data and site data from KiK-net and K-net. The classification model used the average HVSR curve of the labeled site and the combined inputs including frequency, peak, “prominence” and “sharpness” extracted from the curve. The SVM classification model has an accuracy of 76.12% on the test set, with recall rates of 82.69%, 75%, and 63.64% for sites I, II, and III, respectively. The precision rates are 75.44%, 73.77%, and 87.50%, respectively, with F1 scores of 78.90%, 74.38%, and 73.68%. For sites without significant peaks in the HVSR curve, the HVSR curve value was used as the characteristic parameter (input), and the SVM-based site classification model was also trained. The accuracy of class I and II is 75.86%. The results of this study show higher recall and accuracy rates than those obtained using the spectral ratio curve matching method (Wen et al., 2015) and GRNN method (Ji et al., 2021), indicating a better classification performance. Finally, the generalization ability of the model was verified using some basic stations in Xinjiang deployed by the “National Seismic Intensity Rapid Reporting and Early Warning Project”. The SVM-based site classification model that employs strong motion data can provide more reliable classification results for sites without detailed borehole information, and the site classification results can serve as a reference for probing ground motion attenuation relationships, ground motion simulation, and seismic fortification considering the site effect.

Cervical Squamous Cell Carcinoma Diagnosis by FTIR Microspectroscopy.

Cervical cancer was considered the fourth most common cancer worldwide in 2020. In order to reduce mortality, an early diagnosis of the tumor is required. Currently, this type of cancer occurs mostly in developing countries due to the lack of vaccination and screening against the Human Papillomavirus. Thus, there is an urgent clinical need for new methods aiming at a reliable screening and an early diagnosis of precancerous and cancerous cervical lesions. Vibrational spectroscopy has provided very good results regarding the diagnosis of various tumors, particularly using Fourier transform infrared microspectroscopy, which has proved to be a promising complement to the currently used histopathological methods of cancer diagnosis. This spectroscopic technique was applied to the analysis of cryopreserved human cervical tissue samples, both squamous cell carcinoma (SCC) and non-cancer samples. A dedicated Support Vector Machine classification model was constructed in order to categorize the samples into either normal or malignant and was subsequently validated by cross-validation, with an accuracy higher than 90%.

Data-driven estimates of the strength and failure modes of CFRP-steel bonded joints by implementing the CTGAN method

The bond strength between the CFRP and steel usually dominates the final strengthened effectiveness. However, the CFRP-steel bond strength is affected by various geometric and material properties and exhibits different failure modes, making accurate predictions challenging. This study utilises data-driven machine learning (ML) methods to predict the strength and failure modes of CFRP-steel joints. An experimental dataset consisting of 178 single-lap shear test results was first built, after which the Conditional Tabular Generative Adversarial Networks (CTGAN) method was applied to augment the limited available data. Four broadly used ML algorithms: Support Vector Machines (SVM), K-Nearest Neighbours (KNN), Decision Trees (DT) and Artificial Neural Networks (ANN) were applied. The ANN regression model achieved the best performance in predicting joint strength (Rtest2=0.95), while the SVM classification model achieved the best performance in predicting failure modes (accuracy ≥ 92.3 %). The SHapley Additive exPlanations analysis further revealed that the Young's modulus of the adhesive was most significant to the joint strength, while the tensile strength of the adhesive was most significant to the failure modes. The ultimately constructed ML models and the corresponding analyses presented can benefit practical structural engineering applications and provide insights into the optimal CFRP-steel joint design.

Status Identification in Support of Fishing Effort Estimation for Tuna Longliners in Waters near the Marshall Islands Based on AIS Data

Visualising the fishing behaviour of vessels and quantifying the spatial distribution of fishing effort is the scientific basis for assessing and managing fisheries resources. The information on the dynamics of fishing vessel voyages provided by the automatic identification system (AIS) of vessels serves as high-precision fishery data and provides a means of quantifying fishing effort with high spatial and temporal resolution in the tuna longline fishery. Based on the AIS data of five tuna longliners operating in the waters near the Marshall Islands from 2020 to 2021, this study used three methods, namely the threshold screening method, the construction of a BP neural network and the support vector machine (SVM) to identify the fishing and non-fishing status of the tuna longliners, respectively. This study investigates the status identification and fishing effort estimation of the tuna longliner (VESSEL A) in 2021 based on the constructed optimal model, and spatial correlation analyses are performed between the fishing effort estimated in hours based on AIS data and in hooks based on fishing logbook data, by month. The results showed (1) the recognition accuracy of the threshold screening method is 89.9%, the recognition accuracy of the BP neural network classification model is 95.11%, the kappa coefficient is 0.51, the recognition accuracy of the SVM classification model is 95.74% and the kappa coefficient is 0.52; (2) in comparison, the SVM classification model performs better than the other two status identification methods for tuna longliners; and (3) the correlation coefficients between the two types of effort of VESSEL A were greater than 0.79 on all fishing months, indicating that there was no significant difference in the spatial and temporal distribution between the two types of effort. This study suggests that the SVM model can be used to identify the status and estimate the fishing effort of longliners.

