Multivariate Scatter Correction Research Articles

Feasibility and potential of terahertz spectral and imaging technology for Apple Valsa canker detection: A preliminary investigation

Apple Valsa canker (AVC) caused by the Ascomycete Valsa mali, seriously constrains the production and quality of apple fruits. The symptomless incubation characteristics of Valsa mali make it highly challenging to detect AVC at an early infection stage. After infecting the wound of apple bark, the pathogenic hyphae of AVC will expand and colonize the phloem tissue. Meanwhile, various enzymes and toxic substances released by hyphae cause the decomposition of cellulose and lignin, and the generation of poisonous secondary metabolites in bark tissue. However, these early symptoms of AVC are invisible from the bark’s appearance. Fortunately, Terahertz Spectral Imaging (ThzSI) technology with the advantage of penetrating, and fingerprinting is promising for detecting hidden or slight symptoms of the fungal infection. This study is a preliminary investigation of terahertz frequency-domain spectra for AVC in the early stage of infection. Healthy and two-week-infected apple tree branches were prepared for capturing ThzS images, and the spectral data were preprocessed by Multivariate scattering correction (MSC), Savitzky-Golay convolution smoothing (SG), and standard normal variate (SNV) respectively to remove data noise and improve data quality. Principal component analysis (PCA), competitive adaptive reweighted sampling (CARS), and random frog (RFROG) were employed to extract the spectral feature bands to eliminate redundant data and improve computational efficiency. Machine learning models were established based on the spectral features to detect AVC at an early infection stage, where 11 of them exhibited the best performance with F1-score of 99.72%. To further explore disease information in spatial spectra, imaging data were acquired using terahertz imaging technology. Based on imaging data, pseudo-color imaging, histogram equalization, and Otsu segmentation were employed to visualize early infection areas in apple barks. Furthermore, histogram feature (HF), shape feature (SF), and local binary pattern (LBP) extracted from terahertz spectral images were utilized to establish the SVM, RF, and KNN models. HF-SF-KNN and HF-SF-LBP-KNN with the best performance achieved F1-score of 98.82%. This study presents a preliminary application of terahertz spectral and imaging technology for early-stage AVC detection and demonstrates its feasibility. Additionally, it provides a new way to detect AVC, which expands the application of ThzSI technology in tree disease detection in orchards and lays the foundation for further research.

Inversion Modeling of Chlorophyll Fluorescence Parameters in Cotton Canopy via Moisture Data and Spectral Analysis

The study of chlorophyll fluorescence parameters is very important for understanding plant photosynthesis. Monitoring cotton chlorophyll fluorescence parameters via spectral technology can aid in understanding the photosynthesis, growth, and stress of cotton fields in real time and provide support for cotton growth regulation and planting management. In this study, cotton plot experiments with different water treatments were set up to obtain the spectral reflectance of the cotton canopy, the maximum photochemical quantum yield (Fv/Fm), and the photochemical quenching coefficient (qP) of leaves at different growth stages. Support vector machine regression (SVR), random forest regression (RFR), and artificial neural network regression (ANNR) were used to establish a fluorescence parameter inversion model of the cotton canopy leaves. The results show that the original spectrum was transformed by multivariate scattering correction (MSC), the standard normal variable (SNV), and continuous wavelet transform (CWT), and the model constructed with Fv/Fm passed accuracy verification. The SNV-SVR model at the budding stage, the MSC-SVR model at the early flowering stage, the SNV-SVR model at the full flowering stage, the MSC-SVR model at the flowering stage, and the CWT-SVR model at the full boll stage had the highest estimation accuracy. The accuracies of the three spectral preprocessing and qP models were verified, and the MSC-SVR model at the budding stage, SNV-SVR model at the early flowering stage, MSC-SVR model at the full flowering stage, SNV-SVR model at the flowering stage, and CWT-SVR model at the full boll stage presented the highest estimation accuracies.

