MEASURING DEVICE AND PESTICIDES MEASUREMENT METHOD FOR AGRICULTURAL PRODUCTS

Abstract

A mobile device for detecting pesticides in a sample such an agricultural product by a method of Surface-enhanced Raman spectroscopy (SERS) includes: a feeder for raw material, a container for processing the raw material into the required form, a feeder for nanomaterials in the liquid phase, a feeder for nanomaterials in solid phase—substrates with nanomaterials, a container for preparing a sample for measurement, a platform for moving samples in containers or vessels for measurement, Raman spectrometer and a measuring chamber. A related method detects pesticides in a sample of an agricultural product with a mobile device.

Claims

1.-11. (canceled)

12. A mobile device for the detection of pesticides in a sample constituting an agricultural product by Surface-enhanced Raman spectroscopy (SERS), the mobile device comprising: a first feeder for raw material; a first container for processing the raw material to a required form; a second feeder for nanomaterials in a liquid phase; a second feeder for nanomaterials in a solid phase including substrates with nanomaterials; a container for preparing the sample for measurement; a platform for moving the samples in measuring containers or vessels; a Raman spectrometer; and a measuring chamber.

13. The mobile device according to claim 12, wherein the measuring containers or vessels for liquid nanomaterials are equipped with a measuring well, open or closed, made of aluminum or silicon, quartz, CaF.sub.2.

14. The mobile device according to claim 12, wherein the measuring containers or vessels for solid nanomaterials are equipped with a substrate obtained in place.

15. The method of detecting pesticides in a sample of an agricultural product with the mobile device of claim 12, the method comprising: feeding the raw material into the first feeder; automatically pre-processing the sample in the container to the required form by centrifugation and decantation of a supernatant solution and/or a solution filtration, and mixing with a solvent in a manner to avoid degradation of the pesticides; combining the sample after the automatic pre-processing in the container with the nanomaterial in a solid or liquid phase in the ratio 1:1 v/v-1:10 v/v; feeding the material into the chamber via the platform moving the samples in the measuring containers or vessels; illuminating the sample in the measuring chamber with at least one coherent source of electromagnetic radiation, in particular a laser, and registering an emitted radiation with the Raman spectrometer, which is a measurement spectrum of the sample.

16. The method for detecting pesticides according to claim 15, wherein if the nanomaterial is liquid, it is fed through the first feeder, it is a colloid of nanoparticles, mainly made of precious metals, such as gold, silver or mixtures thereof, ensuring the effect of surface amplification of the signal, wherein the plasmon SPR resonance curve of the nanomaterial reaches at least 90% of its maximum at the laser wavelength.

17. The method of pesticide detection according to claim 16, wherein if the nanomaterial is solid, it is fed through the second feeder, it is in the form of a plate, preferably made of glass or silicon oxide, quartz, calcium fluoride, aluminum, steel, paper, cellulose, fabric (cotton), carbon or mixture thereof, permanently bonded to metal (gold, silver, copper, aluminum, platinum) in the form of a surface coating with a thickness of 1-250 nm, where the plasmon SPR resonance curve of the nanomaterial reaches at least 90% of its maximum at the laser wavelength, or in the form of nanoparticles deposited thereon.

18. The method of detecting pesticides according to claim 16, wherein combining of the sample with the nanomaterial includes mixing appropriate proportions with a nanoparticle solution with the nanoparticle solution obtained by a previous centrifugation procedure, with a speed in the range of 500-4000 rcf, preferably 2000 rcf, nanomaterial solution, where the sediment is a solution of nanoparticles.

19. The method for detecting pesticides according to claim 17, wherein combining the sample with a solid nanomaterial consists in spraying the sample on the surface of the nanomaterial, after which it can be heated until it is dried/evaporated or/and adsorbed/soaked.

20. The method for detecting pesticides according to claim 15, wherein the test result contains at least one type of pesticide detected and their quantity or, in case of their absence, information about the absence of pesticides.

21. The method for detecting pesticides according to claim 15, wherein on the basis of the registered measurement spectrum, it determines the measurement result by means of artificial intelligence (machine learning) algorithms, which is sent to the measurement device.

22. The method of pesticide detection according to claim 15, wherein the mobile device is powered a source selected from a group consisting batteries, accumulators, aggregates, solar and combinations thereof, allowing for autonomous work at the measurement site.