A radiomics model based on transrectal ultrasound for predicting prostate cancer.

Prostate cancer (PCa) is one of the most common neoplasms in men. However, the value of ultrasound-based radiomicsfor diagnosing PCa remains uncertain. We retrospectively analyzed ultrasonic and clinicaldata from 373 patients. Patients were divided into two groups according to the pathological results. Radiomics features wereextracted from TRUS, and we screened the optimal features to construct radiomics models. Relationships between clinicalcharacteristics and prostate lesions were identified by univariate and multivariate logistic regression analysis. Finally, aclinical-radiomics model was developed, and then visualized in the form of a nomogram. Of the 373 patients, 178had benign disease and 195 had malignant disease. The support vector machine (SVM) classification model showed the bestperformance, while the diagnostic performance of the clinical model was poorer than that of the radiomics model (p<0.05) orthe combined (clinical-radiomics) model (p<0.05). In general, the combined model demonstrated the highest AUC and provedto be more advantageous. The prediction model we constructed based on TRUS predicted PCa preoperativelywith high efficiency. In addition, combining radiomics with clinical factors improved diagnostic accuracy.

A Molecular Typing Method for Invasive Breast Cancer by Serum Raman Spectroscopy

BackgroundThe incidence of breast cancer ranks highest among cancers and is exceedingly heterogeneous. Immunohistochemical staining is commonly used clinically to identify the molecular subtype for subsequent treatment and prognosis. PurposeRaman spectroscopy and support vector machine (SVM) learning algorithm were utilized to identify blood samples from breast cancer patients in order to investigate a novel molecular typing approach. MethodTumor tissue coarse needle aspiration biopsy samples, and peripheral venous blood samples were gathered from 459 invasive breast cancer patients admitted to the breast department of Sichuan Cancer Hospital between June 2021 and September 2022. Immunohistochemical staining and in situ hybridization were performed on the coarse needle aspiration biopsy tissues to obtain their molecular typing pathological labels, including: 70 cases of Luminal A, 167 cases of Luminal B (HER2-positive), 57 cases of Luminal B (HER2-negative), 84 cases of HER2-positive, and 81 cases of triple-negative. Blood samples were processed to obtained Raman spectra taken for SVM classification models establishment with machine algorithms (using 80% of the sample data as the training set), and then the performance of the SVM classification models was evaluated by the independent validation set (20% of the sample data). ResultsThe AUC values of SVM classification models remained above 0.85, demonstrating outstanding model performance and excellent subtype discrimination of breast cancer molecular subtypes. ConclusionRaman spectroscopy of serum samples can promptly and precisely detect the molecular subtype of invasive breast cancer, which has the potential for clinical value.

The identification of aged-rice adulteration by support vector machine classification combined with characteristic wavelength variables

The aging of rice during long-term storage leads to a decline in quality and a decrease in consumption value. Therefore, the efficient and rapid detection of aged-rice adulteration is crucial for rice quality control. This paper aims to utilize near-infrared spectroscopy (NIRS) in conjunction with chemometrics to identify two types of rice adulteration: Late japonica rice and Southern japonica rice. To achieve this, competitive adaptive reweighted sampling (CARS) and successive projections algorithm (SPA) were utilized to select characteristic wavelengths from the spectral data, both with and without pretreatment methods. In order to establish a support vector machine classification (SVC) model with exceptional predictive capability, cross validation (CV), genetic algorithm (GA), and particle swarm optimization (PSO) were adopted to find the best combination of parameters (C, g). Notably, the prediction accuracy achieved by MMN-CARS-CV-SVC, SNV-CARS-CV-SVC, SNV-CARS-GA-SVC, and SNV-CARS-PSO-SVC models was 98% for Southern japonica rice adulteration, with varying proportions of aging rice. Conversely, the prediction accuracy of the MSC-CARS-CV-SVC model reached 96% for Late japonica rice adulteration, with varying proportions of aged rice. In conclusion, the combination of NIRS and SVC enhances the discriminative analysis of aged-rice adulteration, and the outstanding generalization ability of the SVC model offers new possibilities for the analysis of adulteration in other cereal grains.