Determination of malathion content in sorghum grains using hyperspectral imaging technology combined with stacked machine learning models

The rapid and precise detection of pesticide residues remains a pressing safety issue in the food industry. In this study, a rapid method for analyzing pesticide residues in sorghum was developed, combining hyperspectral imaging (HSI) technology with stacking ensemble learning (SEL) models. The HSI spectral data were preprocessed using the multivariate scatter correction (MSC) algorithm. The gradient boosting decision tree (GBDT), extreme gradient boosting (XGBoost), light gradient boosting machine (LGBM), and categorical boosting (CatBoost) algorithms were employed to identify the feature wavelengths with high contributions to the predictive model, and the performances of SEL, GBDT, XGBoost, LGBM, and CatBoost to accurately predict the pesticide residues in sorghum samples were compared. The SEL model constructed using the characteristic wavelength selected by CatBoost has the best predictive performance, with RMSEP, RP2, and RPD values of 0.6940 mg/kg, 0.9798, and 7.029, respectively. The study demonstrated that the combination of HSI and SEL enabled the accurate analysis of pesticide residues in sorghum, providing a reference for the utilization of HSI methods to accurately measure the concentrations of pesticide residues in sorghum and other food products.

Authenticity Verification of Alcoholic Beverages in IoT Environment through Fluorescence Hyperspectral Technology and Machine Learning

In the Internet of Things (IoT) environment, verifying the authenticity of beverages is an important issue. Baijiu, a unique Chinese distilled liquor, has gained popularity among people due to its distinctive flavor. In recent years, there has been increasing concern about the adulteration of baijiu. Many studies in this field have attempted to combine chemometrics, electronic nose, Raman spectroscopy, and other detection technologies with machine learning for baijiu detection. However, no research has yet applied the mature detection technology of fluorescence hyperspectral technology to this field. Additionally, existing studies have mainly focused on branded baijiu, without addressing the problem of adulteration in bulk baijiu, which remains a long-standing issue. In this study, a combination of fluorescence hyperspectral and machine learning was used for rapid and nondestructive adulteration detection of bulk liquor in Chengdu. The fluorescence hyperspectral data of three types of samples, namely, original liquor, adulterated liquor with water, and adulterated liquor with industrial alcohol, were obtained, and the raw data were preprocessed using Savitzky–Golay (SG), baseline correction (BL), multivariate scatter correction (MSC), and the combination of Savitzky–Golay and baseline correction (SG-BL). Finally, PCA and MDS are also used to extract spectral data characteristics, respectively. For model selection, three models were chosen: back-propagation artificial neural network (BP-ANN), support vector machine (SVM), and one-dimensional convolutional neural network (1D-CNN). For model evaluation, we choose accuracy, recall, precision, F1-score, memory occupied, and time consumed by the model to evaluate. The experiments show that the combination of fluorescence hyperspectral technology and machine learning can provide a new method, MSC-PCA-SVM, for bulk liquor detection, with a final classification accuracy, recall, precision, and F1-score all reaching 100%, The effectiveness and application potential of this method in the IoT environment have been demonstrated. The results of this study can not only be applied to liquor detection but also have the value of reference for the adulthood and detection of the entire food industry. It is of great significance to standardize the market and protect consumer rights.

A detection method of Saposhnikovia divaricata seed vigor based on near-infrared spectral feature extraction

Saposhnikovia divaricata is an important Chinese herbal medicine, the rapid detection of seed vigor is of great significance and practical value for the breeding selection and cultivation production of Saposhnikovia divaricata varieties. It was difficult to solve the problem of rapid testing technology of Saposhnikovia divaricate seed of the shortcomings of traditional chemical testing methods, such as cumbersome operation, time-consuming, easy to cause seed damage, and so on. In this paper, a rapid method for detecting seed vigor was processed for the Saposhnikovia divaricata based on near-infrared spectral feature extraction. First, 612 sets of near-infrared spectral data were obtained using a Fourier transform near-infrared spectrometer, and randomly divided into a calibration set and prediction set according to the ratio of 3:1. Then, the spectral data were preprocessed by the multivariate scatter correction method (MSC), the successive projection algorithm (SPA) was used to extract the feature wave numbers of spectral data. A total of 5 feature wave numbers were selected from the original 1845 wave numbers, with a streamlining rate of 99.73 %. Finally, a detection method of seed vigor was constructed based on the radial basis function neural network (RBF) with 5 feature wave numbers as input data. A network structure of the model MSC-SPA-RBF is of type 5-48-3, the accuracy of seed vigor detection was 99.0 %, and the detection time was 0.0033 s. The result indicates that the established model is a fast, non-destructive, and accurate method for detecting seed vigor of Saposhnikovia divaricata, which provides a theoretical basis and technical support for the rapid detection of Saposhnikovia divaricata seed vigor.