Description

DESCRIPTION OF THE FIGURES

[0023] FIG. 1—presents a diagram of the measuring system, which includes: (1) power supply, (2) user interface, (3) controller, (4) feeder for raw material, (5) container where the raw material is processed, (6) feeder for liquid nanomaterial, (7) feeder for substrates with nanomaterials, (8) container where samples preparation for measurement takes place, (9) platform for moving samples in containers or vessels for measurement, (10) Raman spectrometer, (11) measuring chamber. The arrows present the direction of the sample and nanomaterial passage through the measuring chamber coupled with the Raman spectrometer, where the Raman spectrum is measured, and then the sample is disposed.

[0024] FIG. 2—presents the general operating principle of the device.

[0025] FIG. 3—presents an example of the quantive result of device operation determining pesticide concentration (thiophanate-methyl). Groups of samples with various concentrations are marked with colours. High concentration is marked in red, intermediate in green and low in blue (1000 ppm, 1 ppm, 1 ppb, respectively).

[0026] The invention is illustrated by the following non-limiting examples:

EXAMPLE 1

[0027] Mobile device consists of the following elements: power supply (1); wireless communication and user interface (2); controller (3); feeding and pre-processing raw material in the form of an agricultural product (4); a feeder for liquid or solid nanomaterial (6); processing the sample to be measured (8); Raman spectrometer (10) measuring the sample in a measurement chamber using the nanomaterial to amplify the sample signal; a platform for moving the samples (9) onto containers or vessels for measuring and storing used materials.
In particular, the device shown in (FIG. 1) consists of: a power supply (1) for electrical components; a user interface (2) allowing communication with the user, i.e. entering data and setting system operating parameters as well as displaying information to the user; an electronic controller (3) which controls all components of the system, data storage, data processing, data analysis; a feeder for raw material (4) where the agricultural product is introduced; converting the raw material onto the required form (5); a nanomaterial feeder (6) in liquid form, e.g. a solution of nanoparticles; a feeder for substrates (7) with nanomaterials, preparing the sample for measurement (8); a sample moving platform (9), equipped with containers or vessels for measurement, which in the case of using liquid nanomaterials are equipped with a measuring well, open or closed, made of aluminum, silicon, quartz, CaF.sub.2, etc., while in the case of nanomaterials a solid container or vessel for measurements keeps the substrate in place; Raman spectrometer (10); measuring chamber (11).

EXAMPLE 2

[0028] The method of operation of the device according to (FIG. 2) consists in processing the loaded sample of agricultural product into the form required by the standards for ensuring a unified and representative sample, then processing it into a form which meets the conditions for measuring the Raman spectrum, combining it with the nanomaterial, measuring the Raman spectrum, processing and analysis of measurement data with the usage of artificial intelligence (machine learning) algorithms, and presentation of the pesticide concentration result in an agricultural product.
The detailed operation of the device consists in:
(I) preparing the measurement device by: placing in it the appropriate chemical substances necessary for the processing of the samples; chemical solvents and deionized water; placing liquid nanomaterials in the form of a colloid of metallic nanoparticles consisting of silver, gold or their mixture or consisting of layers or metallic nanoparticles deposited on a solid substrate, e.g. silicon, metal, paper, cellulose, polymers; placing the sample of the agricultural product in the feeder (4); power supply connection (1).
(II) the device performs initial processing of the agricultural product into the required form, defined appropriately for each type and species of agricultural product, where the common element is cleaning and homogenization of the sample in accordance with the standards—a container (5);
(III) the device processes the agricultural product into a sample which is the object of the Raman spectrum measurement—a container (8).
(IV) combining the sample with the nanomaterial in a manner appropriate for the given solid/liquid form of the nanomaterial.
(V) the device in the measuring chamber (11) measures sample of the Raman (surface-enhanced) spectrum combined with the nanomaterial.
(VI) the device cleans the platform moving the samples (9)/measuring chamber (11), discards the used nanomaterial and the sample;
(VII) the device processes measurement data, particularly by comparison with reference data, uses algorithms which change the form of the Raman spectrum by normalizing, reducing noise, removing interference, removing background, etc.
(VIII) the device uses artificial intelligence (machine learning) algorithms, preferably neural networks, to recognize the amount and type of pesticide.
(IX) the device presents the measurement result to the user

