FIELD OF THE INVENTION

The present disclosure relates to the field of cadmium chelate detection, and in particular to a method and system for the in-situ detection of cadmium chelates in a plant.

BACKGROUND OF THE INVENTION

Due to human activities, lots of heavy metal elements enter the soil system, causing heavy metal pollution. Cadmium pollution is a relatively-common soil heavy metal pollution, which has become a major environmental problem that endangers human health due to the migration of cadmium in the soil-plant-food chain system.

The traditional physical and chemical treatment methods for heavy metal pollution (such as earth filling, soil washing, and electrochemical methods) can achieve prominent treatment effects, but have disadvantages such as high cost, difficult management, frequent secondary pollution, and large environmental disturbance. At the end of the last century, Chaney first proposed a phytoremediation method where the content of heavy metals in soil is reduced by growing plants capable of absorbing heavy metals in large quantities. Phytoremediation shows little disturbance to a soil environment, low cost, and no secondary pollution, and can achieve permanent in-situ remediation. Therefore, phytoremediation has become a research hotspot of soil remediation technology. Some acknowledged accumulators such asBrassica junicagrow rapidly, have large biomasses, and can transfer more than 88% of absorbed heavy metals to aboveground parts, thus achieving extremely-high soil purification rates.

However, in practical applications, phytoremediation also has some shortcomings. For example, in some growth environments, an accumulator has low biomass, slow growth, and underdeveloped root systems, which cannot achieve effective accumulation at low heavy metal concentrations, but presents inhibited growth at high heavy metal concentrations, thus reducing the efficiency and environmental applicability of phytoremediation. It can be known that the key to the efficiency of phytoremediation is the adaptability of accumulator varieties to application environments and the accumulation and tolerance of accumulator varieties to heavy metals in the soil. It is a new direction for research of phytoremediation technology to integrate fast-growing, high-biomass, and adaptable varieties with the domestication and hybridization techniques to obtain practically-valuable hyperaccumulator varieties.

Hyperaccumulators are varieties with a heavy metal accumulation capacity 100 times that of ordinary plant varieties. At present, at least 400 species of plants in 45 families have been identified as hyperaccumulators, mainly concentrated inBrassica, Alyssuns, andThlaspiof the cruciferous family, and most germplasms are distributed abroad.Brassica chinensisis one of the main cruciferous crops in China, some varieties of which also have a strong Cd accumulation ability. Studying, cultivating and improving heavy metal hyperaccumulationBrassica chinensisvarieties with independent intellectual property rights and a Cd accumulation capacity thereof is significant for the accumulation of technologies in the field of soil heavy metal phytoremediation, hyperaccumulator germplasm resources, and new variety development technologies in China.

Calculation of many indexes (transfer coefficient, accumulation coefficient, etc.) traditionally used to measure the heavy metal accumulation capacity of a plant is based on a heavy metal content in each part of the plant, and the tolerance can only be indirectly reflected by a degree to which the plant phenotype or quality is inhibited (such as a root-shoot ratio). However, the heavy metal tolerance of some plants is manifested as the excretion of heavy metals through mechanisms such as excretion or senescence, for example, some abscisic acids (ABAs) are secreted to promote the abscission of poisoned leaves. Therefore, plants with strong heavy metal tolerance do not necessarily have strong heavy metal accumulation capacity at the same time, and may not have the function of phytoremediation. In addition, a plant itself synthesizes several small molecular compound ligands to chelate with heavy metal ions to form less toxic complexes, which are then transferred and fixed to a specific area in cells for isolation. Such small molecules include metallothioneins (MTs), organic acids, amino acids, etc. Phytochelatins (PCs, (γ-Glu-Cys)n-X, n=2 to 11, X may be absent or may be Gly, β-Ala, Ser, Glu, etc.) are a group of cysteine-rich polypeptides, which belong to the Class III MTs that are ubiquitous in plants and the most important in heavy metal resistance. PC chelates with a heavy metal ion via mercapto on cysteine to form a non-toxic heavy metal-S chelate. Therefore, a heavy metal-S bond can be used to distinguish between free and complex heavy metals, and a content thereof can be used as an important index for simultaneously characterizing the heavy metal accumulation and tolerance of a plant. Hyperaccumulators capable of synthesizing more chelates can be regarded as having both superior heavy metal adsorption capacity and superior heavy metal tolerance. Such hyperaccumulators are identified for selective breeding to obtain new varieties with further-enhanced phytoremediation performance.

At present, there are few studies on the detection and structure analysis methods of heavy metal chelates. Existing methods generally adopt separation by SEC-HPLC first and then determination by ICP-MS, and the lengthy process and the organic mobile phases and derivatization reagents used will result in damage to a biomass structure, thereby affecting the accuracy of detection results. Moreover, existing near-infrared, Raman and other spectroscopic in-situ rapid detection methods are limited at low resolution, and a sample needs to be crushed uniformly before detection. Research on the characteristic spectral information of a chelate still stagnates at the level of semi-empirical computer simulation. Therefore, it is impossible to quickly detect chelate formation in-situ at present. The potential hyperaccumulation ability of a plant can only be identified by detecting the sequences and expressions of two currently-known genes related to the regulation of accumulation traits, which is costly and labor-consuming.

