The evaluation of antioxidant performance in economic crops is of significant importance due to their potential health benefits and increasing demand for functional foods. In this study, we developed an electrochemical sensor based on guanine binding technology to assess the antioxidant capacity of common economic crops, including tomatoes, blueberries, spinach, grapes, and oranges. The guanine oxidation peak current, measured using square wave voltammetry on a composite film-modified electrode (RGO/G/GCE), served as an indicator of antioxidant activity. Statistical analysis, including one-way analysis of variance (ANOVA) and post-hoc tests, revealed significant differences (p < 0.05) in the guanine oxidation peak currents among the tested crops, highlighting variations in their antioxidant activities. Blueberries exhibited the highest guanine oxidation peak current, indicating the strongest antioxidant capacity, followed by spinach and grapes with significant antioxi-dant activity. Tomatoes and oranges displayed relatively lower antioxidant capacity. The working/linear range for the electrochemical sensor was determined to be 0.5-4.0 mg/L of ascorbic acid (AA), and the limit of detection (LOD) was calculated as 0.35 mg/L. The proposed electrochemical sensor provides a rapid, sensitive, and cost-effective approach for assessing antioxidant capacity, offering potential applications in quality control, nutraceutical analysis, and functional food development.

This study proposed a new vegetation index ADVI to detect the pollution degree of different varieties of maize under copper stress, which provides a new idea for the detection of heavy metal pollution in vegetation. In order to ensure the outdoor growth environment of maize, we put all maize into outdoor greenhouse. The spectral reflectance interval of 450–850 nm of maize leaves was processed by the absorbance conversion (A) and the first-order differential (D), and the absorbance differential (AD) spectral curve was obtained. The Pearson correlation coefficient (r) was used to analyze the AD data and the biochemical data and select characteristic bands that are sensitive to heavy metal copper (Cu). The calculated Pearson correlation coefficients suggested that the AD value at 547–553 nm and 672–674 nm presented a linear positive correlation close to 1 with the Cu2+ contents in soil and leaves, and a linear negative correlation close to − 1 was present in the range of 496–506 nm and 677–679 nm. We selected the AD value of wavelengths 501 nm, 550 nm, 673 nm, and 678 nm to establish ADVI and compared it with conventional vegetation indices (VIs) by calculating Pearson correlation coefficient between them and Cu contents in soil and leaves, and vegetation indices include Water Band Index (WBI), Photochemical Reflectance Index (PRI), MERIS Terrestrial Chlorophyll Index (MTCI), Modified Red Edge Simple Ratio Index (mSR), Modified Chlorophyll Absorption Ratio Index (MCARI), Water Index (WI), Normalized Difference Water Index (NDWI). Maize leaf spectral data obtained from experiments in 2017 were used for verification, and ADVI was also compared with WBI, PRI, MTCI, mSR, MCARI, WI, and NDWI. The calculated Pearson correlation coefficient between ADVI and Cu2+ contents in soil and leaves is 0.9816 and 0.9460 (experimental data of 2016). The calculated Pearson correlation coefficient between ADVI and Cu2+ contents in soil and leaves is 0.9109 and 0.9639 (experimental data of 2017). The results suggested that ADVI showed a significant correlation with Cu2+ stress concentration, and the correlation of ADVI was much stronger than that of other vegetation indices. The proposed ADVI detects the pollution degree of maize with different varieties and in different periods under copper stress has advantages of straightforward calculation, robustness, and high effectiveness. This study focused on the laboratory leaf scale, so it is expected that future work extends it to a wide range of field scale and image scale.