EXAMPLE 3

[0029] The raw material (sample of agricultural product) is fed to the mobile device—feeder (4) by the user, then, according to the type of the sample, it is pre-processed (container 5). Processing may consist of homogenizing the sample and/or centrifuging the sample and/or filtering the sample, or subjecting it to other processes.
a) The raw material (an apple) is pre-processed (container 5) by cleaning the batch with water (washing), cutting the skin or leaving it, and homogenizing the sample and filtering the mixture to extract a sample of the liquid suspension of the flesh, juice, etc.
b) The raw material (carrots) is pre-processed (container 5) by cleaning the batch with water (washing), homogenizing the sample, centrifuging and extracting the liquid sample in the form of juice.
The processed sample is then subjected to another treatment (container 8) in order to prepare it for measurement and to connect it with the nanomaterial. Sample preparation may in particular consist of centrifugation (in the range 100-5000 rcf, preferably 2000 rcf); decanting; mixing with a solvent of a type and concentration depending on the agricultural product and the type of pesticide/pesticides. Nanomaterial the SPR resonance curve is equal to the laser wavelength, ensuring the surface effect of signal amplification in this method.
a) The processed sample (an apple) is mixed with a solvent, preferably acetonitrile, preferably in a volume ratio of 1:1 v/v or 1:2 or other. The sample is centrifuged, preferably at 4000 rcf, followed by decantation of the centrifuged solution to remove the flesh. Then the sample is combined with the nanomaterial. For a liquid nanomaterial, the sample is mixed, preferably in a 1:1 v/v or 1:4 v/v volume ratio and fed into the measuring cuvette for measurement.
In case the nanomaterial is a solid, the liquid sample is sprayed on the nanomaterial (substrate) and, after waiting preferably 30 minutes for it to dry, the substrate with the sample is transferred for measurement using the sample transfer platform.
b) The processed sample (carrot) is mixed with a solvent, preferably methanol, preferably in a 1:1 v/v or 1:2 or other volume ratio. The sample is centrifuged, preferably at 2000 rcf, and then the centrifuged solution is decanted to remove the flesh. For a liquid nanomaterial, the sample is mixed, preferably in a 1:1 v/v or 1:4 v/v volume ratio, and fed into the measuring container or vessel for measurement.
In case the nanomaterial is a solid, the liquid sample is sprayed on the nanomaterial (substrate) and, after waiting preferably 15 minutes for it to dry, the substrate with the sample is transferred for measurement by the sample transfer platform.
The liquid nanomaterial can be a colloid of nanoparticles, made mainly of precious metals, such as gold, silver, and/or silica, or other materials and mixtures thereof, in any proportions, shapes and sizes ensuring the surface signal amplification effect in this method.
Combining with a liquid nanomaterial consists in mixing a liquid sample with a liquid solution containing the nanomaterial and placing it in a measuring cuvette.
It is recommended for the nanomaterial to be activated (produced and prepared for measurement) just before the test is performed to ensure optimal conditions for the most effective signal amplification effect which affects the sensitivity and measurement accuracy. In case liquid nanomaterial is used, the nanomaterial is previously (in the device) centrifuged in the range of 500-4000 rcf, preferably 2000 rcf, liquid decanting and using sediment (nanoparticles) to mix with the sample. After mixing with the sample, the material is placed in the measuring container or vessel on the sample moving platform.
The solid nanomaterial may be plate made of glass, silicon, quartz, metallic, paper, fabric (cotton), semiconductor, carbon wafer, any surface bonded in any way to a precious metal as previously mentioned.
Combining with a solid nanomaterial consists in depositing a liquid sample on a solid nanomaterial which is placed in a measuring cuvette. In case of a solid nanomaterial usage, the sample is sprayed on the surface of the nanomaterial, after which it can be heated to accelerate the drying process (evaporation/adsorption/infiltration).
The container or vessel for measurement is transported by the sample moving platform from the sample application point to the measuring chamber and after the measurement is carried out, to the waste container (external container).
In the measuring chamber, the sample is illuminated with laser radiation with a narrow spectral range, less than 0.5 nm, preferably 0.05 nm, with a continuous power in the range of 1-500 mW, and wavelength in the visible and near infrared range, preferably 500-900 nm, and measurement of the radiation emitted using a Raman spectrometer. In case of contact of the sample with nanomaterial (liquid or solid), Surface-enhanced Raman spectroscopy (SERS) spectrum is recorded.
Measurement of a sample with a solid nanomaterial consists in illuminating the sample with laser radiation and recording Surface-enhanced Raman spectroscopy spectrum (SERS spectrum) with a Raman spectrometer. The measurement is repeated many times at different points of the nanomaterial surface with the sample. The change in measurement location results from the sample shift in relation to the laser beam.
Measurement of a sample with the liquid nanomaterial consists in illuminating the sample with laser radiation and recording Surface-enhanced Raman spectroscopy spectrum (SERS spectrum) with a Raman spectrometer. The measurement of a given sample may be performed several times.
The device processes measurement data (spectrum and other collected data) and sends them to a central database via wireless connection to the Internet. A central database of spectra, using artificial intelligence/machine learning algorithms, determines the measurement result which is sent to the device. The device presents the result to the user, including the type of pesticide as well as information about its quantity (e.g. Below the level/above the level).
Measurement data is analyzed by appropriate artificial intelligence algorithms, based on the developed calibration models built on the basis of the Raman spectra database. The final result is presented in a qualitative form as a type of pesticide as well as in quantitative form as exceeding or not exceeding the permitted level of concentration in a given agricultural product.

