FIELD OF THE INVENTION

[0001 ] This invention relates to "METHOD OF SOIL FERTILITY ANALYSIS BY CHEMICAL AND PHYSICAL PARAMETERS USING VIS-NI R SPECTROSCOPY IN LARGE-SCALE ROUTINE", which field of application is agribusiness.

[0002] More specifically, the product of the invention can be used (i) in agronomic laboratories providing services in soil fertility analysis; (ii) in sectors that use soil analysis information to guide their clients (farmers) on the chemical management of soil fertility and plant nutrition (e.g. cooperatives, agronomic consultancies, fertilizer and pesticide industries, agricultural retailers, agricultural groups, sugar and alcohol mills); or (iii) by rural producers.

BACKGROUND OF THE INVENTION

Soil Analysis Importance

[0003] One very important characteristic of soil is the ability to retain and cede, under ionic form, certain chemical elements which are vital for the development of living beings. This characteristic is basically exerted by the soil colloids, whether organic ("humus") or mineral ("clay"), due to the presence of surface charges, usually negative, making soil the capable of adsorbing cations (Daniel Vidal Perez, Embrapa Solos).

[0004] Soil is a renewable natural resource that plays a key role in agricultural productivity. It happens because soil carries in its composition the essential nutrients for plants. Fertile soil has a great capacity to provide water and nutrients to plants, but its fertility can vary widely on a single farm. Therefore, the farmer must be aware of the soil composition for his crops, which is only possible through the specific analysis of its fertility.

[0005] The planning of fertilization and liming should be done based on accurate information on the fertility level of the areas to be cultivated, which is done by means of soil analysis, followed by the interpretation of the results based on the knowledge of the requirements of the species to be cultivated that is made by an Agronomist (site Embrapa: https://www.embrapa.br/busca-de-noticias/-/noticia/17162564/ tecnologia i no vadora-anal isa-so los-em-apenas-30-seg u ndos) .

[0006] Soil analysis is a standard procedure for any crop and is carried out by a network of public and private laboratories.

[0007] Soil analysis also indicates the level of soil acidity by the pH index measure, which is the reference used to measure the need for its correction through liming or plastering. The ideal pH index for each cultivated species, associated to the characteristics of the acid corrective to be used, is applied to appropriate formulas, by means of which the amount to be applied is measured. (Website Cnptia / Embrapa:https://www.agencia.cnptia.embrapa.br/Repositorio/AG 01_2 298200581532.html). [0008] Soil analysis is one Best Management Practices (BMP) for crop production that remains important in both developed and developing countries because it is agronomically correct, profitable and environmentally responsible.

[0009] Soil analysis will probably be one of the most important management practices for crop production and environmental protection in the future. It will certainly be listed among the best management practices (BMP) to be used by agronomists of industries and universities, consultants and farm managers for the benefit of farmers. Soil analysis will probably be a planning tool and management support service for fertilizer suppliers to use for the benefit of their customers. The environmental benefits of improved soil resource management and fertilizer materials are notable when BMPs of nutrients are tailored to specific fields or areas of the farmer.

[0010] Fertilizer use and crop yield statistics used in many properties indicate a decline of soil fertility due to deficits in nutrient management. The consequences of "mining" (more removal than application) of soil nutrients may not become apparent for several years. (IPNI, International Manual of Soil Fertility, 1998).

[001 1 ] Therefore, analyzing soil is fundamental to improve crop productivity, increase the farmer's income, and contribute to sustainable agriculture practices. Because of its importance, soil analysis has to be carried out by qualified analytical laboratories, since limestone and fertilizers demand large investments and account for a significant part of production cost. In addition, the efficient use of soil analysis is critical to guarantee overall profits and environmental protection.

State of the art

[0012] The discovery of the near-infrared spectral region is credited to the German astronomer living in England, Sir Friedrich Wilhelm Herschel (Herschel, 1800). In 1800 Herschel published a scientific paper entitled "Experiments on the Refrangibility of the Invisible Rays of the Sun" in the scientific journal "Philosophical Transactions of the Royal Society of London". There is a hypothesis that his discovery came accidentally through an experiment that is considered simple today: sunlight was refracted in the colors of the rainbow with the aid of a prism, and calibrated thermometers were positioned in each of the colors to measure their temperatures. Herschel was surprised to conclude that a thermometer that was positioned just beyond the red color, which corresponded to the invisible spectral region, showed a higher temperature than the visible portion of the spectrum. Encouraged by this observation, he conducted a series of experiments involving thermometers to deduce the presence of invisible radiation which is now known to be infrared radiation.

[0013] Herschel and other scientists contributed to the understanding of electromagnetic radiation and spectroscopy: the study of electromagnetic radiation with matter.

[0014] The modern application of NIR spectroscopy has begun on 20 ^th century and Karl Norris is credited as pioneering of its establishment as an analytical tool for chemical analysis. However, NI R spectroscopy only achieved popularity among chemists during the 1990s, driven by the development of analytical instruments and computers (Pasquini, 2003). [0015] The near infrared region is characterized by first, second and third overtones from CH, OH, NH and SH bonds. Due to the nature of these vibrations, the NI R spectrum is characterized by wide and overlapping bands of low intensity absorption when compared to the mid-infrared. These characteristics make the NI R spectrum difficult to interpret using an univariate analysis data treatment, requiring the use of multivariate methods of data analysis called Chemometrics (Pasquini, 2003).

[0016] Numerous possibilities exist for the application of NIR spectroscopy in several areas of science, with an emphasis on practical applications in the development of technologies to meet the demands of the agricultural sector, where soil analysis plays any important role. Current research indicates that NIR spectroscopy is the most promising alternative to determine soil fertility parameters in total or partial replacement with traditional wet chemistry methods. Recent scientific literature was used to find the terms "Near infrared Spectroscopy" and "Soil" as topics in the Web of Science database, and the result was that in the last 3 years (2016, 2015, 2014 and 2013) more than 400 scientific articles were published proposing NI R spectroscopy as an analytical tool for soil analysis. About 94% of these papers were published in English, with 95% being scientific papers and only 0.5% patent registrations.

[0017] Scientific literature shows the use of NIR spectroscopy for determination of soil fertility attributes. And also presents the theoretical aspects about the main chemometric methods commonly used in data treatment.

Near infrared spectroscopy applied to soil analysis

[0018] Most applications of infrared spectroscopy in soil science have been performed in the medium and near infrared regions associated or not to the visible spectral portion (Vis / NI R, 400-2500 nm) in qualitative and quantitative analysis (Reeves III, 2012; Stenberg et al., 2010; Viscarra Rossel et al., 2006).

[0019] Radiation in the infrared region is energetic enough to cause vibrational energy transitions, which basically results in a spectrum which represents the chemical and physical fingerprint of the soil sample.