Beef muscle discrimination based on two-trace two-dimensional correlation spectroscopy (2T2D COS) combined with snapshot visible-near infrared multispectral imaging

The purpose of this work was to assess the potential of 2T2D COS PLS-DA (two-trace two-dimensional correlation spectroscopy and partial least squares discriminant analysis) in conjunction with Visible Near infrared multispectral imaging (MSI) as a quick, non-destructive, and precise technique for classifying three beef muscles -Longissimus thoracis, Semimembranosus, and Biceps femoris- obtained from three breeds - the Blonde d'Aquitaine, Limousine, and Aberdeen Angus. The experiment was performed on 240 muscle samples. Before performing PLS-DA, spectra were extracted from MSI images and processed by SNV (Standard Normal Variate), MSC (Multivariate Scattering Correction) or AREA (area under curve equal 1) and converted in synchronous and asynchronous 2T2D COS maps. The results of the study highlighted that combining synchronous and asynchronous 2T2D COS maps before performing PLS-DA was the best strategy to discriminate between the three muscles (100% of classification accuracy and 0% of error).

Application of Hyperspectral Technology with Machine Learning for Brix Detection of Pastry Pears.

Sugar content is an essential indicator for evaluating crisp pear quality and categorization, being used for fruit quality identification and market sales prediction. In this study, we paired a support vector machine (SVM) algorithm with genetic algorithm optimization to reliably estimate the sugar content in crisp pears. We evaluated the spectral data and actual sugar content in crisp pears, then applied three preprocessing methods to the spectral data: standard normal variable transformation (SNV), multivariate scattering correction (MSC), and convolution smoothing (SG). Support vector regression (SVR) models were built using processing approaches. According to the findings, the SVM model preprocessed with convolution smoothing (SG) was the most accurate, with a correlation coefficient 0.0742 higher than that of the raw spectral data. Based on this finding, we used competitive adaptive reweighting (CARS) and the continuous projection algorithm (SPA) to select key representative wavelengths from the spectral data. Finally, we used the retrieved characteristic wavelength data to create a support vector machine model (GASVR) that was genetically tuned. The correlation coefficient of the SG-GASVR model in the prediction set was higher by 0.0321 and the root mean square prediction error (RMSEP) was lower by 0.0267 compared with those of the SG-SVR model. The SG-CARS-GASVR model had the highest correlation coefficient, at 0.8992. In conclusion, the developed SG-CARS-GASVR model provides a reliable method for detecting the sugar content in crisp pear using hyperspectral technology, thereby increasing the accuracy and efficiency of the quality assessment of crisp pear.

Comparison of principal component regression (PCR) and partial least square regression (PLSR) modeling methods for quantifying polyethylene (PE) in recycled polypropylene (rPP) with near-infrared spectrometry (NIR)

Recycled polypropylene (rPP) often contains a small amount of polyethylene (PE). Since polypropylene (PP) and PE are incompatible, the presence of PE compromises the performance of rPP materials and needs to be closely monitored. In our previous work, Raman and near-infrared (NIR) spectrometries were evaluated to monitor PE content in rPP with partial least square regression (PLSR) modeling. The NIR spectrometry exhibited a wider application range, but the accuracy of the prediction models might be further improved. In the current work, a different modeling method, principal component regression (PCR) was employed to analyze PE content in rPP with NIR spectrometry. Spectrum pretreatment methods, including multivariate scatter correction (MSC), standard normal variate transformation (SNV), smoothing, and first derivative, were investigated to improve the NIR spectrum quality. Forward and backward interval methods were used to optimize spectral range selection. The outcomes were compared with our previous PLSR modeling results. The highest accuracy in independent validation was achieved by a PCR model with an R 2 of 0.9991 and a root-mean-square error of prediction (RMSEP) of 0.1596 PE%. On the other hand, a PLSR model achieved the lowest RMSEP of 0.9712 PE% for a non-colored post-consumer rPP sample. The PCR models might be sensitive to interference and more suitable for post-industrial materials, which have a simpler chemical composition. The PLSR models might have better stability and be more suitable for complicated post-consumer samples. Both the PCR and PLSR models were successfully applied to a gray commercial rPP sample.

Performance of Classification Models of Toxins Based on Raman Spectroscopy Using Machine Learning Algorithms.

