Glyphosate detection: methods, needs and challenges

A. L. Valle1· F. C. C. Mello2· R. P. AlvesBalvedi3· L. P. Rodrigues4· L. R. Goulart1
Received: 24 April 2018 / Accepted: 17 August 2018 © Springer Nature Switzerland AG 2018
Glyphosate is considered toxicologically harmful and presents potential association with human carcinogenesis and other chronic diseases, including mental and reproductive behaviors. The challenges to analyse and demonstrate its toxicity are likely due to its metal-chelating properties, the interference of organic compounds in the environment, and similarity with its by-products. Whereas there is a link with serious health and environmental problems, there is an absence of public health policies, which is probably due to the difficulties in detecting glyphosate in the environment, further complicated by the undetectable hazard in occupational safety and health. The historical lenient use of glyphosate in transgenic-resistant crops, corroborated by the fact that it is not easily detected, creates the “Glyphosate paradox”, by which it is the most widely used herbicide and one of the most hardly determined. In this review, we revisited all available technologies for detection and quantification of glyphosate, including their drawbacks and advantages, and we further discuss the needs and challenges. Briefly, most of the technologies require high-end equipment and resources in low throughput, and none of them are adequate for real-time field tests, which may explain the lack of studies on occupational health associated with the chemical hazard. The real-time detection is an urgent and highly demanded need to improve public policies.

General considerations
Glyphosate [(N-phosphonomethyl)glycine] (GLY) is a non-selective  and  broad-spectrum  herbicide  and  is  the  most widely  used  worldwide  (Castle  et  al.  2004;  Woodburn  2000). Since the introduction of genetically modified GLY-resistant crops at the end of the twentieth century, its use has increased dramatically (Giesy et al. 2000). The main commercial formulation of GLY is “Roundup”, which consists of isopropylamine salt, and the surfactant polyoxyethylene amine is also added according to the manufacturer to increase its efficiency (Tsui and Chu 2003). World Health Organization et al. (1994) had considered GLY as “toxicologically harmless” for humans, other mammals, birds and environment (Tsui and Chu 2003, 2008; Williams et al. 2000) due to its degradability by soil microbes (Zhang et al. 2015a; Napoli et al. 2015) and binding ability to soil colloids (González-Martínez et al. 2005). However, new studies have pointed out GLY as a possibly carcinogenic agent due to its accumulation in the water and the environment. It is believed that this poisonous is probably related to the ability of GLY to form metal complex (Coutinho et al. 2007; Tsui et al. 2005). Actually, the diagnostic strategies and tools have frequently failed to detect GLY and its by-products, and therefore such assumptions need to be confirmed (Simonetti et al. 2015). This review summarizes methods most used during the past 36 years for GLY detection.

Environmental risks and animal’s health
The European Glyphosate Task Force (GTF) published an enormous list of scientific citations about toxicological effects of GLY. Interestingly, GLY has been associated with fungus infestation in wheat plantations (Ho and Cherry 2010), and additionally, it has been related to more than 30 plant diseases (Johal and Huber 2009; Huang et al. 2015). An extensive review has compiled evidence for the widespread contamination of GLY and its derivatives in  groundwater,  surface waters  (creeks,  brooks,  lakes,  rivers and drains), marine sediments, seawater and rain (Watts 2009; Allinson et al. 2016; Bradley et al. 2017; Okada et al. 2018). Furthermore, GLY can also be transported by particles in the air (Humphries et al. 2005) and be deposited in the snow (European Commission 2002). GLY may also affect the marine microbial community (Stachowskihaberkorn et al. 2008). The observed concentration of GLY found in coastal areas may be enough to cause considerable changes in the ecosystem, including the obstruction of biomass trophic transfer to different levels (DeLorenzo et al. 1999).

Chronic exposure of GLY is associated with many human health hazards that include: endocrine function disruption (Gasnier et al. 2009; Chalubinski and Kowalski 2006; Ejaz et al. 2004), attention-deficit/hyperactive disorder (ADHD), colitis, diabetes, heart disease, inflammatory bowel disease, amyotrophic lateral syndrome, multiple sclerosis, obesity, depression, non-Hodgkin lymphoma and Alzheimer’s disease (Samsel and Seneff 2013a), anencephaly (Rull 2004), autism (Beecham and Seneff 2015), pineal gland disorders (Seneff et al. 2015), birth defects (Paganelli et al. 2010), brain and breast cancers (Shim et al. 2009; Cattani et al. 2014; Thongprakaisang et al. 2013), celiac disease and gluten intolerance (Samsel and Seneff 2013b), chronic kidney disease (Jayasumana et al. 2014, 2015), Parkinson’s disease (Gui et al. 2012), pregnancy problems (Richard et al. 2005; Garry et al. 2002; Benachour and Séralini 2009; Hokanson et al. 2007; Poletta et al. 2009), abnormal cell cycle (Marc et al. 2004), allergies (Slager et al. 2010; Heras-Mendaza et al. 2008; Nielsen et al. 2007) and intestine problems (She-hata et al. 2013).

In fish, GLY has affected the energy metabolism, free radical processes, acetylcholinesterase activity (Glusczak et al. 2006, 2007; do Carmo Langiano and Martinez 2008), modified parameters of the micronucleus test and caused DNA damage as evidenced by the comet assay (Grisolia 2002; Cavalcante et al. 2008; Cavaş and Könen 2007) and caused significant alterations in the immune response (El-Gendy et al. 1998) and in hepatocytes histology (Jiraungkoorskul et al. 2003; Szarek et al. 2000). Besides such effects, preference and avoidance reactions of rainbow trout could also be induced by different GLY concentrations (Tierney et al. 2007).

It has been demonstrated that low GLY exposure may induce mild oxidative stress in goldfish tissues by sup-pressing molecules that modulate reactive oxygen species (ROS), such as superoxide dismutase (SOD), glutathione reductase,  glutathione  S-transferase  (GST)  and  glucose  6-phosphate dehydrogenase (Winfield 1990). Additionally, the increase in alkaline phosphatase activity at the heart and liver of fish with sublethal GLY doses has also affected the oxaloacetic and glutamic-pyruvic transaminases activities, leading to epithelial hyperplasia and subepithelial edema in gills, and morphological changes in the liver (Nešković et al. 1996; Lushchak et al. 2009). In amphibians, it has induced morphological changes on tadpole development, probably breaking their antipredator responses (Relyea 2012). A very broad review on the impact of GLY on native amphibians was published in 2008 (Govindarajulu 2008).

Reasons to determine precise levels of glyphosate
Detection and quantification of glyphosate (GLY) is expensive and slow; consequently, governmental control measures are ineffective since GLY usually cannot be detected by methods that simultaneously analyze different kinds of chemical and their metabolites in the same assay, in a unique multi-residue method. Therefore, the impact of this knowledge gap on public economy and in the health system is not known. Hence, the concept of the “Glyphosate paradox” is raised, which means that besides GLY being the most widely used agrochemical in the world, it is also the most hardly determined by analytical methods.

Currently, there is no continuous monitoring of GLY or any systematic information about environmental contaminated areas worldwide. The European Union (EU) authorities conducted 186,852 tests in 2009 on cereal samples for pesticide residues, but such survey was performed in only five countries, reaching only 462 sites, from which 42 tested positive. Since 2010, EU authorities have performed regular monitoring of GLY in cereals, but the challenge still remains in testing GLY residues on imported genetically modified soybeans, in which Brazil is one of the biggest producers in the world with indiscriminate use of GLY. Even in the EU, only a small number of testing laboratories are able to detect this chemical (Poulsen et al. 2009). The consequence of this lack of information means greater difficulties to find out how much GLY people have been daily exposed to, and how governments should protect human and environment health from the adverse effects of it.

Our perception is that the Europe Community is more concerned in applying the precautionary principle than  many other countries. For example, the Codex Alimentarius Commission and the US Environmental Protection Agency (EPA) established the maximum residue limit (MRL) of 20 mg kg−1 for GLY in soybean and, in a most preventive way, the National Health Surveillance Agency (ANVISA) in Brazil set the MRL of 10 mg kg−1. For drinking water, the regulatory rules adopted by each country differ significantly. The EU has set a MRL of pesticides independently of the chemical structure or biological activity of the compound in 0.1 ng mL−1. The EPA established the MRL in terms of persistence and toxicity of each pesticide individually at 700 ng mL−1 (Winfield 1990). The Canadian Drinking Water Guideline recommends a maximum level of 280 ng mL−1. In Brazil, the ANVISA and the Ministry of Health has established the MRL in water of max 500 ng mL−1.