Calculation of various classic indexes (accumulation coefficient, transfer coefficient, etc.) to measure phytoremediation performance is based on a heavy metal content in each part of a plant, and the heavy metal tolerance can only be indirectly reflected by various classic plant phenotype indexes (dry weight, root-shoot ratio, etc.). Therefore, the overall evaluation process is cumbersome due to many indexes involved. Studies on the binding ratio of heavy metals with MTs and on the molecular structure of chelates also still stagnate at the level of computer simulation. Therefore, it is not yet possible to quickly detect chelate formation in-situ to comprehensively evaluate the heavy metal accumulation and tolerance of a variety.

Therefore, developing a method for rapid in-situ detection of the heavy metal accumulation capacity of a plant and for rapid identification of a variety with potential hyperaccumulation is of great significance for the technical reserves in the field of soil heavy metal phytoremediation in China.

SUMMARY OF THE INVENTION

The present disclosure is intended to provide a method and system for the in-situ detection of cadmium chelates in a plant, which can quickly and in-situ detect the cadmium accumulation capacity of a plant and thus can accurately and quickly evaluate the phytoremediation performance.

To achieve the above purpose, the present disclosure provides the following technical solutions.

A method for the in-situ detection of cadmium chelates in a plant includes:

preparing cadmium chloride samples with different concentrations and phytochelatin (PC) 2 samples with different concentrations;

using terahertz time-domain spectroscopy (THz-TDS) to determine a terahertz (THz) reflection image for the cadmium chloride samples and the PC 2 samples;

building a cadmium chloride content detection model according to first spectral data of the THz reflection images of the cadmium chloride samples;

building a PC 2 content detection model according to second spectral data of the THz reflection images of the PC 2 samples;

determining an optimal mixed sample according to the cadmium chloride content detection model and the PC 2 content detection model, where, the optimal mixed sample is a mixed sample of a cadmium chloride sample and a PC 2 sample and has the lowest cadmium chloride and PC 2 contents;

building a Cd—PC2content detection model according to the optimal mixed sample; and

using the Cd—PC2content detection model to determine a cadmium chelate content in a to-be-tested plant.

Optionally, after the using THz-TDS to determine a THz reflection image for the cadmium chloride samples and the PC 2 samples, the method may further include:

subjecting spectral data in the THz reflection image of the cadmium chloride sample successively to noise signal elimination, scattering elimination, spectral baseline zeroing, and horizontal adjustment to obtain processed cadmium chloride spectral data;

subjecting spectral data in the THz reflection image of the PC 2 sample successively to noise signal elimination, scattering elimination, and horizontal adjustment to obtain processed PC 2 spectral data;

applying density-functional theory (DFT) to molecular structures of the cadmium chloride and PC 2 to determine effective spectral data for the cadmium chloride and PC 2;

screening the processed cadmium chloride spectral data using the effective cadmium chloride spectral data to obtain the first spectral data; and

screening the processed PC 2 spectral data using the effective PC 2 spectral data to obtain the second spectral data.

Optionally, the building a cadmium chloride content detection model according to first spectral data of the THz reflection images of the cadmium chloride samples may specifically include:

dividing the first spectral data into a first modeling data set and a first correction data set;

according to the first modeling data set, using a partial least squares (PLS) regression algorithm to build a first regression model for the cadmium chloride;

according to the first modeling data set, using a principal component regression (PCR) algorithm to build a second regression model for the cadmium chloride;

using the first correction data set to determine a determination coefficient and a root-mean-square error (RMSE) for the first regression model and the second regression model, separately; and

determining a regression model for the cadmium chloride with high determination coefficient and low RMSE as the cadmium chloride content detection model.

Optionally, the building a PC 2 content detection model according to second spectral data of the THz reflection images of the PC 2 samples may specifically include:

dividing the second spectral data into a second modeling data set and a second correction data set;

according to the second modeling data set, using a PLS regression algorithm to build a third regression model for the PC 2;

according to the second modeling data set, using a PCR algorithm to build a fourth regression model for the PC 2;

using the second correction data set to determine a determination coefficient and an RMSE for the third regression model and the fourth regression model, separately; and

determining a regression model for the PC 2 with high determination coefficient and low RMSE as the PC 2 content detection model.

Optionally, the determining an optimal mixed sample according to the cadmium chloride content detection model and the PC 2 content detection model may specifically include:

preparing mixed samples in different mixing ratios;

using THz-TDS to determine a THz reflection image for the mixed samples;

screening spectral data in the THz reflection images of the mixed samples using the effective cadmium chloride spectral data and the effective PC 2 spectral data to obtain third spectral data;

according to the third spectral data, using the cadmium chloride content detection model and the PC 2 content detection model to determine contents of the cadmium chloride and PC 2; and

determining a mixed sample with the lowest cadmium chloride and PC 2 contents as the optimal mixed sample.