[0020] Infrared (near and medium) spectroscopy techniques are increasingly being employed in the counting of various soil attributes, both those related to chemical composition (e.g. organic carbon, cation exchange capacity, pH) as well as the physical parameters of soils [Canasveras, J.C., Barron, V., del Campillo, M.C., Torrent, J., Gomez, J. a., 2010. Estimation of aggregate stability indices in Mediterranean soils by diffuse reflectance spectroscopy; Dematte, J. a. ., Campos, R.C., Alves, M.C., Fiorio, P.R., Nanni, M.R., 2004. Visible— NIR reflectance: a new approach on soil evaluation.; Fidencio, P., Poppi, R., 2002. Determination of organic matter in soils using radial basis function networks and near infrared spectroscopy; Fontan, J.M., Calvache, S., Lopez-Bellido, R.J., Lopez-Bellido, L, 2010. Soil carbon measurement in clods and sieved samples in a Mediterranean Vertisol by Visible and Near-Infrared Reflectance Spectroscopy; Guerrero, C, Rossel, R. a. V., Mouazen, A.M., 2010. Special issue 'Diffuse reflectance spectroscopy in soil science and land resource assessment.'; Nocita, M., Kooistra, L, Bachmann, M., Miiller, A., Powell, M., Weel, S., 2011. Predictions of soil surface and topsoil organic carbon content through the use of laboratory and field spectroscopy in the Albany Thicket Biome of Eastern Cape Province of South Africa; Rossel, R. a. V., Behrens, T., 2010. Using data mining to model and interpret soil diffuse reflectance spectra; Stenberg, B., 2010. Effects of soil sample pretreatments and standardised rewetting as interacted with sand classes on Vis-NI R predictions of clay and soil organic carbon; Vohland, M., Ludwig, M., Thiele-bruhn, S., Ludwig, B., 2014. Geoderma Determination of soil properties with visible to near- and mid-infrared spectroscopy : Effects of spectral variable]. The effectiveness of its widespread use as a routine analytical technique in soil laboratories is still in the process of maturation but is growing fast.

[0021 ] The increasing interest in NI R spectroscopy as an alternative technique to soil analysis can be justified by the innumerable and remarkable advantages that this technique presents in relation to conventional analyzes. NIR technology can provide the development of analytical methods that can be non-destructive, free of undesirable residues and environmental impacts, in addition to being inexpensive, fast, requiring limited sample handling when combined with Chemometrics. However, interpretation of NIR spectrum is not immediate, since in its spectral region there is overlapping and discussed previously.

Chemometrics

[0022] Several authors agree that NI R spectroscopy and Chemometrics coexist in symbiosis. Since NIR spectroscopy has characteristics that limit a univariate analysis of the spectrum, it benefits from Chemometrics to become an increasingly strong tool for identification and quantification of several parameters in different kind of samples. Chemometrics benefits from the type and high amount of spectral information generated by NIR spectroscopy for the development and application of new methods for calibration and classification (Bellon-Maurel et al., 2010; McClure, 1994; Pasquini, 2003).

[0023] There are many definitions for Chemometrics reported on the literature, one possible alternative is: "Chemometrics can be the defined as the application of mathematical, statistical, logical and computational methods to plan, investigate and predict sets of multivariate chemical data". The term was introduced in 1971 by Prof. Svante Wold (Umea University) who defined Chemometrics as "the art of extracting chemically relevant information from data produced in chemical experiments" (Wold, 1995).

[0024] The birth of Chemometrics in the 1970s was driven by the development of computerized instrumental methods for chemical analysis, which made it possible to obtain large amounts of data in a much faster and more efficient way (Neto et al., 2006). As a direct consequence, the number of variables that can be measured in a single sample increased significantly. A notable example is the intensity of absorption of more than one wavelength which is routinely recorded in a single spectrum. To obtain instrumental responses from multiple variables, it was also necessary to extract reliable results and relevant information quickly and efficiently, which was done with high levels of efficacy by the Chemometrics method (Ferreira et al., 1999).

[0025] Although Chemometrics reaches all branches of Chemistry, Analytical Chemistry was the area most benefited by the development of analytical instrumentation and its association with computers (Bruns and Faigle, 1985). Ultraviolet-visible (UV-Vis) spectroscopy, Near and Mid-infrared spectroscopy, Mass Spectrometry (MS), Nuclear Magnetic Resonance (NMR), Atomic Absorption / Emission and Liquid / Gas Chromatography are examples of analytical techniques that offer the possibility of data generation characterized as multivariate.

[0026] Many chemometric methods and their applications depend on the nature of the problem to be solved or the type of information to be obtained. In general, the methods that form the basis of Chemometrics can be classified as pattern recognition, multivariate calibration, multivariate resolution of signals and experiment design. Application of these methods basically requires fundamental steps for the correct interpretation of the data, such as preprocessing of instrumental responses, selection of variables, removal of outliers and counting of some figures of merit, to ensure the validity of constructed models.

[0027] Some of the major chemometric methods used as a basis for this work are Principal component Analysis (PCA) and Partial Least Squares (PLS) regression. The aspects related to validating the results can be known below.

Principal Component Analysis

[0028] PCA is a method that allows the reduction of data dimensionality by representing the data set in a new system of axes called Principal Component (PC), allowing the visualization of the multivariate nature of the data in a few dimensions. In the original space, the samples are points located in an n-dimensional space, where 'n' is equal to the number of variables. With the reduction of dimensionality provided by the PCA, the samples become points located in spaces of reduced dimensions defined by the PCs, for example, bi or tri-dimensional. Mathematically, in PCA the matrix X is decomposed into a product of two matrices, called scores (T) and weights (P), plus an error matrix (E) (Wold and Sjostrom, 1998), as shown in Equation 1 :

X = TP' + E(l)

[0029] The scores refer to the similarity between the samples and represent their coordinates in the system of axes formed by the Principal Components (PC). The PC is formed by the linear combination of the original variables and the coefficients of this combination are called weights. The weights are the cosines of the angles between the original variables and the PCs, representing, therefore, how much each original variable contributes to a particular PC. The first major component (PC1 ) is plotted in the direction of the greatest variation in the data set; the second (PC2) is plotted orthogonally to the first, in order to describe the largest percentage of the variation not explained by PC1 and so on. The evaluation of the weights allows to understand which variables contribute more to the groupings observed in the chart of scores. Through the joint analysis of the scores and weights graph, it is possible to verify which variables are responsible for the differences observed between the samples. The number of PCs to be used in the PCA model is determined by the percentage of variance explained. Thus, a number of components are selected in such a way that the highest percentage of the variation present in the original data set is captured (Wold, 2002). Several algorithms are available for the realization of PCA, and four of them frequently appear in the literature: Non linear Iterative Partial Least Squares (NIPALS), Singular Value Decomposition (SVD), which use the data matrix X, POWER and (Eigen value Decomposition - EVD) that work with the cross product matrix X'X. SVD and EVD extract the core Components simultaneously, while NIPALS and POWER calculate the PC sequentially (Andersson, 2009; Geladi and Kowalski, 1986).