The level of exposure that is deemed safe for humans over a long period of time is called ADI. It has been set at 0.3 mg kg−1 of body-weight per day (bw/d) in EU and Canada and 1.75 mg kg−1 bw/d in the USA. The ADI is the highest dose at which no adverse effect is found (the No Observed Adverse Effect Level or NOAEL), which is also lower than the lowest dose that has a toxic effect (the Lowest Observed Adverse Effect Level or LOAEL). However, it is important to emphasize that analyses on the current approvals by the EU and in the USA regarding GLY levels suggest that the established ADIs are questionable (Antoniou et al. 2012), especially because agencies used the information provided by studies performed by the industries, which support regulators to calculate and approve the application of chemical levels without adverse effects. All these facts have raised questions about how safe GLY levels are, which is further complicated by the fact that many approaches present Limits of Detection (LOD) far away from Agency’s control interest. GLY has some special characteristics that go far from the fact that it has been broadly used. It is usually applied to soils in the form of aqueous solutions, in high concentrations of around 0.03 mol L−1 (Candela et al. 2010; Laitinen et al. 2009; Tuesca and Puricelli 2007).
So, to understand and predict the transport of GLY in soils, one needs to measure it in a wide spectrum of concentrations, focusing on how GLY interacts with the soil complexity under variable conditions. In fact, this challenge is quite difficult, both technically and financially, which is mainly due to the complexity of molecular  interactions among GLY, metals, nutrients and organic matter, and also because there is no good technology for real-time and sensitive measurements of GLY. Simple, portable and low-cost methods and instruments are highly desirable, but difficult to attain for all different environmental conditions.

Glyphosate: metabolites and analogues, formulation toxicity and detection problems
Glyphosate (GLY) is generally formulated by a series of zwitterions with adjuvants or surfactants to improve its activity. It is an aminophosphonic analogue of the natural amino acid glycine, which is protonated and presented in different ionic states depending on pH. The carboxylic and the phosphonic acid can be ionized, and the amine group can be protonated (Winfield 1990; Chenier 2002). The GLY primary natural decomposition pathway occurs through degradation by soil microflora under both aerobic and anaerobic conditions (Franz et al. 1997). The main deactivation path is the hydrolysis to aminomethylphosphonic acid (AMPA). This compound presents a low toxicity weak organic acid with a phosphoric acid group (Winfield 1990; Schuette 1998). AMPA is then broken down further by manganese oxide, which naturally occurs in soil (Barrett and McBride 2005), or to phosphoric acid via bacterial action (Forlani et al. 1999; Pipke and Amrhein 1988), and ultimately to carbon dioxide and inorganic phosphate (Winfield 1990; Tuesca and Puricelli 2007). The second catabolic pathway is sarcosine as the intermediate metabolite. In hard water, the decomposition process is slower, and GLY forming salt, mainly by complexation to Ca2+ (Coutinho and Mazo 2005). GLY has more than one thousand analogues (Winfield 1990; Pollegioni et al. 2011), but seems that there are only two, very similar analogues, which are as effective to the same extent as GLY, the N-hydroxy-glyphosate and N-amino-glyphosate (Winfield 1990; Laitinen et al. 2009; Singh 1998).

Interestingly, the oxidative stress generated by GLY, AMPA and its commercial formulation was examined in a hepatocyte cell line (HepG2) under dilution levels below agricultural applications, but surprisingly, the AMPA exposure produced an increase in glutathione (GSH) levels only, and no effects were observed for GLY. However, the GLY formulation induced a significant increase in reactive oxygen species, nitrotyrosine formation, superoxide dismutase activity and GSH levels, suggesting that adjuvants associated with the active GLY may be causing part of the toxic effects (Chaufan et al. 2014).
The challenge to detect GLY residue using a simple analytical method is due to its ionic character, high polarity and solubility in water, difficult evaporation, poor solubility in common organic solvents, low volatility, low mass and favored complexing behavior (Ibáñez et al. 2006; de Llasera et al. 2005; Koskinen et al. 2016; Skeff et al. 2016). The photometric and fluorometric detection of these substances is not viable due to the absence of chromophore or fluorophore groups in GLY structures. Moreover, similarity with amino acids or other natural plant components can cause interferences. The GLY capacity to adsorb strongly on clay minerals (Hance 1976; Arroyave et al. 2016) and organic (Zheng et al. 2015) or mineral particles in water (Thompson et al. 1989; Rueppel et al. 1977) and its high affinity to metal cations that complex with it, make it hard to detect without a pretreatment method (Glass 1984).

Measurement methods: Chromatography techniques
Chromatography can be used to break apart mixtures into their components allowing each part to be analyzed separately. Many approaches to detect glyphosate (GLY) residues use liquid chromatography (LC) or high-performance liquid chromatography (HPLC), gas chromatography (GC) and ion chromatography (IC) (Zelenkova and Vinokurova 2008). Alternatively, the eluates from the chromatographic columns can be fed into mass spectrometer (MS) detectors (LC/MS).

Liquid chromatography
Liquid chromatography (LC) is the most suitable method to detect GLY. It needs a derivatization procedure, for which several approaches have been used, such as pre-column, e.g., and post-column (Winfield 1990; Patsias et al. 2001; Hogendoorn et al. 1999; Mallat and Barceló 1998). Normally, LC has been used in combination with fluorescence and UV/visible (LC/UV–Vis) detection after derivatization and has also been used with fluorescence detector (LC–FLD) (Khrolenko and Wieczorek 2005; Merás et al. 2005; Nedelkoska and Low 2004; Ridlen et al. 1997). The recommended EPA method for GLY in drinking water uses LC with direct injection of the sample, post-column derivatization and fluorescence detection without pre-concentration (Barcelo 2000). The derivatization reagents for UV detector are p-toluenesulfonyl chloride (Si et al. 2009; Kawai et al. 1991), o-nitrobenzenesulfonyl chloride (Fang et al. 2011) and 2,5-dimethoxy-benzenesulfonyl chloride (Fang et al. 2014). LC methods for GLY often adopt pre-column 9-fluorenylmethyl chloroformate (FMOC-Cl) derivatization and fluorimetric detection. On FLD detections used 9-fluorenylmethyl chloroformate (FMOC) and o-phthalaldehyde (OPA) in post-column (Nedelkoska and Low 2004; Zhou et al. 2007; Hidalgo et al. 2004; Sancho et al. 1996; Sun et al. 2017). The pre-column is more precise than post-column derivatization due to the difficulty  in  controlling  reaction  in  the  reflux  system  of  HPLC for post-column. Pre-column derivatization reaches LOD as low as 0.02 ng mL−1 in water and 0.02 mg kg−1 in soil, while post-column derivatization reaches on aqueous sample 2.0 ng mL−1. LC is a fast, sensitive and repeatable method to GLY residue detection, but it needs derivatization processes and requires high-end equipments.

Gas chromatography
Gas chromatography (GC) is not commonly used to detect GLY due to the complicated derivatization procedure, but the evaporation properties have been improved through esterification and acylation. Generally, GC is performed after pre-column derivatization of GLY to convert it to volatile and thermally stable derivative (Hu et al. 2008; Kudzin et al. 2002, 2003; Börjesson and Torstensson 2000; Tadeo et al. 2000). The C, P and H in the GLY molecule permit the use of associated techniques as flame photometric detector (GC/FPD) (Tseng et al. 2004; Kataoka et al. 1996), flame ionization detector (GC/FID) (Kudzin et al. 2003), electron capture detector (GC/ECD) and nitrogen phosphorus detector (GC/NPD) (Hu et al. 2008). The most used derivatiza-tion reagents are N-methyl-N-ter-butyldimethylsilicontri-fluoroacetamide and dimethylformamide (Tsunoda 1993), trifluoroacetic anhydride and 4,4,4-trifluoro-1-butanol (Hu et al. 2007; Lou et al. 2001; Ding et al. 2015), isopropyl chloroformate  and  diazomethane  (Kataoka  et  al.  1996), trifluoroacetic acid, trifluoroacetic anhydride and trimethyl orthoformate (Kudzin et al. 2002), propionic anhydride and methanol (Ding et al. 2015; Pei and Lai 2004).

