# Development of a new spectral database of photo-induced pesticide compounds for an automatic monitoring and identification system

Coumba Faye, El Hadji T. Bodian, Diène D Thiaré, Diéry Diouf, Ndeye A Diop, Jean-Pierre Bakhoum, Lamine Cisse, Pape A Diaw, Atanasse Coly, Philippe Giamarchi

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Coumba Faye, El Hadji T. Bodian, Diène D Thiaré, Diéry Diouf, Ndeye A Diop, et al. Development of a new spectral database of photo-induced pesticide compounds for an automatic monitoring and identification system. Analytica Chimica Acta, 2025, 1335, pp.343475. 10.1016/j.aca.2024.343475. hal-05069570

HAL Id: hal-05069570

https://hal.univ-brest.fr/hal-05069570v1

Submitted on 15 May 2025

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Development of a new spectral database of photo-induced pesticide
compounds for an automatic monitoring and identification system

Coumba Faye<sup>a,b</sup>, El Hadji T. Bodian<sup>a,b</sup>, Diène D. Thiaré<sup>a</sup>, Diéry Diouf<sup>a,b</sup>, Ndeye A. Diop<sup>b,c</sup>,
Jean-Pierre Bakhoum<sup>a,b</sup>, Lamine Cisse<sup>a</sup>, Pape A. Diaw<sup>c</sup>, Atanasse Coly<sup>a</sup>, Philippe Giamarchi<sup>b,*</sup>

HIGHLIGHTS

* Innovative spectrofluorimetric database of photo-induced pesticide compounds.

* Original construction of the database to allow a simple identification protocol.

* Pesticide identification by spectral similarity.

* Good pesticide identification results.

* Leads to an automated pesticide monitoring and identification system.

GRAPHICAL ABSTRACT

<figure><img src="image_3.png" /><figcaption>Fluorescence database for photoinduced pesticide compounds</figcaption></figure>

ARTICLE INFO

Keywords:

Pesticides

Photoinduced

Fluorescence

Database

A B S T R A C T

**Background:** Due to the widespread use of pesticides, it is necessary to monitor surface water quality for environmental protection, industrial use and tap water production. Many analytical methods based on chromatographic separations and mass spectrometry detection can be used, but they are complex and expensive. They also require sampling and transport to the laboratory, which delays the results. We therefore need automatic monitoring systems that can detect and identify pesticides at low cost and with simple technology.

**Results:** As many pesticides are non-fluorescent, we have chosen the photoinduced fluorescence method for detection. However this method lacks of specificity. To improve this, we present a new fluorescence database of photoinduced pesticide compounds. It is designed in an original way to be lightweight, containing only four spectra of each pesticide concatenated into a single 'combined fluorescence spectrum'. It can therefore be used with a simple spreadsheet to identify pesticides by spectral similarity. The database is then combined with an automated pesticide monitoring system using photo-induced fluorescence to add identification capability.

<footnote><sup>a</sup> Laboratoire de Photochimie et d'Analyse, Université C. A. Diop, Dakar, Senegal</footnote>

<footnote>b Laboratoire OPTIMAG, Université de Brest, 6 Av. Victor Le Gorgeu, 29285, Brest, Cedex, France</footnote>

<footnote>c Laboratoire Matériaux, Electrochimie et Photochimie Analytique, Université A. Diop, Bambey, Senegal</footnote>

<footnote>* Corresponding author. Laboratoire OPTIMAG, Université de Brest, 6 Av. Victor Le Gorgeu, 29285, Brest, Cedex, France.
E-mail address: philippe.giamarchi@univ-brest.fr (P. Giamarchi).</footnote>
Analytical applications are carried out on-line to determine rimsulfuron and sulfometuron-methyl, with good analytical results. The identification process using the database is then self-tested against the spectra of these two pesticides, leading to correct results.

**Significance:** This work presents the first spectrofluorimetric database of photoinduced compounds of pesticides. It has been designed to be easy to use, even by an unskilled user. Combined with an automated monitoring system, it becomes the first to allow simultaneous detection and identification of pesticides in surface water. The results showed good identification results and good analytical performance.

# 1. Introduction

Pesticides are extensively used in contemporary farming to enhance yield. Nevertheless, multiple pesticides are water soluble, causing persistent pollution of surface and ground water even after prolonged spraying [1,2] In Africa, pesticides tend to be overused, often due to a lack of knowledge of good agricultural practices, leading to more severe environmental pollution issues than in developed nations. Traces of pesticides in groundwater can contaminate other crops, including organic ones, through irrigation, thereby disrupting the habitats of numerous aquatic species. Pesticide traces, at concentrations above World Health Organization guidelines of 2–20 ng mL⁻¹ [3], are also harmful to human health through consumption of contaminated food and water [4–6].

Many pesticides have no natural fluorescence, but exhibit high photochemical reactivity under UV irradiation, leading to the formation of photoproducts. Due to the loss of some electron withdrawing groups, known as fluorescence quenchers (such as –NO, –COOH, –CHO), the resulting photoproduct exhibits a strong fluorescence. This allows pesticides to be determined indirectly by measuring the fluorescence of their photoproducts. This is called the Photo Induced Fluorescence (PIF) method [7]. This method is cost-effective and suitable for developing countries. It is commonly applied to the analysis of herbicides [8,9], insecticides [10–12] and others pesticides [13–15] in natural waters. For example, Diaw et al. determined fenuron and diflubenzuron in Senegalese natural water with satisfactory mean recovery values in the range 95–105 %, by the classical-PIF method using UV irradiation [16], and by a PIF method using laser irradiation as Diaw et al. [17] and Bakhoum et al. [18]. Moreover Diop et al. reach limits of detection (LOD) of 0.32 ng mL⁻¹ for monolinuron and 1.24 ng mL⁻¹ for fipronil [19], and Mbaye et al. 194 ng mL⁻¹ for propanil [20].