Rapid and accurate detection of protein toxins is crucial for public health. The Raman spectra of several protein toxins, such as abrin, ricin, staphylococcal enterotoxin B (SEB), and bungarotoxin (BGT), have been studied. Multivariate scattering correction (MSC), Savitzky-Golay smoothing (SG), and wavelet transform methods (WT) were applied to preprocess Raman spectra. A principal component analysis (PCA) was used to extract spectral features, and the PCA score plots clustered four toxins with two other proteins. The k-means clustering results show that the spectra processed with MSC and MSC-SG methods have the best classification performance. Then, the two data types were classified using partial least squares discriminant analysis (PLS-DA) with an accuracy of 100%. The prediction results of the PCA and PLS-DA and the partial least squares regression model (PLSR) perform well for the fingerprint region spectra. The PLSR model demonstrates excellent classification and regression ability (accuracy = 100%, Rcv = 0.776). Four toxins were correctly classified with interference from two proteins. Classification models based on spectral feature extraction were established. This strategy shows excellent potential in toxin detection and public health protection. These models provide alternative paths for the development of rapid detection devices.

Study on Dissipation Law of Pesticides in Cauliflower Based on Hyperspectral Image Technique

In order to rapidly and non-destructively detect the residual rate of emamectin benzoate+indoxacarb pesticides on cauliflower, a study was conducted using hyperspectral technology to investigate the dissipation law of this pesticide over time. Hyperspectral imaging was employed to capture spectral data from cauliflower samples with and without the pesticide, focusing on the region of interest. The spectral data, consisting of 216 bands (ranging from 950 nm to 1666 nm), were preprocessed using techniques such as Savitzky–Golay convolution smoothing (S-G), multivariate scattering correction (MSC), and standard normal variate (SNV). Next, characteristic spectra for each pesticide were extracted using the competitive adaptive reweighted sampling algorithm (CARS). This study utilized the partial least squares (PLS) algorithm to construct a discriminative model aimed at identifying pesticide residues on cauliflower. The accuracy of the hyperspectral imaging technique was validated by comparing the results with those obtained through chromatography. The PLS model, optimized using the SNV method, exhibited the highest discriminant accuracy, achieving a recognition rate of 100%. The residual rate of indoxacarb detected through hyperspectral technology closely corresponded to the results obtained through chromatography. It was found that the discrepancy in the half-life of pesticides as detected by hyperspectral and chromatographic methods is a mere 0.14 days. These findings highlight the potential of hyperspectral imaging technology for studying pesticide dissipation on cauliflower and detecting pesticide residues.

Forensic Characterization of Mobile Phone Cases Using Infrared Spectroscopy with Deep Learning Methods

The fast and nondestructive identification of mobile phone cases is a crucial task for law enforcement personnel in the field of forensic science. Attenuated total reflection-Fourier transform infrared spectroscopy (ATR-FTIR) has been employed in conjunction with feature extraction and deep learning algorithms to develop a method for distinguishing mobile phone cases. As preprocessing steps, multivariate scatter correction and standard normal variate were performed. Feature variables were extracted using the competitive adaptive reweighted sampling and shuffled frog leaping algorithm. Several supervised pattern recognition methods, namely ResNet50, VGG16, and multilayer perceptron (MLP), were utilized for constructing classifiers. The results indicated that the model’s performance based on multivariate scattering correction (MSC) with competitive adaptive re-weighted sampling (CARS) surpassed that of other approaches. Notably, the ResNet50 model exhibited superior discriminative ability compared to the VGG16 and MLP models, achieving 100% accuracy in the differentiation of all samples. This designed method represents a potentially rapid and nondestructive approach for identifying mobile phone cases in forensic science.

