Original Spectral Data Research Articles

Rapid detection of corn moisture content based on improved ICEEMDAN algorithm combined with TCN-BiGRU model

Rapid detection of corn moisture content(MC) during maturity is of great significance for field cultivation, mechanical harvesting, storage, and transportation management. However, cumbersome operation, time-consuming and labor-intensive operation were the bottleneck in the traditional drying process and dielectric parameter method. Thus, to overcome the above problems, a rapid detection method for corn MC based on improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) combined with temporal convolutional network-bidirectional gated recurrent unit (TCN-BiGRU) model. First, based on the 405 groups of NIR spectral data of corn seeds, the crested Porcupine Optimizer (CPO) algorithm was used to optimize ICEEMDAN to reduce the noise of the original spectral data. Then the Chaotic-Cuckoo Search (CCS) algorithm was applied to extract 203 characteristic wavenumbers from the original spectrum, which were input into the constructed TCN-BiGRU network model to realize corn MC detection. Finally, the CPO-ICEEMDAN-CCS-TCN-BiGRU corn MC classification detection model was constructed. The result showed that the model accuracy was 97.54 %, which was 9.22 %, 5.58 %, 2.34 %, 4.74 %, and 5.94 % higher than those of convolutional neural networks (CNN), long short-term memory networks (LSTM), temporal convolutional network (TCN), partial least squares (PLS), and support vector machine (SVM) models, respectively. The research results can provide a reliable basis for improving corn yield, quality and economic benefits.

An optimized spectral reconstruction method for shift excitation Raman differential spectroscopy

Raman spectroscopy is a powerful analytical method, but when the composition of the test sample is intricate, the original spectral data may contain noise and fluorescence background interference, making it more difficult to extract Raman spectral information from the original spectra. Especially the fluorescence background signal, which is typically several orders of magnitude stronger than the Raman signal, can even overwhelm or obscure the Raman signals, thereby impeding the qualitative or quantitative analysis of the Raman spectra. One effective method for removing the fluorescence background is shift excitation Raman differential spectroscopy (SERDS), which typically involves measuring two raw Raman spectra using slightly different excitation wavelengths, combined with reconstruction algorithms, to obtain Raman spectra free from fluorescence interference. For this purpose, a reconstruction method based on Tikhonov regularized least squares (TRLS) was developed in this study, which mitigated the oscillations caused by the direct unconstrained least squares (DULS) reconstruction method. The method was verified and optimized using four groups of artificial datasets with different characteristics. By selecting an appropriate value for parameter α, the relative standard deviation (RSD) of the reconstructed datasets was lower than that of the artificial datasets in most cases. Additionally, we evaluated the performance of the TRLS reconstruction algorithm based on a quantitative model of real Raman spectral datasets, assessing the algorithm’s performance from three perspectives: the root mean square error (RMSE), the correlation coefficient (R), and the ratio of prediction to deviation (RPD). The quantitative results indicate that using the TRLS method for reconstruction enhances both prediction accuracy and practicality. In summary, findings from both simulated data and actual experiments demonstrate that the TRLS-based reconstruction method substantially improves the stability and reliability of differential Raman spectra reconstruction.

Estimation of Malondialdehyde Content in Medicago truncatula under Salt Stress Based on Multi-Order Spectral Transformation Characteristics

Salt stress is a significant abiotic factor affecting the growth and development of alfalfa. Malondialdehyde (MDA) serves as a critical biomarker for assessing alfalfa’s salt tolerance. Traditional methods for measuring MDA are often time-consuming and labor-intensive. Recent advances in remote sensing technology have made non-destructive estimation of metabolites feasible, positioning the accurate estimation of MDA content in alfalfa as a key focus in intelligent breeding. To address the challenge of detecting subtle changes in MDA content, this study developed a partial least squares regression (PLSR) model specifically for Medicago truncatula. This study utilized leaf reflectance hyperspectral data across the visible near-infrared–shortwave infrared (VIR-NIR-SWIR) spectrum, applying multi-order spectral transformation methods, including continuous wavelet transform (CWT), fractional differential (FD), and multi-granularity spectral segmentation (MGSS). Feature selection techniques, such as sequential forward selection (SFS), Least-Squares Boosting (LSBoost), and feature selection using neighborhood component analysis for regression (FSRNCA), were employed to enhance the efficiency of the MDA estimation. The findings revealed that the optimal PLSR model for MDA estimation was achieved by integrating CWT features across orders 1–30 with the SFS method. This model demonstrated robust estimation capabilities under varying salt stress conditions, significantly outperforming the original spectral data (R2 = 0.654, RMSE = 22.567 vs. R2 = 0.242, RMSE = 33.411). A comparative analysis of feature selection methods confirmed that SFS was the most effective for estimating MDA content in alfalfa. These results provide valuable insights and methodologies for MDA estimation and evaluating salt tolerance in alfalfa.