The main objective of this work is to address the critical limitation of the PIF method, its lack of specificity. For this purpose, we have developed a new and innovative fluorescence spectral database of photo-induced compounds of pesticides (DATAPIF). We will explain in detail the original construction of DATAPIF, which was specifically done with the most commonly used pesticides in Senegal [21,22]. It contains only four emission spectra corresponding to the most efficient excitation wavelengths for the photoinduced compounds of pesticides [23]. The four spectra of each pesticide are concatenated into a single 'combined fluorescence spectrum' to allow easy calculation with a classical spreadsheet. The pesticide is then identified by searching for the closest spectral similarity.

DATAPIF is then combined with a prototype of automatic pesticide monitoring of surface waters by PIF [24]. With this new capability, it consequently becomes the first Pesticide Automatic Monitoring and Identification System (PAMIS). To evaluate the identification and analytical performance of the PAMIS, we have chosen two pesticides, Rimsulfuron (RIM) and Sulfometuron-Methyl (SUL), widely used in Senegal.

As a preamble, the analytical performance of the system will be evaluated on these two pesticides by measuring detection limits, matrix effects, interferences from organic and inorganic species and accuracy. Subsequently, the effectiveness of DATAPIF in identifying these pesticides by spectral similarity will be evaluated.

# 2. Material and methods

## 2.1. Reagents and samples

Rimsulfuron, sulfometuron methyl (technical-grade purity >99 % m/m, Table 1) and methanol (analytical reagent grade) were obtained from Sigma Aldrich (St Quentin Fallavier, France). Ultra-pure water (Millipore Mro-MQ System) was used for working solutions.

## 2.2. Materials

Solid phase extraction C18 cartridge (LiChrolut RP-18E 200 mg 40–63 mm). Peristaltic pump (Minipuls, Gilson, Middleton, WI, USA). Teflon tube coil (3 m long, 3 mm internal diameter). Cylindrical quartz flow cell (0.5 mL volume). UV-C fluorescent compact E27 15W 240V Germicide TUV HNS 55 × 230mm (Bailey Electric & Electronics bv, Oosterhout, Netherlands). 250, 260, 280 and 300 nm, 1 mW UV LEDs with typical specifications 8 V and 80 mA and LED driver 'DC2200' with a current resolution of ± 0.1 mA (Thorlabs Inc, New Jersey). High-pass filters WG295 and WG320 (IDEX Health & Sciences, West Henrietta, NY state, USA). Diode array 200–800 nm spectrometer named 'Flame' and OceanView acquisition software (Ocean Insight, Duiven, The Netherlands). Cary Eclipse fluorimeter (Agilent, Les Ulis, France). Statistics 19 software (The Plains, Virginia, USA).

## 2.3. Experimental setup

The experimental set-up has been presciently described [21,22,25]. It uses a peristaltic pump for sampling and sending it through the system. The photoreactor is composed of a Teflon tube coil (3 m long, 3 mm internal diameter), three UV-C lamps, are disposed in triangle in its centre [17]. The speed of the pump allows adjusting the irradiation time. Then the sample passes through a cylindrical detection cell (0.5 mL). Four UV LEDs (250, 260, 280 and 300 nm, are disposed along the detection cell to excite the fluorescent photoproducts and are sequentially switched on to record four spectra. An electronic controller is used to precisely maintain the current intensity (80±0.1 mA) in order to avoid the variation of the power of the LEDs. For each LED, a check of the Rayleigh intensity is regularly performed on ultrapure water. At each wavelength, two UV LEDs are used simultaneously, disposed face to face on both sides of the detection cell. High-pass filters are disposed at the output of the optic fibre to cut the Rayleigh signal of the LEDs and avoid the saturation of the spectrophotometer. The WG295 filter (transmittance of 1 % at 277 nm, 50 % at 299 nm and 90 % at 333 nm), is used to cut the signal of the 250 and 260 nm LEDs. The WG320 filter (transmittance of 1 % at 308 nm, 50 % at 325 nm and 90 % at 360nm), is used to cut the signal of the 280 nm and 320 nm LEDs. Among the four recorded spectra, the most intense is kept in order to obtain the best sensitivity (Fig. 1, supplementary material). The prototype was designed to be integrated into a stand-alone cabinet, controlled by a touchscreen interface, and then installed near a river or water supply for automatic monitoring.

## 2.4. Methods

Stock standard solutions of the pesticides (in the range of 350 mg
$L^{-1}$) were prepared in methanol. Serial dilutions were performed to obtain the working standard solutions in water. To test the method on natural matrix free of contaminants, the samples of water were first filtered through a 0.45 µm disc to remove suspended organic matter, then purified by solid phase extraction (SPE) to remove dissolved organic matter (50 mL of samples were treated on C18 cartridge, previously conditioned by 5 mL of methanol followed by 5 mL of ultrapure water) [8,9].

### 3. Experimental results on rimsulfuron and sulfometuron

#### 3.1. Photo-induced fluorescence properties of rimsulfuron and sulfometuron methyl

The excitation fluorescence spectra of the PIF compounds show that the main excitation bands were at 227 and 315 nm for RIM and 227 and 285 nm for SUL (Table 1 and Fig. 1).

Both pesticides share the first excitation band (227 nm) with the same intensity at the same concentration. There is a clear difference in wavelength and intensity for the second excitation band of the two pesticides. That of RIM is significantly redshifted (315 nm) and has an intensity 60 % higher, than that of SUL (285 nm). Comparing the excitation bands for each pesticide, the first excitation band of SUL is higher in intensity than the second, while the opposite is true for RIM. The emission spectra show that there is only one emission band for each herbicide, indicating that they formed only a single photoproduct. The bands are located at 352 nm for SUL and redshifted at 375 nm for RIM with an intensity about 20 % higher at the same concentration.

The photoinduced compounds were formed after an optimum irradiation time of 40 s for SUL and 200 s for RIM (Fig. 2). This may be due to the higher absorbance of SUL observed at 254 nm. This significant difference in reactivity can be explained by the fact that the RIM molecule is more rigid under the influence of light [26]. The initial concentration of the pesticides had no significant effect on the optimum irradiation time, and the same fluorescence intensity was achieved after only 40 s.