Quantification of GLY in soil and water through NPD has reached LOD equivalents of 0.02 mg kg−1 (Ding et al. 2015; Pei and Lai 2004) and 0.5 ng L−1, respectively (Hu et al. 2007). One point that should be emphasized is the use of less toxic acetone, ethyl acetate and methanol instead of the carcinogenic chloroform, dichloromethane and neurotoxic n-hexane as eluent solvents (Tseng et al. 2004). Therefore, GC and LC can determine GLY derivatives in a sensitive and selective way, but the steps to transform GLY in a product that could be read are quite complicated, besides generating unstable products.

Ion chromatography
Ion chromatography (IC) is a type of LC in which retention of molecules is based on the attraction between solute ions and charged sites bound to the stationary phase. Once GLY is an ionic compound (pKa1 = 2.27,  pKa2 = 5.58 and pKa3 =   10.25), an anion-exchange column can be used followed by elution with an alkaline buffer. IC was used to  measure  GLY  in  a  simple  and  sensitive  method  with  emphasis on a simple clean-up procedure based on IC with suppressed conductivity detection (Zhu et al. 1999). The highlight of this study was the very short retention time of common inorganic anions of GLY, such as chloride, phos-phate, nitrate and sulfate, without any interference. In a few cases, GLY could be determined directly by IC with UV (Ibáñez et al. 2005) or by suppressing conductivity detection due to its limited sensitivity. Furthermore, an IC method with integrated pulsed amperometric detection (IC/IPAD) could determine GLY with the advantages of not requiring derivatization, pre-concentration and mobile-phase conductivity inhibition (Sato et al. 2001). It is important to consider the complexity of soils, which includes the presence of several competing ions in different concentrations and other environmental variations, such as pH, organic matter and microorganisms that make the extraction methods harder to be attained and leading to unreproducible results.

Chromatography–mass spectrometry
Chromatography–mass  spectrometry  (LC/MS),  or  alternatively HPLC/MS, is the most common method to detect GLY in environmental samples due to its higher sensitivity (Liao et al. 2018). Low analysis time has been achieved using solid-phase extractions with LC–SPE (Delmonico et al. 2014), but with higher LOD. LC/MS methods are already used with a technique called electrospray ionization (ESI) that works as an ion source (LC/ESI–MS) (Sato et al. 2009). Sensitivity can be significantly improved by LC/MS–MS, which also avoids the derivatization procedure. MS/MS combines two mass analyzers in one instrument, in which the first MS filters the precursor ion followed by its fragmentation with high energy, and the second MS analyzer then filters the produced ions generated by fragmentation. The advantage of the MS/MS is the increased sensitivity due to the noise reduction.It was reported that the LC/MS–MS method sufficiently detects GLY, but this method requires longer equilibration time,  suffers  from  poor  robustness  and  still  has  adverse  impacts on column lifetime (Liao et al. 2018). Kaczyński and  Łozowicka  compared  LC/MS–MS and LC/FLD to detect traces of GLY in rapeseeds. Good results have been achieved  with  LC/MS–MS, but some  factors  may  have  affected the method’s performance such as metal ions, sample preservation and storage time (Kaczyński and Łozowicka 2015).  However, while LC/FLD  requires less expensive  equipment, the LC/MS–MS presents simpler sample preparation, easier procedure, faster and more sensitive (Hao et al. 2011). Routine analysis can be performed without laborious instrumental changes using this technique. The results suggest that LC/MS–MS may also be used to analyze residues of these compounds in oil plants, where GLY is widely used. Flow injection associated with tandem mass spectrometry (MS/MS) was researched for the rapid detection of polar pesticides, such as GLY (Mol and van Dam 2014).

Searching for an analysis without derivatization procedures has led to the development of an alternative methodology to determine GLY and AMPA residues using a fast-chromatographic  analysis  with  sensitive  detection,  with calibration curves prepared in the matrix after a simple sample extraction and liquid–liquid partition followed by protein precipitation step with organic solvent to minimize the complexity of the sample (Martins-Júnior et al. 2009, 2011). These authors investigated the potential of reversed-phase LC–ESI/MS/MS for the quantification of these residues in soybean-spiked samples, suggesting that this method could be expanded to corn and cotton crops. LC–ESI–MS/MS does not need derivatization procedure, but the instrumentation demands are substantial (Byer et al. 2008). A fully automated SPE–LC–ESI–MS/MS was developed and validated to analyze potable water, surface water and waste-water with good LOD, but with derivatizations (Vreeken et  al. 1998).  Similarly,  a  selective  and  sensitive  online SPE–LC–ESI–MS/MS approach reached incredible LOD for GLY and AMPA in soil and water samples, reaching as low as 50 ng g−1 and 0.0005 ng mL−1, respectively (Ibáñez et al. 2005, 2006; Hanke et al. 2008). It is also interesting to highlight that using labeled GLY as internal standard, even applying powerful approaches as SPE–LC–ESI–MS/MS detection, its application to real-world samples failed.

Most reported methods for GLY analysis did not perform acidification of sample before derivatization, and some data reported on GLY concentrations in water might be questionable due to the presence of some organiccompounds and metal ions that were neglected, which act as chelating agents that form complexes with GLY, becoming unavailable for the derivatization step. The nature of the formed complex was not elucidated yet, and more studies are necessary to establish whether acidification of samples is a general approach that should be applied to all water samples (Ibáñez et al. 2006).

GC/MS is another method that requires derivatization to confer volatility to GLY (de Llasera et al. 2005; Kudzin  et  al.  2003).  Three technologies  based  on  GC/MS  have been used to detect GLY: GC–CI (chemical ionization)–MS,  GC–FID  (flame  ionization  detector)–MS  and  GC–EI (electron impact)–MS. Generally, the methods are time-consuming, tedious and require a substantial amount of sample manipulation. Although these methods present high sensitivity and capability of detecting very low GLY concentrations, they are laborious and require the use of high-end specialized equipment. Tsunoda developed a sensitive GC/ion-trap-MS (GC/IT-MS) method to determine simultaneously GLY, glufosinate (GLU) and bialaphos (BIA), their major metabolites, besides another nineteen amino acids (Tsunoda 1993). Royer et al. (2000) used this method to determine GLY and AMPA in water with different hardnesses. Börjesson and Torstensson (2000) reached LOD as low as 0.1 ng mL−1 in groundwater and 6 ng g−1 for both compounds in soil. The preferred detection system according to many scientists is MS (Kudzin et al. 2002; Börjesson and Torstensson 2000; Royer et al. 2000; Alferness and Iwata 1994). Another  approach  based  on  by ion-pairing  reversed-phase  liquid  (RP-LC)  coupled  to  inductively  coupled  plasma mass spectrometry with octapole (ICP/MS) did not require derivation and obtained lower sensitivity with LOD at 25–32 ng mL−1 (Sadi et al. 2004).

Guo and colleagues also built an IC/ICP–MS method in order to determine the GLY in water. The method was sensitive, simple, did not require sample pre-concentration or mobile-phase conductivity suppression and did not suffer anions’ interference (nitrate, nitrite, sulfate, chloride, etc.) and metallic ions from the matrix (Guo et al. 2005). Later in 2007, they developed an IC/ICP–MS method to determine simultaneously four water-soluble organophosphorus herbicides. The detection was fast, simple, selective and free from tedious sample preparation or chemical derivatization and was applicable to highly polluted water samples. However, environmental water applicability depends on further research using instrumental upgrading or applying a pre-concentration step to improve its sensitivity (Guo et al. 2007).
Yoshioka and col-leagues also avoided derivatization and ion-pair reagents and aimed the study to the emergency medicine, where time is the utmost aim, especially in poisonings cases. In this situation, a rapid method for detecting multiple herbicides would allow rapid treatment. Besides GLY, this method could also detect GLU, BIA, AMPA and 3-methylphosphinicopropionic acid (3-MPPA) in human serum. These amphoteric and polar phosphorus herbicides contain amino acids. Their detection without derivatization or ion-pair reagents, and under the use of conventional columns, such as reversed-phase (RP) or ion-exchange column may lead to poor peak shapes and insufficient peak separation in LC chromatograms (Yoshioka et al. 2011). In order to solve this problem, hydro-philic interaction chromatography (HILIC) columns were used (Coutinho et al. 2007; Li et al. 2009; Vass et al. 2016).

