Set Of Predictors Research Articles

Identification of millet origin using terahertz spectroscopy combined with ensemble learning

It’s crucial for both producers and consumers to accurately trace the origin of millet, given the significant differences in price and taste that exist between millets from various origins. The traditional method of identifying the origin of millet is time-consuming, laborious, complex, and destructive. In this study, a new method for fast and non-destructive differentiation of millet origins is developed by combining terahertz time domain spectroscopy with ensemble learning. Firstly, three machine learning algorithms, namely support vector machine (SVM), random forest (RF), and kernel extreme learning machine (KELM), were used to build different discriminative models, and then the impact of six different preprocessing methods on the models’ classification performance was compared. It was observed that models employing Savitzky-Golay preprocessing exhibited pronounced superiority in accurately determining the millet’s geographical origins. Building upon these findings, the research introduces an innovative ensemble learning strategy, leveraging both topsis and stacking techniques, to harness the collective strengths of the three algorithms. The outcomes of this approach reveal its remarkable capacity to distinguish millets originating from five distinct locations without the necessity for any parameter fine-tuning. The accuracy, F1 score, and Kappa on the prediction set are all 100 %, which significantly outperforms the single model, traditional voting method, and stacking method. The culmination of this study suggests that the integration of terahertz time-domain spectroscopy and TOPSIS-Stacking ensemble learning emerges as a promising method for the swift and non-intrusive discrimination of millet geographical origins with remarkable precision.

KnowledgeVIS: Interpreting Language Models by Comparing Fill-in-the-Blank Prompts.

Recent growth in the popularity of large language models has led to their increased usage for summarizing, predicting, and generating text, making it vital to help researchers and engineers understand how and why they work. We present KnowledgeVIS, a human-in-the-loop visual analytics system for interpreting language models using fill-in-the-blank sentences as prompts. By comparing predictions between sentences, KnowledgeVIS reveals learned associations that intuitively connect what language models learn during training to natural language tasks downstream, helping users create and test multiple prompt variations, analyze predicted words using a novel semantic clustering technique, and discover insights using interactive visualizations. Collectively, these visualizations help users identify the likelihood and uniqueness of individual predictions, compare sets of predictions between prompts, and summarize patterns and relationships between predictions across all prompts. We demonstrate the capabilities of KnowledgeVIS with feedback from six NLP experts as well as three different use cases: (1) probing biomedical knowledge in two domain-adapted models; and (2) evaluating harmful identity stereotypes and (3) discovering facts and relationships between three general-purpose models.

Application of handheld near infrared spectrometer in quality control of traditional Chinese medicine: Rapid screening and quantitative analysis of Lonicerae Japonicae Flos adulteration

Traditional Chinese medicine (TCM) prescription, with its intricate formulations and nuanced compositions, is a cornerstone of holistic health practices. However, the expansion of the TCM market has led to a surge in herb adulteration, which significantly undermines the quality and safety of these medicinal products. A case in point is Lonicerae Japonicae Flos (LJF), a widely utilized herb for treating colds, which has been adulterated by the cheaper Lonicerae Flos (LF), thereby affecting its therapeutic effectiveness. Therefore, a method utilizing handheld NIR spectroscopy combined with chemometrics has been developed to provide a portable, real-time solution for the rapid and accurate detection and quantification of adulterants in TCM. By collecting NIR spectra from LJF, LF and adulterated samples (AS), we’ve established a spectral database enabling deep insights into the correlation between spectral features and sample compositions. Resultantly, a classification model with a 99.58 % cross-validation accuracy, reaching 100 % for test set, effectively identified adulterants. And further spectral similarity analysis and classification identification of samples with different adulteration ratios were carried out. The cross-validation accuracy under the optimal model reached 98.38 %, and the test set accuracy was 99.20 %. In addition, the study extends to quantifying different levels of adulteration, employing 20 standard adulterated samples across a 0–100 % adulteration gradient. Via data preprocessing, feature extraction, and regression techniques, the full concentration prediction models were developed, later refined by segmenting samples based on high and low adulteration ratios. Under the SGFD_CARS_PLS (Savitzky-Golay smoothing with the first derivative_competitive adaptive reweighted sampling_partial least squares) model, exceptional performance was achieved, with a R2p of 0.983, RMSEp of 3.402, and RPDp of 7.757 for the homemade adulterated prediction set. In conclusion, the application of this technology not only improves the efficiency and accuracy of screening, but also has the advantages of low cost, easy operation and rapid results compared with traditional chemical analysis methods. It effectively protects the safety of drugs for consumers, maintains the integrity of the TCM market, and provides a strong technical support for the on-site rapid detection of TCM.