The situation of heavy metal pollution in farmland isn't optimistic. The heavy metals in soil can affect normal growth and development of crops after being absorbed by the roots, reduce quality of agricultural products, and then enter human body through food chain, endangering human health. Hyperspectral Remote Sensing provides possibility for a real-time, dynamic and efficient monitoring of heavy metal pollution in crops. The potted corn experiment with different Cu2+ stress gradients was set up, the spectral data of old, middle and new leaves in seedling, jointing and spike stages were collected, and the chlorophyll content and leaves Cu2+ content were determined in different growth periods. Based on the spectral data, chlorophyll content and leaves Cu2+ content, OIF-PLS method was constructed to extract feature bands containing Cu2+ pollution information by combining correlation analysis, optimal index factor (OIF) and partial least square (PLS). Firstly, the characteristic bands were preliminarily screened according to correlation coefficient between chlorophyll content in leaves at seedling stage, jointing stage and spike stage and Cu2+ content in leaves at spike stage and corresponding leaf spectra. Then, three bands were selected to calculate optimum index factor, and the three bands were taken as independent variables to carry out partial least squares regression analysis on Cu2+ content in corn leaves to calculate root mean square error. Finally, the best feature band was selected according to principle of maximum optimum index factor and minimum root mean square error. The vegetation index OIFPLSI was constructed based on the characteristic bands selected by OIF-PLS method to monitor heavy metal copper pollution, and compared with red edge normalized difference vegetation index (NDVI705), modified red edge simple ratio vegetation index (mSR(705)) , red-edge vegetation stress index (RVSI) and photochemical reflectance index (PRI) monitoring results to verify the effectiveness and superiority of OIFPLSI. In addition, the applicability and stability of OIFPLSI were verified by using the data obtained from different years under same experimental method. The experimental results show that the feature bands (542, 701, 712 nm) extracted from OIF-PLS can better reflect Cu2+ pollution information than the feature bands (602, 711, 712 nm) based on OIF. OIFPLSI was significantly positively correlated with leaf Cu2+ content, and the correlation was better than NDVI705, mSR(705), RVSI and PRI. OIFPLSI was significantly negatively correlated with leaf chlorophyll content and positively correlated with Cu2+ content in soil. The correlation between OIFPLSI and Cu2+ content in soil at different growth stages is successively higher in jointing stage, ear stage and seedling stage. Based on the data of different years, the results show that OIFPLSI is positively correlated with leaf Cu2+ content, and OIFPLSI has strong stability. OIFPLSI based on the characteristic bands extracted by OIF-PLS method can better diagnose and analyze copper pollution level of corn leaves, which can provide a certain technical reference for crop heavy metal pollution monitoring.

A new index Absorbance Differential Vegetation Index (ADVI), which realized the monitoring of heavy metals copper and lead stress in two varieties of maize with different cultivation periods and the pollution of heavy copper and lead, was distinguished. Experiments of copper and lead pollution with different concentrations were designed and measured spectral reflectance and contents of copper ion (Cu 2+ ) and lead ion (Pb 2+ ) of maize under concentrations of copper and lead stress. The spectral reflectance was processed by the absorbance conversion ( A ) and the first-order differential ( D ), and the Absorbance Differential (AD) spectral curve was obtained. The Pearson correlation coefficient ( r ) was used to analyze the AD data and the biochemical data and select characteristic bands that sensitive to heavy metal Cu (Copper). We selected the AD value of wavelengths 501 nm, 550 nm, 673 nm and 678 nm to establish ADVI and compared it with conventional vegetation indices by calculating Pearson correlation coefficient between them and Cu contents in soil and leaves. The results suggested that ADVI showed a significant correlation with Cu 2+ and Pb 2+ stress concentration, the correlation of ADVI was much stronger than that of other vegetation indices and effectively distinguished copper and lead stress. It can provide theoretical basis for monitoring heavy metal stress on canopy scale.

Soil environmental safety is of great significance. When soil is contaminated by heavy metals, it will affect the safety of crops and foods and endanger human health. Therefore, it is particularly critical to look for ways to rapidly and efficiently measure heavy metal pollution in soil. Traditional chemical analysis methods have some disadvantages such as complicated process, time-consuming and labor-consuming. Hyperspectral remote sensing has obvious advantages in environmental monitoring and other applications because of its high spectral resolution, large amount of information, and rapid losslessness. Due to the complex reflection and radiation process of electromagnetic remote sensing signals, the soil hyperspectral data acquired by the instrument is difficult to directly interpret the information of heavy metal pollution. Therefore, it is very important to find out a method that can effectively excavate heavy metal pollution information in soils. The soil physicochemical properties will change because of different concentrations pollution of Cu, causing slight changes in the soil spectrum, the purpose of this study is to identify, extract and analyze the characteristics and weak difference information in the spectrum of Cu contaminated soil, and then tap the heavy metal pollution information in the spectra. In this paper, the continuum removal(CR) was used to preprocess the spectrum, the LH-PSD analysis model for analyzing soil spectra was constructed bydefining local maximum mean (LMM) and half wave height(HWH) combined with Short-time Fourier transform (STET) of the time-frequency analysis method and power spectral density (PSD). The extremely similar soil spectrum was processed by the LH-PSD model, and the PSD distribution map visualized the faint differences between the spectra, and significantly distinguished the similar spectra, which verified the ability of the model to discriminate spectral features and weak differential information.At the same time, this model was used to extract and analyze heavy metal pollution information from experimental soil spectra with different Cu pollution gradients. The results of the study show that CR can plan the spectrum to the same background and highlight the differences between spectra, LMM and HWH of LH-PSD detection model can effectively extract the characteristics of the difference between the spectra and appear in a ladder. The visualized PSD map obtained after the model processing can directly and qualitatively discriminate whether the soil is contaminated by heavy metal Cu. Specifically, when the soil is contaminated by heavy metal Cu, at the same sampling frequency, the PSD distribution at frequencies of 100 and 600 Hz will be obviousvacant separation, with the increase of Cu pollution concentration, the distribution of PSD between 100 similar to 600 Hz is gradually sparse. The energy value E can be used to quantitatively monitor the degree of soil Cu pollution. That is, as the concentration of Cu in the soil increases, the E value decreases, and the correlation coefficient with the Cu content reaches -0. 910 5, which is significantly correlated. In order to test the reliability of the model, the soil spectra of the planted corn crop was combined and analyzed by the LH-PSD detection model. The result of the visualized PSD map was basically similar to that in the experimental analysis, The correlation coefficient of the energy value E with Cu content in soil reaches -0. 973 9, which has a significant correlation. The monitoring effect is ideal and the reliability of the model is verified. Therefore, through the LH-PSD analysis model, the dissection of soil spectrum from the spectral domain to the time-frequency domain provides a new idea for deepening the spectral features and weak information of heavy metal pollution spectra.