Once hydrophilic and polar compounds cannot be retained by conventional RP chromatography, the HILIC column is suitable. The greatest advantage of IC testing is the simple treatment for samples. However, it is only applied in water and soil analysis.

Spectroscopic methods
Spectroscopy analysis studies the interaction between matter and electromagnetic radiation as a function of its wave-length or frequency. The data are represented by a plot of the response of interest as a function of the wavelength, wave-number or frequency.

Methods of absorption and emission
Although accurate and sensitive, the technologies related to atomic absorption spectrometry, electrothermal atomization atomic absorption spectrometry, flame atomic absorption spectrometry, fluorimetry and fading spectrophotometry, suffer from the requirement of well-established laboratory settings, high complexity and long testing times. However, a simple and cost-effective fluorimetric sensor (FS) has been developed, which is based on the detection of oligonucleotides by fluorescence. It is based on fluorescence magnetic nanoparticles (FMPs) coupled to specific DNA probe (FS-FPMs/DNA). The principle of detection was based on a competitive inhibition of conjugated GLY-double target/probe-FMP (Lee et al. 2013). GLY could be easily quantified using confocal laser scanning microscopy and low-cost UV photometric analysis. Unfortunately, this study did not explore the possible cross-reactions with GLY analogues and possible environmental interferents. This study further improves the previous report by the same authors, who developed a competitive inhibition assay by free GLY using GLY-dsDNA-gold conjugate nanoparticles, which was used to quantify fluorescence intensity through an immunoassay (FS-AU/DNA) (Lee et al. 2010).

Another immunosensor (IS) was developed using carbon dot-labeled antibodies (lgG-CDs) that were able to specifically recognize GLY (Wang et al. 2016a). The fluorescent properties of this IS allowed the visualization of the GLY distribution into plant tissues. The excess of IgG-CDs is removed from the system using magnetic nanoparticles Fe3O4 allowing a linear relationship between the fluorescence intensity of IgG-CDs and the logarithmic concentration of GLY.
Silva and colleagues employed diffuse reflectance spectroscopy (DRS) using a spot test on a filter paper (da Silva et al. 2011; Metzger 1997). Although the technique is simple, precise, inexpensive, environmentally friendly, requires minimal amounts of samples and reagents and is applicable to environmental, drinking water and commercial formulations, it presents very low sensitivity and may not be applicable to soil samples.

Most of the spectrophotometric methods require colored reagents and chromophore groups. To surpass this difficulty, a simple and rapid method was developed by transforming the amino group of GLY into a dithiocarbamate derivative. A copper (I) perchlorate reaction formed a yellowish green-colored  complex  with  maximum  absorbance  at  392  nm  (Sharma et al. 2012). The color intensity and stability were obtained at 60 s, and remained for at least 90 min, which was an advantage over the commonly used spectrophotometric methods. In some cases, it is useful to apply a low-cost, simple and fast method, despite its lower sensitivity, when compared with chromatographic methods or CE. For example, some researchers have used UV–Vis spectroscopy for GLY quantification in laboratory experiments to evaluate the adsorption capacity in soil sample under different pH values by performing adsorption isotherms under well-controlled conditions and was able to quantify GLY in the range from 0.084 to 21.8 mg L−1(Waiman et al. 2012). However, a derivatization step was performed, in which the GLY amine group was modified by FMOC-Cl in acetonitrile at pH 9.0. Besides,  a  non-characterized  soil  sample  was  incubated  overnight in buffer solution and adjusted to different pH values, and therefore, the potential use for field conditions with different soils is yet to be demonstrated, since differences in the concentration of organic compounds and metal ions were not referred to, or considered (Waiman et al. 2012).

Another method has also been proposed, which uses carbon disulfide to convert the GLY amine group into dithiocarbamic acid. Dithiocarbamate by-product is then used as a copper-chelating group that results in a yellowish-colored complex used for measurements (Jan et al. 2009).

Another colorimetric sensor for GLY detection has specifically been made by aggregating 2-mercapto-5-nitrobenzimidazole-capped silver nanoparticles (MNBZ-Ag NPs) and Mg2+ ions. This structure suffers a reduction in the distance of its interparticle complex formation between MNBZ-Ag NPs–Mg2+ ion and GLY, which promotes a color switch from yellow to orange-red (Rawat et al. 2016). The colorimetric property was based on the inhibition of peroxidase-like activity of Cu2+ through the oxidation of 3,3′,5,5′-tetra-methylbenzidine  (TMB).  The color solution changed according to the concentration of GLY when complexed with  Cu2+ (Chang et al. 2016a). The indirect colorimetric determination method of GLY was developed after its oxidation with hydrogen peroxide to orthophosphate, reaching levels between 1000 and 20,000 ng mL−1 (Glass 1981). Although the complex is pH dependent and needs a pre-concentration step before measurement, it can detect GLY in different samples.

Recently, the dithiocarbamic acid was used as an optical color changer of the polyvinyl alcohol (cd-PVA; copper-doped polyvinyl alcohol) nanofiber from blue to yellow (De Almeida et al. 2015). Although advantageous, requiring small sample volume, with a fast response time (~ 1–3 s), good color spot stability (4 h) and low cross-reactivity with GLY derivatives and structural  analogues, AMPA and glycine, respectively, the sensor was not very sensitive and could not keep stability for longer periods (> 20 days). Another drawback was the system susceptibility to compounds and ions commonly found in environmental waters at a lower concentration (60,000 ng mL−1), which could require pre-treatment, besides being strongly dependent on pH 11–12.

An additional colorimetric sensor strip was capable to detect not only GLY, but also three other organophosphorus compounds: dimethoate, dichlorvos and chlorpyrifos (Liu et al. 2015). It presents some advantages, such as easy read-out, fast analysis, easy operation, low cost, simple transportation and storage. However, although its detection limit has met the maximum residue limits reported in the EU pesticides database, naked eyes cannot distinguish very large ranges, so specific photonic equipments are required for measurements. Briefly, stabilized gold nanoparticles (NPs) with cysteamine (CS-AuNPs) without aggregation present a red color, and when GLY aggregates to these NPs, the color switches to a blue or purple color (Zheng et al. 2013).

Another optical sensor was designed using hollow-core metal-cladded waveguide (HCMW) with double-metal surface. The insertion of chromogenic GLY in the hollow core promoted the orientation for the wave propagation exciting highly sensitive ultra-high-order modes, through small incident angle coupling (Dai et al. 2014). Detection of GLY concentrations as low as 0.23 ng mL−1 was unambiguously identified within several minutes. Several interesting advantages are mentioned, such as the small analyte volume required, environmentally friendly, compactness, inexpensive, label-free and real-time detection. However, the system behavior is unknown in field samples since detection was performed only in ultrapure water.Reporter spacer receptors (RSR), both colorimetric- and also luminescence-based systems, are the most widely used optical chemosensors (OC) (Roberts). But, the sensor synthesis requirement is very expensive. To overcome this setback, Minami et al. (2014) developed an optical che-mosensor named “Intramolecular Indicator Displacement Assay (IIDA)” (OC-IIDA). In this sensor, an attached dye works as an anionic chromophore, which is bound to the receptor. The anionic analyte GLY competes for receptor binding leading to changes in photophysical properties of the dye. Besides the possibility of reusing it, one of the benchmarks of this work is the study of phosphate-type anions, e.g., phosphate (Pi), pyrophosphate (PPi), AMPA and phosphonate GLY in aqueous solutions with and without an excess of NaCl as a competing electrolyte, which showed no differences (Minami et al. 2014).

Quantum dots (QDs) are also used to develop bioanalytical methods based on fluorescence resonance energy transfer (FRET) (Algar and Krull 2008). QDs act as donor fluoro-phore to a proximal ground-state acceptor (Guo et al. 2014). In this work, gold NPs stabilized with  cysteamine (CS-AuNPs) were used as acceptors of fluorescence emission by QDs capped with thioglycolic acid (TGA-CdTe-QDs). The presence of GLY created electrostatic interactions with CS-AuNPs, promoting disaggregation between CS-AuNPs and TGA-CdTe-QDs, and consequently generating fluorescence.