A system for the in-situ detection of cadmium chelates in a plant includes:

a first preparation module configured to prepare cadmium chloride samples with different concentrations and PC 2 samples with different concentrations;

a THz reflection image determination module configured to use THz-TDS to determine a THz reflection image for the cadmium chloride samples and the PC 2 samples;

a cadmium chloride content detection model building module configured to build a cadmium chloride content detection model according to first spectral data of the THz reflection images of the cadmium chloride samples;

a PC 2 content detection model building module configured to build a PC 2 content detection model according to second spectral data of the THz reflection images of the PC 2 samples;

an optimal mixed sample determination module configured to determine an optimal mixed sample according to the cadmium chloride content detection model and the PC 2 content detection model, where, the optimal mixed sample is a mixed sample of a cadmium chloride sample and a PC 2 sample and has the lowest cadmium chloride and PC 2 contents;

a Cd—PC2content detection model building module configured to build a Cd—PC2content detection model according to the optimal mixed sample; and

a cadmium chelate content determination module configured to use the Cd—PC2content detection model to determine a cadmium chelate content in a to-be-tested plant.

Optionally, the system for the in-situ detection of cadmium chelates in a plant may further include:

a first data processing module configured to subject spectral data in the THz reflection image of the cadmium chloride sample successively to noise signal elimination, scattering elimination, spectral baseline zeroing, and horizontal adjustment to obtain processed cadmium chloride spectral data;

a second data processing module configured to subject spectral data in the THz reflection image of the PC 2 sample successively to noise signal elimination, scattering elimination, and horizontal adjustment to obtain processed PC 2 spectral data;

an effective spectral data determination module configured to apply DFT to molecular structures of the cadmium chloride and PC 2 to determine effective spectral data for the cadmium chloride and PC 2;

a first spectral data determination module configured to screen the processed cadmium chloride spectral data using the effective cadmium chloride spectral data to obtain the first spectral data; and

a second spectral data determination module configured to screen the processed PC 2 spectral data using the effective PC 2 spectral data to obtain the second spectral data.

Optionally, the cadmium chloride content detection model building module may specifically include:

a first data division unit configured to divide the first spectral data into a first modeling data set and a first correction data set;

a first regression model building unit configured to use a PLS regression algorithm to build a first regression model for the cadmium chloride according to the first modeling data set;

a second regression model building unit configured to use a PCR algorithm to build a second regression model for the cadmium chloride according to the first modeling data set;

a first index data determination unit configured to use the first correction data set to determine a determination coefficient and an RMSE for the first regression model and the second regression model, separately; and

a cadmium chloride content detection model determination unit configured to determine a regression model for the cadmium chloride with high determination coefficient and low RMSE as the cadmium chloride content detection model.

Optionally, the PC 2 content detection model building module may specifically include:

a second data division unit configured to divide the second spectral data into a second modeling data set and a second correction data set;

a third regression model building unit configured to use a PLS regression algorithm to build a third regression model for the PC 2 according to the second modeling data set;

a fourth regression model building unit configured to use a PCR algorithm to build a fourth regression model for the PC 2 according to the second modeling data set;

a second index data determination unit configured to use the second correction data set to determine a determination coefficient and an RMSE for the third regression model and the fourth regression model, separately; and

a PC 2 content detection model determination unit configured to determine a regression model for the PC 2 with high determination coefficient and low RMSE as the PC 2 content detection model.

Optionally, the optimal mixed sample determination module may specifically include:

a mixed sample preparation unit configured to prepare mixed samples in different mixing ratios;

a THz reflection image determination unit configured to use THz-TDS to determine a THz reflection image for the mixed samples;

a third spectral data determination unit configured to screen spectral data in the THz reflection images of the mixed samples using the effective cadmium chloride spectral data and the effective PC 2 spectral data to obtain third spectral data;

a cadmium chloride and PC 2 content determination unit configured to use the cadmium chloride content detection model and the PC 2 content detection model to determine contents of the cadmium chloride and PC 2 according to the third spectral data; and

an optimal mixed sample determination unit configured to determine a mixed sample with the lowest cadmium chloride and PC 2 contents as the optimal mixed sample.

According to specific examples provided in the present disclosure, the present disclosure discloses the following technical effects:

In the method and system for the in-situ detection of cadmium chelates in a plant according to the present disclosure, terahertz time-domain spectroscopy (THz-TDS) is used to determine terahertz (THz) reflection images of CdCl2and PC 2 samples; first spectral data of the THz reflection images of cadmium chloride samples are used to build a cadmium chloride content detection model, and second spectral data of the THz reflection images of the PC 2 samples are used to build a PC 2 content detection model; a mixed sample with the lowest CdCl2and PC 2 contents is determined as an optimal mixed sample according to spectral data of mixed samples obtained by mixing CdCl2and PC 2 samples in different ratios; the optimal mixed sample is used to build a Cd—PC2content detection model; and the Cd—PC2content detection model is used to determine a cadmium chelate content in a to-be-tested plant, thus realizing the rapid in-situ detection of a chelated Cd content in different varieties ofBrassica chinensis. Moreover, the present disclosure can accurately and quickly evaluate the phytoremediation performance of a plant by simultaneously characterizing the heavy metal accumulation capacity and heavy metal tolerance of the plant.