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.

Applications of an Electrochemical Sensory Array Coupled with Chemometric Modeling for Electronic Cigarettes.

This study investigates the application of an eNose (electrochemical sensory array) device as a rapid and cost-effective screening tool to detect increasingly prevalent counterfeit electronic cigarettes, and those to which potentially hazardous excipients such as vitamin E acetate (VEA) have been added, without the need to generate and test the aerosol such products are intended to emit. A portable, in-field screening tool would also allow government officials to swiftly identify adulterated electronic cigarette e-liquids containing illicit flavorings such as menthol. Our approach involved developing canonical discriminant analysis (CDA) models to differentiate formulation components, including e-liquid bases and nicotine, which the eNose accurately identified. Additionally, models were created using e-liquid bases adulterated with menthol and VEA. The eNose and CDA model correctly identified menthol-containing e-liquids in all instances but were only able to identify VEA in 66.6% of cases. To demonstrate the applicability of this model to a commercial product, a Virginia Tobacco JUUL product was adulterated with menthol and VEA. A CDA model was constructed and, when tested against the prediction set, it was able to identify samples adulterated with menthol 91.6% of the time and those containing VEA in 75% of attempts. To test the ability of this approach to distinguish commercial e-liquid brands, a model using six commercial products was generated and tested against randomized samples on the same day as model creation. The CDA model had a cross-validation of 91.7%. When randomized samples were presented to the model on different days, cross-validation fell to 41.7%, suggesting that interday variability was problematic. However, a subsequently developed support vector machine (SVM) identification algorithm was deployed, increasing the cross-validation to 84.7%. A prediction set was challenged against this model, yielding an accuracy of 94.4%. Altered Elf Bar and Hyde IQ formulations were used to simulate counterfeit products, and in all cases, the brand identification model did not classify these samples as their reference product. This study demonstrates the eNose's capability to distinguish between various odors emitted from e-liquids, highlighting its potential to identify counterfeit and adulterated products in the field without the need to generate and test the aerosol emitted from an electronic cigarette.

Research on rubber classification and recognition based on terahertz time-domain spectroscopy and improved honey badger algorithm

The identification of different rubber materials is crucial to ensuring the quality of rubber products. In order to quickly and effectively identify the types of rubber, reduce the impact of counterfeit rubber on the market. This study proposes a rubber identification method based on terahertz time-domain spectroscopy (THz-TDS), Chemometry, and Improved Honey Badger Algorithm (IHBA). Initially, the absorption spectra of eight types of rubber within the 0.2–1.6 THz range are obtained and calculated using THz-TDS. This is followed by data preprocessing using Savitzky-Golay and Principal component analysis(PCA). The optimization effects of genetic algorithm (GA), grid optimization algorithm (GRID), particle swarm optimization algorithm (PSO) and honey badger algorithm (HBA) on support vector machine (SVM) model parameters were compared respectively. The HBA-SVM model achieves 96.88 % recognition accuracy on the prediction set, which is higher than other models and shows excellent parameter optimization ability.To further improve accuracy, Bernoulli chaotic mapping, cosine density factor, and Cauchy mutation are introduced for improvement. Compared with the original model, the IHBA-SVM model improves the accuracy of rubber recognition from 96.88 % to 98.96 %. Furthermore, compared with other models, the IHBA-SVM model achieved the highest classification accuracy. In summary, this study provides technical support and reference for the rapid identification of rubber, which is of great significance for ensuring the quality of rubber products.

Qualitative discrimination and quantitative prediction of salt in aqueous solution based on near-infrared spectroscopy

