A method for non-targeted identification of small molecule phytostimulants in sludge stabilization products

Abstract

This invention relates to the field of analytical chemistry, and more particularly to a method for non-targeted identification of small-molecule phytostimulants in sludge stabilization products. Based on the definitions of phytostimulants from the European Biostimulant Industry Alliance, this invention constructs a phytostimulant database and establishes a phytostimulant identification process based on non-targeted analysis and suspected screening. Using secondary mass spectrometry fragment ion matching as a foundation, supplemented by fractionation, the accuracy and reliability of phytostimulant identification are ensured. This invention provides a theoretical basis and technical support for the distribution, formation mechanism, and targeted regulation of phytostimulants during sludge stabilization treatment.

Description

Technical Field

[0001] This invention relates to the field of analytical chemistry, and in particular to a method for non-targeted identification of small molecule phytostimulants in sludge stabilization products. Background Technology

[0002] Land application is one of the mainstream solutions for the disposal of sludge stabilization products. During stabilization, a large amount of organic matter in sludge is transformed into various phytostimulants, such as free amino acids, carboxylic acids, and plant hormones (indoleacetic acid, salicylic acid, etc.). These substances can significantly promote plant growth, regulate plant physiological and biochemical processes, and improve the land application efficiency of sludge stabilization products. Given the abundance of phytostimulants in sludge, effectively identifying and detecting dominant phytostimulants can provide crucial information for optimizing sludge stabilization processes and iterating technologies, as well as providing methodological support for the high-value land application of sludge.

[0003] Current technologies mainly focus on the targeted detection of small molecules that promote plant growth, such as amino acids and auxin-like substances, in sludge stabilization products. However, phytostimulants, in addition to regulating plant growth, also enhance nutrient utilization efficiency and stress resistance, and stimulate natural physiological processes in crops. For example, Dai Xiaohu's method for quantitative detection of bioactive plant growth hormone-like substances in sludge using liquid chromatography-mass spectrometry (CN 113125591 A) only considers some auxin-like molecules, using indoleacetic acid and hydroxyphenylacetic acid as detection targets. It cannot detect other phytostimulants in the products. Understanding the changes in the molecular composition and relative content of phytostimulants during sludge stabilization is beneficial for guiding process optimization and further improving land use efficiency. Therefore, there is an urgent need for a method that can simultaneously detect the molecular composition and relative content of multiple phytostimulants in sludge stabilization products. Summary of the Invention

[0004] The purpose of this invention is to address the aforementioned shortcomings of the prior art by proposing a method for non-targeted identification of small molecule phytostimulants in sludge stabilization products.

[0005] The present invention provides a method for non-targeted identification of small molecule phytostimulants in sludge stabilization products, comprising the following steps:

[0006] S1. Based on the definition of plant stimulants by the European Biostimulant Industry Alliance, a plant stimulant database was constructed, which includes the names, molecular formulas, and CAS numbers of common plant stimulants. The plant stimulant database was then imported into Compound Discover 3.3 software.

[0007] S2. LC-MS / MS is used to identify samples without targeting, and the raw data of the samples in positive and negative ion modes acquired by LC-MS / MS are imported into Compound Discover 3.3 software. The software predicts the molecular formula by molecular ion peak and fragment ion, and performs quantitative integration on the peak area to obtain the peak area and molecular formula of the sample in different ion modes. The identified results are exported for data screening, and compounds with a deviation (δ) within ±5ppm are selected.

[0008] S3. Compare the secondary mass spectra of the compounds selected in step S2 with the secondary mass spectra of the standard substances to further screen for possible phytostimulants and classify them into levels 2-4. Molecular formulas that have secondary mass spectra and can be matched with the secondary mass spectra of the standard substances are classified as level 2. Molecular formulas that have secondary mass spectra but no corresponding standard substance mass spectra data in the existing database are classified as level 3. Level 4 refers to molecules that only have molecular formulas and molecular weights but no secondary mass spectra data.