[0031 ] The SVD (Equation 2) is based on the linear algebra theorem which states that a matrix X (mxn), 'm' columns and 'n' lines, can be transformed into a product of three matrices U, S, V (' subscript 'means transpose) which have specific properties, where: the matrices U and V are square and orthonormal; the matrix S is a diagonal rectangular matrix containing the singular values on the diagonal and all the non- diagonal elements equal to zero (Wold, 2002). In this case, the weights are given by the matrix V and the scores by: T = US.

X = USV'(2)

[0030] EVD is a method similar to SVD used to calculate eigenvalues and eigenvectors of an array. Both SVD and EVD are mathematically easy to use methods in Matlab because in this software there are internal functions for the decomposition of singular values and to estimate eigenvectors and eigenvalues of a matrix.

[0031 ] NIPALS is a commonly used method for calculating Core Components of a data set, in which the weights and scores vectors are calculated iteratively, one at a time, in order of decreasing importance. The iterative process, for the first main component, is initialized with a first estimate of scores, which may be the column of X that has the largest variance. Using these scores, weights are calculated as: p = tX / t't, normalized to length equal to one. After this, the scores are calculated as: t = Xp / p'p. These score values are compared to the previous ones and if they are different (within a pre-established criterion), the weights are recalculated as shown above. This process continues until the scores are similar or a given number of iterations has been performed. After convergence, the product tp 'is subtracted from X, obtaining residue E

E = X - tp'(3)

[0032] The process proceeds to the next major component, replacing E with X (Andersson, 2009; Wold, 2002).

Partial Least Squares (PLS)

[0033] Certainly PLS is one of the most prominent methods used in Chemometrics alongside PCA, being considered the multivariate calibration method used as reference for this type of application. In PLS the spectral response variation is related to the attributes (or properties) of interest through a multivariate regression (Geladi and Kowalski, 1986). PLS uses PCA to reduce the dimensionality of the data set for later correlation between the spectra (matrix X) and the properties of interest (vector y) (Geladi and Kowalski, 1986). The property of interest is often the concentration of an analyte, but not limited thereto, and may even be physical-chemical properties, such as density and viscosity, which are related to the composition of the sample. The matrix X and vector y are decomposed by PCA by equations 4 and 5:

X = T P' + E (4) y = Uq' + f (5) Where: P and q are the weights of X and y, respectively; T and U are the scores of X and y, respectively, and E and f represent the residue matrices of X and y, respectively. The matrix of scores T is estimated as a linear combination of X with the weighted coefficients powder W (called weights);

T = XW (6)

From W, the regression coefficients of the PLS model can be estimated by:

bp _LS = W(P' W) ^_1 q' (7)

The linear PLS model can be represented by: y = bp _L s (8)

Preprocessing

[0034] The preprocessing step of the data is critical to the success of the multivariate analysis. The main objectives of the application of the preprocessing techniques are to eliminate information that is not relevant from the chemical point of view and to make the data matrix better conditioned for the analysis, allowing the subsequent exploratory analysis of the data set with efficiency. There is extensive literature available on the various methods of data processing in spectroscopy (Xu et al., 2008). The standardization of spectra, data centering, derivation and smoothing using the Savitzky-Golay algorithm and applying the Multiplicative Scatter Correction (MSC) are some of the most applied methods (Rinnan et al., 2009).

[0035] Centering the data in the mean (Equation 9) is to calculate the mean of the intensities for each wavelength and subtract each of the intensities of the mean value. In this way, each variable will have zero mean, that is, the coordinates are moved to the data center, allowing differences in the relative intensities of the variables to be easier to perceive.

(mean center) = Xij raw dataset ) x _i j(average) 9 ^' )

[0036] Baseline displacement and slope can be corrected by derivation of the spectra. Smoothing methods are used to mathematically reduce noise, thereby increasing the signal-to-noise ratio. In these methods, a window is selected, which contains a number of variables. The dots in the window are then used to determine the value at the center point of the window, and thus the window size directly influences the smoothing result. In the Savitzky-Galoy method, a low order polynomial is adjusted to the points in the window and used to recalculate the center point (Savitzky and Golay, 1964).

[0037] Multiplicative scatter correction is a transformation method used to compensate for additive and / or multiplicative effects in spectral data. This method removes the influence of physical effects on spectra, such as particle size, roughness and opacity, which do not bring chemical information about the samples and introduce spectral variations such as baseline displacement. To make the correction, the MSC method assumes that each spectrum is determined by the chemical characteristics of the sample added to the unwanted physical characteristics. Equation 10 describes the principle of operation of the MSC: (transformed) (10) where: x _tk (raw dataset) and x _tk (transformed) are the absorbance values before and after correction with the MSC at k wavelengths; a _t and b _t are constants estimated from a least squares regression of an individual spectrum x _ik against a mean spectrum x of the calibration set at all wavelengths or in a subset, following Equation 1 1 : xt _k = _t + b _t x + e _ik (11) where: e _lk corresponds to all other effects on the spectra that were not modeled (Rinnan et al., 2009). Outlier Detection

[0038] Outliers (or anomalies) is the term used to designate anomalous samples that may appear in the calibration and validation sets used in the construction of multivariate calibration models. Normally, these anomalous samples have different behavior from the other samples of the data set, and their presence in the calibration set can result in models with low predictive capacity, that is, they produce high prediction error values (Naes and Martens, 1984; Valderrama et al., 2007).

[0039] Methods for their detection have already been described in several papers (ASTM E1655-05, 2012; Naes and Martens, 1984). The three simplest forms to identify abnormal samples, usually recommended (Naes and Martens, 1984), are based on data with extreme leverage, unmodeled residuals in spectral data, and unmodeled residuals in the dependent variable (Valderrama et al., 2007).

Validation

[0040] The proposition or development of an analytical method necessarily requires a careful and systematic evaluation of its performance and robustness against the conditions in which it will be applied. To this analytical suitability process - in which the requirements specified in the method are suitable for an intended use - is given the name of validation (VIM, 2008).

[0041 ] The validation of an analytical method can be confirmed by determining parameters known as figures of merit (Lorber, 1986; Valderrama, 2005; Valderrama et al., 2009). In analytical chemistry the main figures of merit commonly used in a validation process are: accuracy, precision, sensitivity, analytical sensitivity, selectivity, signal-to-noise ratio, detection limit, quantification limit, confidence intervals and bias or bias tests (Souza et al., 2016, Valderrama et al., 2009).