The pH is a factor that strongly affects the fluorescence signal of organic compounds in an aqueous medium [27,28], its influence on the photoinduced fluorescence intensity of SUL and RIM was investigated in water between pH 2.0 and 13.0 (Fig. 3). For the SUL, an increase in PIF intensity was observed with the pH, from pH 6, the PIF signal increases slightly and reaches a maximum value at pH 10 and decreases up to pH 13. For RIM from pH 6 there is a plateau until pH 10 and it begins to decrease until pH 13. For the case of RIM from pH 2 to 4 the signal increases gradually and from 4 to 6 the signal is constant and reaches a maximum at pH 7 before decreasing to pH 13. These similar results were found by Coly et al. [29]. For further analysis, the pH of SUL was set at 10 and RIM at 7.

<figure><img src="image_4.png" /><figcaption>Fig. 1. PIF excitation and emission spectra of rimsulfuron and sulfometuron methyl (40 ng mL$^{-1}$) in water after respectively 2 and 1 min of irradiation time.</figcaption></figure>

<figure><img src="image_5.png" /><figcaption>Fig. 2. Formation of the PIF photoproducts of rimsulfuron and sulfometuron methyl (40 ng mL$^{-1}$) in water as a function of the UV irradiation time.</figcaption></figure>

#### 3.2. Method validation for of rimsulfuron and sulfometuron methyl determination

In order to evaluate the performance of the method, analytical merit figures were determined. Calibration curves were constructed by preparing samples in triplicate, containing increasing concentrations of each pesticide (Fig. 4). The study was performed in the concentration ranges from 4 to 340 ng mL$^{-1}$ for RIM 3–360 ng mL$^{-1}$ for SUL. The calibration linearity was tested by variance analysis (Table 2). For both pesticides the regression is explicative (regression variance, $V_{REG}$, significantly higher than residual variance, $V_{RES}$, p-value ≤5 %); and the model is descriptive (lack of fit variance $V_{LOF}$, not significantly higher than pure error variance $V_{PE}$, p-value >5 %). Moreover, the intercepts

<table><caption>**Table 1**

Chemical properties of Sulfométuron methyl and rimsulfuron.</caption><thead><tr><th>Pesticides</th><th>Chemical structure</th><th>Formula</th><th>Molecular weight (g mol<sup>-1</sup>)</th><th>Water solubility At 25 °C (mg L<sup>-1</sup>)</th><th>Excitation wavelength (nm)</th><th>Emission wavelength (nm)</th></tr></thead><tbody><tr><td>Sulfometuron methyl (SUL)</td><td><img src="chemical_structure.png" alt="Chemical structure of Sulfometuron methyl (SUL)"></td><td>C<sub>15</sub>H<sub>16</sub>N<sub>4</sub>O<sub>5</sub>S</td><td>364.38</td><td>244</td><td>227-285</td><td>376</td></tr><tr><td>Rimsulfuron (RIM)</td><td><img src="chemical_structure.png" alt="Chemical structure of Rimsulfuron (RIM)"></td><td>C<sub>14</sub>H<sub>17</sub>N<sub>5</sub>O<sub>7</sub>S<sub>2</sub></td><td>431.44</td><td>10</td><td>227-315</td><td>348</td></tr></tbody></table>
<figure><img src="image_6.png" /><figcaption>Fig. 3. Effect of pH on fluorescence intensity of sulfometuron methyl and rimsulfuron (40 ng mL⁻¹).</figcaption></figure>

are not significantly different from zero (Student *t*-test, *p*-value >5 %). We obtained low LODs of 0.092 ng mL⁻¹ for RIM and 0.17 ng mL⁻¹ for SUL, which are in the range to monitor surface water quality standard as defined by the OMS or other environmental regulations agencies [30]. These results are better than for example those obtained in water media by Diop et al. (0.32 ng mL⁻¹ for monolinuron and 1.24 ng mL⁻¹ for fipronil [18]); or by Mbaye et al. [19] (194 ng mL⁻¹ for propanil); or at last by Bautista et al. who obtained LODs for diuron, linuron, and neb-uron of 580–700 ng mL⁻¹ that were approximately one thousand times higher than our results [31].

Other analytical laboratory methods are currently used to analyse pesticides, mainly gas or liquid chromatography, both with different detection systems including mass spectrometry. For example, by liquid chromatographic with UV detection, Fernandez-Ramos et al. find a LOD higher than ours of 7.7 ng mL⁻¹ for tau-fluvalinate [32]. After dispersive SPE extraction followed by HPLC separation and UV detection Maleki et al. find a LOD of 7.7 ng for fenvalerate [33]. With SPE extraction followed by HPLC-UV Zhou et al. find LOD lower than our results, from 0.02 to 0.08 ng mL⁻¹ for fenvalerate, fenpropathrin, cyhalothrin and deltamethrin [34]. After QuEChERS microextraction followed by HPLC separation and MS/MS detection Ccanccapa-Cartagena et al., obtained are lower LOD than our results, of 0.12 ng mL⁻¹ for flumethrin [35]. By ultrasound-assisted emulsification-extraction followed by gas

chromatography separation and MS detection Feo et al. find LOD ranging from 0.03 to 35 ng mL⁻¹ for several pyrethroids pesticides [36]. However, these methods need manual sampling, transport to the laboratory, and do not give on time results. Moreover, chromatographic methods are too complex to be installed on site for a continuous monitoring.

### 3.3. Analytical applications for rimsulfuron and sulfometuron methyl in natural matrix

To put our method into practice, we tested its applicability on natural matrix of tap water from Brest (France) and well water from the Niayes agricultural region (Senegal river valley). The water samples were initially spiked with 13 or 11 ng mL⁻¹ and the standard addition method was used to analyse the pesticides present in the water samples (Fig. 4). To demonstrate the parallelism of the standard addition curves with the calibration curves, the Student's *t*-test was performed for each herbicide. The Student's *t* value was calculated (at 5 % confidence level) and it exceeds the *t* value of the difference. This indicates that the slopes of the standard addition curves are not significantly different from those of the calibration curves (Table 3). As a consequence, it is shown that there is no significant matrix effect.

<caption>Table 2</caption>

Evaluation parameters of the linear functions of the EWMIS method by analysis of variance at 5 % confidence level. Equations of the calibration curves, estimation of the quality of the intercept by comparing it with 0 using a Student's *t*-test.

