Variable Combination Population Analysis Research Articles

Quantitative analysis of three ingredients in Salvia miltiorrhiza by near infrared spectroscopy combined with hybrid variable selection strategy

Rosmarinic acid (RA), Tanshinone IIA (Tan IIA), and Salvianolic acid B (Sal B) are crucial compounds found in Salvia miltiorrhiza. Quickly predicting these components can aid in ensuring the quality of S. miltiorrhiza. Spectral preprocessing and variable selection are essential processes in quantitative analysis using near infrared spectroscopy (NIR). A novel hybrid variable selection approach utilizing iVISSA was employed in this study to enhance the quantitative measurement of RA, Tan IIA, and Sal B contents in S. miltiorrhiza. The spectra underwent 108 preprocessing approaches, with the optimal method being determined as orthogonal signal correction (OSC). iVISSA was utilized to identify the intervals (feature bands) that were most pertinent to the target chemical. Various methods such as bootstrapping soft shrinkage (BOSS), competitive adaptive reweighted sampling (CARS), genetic algorithm (GA), variable combination population analysis (VCPA), successive projections algorithm (SPA), iteratively variable subset optimization (IVSO), and iteratively retained informative variables (IRIV) were used to identify significant feature variables. PLSR models were created for comparison using the given variables. The results fully demonstrated that iVISSA-SPA calibration model had the best comprehensive performance for Tan IIA, and iVISSA-BOSS had the best comprehensive performance for RA and Sal B, and correlation coefficients of cross-validation (R2cv), root mean square errors of cross-validation (RMSECV), correlation coefficients of prediction (R2p), and root mean square errors of prediction (RMSEP) were 0.9970, 0.0054, 0.9990 and 0.0033, 0.9992 0.0016, 0.9961 and 0.0034, 0.9998, 0.0138, 0.9875 and 0.1090, respectively. The findings suggest that NIR spectroscopy, along with PLSR and a hybrid variable selection method using iVISSA, can be a valuable tool for quickly quantifying RA, Sal B, and Tan IIA in S. miltiorrhiza.

Comparison of optimization algorithms for variable selection to enhance the predictive performance of PLS regression model in determining the concentration of heavy metal Cd in peanut oil

Peanut oil is a widely loved edible vegetable oil in the world. The raw material of peanut oil may be contaminated by heavy metals in the process of planting, processing, and transportation. In this study, the heavy metal Cd content in peanut oil was rapidly determined by FT-NIRS combined with a machine learning model. Three variable optimization algorithms were used for FT-NIRS obtained by experiments, namely, the variable combination population analysis (VCPA), the iteratively variable subset optimization (IVSO), and the multi-feature spaces ensemble strategy by least absolute shrinkage and selection operator (MFE-LASSO). A partial least squares (PLS) regression model was used to determine Cd content in peanut oil. The experimental findings demonstrate that the three variable selection optimization algorithms are effective in identifying key combinations of variables. Among them, the MFE-LASSO-PLS model has strong robustness and generalization performance. Its root mean square error of prediction (RMSEP) was 3.7753 mg·kg-1, prediction coefficient of determination (RP2) was 0.9675, and the relative prediction deviation (RPD) was 5.5485. The research findings demonstrate that the rapid and accurate determination of Cd in peanut oil can be achieved through the integration of FT-NIRS technology with a machine learning model.

Research on the Detection Method of Organic Matter in Tea Garden Soil Based on Image Information and Hyperspectral Data Fusion.

Soil organic matter is an important component that reflects soil fertility and promotes plant growth. The soil of typical Chinese tea plantations was used as the research object in this work, and by combining soil hyperspectral data and image texture characteristics, a quantitative prediction model of soil organic matter based on machine vision and hyperspectral imaging technology was built. Three methods, standard normalized variate (SNV), multisource scattering correction (MSC), and smoothing, were first used to preprocess the spectra. After that, random frog (RF), variable combination population analysis (VCPA), and variable combination population analysis and iterative retained information variable (VCPA-IRIV) algorithms were used to extract the characteristic bands. Finally, the quantitative prediction model of nonlinear support vector regression (SVR) and linear partial least squares regression (PLSR) for soil organic matter was established by combining nine color features and five texture features of hyperspectral images. The outcomes demonstrate that, in comparison to single spectral data, fusion data may greatly increase the performance of the prediction model, with MSC + VCPA-IRIV + SVR (R2C = 0.995, R2P = 0.986, RPD = 8.155) being the optimal approach combination. This work offers excellent justification for more investigation into nondestructive methods for determining the amount of organic matter in soil.