Freshwater resources have been gradually salinized in recent years, dramatically impacting the ecosystem and human health. Therefore, it is necessary to detect the salinity of freshwater resources. However, traditional detection methods make it difficult to check the type and concentration of salt quickly and accurately in solution. This paper uses a portable near-infrared spectrometer to qualitatively discriminate and quantitatively predict the salt in the solution. The study was carried out by adding ten salts of NaCl, KCl, MgCl2, CaCl2, Na2CO3, K2CO3, CaCO3, Na2SO4, K2SO4, MgSO4 to 2 milliliters of deionized water to prepare a single salt solution (0.02% - 1.00%) totaling 100 sets. It was found that the Support vector machine (SVM) model was only effective in discriminating the class of salt anions in the solution. The Partial least squares-discriminant analysis (PLS-DA) model, on the other hand, can effectively discriminate the classes of salt in solution, and the accuracies of the optimal model prediction set and the interactive validation set are 98.86% and 99.66%, respectively. Furthermore, the Partial least squares regression (PLSR) models can accurately predict the concentration of NaCl, KCl, MgCl2, CaCl2, Na2CO3, K2CO3, CaCO3, Na2SO4, K2SO4, MgSO4 salt solutions. The coefficients of determination R2 of their model interactive validation sets were 0.99, 0.99, 0.99, 0.97, 0.99, 0.99, 0.98, 0.99, 0.98, and 0.98, respectively. This study shows that NIRS can achieve rapid and accurate qualitative and quantitative detection of salts in solution, which provides technical support for the utilization of safe water resources.

Building a Defence Manpower Planning Tool Using Survival Analysis Models

ABSTRACT A vibrant economy results in strong competition in the marketplace for highly skilled and versatile workforce. Therefore, the retention of such workers is an important issue for most organisations, including the Royal Navy, which is sensitive to economic pressures from the civilian economy. This paper considers early exits from the Royal Navy as the termination of an individual’s career in the naval force at any time before the termination of their contractual agreement with the organisation. The paper uses a survival analysis model to forecast/predict early exits from the Royal Navy and help build a defence manpower planning tool to manage retentions. At the micro-level, it provides two sets of predictions, the first on who is more likely to leave early, and the second on when that event is most likely to occur. At the aggregate level, the predictive model provides a management tool for performing stress testing in relation to potential changes in economic and labour market conditions and how the heterogeneous skilled workforce within the organisation is likely to respond to different changes.

Transfer of near-infrared analysis models for gasoline RON based on ensemble learning

The research octane number (RON) has guiding significance for evaluating the quality of gasoline, while near-infrared (NIR) spectroscopy analysis technology provides an important means for the detection of RON non-destructively and rapidly. When using a near-infrared spectrometer to obtain the RON of gasoline, if the analysis model can be shared among different instruments, it will greatly reduce the cost of re-modeling or model maintenance. Aiming to achieve the sharing of a NIR spectroscopy analysis model for RON between two portable near-infrared spectrometers of the same model, two ensemble learning algorithms, random forest (RF) and extreme gradient boosting (XGBoost), were employed for investigation, as well as two other machine learning algorithms, support vector regression (SVR) and decision tree (DT). Based on the RON of 120 gasoline samples and their NIR spectroscopy collected on the two spectrometers, hybrid and pure models were established to evaluate their sharing performance among SVR, DT, RF and XGBoost. In order to further simplify the model and improve its robustness and prediction accuracy, the characteristic wavelength selection strategies, including elimination of uninformative variables (UVE), successive projections algorithm (SPA), and competitive adaptive reweighted sampling (CARS), were also adopted to optimize the model. The results showed that the hybrid model based on the CARS-RF method yielded the best prediction performance, with the coefficient of determination (R2) of 0.96, 0.86, and 0.94 for the single prediction sets of instrument A, instrument B, and the hybrid prediction set of the two instruments, respectively. Therefore, the hybrid modeling method based on ensemble learning algorithms combined with an appropriate wavelength selection strategy can effectively improve the robustness and universality of the model, and achieve the sharing of gasoline RON models on two near-infrared spectrometers of the same model.

A Peak-Finding Siamese Convolutional Neural Network (PF-SCNN) for Aero-Engine Hot Jet FT-IR Spectrum Classification

Aiming at solving difficulties related to aero-engine classification and identification, two telemetry Fourier transform infrared spectrometers are utilized to measure the infrared spectra of six types of aero-engine hot jets, and create a spectral data set, which is divided into a training set (80%), a validation set (10%), and a prediction set (10%). A peak-finding Siamese convolutional neural network (PF-SCNN) is used to match and classify the spectral data. During the training stage, the Siamese convolutional neural network (SCNN) is designed to extract spectral features and calculate the distance similarity. In order to improve the efficiency of the SCNN, a peak-finding method is introduced to extract the spectral peaks, which are used to train the model instead of the original spectral data. During the prediction stage, the trained model is used to calculate the similarity between the prediction set and the combined set of the training set and validation set, and the label of the most similar training data in each prediction set is used as the prediction label. The performance measures of the classification results include accuracy, precision, recall, confusion matrix, and F1-score. The experimental results show that the PF-SCNN can achieve a high classification accuracy rate of 99% and can complete the task of classifying the infrared spectra of aero-engine hot jets.