[0042] The way some of these parameters are determined in multivariate calibration models is estimated in a very similar way to the methods of multivariate calibration. However, this similarity can't be observed for parameters such as confidence intervals or uncertainty, linearity, sensitivity, signal-to-noise ratio and selectivity (Souza et al., 2010; Valderrama et al., 2009).

[0043] In literature there are several studies addressing theoretical aspects regarding merit figures in multivariate analysis (Lorber, 1986) and application of figures of merit in NIRS data (Rambo et al., 2013, Souza et al., 2016; Valderrama et al., 2009). In the specific case of the use of merit figures in the validation of NIRS for soil analysis, Bellon-Maurel et al. (2010) presents a critical evaluation on the quality indicators in multivariate calibration models.

[0044] A detail of the validation of the analytical methodology employed in the present patent application can be found in previous research (Souza et al., 2016). Table 1 presents the figures of merit employed in this document.

Table 1 . Figures of merit and outlier detection equations employed in this work.

Figures of Merit

Ration of performance to deviation

Precision -,

Sensitivity

Analytical sensitivity

Limit of detection

Limit of quantification

+^-) M

1

Confidence interval

1

Outliers detection

[0045] Hubert et al. describe a method of analyzing spectral data for the selection of a calibration model. In this method the spectra of a given substance are related to a physical or chemical parameter of the substance, within a predetermined range of concentration of that parameter, following the steps: a) the spectral data of a substance were captured and related to their respective physical parameters - chemists in a given concentration range; b) several calibration models were created using the spectral data and the physical-chemical parameters employing resampling; c) the tolerance intervals were calculated for each reference level and calibration model e; d) the tolerance intervals were displayed at each of the reference levels over the predetermined interval for each calibration model. In this way, a possibility of spectral data analysis was proposed aiming an automated calibration selection.

[0046] Jiliang China University has also filed some patents on near infrared spectroscopy, among which may be cited:

- CN106096656, published 1 1 /1 1 /2016, describes a method of analysis of near-infrared spectroscopy, based on poor representation and neural network technique BP; - CN205157419, issued 04/13/2016, describes a soil nutrient detection device based on near infrared spectroscopy;

- CN20515741 , issued 02/10/2016, describes a soil nutrient detection device based on near-visible infrared spectroscopy;

- CN105319172, issued 02/02/2016, describes a soil nutrient detection device based on near infrared spectroscopy;

- CN204832023, issued 02/12/2015, describes a Rapid Ground Moisture Detection Device based on near infrared spectroscopy;

- CN204630922, published 09/09/2005, describes an organic system for the detection of organic matter and moisture based on the technique of near infrared spectroscopy.

[0047] We note from the related documents some to be particularly cited, such as CN106124449-A, Wang R. et al., describing a process for analyzing and predicting soil depth by near infrared spectroscopy involving sampling of training and pre-processing, performing spectrometry, spectroscopic data acquisition, prediction model construction and soil sample scanning.

[0048] CN105784629-A, from Huang et al., describes the detection of a stable carbon isotope ratio of soil, i.e., delta-13 carbon value using medium infrared spectroscopy, involving obtaining original spectra and quantitative relationship between the treated spectra and the stable carbon isotope ratio.

[0049] CN105699314-A, from Kang H. et al., describes a method for detecting the stable isotope ratio of carbon using infrared spectroscopy, involving the establishment of a quantitative ratio model according to an isotopic ratio of stable carbon of calibrated soil samples.

[0050] CN105486663-A, Kang H. et al., discloses a soil nutrient detection device based on visible and near infrared spectroscopy technology, having a fiber optic probe connected to the controller through the spectrograph and GPS module and LCD module connected to the controller.

[0051 ] WO201 1051 166-A1 , Janik LJ et al., , discloses a device for the determination of polycyclic aromatic hydrocarbons in general. The soil comprises a unit for exposing the sample of diffuse reflection infrared spectroscopy, and detection unit for recording spectroscopic sample parameter and computing unit.

[0052] The classical wet methods are the most used for physical-chemical analysis of soil fertility. They incorporate products and processes employed by a person skilled in the art , and in terms of process, it is worth mentioning that the methods for chemical analysis of soil samples involve three (3) main steps:

- Preparation of the sample: dry, de-rotate, pass through the first 2 mm mesh and withdraw the amount of sample (volume or weight) to analyze each attribute of interest, according to the Manual of Methods;

- Extraction: add extractive solution (chemical reagent) and;

- Analytical Determination: sample analysis by several methods, such as Atomic Flame Absorption Spectrometry (FAAS), Inductively Coupled Plasma Optical Emission Spectrometry (ICP-OES), Colorimetry, Titration, among others. [0053] The most widespread classical methods for soil physical analysis (particle size) are based on the application of pre-treatments for the removal of cementing and flocculating agents; dispersion of the soil sample through the use of a combination of chemical processes and mechanical disaggregation; and quantification of the soil fractions by sieving, for the fine sand and fine sand fractions, and by sedimentation, for the silt and clay fractions.

[0054] Despite the technologies currently known, the near infrared (NI R) spectrophotometers, which have generalized applications for any type of matrix or sample. These instruments can be classified, according to the optical system of analysis, into interferometric and dispersive. The interferometric instruments are those that make use of Fourier transform to realize the spectroscopic measurements and to generate the infrared spectrum. Dispersive equipment makes use of diffraction gratings to perform such measurements. In the market there are commercial equipment of both classes.

[0055] The system of acquisition of the spectra (analysis) of solids can present at least 3 (three) types of design: fixed, rotating or translational. As far as fixed systems are concerned, the spectra are acquired at a single point in the sample. In rotary systems, the sample is usually analyzed on a petri dish-like support with diameters ranging from 10 mm to 90 mm. In this case, a spinner causes the sample to rotate continuously throughout the spectrum acquisition process. At the end of the scan a spectrum is generated which is the average of all the spectra acquired during the sample scan. The translational system can function in a similar way to the rotational one, however, the supports are usually rectangular. Typically, the sample holder undergoes translation and a spectrum is acquired at a given fixed number of points. At the end of the process a spectrum is obtained which represents the mean of all points analyzed in the sample. These devices analyze solid samples in the form of powder or grain in diffuse reflectance mode, and only one sample can be analyzed at a time.