<table><thead><tr><th></th><th>RIM</th><th>SUL</th></tr></thead><tbody><tr><td>Slope</td><td>1.902</td><td>10.40</td></tr><tr><td>Intercept</td><td>-12.88</td><td>-4.57</td></tr><tr><td>STD<sup>a</sup></td><td>35.80</td><td>0.213</td></tr><tr><td>P value</td><td>0.72</td><td>0.37</td></tr><tr><td>Difference to 0</td><td>No</td><td>No</td></tr><tr><td>V<sub>REG</sub></td><td>2.00 10<sup>6</sup></td><td>13.91 10<sup>6</sup></td></tr><tr><td>V<sub>RES</sub></td><td>9.99 10<sup>3</sup></td><td>37.40 10<sup>3</sup></td></tr><tr><td>p value</td><td>0.00</td><td>0.00</td></tr><tr><td>V<sub>LOF</sub></td><td>3.10 10<sup>3</sup></td><td>44.77 10<sup>3</sup></td></tr><tr><td>V<sub>PE</sub></td><td>15.73 10<sup>3</sup></td><td>4.20 10<sup>3</sup></td></tr><tr><td>p value</td><td>0.89</td><td>0.09</td></tr><tr><td>LOD<sup>b</sup> (ng mL<sup>-1</sup>)<sup>b</sup></td><td>0.092</td><td>0.17</td></tr><tr><td>LOQ<sup>c</sup> (ng mL<sup>-1</sup>)<sup>c</sup></td><td>0.29</td><td>0.59</td></tr></tbody></table>

<figure><img src="image_7.png" /><figcaption>Fig. 4. Calibration and standard addition curves of rimsulfuron (left) and sulfometuron methyl (right).</figcaption></figure>
The recovery study (Table 4) shows, firstly, that the mean recovery rates are in the range of 95–105 % (i.e. 98.88–102.95 %), demonstrating the accuracy of the method. Secondly, the intra-day relative standard deviation values are less than 5 % (i.e.: 1.01–4.78 %), demonstrating the repeatability of the method. Thirdly, the inter-day uncertainty was evaluated over five days using freshly prepared RIM and SUL solutions at the same concentration. The relative standard deviation values obtained are less than 5 % (i.e. for RIM 1.7 % in water, 1.9 % in tap water and 0.7 % in well water; for SUL 1.8 % in water, 2.2 % in tap water and 0.9 % in well water), demonstrating the reproducibility of the method. We can therefore conclude that the method is precise and accurate, consequently RIM and SUL can be directly determined in raw waters using this prototype.

Similar results have been reported in the literature. For example, coly et al. [12] detected both herbicides in samples of tap, river and sea water with recoveries ranging from 90 to 96 %. Other researchers [37, 38] have also used photo-induced fluorescence to quantify pesticides in natural waters, yielding recovery rates similar to ours. In this regard, Bakhoum et al. [28] reported recovery rates between 97 and 117 % for oxadiazon in tap and river water.

### 3.4. Interference study for rimsulfuron and sulfometuron methyl

To evaluate the robustness of the method we investigated, the potential interferences of certain species likely to be present in natural waters on the fluorescence measurements. Indeed, other pesticides commonly used in Niayes agricultural region can contaminate natural water sources. In addition, the proximity of the sampling area to the Atlantic Ocean and small industrial units can lead to the presence of inorganic ions in the water.

In a first part, as organic contaminants, we specifically focused on four pesticides - chlorsulfuron, metsulfuron methyl, oxadiazon and monolinuron - used in this region. Fixed concentrations of RIM (43 ng mL⁻¹) or SUL (36 ng mL⁻¹) were used, while varying concentrations of chlorsulfuron (3–180 ng mL⁻¹), metsulfuron methyl (3–190 ng mL⁻¹), oxadiazon (3.6–350 ng mL⁻¹), and monolinuron (2.1–210 ng mL⁻¹) were added. The SUL concentration ranged from 4.3 to 220 ng in the RIM study, while the SUL concentration ranged from 3 to 180 nm in the RIM study. Our findings suggest that the introduction of foreign species does not affect the shape of the emission spectra. The interference effect of oxadiazon on RIM it is manifested as a slight increase in the PIF intensity (17 ng mL⁻¹). The same in obtained, less pronounced for monolinuron on SUL (11 ng mL⁻¹).

In a second part, we investigate the impact of inorganic ions on the assessment of RIM and SUL levels. The fluorescence intensity of PIF of these two herbicides shows significant fluctuations, with tolerance thresholds varying between 25 and 1010 ng mL⁻¹ for RIM, and between 30 and 4200 ng mL⁻¹ for SUL, contingent on the type of ion. Ions (Ca²⁺, Cl⁻) have a notable impact on the PIF signal of both herbicides, with a tolerance limit of 5.5 ng mL⁻¹ for RIM and 11 ng mL⁻¹ for SUL. Table 5 presents the tolerance limit values for interfering species, indicating the maximum concentration where the percentage variation in the pesticide

<caption>**Table 3**</caption>

Comparison between the slope of the standard addition method and the slope of the calibration curve, using a Student's t-test of the difference.

<table><caption>**Table 4**</caption><thead><tr><th rowspan="2"></th><th colspan="3">Rimsulfuron</th><th colspan="3">Sulfometuron methyl</th></tr><tr><th>Ultra-pure water</th><th>Tap water</th><th>Well water</th><th>Ultra-pure water</th><th>Tap water</th><th>Well water</th></tr></thead><tbody><tr><td>Slope</td><td>1.902</td><td>1.99</td><td>1.950</td><td>10.4</td><td>10.74</td><td>10.527</td></tr><tr><td>STD</td><td>0.0783</td><td>0.787</td><td>0.083</td><td>0.213</td><td>0.256</td><td>0.321</td></tr><tr><td>d.o.f</td><td></td><td>10</td><td>10</td><td></td><td>10</td><td>10</td></tr><tr><td>t<sub>DIFF</sub></td><td></td><td>1.99</td><td>1.06</td><td></td><td>1.44</td><td>1.88</td></tr><tr><td>t<sub>TAB</sub> (5 %)</td><td></td><td>2.23</td><td>2.23</td><td></td><td>2.23</td><td>2.23</td></tr><tr><td>SD</td><td></td><td>No</td><td>No</td><td></td><td>No</td><td>No</td></tr></tbody></table>

Analytical applications of the method to the determination of sulfometuron methyl and rimsulfuron in natural water samples fortified by the standard addition method and evaluation of the recovery rates obtained.