Paddy seed viability prediction based on feature fusion of color and hyperspectral image with multivariate analysis

Seed viability is essential to have a homogeneous plant population. The seed industry cannot adopt traditional procedures for seed viability evaluation since they are destructive, time-consuming, and need chemicals. This study aimed to investigate the potential of combining hyperspectral and color image features to differentiate viable and non-viable paddy seeds. The hyperspectral and color image of the 355 paddy seeds was captured and later used to examine their viability. An image processing algorithm was developed to extract features from color images of paddy seeds and investigated significant differences in the retrieved feature data using variance analysis. The spectra were extracted from the selected region of interest (ROI) of the hyperspectral paddy seed image and averaged. In the next step, the partial least square discrimination analysis (PLS-DA) model was developed to distinguish viable and non-viable paddy seeds. Initially, the PLS-DA model was developed using spectral data with different preprocessing techniques, and the result obtained an accuracy of 88.9 % in the calibration set and 86.1 % in the prediction set using Savitzky-Golay 2nd derivative preprocessed spectra. With the fusion of spectral and significant color image features, the model's accuracy improved to 93.3 % and 90.9 % in the calibration and prediction sets, respectively. Results also showed that the fusion of selected color image features with Savitzky-Golay 2nd derivative preprocessed spectra could achieve higher F1-score, recall, and precision values. The visualization map for the viable and non-viable paddy seeds was also developed utilizing the most effective predictive model. The results demonstrate the possibility of using the fusion of the hyperspectral and color image features to sort seeds according to viability, which may be applied in developing an online seed sorting method.

Rapid and accurate identification of steel alloys by femtosecond laser-ablation spark-induced breakdown spectroscopy and machine learning

This work investigates the application of femtosecond laser-ablation spark-induced breakdown spectroscopy (fs-LA-SIBS) combined with machine learning algorithms for the rapid and accurate identification of steel alloys. Three algorithms, namely random forest (RF), support vector machine (SVM), and partial least squares identification analysis (PLS-DA), were compared and evaluated. The results indicate that, in 100 independent classifications, the RF model demonstrated an average accuracy of 0.9337, significantly surpassing the accuracies of the SVM model at 0.8281 and the PLS-DA model at 0.8646. In addition, in the evaluation of 5-fold cross-validation and the prediction set, the RF model achieved a near-perfect micro-average area under curve (AUC) of 0.9996, surpassing the AUCs of the SVM model at 0.9761 and the PLS-DA model at 0.9847. The PCA results provided valuable insights into the spectral features that most significantly contributed to the classification accuracy, further confirming the RF model's robustness and effectiveness. This integrated approach offers a powerful tool for the rapid classification and accurate identification of steel alloys in industrial applications.

Intelligent identification of picking periods of Lu’an Guapian tea by an indicator displacement colorimetric sensor array combined with machine learning

Lu’an Gua Pian (LAGP) tea is one of the most famous green teas in China. The quality of green tea is related to its picking periods, especially the green tea before Qingming Festival (usually April 6th) is highly praised as precious in the market. In this work, a simple and cheap indicator displacement colorimetric sensor array combined with smartphone was developed to rapidly identify LAGP picked during different picking periods. First, the chemical component contents of LAGP picked before and after Qingming Festival were analyzed. Second, a well-designed colorimetric sensor array was proposed based on the tea component contents differences. Finally, machine learning was used to process the array data taken by a smartphone. By comparison, the accuracy of the best model for the prediction set was 97%. Meanwhile, the multi-channel advantages of the sensing array were demonstrated by an ablation experiment. In addition, the method achieved an AGREE analysis score of 0.88, indicating that it was environmental-friendly.

Evaluating Synthetic Data Augmentation to Correct for Data Imbalance in Realistic Clinical Prediction Settings.

Predictive modeling holds a large potential in clinical decision-making, yet its effectiveness can be hindered by inherent data imbalances in clinical datasets. This study investigates the utility of synthetic data for improving the performance of predictive modeling on realistic small imbalanced clinical datasets. We compared various synthetic data generation methods including Generative Adversarial Networks, Normalizing Flows, and Variational Autoencoders to the standard baselines for correcting for class underrepresentation on four clinical datasets. Although results show improvement in F1 scores in some cases, even over multiple repetitions, we do not obtain statistically significant evidence that synthetic data generation outperforms standard baselines for correcting for class imbalance. This study challenges common beliefs about the efficacy of synthetic data for data augmentation and highlights the importance of evaluating new complex methods against simple baselines.