Rapid detection of ash content in black tea using a homemade miniature near-infrared spectroscopy

Ash is a testing index with both health inspection value and quality decision value, and it is an essential detection item in the import and export trade of tea. To realize the rapid and effective quantitative analysis of ash content in tea, this study proposed the use of a homemade miniature near-infrared (NIR) spectroscopy combined with multivariate analysis for the rapid detection of ash content in black tea. First, NIR data of black tea samples from different countries were acquired and optimized by the spectral preprocessing method. Then, the optimized pre-processed spectral data were used as features, and four feature wavelength selection algorithms, such as competitive adaptive reweighted sampling, iteratively retaining informative variables (IRIV), variable combination population analysis (VCPA)-IRIV, and interval variable iterative space shrinkage approach (IVISSA), were utilized to optimize the feature spectra. Finally, the support vector machine regression (SVR) algorithm was employed to construct the quantitative models of ash content in black tea by combining the optimal wavelengths obtained from the four feature selection methods mentioned above. The experimental results showed that the IVISSA-SVR model had the best performance, with correlation coefficient (Rp), root mean square errors of prediction (RMSEP), and relative prediction deviation (RPD) of 0.9546, 0.0192, and 5.59 for the prediction set, respectively. The results demonstrate that a miniature NIR sensing system combined with chemometrics as an effective analytical tool can realize the rapid detection of ash content in black tea.

Applications of colorimetric sensors for non-destructive predicting total volatile basic nitrogen (TVB-N) content of Fujian oyster (Crassostrea angulata)

Total volatile basic nitrogen (TVB-N) content is one of the critical safety evaluation indexes in seafood. This study investigated the colorimetric sensing array (CSA) technique with two data collection blocks, imaging and visible near-infrared spectroscopy, for non-destructive prediction of TVB-N content in Fujian oysters. Three data fusion strategies coupled with variable selection approaches including variable combination population analysis (VCPA), ant colony optimization, and competitive adaptive reweighted sampling were applied to establish the partial least squares (PLS) model for predicting the TVB-N content of oysters. Better prediction performance was obtained using data fusion of two information blocks than using individual block data. The results showed that the high-level-VCPA strategy showed the best performance with the correlation coefficient of the prediction set (Rp) at 0.9128. This work demonstrates that the use of CSA has the potential for non-destructive monitoring of TVB-N content in Fujian oysters.

Detection of anthocyanin content in fresh Zijuan tea leaves based on hyperspectral imaging

Anthocyanins are characteristic substances that determine the leaf color and sensory quality of Zijuan tea. The qualitative determination of anthocyanin content in fresh Zijuan tea leaves by artificial vision may lead to the uneven quality of tea products. Hyperspectral images at 400–956 nm were obtained. K-nearest neighbor (KNN) and support vector machine (SVM) models were used to identify the tenderness grade, and partial least squares regression (PLSR) and support vector regression (SVR) models were established based on principal component analysis for prediction. In short, the classification accuracy of the SVM model was better than 90%; variable combination population analysis (VCPA) and variables combination population analysis combined with iterative retained information variable (VCPA-IRIV) can effectively simplify the model and the prediction accuracy is over than 0.92. The best predictive models of total anthocyanins, Cya-3,5-O-diglucoside, Cya-3-O-glucoside, and petunidin were VCPA + SVR, VCPA-IRIV + SVR, VCPA-IRIV + PLSR, and VCPA + PLSR, and the relative percent deviation were 3.233, 2.868, 3.529 and 3.298, respectively. The total anthocyanins were visualized by the spatial distribution of anthocyanins in samples with different tenderness. This study provided a rapid nondestructive method of fresh Zijuan tea leaf tenderness grade and quality components to control the quality of fresh leaf picking of tea with specific leaf color.

Application of visible-near infrared spectroscopy in tandem with multivariate analysis for the rapid evaluation of matcha physicochemical indicators.