Rapid Detection of Protein and Starch Content in Brewing Wheat Using Hyperspectral Imaging Technology Combined with a Convolutional Neural Network Regression Model

Wheat is the main raw material in liquor brewing. However, the protein content (PC) and starch content (SC) in wheat will affect the quality and flavor of the final liquor. In this study, the rapid, non-destructive determination of the PC and SC in wheat was achieved by combining hyperspectral imaging (HSI) with a convolutional neural network regression (CNNR) model established using the original spectral data in the hyperspectral images. The best preprocessing method was first determined, and then the performance of CNNR, extreme gradient boosting (XGBoost) and partial least squares regression (PLSR) models were compared at full wavelength, and it was concluded that CNNR based on the original spectrum was the optimal model for predicting wheat PC (R-square (R2) = 0.9942, root mean square error (RMSE) = 0.1041, relative percentage difference (RPD) = 13.1306) and SC (R2 = 0.9329, RMSE = 0.8633, RPD = 3.8605) at full wavelength. Finally, to further explore the feature extraction capability of the CNN model, different feature selection and extraction methods are used to build the PLSR model, and the comparison revealed that the PLSR model built by feature extraction using convolutional neural network (CNN_F) performed best in predicting wheat PC and SC. These results showed that HSI combined with a CNNR model could enable the rapid and accurate analysis of the PC and SC in wheat, since the CNNR can extract features from samples better than the traditional machine learning models.

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.

A 1D-inception-ResNet based global detection model for thin-skinned multifruit spectral quantitative analysis

Near-infrared spectroscopy (NIRS) analysis technology has important research and application value in the inspection of agricultural product quality due to its nondestructive characteristics. Deep learning methods represented by convolutional neural networks (CNNs) reduce the reliance on expert experience and prior knowledge in spectral analysis. They can automatically extract effective feature information from the original spectral data to improve the prediction accuracy of the model. However, the diverse characteristics of fruit varieties present a challenge to the robustness of the model. This study proposes1D-Inception-ResNet, an end-to-end CNN model for multifruit spectral quantitative analysis. The effectiveness of the model is validated on two thin-skinned fruit datasets with similar physicochemical properties. The root mean square error of prediction (RMSEP) for soluble solids content (SSC) and dry matter (DM) were 0.48 °Brix and 0.60 %, and the coefficients of determination of prediction (Rp2) were 0.95 and 0.89, respectively. Compared with traditional partial least squares regression (Rp2 of 0.86 and 0.80, RMSEP of 0.84 °Brix and 0.81 %, respectively) and extreme learning machine (Rp2 of 0.82 and 0.77, RMSEP of 0.95 °Brix and 0.86 %, respectively), our proposed method resulted in a lower RMSEP (46.1% and 28.1% lower, respectively) and higher RP2 (13.1% and 13.4% higher, respectively) for SSC and DM prediction. The results show that compared to other linear and nonlinear algorithms, 1D-Inception-ResNet significantly enhances the modeling effectiveness. Furthermore, external validation using a new sample set showed RMSEP values of 0.62 °Brix and 1.45 % for the SSC and DM models of Inception-ResNet, respectively. In conclusion, using an appropriate CNN network structure to establish a global model for diverse fruits expands the application scope of the model and improves the prediction stability across different species, which is significant in the future application of agricultural products and foods.