[0009] Furthermore, the database also includes material function information and structural function information.

[0010] Furthermore, the material functional information includes the KEGG number, properties, and uses.

[0011] Furthermore, the phytostimulant database contains 595 phytostimulants in 44 categories.

[0012] Furthermore, in step S2, the sludge stabilization product is added to methanol and water for ultrasonic dissolution; after standing, it is centrifuged, and the supernatant is taken and diluted to volume in a volumetric flask. The sample is filtered with a microporous membrane and stored in a sample vial for LC-MS / MS analysis.

[0013] Furthermore, the volume ratio of methanol to water is 1:1.

[0014] Furthermore, the mass-to-volume ratio of the sludge stabilization product to methanol and water is 1:5 g / ml.

[0015] Furthermore, the pore size of the microporous filter membrane is 0.22 μm.

[0016] This invention establishes a non-targeted identification process for phytostimulants in sludge stabilization products based on a self-built phytostimulant database and LC-MS / MS non-targeted analysis process. It is the first to achieve non-targeted identification of phytostimulants in sludge, providing technical support for subsequent sludge treatment and high-value land use.

[0017] This invention, based on the definitions of plant stimulants from the European Biostimulant Industry Alliance, constructs a plant stimulant database containing information on the names, molecular formulas, CAS numbers, KEGG numbers, properties, uses, and structural classifications of 595 plant stimulants across 44 categories. A plant stimulant identification process based on non-targeted analysis and suspected screening is established, using secondary mass spectrometry fragment ion matching supplemented by grading to ensure the accuracy and reliability of plant stimulant identification.

[0018] This invention provides a theoretical basis and technical support for the distribution, generation mechanism and targeted regulation of plant stimulants during sludge stabilization treatment. Attached Figure Description

[0019] Figure 1 This is a flowchart of a method for non-targeted identification of small molecule phytostimulants in sludge stabilization products according to the present invention.

[0020] Figure 2 Exported graph of the results of adding compost products to iron-manganese minerals and identifying them in CD software;

[0021] Figure 3 This is a secondary mass spectrum of benzoic acid standard material;

[0022] Figure 4 The second-order mass spectra of substances identified by a suspected screening method in actual compost products;

[0023] Figure 5 A graph showing the number and grade of plant growth promoters in waste sludge, aerobic composting, and mineral conditioning-compost sludge products.

[0024] Figure 6 A statistical chart showing the identification of phytostimulants in four sludge stabilization products; AC: aerobic composting, AD: anaerobic digestion, HT: hydrothermal treatment, AT: alkaline thermal treatment. Detailed Implementation

[0025] The following are specific embodiments of the present invention, which are described in conjunction with the accompanying drawings. However, the present invention is not limited to these embodiments.

[0026] The method for non-targeted identification of small molecule phytostimulants in sludge stabilization products according to the present invention is as follows: Figure 1 As shown.

[0027] Pretreatment: Methanol and water (1:1) were used to extract the organic matter from the stabilized sludge. The 1:1 methanol:water ratio is highly efficient because it leverages the non-polar and polar properties of methanol and water to extract both polar and non-polar substances from the sludge, resulting in high extraction efficiency and no selectivity. The solid-liquid ratio was 1:5 (w / v), and the mixture was ultrasonically dissolved for 20 min. After standing, it was centrifuged for 15-30 min at 3000 rpm. The supernatant was diluted to a 5 mL volumetric flask, filtered through a 0.22 μm microporous membrane, and stored in a sample vial for LC-MS / MS analysis.