Transformer fault diagnosis based on MPA-RF algorithm and LIF technology

Power transformers are essential components in power systems used to regulate voltage, transmit electrical energy, provide isolation, and match loads. They contribute to efficient and reliable electricity transmission and distribution. However, traditional methods for diagnosing transformer faults are time-consuming, not suitable for online monitoring, and greatly affected by environmental conditions. In this experiment, we propose the use of laser-induced fluorescence (LIF) technology for transformer fault detection. LIF technology is a method for analyzing and detecting specific molecules or atoms in samples. It combines laser technology with fluorescence measurements, making it a powerful analytical tool. It achieves high sensitivity and selectivity in analyzing molecules and atoms by exciting and detecting fluorescence in the sample. This makes it an important technology in scientific research and practical applications. Furthermore, LIF technology has not been previously applied to power transformer fault diagnosis. Therefore, this experiment introduces a transformer fault diagnosis model based on the marine predators algorithm (MPA) optimized random forest (RF) algorithm and LIF spectroscopy technology. Four different oil samples were selected for experimentation: crude oil, thermally faulty oil, partially moist oil, and electrically faulty oil. First, LIF technology for collect spectral images and data from the different fault oil samples. The obtained spectral data was preprocessed using two methods, multivariate scatter correction (MSC) and standardization method (SNV). Then, principal component analysis (PCA) and kernel principal component analysis (KPCA) for reducing the dimensionality of the preprocessed spectral data. Finally, the RF model, MPA-RF model, and PSO-RF model were established; and the reduced data was input into the model for training. Through comparisons of the predictions on the test set, evaluation metrics of the algorithm (including fitting coefficient, MSE, RMSE, and RMSE), and iteration convergence curves, the best transformer fault diagnosis model was identified. The results show that the MSC-KPCA-MPA-RF model has the best matching resule, with a fitting coefficient of 0.9963 and a mean square error of 0.0047. The SNV-PCA-MPA-RF model has the worst fitting effect, with a fitting coefficient of 0.9840 and a mean square error of 0.0199. Through comparisons of the convergence of different models, the MSC-KPCA-MPA-RF model has the best convergence and is the most applicable model for transformer fault diagnosis in this experiment. This model has significant implications for ensuring the safety of the power system.

A quality detection method of corn based on spectral technology and deep learning model

Corn is an important food crop in the world. With economic development and population growth, the nutritional quality of corn is of great significance to high-quality breeding, scientific cultivation and fine management. Aiming at the problems of cumbersome steps, time-consuming and laborious, and low accuracy in the current research on corn quality detection. This paper proposes to combine near-infrared (NIR) spectroscopy technology with deep learning technology to build a corn quality detection model based on convolutional neural network (LeNet-5). The original spectral data were preprocessed by wavelet transform (WT) and multivariate scattering correction (MSC) to remove noise interference and spectral scattering information. The Competitive Adaptive Reweighted Sampling Algorithm (CARS) was applied to optimize the characteristic wavenumber and reduce redundant data. According to the optimized characteristic wave number, it was input into the constructed corn quality detection model for simulation test, and the average detection accuracy rate of the test set was 96.46%, the average precision rate was 95.42%, the average recall rate was 97.92%, the average F1score was 96.64%, and the average recognition time was 51.95 s. Compared with traditional machine learning models such as BP neural network, K Nearest Neighbor (KNN), Support Vector Machine (SVM), Generalized Linear Model (GLM), Linear Discriminant Analysis (LDA), and Naive Bayesian (NB), the deep learning LeNet-5 network model constructed in this paper has an average accuracy increase of 39.32%, and has a higher detection accuracy.

Spectral non-destructive inspection of pigments via multivariate analysis

The inspection and identification of pigments is a critical task in forensic identification. Previously, investigators mainly analyzed them manually by comparing infrared spectra one by one, which was subject to significant subjective factors and time-consuming. This article proposes a non-destructive, fast, and accurate method for inspecting and identifying pigment evidence. In the experiment, 191 pigment samples from different brands were collected and analyzed for their infrared spectra. Multivariate scatter correction, Savitzky-Golay smoothing, and peak area normalization were used for preprocessing. Filter and multi-order derivative preprocessing were carried out, and feature extraction was performed using the CARS algorithm. Based on Fisher's discriminant, a classification model was established to distinguish between different brands of pigments accurately. The experimental results showed that infrared spectra combined with multivariate classification models could accurately identify pigment samples. The method was fast, non-destructive, and accurate, reducing inspection costs and improving efficiency. It can provide reference and guidance for inspecting and identifying relevant cases and other physical evidence inspections.

Near-infrared spectroscopy analysis of compound fertilizer based on GAF and quaternion convolution neural network