[0056] The sample acquisition systems shown above may analyze the sample from below or above, i.e. the location of the infrared radiation beam may be below or above the sample carriers. In case of reading devices below the sample holders, the optical path or the distance between the beam and the sample is easily kept constant since the surface of the sample holders, usually quartz or glass, functions as a border between the sample and the radiation beam. However, the commercial spectrophotometers that make up the spectrum above the sample, the detector is fixed and the distance between the beam and the sample may vary depending on the amount of sample placed on the sample carrier and the irregularity on the sample surface when disposed in the container.

[0057] In general, the current methods for physical-chemical analysis of soil fertility require several different tests to analyze different soil characteristics. They are also laborious, can take a significant time to execute, require a significant number of labor, make use of various chemical reagents and produce waste that can generate negative environmental impacts.

[0058] As related to Total Organic Carbon (TOC) analysis in the soil, the analytical laboratories usually employ the Walkley & Black method, which is based on the oxidation of the carbon forms by dichromate (Ο ₂ 07 ₂ ^" ). In addition to the high cost and high time of data acquisition for the determination of organic carbon by this method, a toxic reagent is used in the analysis, which at the end of the process generates a chemical residue that is highly damaging to the environment and to human health.

[0059] It is worth mentioning that the complexity in the official methodologies for soil analysis is related to several steps to perform the test. Therefore, for a laboratory to guarantee the quality of the analysis result, it is necessary to implement several controls in its process, because each stage contributes to the final uncertainty of the result. In addition, it is extremely important to observe the quality of the water and reagents used in the test, to have a plan of preventive maintenance and calibration of equipment and to constantly invest in the training of its collaborators. It is desirable that laboratories seek the implementation of quality management systems, as in ISO / IEC 17.025, to improve process control and quality of analysis.

[0060] Another important point to consider is that, for the most part, the analytical capacity of the soil laboratory is directly related to the amount of human resources employed in the process. In addition, the demand for soil analysis in agriculture is seasonal, that is, there are periods of the year when the demand for analysis is intense, to the detriment of other periods. Therefore, it is a very difficult task for laboratories to adequately scale their analytical capacity and allocate adequate resources for this, since when the analytical capacity is undersized, the laboratory may fail to meet the market and reduce its billing potential, while allocate a lot of resources and for some nuance of the market do not get samples, the damage can be very large.

[0061 ] All of these factors contribute to hampering the implementation and quality operation of soil analysis laboratories in remote regions with an agricultural hub where the availability of skilled labor and suppliers of reagent inputs is limited; water quality is often not suitable for use in the test; access to service providers for equipment maintenance and calibration is scarce.

[0062] With regard to the NI R spectrophotometers available in the market or described in the state of the technique, it should be noted that no near-infrared spectrophotometer (NI R) of the knowledge base of the matter has a solution dedicated to soil analysis, with an associated digital platform, based (web), for forecasting and making results available. In general, these devices have complex software for the construction of calibration models that require constant intervention by analyst, and the lack of specialized labor for multivariate data treatment is recurrent. Those software have limited resources to perform the calculations and exploratory analysis required in multivariate calibration; are very unintuitive systems, allowing the analyst to make gross errors that can compromise the quality of the results.

[0063] As far as data modeling is concerned, commercially supplied services, most of the time offered by the companies that sell the NIR equipment themselves, boils down to offering customers the so-called pre- made "calibration curves". This type of approach is extremely technically not suggested because any variation in matrix composition can seriously affect the calibration model causing systematic errors and impairing the accuracy of the analyzes. In some cases, these companies offer a recalibration service of the models built into the system using customer data. However, this service has shown to be inefficient because it does not have a systematized software solution or digital platform for multivariate data processing. Typically, these companies send the data to specialists packed in their headquarters, located mainly in Europe and the United States, for recalibration to take place. It should be noted that this type of approach has contributed negatively to the popularization, acceptance and understanding of NIR spectroscopy for the analysis of soils and other matrices.

[0064] Another drawback inherent in the infrared instruments available on the market is that they allow the analysis of only one sample at a time. Considering the high investment for the acquisition of a commercial infrared spectrophotometer and, in general, the low value added of the soil analysis services, if the NIR equipment does not have a high operational performance, it becomes impracticable to apply it in soil analysis routines. SUMMARY OF THE INVENTION

[0065] The new technology dedicated to the chemical and physical analyzes of soil fertility by Vis-NIR spectroscopy greatly simplifies the analytical procedure, being limited to only two steps:

Preparation of the sample: Dry, de-rotate, pass through the first 2 mm mesh and transfer the amount of soil sample sufficient to cover the bottom of the sample vessel (plastic cup), approximately 150 g;

- Analytical Determination: collection of the sample spectrum with the Vis-NI R spectrophotometer of the invention, which automatically sends the information to a cloud-hosted database, where the calibration models (mathematical models) are stored, which will produce the analytical results and made available for inquiry on the website, in less than 30 (thirty) seconds. It also has the advantage of analyzing different soil attributes with only one spectral reading: dozens of soil fertility parameters can be analyzed simultaneously in a non-destructive, fast and economical way.

[0066] The controls necessary to evaluate the correct functioning of the Vis-NIR spectrophotometer and to guarantee the quality of the analytical result are made through the use of a white reference standard (Spectralon), with a known reflection of around 0.99 through the spectrum and a standard soil sample with known analytical results. Before starting the process of analyzing soil samples in the tray, the spectrophotometer needs to be calibrated by reading Spectralon. This calibration has the objective of equalizing the energy beams, within a limit of 0 to 100%. If the reading of the Spectralon is approved, the autosampler starts to read the second control, which is the standard sample of soil. The analytical result of the standard sample is predicted in the database and evaluated through the Online Control Chart. Only if the spectrum of the standard sample is approved, the equipment proceeds with the analysis of the entire tray.

[0067] Thus, the solution presented democratizes the realization and access to the technology of soil analysis, because: (i) dispenses with the need for specialized labor in the process; (ii) significantly reduces the operational cost of the analysis, allowing a reduction of the final price of the service, allowing the farmer to intensify the number of samples collected to the field aiming at greater representativeness and better knowledge of the production area; (iii) reduces the analytical time to less than thirty (30) seconds, which meets the market demands for faster and more accurate information; (iv) standardization of the analytical process, giving greater repeatability and quality in the analysis results; (v) possibility of implantation in remote areas, such as in agricultural poles, promoting greater access of rural producers to the use of analysis technology, with the benefit of facilitating logistics and shortening the time of sending the samples to the laboratory; (vi) dispensing with the use of chemical reagents that generate toxic wastes that are highly harmful to the environment and human health, and to bureaucratize the process of implementing soil analysis laboratories because it does not require the handling of hazardous products and controlled waste disposal.