When crops are contaminated with heavy metals, their tissue structure and chlorophyll content will be destroyed, which will affect the metabolism and health of crops. People and animals will have fatal injuries if they eat the contaminated crops. Hyperspectral remote sensing is now widely used to monitor the extent of crops affected by heavy metals, and in the heavy metal pollution, the spectral crop leaves are still very similar to those of the traditional monitoring methods and spectral characteristic parameters of routine, so it is difficult to distinguish between different spectral weak information, and application of hyperspectral remote sensing is the focus and difficulty of the study. The maize leaf spectral data, chlorophyll content and relative content of heavy metals Cu2+ and Pb2+ were collected by setting different concentrations of Cu2+ and Pb2+ stress. A LDCR-SIDSCAtan model combined with the continuum removal (CR) , spectral correlation angle (SCA) , spectral information divergence (SID) and tangent function (Tan) and Langmuir distance (LD) is proposed in this study, and the traditional measure, such as spectral correlation coefficient (SCC) , SA (spectral angle), tangent spectrum (DSA), spectral information divergence and spectral correlation tangent (SIDSAM(tan)), spectral information divergence and spectral gradient tangent (SIDSGA(tan)) and conventional spectral characteristic parameters, such as the maximum value of red edge (MR), green peak height (GH) and red edge area surrounded by a first order differential (FAR), red edge derivative curve steepness (FCDR), blue (DB), red band depth (RD) compared to verify the feasibility and superiority of the model. The LD-CR-SIDSCA ton model was applied to measure the spectral difference information about the overall waveform and the subband of maize leaves under Cu2+ and Pb2+ stress at different concentrations. The results show that the LD-CR-SIDSCA, m , model realized the qualitative analysis of heavy metal Cu2+ and Pb2+ pollution, could measure the spectral correlation coefficient of more than 0.99 of the difference information between the similar spectral information, and waveform difference information was significantly related to the leaf chlorophyll content and the relative content of heavy metals Cu2+ and Pb2+ that measured, and also found the spectra response wave band under the stress of heavy metals Cu2+ and Pb2+. When the whole spectral range of spectral data is measured, the spectral difference is more obvious when the value of the model is negative. When the value of the model is positive, the larger the value of the model is, the larger the spectral difference will be. Therefore, with the increase of heavy metals Cu2+ and Pb2+ concentration, the difference of spectra increased, which means that the heavy metal Cu2+ and Pb2+ pollution degree is more serious; maize plants suffer from heavy metal pollution in Cu2+ stress when measuring the local subband range of spectral data, in the "blue" and "red edge", "near the Valley", "at the peak of B" were specially sensitive to heavy metal Cu2+ stress pollution response and can be used as an effective band of monitoring heavy metal pollution Cu2+; when the maize plants are under heavy metal pollution in Pb2+ stress, in the "Purple Valley", "blue", "yellow" and "red Valley", "red edge", "near at the peak of A" were specially sensitive to heavy metal Pb2+ stress pollution response and can be used as an effective band of monitoring heavy metal pollution Pb2+. Finally, through the linear fitting analysis of the application results of LD-CR-SIDSCA(tan) model and the content of Cu2+ and Pb2+ in maize leaves, the pollution degree of heavy metals Cu2+ and Pb2+ to maize plants was inversed and predicted.