LITERATURE

[0030] [1] Regulation (EC) No. 396/2005 of the European Parliament and of the Council of Feb. 23 2005 on maximum residue levels of pesticides in or on food and feed of plant and animal origin and amending Council Directive 91/414/EEC (Text with EEA relevance)
[2] Vidal, J L Martinez, et al. “Determination of pesticide transformation products: a review of extraction and detection methods.” Journal of Chromatography A 1216.40 (2009): 6767-6788.
[3] Hernandez, F., J. V. Sancho, and O. J. Pozo. “Critical review of the application of liquid chromatography/mass spectrometry to the determination of pesticide residues in biological samples.” Analytical and bioanalytical chemistry 382.4 (2005): 934-946.
[4] Aulakh, J. S., et al. “A Review on solid phase micro extraction—high performance liquid chromatography (SPME-HPLC) analysis of pesticides.” Critical Reviews in Analytical Chemistry 35.1 (2005): 71-85.
[5] Bruzzoniti, Maria Concetta, et al. “QuEChERS sample preparation for the determination of pesticides and other organic residues in environmental matrices: a critical review.” Analytical and Bioanalytical Chemistry 406.17 (2014): 4089-4116.
[6] Jiang, Xuesong, et al. “Immunosensors for detection of pesticide residues.” Biosensors and Bioelectronics 23.11 (2008): 1577-1587.
[7] Van Dyk, J. Susan, and Brett Pletschke. “Review on the use of enzymes for the detection of organochlorine, organophosphate and carbamate pesticides in the environment.” chemosphere 82.3 (2011): 291-307.
[8] Songa, E. A. & Okonkwo, J. O. Talanta Recent approaches to improving selectivity and sensitivity of 537 enzyme-based biosensors for organophosphorus pesticides : A review. Talanta 155, 289-304 (2016).
[9] Robinson, A. M., S. G. Harroun, J. Bergman, and C. L. Brosseau. “Portable electrochemical surface-enhanced Raman spectroscopy system for routine spectroelectrochemical analysis.” Analytical chemistry 84, no. 3 (2012): 1760-1764.
[10] Fanjul, P., Ibáñez, D., Junquera-Pérez, A., González-Garcia, M. B., & Hernández-Santos, D. Versatility of Time-Resolved Raman Spectroelectrochemistry: From the Resolution of Mixture of Vitamins to the Detection Pesticides. In Meeting Abstracts, The Electrochemical Society, (2019); 38: 1917-1917.
[11] Xu M L, Gao Y, Han X X, Zhao B. Detection of Pesticide Residues in Food Using Surface-Enhanced Raman Spectroscopy: A Review. J Agric Food Chem. (2017); 65(32): 6719-6726.
[12] Pang, Shintaro, Tianxi Yang, and Lili He. “Review of surface enhanced Raman spectroscopic (SERS) detection of synthetic chemical pesticides.” TrAC Trends in Analytical Chemistry 85 (2016): 73-82.