Seismic fault identification in coal mines based on the self-organizing map-gray wolf optimizer-support vector machine algorithm

Accurate fault identification in coal mines is important to improve mine safety and economic benefits. We compare various intelligent algorithms for data preprocessing and optimization and analyze the construction methods of seismic attribute data sets and the performance of intelligent optimization algorithms using fault identification accuracy as the discrimination index to find a better combined model for seismic fault identification. First, the training data set is constructed by mining the fault and nonfault information revealed by the roadway. The distribution characteristics of the seismic attribute data indicate similarities among them, and they are nonlinearly separable. Directly using the attributes to construct the data set, the accuracy of fault identification using the support vector machine (SVM) model is 78.41%. Principal component analysis (PCA) and self-organizing mapping (SOM) neural networks are used to extract effective information and then combined with the SVM classification model, and the accuracy of fault identification is 83.82% and 87.47%, respectively. Compared with the original data and PCA dimensionality reduction data, the accuracy of fault detection is improved by 9.06% and 3.66%, respectively, indicating that SOM can effectively improve the accuracy of fault detection by eliminating similar attributes and reducing the weight of redundant information. Then, through a fixed attribute data set, genetic algorithm (GA), particle swarm optimization (PSO), and gray wolf optimizer (GWO) intelligent optimization algorithms are used to find the optimal kernel function parameter and penalty parameter of the SVM classifier. The accuracy rate of the SOM-GWO-SVM model reaches 91.12%, compared with the SOM-PSO-SVM and SOM-GA-SVM, and the model accuracy is increased by 5.2% and 5.61%, respectively. Compared with PSO and GA, the GWO algorithm has a better global search ability. The identification result of the SOM-GWO-SVM model is closest to the actual fault exposure, especially for the identification of “short” faults and associated faults, which has obvious advantages over the traditional manual interpretation in terms of efficiency and accuracy.

Identifying IDH-mutant and 1p/19q noncodeleted astrocytomas from nonenhancing gliomas: Manual recognition followed by artificial intelligence recognition.

The T2-FLAIR mismatch sign (T2FM) has nearly 100% specificity for predicting IDH-mutant and 1p/19q noncodeleted astrocytomas (astrocytomas). However, only 18.2%-56.0% of astrocytomas demonstrate a positive T2FM. Methods must be considered for distinguishing astrocytomas from negative T2FM gliomas. In this study, positive T2FM gliomas were manually distinguished from nonenhancing gliomas, and then a support vector machine (SVM) classification model was used to distinguish astrocytomas from negative T2FM gliomas. Nonenhancing gliomas (regardless of pathological type or grade) diagnosed between January 2022 and October 2022 (N = 300) and November 2022 and March 2023 (N = 196) will comprise the training and validation sets, respectively. Our method for distinguishing astrocytomas from nonenhancing gliomas was examined and validated using the training set and validation set. The specificity of T2FM for predicting astrocytomas was 100% in both the training and validation sets, while the sensitivity was 42.75% and 67.22%, respectively. Using a classification model of SVM based on radiomics features, among negative T2FM gliomas, the accuracy was above 85% when the prediction score was greater than 0.70 in identifying astrocytomas and above 95% when the prediction score was less than 0.30 in identifying nonastrocytomas. Manual screening of positive T2FM gliomas, followed by the SVM classification model to differentiate astrocytomas from negative T2FM gliomas, may be a more effective method for identifying astrocytomas in nonenhancing gliomas.

Machine Learning-Based Lane-Changing Behavior Recognition and Information Credibility Discrimination

Intelligent Vehicle–Infrastructure Collaboration Systems (i-VICS) put forward higher requirements for the real-time security of dynamic traffic information interaction. It is difficult to ensure the safety of dynamic traffic information interaction by means of traditional static information security. In this study, a method was proposed through machine learning-based lane-changing (LC) behavior recognition and information credibility discrimination, based on the utilization and exploitation of traffic business characteristics. The method consisted of three stages: LC behavior recognition based on Support Vector Machine (SVM), LC speed prediction based on Recurrent Neural Network (RNN), and credibility discrimination of speed information under LC states. Firstly, the labeling rules of vehicle LC behavior and the input/output of each stage model were determined, and the raw NGSIM data were processed to obtain data sets for LC behavior identification and LC speed prediction. Both the SVM classification and RNN prediction models were trained and tested, respectively. Afterwards, a model of credibility discrimination speed information under an LC state was constructed, and the real vehicle speed data were processed for model verification. The results showed that the overall accuracy of vehicle status recognition by the SVM model was 99.18%, and the precision of the RNN model was on the order of magnitude of cm/s. Considering transverse and longitudinal abnormal velocity, the accuracy credibility discrimination of LC velocity was more than 97% in most experimental groups. The model can effectively identify the abnormal speed data of LC vehicles and provide support for the real-time identification of LC vehicle speed information under i-VICS.

Endovascular Detection of Catheter-Thrombus Contact by Vacuum Excitation.