<table><thead><tr><th></th><th>Added<br>(ng<br>mL<sup>-1</sup>)</th><th>Measured<br>(ng mL<sup>-1</sup>)</th><th>Recovery<br>(%)</th><th>Mean<br>recovery<br>(%)</th><th>Intra-day<br>relative<br>standard<br>deviation<br>(%)</th></tr></thead><tbody><tr><td><strong>Tap water</strong></td><td>40.46</td><td>41.98 ±<br>0.12</td><td>103.75 ±<br>0.29</td><td>102.02 ±<br>0.17</td><td>1.31</td></tr><tr><td rowspan="5">Sulfometuron<br>methyl</td><td>76.90</td><td>78.27 ±<br>0.06</td><td>101.78 ±<br>0.08</td><td></td><td></td></tr><tr><td>113.33</td><td>117.03 ±<br>0.41</td><td>103.26 ±<br>0.35</td><td></td><td></td></tr><tr><td>149.80</td><td>152.81 ±<br>0.32</td><td>102.01 ±<br>0.21</td><td></td><td></td></tr><tr><td>183.21</td><td>183.78 ±<br>0.01</td><td>100.31 ±<br>0.01</td><td></td><td></td></tr><tr><td>222.65</td><td>224.82 ±<br>0.20</td><td>100.97 ±<br>0.09</td><td></td><td></td></tr><tr><td rowspan="8">Rimsulfuron</td><td>34.48</td><td>36.05 ±<br>0.13</td><td>104.55 ±<br>0.36</td><td>102.26 ±<br>0.13</td><td>2.10</td></tr><tr><td>68.96</td><td>70.45 ±<br>0.01</td><td>102.16 ±<br>0.01</td><td></td><td></td></tr><tr><td>77.58</td><td>78.62 ±<br>0.03</td><td>101.34 ±<br>0.04</td><td></td><td></td></tr><tr><td>99.13</td><td>97.77 ±<br>0.12</td><td>98.63 ±<br>0.12</td><td></td><td></td></tr><tr><td>120.68</td><td>125.04 ±<br>0.20</td><td>103.615 ±<br>0.16</td><td></td><td></td></tr><tr><td>142.18</td><td>146.82 ±<br>0.09</td><td>103.26 ±<br>0.06</td><td></td><td></td></tr><tr><td rowspan="2">Well water<br>Sulfometuron<br>methyl</td><td>34.74</td><td>35.94 ±<br>0.08</td><td>103.45 ±<br>0.22</td><td>102.78 ±<br>0.28</td><td>0.90</td></tr><tr><td>71.18</td><td>73.10 ±<br>0.20</td><td>102.70 ±<br>0.27</td><td></td><td></td></tr><tr><td rowspan="8">Rimsulfuron</td><td>107.61</td><td>110.54 ±<br>0.47</td><td>102.72 ±<br>0.43</td><td></td><td></td></tr><tr><td>144.08</td><td>149.84 ±<br>0.63</td><td>103.99 ±<br>0.42</td><td></td><td></td></tr><tr><td>177.49</td><td>181.85 ±<br>0.38</td><td>102.46 ±<br>0.21</td><td></td><td></td></tr><tr><td>216.93</td><td>219.87 ±<br>0.26</td><td>101.36 ±<br>0.12</td><td></td><td></td></tr><tr><td>34.48</td><td>34.51 ±<br>0.04</td><td>100.10 ±<br>0.09</td><td>98.88 ±<br>0.19</td><td>1.32</td></tr><tr><td>68.96</td><td>67.62 ±<br>0.14</td><td>98.07 ±<br>0.04</td><td></td><td></td></tr><tr><td>77.58</td><td>75.51 ±<br>0.03</td><td>97.33 ±<br>0.09</td><td></td><td></td></tr><tr><td>99.13</td><td>96.94 ±<br>0.50</td><td>97.79 ±<br>0.03</td><td></td><td></td></tr><tr><td rowspan="2">Rimsulfuron</td><td>120.68</td><td>120.13 ±<br>0.09</td><td>99.55 ±<br>0.05</td><td></td><td></td></tr><tr><td>142.18</td><td>142.83 ±<br>0.23</td><td>100.45 ±<br>0.02</td><td></td><td></td></tr></tbody></table>

signal ΔF(%) does not exceed ±5 % for each interfering concentration.

In comparison with the findings of other studies on tolerance thresholds, the methodology employed in this investigation can be considered to be robust and valid [20,39].

$$ \Delta F(\%) = ((F-F_0)*100)/F_0 $$

ΔF(%): percentage of variation of the fluorescence signal.