Habitat Prediction of Bigeye Tuna Based on Multi-Feature Fusion of Heterogenous Remote-Sensing Data

Accurate habitat prediction of Bigeye Tuna, the main fishing target of tuna pelagic fishery, is of great significance to the fishing operation. In response to the fact that most of the current studies use single-source data for habitat prediction, and the association between spatiotemporal features and habitat distribution is not fully explored and that this has limited the further improvement of prediction accuracy, this paper analyzes the spatiotemporal distribution of the characteristics of Bigeye Tuna’s highly migratory nature. Additionally, it puts forward a method of habitat prediction that utilizes heterosource remote-sensing data for the four-dimensional time–space–environment–spectrum (TSES) for deep-level feature extraction. First, a multi-source heterogeneous dataset was constructed by combining the spatiotemporal distribution characteristics of the product-level environmental remote-sensing data and the L1B-level original spectral remote-sensing data, and then a multi-branch, dynamic spatiotemporal feature extraction, Long Short-Term Memory Network (LSTM) time-series model was constructed to extract the characteristics of the heterogeneous data. This model was constructed to fully explore and utilize the multidimensional deep-level TSES distribution features affecting the habitat prediction. Finally, the two types of heterogeneous data were subjected to the weighted average-based decision-level fusion to obtain the final prediction results. The experimental results show that compared with other methods, the proposed method in this paper outperforms traditional machine-learning models and other single-source, data-based time-series models, with R2 reaching 0.96278 and RMSE decreasing to 0.031361 in the validation experiments of these models. In contrast, the method in this paper demonstrates good generalization ability and achieves accurate prediction of future fishery distribution.

Apple firmness detection method based on hyperspectral technology

Firmness is a key indicator of apple quality. Building a predictive model for apple firmness based on hyperspectral technology and regression algorithms can achieve rapid, non-destructive, and high-throughput detection of apple firmness. This paper adopts an Adaptive Window Length Savitzky-Golay Smoothing (AWL-SG smoothing) algorithm based on the Savitzky-Golay Smoothing (SG smoothing) algorithm, which can adaptively adjust the window length according to the change rate of spectral data at different wavelengths. SG smoothing, AWL-SG smoothing, Standard Normal Variate (SNV), and Multiplicative Scatter Correction (MSC) algorithms were used to preprocess the original spectral data, and Partial Least Squares (PLS), Ridge Regression (Ridge), and Kernel Ridge Regression (Kernel Ridge) predictive models were constructed to analyze the impact of different preprocessing methods on model prediction accuracy. The prediction models established with spectral data preprocessed by SG smoothing and AWL-SG smoothing algorithms showed significant improvement in predictive performance on the basis of the original spectral data, among which the AWL-SG smoothing algorithm performed the best. The Ridge model established with spectra data preprocessed by AWL-SG smoothing achieved an R2 of 0.8914 in the test set. Successive Projection Algorithm (SPA), Principal Component Analysis (PCA), and Independent Component Analysis (ICA) dimensionality reduction algorithms were used to reduce the dimensions of the full-band spectral data preprocessed by SG smoothing and AWL-SG smoothing algorithms, and Ridge and Kernel Ridge prediction models were constructed. The results showed that both SPA and PCA algorithms could improve the predictive performance of the models, with the PCA performing the best. The combination of AWL-SG + PCA + Ridge achieved the best predictive effect, with an R2 of 0.9146 in the test set.

Identification of Cortex Cercis chinensis Decoction Pieces from Different Growth Origins Using Raman Spectroscopy