[0028] Phytostimulant Database: A thorough review of relevant literature, such as "The Role of Unsaturated Fatty Acids and Their Derivatives in Plant Stress Resistance" and "Research Progress on the Allelopathic Effects of Phenolic Acids," was conducted to compile the Chinese and English names and molecular formulas of common phytostimulants. Based on these molecular formulas, the relevant substance categories and CAS numbers were retrieved from PubChem and ChemSpider, resulting in the phytostimulant database. The constructed database contains 44 categories of phytostimulants, totaling 595 species. The most numerous category is amino acids and their derivatives, containing 99 related compounds, followed by flavonols, flavones, cytokinins, and indoles, containing 85, 54, 43, and 39 compounds respectively.

[0029] Non-targeted identification process for phytostimulants: First, import the established phytostimulant database into CompoundDiscover 3.3 software, then import the raw data (positive ion mode and negative ion mode) acquired by LC-MS / MS. The software predicts the molecular formula through molecular ion peaks and fragment ions, and simultaneously performs integrated quantification of peak areas to obtain preliminary results such as peak area and molecular formula of the sample under different ion modes. The identified results are then exported (as follows). Figure 2 Data filtering was performed (as shown), selecting samples with a deviation (δ) within ±5ppm (1ppm = 1 × 10⁻⁶). -6 Compounds within the range of 0.5 are included. Simultaneously, the secondary mass spectra of the matching molecules are compared with those of the standard reference material to further screen for potential phytostimulants and classify them into levels 2-4 according to "Identifying Small Molecules via High Resolution Mass Spectrometry: Communicating Confidence". Molecules with secondary mass spectra that match those of the standard reference material are classified as level 2; molecules with secondary mass spectra but no corresponding standard reference mass spectra data in the existing database are classified as level 3; and level 4 includes molecules with only molecular formula and molecular weight but no secondary mass spectra data. Based on this classification, phytostimulants of different levels can be selected for further analysis according to requirements.

[0030] Example 1

[0031] Using the above method, non-targeted identification of aerobic compost products revealed 356 phytostimulant molecules detected in both positive and negative ion modes. The software matching score for the identified substances ranged from 85-97. Generally, a non-targeted identification matching score higher than 85 is considered acceptable. For example, benzoic acid standard material secondary mass spectrometry (...) Figure 3 ) and secondary mass spectrometry of substances identified by a suspected screening method in actual compost products ( ) and measured substances ( ) Figure 4 The CD software has a matching score of 94.2, and it is rated as level 2 after comparison.

[0032] Example 2

[0033] Take a sludge composting system as an example. (The sentence is incomplete and requires more context to translate accurately.) Figure 5 Analysis using a non-targeted matching and LC-MS / MS process revealed that among the plant stimulants identified in waste sludge, iron-manganese mineral-added compost products, manganese mineral-added compost products, and aerobic compost products, 43, 43, 31, and 44 molecules belonged to level 2, respectively; 105, 109, 100, and 92 molecules belonged to level 3, respectively; and 38, 33, 28, and 23 molecules belonged to level 4, respectively. Most identified molecules belonged to level 3, with slightly more level 2 molecules than level 4 molecules. Furthermore, a comparison of the total number of plant stimulant molecules produced by different composting technologies showed that iron mineral addition promoted the production of plant stimulants in the composting system.

[0034] in, Figure 2 Exported graph of the results of adding compost products to iron-manganese minerals in CD software.

[0035] Example 3

[0036] Taking the sludge stabilization products obtained from different treatment technologies as examples. Figure 6The study revealed the distribution of plant stimulants in the products of different sludge treatment technologies (aerobic composting, anaerobic digestion, hydrothermal treatment, and alkaline thermal treatment). Results showed that amino acids and their derivatives (23.42%), flavonoids (22.15%), and indoles (9.49%) were the most abundant in aerobic compost products, while amino acids and their derivatives, indoles, and carboxylic acids and their derivatives were the most abundant in anaerobic digestion, hydrothermal treatment, and alkaline thermal treatment products. Notably, compared to aerobic composting, anaerobic digestion, hydrothermal treatment, and alkaline thermal treatment all resulted in a decrease in the proportion of amino acids, flavonoids, and gibberellins in the products, while a slight increase in carboxylic acids and their derivatives was observed. Gibberellins were essentially eliminated in the products of anaerobic digestion and hydrothermal treatment. Simultaneously, the proportion of salicylic acid increased in the anaerobic digestion products (6.25%). Since salicylic acid and carboxylic acids can affect plant stress resistance, the effects of anaerobic digestion products on plants may be more focused on increasing stress resistance compared to aerobic compost products. This demonstrates that the method can be used to reveal the differences in the composition of phytostimulants produced by different sludge stabilization methods.