Consumer preference for matcha is heavily influenced by its physicochemical properties. The visible-near infrared (Vis-NIR) spectroscopy technology coupled with multivariate analysis was investigated for rapid and non-invasive evaluation of particle size and the ratio of tea polyphenols to free amino acids (P/F ratio) of matcha. The multivariate selection algorithms such as synergy interval (Si), variable combination population analysis (VCPA), competitive adaptive reweighted sampling (CARS), and interval combination population analysis (ICPA) were compared, and eventually, the variable selection strategy of ICPA and CARS hybridization was firstly proposed for selecting the characteristic wavelengths from Vis-NIR spectra to build partial least squares (PLS) models. Results indicated that the ICPA-CARS-PLS models achieved satisfactory performance for the evaluation of matcha particle size (Rp=0.9376) and P/F ratio (Rp=0.9283). Hence the rapid, effectual, and nondestructive online monitoring, Vis-NIR reflectance spectroscopy in tandem with chemometric models is significant for the industrial production of matcha.

Quantification of polycyclic aromatic hydrocarbons in roasted Tan lamb using fluorescence hyperspectral imaging technology

Polycyclic aromatic hydrocarbons (PAHs) are one of the main harmful substances produced during the roasting of lamb. In this study, fluorescence hyperspectral imaging (FHSI) at different excitation wavelengths was used for the rapid determination of total PAHs (T-PAHs) content in roasted Tan lamb. Heterogeneous two-dimensional correlation spectroscopy (H2D-COS) was performed to resolve the fluorescence hyperspectral peaks. Competitive adaptive reweighed sampling (CARS), uninformative variable elimination (UVE), variable combination population analysis (VCPA), iterative retained information variable (IRIV) and interval variable iterative spatial shrinkage analysis (iVISSA) methods were employed to extract effective wavelengths to reduce data dimensionality. Partial least squares regression (PLSR), least squares support vector machine (LS-SVM) and convolutional neural network (CNN) algorithms were used to develop the T-PAHs content prediction model. For the FHSI at 357 nm excitation wavelength, the Smoothing-CARS-PLSR quantitative prediction model worked the best, with RC2 and RP2 reaching 0.9011 and 0.9169, and RMSEC and RMSEP reaching 0.0179 mg/kg and 0.0181 mg/kg, respectively. For the FHSI at 452 nm excitation wavelength, Smoothing-VCPA-CNN showed the best results, with RC2 and RP2 of 0.9199 and 0.9189, and RMSEC and RMSEP reaching 0.0175 mg/kg and 0.0171 mg/kg, respectively. This study provided a reference for the quantitative detection of PAHs.

Determination of residual levels of procymidone in rapeseed oil using near-infrared spectroscopy combined with multivariate analysis

The issue of pesticide residues has always been a hot topic at home and abroad. A method for the quantitative detection of procymidone residues in grain and oil products using near-infrared (NIR) spectroscopy has been proposed. First, a NIR spectrometer was used to collect spectral data from rapeseed oil samples with different concentrations of procymidone residues. Based on full-spectrum data, the wavelength points selected by bootstrapping soft shrinkage (BOSS) algorithm, competitive adaptive reweighted sampling (CARS) algorithm, and variable combination population analysis (VCPA) algorithm then were compared and were quantified using support vector regression (SVR) model. Simultaneously, the prediction results of the SVR model optimized by dung beetle optimizer (DBO) algorithm and pigeon-inspired optimization (PIO) algorithm were compared using the full-spectrum data. Finally, the wavelength selection algorithms and parameter optimization algorithms with the best prediction results were selected for comparison and combination. In light of the outcomes, the three spectral characteristic wavelength selection algorithms and the two optimization algorithms can improve the coefficient of determination (RP2) and reduce the root mean square error of prediction (RMSEP). The SVR model that utilizing CARS and PIO algorithm demonstrates the best generalization performance among all models evaluated, and the RP2 is 0.9939 with a RMSEP of 2.3435 mg·kg−1. The results indicate that the high-precision and rapid detection of procymidone in edible oil can be achieved using the SVR model optimized by input feature and parameter based on NIR spectral data. This has great significance in ensuring the safety of grain and oil food.

Retrieval of Leaf Chlorophyll Contents (LCCs) in Litchi Based on Fractional Order Derivatives and VCPA-GA-ML Algorithms.