The complexity of traditional Chinese medicine (TCM) components and the time-consuming of traditional detection methods make it necessary and meaningful to establish rapid and efficient identification techniques. This study explores the potential of Raman spectroscopy, a non-destructive technique offering details of molecular structure, for rapid and accurate identification. Cortex Cercis chinensis (CCC) decoction pieces from diverse geographical origins, Anhui, Sichuan, Zhejiang, and Hubei, were collected and analyzed using Raman spectroscopy at 785 nm, and the Raman characteristic peaks were analyzed. MATLAB software was employed to analyze the similarity between the spectra of CCC decoction pieces, and the original Raman spectral data were transformed into first and second derivative spectra. The results revealed distinct Raman spectral characteristics of carbohydrates and glycosidic bonds (characteristic peaks at 480, 531, 549, 873, 946 and 1086 cm−1). The correlation coefficients of the all the four samples from different origins ranged from 0.9625 to 0.9912, while the coincidence coefficients ranged from 0.9602 to 0.9934. The first and second derivative demonstrated significantly different peaks within specific ranges, 180–200, 280–380, and 680–740 cm−1 for first derivatives, 160–300, 340–400 and 420–480 cm−1 for second derivatives. These obvious differences in first and second derivative spectra of Raman spectra of CCC decoction pieces demonstrated the different growth origins. In conclusion, the study demonstrated the ability of Raman spectroscopy to accurately differentiate CCC decoction pieces from different geographical growth origin. These findings provided a basis for further application of Raman spectra characteristic fingerprints to be used in quality control for rapid identification of the quality and origin of TCM raw materials.

Self-immunological disease aid diagnosis with ConvSANet and Eu-clidean distance

BackgroundAnkylosing spondylitis (AS), Osteoarthritis (OA), and Sjögren's syndrome (SS) are three prevalent autoimmune diseases. If left untreated, which can lead to severe joint damage and greatly limit mobility. Once the disease worsens, patients may face the risk of long-term disability, and in severe cases, even life-threatening consequences. ResultIn this study, the Raman spectral data of AS, OA, and SS are analyzed to auxiliary disease diagnosis. For the first time, the Euclidean distance(ED) upscaling technique was used for the conversation from one-dimensional(1D) disease spectral data to two-dimensional(2D) spectral images. A dual-attention mechanism network was then constructed to analyze these two-dimensional spectral maps for disease diagnosis. The results demonstrate that the dual-attention mechanism network achieves a diagnostic accuracy of 100 % when analyzing 2D ED spectrograms. Furthermore, a comparison and analysis with s-transforms(ST), short-time fourier transforms(STFT), recurrence maps(RP), markov transform field(MTF), and Gramian angle fields(GAF) highlight the significant advantage of the proposed method, as it significantly shortens the conversion time while supporting disease-assisted diagnosis. Mutual information(MI) was utilized for the first time to validate the 2D Raman spectrograms generated, including ED, ST, STFT, RP, MTF, and GAF spectrograms. This allowed for evaluation of the similarity between the original 1D spectral data and the generated 2D spectrograms. SignificantThe results indicate that utilizing ED to transform 1D spectral data into 2D images, coupled with the application of convolutional neural network(CNN) for analyzing 2D ED Raman spectrograms, holds great promise as a valuable tool in assisting disease diagnosis. The research demonstrated that the 2D spectrogram created with ED closely resembles the original 1D spectral data. This indicates that ED effectively captures key features and important information from the original data, providing a strong descript.

Non-Destructive Detection of Cerasus Humilis Fruit Quality by Hyperspectral Imaging Combined with Chemometric Method

Cerasus Humilis fruit is susceptible to rapid color changes post-harvest, which degrades its quality. This research utilized hyperspectral imaging technology to detect and visually analyze the soluble solid content (SSC) and firmness of the fruit, aiming to improve quality and achieve optimal pricing. Four maturity stages (color turning stage, coloring stage, maturity stage, and fully ripe stage) of Cerasus Humilis fruit were examined using hyperspectral images (895–1700 nm) alongside data collection on SSC and firmness. These samples were divided into a calibration set and a validation set with a ratio of 3:1 by sample set partitioning based on the joint X-Y distances (SPXY) method. The original spectral data was processed by a spectral preprocessing method. Multiple linear regression (MLR) and nonlinear least squares support vector machine (LS-SVM) detection models were established using feature wavelengths selected by the successive projections algorithm (SPA), competitive adaptive reweighted sampling (CARS), uninformative variable elimination (UVE), and two combined downscaling algorithms (UVE-SPA and UVE-CARS), respectively. For SSC and firmness detection, the best models were the SNV-SPA-LS-SVM model with 18 feature wavelengths and the original spectra-UVE-CARS-LS-SVM model with eight feature wavelengths, respectively. For SSC, the correlation coefficient of prediction (Rp) was 0.8526, the root mean square error of prediction (RMSEP) was 0.9703, and the residual prediction deviation (RPD) was 1.9017. For firmness, Rp was 0.7879, RMSEP was 1.1205, and RPD was 2.0221. Furthermore, the optimal model was employed to retrieve the distribution of SSC and firmness within Cerasus Humilis fruit. This retrieved information facilitated visual inspection, enabling a more intuitive and comprehensive assessment of SSC and firmness at each pixel level. These findings demonstrated the effectiveness of hyperspectral imaging technology for determining SSC and firmness in Cerasus Humilis fruit. This paves the way for online monitoring of fruit quality, ultimately facilitating timely harvesting.