Surfaceenhanced Raman scattering
Surface-enhanced Raman scattering (SERS) can magnify molecular vibrations in a system. The enhancement factor can be as much 10, 14 or 1015, which is sufficient to allow even a single molecule to be detected. The enhancement takes place at a nanoscale roughness reflective metal surface where the molecules are adsorbed. Gold nanorod particles can be synthesized with controllable size and numerous surface functionalities, and due to its tunable optical properties, it can be used as SERS substrates. Therefore, GLY was detected in attomol levels through gold nanorods derivatized with 4-mercaptophenylboronic acid (Torul et al. 2010). At the range of 110−16 mM the SERS signal exhibited a linear dependence within the GLY. A disadvantage was that all solutions were prepared using deionized water, free of any organic matter, and considering the high complexity of organic compounds and metal ions interactions with GLY, studies should be performed to better understand and dis-criminate such complex profiles.

Surface plasmon resonance
Surface plasmon resonance (SPR) can be used as an optical biosensor that monitors the interactions between an analyte in a solution and a bioelement immobilized on the SPR sensor surface through special electromagnetic waves—surface plasmon polaritons. One of the advantages provided by SPR biosensors is its label-free real-time analytical technology in which the main application is to detect biological analytes through biomolecular interactions (Homola 2003). Using bacteriophages (SPR-pd), it has been developed a specificoligopeptide that presents good specificity against glycine, thiacloprid and imidacloprid (Ding and Yang 2013). SPR is much more sensitive than nuclear magnetic resonance spectroscopy (NMR); however, the immobilization of binding partners creates several undesirable issues. In particular, the molecular binding site may be near the surface and induce steric hindrances that could affect binding energy and/or kinetics, and the surface layers often exhibit decreased activity during the analysis (Ding and Yang 2013).

Nuclear magnetic resonance
NMR provides detailed information about the molecular structure through the exploration of magnetic properties of certain atomic nuclei. Using 31P NMR it was possible to determine GLY in blood, liver and urine in postmortem samples, reaching levels of 1 mg mL−1 in less than a minute (Dickson et al. 1988). Using 31P and 1H NMR, GLY could be detected in biological fluids in between 10 and 20 min in a small sample size without any pretreatment (Cartigny et al.

2004). In fact, 31P NMR has been used to detect organophosphorus compounds as endogenous phosphorus metabolites present in plasma or urine. Interestingly, other components can be detected in the same NMR spectrum, e.g., the occurrence of metabolic acidosis in salicylate and alcohol/glycol poisonings (Cartigny et al. 2004). The main limitation of NMR analysis is the quantification analysis, particularly when therapeutic agents are administered, because several signals can overlap. However, in the clinical emergency context, the diagnostic problem is partially solved when only detection is needed, as is the case of monitoring the effectiveness of drug elimination. In an emergency clinical context, the diagnosis problem could be at least partly solved if a rapid identification procedure was available. The NMR method should be useful in rapidly confirming the diagnosis of poisoning and in evaluating the effectiveness of elimination procedures such as gastric lavage, forced diuresis or hemodialysis (Cartigny et al. 2004).

Chemiluminescencemolecular imprinting sensor
Chemiluminescence-molecular imprinting sensor (CL-MIS) can be made using small dimension microspheres (MIMs) as a molecular printer, reaching extremely high surface-to-volume ratio (Zhao et al. 2011). It was synthesized onto a molecularly imprinted polymer base, using precipitation polymerization with GLY as template. A circular glass sheet was used as a form to be coated by GLY-MIMs suspension.

After, placing it into the well, the microplate is prepared as a recognition element, acting as a chemiluminescence (CL)-molecular imprinting (MI) sensor able to perform 96 sequentially independent measurements in just 10 min. Stability tests showed around 90% of its initial CL intensity for 3 months when stored in air at 4 °C. The authors pointed out that CL-MIS may become a useful and quick analytical technology for molecular recognition due to its excellent selectivity for GLY determination; however, they did not compare the GLY recognition sensor capacity with its derivatives as AMPA. Therefore, specificity was not considered.

Electrochemical sensors
Amperometric and voltammetric methods
To access a simple and fast way to determine GLY residue in soil samples, a single-sweep oscillo-polarographic method was developed (Sun et al. 2007). This is an adaptation of an old method (Brłnstad and Friestad 1976) that detected GLY in natural water by nitrosation, converting GLY in N-nitroso-N-(phosphonomethyl) glycine after derivatiza-tion with sodium nitrite, followed by detection with differential pulse polarography. This derivative showed a sensitive cathodic peak at − 0.81 V against saturated calomel electrode in pH 0.7 and resulted in efficient determination of GLY in formulations and soil samples. However, the presence of concomitant metal ions or organic compounds may have probably affected the analysis, and interference of any potential confounding effect should be further investigated to validate the proposed method for GLY quantification. Glyphosate could be detected electrochemically in 20 min by its ability to bind to horseradish peroxidase (ES-HRP). Although the LOD of 1.70 ng mL−1 achieved was very good, it is not known its applicability to real samples. Another ES-HRP with good reproducibility was also developed using a gold disk electrode. It is a sensitive, simple and low-cost method. Besides it can detect AMPA too. One interesting characteristic of this biosensor is the possibility to reuse it for up to three measurements before surface saturation (Songa et al. 2009a). Another proposed sensor also uses HRP electrostatically immobilized onto the surface of a rotating  gold  disk  electrode  modified  with  PDMA-PSS  [poly(2,5-dimethoxyaniline)-poly(4-styrenesulfonic acid) nanoparticles for amperometric detection. Before the expo-sure of GLY onto the electrolyte solution the activity of the enzyme was measured with hydrogen peroxide. The stability of this enzymatic electrode was very good and could be used for over 60 measurements (Songa et al. 2009b).

Another electrochemical sensor study uses enzymatic inhibition method to determine GLY through a modified nanoclay that immobilizes atemoya peroxidase (ES-Ate-moya).  It  is  applicable to real water samples, stable for 8 weeks, and does not need pretreatment process. Unfortunately, there is no information regarding its portability and cross-reactivities with analogues (Oliveira et al. 2012).

Other two reports have been published on nano-film-modified  amperometric  sensors.  One  used an electrogenerated NiAl-LDH (Ni1−Alx(OH)2NO3x·nH2O-layered double hydroxides) thin film by electrodeposition on the Pt electrode surface. The principle of detection is based on oxidation of amine group by Ni (III). The electrocatalytic efficiency and morphology of the obtained LDH film was strongly dependent on the electrodeposition time. It is important to note that this sensor could not properly work at strong alkaline pH (Khenifi et al. 2009). Despite these electroactive NiAl-LDH films easily electrodeposited, lower LODs could not be achieved. The second sensor was capable of detecting chemicals in soil, human serum samples and water simultaneously without cross-reaction. However, a derivatization by nitrosation is needed for GLY in order to distinguish herbicides, leading to an N-nitroso Glyphosate derivative (Prasad et al. 2014).

Toward a sensor fabrication, (N-methacryloyl-l-cysteine) monomers through S–Au bonds were used to immobilize a nanostructured polymer film that was grown directly onto the electrode surface. These molecules were polymerized in the presence of templates, cross-linker, initiator and carbon nanotubes as pre-polymer mixture. It reached limits as low as 0.35 ng mL−1. Although it is clear there is no cross-reaction between GLY and GLU, there is no information about other possible cross-reactions among its metabolites. However, these procedures are generally very slow, need laboratory apparatus of high cost and are inadequate for on-site or in situ monitoring (Prasad et al. 2014).

A voltammetric electronic tongue (VET) was used in the determination of GLY. The VET consisted of three metallic electrodes of cobalt, copper and platinum, which produced a signal pattern when subjected to GLY in aqueous sample. Besides its simplicity, speed (2 s) and low cost, the electronic tongue was also capable of detecting this analyte, even in the presence of different concentrations of potential interferents, such as  Ca2+ and humic acids (Bataller et al. 2012). Another voltammetry-based detection system used rhodium, cobalt and copper electrodes coupled to a mathematical model to predicted GLY concentration, but despite the presence of fertilizers (ammonium nitrate) and organic substances, the system proved to be effective (Martínez Gil et al. 2013). Finally, voltammetric determination of GLY using a copper electrode in natural waters was performed in agreement with the green chemistry concept. The optimization showed ideal condition in neutral pH, reaching an LOD of 59 μg L−1 (Garcia and Rollemberg 2007). Still with copper electrode, an electrochemical determination of the AMPA in drinking waters was demonstrated (Pintado et al. 2012). Electrochemical and spectroscopic investigations of GLY and AMPA were performed successfully on pure samples of GLY and commercial products (Habekost 2015). Electrochemical behavior of GLY on nickel and copper electrode was measured in the development of a sensor by cyclic voltammetry (Sierra et al. 2008).