The accurate estimation of leaf chlorophyll content (LCC) is a significant foundation in assessing litchi photosynthetic activity and possible nutrient status. Hyperspectral remote sensing data have been widely used in agricultural quantitative monitoring research for the non-destructive assessment of LCC. Variable selection approaches are crucial for analyzing high-dimensional datasets due to the high danger of overfitting, time-intensiveness, or substantial computational requirements. In this study, the performance of five machine learning regression algorithms (MLRAs) was investigated based on the hyperspectral fractional order derivative (FOD) reflection of 298 leaves together with the variable combination population analysis (VCPA)-genetic algorithm (GA) hybrid strategy in estimating the LCC of Litchi. The results showed that the correlation coefficient (r) between the 0.8-order derivative spectrum and LCC had the highest correlation coefficients (r = 0.9179, p < 0.01). The VCPA-GA hybrid strategy fully utilizes VCPA and GA while compensating for their limitations based on a large number of variables. Moreover, the model was developed using the selected 14 sensitive bands from 0.8-order hyperspectral reflectance data with the lowest root mean square error in prediction (RMSEP = 5.04 μg·cm-2). Compared with the five MLRAs, validation results confirmed that the ridge regression (RR) algorithm derived from the 0.2 order was the most effective for estimating the LCC with the coefficient of determination (R2 = 0.88), mean absolute error (MAE = 3.40 μg·cm-2), root mean square error (RMSE = 4.23 μg·cm-2), and ratio of performance to inter-quartile distance (RPIQ = 3.59). This study indicates that a hybrid variable selection strategy (VCPA-GA) and MLRAs are very effective in retrieving the LCC through hyperspectral reflectance at the leaf scale. The proposed methods could further provide some scientific basis for the hyperspectral remote sensing band setting of different platforms, such as an unmanned aerial vehicle (UAV) and satellite.

Application of portable near‐infrared spectroscopy technology for grade identification of Panax notoginseng slices

AbstractPanax notoginseng slices (PNS) are prepared from the taproot of a rare Chinese herbal plant, Panax notoginseng. The price and efficacy of PNS change depending on its grade, but substandard PNS are more prevalent in the Asian market. In this study, a portable near‐infrared spectrometer was used to collect the spectra of 240 PNS samples divided into four grades. The spectral data were preprocessed by the Savitzky–Golay (SG) filter to eliminate noise interference. Principal component analysis (PCA), competitive adaptive reweighted sampling, and variable combination population analysis were used to extract the feature variables of the spectral data. The selected feature variables were used to establish least squares support vector machine (LSSVM), support vector machine (SVM), and extreme learning machine (ELM) classification models. To further improve the classification accuracy of the most effective of these models, a grey wolf optimizer (GWO) was introduced, and particle swarm optimization (PSO) as well as genetic algorithm (GA) were used to conduct comparative analyses. The results showed that PCA provided accurate identification information of different PNS grades and that the classification effect of the LSSVM model was better than that of the ELM and SVM models. During the optimization process, the optimization accuracy of the GWO was better than that of the PSO and GA systems. Therefore, the optimal classification model was established as GWO–PCA–LSSVM, and the classification accuracy of the test set was 91.67%. Therefore, portable near‐infrared spectroscopy technology can be used to identify the grade of PNS effectively.

Effect of potato peel on the determination of soluble solid content by visible near-infrared spectroscopy and model optimization.

The quantitative determination of the soluble solid content (SSC) of potatoes using NIR spectroscopy is useful for predicting the internal and external quality of potato products, especially fried products. In this study, the effect of peel on the partial least squares regression (PLSR) quantitative prediction of potato SSC was investigated by transmission and reflection. The results show that the variable sorting for normalization (VSN) pre-processing method improved model accuracy. Additive multiplicative scattering effects and intensity drift interference of the peels were reduced. The model accuracy reached a correlation coefficient of prediction (RP) of 0.85. The selection algorithm using variable combination population analysis and iterative retention of information variables (VCPA-IRIV) demonstrated that peel increases unnecessary information. When the effect of irrelevant variables was reduced, the results reached RP = 0.88 and the root mean square error of prediction (RMSEP) = 0.25 in the transmission mode was close to that of the full-wavelength peeled PLSR model (RP = 0.89 and RMSEP = 0.25). This indicates that the use of the combined algorithm (VSN-VCPA-IRIV) reduces the effect of the peel and enables samples with a peel to still be predicted accurately in the full-wavelength model. It also improves detection efficiency through the extraction of the necessary variables and optimizes the stability and accuracy of the model.