Efficient identification of broad absorption line quasars using dimensionality reduction and machine learning

Efficient identification of broad absorption line quasars using dimensionality reduction and machine learning

Stacking and ridge regression-based spectral ensemble preprocessing method and its application in near-infrared spectral analysis

Spectral preprocessing techniques can, to a certain extent, eliminate irrelevant information, such as current noise and stray light from spectral data, thereby enhancing the performance of prediction models. However, current preprocessing techniques mostly attempt to find the best single preprocessing method or their combination, overlooking the complementary information among different preprocessing methods. These preprocessing techniques fail to maximize the utilization of useful information in spectral data and restrict the performance of prediction models. This study proposed a spectral ensemble preprocessing method based on the rapidly developing ensemble learning methods in recent years and the ridge regression (RR) model, named stacking preprocessing ridge regression (SPRR), to address the aforementioned issues. Different from conventional ensemble learning methods, the proposed SPRR method applied multiple different preprocessing techniques to the original spectral data, generating multiple preprocessed datasets. These datasets were then individually inputted into RR base models for training. Ultimately, RR still served as the meta-model, integrating the output results of each RR base model through stacking. This approach not only produced diversity in base models but also achieved higher accuracy and lower computational complexity by using a single type of base model. On the apple spectral dataset collected by our team, correlation analysis showed significant complementary information among the data produced by different preprocessing techniques. This provided robust theoretical support for the proposed SPRR method. By introducing the currently popular averaging ensemble preprocessing method in a comparative experiment, the results of applying the proposed SPRR method to six datasets (apple, meat, wheat, olive oil, tablet, and corn) demonstrated that compared to the single preprocessing method and averaging ensemble preprocessing method, SPRR yielded the best accuracy and reliability for all six datasets. Furthermore, under the same conditions of the training and test datasets, the proposed SPRR method demonstrated better performance than the four commonly used ensemble preprocessing methods.

Blank sample denoising algorithm (BSDA): An effective spectral noise reduction in water sample LIBS detection

Laser-induced breakdown spectroscopy (LIBS), as an elemental composition analysis technique, has many unique advantages and great potential for applications in water detection. However, the quality of LIBS spectral signals, such as signal-to-noise ratio and stability, is often poor due to the matrix effects of water, limiting its practical performance. To effectively remove the inherent weak radiation in experimental spectral data that can be easily mistaken for noise, this paper proposes a denoising algorithm for processing spectral data using a self-built blank sample spectral database of deionized water samples, and designs a complete data processing workflow. It includes steps such as blank sample data screening, internal standard correction, blank sample correction, and spectral smoothing. Against the backdrop of marine applications, experimental spectral data for target elements Na, Mg, Ca, K, Sr, and Li were processed with this algorithm. The results show that after algorithm processing, the spectral quality was significantly improved, with the signal-to-noise ratio and detection limits of various elements improved by at least one order of magnitude. The signal-for Li increased by up to 36 times, and the detection limit for K decreased by up to 25.2 times. Additionally, tiny spectral peaks that could not be observable in the original spectral data could be effectively extracted after processing. From a technical implementation perspective, the database establishment and data process are simple and practical, with universal applicability. Therefore, this method has good potential and wide foregrounds in many other water sample LIBS detection technologies.