Exploring the analytical potential of total reflection X-ray fluorescence (TXRF) combined with partial least square (PLS) for simple determination of ash content in various coal types

Ash content, as a crucial indicator of coal quality, its rapid and accurate determination is pivotal to improve the energy utilization of coal and reduce environmental pollution. Traditional spectroscopic methods face significant challenges in acquiring accurate information from coal samples due to the notorious matrix effects arising from their complex composition, vast molecular structure, and diverse coal types. In this study, the feasibility of total reflection X-ray fluorescence (TXRF) combined with partial least squares (PLS) for the determination of coal ash was firstly investigated based on the TXRF being unaffected by matrix effects. Firstly, coal samples were prepared as suspensions, and the effects of sample particle size and different dispersants on the results of TXRF analyses were evaluated. The accuracy and applicability of the chosen sample preparation strategies were further validated using inductively coupled plasma mass spectrometry (ICP-MS) and two certified reference materials (CRMs). Subsequently, based on the analysis of 19 coal samples, the impact of three different predictive variables on the performance of the PLS model was investigated: (a) TXRF full spectrum normalized by the net intensity of the internal standard; (b) net intensity of characteristic peaks for 12 elements (Al, Si, K, Ca, Ti, Fe, Cr, Mn, Cu, Ni, and Sr) normalized by the net intensity of the internal standard; (c) concentrations of the aforementioned 12 elements. The results demonstrate that the PLS model constructed usingthe TXRF full spectrum normalized by the net intensity of the internal standard exhibits the best predictive capabilities, with the determination coefficient of calibration set (R2) and mean square error (MSE) of the prediction set reaching 0.9736 and 0.99 %, respectively. Moreover, the measurement accuracy of this model was six times greater than that obtained with traditional X-ray fluorescence (XRF). Presented analytical results display the possibilities of combining TXRF with PLS for coal quality evaluation.

Process Optimization for Antarctic Krill (Euphausia superba) Sauce Based on Back Propagation Neural Network Combined with Genetic Algorithm

Using Antarctic krill (Euphausia superba) as the research object, we optimized the process conditions for Antarctic krill sauce (AkS) by including three factors (salt addition, rock sugar addition, and the oil-to-material ratio) and sensory evaluation as response values. The data from the response surface were fed into the back propagation (BP) neural network training, generating a model mapping the process conditions and sensory scores, which were subsequently combined with the genetic algorithm (GA) for global optimization to determine the optimal process for AkS preparation. The results revealed that the response surface model was well suited to the BP neural network training and prediction sets, with correlation values of 0.98 and 0.95, respectively. The fitting prediction effect was obvious for the sensory scoring results of the product. The parameters obtained from the GA’s global optimization search accord with the analytical results of the response surface. The findings demonstrated that combining a BP neural network with a GA can enhance the AkS preparation technique. Under optimal processing conditions, AkS has a high sensory score and protein and carbohydrate contents, moderate fat content, minimal fat oxidation, and non-detectable pathogens, indicating that the AkS in this study was nutritious and safe to consume.

Dysregulated immunologic landscape of the early host response in melioidosis.

Melioidosis, a neglected tropical infection caused by Burkholderia pseudomallei, commonly presents as pneumonia or sepsis with mortality rates up to 50% despite appropriate treatment. A better understanding of the early host immune response to melioidosis may lead to new therapeutic interventions and prognostication strategies to reduce disease burden. Whole blood transcriptomic signatures in 164 patients with melioidosis and in 70 patients with other infections hospitalized in northeastern Thailand enrolled within 24 hours following hospital admission were studied. Key findings were validated in an independent melioidosis cohort. Melioidosis was characterized by upregulation of interferon (IFN) signaling responses compared with other infections. Mortality in melioidosis was associated with excessive inflammation, enrichment of type 2 immune responses, and a dramatic decrease in T cell-mediated immunity compared with survivors. We identified and independently confirmed a 5-gene predictive set classifying fatal melioidosis (validation cohort area under the receiver operating characteristic curve 0.83; 95% CI, 0.67-0.99). This study highlights the intricate balance between innate and adaptive immunity during fatal melioidosis and can inform future precision medicine strategies for targeted therapies and prognostication in this severe infection.