The monitoring of heavy metal pollution in crops is one important application of hyperspectral remote sensing study. The objective of this work was to develop a new narrow-band vegetation index to characterize the Cu (copper) stress degree in two corn species at two growing years. The experiment on the copper pollution was designed based on its different concentrations, meanwhile, the hyperspectral reflectance of corn leaves stressed by different Cu2+ concentrations were measured using hand-held spectrometer(SVC, USA) and leaf Cu2+ contents were also measured. The first difference reflectance and biochemical data of corn were analyzed using Pearson correlation coefficient (r) to select wavelengths sensitive to Cu stress. The calculated Pearson correlation coefficients suggested that the first difference reflectance near 489 similar to 497, 632 and 677 nm wavelengths was significantly correlated with Cu2+ contents in leaves. The selected wavelengths of 489 similar to 497, 632 and 677 nm were used to establish the Cu stress vegetation index based on the first difference reflectance (dVI). To select index with the highest possible correlation to Cu stress, all possible dVIs were related through simple regression models with Cu2+ contents andthe predictive abilities of those models were evaluated through the R-2 values and the root mean square error (RMSE). The stability of the sensitive bands and the applicability of dVI were assessed using corn data from different growth years. Meanwhile, the performance of dVI was compared with that of existing popular vegetation index (VIs) related to heavy metal stress, such asnormalized difference vegetation index (NDVD, red-edge chlorophyll index (CIred-edge), red-edge position (REP), photochemical reflectance index (PRI). The results suggest that the corn spectral characteristics in response to copper stress are enhanced with the first-order difference treatment. Compared with the original reflectance, the correlation coefficient between first difference reflectance at wavelengths of 450 similar to 500, 630 similar to 680 and 677 nm and Cu2+ content increases. The wavelength position of copper stress sensitive band based on the first-order differential reflectance is stable for the data sets of different growth years. The index that combined the first difference reflectance in 497, 632 and 677 nm wavelengths is found to be a potential useful index to predict leave Cu concentration for different data sets. And the correlation of dVI was much stronger than that of other VIs for all the tested data sets from two corn species at two growing years. The proposed dVI characterizes the Cu stress degree on vegetation with advantages of better effectiveness and robustness. This study focuses on the spectral reflectance at the leaf scale, so it is expected that future work will extend it to canopy scale.

This study proposed a new vegetation heavy metal pollution index VHMPI to detect the pollution degree of different varieties of maize under copper stress, which provides a new idea for the detection of heavy metal pollution in vegetation. In order to ensure the outdoor growth environment of maize, we put all maize into outdoor greenhouse. The spectral reflectance interval of 450 nm-850 nm of maize leaves was processed by the first order differential (D) and continuum removal (CR), and the DCR spectral curve was obtained. The Pearson correlation coefficient (R) was used to analyze the DCR data and the biochemical data and select characteristic bands that sensitive to heavy metal Cu. The calculated Pearson correlation coefficients suggested that the DCR value at 490 nm-520 nm and 680 nm-700 nm presented a linear positive correlation close to 1 with the Cu2+ contents in soil and leaves, and a linear negative correlation close to -1 was present in the range of 630 nm-650 nm and 710 nm-750 nm. We selected the DCR value of wavelengths 505 nm, 640 nm, 690 nm and 730 nm to establish VHMPI, and compared it with conventional vegetation indices (VIs) by calculating Pearson correlation coefficient between them and Cu contents in soil and leaves, Vegetation indices include WBI (Water Band Index), PSNDa (Pigment Specific Normalized Difference a), PRI (Photochemical Reflectance Index), NDVI (Normalized Difference Vegetation Index). Maize leaf spectral data obtained from experiments in 2017 were used for verification, VHMPI was also compared with WBI, PSNDa, PRI and NDVI.The results suggested that VHMPI showed a significant correlation with Cu2+ stress concentration,and the correlation of VHMPI was much stronger than that of other vegetation indices. The proposed VHMPI detects the pollution degree of maize with different varieties and in different periods under copper stress has advantages of straightforward calculation, robustness, and high effectiveness. This study focused on the laboratory leaf scale, so it is expected that future work extends it to a wide range of field scale and image scale.

This study analysed the spectral characters of corn grown in metal-spiked soil, and thus to develop a new vegetation index to characterize copper and lead concentrations in corn leaves. The significant correlation between the leaf-metal and the leaf-spectra suggested that the Cu and Pb stress caused the spectra changes in visible and near-infrared ranges. Heavy Metal Stress Vegetation Indices based on the first difference reflectance (dVIs) were established using the characteristic wavelengths. The results of regression models show that the index that combines the first difference reflectance in 497, 632 and 677 nm wavelengths is the best potential indicator of the Cu stress, and the index that combines the first difference reflectance in 456, 668 and 686 nm wavelengths most strongly indicates the Pb stress. In addition, compared with six normal vegetation indices, the proposed dVI has better robustness and effectiveness. This study may provide a theoretical basis for the monitoring of heavy metal stress at corn canopy scale.