Inversion of soil carbon, nitrogen, and phosphorus in the Yellow River Wetland of Shaanxi Province using field in situ hyperspectroscopy

Soil nitrogen and phosphorus are directly related to soil quality and vegetation growth and are, therefore, a common research topic in studies on global climate change, material cycling, and information exchange in terrestrial ecosystems. However, collecting soil hyperspectral data under in situ conditions and predicting soil properties, which can effectively save time, manpower, material resources, and financial costs, have been generally undervalued. Recent optimization techniques have, however, addressed several of the limitations previously restricting this technique. In this study, hyperspectral data were taken from surface soils under different vegetation types in the wetlands of the Shaanxi Yellow River Wetland Provincial Nature Reserve. Through in situ original and first-order differential transformation spectral data, three prediction models for soil carbon, nitrogen, and phosphorus contents were established: partial least squares (PLSR), random forest (RF), and Gaussian process regression (GPR). The R2 and RMSR of the constructed models were then compared to select the optimal model for evaluating soil content. The soil organic carbon, total nitrogen, and total phosphorus content models established based on the first-order differential had a higher accuracy when modeling and during model validation than those of other models. Moreover, the PLSR model based on the original spectrum and the Gaussian process regression model had a superior inversion performance. These results provide solid theoretical and technical support for developing the optimal model for the quantitative inversion of wetland surface soil carbon, nitrogen, and phosphorus based on in situ hyperspectral technology.

Prediction of soil organic carbon in black soil based on a synergistic scheme from hyperspectral data: Combining fractional-order derivatives and three-dimensional spectral indices

Monitoring soil organic carbon (SOC) content is crucial for climate change mitigation and sustaining ecological balance. Despite the unparalleled advantages of hyperspectral data in capturing nuanced variations in soil properties through its high spectral resolution, effectively extracting useful features from numerous bands via spectral processing techniques remains a formidable challenge. This study proposes an integrated approach combining fractional-order derivative (FOD) technique and optimal band combination algorithm using ZY1-02D satellite hyperspectral data to estimate SOC in Northeast China’s Black soil region. Three modeling strategies were compared: (1) FOD-transformed reflectance (FOD spectra), (2) FOD spectra with traditional 2D spectral indices (FOD + 2D SI), and (3) FOD spectra with new 3D spectral indices (FOD + 3D SI). These strategies were implemented using the random forest model with the aim of the optimal SOC prediction. Results showed that the application of FOD technique for spectral transformation effectively addressed the challenges posed by overlapping peaks and baseline drift inherent in the original spectral reflectance. Additionally, FOD transformation enhanced subtle soil spectral features and yielded more pronounced spectral variations with increasing fractional order, as compared to the original spectral data and conventional integer-order derivatives (i.e., first and second-order derivatives). However, as the FOD order continued to increase beyond 1.4, the spectral curve exhibited amplified noise and distortion, thereby adversely impacting subsequent model development. The 3D spectral indices correlate more robustly with SOC than 2D indices. The model that combines 0.6-order FOD and 3D spectral indices achieved the best accuracy (R2 = 0.66, RMSE = 2.99 g/kg and MAE = 2.42 g/kg), significantly outperforming the models built by 0.6-order FOD spectra (R2 = 0.48, RMSE = 3.65 g kg−1, and MAE = 2.93 g kg−1) and 0.8-order FOD + 2D SI modeling strategy (R2 = 0.55, RMSE = 3.54 g kg−1, and MAE = 2.85 g kg−1). These findings indicated that FOD and 3D spectral indices exhibit superior synergistic performance in SOC prediction, demonstrating their feasibility and providing valuable insights for large-scale soil property prediction and mapping using satellite hyperspectral data.

A new application of Elasticnet regression based near-infrared spectroscopy model: Prediction and analysis of 2,3,5,4′-tetrahydroxy stilbene-2-O-β-D-glucoside and moisture in Polygonum multiflorum

This study constructed a near-infrared spectroscopy (NIRS) quantitative analysis model of 2,3,5,4′-tetrahydroxy stilbene-2-O-β-D-glucoside (TSG) and Polygonum multiflorum's water content based on Elasticnet regression (ER). NIRS data, TSG and the moisture content data sets of 117 samples of P. multiflorum were obtained by using NIR scanning and high-performance liquid chromatography analysis (HPLC) methods. MSE was used to determine the hyperparameters α and λ of Elasticnet. The optimal α values of TSG and water content were 0.83 and 0.01, and the optimal λ values were 0.03 and 1.13, respectively. By preprocessing the original spectral data, the NIRS model was established based on the source data set using ER, compared with the model established by PLS and its optimization algorithm. The results show that the ER model has good performance compared with other methods, with R2c value was 0.9879, RMSEC value was 0.09, R2p value was 0.9784, RMSEP value was 0.13 and RPD value was 6.8. In addition, according to the properties of Elasticnet's variable selection, we speculate that the final coefficients of spectral features may indirectly reflect the spectral features of different components. The results showed better prediction accuracy and superior performance. This is a much more reliable method used to forecast the content of effective components in plants.