[0068] Another positive aspect of the technology of the invention is the high operational efficiency and low demand for dedicated workforce in the process, enabling its application in large scale routines. The technology charging model is based on each analyzed sample (click-through), the fixed cost for maintenance of the analytical laboratory structure will be drastically reduced, and will be mainly variable cost (when performing the analysis). Thus, all work to plan the analytical capacity of the laboratory to meet the intrinsic seasonality's of the agricultural market will disappear, since the technology allows with a reduced number of employees to be able to produce large volumes of soil samples per day. [0069] Considering the determination of Total Organic Carbon (TOC) in the soil to be a routine analysis in the agronomic laboratories and the increasing demand for this type of analysis in function of global initiatives for monitoring the amount of carbon in the soil with programs of Low Agriculture Carbon, it is necessary the development of new analytical technologies of high yield and that are not based on the oxidation by dichromate. The IPCC - Intergovernmental Panel on Climate Change, recognizes the analysis of Total Organic Carbon in the soil by the dry combustion method using a CHN elemental analyzer. However, this method, besides being very expensive, does not have an adequate yield for application in laboratory routines to meet the market demands. For this reason, new technology presented here for soil analysis by Vis-NIR spectroscopy becomes even more relevant. According to the Agency for Toxic Substances and Disease Registry (ATSDR) located in Atlanta, USA, chromium is a natural element in plants, rocks, soils and is present in different forms ( Cr (0), Cr (II I) and Cr (VI), where Cr (I II) is naturally occurring and Cr (0) and (VI) are generally the results of industrial processes, with chromium being hexavalent in 18th in the list of hazardous substances published in 2007 (ATSDR, 2010).

[0070] According to NBR 10.004, from the Brazilian Association of Technical Standards, wastes containing chromium (VI) are classified as hazardous, or Class I (ABNT, 2004) and require appropriate forms for transportation and packaging.

[0071 ] There is no scientific proof to confirm, but considering Embrapa Solos' estimate that in Brazil at least 4 (four) million soil analyzes are performed; that the Walkley & Black method establishes for TOC the use of 10mL potassium dichromate + 10mL concentrated sulfuric acid + 200mL of distilled water + 10mL concentrated phosphoric acid (Total Chromium VI solution used per sample: 240mL); the TOC analysis is a basic routine analysis, being demanded in all the analytical services; therefore, at least 900,000 liters of solution containing Chromium VI are generated in Brazil from soil analysis.

[0072] The great innovative reference of the automated Vis-NI R spectrophotometer, , is that it has been developed customized for soil fertility analysis in large-scale routines. It has a tray with capacity to accommodate 40 (forty) soil samples at a time and autosampler for sequential analysis of the samples. The equipment may also be associated with a conveyor belt that loads the samples as an alternative to the 40 (forty) sample tray.

[0073] The spectrum acquisition system of the SpecSoil-Scan spectrophotometer of the invention is made above the sample, i.e., the infrared beam is coupled to the autosampler above the sample carriers (test cup / vessel). In addition, the autosampler is equipped with a height sensor that adjusts according to the amount of sample placed in the vessel, ensuring that the optical path or the distance between the beam and the sample is always the same between samples, standardizing and improving the quality of spectra collection and, consequently, the robustness of the model and the quality of the result.

[0074] Identification of the samples by the system is performed by reading the bar code affixed to the sample vessel by means of a reader which is coupled to the autosampler. Therefore, the samples can be arranged in any position of the tray (or mat) that the equipment will be able to identify them at the moment of the collection of the spectrum, minimizing the intervention of the analyst and the chances of errors.

[0075] The spectrophotometer of the invention (SpecSoil-Scan) is integrated into a software platform dedicated to soil analysis, called the Digital Platform SpecSolo, which controls the equipment and manages the entire routine of analysis, from the receipt of the sample to the availability of the analytical results on the website. The Digital SpecSolo platform consists of the following modules: SpecSolo- Manager, SpecSolo-Tray, SpecSolo-Lims, SpecSolo-Services and SpecSolo-Toolbox.

[0076] It is an object of the present invention to provide a new method, i.e. a process, and its product (hardware + software) system to improve the chemical and physical analysis procedures of soil fertility, through package technology that combines the use of a Vis-NI R spectrophotometer called SpecSoil-Scan, efficient chemometric algorithms and integrated cloud-based software to produce analytical results.

[0077] Another object of the invention is to democratize the realization and access to soil analysis technology, significantly reducing its operational cost, reducing the analysis time to less than 30 seconds, dispensing with the use of chemical reagents that generate toxic residues and enabling its implantation in remote areas, as in agricultural poles. It has the advantage of analyzing soil samples in a non-destructive, quick and economical way: dozens of soil fertility parameters can be analyzed simultaneously in just a few seconds.

DESCRIPTION OF THE DRAWINGS

[0078] The invention will now be described in a non-limiting embodiment, wherein, for the better understanding of the developed technology, references may be made to the accompanying drawings, in which:

FIGURE 1 : Perspective view of the equipment dedicated to soil analysis by Vis-NI R spectroscopy, according to the invention, called SpecSoil-Scan;

FIGURE 2: Illustrative view of the technology components of the invention;

FIGURE 3: Illustrates the interaction between the factors that compose SpecSolo technology;

FIGURE 4: Diagram showing the algorithm of the process of construction of the multivariate calibration model and prediction of the results;

FIGURE 5: Shows the flowchart of the analysis process with the product (SpecSoil-Scan spectrophotometer) according to the invention.

DETAILED DESCRIPTION OF THE INVENTION

[0079] The "METHOD OF SOIL FERTILITY ANALYSIS BY CHEMICAL AND PHYSICAL PARAMETERS USING VIS-NI R SPECTROSCOPY IN LARGE-SCALE ROUTINE" comprises the provision of a new method, i.e. a process , and its product (hardware + software) system in order to improve the chemical and physical analysis procedures of soil fertility, through a technological package dedicated to soil analysis, according to its own routine, a package that combines the use of a spectrophotometer Vis-NIR, called SpecSoil-Scan with its respective digital platform, chemometric algorithms of multivariate analysis and an integrated software based on cloud computing (service server) to produce the analytical results.

[0080] Thus, according to the invention, for the routine application of the infrared spectroscopy technique for determining the chemical and physical attributes of the soil, it is first necessary to construct the calibration models for each of the attributes that will be analyzed in the soil , associating the result of the wet analysis, performed according to the reference methods, with the corresponding spectrum of each soil sample, acquired through Vis-NIR spectrophotometer of the invention, or SpecSoil-Scan. [0081 ] On the other hand, the equipment (1 ) dedicated to the soil analysis by VisNIR spectroscopy according to the invention, presents positive aspects in relation to the other near infrared spectrophotometers available in the market, as follows:

- presence of the autosampler for sequential analysis of the soil samples arranged in the tray (2), with a capacity of at least 40 (forty) positions;

- proximity sensor coupled to the autosampler to standardize the distance of the spectrum collection, i.e. the distance between the infrared beam and the sample;

- presence of a bar code reader coupled to the autosampler for identification and registration of the sample in the tray.