F₀ and F: PIF signal recorded without and with interfering species respectively.
<table><caption>**Table 5**
Study of interference from foreign species with rimsulfuron and sulfometuron methyl.</caption><thead><tr><th rowspan="2">Pesticides</th><th colspan="2">Interference with rimsulfuron</th><th colspan="2">Interference with sulfometuron methyl</th></tr><tr><th>Concentrations range (ng mL<sup>-1</sup>)</th><th>Tolerance Limit (ng mL<sup>-1</sup>)</th><th>Concentrations range (ng mL<sup>-1</sup>)</th><th>Tolerance Limit (ng mL<sup>-1</sup>)</th></tr></thead><tbody><tr><td>Rimsulfuron</td><td>–</td><td>–</td><td>4.3–220</td><td>12</td></tr><tr><td>Sulfometuron methyl</td><td>3.0–180</td><td>36</td><td>–</td><td>–</td></tr><tr><td>Chlorsulfuron</td><td>3.0–180</td><td>53</td><td>3.0–180</td><td>28</td></tr><tr><td>Metsulfuron methyl</td><td>3.0–190</td><td>38</td><td>3.0–190</td><td>–</td></tr><tr><td>Oxadiazon</td><td>3.6–350</td><td>17</td><td>3.6–350</td><td>17</td></tr><tr><td>Monolinuron</td><td>2.1–210</td><td>81</td><td>2.1–210</td><td>11</td></tr><tr><td colspan="5"><strong>Inorganic ions</strong></td></tr><tr><td>K<sup>+</sup>NO<sub>3</sub><sup>-</sup></td><td>3.0–40 10<sup>3</sup></td><td>1.0 10<sup>3</sup></td><td>3.0–40 10<sup>3</sup></td><td>30</td></tr><tr><td>Ca<sup>2+</sup> 2Cl<sup>-</sup></td><td>3.3–44 10<sup>3</sup></td><td>5.5</td><td>3.3–44 10<sup>3</sup></td><td>11</td></tr><tr><td>Na<sup>+</sup> NO<sub>3</sub><sup>-</sup></td><td>2.0–34 10<sup>3</sup></td><td>25</td><td>2.0–34 10<sup>3</sup></td><td>4.2 10<sup>3</sup></td></tr></tbody></table>

Fixed concentrations = 43 ng mL⁻¹ for Rimsulfuron and 36 ng mL⁻¹ for Sulfo-meturon methyl.

# 4. Construction of the database and pesticides identification

## 4.1. Construction of the database

All the pesticides in the database are non-fluorescent in their native form and have been evaluated, in previous work [17–19,21], to ensure that the PIF method is validated for each of them. Spectra obtained in this work for SUL and RIM have been added to extend the database. A complete database would contain the full excitation-emission fluorescence matrix for each pesticide. This matrix is generally used to perform chemometric analyses on the data. However, the main novelty of this work is the development of a simple and lightweight database, specifically designed for the PAMIS prototype, which can be easily processed on simple spreadsheets and does not require chemometric calculations. Therefore, for each pesticide, we selected only the four excitation wavelengths used by the PAMIS (250, 260, 280 and 300 nm) to build this specific database, called "DATAFIF". It currently contains 37 pesticides classified into five categories classically used [40–42]: organochlorines, organophosphates, carbamates, pyrethrin & pyrethroids, and others. The latter is then divided into seven subcategories [43] (Table 6). The DATAPIF has been developed for use in the agricultural region of

Niayes (Senegal River valley), and the 37 pesticides have been selected because they are currently the most widely used in that area [21,22]; for use in another district or country, an additional selection of pesticides may be necessary.

Fig. 5 shows that the PIF fluorescence spectra evolve differently for each pesticide depending on their optimal excitation wavelength, as an example, for SUL and RIM (this work), and for simazine and folpet (from DATAPIF). It can be seen that SUL and RIM have comparable emissions spectrum, the main difference appearing for the low RIM sensitivity at the excitation wavelength of 250 and 260 nm. For simazine and folpet the differences are more important. The highest emission intensity of simazine decreases from excitation at 260 nm to excitation at 300 nm, while for flopet it evolves in the opposite way. Consequently, the comparison of the spectra obtained at the four excitation wavelengths brings an important discriminatory criterion without needing the full excitation-emission fluorescence matrix.

To simplify usage of the four emission spectra, they are concatenated into a single spectrum of arbitrary design for each pesticide. This process involves arranging the emission spectra, corresponding to excitations at 250, 260, 280, and 300 nm, in a consecutive sequence after removing the Rayleigh, Raman, and second-order signals (Fig. 6). The initial spectrum corresponding to excitation at 250 nm is recorded between 288 and 600 nm. It is then concatenated with the second spectrum, exited at 260 nm and recorder between 300 and 600 nm, but arbitrarily coded from 601 to 913 "nanometres". The same procedure is applied to the following spectra, as detailed in Table 7. Therefore, for each analysed sample, we will simply compare its single concatenated spectrum to the DATAPIF to determine the closest match and suggest an identification.

## 4.2. Spectral identification of for rimsulfuron and sulfometuron methyl and other pesticides

The protocol is tested here on RIM and SUL as samples, but it is intended for use with unknown samples. The concatenated spectrum of each sample, is compared to the DATAPIF to determine the highest similarity. The sum of squares of deviations (SSD) are computed between the sample spectrum and all the spectra of the database. The minimum value of the SSD corresponds to the highest similarity score. A similarity score of 100 % would correspond to an exact matching of two spectra, meaning an SSD equal to 0 %. A similarity score of 0 % is arbitrary affected to the highest SSD found.

When comparing the RIM spectra to the 36 other pesticides of the

<caption>**Table 6**</caption>

Pesticides used in the database, classified into the five main categories: organochlorines; organophosphates; carbamates; pyrethrin & pyrethroids; and others, subdivided into seven subcategories.