The objective of this work is to introduce and demonstrate the effectiveness of a novel sensing modality for contact detection between an off-the-shelf aspiration catheter and a thrombus. A custom robotic actuator with a pressure sensor was used to generate an oscillatory vacuum excitation and sense the pressure inside the extracorporeal portion of the catheter. Vacuum pressure profiles and robotic motion data were used to train a support vector machine (SVM) classification model to detect contact between the aspiration catheter tip and a mock thrombus. Validation consisted of benchtop accuracy verification, as well as user study comparison to the current standard of angiographic presentation. Benchtop accuracy of the sensing modality was shown to be 99.67%. The user study demonstrated statistically significant improvement in identifying catheter-thrombus contact compared to the current standard. The odds ratio of successful detection of clot contact was 2.86 (p=0.03) when using the proposed sensory method compared to without it. The results of this work indicate that the proposed sensing modality can offer intraoperative feedback to interventionalists that can improve their ability to detect contact between the distal tip of a catheter and a thrombus. By offering a relatively low-cost technology that affords off-the-shelf aspiration catheters as clot-detecting sensors, interventionalists can improve the first-pass effect of the mechanical thrombectomy procedure while reducing procedural times and mental burden.

Feasibility for rapid on-site screening of pesticide residues in fresh produce using machine learning-assisted diffuse reflectance spectroscopy

Ensuring food safety by minimizing pesticide residues in crops is crucial to safeguarding human health. However, farmers often fail to follow the minimal time interval known as the Pre-Harvest Interval (PHI) between pesticide application and crop harvest. This lapse leads to high pesticide residue levels, often exceeding the recommended maximum residue levels (MRLs). Conventional methods for detecting pesticide residues are laboratory-based and rely on complex wet chemistry procedures. Although highly accurate, these techniques are destructive, expensive, and time-consuming. In addition, they take several hours before establishing results, making them unsuitable for analyzing large volumes of perishable fresh produce, particularly in resource-constrained settings. In this study, we investigated the feasibility of utilizing machine learning-assisted diffuse reflectance spectroscopy (DRS), a direct, rapid, and cost-effective approach for conducting an on-site preliminary assessment of pesticide residue levels in fresh produce, referred to as “screening.” This method facilitates the validation of fresh produce compliance with crop-specific PHI requirements after pesticide application, indicating adherence to MRLs. We used a portable Vis/NIR spectrometer to acquire DRS measurements from tamarillo fruits that had just been sprayed with mancozeb at the cultivation site. In total, 646 measurements were collected from treated and control (untreated) fruits over five alternating days, covering the duration between pesticide application and PHI lapse. We used spectral data pre-processing techniques and principal component analysis (PCA) to differentiate the fruits as the residue levels decreased, approaching the recommended PHI and demonstrating their suitability for harvesting. Furthermore, using two principal components that explained 88% of the total variance, we developed a support vector machine (SVM) classification model. The model successfully classified both the treated and control samples, as well as the samples based on their PHI status, distinguishing between ‘before PHI’ (days 1 - 7), ‘after PHI’ (day 9), and ‘Control’ (no residues), all with 100% accuracy. Given the quick turnaround time for results, typically within five minutes, we conclude that DRS combined with machine learning holds the potential for rapid and cost-effective on-site screening of large volumes of fresh produce in a market setting. This approach allows for the rapid identification of samples that require additional investigation in a food safety laboratory, thereby improving the efficient use of laboratory resources.

Identification of DNA methylation episignature for the intellectual developmental disorder, autosomal dominant 21 syndrome, caused by variants in the CTCF gene

PurposeThe main objective of this study was to assess clinical features and genome-wide DNA methylation profiles in individuals affected by intellectual developmental disorder, autosomal dominant 21 (IDD21) syndrome, caused by variants in the CCCTC-binding factor (CTCF) gene. MethodsDNA samples were extracted from peripheral blood of 16 individuals with clinical features and genetic findings consistent with IDD21. DNA methylation analysis was performed using the Illumina Infinium Methylation EPIC Bead Chip microarrays. The methylation levels were fitted in a multivariate linear regression model to identify the differentially methylated probes. A binary support vector machine classification model was constructed to differentiate IDD21 samples from controls. ResultsWe identified a highly specific, reproducible, and sensitive episignature associated with CTCF variants. Six variants of uncertain significance were tested, of which 2 mapped to the IDD21 episignature and clustered alongside IDD21 cases in both heatmap and multidimensional scaling plots. Comparison of the genomic DNA methylation profile of IDD21 with that of 56 other neurodevelopmental disorders provided insights into the underlying molecular pathophysiology of this disorder. ConclusionThe robust and specific CTCF/IDD21 episignature expands the growing list of neurodevelopmental disorders with distinct DNA methylation profiles, which can be applied as supporting evidence in variant classification.