Rapid Nondestructive Detection of Chlorophyll Content in Muskmelon Leaves under Different Light Quality Treatments

In order to select the light quality suitable for plant growth, a quantitative detection model of chlorophyll content in muskmelon leaves was established to monitor plant growth quickly and accurately. In the paper, muskmelon “Boyang 91” was used as the experimental material, and six different light proportion treatments were set up. Through measuring plant height, stem diameter, number of leaves, nodes, and other growth indicators, in addition to leaf chlorophyll content, the response difference of muskmelon to different light qualities was explored in a plant factory. The hyperspectral imaging technology was used to establish the prediction model for the chlorophyll content of muskmelon. The original spectrum was preprocessed and optimized by five pretreatments, and then the characteristic wavelengths were extracted by six methods. Partial least squares regression (PLSR), least squares support vector machine (LSSVM), and convolutional neural network (CNN) were established for optimal feature wavelength. The results showed that the plant height and stem diameter of the T3 treatment were higher than those of other treatments, and their values were 14.48 (cm) and 5.02 (mm), respectively. The chlorophyll content of the T3 treatment was the highest, and its value was 40.16 (mg/g), which was higher than that of other treatments. Through comprehensive analysis, the T3 treatment (light ratio: 6R/1B/2W, light quantum flux: 360 μmol/(m2·s), photoperiod: 12 h) was optimal. Meanwhile, the average spectral reflectance data of 216 leaf samples were extracted, and the S-G preprocessing method was selected to preprocess the original spectral data (Rc = 0.860, RMSEC = 1.806; Rcv = 0.790, RMSECV = 2.161). By comparing and analyzing the correlation coefficients and root mean square errors of six feature wavelength extraction methods, it was concluded that the variable combination population analysis (VCPA) method had the best model effect for feature wavelength extraction (RP = 0.824, RMSEP = 1.973). Ten characteristic wavelengths ( 396, 409, 457, 518, 532, 565, 687, 691, 701, and 705 nm) extracted by the VCPA method were used to establish the chlorophyll content prediction model, and the chlorophyll content prediction model of S-G-VCPA-CNN had the best performance (Rc = 0.9151, RMSEC = 1.445; Rp = 0.811, RMSEP = 2.055). The results of this study provide data support and a theoretical basis for screening the light ratio of other crops, and also present technical support for online monitoring of crop growth in plant factories.

Rapid determination of acidity index of peanut during storage by a portable near-infrared spectroscopy system

In the process of peanut storage, the acidity index can be used as one of the important evaluation criteria of peanut storage characteristics. This study proposed a rapid detection method of acidity index of peanuts during storage based on a portable near-infrared (NIR) spectroscopy system. The portable spectroscopy system was built to collect the NIR spectra of peanuts during storage. The Savitzky-Golay (SG) smoothing combined with multiple scattering correction (MSC) was used to preprocess the raw spectra and normalize it. Three model population analysis-based (MPA-based) wavelength variable selection methods (namely: variable combination population analysis (VCPA), iterative retained information variable (IRIV) and (VCPA-IRIV) were employed comparatively to optimize the characteristics of the pre-treated spectra. And support vector machine (SVM) models were established based on optimized features to achieve rapid detection of acidity index of peanuts during storage. The results obtained showed that compared with the full-spectrum SVM model, the prediction performance of the SVM model based on optimized features has been improved. In addition, the VCPA-SVM model obtained the best prediction performance. The root mean square error (RMSEP) of the model was 0.61 g·kg−1, the coefficient of determination of the prediction set (RP2) was 0.95, and the residual predictive deviation (RPD) was 4.31. The overall results demonstrate that the portable NIR spectroscopy system can realize the rapid detection of the acidity index during peanut storage, and the VCPA algorithm can efficiently obtain the characteristic wavelengths of the NIR spectra of peanuts during storage.