Interestingly, despite the success of several electrochemical sensors in the last decade, none of them has become a reality, and it is still questionable their reliability and reproducibility in real-time detection without controlled complex environments, especially because of variations and complexity of production of such electrodes, and also because of the complex interactions of GLY with other compounds. Although the proof of concepts were presented, one should be able to demonstrate the production cost-effectiveness, sensitivity and reproducibility in field detections for final validation.

A  photoelectrochemical  sensor (PEC)  using a glassy carbon electrode (GCE) firstly modified with nanosheets of graphitic carbon nitride g-C3N4NSs (g-C3N4/GCE) and then self-assembled with Ag+ onto the g-C3N4/GCE was performed (Li et al. 2016). The pyridine nitrogen units on g-C3N4 backbone could absorb chemically the Ag+ and then photogenerated electrons would be used to reduce Ag+/Ag, leading to the inhibition of electrons transfer and decrement of photocurrent. However, GLY can displace the Ag to form a very stable chelate, promoting an increase in current in a process called “Binding-induced internal-displacement of signal-on photoelectrochemical response.” Response was given in 5–15 min.

The PEC sensor possesses fine fabrication reproducibility, detection precision and excellent selectivity, even in the presence of the interferences, such as sulfluramid, glucose, vitamin B1, carbendazim, starch, sucrose and acetochlor. Even with excess of other interfering ions, such as Ca2+, Zn2+, Al3+,  Pd2+,  Fe2+,    Fe3+,  Na+, K+, Cd2+, and all interferences mixed in Ag+ solution, the photocurrent remained practically constant. Moreover, the mixture of the nine metal ions did not influence the signal response to Ag+. The question that remains is—could Ag+ of the electrode be strong enough to displace other chemicals that commonly bind GLY? This sensor still needs to be tested in the presence of humic acid. Some drawbacks of it are the pH- and time-dependent responses, besides losing its photocurrent response very quickly, even if stored in ideal conditions (dark sealed environment at 4 °C). Lastly, it is not known its behavior in the presence of GLY analogues main (Li et al. 2016).

Capillary electrophoresis
Capillary electrophoresis (CE) is a common method to detect GLY or AMPA. This method requires derivatization for the same reasons cited before. CE is generally associated with UV–Vis (Cikalo et al. 1996; Chang and Wei 2005), fluorescence (Molina and Silva 2002) and MS (Goodwin et al. 2003) detectors, and in this latter method, derivatization is not required. A rapid and direct pre-concentration technique followed by CE was utilized, and detection was based on a capacitively coupled contactless conductivity system (CE-C(4)D). The method showed good  reproducibility for GLY and its derivatives and analogues, AMPA and GLU, respectively (See et al. 2010). Comparing CE with LC, in samples of low to medium conductivity, the GLY concentration might be effectively determined, but there is the necessity to adjust the sample volume to the required sensitivity. Considering this and the fact that CE is much cheaper and less time-consuming than LC, CE should be the preferred method. On the other hand, in samples with high concentration of salts, AMPA is poorly extracted by the strong anion-exchange resin that was used to pre-concentrate both analytes in environmental aqueous samples (Corbera et al.

2005). Clikalo et al. (1996) used the same CE/UV procedure, however using tetradecyltrimethylammonium bromide (TTAB) as an electro-osmotic flow modifier and reached LOD with gains of 85 ng mL−1 to GLY and 60 ng mL−1 to AMPA in pure water samples in contrast, with 5000 ng mL−1 for and 4000 ng mL−1 reached by the previous report. Molina and Silva also reached even better LOD, from 0.06 to 0.16 ng mL−1 (Molina and Silva 2002), by using a non-ionic surfactant MEKC-LIF as a selective agent, which was fast and sensitive tool for the determination of GLY, GLU and their metabolites. Besides, once it does not need a previous enrichment steps, it increases its potential for analysis of environmental samples. Chang and Liao (2002) also used indirect fluorescence as a detection method in commercial formulations and showed that this technique can be applied in routine analysis, but direct analysis of GLY in ground-water is still problematic. Finally, Goodwin et al. (2003) combined CE with MS for simultaneous determination of GLY, GLU and their metabolites using a simple micro-electrospray interface (mESI). To drive separation and generate the electrospray, the interface uses the voltage applied to the CE capillary, thus avoiding sample dilution. Other advantage of mESI in relation an ESI is that it has no physical contact between the capillary outlet and the ground-state electrode because electrical contact is achieved by placing the capillary tip 1 mm away from the MS, that is, under these conditions the voltage generates the electrospray and promotes the necessary electrophoretic separation (Mazereeuw et al. 1997). This technique presents a hindrance, because only high resistivity background electrolytes (BGEs) can be used. Besides, if the BGE concentration is too high, interference may occur during detection due to electrical discharges. Some of the operational limitations of the “homemade” mESI used were the restricted range of acceptable sample matrices.

On the other hand, when compared to the typical sheath liquid interface systems, it has the advantage that analyte dilution is not required. The microchip electrophoresis system with laser-induced fluorescence (LIF) was also used as detection system for fast and sensitive analysis of GLY and GLU residues. In order to minimize the cost of cyclic olefin copolymer microchips was used (Mazereeuw et al. 1997); moreover, the technology is portable and user-friendly.

Enzymelinked immunosorbent assays
The enzyme-linked immunosorbent assay (ELISA) has been presented as an alternative approach to the drawbacks exposed in the other techniques, such as the requirement of derivatization procedures, hard sample pre-treatments, high-cost end equipments and reactions and time for analysis. Immunoassay offers some advantages over chemistry methods, since labeled antibodies can be used in competitive reactions to detect herbicides. It is also selective and sensitive to determine GLY and enables prompt environmental surveys.  Besides, that the ELISAs’s LOD are higher than those typically achieved by LC/MS/MS, better than GC/MS methods, and even similar of those obtained by HPLC (Rubio et al. 2003).

Two kinds of ELISA have been used to identify GLY. The first includes a derivatization step with acetic anhydride followed by detection with immobilized antibodies, resulting in an LOD equal to or less than 0.6 ng mL−1 (Rubio et al. 2003).  The second, an indirect  ELISA (CI-ELISA) just needs water pretreatment. Moreover, it was found to be highly specific for GLY detection with cross-reactivity less than 0.1%, even in the presence of related compounds, e.g., AMPA and GLU (Clegg et al. 1999). A so-called linker-assisted enzyme-linked immunosorbent assay (L’ELISA)  method  that  first  derivatized GLY with succinic anhydride achieved LOD values as low as 0.1 ng mL−1(Lee et al. 2002). Additionally, González-Martínez et al. (2005) also improved the LOD to 0.021 ng mL−1 by using a GLY ELISA sensor. In contrast, the drawback of ELISA methods is the high limits of AMPA detection, which under certain circumstances may be present in the absence of its parent pesticide (e.g., high use of GLY and vulnerable hydrogeological settings) (Scribner et al. 2007). Therefore, the quantification of AMPA through conventional analytical methods should be concurrently applied along with determination of GLY by ELISA (Sanchís et al. 2012). The difficulty of moni-toring mixed herbicides is due to the requirement of specific antibodies, which are not always available, because generation  of  antibodies  against  poisonous  chemicals  cannot be produced by conventional methods. In conclusion, ELISA is the most cost-effective method for routine analysis, but the commercially available kits are still high relatively expensive (González-Martínez et al. 2005), and applications for soil samples in field conditions have not been demonstrated.

Cell biosensor
A cyanobacterium sensor was developed based on the luciferase activity present in a modified cyanobacterium Synechocystis sp. cell. The results showed that the decrease in bioluminescence could be correlated with the herbicide concentration and with increasing incubation time. The reduction bioluminescence by 20% and 50% (EC20 and EC50) of the herbicide Glyphosate was determined at 6 h and 1 day, respectively. The EC20 at 6 h was 3.62 ×  103 ± 0.79 ng L−1, and the EC50 at 1 day was 3.10 × 103 ± 0.17 ng L−1. One of the major restrictions of this method is its low selectivity, presenting cross-reactions with other herbicides as diuron, paraquat, mcpa, mecoprop, atrazine, propazine and simazine. Besides, the pH conditions must be optimized in order to obtain reproducible responses (Shao et al. 2002). The use of the green alga Selenastrum capricornutum demonstrated to be less sensitive to GLY when two parameters are considered: sensitivity and reaction time. The EC50 of 1050 ng mL−1 could only be reached after 4 days (Abdel-Hamid 1996). In comparison with other methods, such as the algal biosensor, chlorophyll fluorescence-based and isolated photosystem II (PSII) (Campanella et al. 2001; Frense et al. 1998; Koblizek et al. 1998), it is simpler, faster, economical and accurate. It is more suitable for prediction of long-term effects of chronic toxicity of pollutants, because of the longer doubling time of cyanobacteria. Unfortunately, to decrease the detection limit, it is necessary to increase the assay time (Schafer et al. 1994).