<table><thead><tr><th></th><th>Carbamates</th><th>Organochlorines</th><th>Organo-phosphates</th><th>Pyrethroids</th></tr></thead><tbody><tr><td>1</td><td>Carbaryl</td><td>2,4 D</td><td>Chlorpyrifos</td><td>Cyhalothrin</td></tr><tr><td>2</td><td>Carbofuran</td><td>Acetamiprid</td><td>Fenthion</td><td>Cypermethrin</td></tr><tr><td>3</td><td>Propoxur</td><td>Dicamba</td><td>Fenuron</td><td>Delthamethrin</td></tr><tr><td>4</td><td></td><td>Flupyradifurone</td><td></td><td>Flumethrin</td></tr><tr><td>5</td><td></td><td>Folpet</td><td></td><td>Tau fluvalinate</td></tr><tr><td>6</td><td></td><td>Mecoprop</td><td></td><td></td></tr><tr><td>7</td><td></td><td>Monalide</td><td></td><td></td></tr><tr><td>8</td><td></td><td>Oxadiazon</td><td></td><td></td></tr><tr><td>8</td><td></td><td>Propanil</td><td></td><td></td></tr></tbody></table><table><thead><tr><th></th><th>Others</th><th></th><th></th><th></th><th></th><th></th><th></th></tr><tr><th></th><th>Anilides</th><th>Benzoic acid</th><th>Dipyrids</th><th>Phenylpyrazoles</th><th>Phthalimides</th><th>Benzoylureas and Ureas</th><th>Trazines</th></tr></thead><tbody><tr><td>1</td><td>Carboxin</td><td>Naptalam</td><td>Diquat</td><td>Fipronil</td><td>Captan</td><td>Diflubenzuron</td><td>Atrazine</td></tr><tr><td>2</td><td rowspan="7">Pendimethalin</td><td></td><td rowspan="7">Paraquat</td><td></td><td></td><td>Fluometuron</td><td rowspan="7">Simazine</td></tr><tr><td>3</td><td></td><td></td><td></td><td>Isoproturon</td></tr><tr><td>4</td><td></td><td></td><td></td><td>Linuron</td></tr><tr><td>5</td><td></td><td></td><td></td><td>Monolinuron</td></tr><tr><td>6</td><td></td><td></td><td></td><td>Sulfometuron Me</td></tr><tr><td>7</td><td></td><td></td><td></td><td>Rimsulfuron</td></tr><tr><td></td><td></td><td></td><td></td><td>Flufenoxuron</td></tr></tbody></table>
<figure><img src="image_8.png" /><figcaption>Fig. 5. Relative fluorescence emissions spectra obtained at the four excitation wavelengths available (250, 260, 280 and 300 nm) for rimsulfuron and sulfometuron methyl (this study); simazin and folpet (from the database).</figcaption></figure>

<figure><img src="image_9.png" /><figcaption>Fig. 6. Relative fluorescence emission of the concatenated spectra of the 37 pesticides of the database.</figcaption></figure>
<table><caption>**Table 7**
Construction of the concatenated spectra.</caption><thead><tr><td>Excitation (nm)</td><td>Emission domain recorded (nm)</td><td>Corresponding “coded” wavelength (“nm”)</td></tr></thead><tbody><tr><td>250</td><td>288–600</td><td>288–600</td></tr><tr><td>260</td><td>300–600</td><td>601–901</td></tr><tr><td>280</td><td>300–600</td><td>901–1202</td></tr><tr><td>300</td><td>315–600</td><td>1203–1488</td></tr></tbody></table>

DATAPIF, a similarity score of 99.8 % is evidently obtained to itself (Table 8); however, if it is not in the database, the second and third closest match proposed would be flupyradifurone (85.6 %) or acetamipride (76.4 %). Fig. 7 illustrates that flupyradifurone exhibits a very similar shape with the same maximum emission at 348 nm, differing only by a more intense relative fluorescence value for the excitation wavelengths of 250, 260 and 280 nm. For acetamipride, the maximum emission wavelengths are similar, but the weaker similarity rate can be attributed to a more significant difference in relative fluorescence intensity at 250, 260, and 280 nm. On the contrary, for flufenoxuron, which has one of the lowest similarity scores (8.6 %), one can see the very different shapes and intensities obtained. Despite having the closest similarity score, RIM and flupyradifurone do not belong to the same class (i.e. ureas and organochlorines, Table 8). Their chemical structures are different. They both contain one pyridine aromatic ring, but with different substituents. The results presented in Fig. 7 are voluntarily

limited to the three pesticides with the closest match and the pesticide with the lowest match, but the system can of course calculate the similarity score for all pesticides.

Comparing the SUL spectrum to the DATAPIF (Table 8 and Fig. 2 in supplementary material file) produces similar outcomes. A similarity score of 99.7 % is obtained to itself. Carbaryl and mecoprop (79.3 % and 79.2 % similarity score) exhibit spectra closely shaped with a maximum emission wavelength five manometers blue shifted, and a relative fluorescence emission lower when excited at 300 nm and higher when excited at 250 and 260 nm. At the opposite, dicamba which has a low similarity scores (10.9 %), displays distinct variations in the shapes and intensities of the spectra. It is notable that the similarity score between sulfometuron and mecoprop (79.3 %) is significantly lower than that between RIM and flupyradifurone (85.6 %), which reflect their very distinct chemical structures (Table 8).

The good identification match for RIM and SUL (99.8 % and 99.7 % respectively) and their significance difference to the second closest similar pesticides found (flupyradifurone 85.6 % and carbaryl 79.3 % respectively) supports the reliability of the proposed identification protocol using the DATAPIF.

In case of the sample contains a mixture of two (or more) pesticides. We can then compare this spectrum to a combination of spectra from two (or more) pesticides from the database by adjusting the coefficient for each of them to a total of 1. By iteration, it is possible to construct the spectrum of a mixture of pesticides from the DATAPIF that is closest to