This paper combines near infrared spectroscopy with deep learning theory to propose a rapid identification method for compound fertilizer based on gramian angular field (GAF) image coding and quaternion convolutional neural network (QCNN). First, the near-infrared (NIR) spectra of 200 samples of two types of compound with and without polyglutamic acid (γ-PGA) were collected and pretreated by multivariate scattering correction(MSC) and first derivative methods. Then, the one-dimensional (1D) NIR spectra were transformed into two-dimensional (2D) images by GAF coding method to express the spectral information more intuitively. Finally, three images of Gramian angular difference field (GADF), Gramian angular summation field (GASF) and their average images were formed into a pure quaternion matrix for parallel representation and were analyzed by the designed QCNN. The peak features and minor features were mined based on the automatic learning function of multi-layer structure of QCNN to establish a high-performance, qualitative model for identifying compound fertilizer. The experimental results show that the classification accuracy, sensitivity, and specificity of the identification model were 95.84%, 95.96% and 94.25%, respectively. Compared with the classification results based on traditional partial least square discrimination analysis(PLS-DA), principal component analysis combing with support vector machine classification(PCA-SVM), 1D convolution neural network (1DCNN) and GAF-CNN methods, the classification accuracy and adaptability of the model based on the proposed GAF-QCNN method have been significantly improved. The combination of GAF image of NIR spectra and QCNN developed in this study provides a novel approach for the intelligent modeling algorithm of NIR spectroscopy technology.

Identifying Freshness of Shrimp Following Refrigeration Using Near-Infrared Hyperspectral Imaging

Shrimp tends to deteriorate during the refrigeration process. To monitor the freshness of shrimp during refrigeration, near-infrared (NIR) hyperspectral imaging was utilized to non-destructively identify the freshness of shrimp. In the process, three preprocessing methods (multivariate scatter correction [MSC], standard normal variate [SNV], and direct orthogonal signal correction [DOSC]) were employed to preprocess the full-wavelength spectral data, and three characteristic wavelength extraction algorithms (competitive adaptive reweighted sampling [CARS], and random forest [RF] simulated annealing [SA]) were used to extract the best-pre-processed data. Because extreme learning machine (ELM) and kernel extreme learning machine (KELM) are easily affected by parameters, ELM (based on teaching-learning-based optimization [TLBO]) and KELM (based on teaching-learning-based optimization [TLBO]) were proposed. In this study, four discriminant models (ELM, TLBO– ELM, KELM, and TLBO–KELM) were used for the full wavelength modeling analysis and the characteristic wavelength modeling analysis. In this work, the results of the final selected models are presented.

Hyperspectral Detection of Moisture Content in Rice Straw Nutrient Bowl Trays Based on PSO-SVR

In the process of rice straw nutrient bowl tray drying, real-time detection of changes in moisture content to achieve automatic adjustment of drying factors is one of the important means to ensure its drying quality. At present, the main method for measuring the moisture content of rice straw nutrient bowl trays is the drying and weighing method. This method is not only time consuming, labor intensive, and complex to operate, but also has poor real-time performance, which cannot meet the demand for real-time detection of the moisture content in the production process of rice straw nutrient bowl trays. In this regard, a real-time moisture content detection method for rice straw nutrient bowl trays based on hyperspectral imaging technology was studied. In this study we took the rice straw nutrient bowl tray during the drying process as the research object, adopted a single factor experiment, took microwave power, hot air temperature, and hot air speed as the drying factors, and took the moisture content of the rice straw nutrient bowl tray as the drying index. The rice straw nutrient bowl tray was dried according to the designed drying conditions. When drying, we removed the rice straw nutrient bowl tray every 5 min for weighing and collected hyperspectral image data within the wavelength range of 400~1000 nm until its quality remained unchanged. A total of 204 samples were collected. Using the average spectrum of the region of interest as the sample for effective spectral information, spectral preprocessing was performed using multivariate scattering correction (MSC), standardization normal variables (SNV), and Savitzky–Golay convolution smoothing (SG) methods. Principal component analysis (PCA) and competitive adaptive reweighting (CARS) methods were adopted for the dimensionality reduction of the spectral data. Three prediction models of rice straw nutrient bowl tray moisture content, namely random forest regression (RF), particle swarm optimization support vector regression (PSO-SVR), and XGBoost model were constructed using the reduced dimension spectral data. Finally, the performance of the model was compared using the coefficient of determination (R2) and mean square error (RMSE) as evaluation indicators. The research results indicate that the PCA-PSO-SVR model established based on SG method preprocessing has the best predictive performance, with a training set decision coefficient R2C of 0.984, a training set mean square error RMSE-C of 2.775, a testing set decision coefficient R2P of 0.971, and a testing set mean square error RMSE-P of 3.448. The model therefore has a high accuracy. This study achieved rapid detection of water content in rice straw nutrition trays. This method provides a reliable theoretical basis and technical support for the rapid detection of rice straw nutrient bowl tray moisture content, and is of great significance for improving the quality of rice straw nutrient bowl trays; promoting the popularization and application of raising rice straw nutrient bowl tray seedlings and whole process mechanized planting technology system; improving soil structure; and protecting the ecological environment.