Other cell biosensors preserve cell “physiological” functions by the utilization of an agarose gel matrix with immobilized cell components, to access electrophysiological interactions by measuring its potential. This method was called Bioelectric Recognition Assay (BERA). In a preliminary work it was able to specifically detect GLY in 3–5 min in concentrations lower than 0.1 ng mL−1, even among other compounds with similar structure in water solution (Kintzios et al. 2001).

BERA  biosensors  can  determine  GLY in a fast  and cost-efficient way without prior knowledge of the sample. Besides, it has kept its stability even after a 2-month storage in low temperature. This method responds differently to GLY and AMPA herbicides. Another characteristic of this sensor is that, rather than operating the biosensor electrode in direct contact with a single cell, BERA’s electrodes are inserted into the matrix of a group of cells. It approaches the measurements made in natural tissues. It is expected that an evolution of this type of sensors should be made with the interface of luminescent cells with optical transducers.

Finally, the factors that can affect the biosensor response are, among others, gel density, cell density in the matrix, and cell size, because it has a direct correlation with gel porosity (Frense et al. 1998). However, it is not known how the bio-sensor will behave in field samples. An important drawback of this method is that the sensor depends on many careful and detailed steps, including cell culture.

Crossresponses from multiple sensors
Different detection methods using data from conventional measurements  of  water  quality  have  been published in numerous publications, which include artificial intelligence, statistical analyses and data mining. Cross-responses from multiple sensors (CRMS) are also a proposed method to detect some contaminants. An online water quality monitoring system can detect GLY from simultaneous and continuous measurements of eight parameters: UV-254, pH, temperature, conductivity, turbidity, oxidation–reduction potential (ORP), nitrate-nitrogen and phosphate, even if the contaminant in concentrations as low as 2000 ng mL−1 had been introduced 1 min before (Che and Liu 2014). However, for each contaminant it is necessary to optimize the analytical parameters. Another drawback of such algorithm is the use of conventional parameters that are highly affected by other environmental factors, such as different soil compositions, different fertilizer formulations, among others.

Commercial glyphosate contains toxic agents called adjuvants  (Mesnage  et  al.  2013).  Most investigators have neglected the analysis of these toxic products. This is clear from analyzing Table 1 where basically only AMPA and GLU are the most common chemicals simultaneously analyzed with glyphosate. In clinical tests, immunosensors are usually more sensitive than ELISA; however, for GLY analysis, ELISA has shown to be more sensitive than most of the methods presented in this review. Among chromatographic methods, the most sensitive one for GLY detection is liquid chromatography using solid-phase extraction coupled to mass spectrometry with electrospray ionization (LC–SPE–ESI/MS/MS). However, SERS was much more sensitive reaching attomole levels of GLY using gold nanorods, far surpassing the other methods, although it is not yet applicable to field conditions. Recovery studies are a classical technique for validating the performance of an analytical method, mainly in the absence of a reliable comparison method. Average recovery analytes (ARA) showed superior performance for diffuse reflectance spectroscopy.

The detection of Glyphosate in living tissues with high protein content appears to exhibit a systematic negative error. Studies with bluegill sunfish exposed to 14C-radiolabeled Glyphosate showed subsequent contamination in which the amount of radiolabeled extracted with EDTA was greater than the GLY content detected in these fish. After the digestion procedure of these samples with protein K and a new extraction with EDTA, a significant increase of radiolabeled occurred, suggesting that the GLY is strongly incorporated to the protein. Probably GLY is misleadingly replacing the amino acid coding for glycine during protein synthesis (Anthony Samsel and Stephanie Seneff 2017). Generally, the analytical chemistry is faced with problems in method development, reachable detection and quantification limits, for GLY (Huhn 2018).

Nowadays, many kinds of glucometers are known as reference platforms for detection,  due  to  their  sensitivity, portability, reproducibility, fastness, specificity, selectivity, stability, low cost and easiness to operate. However, these characteristics cannot be found in Glyphosate detectors. There are three classes of security levels for food and potable water in which a detector can operate: below the 0.1 ng mL−1 limit (EU), above 700 ng mL−1 limit (US) and between both. Most of the sensors that reach EU values fail in other aspects as reproducibility, possible use in real samples, stability, portability or selectivity. It should be pointed out, however, that sometimes the method of choice should be cheaper and less time-consuming, instead of being highly sensitive. Sensors that do not need pre- or post-derivatization, or pretreatment of samples, are the most needed characteristic, and this is one of the drawbacks of the current methods. There is an urgent need to investigate residual applications of GLY directly in environmental samples on site, and for this, sensitivity, specificity, portability and speed are essential. Interestingly, such characteristics have been reported for GLY sensing using colorimetric or electrochemical biosensors, but these biosensors are difficult to prepare and maintain, due to the use of antibodies as probes, which require controlled conditions for optimal operation. In this sense, the major concern is the shelf life of such sensors, and solutions must search for greater stability prior to detection. Several authors have also claimed the development of low-cost methods to detect GLY, but none of them have published their costs or compared with other methods. Real-time detection at lower cost, faster, with good sensitivity is important issues, and at the moment no method can reach the required parameters for field tests with environmental samples.

Another important issue is that GLY is never used alone, which means that commercial formulations contain Chang and Wei (2005) adjuvants as additional toxic agents. They are used to increase Glyphosate toxicity by allowing its penetration into plants and in some cases are more toxic than GLY, but they are never included in GLY long-term toxicity tests and are considered to be inert. They constitute a “black hole” in pesticide toxicology, because they are often kept secret by companies, and are never measured in the environment, and so, they are not included in the establishment of pesticide acceptable daily intakes. So, pure GLY purchased from chemical companies is not the commercial form used, and the pure form is the one used for the development of sensors. Therefore, the true need is the ability to quantify GLY in real environmental complex matrices and not as a pure GLY form dissolved in ultrapure water. The ability to quantify GLY bound to metal ions and cations (Ca2+) in soil or in water in a fast, simple and sensitive way using a stable portable device is still a challenge.