Rapid detection of yeast growth status based on molecular spectroscopy fusion (MSF) technique

This work innovatively proposes a novel method for quantitative detection of yeast growth status based on molecular spectroscopy fusion (MSF) technique. First, the Raman spectra and near-infrared (NIR) spectra of the samples during the yeast culture process were collected by spectrometers, and the raw spectra were respectively preprocessed using Savitzky-Golay (SG) smoothing + standard normal variate (SNV). Then, the two kinds of molecular spectra after preprocessing were respectively optimized for characteristic wavelength using variable combination population analysis (VCPA), and feature fusion was performed on the feature layer. Finally, support vector machine (SVM) models based on the fusion features were established to achieve quantitative detection of yeast growth status. The experimental results showed that, compared with the best VCPA-SVM model based on single-molecule spectral features, the best VCPA-SVM model based on the fusion features could obtain better prediction performance. The root mean square error of prediction (RMSEP), coefficient of determination (RP2) and relative percent deviation (RPD) of the best model were 0.47, 0.98, and 6.69, respectively. The results obtained reveal that the MSF technique can effectively improve the detection accuracy of yeast growth status; in addition, the VCPA method has a certain potential in feature wavelength mining, which can effectively reduce the dimensionality of fusion features.

Efficient monitoring for the nutrient changes in stir-fried Moutan Cortex using non-destructive near-infrared spectroscopy sensors

Now, almost all solutions are moving toward miniaturization of devices. Stir-frying, as a food processing process, presents field analysis challenges to the NIR foundation. Therefore, in this paper, Moutan Cortex, a functional food product that requires strict control of stir-frying degree, with a variety of biological activities such as antioxidant, anti-inflammatory and protection of cardiovascular system, was investigated to realize the qualitative and quantitative analysis of the product on site through miniaturized spectrometer NIR-S-G1. TANGO FT-NIR, a precision benchtop instrument, was applied as a reference to observe the field analysis performance of the instrument. The results show that the handheld device can accurately judge the products of different stir-frying degrees by SVM model with the accuracy rate of 100%. At the same time, it can accurately and quantitatively monitor the four main components in the process. Also combinations of optimization algorithms (Competitive Adaptive Weighted sampling, variable combination population analysis combined with iteratively retaining informative variables and genetic algorithm) and regression models (Partial least squares regression and Support Vector Machine) were investigated to perform a better result. Therefore, this work provided a reference for the field analysis of NIR technology from qualitative and quantitative analysis in this specific analytical application.

Non-destructive determination of total sugar content in tobacco filament based on calibration transfer with parameter free adjustment

The advantages of near-infrared spectroscopy (NIR) for non-destructive determination have an important impact on quality assessment in the tobacco industry. However, spectral measurements are very sensitive to external conditions, such as moisture content and sample morphology. The accuracy and stability of analytical models are often affected by variations in sample spectra. In this study, an efficient non-supervised parameter-free calibration enhancement (NS-PFCE) method was proposed for the calibration transfer from dried tobacco powder to undried tobacco samples in order to correct the effects of moisture and material scattering on spectral absorption. While comparing the transfer effects of piecewise direct standardization (PDS), spectral space transformation (SST), and slope/bias correction (S/B) methods. The results showed that the best prediction (RMSEP = 0.9800) was obtained with the NS-PFCE method, with a decrease of 14.19 %, 2.26 %, and 27.19 % in RMSEP compared to the prediction results calibrated transferred by PDS, SST and S/B, respectively. In addition, to further optimize the calibration effect and improve the transfer efficiency, competitive adaptive reweighted sampling (CARS), variable combination population analysis employing the iteratively retaining informative variables (VCPA-IRIV), and variable combination population analysis genetic algorithm (VCPA-GA) were employed to select the important wavelengths to simplify the model. The results showed that the VCPA-IRIV-PLSR model combined with NS-PFCE method obtained the optimal prediction results of undried filament samples (RMSEP = 0.7252), which were 50.08 %, 6.74 % and 33.10 % lower than those RMSEP values by the other three methods under the same treatment conditions, respectively. Therefore, it can be concluded that NS-PFCE method combined with VCPA-IRIV-PLSR model can effectively compensate for the spectral differences caused by moisture and sample morphology, and achieve an excellent quantitative analysis of moist samples in non-homogeneous form. The method helps to achieve rapid non-destructive testing at-line in the tobacco industry.