[0037] For any points not covered above, existing technologies shall apply.

[0038] Although specific embodiments of the present invention have been described in detail by way of examples, those skilled in the art should understand that the above examples are for illustrative purposes only and are not intended to limit the scope of the invention. Those skilled in the art can make various modifications or additions to the described specific embodiments or use similar methods to replace them, without departing from the direction of the invention or exceeding the scope defined by the appended claims. Those skilled in the art should understand that any modifications, equivalent substitutions, improvements, etc., made to the above embodiments based on the technical essence of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for non-targeted identification of small molecule phytostimulants in sludge stabilization products, characterized in that: Includes the following steps: S1. Based on the definition of plant stimulants by the European Biostimulant Industry Alliance, a plant stimulant database was constructed, which includes the names, molecular formulas, and CAS numbers of common plant stimulants; and the plant stimulant database was imported into Compound Discover 3.3 software. S2. Extract organic matter from the sludge stabilization products to obtain the sample to be tested; S3. LC-MS / MS is used to identify samples without targeting, and the raw data of the samples in positive and negative ion modes acquired by LC-MS / MS are imported into Compound Discover 3.3 software. The software predicts the molecular formula by molecular ion peak and fragment ion, and performs quantitative integration on the peak area to obtain the peak area and molecular formula of the sample in different ion modes. The identified results are exported for data screening, and compounds with a deviation (δ) within ±5 ppm are selected. S4. Compare the secondary mass spectra of the compounds selected in step S2 with the secondary mass spectra of the standard substances to further screen for possible phytostimulants and classify them into levels 2-4. Molecular formulas that have secondary mass spectra and can be matched with the secondary mass spectra of the standard substances are classified as level 2. Molecular formulas that have secondary mass spectra but no corresponding standard substance mass spectra data in the existing database are classified as level 3. Level 4 refers to molecules that only have molecular formulas and molecular weights but no secondary mass spectra data.

2. The method for non-targeted identification of small molecule phytostimulants in sludge stabilization products as described in claim 1, characterized in that: The database also includes material function information and structural function information.

3. The method for non-targeted identification of small molecule phytostimulants in sludge stabilization products as described in claim 2, characterized in that: The material's functional information includes its KEGG number, properties, and uses.

4. The method for non-targeted identification of small molecule phytostimulants in sludge stabilization products as described in claim 1, characterized in that: The phytostimulant database contains 595 phytostimulants in 44 categories.

5. The method for non-targeted identification of small molecule phytostimulants in sludge stabilization products as described in claim 1, characterized in that: In step S2, the sludge stabilization product is added to methanol and water and dissolved by ultrasonication; after standing, it is centrifuged, and the supernatant is taken and diluted to volume in a volumetric flask. The sample is filtered with a microporous membrane and stored in a sample vial for LC-MS / MS analysis.

6. The method for non-targeted identification of small molecule phytostimulants in sludge stabilization products as described in claim 5, characterized in that: The volume ratio of methanol to water is 1:

1.

7. The method for non-targeted identification of small molecule phytostimulants in sludge stabilization products as described in claim 1, characterized in that: The mass-to-volume ratio of sludge stabilization products to methanol and water was 1:5 g / ml.

8. The method for non-targeted identification of small molecule phytostimulants in sludge stabilization products as described in claim 1, characterized in that: The pore size of the microporous filter membrane is 0.22 µm.

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