Identification of appetite suppressants through Fourier transform infrared spectroscopy and filtered spectral feature extraction

Rapid inspection of adulterated dietary supplements containing appetite suppressants seized at the scene can provide clues and directions for case investigations. Meanwhile, applying filters and feature extraction techniques can effectively remove noise and interference from the original spectral data, thereby improving the recognition performance of the model. In this study, a total of 419 samples of 7 appetite suppressants confiscated from actual cases were collected. Fourier and Hilbert transform filters were used to preprocess the original spectral data of the samples. Feature extraction was performed using competitive adaptive reweighted sampling, and classification models were established using partial least squares discriminant analysis and random forest algorithms for identification purposes, aiming to select the optimal filter for noise removal. The results showed that the recognition rate and stability of the original spectral data significantly improved after filter processing, with the Hilbert transform filter demonstrating the best noise removal effect. The random forest model outperformed the partial least squares discriminant analysis model regarding recognition rate and stability. The optimized model achieved a recognition rate of 99.21%. This method effectively filters out noise from the spectral data using filters, enhancing the qualitative identification capability of the model and providing reference value for the rapid identification of appetite suppressants in forensic science.

Predictions of Spartina alterniflora leaf functional traits based on hyperspectral data and machine learning models

ABSTRACT Investigating the functional traits of Spartina alterniflora can provide insights towards understanding its invasion mechanism, and developing a method leaves can improve its management in coastal wetlands. Here, we examined the relationship between 11 leaf functional traits of S. alterniflora and hyperspectral data and investigated the feature bands through importance score analysis. Using original spectral and first-order differential conversion data of feature bands, we established four prediction models: random forest (RF), support vector machine (SVM), eXtreme Gradient Boosting (XGBoost), and back propagation neural network (BPNN). The study results showed that: (1) the SVM model based on Random Forest Importance Score is well-suited for S. alterniflora leaf functional trait inversion; (2) the importance score of leaf functional traits differed, and first-order differential spectral data produced more bands with high scores compared with the original hyperspectral reflectance data; (3) first-order differential data modelling effects were slightly better than those of the original spectral data. However, the first-order differential treatment did not show a significant improvement in the validation accuracy compared with the original data, and the accuracy of some traits decreased. Our study provides a new methodological approach for improving the monitoring and management of S. alterniflora in coastal wetlands.

Use of Portable Fluorescence Spectroscopy and SIMCA Method to Test The Authenticity of Apis mellifera Honey From Coffee Flower Nectar Mixed With Two Artificial Sweeteners

Honey with coffee flowers nectar is native honey formed from flower nectar. In this investigation, corn syrup and rice syrup, two artificial sweeteners, were utilized as an adulterant. Portable fluorescence spectroscopy and the SIMCA method are the tools and techniques employed. There were up to 20 samples of pure Apis mellifera honey and up to 120 samples of mixed honey (MC), each used twice. Data on the emission spectra, which are excited at a wavelength of 365 nm, were measured over the wavelength range of 300-800 nm. To improve accuracy, sensitivity, and specificity, the original spectral data was altered using a number of pre-treatments. Pretreatment with the original data with smoothing moving average may accurately identify samples and provides 100% accuracy, sensitivity, and specificity. One of the steps of the SIMCA approach, the cumulative PC, has a value of 92%, which indicates that it well explains the variation of the data. The x-loading plot's values are near the peak of the waves at 378 and 460 nm, indicating the existence of phenolic and flavonoid chemicals at those wavelengths. Keywords: Apis mellifera honey, Corn syrup, Rice syrup, Portable Spectroscopy, SIMCA.