Combining multispectral and hyperspectral data to estimate nitrogen status of tea plants (Camellia sinensis (L.) O. Kuntze) under field conditions

Nitrogen (N) plays a pivotal role in management of tea plantation, with significant impacts on the growth, productivity, and nutrition status of tea plants. The existing methods for N content monitoring of tea leaves are complicated and can not realize in suite and in real time way. This study proposed a method for estimating the N content of tea plants in field conditions based on a combination of a multispectral imaging system and hyperspectral data. A total of 32 parameters were extracted from five tea gardens using calibrated multispectral images of the tea plant canopy, and 27 indices were selected by Pearson correlation analysis. A total of 28 wavelengths selected by competitive adaptive reweighted sampling from hyperspectral data were combined with 27 multispectral indices as the original data. Subsequently, five variables of fused data (H, VOG, BGI, 1664 nm and 1665 nm) were selected by variable combination population analysis based on the 55 combination parameters. Partial least squares regression, random forest regression, and support vector machine regression (SVR) models all showed excellent performance for both the calibration and prediction sets. The overall results indicated that the infused data of multispectral and hyperspectral data combined with SVR are effective in monitoring the N level under field conditions, and the R2 (coefficient of determination) and root mean square error values of the prediction were 0.9186 and 0.0560, respectively. The findings of this study are important in retaining the nutritional and quality attributes of agricultural commodities.

Fourier transform near-infrared spectroscopy coupled with variable selection methods for fast determination of salmon fillets storage time

To address the fast and nondestructive determination of salmon fillets storage time associated with its freshness, Fourier transform near-infrared (FT-NIR) spectroscopy coupled with advanced variable selection methods was attempted in this work. Fresh salmon fillets were divided into three groups and stored at -18℃, 4℃ and 20℃, respectively. The spectra under each temperature were collected by a FT-NIR spectrometer with the range of 4000–11000 cm−1. Advanced variable selection methods, including random frog (RF), successive projections algorithm (SPA), competitive adaptive reweighted sampling (CARS), CARS combined with SPA (CARS-SPA) and variable combination population analysis combined with iteratively retaining informative variables (VCPA-IRIV), were utilized to extract key feature variables and shrink variable space. The prediction models were constructed by using partial least squares regression (PLSR) based on full spectral variables and selected feature variables. Variable selection method VCPA-IRIV coupled with PLSR (VCPA-IRIV-PLSR) showed the best prediction performance in each temperature, with determination coefficients of prediction set (R2P) of 0.9988, 0.9976 and 0.9998, and root mean square error of prediction set (RMSEP) of 0.145, 0.209 and 0.024 for -18℃, 4℃ and 20℃, respectively. The overall results showed the great feasibility of FT-NIR combined with variable selection methods in the rapidly, nondestructively and accurately predicting freshness of salmon fillets stored at different temperatures.

Nondestructive detection of Panax notoginseng saponins by using hyperspectral imaging

SummaryPanax notoginseng saponin (PNS) is the most important physical and chemical index of panax notoginseng. In order to detect PNS rapidly and non‐destructively, 160 hyperspectral images of panax notoginseng rhizome and main root were acquired by using a visible‐near infrared hyperspectral image acquisition system (400–1000 nm), and the original spectrum were extracted from hyperspectral images. The signal‐to‐noise ratio of the spectrum was improved by savitzky‐golay mixed multiplication scatter correction (SG‐MSC) pretreatment. Feature wavelengths were extracted by using competitive adaptive reweighted sampling (CARS), variable combination population analysis (VCPA) and bootstrapping soft shrinkage (BOSS), and support vector regression (SVR) model was established based on the feature spectrum and the original spectrum. By comparing, it was found that BOSS had the best effect of feature selection. In order to improve the accuracy of the model, equilibrium optimizer (EO) was used to optimise the parameters (c, g) of the BOSS‐SVR model. The results showed that BOSS‐EO‐SVR of the optimal prediction model of PNS, achieving and RMSEP of 0.95 and 0.32%, respectively. Therefore, hyperspectral imaging combined with BOSS‐EO‐SVR model is a feasible method to detect PNS.