<table><caption>**Table 8**
Similarity results obtained for rimsulfuron and sulfometuron methyl.</caption><thead><tr><th>Pesticide</th><th>Similarity rate (%)</th><th>Matching pesticides</th><th>Chemical class</th><th>Chemical structure</th></tr></thead><tbody><tr><td rowspan="5">Rimsulfuron</td><td>99.8</td><td>Rimsulfuron</td><td>Ureas</td><td><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAYAAAAf8/9hAAAAAXNSR0IArs4c6QAAAARnQU1BAACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAABYSURBVDhPY6AGaAKIAmJgYPgP8P8/A0MDIwMDwz8DA8N/BgYGFwMDw38GBoY/BgYGFwMDw38GBoY/BgYGFwMDw38GBoY/BgYGFwMDw38GBoY/BgYGFwMDw38GBoY" alt="Chemical structure of rimsulfuron"></td></tr><tr><td>85.6</td><td>Flupyradifurone</td><td>Organochlorines</td><td><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAYAAAAf8/9hAAAAAXNSR0IArs4c6QAAAARnQU1BAACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAABiSURBVDhPY6AGaAKIAmJgYPgP8P8/A0MDIwMDw38DA8N/BgYGFwMDw38GBoY/BgYGFwMDw38GBoY/BgYGFwMDw38GBoY/BgYGFwMDw38GBoY/BgYGFwMDw38GBoY/BgYGFwMDw38GBoY" alt="Chemical structure of flupyradifurone"></td></tr><tr><td>76.4</td><td>Acetamipride</td><td>Organochlorines</td><td><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAYAAAAf8/9hAAAAAXNSR0IArs4c6QAAAARnQU1BAACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAABiSURBVDhPY6AGaAKIAmJgYPgP8P8/A0MDIwMDw38DA8N/BgYGFwMDw38GBoY/BgYGFwMDw38GBoY/BgYGFwMDw38GBoY/BgYGFwMDw38GBoY/BgYGFwMDw38GBoY/BgYGFwMDw38GBoY" alt="Chemical structure of acetamipride"></td></tr><tr><td>...</td><td>...</td><td>...</td><td>...</td></tr><tr><td>8.6</td><td>Flufenoxuron</td><td>Benzoylureas</td><td><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAYAAAAf8/9hAAAAAXNSR0IArs4c6QAAAARnQU1BAACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAABiSURBVDhPY6AGaAKIAmJgYPgP8P8/A0MDIwMDw38DA8N/BgYGFwMDw38GBoY/BgYGFwMDw38GBoY/BgYGFwMDw38GBoY/BgYGFwMDw38GBoY/BgYGFwMDw38GBoY/BgYGFwMDw38GBoY" alt="Chemical structure of rimsulfuron"></td></tr><tr><td rowspan="4">Sulfometuron methyl</td><td>99.7</td><td>Sulfometuron methyl</td><td>Ureas</td><td><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAYAAAAf8/9hAAAAAXNSR0IArs4c6QAAAARnQU1BAACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAABiSURBVDhPY6AGaAKIAmJgYPgP8P8/A0MDIwMDw38DA8N/BgYGFwMDw38GBoY/BgYGFwMDw38GBoY/BgYGFwMDw38GBoY/BgYGFwMDw38GBoY/BgYGFwMDw38GBoY/BgYGFwMDw38GBoY" alt="Chemical structure of rimsulfuron"></td></tr><tr><td>79.3</td><td>Carbaryl</td><td>Carbamates</td><td><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAYAAAAf8/9hAAAAAXNSR0IArs4c6QAAAARnQU1BAACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAABiSURBVDhPY6AGaAKIAmJgYPgP8P8/A0MDIwMDw38DA8N/BgYGFwMDw38GBoY/BgYGFwMDw38GBoY/BgYGFwMDw38GBoY/BgYGFwMDw38GBoY/BgYGFwMDw38GBoY/BgYGFwMDw38GBoY" alt="Chemical structure of carbaryl"></td></tr><tr><td>79.2</td><td>Mecoprop</td><td>Organochlorines</td><td><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAYAAAAf8/9hAAAAAXNSR0IArs4c6QAAAARnQU1BAACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAABiSURBVDhPY6AGaAKIAmJgYPgP8P8/A0MDIwMDw38DA8N/BgYGFwMDw38GBoY/BgYGFwMDw38GBoY/BgYGFwMDw38GBoY/BgYGFwMDw38GBoY/BgYGFwMDw38GBoY/BgYGFwMDw38GBoY" alt="Chemical structure of mecoprop"></td></tr><tr><td>...</td><td>...</td><td>...</td><td>...</td></tr><tr><td>10.9</td><td>Dicamba</td><td>Organochlorines</td><td><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAYAAAAf8/9hAAAAAXNSR0IArs4c6QAAAARnQU1BAACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAABiSURBVDhPY6AGaAKIAmJgYPgP8P8/A0MDIwMDw38DA8N/BgYGFwMDw38GBoY/BgYGFwMDw38GBoY/BgYGFwMDw38GBoY/BgYGFwMDw38GBoY/BgYGFwMDw38GBoY/BgYGFwMDw38GBoY" alt="Chemical structure of dicamba"></td></tr></tbody></table>
<figure><img src="image_10.png" /><figcaption>**Fig. 7.** Comparison of rimsulfuron spectra to the two closest matches: flupyradifurone (85.6 %) or acetamipride (76.4 %); and to one of the farthest: flufenoxuron (8.6 %).</figcaption></figure>

the mixture found in the sample. In this way, a mixture of several pesticides can be identified.

## 5. Conclusion

We have constructed a new database of fluorescence spectra of photoinduced compounds of the most commonly used pesticides in Senegal. Designed to be simple and lightweight, it contains only four emission spectra corresponding to the most efficient excitation wavelengths for the photoinduced compounds of pesticides (250, 260, 280 and 300 nm). We have associated this database to a prototype of on-line monitoring of pesticides by photoinduced fluorescence. Using this new system and a simple spreadsheet, without chemometric calculations, we have demonstrated that it is possible to obtain an identification of unknown pesticides analysed using the closest spectral similarity. As a result, the new database associated to the prototype constitute the first automatic pesticide monitoring and identification system.

Using this new system, we achieved linear calibrations with low detection limits of 0.092 ng mL⁻¹ for rimsulfuron and 0.17 ng mL⁻¹ for sulfometuron methyl, which are often lower than those reported in the literature. We have obtained better results than some works in the literature, for example Bakhoum et al. [18] obtained LODs about ten times higher between 1.98 and 17.47 ng mL⁻¹ for benzoylurea pesticides; Gil-Garcia et al. [44] obtained a LOD of 17.47 ng mL⁻¹ for lufenuron in river water. Furthermore, we did not observe any matrix effects. We also demonstrated that the method is accurate, reproducible and repeatable, thus ensuring precision.

We can therefore conclude that the use of the PAMIS gives good results in term of analysis and identification. We have thus overcome the limitation associated with the lack of specificity of the PIF method. This method is well suited for countries that need a simple and inexpensive technology.

### CRediT authorship contribution statement

**Coumba Faye:** Investigation. **El Hadji T. Bodian:** Investigation. **Diène D. Thiare:** Validation. **Diéry Diouf:** Investigation. **Ndeye A. Diop:** Investigation. **Jean-Pierre Bakhoum:** Validation. **Lamine Cisse:** Validation. **Pape A. Diaw:** Validation. **Atanasse Coly:** Validation. **Philippe Giamarchi:** Supervision.

### Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence

the work reported in this paper.

## Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.aca.2024.343475.

### Data availability

Data will be made available on request.

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