[0082] With a command through the software controlling the equipment, the spectra of the soil samples are acquired in all 40 (forty) positions available on the carrier. The scanning time of a sample for spectrum collection is less than 10 (ten) seconds.

[0083] The Digital SpecSolo platform is the technological package that integrates SpecSoil-Scan (1 ) systems for soil sample management and calculation of analytical results. It is composed of the following modules:

- SpecSolo-Manager: software for managing and controlling access of partners accredited in SpecSolo;

- SpecSolo-Tray: software for control of SpecSoil-Scan equipment and organization of samples for analysis;

- SpecSolo-Lims: software for soil sample management and analytical services to be run. Manages every analytical routine from the arrival of the sample to the availability of the result;

- SpecSolo-Services: portal for integration services (communication) of the SpecSolo Digital Platform with its own modules or third-party modules;

- SpecSolo-Toolbox: own software for data modeling and prediction of results, where the routines of spectral preprocessing, construction and choice of the best models, validation and prediction of results were automated through efficient chemometric algorithms. The system is cloud-hosted where the calibration models responsible for performing the multivariate analysis calculations required to generate the analytical results of each of the calibrated chemical and physical attributes are stored from the spectra generated by the SpecSoil-Scan instrument. According to the invention, therefore, the analyst is not required to have specific chemometrics knowledge for the treatment and interpretation of the results generated since the system performs this task automatically. On a daily basis the robustness of the models is evaluated through trained algorithms and the updating of these models, or "recalibration", happens automatically by the insertion in the system of new analytical and spectral data of relevant soil samples.

[0084] Table 2 shows the main features of each module that makes up the digital platform of the invention, as follows: Access control and privileges of the partner accredited SpecSolo SpecSolo-Manager

Sample registration with the corresponding analytical services requests SpecSolo-Lims

and printing the sample identification labels

Record receipt of the sample by reading the bar code attached to the SpecSolo-Lims

sample packaging

Identify the sample for analysis using your bar code SpecSolo-Tray

Control the equipment to perform sample collection of samples SpecSolo-Tray

Associate the collected spectrum of the sample with its identification SpecSolo-Tray

(sample ID)

Store spectra collected from samples in a local database and send a SpecSolo-Tray

copy of this spectrum through SpecSolo-Services to the Cloud-hosted

SpecSolo-Toolbox

Calculate analytical results using calibration models SpecSolo-Toolbox

SpecSolo-Lims automatically captures, through SpecSolo-Services, the SpecSolo-Lims e SpecSolo- analysis result generated by the SpecSolo-Toolbox and made available Services

for consultation on the SpecSolo portal

Printing of test reports with analytical results SpecSolo-Lims

Provide the analytical result to be consumed by other applications SpecSolo-Services through SpecSolo-Services APIs

Management of access control services per client SpecSolo-Manager

Financial management of the analytical services consumed by the SpecSolo-Manager accredited partner SpecSolo

Availability of electronic payment methods for the analytical services SpecSolo-Manager consumed by the accredited partner SpecSolo

[0085] Regarding the multivariate analysis algorithms to be sequentially addressed to the digital platform, they are stored in the SpecSolo-Toolbox and use multivariate calibration methods to generate accurate and reliable results.

[0086] The invention includes a 'business model' based on the SpecSolo technology, which is to accredit local partners to provide the soil analysis service to farmers in their region. The SpecSolo accredited partners - clients - can be selected from: (i) agronomic laboratories that provide services in soil fertility analysis; (ii) cooperatives, agronomic consultancies, fertilizer and pesticides industries, agricultural resellers, agricultural groups, sugar and alcohol plants, among others, that use soil analysis information to guide their clients (farmers) on the chemical management of soil fertility and plant nutrition; or (iii) by rural producers.

[0087] SpecSolo's accredited partner will have an initial cost to purchase the SpecSoil-Scan equipment, in addition to a variable cost depending on the number of soil samples processed through the platform. Therefore, the business model is based on the collection of a fee - value - per sample analyzed through the SpecSolo platform.

[0088] As far as method (or process of the invention) is concerned, and for routine application of the infrared spectroscopy technique for determining the chemical and physical attributes of the soil, first it is necessary to construct calibration models for each of the attributes that will be analyzed in the soil, associating the wet analysis results, performed according to the reference methods, with the corresponding spectrum of each soil sample, acquired through the SpecSoil-Scan equipment (1 ). For this, we collected spectra of more than 100,000 representative soil samples from all Brazilian territory and associated with the analytical results obtained by wetting these samples. By means of multivariate data treatment - chemometrics - the calibration models were constructed for each of the 36 (thirty-six) parameters that were analyzed by the reference method in more than 100,000 representative samples. The multivariate data processing was done with SpecSolo-Toolbox software, where the spectral preprocessing routines, construction and choice of the best models, validation and prediction of results were optimized by means of efficient chemometric algorithms. Therefore, in a number of "n" spectra collected in a number of at least thousands samples of a representative soil of a certain place, by association with analytical results obtained by wet of these samples, when applying the treatment of multivariate data - chemiometry - one obtains the calibration models for each of the dozens of parameters analyzed by the reference method.

[0089] After the calibration models are constructed, the user (or SpecSolo accredited partner) may use the SpecSolo platform to perform routine soil fertility analyzes.

[0090] For the construction of the calibration models, some sequential procedures must be observed, namely:

- Collection of the infrared spectrum of representative soil samples (> 100,000) using SpecSoil-Scan equipment (1 ). These samples had been previously dried at 40 ⁰ C, milled through a 2 mm mesh sieve and analyzed by the reference method for the following parameters: P (resin, mg / dm3), P (mehlich, mg / dm3), P (oxidation, mg / dm3), pH (H20), pH (CaCI2), pH (SMP), K (resin, mmolc / dm3), COS (oxidation, mg / dm3) Ca (resin, mmolc / dm 3), Mg (resin, mmolc / dm 3), Na (Mehlich, dm 3), H 3 + Al 3 (calculation, mmolc / dm 3), S.B. (mg / dm3), B (hot water, mg / dm3), Cu (DTPA, mg / dm3), V (%), m% (Mehlich, mg / dm3), Mn (DTPA, mg / dm3), Mn (Mehlich, mg / dm3), Zn (DTPA, mg (%), Al in the CTC (calculation,%), H in the CTC Ca / K (calculation), Mg / K (HMS + NaOH, g / Kg), Silt (HMFS + NaOH, g / kg), Total Sand (HMS + NaOH, g / Kg);

- The SpecSolo-Toolbox associates the analytical data of each sample, obtained by the reference method, with its respective infrared spectrum, creating a robust database;

- Through the processing routines and chemometric algorithms embedded in the SpecSolo-Toolbox, the best multivariate calibration models were created and validated for each of the 36 (thirty-six) parameters;

- After the modeling, the SpecSolo-Toolbox software saves the calibration models to be used to predict the concentrations of the new samples of analyzed soils in the infrared.