Quantitative analysis of zearalenone in wheat leveraging support vector machine and olfactory visualization technology

Zearalenone (ZEN) is a contaminant found naturally in grains such as wheat that poses a potential threat to human and animal health. This study describes a colorimetric sensor array detection system based on chemically reactive dyes and chemometric algorithms for the determination of ZEN in wheat. Firstly, six chemically responsive dyes were screened to construct a colorimetric sensor array to obtain the response fingerprints of the volatile components of wheat. The RGB differences of the chemically reactive dyes before and after the response were then calculated to obtain the color component data of the image. The maximum information coefficient (MIC) and ReliefF methods were then used to select the best color features. Finally, the Support Vector Machine Regression (SVR) prediction model was developed and validated based on the optimized features on 132 sample sets. The results showed that the SVR model developed on the initial 132 wheat samples had good predictive ability. Its root mean square error (RMSE) on the prediction set was 38.1625 μg∙kg−1 with a correlation coefficient (R) 0.9516. In particular, the SVR model combining 13 color components reduced the RMSE on the prediction set to 30.4484 μg∙kg−1, with an improved R of 0.9695. Olfactory visualization techniques were shown to be effective for the determination of ZEN in wheat. This study is expected to provide a scientifically novel approach for quality stabilization during the storage of wheat or other cereals.

Objective identification of meteorological fronts and climatologies from ERA-Interim and ERA5

Abstract. Meteorological fronts are important due to their associated surface impacts, including extreme precipitation and extreme winds. Objective identification of fronts is therefore of interest in both operational weather prediction and research settings. The aim of this study is to produce a front identification algorithm based on earlier studies that is portable and scalable to different resolution datasets. We have made a number of changes to an earlier objective front identification algorithm, applied these to reanalysis datasets, and present the improvements associated with these changes. First, we show that a change in the order of operations yields smoother fronts with fewer breaks. Next, we propose the selection of the front identification thresholds in terms of climatological quantiles of the threshold fields. This allows for comparison between datasets of differing resolutions. Finally, we include a number of numerical improvements in the implementation of the algorithm and better handling of short fronts, which yield further benefits in the smoothness and number of breaks. This updated version of the algorithm has been made fully portable and scalable to different datasets in order to enable future climatological studies of fronts and their impacts.

Nutritional Quality Analysis and Classification Detection of Buckwheat in Different Harvest Periods.

For buckwheat, the optimal harvest period is difficult to determine-too early or too late a harvest affects the nutritional quality of buckwheat. In this paper, physical and chemical tests are combined with a method using near-infrared spectroscopy nondestructive testing technology to study buckwheat harvest and determine the optimal harvest period. Physical and chemical tests to determine the growth cycle were performed at 83 days, 90 days, 93 days, 96 days, 99 days, and 102 days, in which the buckwheat grain starch, fat, protein, total flavonoid, and total phenol contents were assessed. Spectral images of buckwheat in six different harvest periods were collected using a near-infrared spectral imaging system. Four preprocessing methods (SNV, S-G, DWT, and the normaliz function) and three dimensionality reduction algorithms (IVSO, VCPA, VISSA) were used to process the raw buckwheat spectral data, and the full and eigen spectra were established as a random forest (RF). Random forest (RF) and Least Squares Support Vector Machine (LS-SVM) classification models were used to determine the full and eigen spectra, respectively, and the optimal model for the buckwheat single harvest period was determined and validated. Through physical and chemical tests, it was concluded that the 90-day harvest buckwheat grain protein, fat, and starch contents were the highest, and that the total flavonoid and total phenolic contents were also high. The SNV preprocessing method was the most effective, and the feature bands extracted using the IVSO algorithm were more representative. The IVSO-RF model was the best discriminative model for the classification of buckwheat in different harvest periods, with the correct rates of the training and prediction sets reaching 100% and 96.67%, respectively. When applying the IVSO-RF model to the buckwheat single harvest period to verify the classification, the correct rate of the training set for each harvest period reached 96%, and that of the prediction set reached 100%. Near-infrared spectroscopy combined with the IVSO-RF modeling method for buckwheat harvest period detection is a rapid, nondestructive classification method. When this was combined with physical and chemical analyses, it was determined that a growth cycle of 90 days is the best harvest period for buckwheat. The results of this study can not only improve the quality of buckwheat crops but also be applied to other crops to determine their optimal harvest period.