An optimization strategy for detection of fertile pigeon egg based on NIR spectroscopy analysis

During the incubation of pigeon eggs, unfertilized eggs not only cause a waste of hatching resources but may also cause contamination from exploder eggs for other eggs. The fertilization status of pigeon eggs can be determined using the visible near-infrared (Vis-NIR) spectroscopy technique, allowing for the removal of unfertilized eggs. We have constructed a strategy for the individual and combined use of mean centralization, multivariate scatter correction, and standardized spectral preprocessing methods. The effect of the individual and combined preprocessing methods on the performance of the classification model under this strategy is explored using the mobile window partial least squares discriminant analysis (MWPLS-DA) model combined with grid search technology to find the optimal discriminant model. Experimental results have shown that the model effort of the MWPLS-DA model combined with the combined preprocessing method is generally better than the model with the individual preprocessing method. The model based on the combination of standardized and multi-scattering correction preprocessing methods (STD-MC) achieved higher classification accuracy than the model based on other preprocessing methods. The classification accuracy (ACC) of the model performed on the test set is 98.60%, which meets the requirements for fertilization identification of pigeon eggs.

Quantitative Detection of Quartz Sandstone SiO2 Grade Using Polarized Infrared Absorption Spectroscopy with Convolutional Neural Network Model

As an independent characteristic of electromagnetic radiation, the polarization of light is sensitive to the scattering and absorption characteristics of the mineral particles. The combination of polarization and infrared absorption spectroscopy is conducive to rapidly and accurately detecting the SiO2 content of metallurgical sandstone deposits. In this study, the 8–14 μm polarized infrared absorption spectra and the grade of the sandstone ore samples were used to analyse the spectral characteristics of the sandstone powder samples. Principal component analysis (PCA) and the successive projection algorithm (SPA) were used to reduce the dimension of the original data, first-order derivative, reciprocal logarithm, and multivariate scattering correction (MSC) data. Then, generalized regression neural network (GRNN), partial least squares regression (PLSR), and convolutional neural network (CNN) were employed to establish a hyperspectral prediction model of SiO2 grade. The results show that the quantitative model by the PCA-CNN algorithm has the better prediction precision for the reciprocal logarithm data, with a coefficient of determination (R2), root mean square error (RMSE), and ratio of performance to interquartile range (RPIQ) of 0.907, 0.023, and 5.11, respectively. This method indicates that the polarized infrared absorption spectra and the PCA-CNN model can provide a more robust and significant spectral interpretation than single infrared spectra, and it is expected to be applied to any high-purity quartz deposit type for in situ and rapid analysis.

Detection of peanut seed vigor based on hyperspectral imaging and chemometrics.

Rapid nondestructive testing of peanut seed vigor is of great significance in current research. Before seeds are sown, effective screening of high-quality seeds for planting is crucial to improve the quality of crop yield, and seed vitality is one of the important indicators to evaluate seed quality, which can represent the potential ability of seeds to germinate quickly and whole and grow into normal seedlings or plants. Meanwhile, the advantage of nondestructive testing technology is that the seeds themselves will not be damaged. In this study, hyperspectral technology and superoxide dismutase activity were used to detect peanut seed vigor. To investigate peanut seed vigor and predict superoxide dismutase activity, spectral characteristics of peanut seeds in the wavelength range of 400-1000 nm were analyzed. The spectral data are processed by a variety of hot spot algorithms. Spectral data were preprocessed with Savitzky-Golay (SG), multivariate scatter correction (MSC), and median filtering (MF), which can effectively to reduce the effects of baseline drift and tilt. CatBoost and Gradient Boosted Decision Tree were used for feature band extraction, the top five weights of the characteristic bands of peanut seed vigor classification are 425.48nm, 930.8nm, 965.32nm, 984.0nm, and 994.7nm. XGBoost, LightGBM, Support Vector Machine and Random Forest were used for modeling of seed vitality classification. XGBoost and partial least squares regression were used to establish superoxide dismutase activity value regression model. The results indicated that MF-CatBoost-LightGBM was the best model for peanut seed vigor classification, and the accuracy result was 90.83%. MSC-CatBoost-PLSR was the optimal regression model of superoxide dismutase activity value. The results show that the R2 was 0.9787 and the RMSE value was 0.0566. The results suggested that hyperspectral technology could correlate the external manifestation of effective peanut seed vigor.