[0100] Figure 3 illustrates the interaction between the factors that compose the SpecSolo technology, that is:

- results of soil attributes obtained by humid methods (or reference method) (3);

- NI RS spectra (SpecSoil-Scan) (4);

- database (5) and;

- algorithms (6) (SpecSolo-Toolbox). [091 ] On the other hand, Figure 4 illustrates the operation flow diagram of the algorithm for model construction and multivariate calibration and prediction of results.

[092] According to the flowchart of Figure 4, the model construction and multivariate calibration process begins with the collection and records of the samples (7), followed by the preparation in the condition of air dry thin earth (TFSA) (8). Samples are analyzed by NIR spectroscopy (9) and by Reference Methods (10) and used in the construction of the calibration model (1 1 ). Then, the presence of the outliers in the calibration model (12) is verified, if so, they return to the spectral (9) and reference (10) measurements. The outiliers are evaluated based on the Leverage vs. Residuals graph in X (1 1 B) and Y (1 1 C), if not, goes to the spectral (1 D) preprocessing step. The available preprocessing methods (13) are: Centering on Mean, Derivatives, MSC, SNV and Auto scaling. After pre-processing (13), the data are used in the construction of the calibration (14) and validation (15) models. The calibration model is evaluated through the following figures of merit (16): Analytical sensitivity, signal-to-noise ratio, selectivity, accuracy (RMSEC and RMSEP), precision and coefficient of determination (R2, cal and val) (17). After the construction and evaluation of the calibration model, the new samples are started, i.e. routine use of soil analysis by NI R spectroscopy (18). The samples provided in the routine are evaluated for outliers (19); if so, the outliers are added to the calibration model in order to update it (1 1 ); in the negative case 20, the results are released 21 in three different ways 22: supplied individually 23, by strip 24 or by fertilization and liming recommendation 25.

[093] This invention ^' s main characteristic is to bring real solutions to chemical and physical analysis of soil fertility by using Vis-Nir spectroscopy. It simplifies, in an extraordinary way, the analytical procedure, demanding only two steps:

- Sample preparation: dry, grind, sift and transfer the amount of soil sample sufficient to cover the bottom of the sample container (plastic cup), approximately 150g;

- Analytical Determination: collection of the sample spectrum with the Vis-NIR (1 ) spectrophotometer, which automatically sends information to the cloud-hosted services server, where the calibration models that produce the analytical results are stored and available for consultation in the portal, in less than 30 (thirty) seconds. It also has the advantage of analyzing different soil attributes with only one spectral reading: dozens of soil fertility parameters can be analyzed simultaneously in a non-destructive, fast and economical way.

[094] Table 3 details each step of the analysis process with SpecSolo, while Figure 5 illustrates the flowchart of this process, as follows:

Block numbers

at the

Step Description Advantages

Fluxogram of

the Figure 5

Partner In order to use the soil analysis services with Management of Accreditation SpecSolo it is necessary to do an accreditation customer

26 e 27

first. This stage of partner accreditation in the registration SpecSolo Digital Platform is performed by the through digital internal project team through the SpecSolo- platform

Manager Module of the system, and the partner

access rules and privileges are defined at the

moment.

Client register Once accredited, the partner can register their Sample

customers (farmers) through the SpecSolo- registration can Limes Module, and provide a login and be done online password for them to access the platform. by the farmer

(end user),

28 streamlining the operational process in the routine and avoiding errors of registration.

Sample register Sample registration in the system can be done Record of receipt by the accredited partner or your client (farmer). of the sample by Both will use the same interface for registration, code reader

29, 30, 32, 33, the SpecSolo-Lims Module. (bars or 34, 43 QRCode),

avoiding typing and streamlining the process.

Registration by the Farmer: the farmer accesses

the digital platform with his login and password

and registers the samples with the respective

requests for analytical services and defines the

form of payment for the service (payment

31

method selection). In this step, it generates for

printing the identification labels (bar code or

QRCode) to fix in the packages that it will put

the soil samples collected for shipping for

analysis.

Preparation of the Registration by the Accredited Partner: in case Quick, accurate sample the Accredited Partner receives clients samples analysis,

without registration, he must proceed with the dispensing process of registration of the samples with the reagent use,

36 respective analytical services and define the reduction of form of collection. In this step, the system will operational cost, generate for printing the job labels (bar code) to results available be affixed to the analysis package. through digital platform.

Spectrum 37 Sample Receipt Record Registered by the Acquisition, Data Farmer: if the Accredited Partner receives Processing and samples already registered by the farmer

Publication of through the SpecSolo-Lims Module, he / she will

Results. read the identification code fixed on the package

and generate for printing a work label (bar code)

to be fixed in the analysis package.

Sample preparation consists of drying the

sample in an oven at 40 ⁰ C, grinding the

sample in a hammer-type mill, passing the

sample in a 2 mm mesh (TFSAR) and

38 e 39 transferring the sample to the analysis (plastic

cup) identifying this cup with the working label

(bar code). The analysis cups should then be

distributed in the tray (2) of the SpecSoil-Scan

equipment (1 ), which has 40 positions.

To initiate the analysis process, the Accredited

Partner must only activate the SpecSoil-Scan

40

equipment, through the software integrated with

the SpecSolo-Tray equipment.

Once the SpecSoil-Scan is started, before Transparency starting the process of analyzing the samples in and convenience the tray, it will calibrate the spectrophotometer

through the Spectralon. If the reading is

approved by the system, the autosampler

passes the second control that is the reading of

the standard sample, provided by SpecSolo for

process quality control. The collected spectrum

41 , 42, 43

of the standard sample is automatically

evaluated by a Control Chart and, if approved,

the autosampler initiates the collection of

spectra of the samples distributed in the tray,

and in each position it also captures the

identification of the sample by reading the code

of in the cups. This entire process is controlled

by the SpecSolo-Tray Module.

Once the readings are completed, the system Management of stores the spectra in a local database and sends customer

45 a copy of the spectrum through SpecSolo- registration

Services to the Cloud-hosted SpecSolo- through digital Toolbox. platform

[095] For the sake of clarity, and for better definition of the parameters of use of the product required according to the invention, it should be noted that the infrared spectra are acquired at ambient temperature and pressure, in the range of 400 to 2500 nm.