Table 1. Comparative analysis among glyphosate detection methods
Method    Samples      Simultaneous identification     LOD ng mL−1 or ng g−1 (RSD)    Other analytical characteristics   References
HPLC     Pregnant women Umbilical cord   Paraquat      0.4  94.33–99.03% (ARA)     Fluorescence (detection)         Kongtip et al. (2017)
HPLC    General water    –        0.02–6.25 × 103    –    Ding et al. (2013)
HPLC    Soils and sludges    –        10 (< 15%)   9-Fluorenyl methoxycarbonyl chloride (derivatization) 75-110% (ARA) Fluorescence (detection)        Sun et al. (2017)
HPLC    Seawater         AMPA        0.69-Fluorenyl methoxycarbonyl chloride (derivatization) Borate buffer  Fluorescence (detection) Wang et al. (    2016b)
HPLC/MS (HILIC–MS/MS)  Olive oil and olives  Amitrol, cyromazine, diquat, paraquat, mepiquat, trimethylsulfonium, fosetyl aluminum  50 Liquid partitioning with methanol         Nortes-Méndez et al. (2016)
HPLC/MS    Milk and urine produced by lactating women  AMPA  0.28 (urine)  The produtes aren’t detectable in milk    Nortes-Méndez et al. (2016)
HPLC/MS/MS    Water matrices (drinking, surface and groundwater) AMPA 0.1 No derivatization 85–113% (ARA)     Guo et al. (2016)
LC–ESI/MS/MS    Soybean  AMPA, GLU    300 (5.3–13%) 73.9–109.1% (ARA)          Martins-Júnior et al. (2009)
LC–ESI/MS/MS    Soybean    AMPA        5 (5.3–13%)  73.9–109.1% (ARA)              Martins-Júnior et al. (2011)
LC/FLD        Fatty matrix (rapeseed)  AMPA        20 (12.8–14.7%) 70.8–74.1% (ARA)      Kaczyński and Łozowicka (2015)
LC–SPE–ESI/MS/MS        General water    AMPA, GLU    0.0002  (< 7%)  91–107% (ARA)       Ibáñez et al. (2005, 2006), Vreeken et al. (1998)
LC–SPE–ESI/MS/MS        Surface, drinkable and waste water    AMPA, GLU    0.03  (< 8.4%)    50 samples     (62 min run−1) in a sequence (analysis time)    96% (ARA)  9-Fluorenyl methoxycarbonyl chloride (derivatization)        Vreeken et al. (1998)
LC/MS–ESI    Urine and serum    BIA, GLU, AMPA, 3-MPPA  0.05    –Sato et al. (2009)
LC/MS/MS    Water    AMPA, GLU    1.2 (6.3–10.2%)    12 min (analysis time)        77.0–102% (ARA)    Metal ions, sample preservation, and storage time (interferents)    Hao et al. (2011)
LC/MS/MS    Water    AMPA    0.025 (groundwater)    0.066 (surface water)    0.105 (WWTP effluent)    Fluorenylmethyl chloroformate (derivatization)  97.0–100% (ARA)  Poiger et al. (2017)
LC/MS/MS    Fatty matrix (rapeseed)    AMPA      5 (6.9–9.2%)    88.8–95.0% (ARA)    Kaczyński and Łozowicka  (2015)
Table 1  (continued)
Method        Samples    Simultaneous identification      LOD ng mL−1  or ng g−1     (RSD)  Other analytical characteristics    References
LC/MS/MS    Serum    AMPA, GLU, BIA, AMPA, 3-MPPA    30 (5.9%)  30 min (analysis time)  94–108% (ARA)  Often observed (interferents)    Filter (pretreatment)         Yoshioka et al. (2011)
LC/MS/MS    Blood, urine and gastric content samples    Paraquat, diquat, GLU    100  pH (4–8)    Tsao et al. (2016)
LC–MS        Coffee leaves    AMPA     41     FMOC (derivatization)            Schrübbers et al. (2016)
LC–FLD + MS/MS    Water canals    AMPA    0.058    Lyophilization (3–4 days for 72 samples) pH 9 FMOC-Cl (derivatization)    Ramirez et al. (2014)
LC–SPETap,     filtered and river water    AMPA    200    8 min (analysis time) 67.1–104.0% (ARA)    Delmonico et al. (2014)
GC/CI/MS        Biological        Phosphonoglycine, phosphonosarcosine, phosphonoalanine, phosphono-b-alanine, phosphonohomoalanine, phosphono-gama-homoalanine, GLU    1    TFA–Gly (OMe)2 (derivatization)        Kudzin et al. (2003)
GC/FID    Biological    Phosphonoglycine, phosphonosarcosine, phosphonoalanine, Phosphono-b-alanine, phosphonohomoalanine, phosphono-gama-homoalanine, GLU    30    TFA–Gly (OMe)2 (derivatization)    Kudzin et al. (2003)
GC/FPD    Rice, soybean sprouts        GLU, AMPA, 3-MPPA    20    Trimethyl orthoacetate (TMOA) (derivatization)    Tseng et al. (2004)
GC/FPD    River water, soil, carrot    GLU, AMPA    8,1 2 and 20 pg, respectively/injection 20 min (analysis time) 91–106% (ARA) N-isopropoxycarbonylmethyl (derivatization)        Kataoka et al. (1996)
GC/MS    Biological        Phosphonoglycine, phosphonosarcosine, phosphonoalanine, phosphono-
b-alanine, phosphonohomoalanine, phosphono-gama-homoalanine, GLU    1.5    TFA–Gly (OMe)2 (derivatization)    Kudzin et al. (2003)
GC/MS    Human serum    AMPA    250    t-BDMS (derivatization) > 73% (ARA)        de Llasera et al. (2005)
GC/MS    Groundwater    AMPA    0.1 (10%)    103% (ARA) Trifluoroacetic anhydride (TFAA) and trifluoroethanol (TFE) (derivatization)    Kudzin et al. (2002)
GC/MS    Soil        AMPA    6 (23%)     78% (ARA) Trifluoroacetic anhydride (TFAA) and trifluoroethanol (TFE) (derivatization)    Kudzin et al. (2002)
GC/MS/MS    Deionized water    –    0.24Trifluoroacetic anhydride (TFAA) and 2,2,3,3,4,4,4-heptafluoro-1-butanol (HFB) (derivatization) Nitrite nitroso ion and amylum and iodine (pre-treatment)        Lou et al. (2001), Ding et al. (2015) and Pei and Lai (2004)
GC/IT–MS    GLY, GLU and bialaphos GLU, BIA, their metabolises and nineteen amino acids    10–20     N-methyl-N-(tert-butyldi-methylsilyl) trifluoroacetamide in dimethylformamide (derivatization)        Tsunoda (1993)
IC    Water Bentazone and picloram    1.54 11.0–106.0% (ARA) 50 min per assay (analysis time)        Luo et al. (2015)
IC    Aquatic environment    Not informed    0.04 (1.94%)    96.4–103.2% (ARA) inorganic ion and organic acids (no interferents)    Zhu et al. (    1999)
IC/ICP–MS    Water    AMPA, polyphosphates    0.7 (≤ 7.4% for n = 3) 97.1–107.0% (ARA) 500 μL (sample injection volume)    Guo et al. (2005)
IC/ICP–MS    Reservoir and treated water, and clean water reclaimed from waste water    GLU, fosamine, ethephon 1.1–1.4    95–109% (ARA)    Guo et al. (2007)
FS-FPMs/DNA    Distilled water    –    0.04    Unknown (interferents)        Lee et al. (2013)
DRS    Commercial formulations, environmental and drinking waters    –    7280 (4.6–5.4%)    93.2–102.6% (ARA)    Cu2+,    Fe3+,   Zn2+,   Mn2+ and SO4 2−,    CO3 2−,   C6H5O7 3−,  PO4 3−,    NO3−     (Interferents) 20 μL (sample volume)    da Silva et al. (2011)
Table 1 (continued)
Method    Samples    Simultaneous identification    LOD ng mL−1 or ng g−1 (RSD)
Other analytical characteristics        References
IS        Water        –     0.021    25 min per assay (analysis time)  Automated (derivatization) Occasionally (Pretreatment) > 500 × (reusability) 500 μL (sample volume) 48 h (stability) González-Martínez et al. (2005)
IS    Soil (min. 10 g)        –    7.9    25 min (analysis time)    Automated (derivatization) Occasionally (Pretreatment) > 500 × (reusability) 48 h (stability)    González-Martínez et al. (2005)
IS    Pearl River water, tea and soil    –    8    87.4–103.7% (ARA)    Wang et al. (2016a)
Spectrophotometric    Groundwater    Gibberellins    0.82    –    Zhang et al. (2015b)
Spectrophotometric    Commercial formulation in soil and water samples    –    3380 (0.5–1.02%) 60 s (analysis time) 90.3–96.5% (ARA)    Dithiocarbamate (derivatization)    Sharma et al. (2012)
Spectrophotometric    Soil        –    1100 (2.7%) 80.0–87.0% (ARA)    Jan et al. (2009)
Spectrophotometric    Wheat grain    –    1100 (2.7%)    95.0–102.0% (ARA)    Jan et al. (2009)
Spectrophotometric    Distilled water    –    1100 (2.7%)    92.0–5.0% (ARA)    Jan et al. (2009)
Spectrophotometric    Legume    –    210    98.0–102.0% (ARA)        Çetin et al. (2017)
Fluorescence        Agricultural products    GLU    –    Laser-induced fluorescence Microchip electrophoresis    Wei and Pu (2015)
Fluorescence        Water        –    670    Sensor synthesized by combining copper (II) oxide and multiwall carbonnano-tubes     (MWCNTs) 96–107% (ARA)        Chang et al. (2016b)
Fluorescence        Milli-Q water    –    12    Fluorescence (CDs/AgNPs)    Wang et al. (2016c)
Fluorescence        Water, tea, soil    –    8     Carbon dot-labeled antibodies (CD-IgG) Antigen and magnetics beads (GLY-Fe3O4)    87.4–103.7% (ARA)            Wang et al. (2016a)
UV–Vis spectroscopy    Aqueous media    –    84    9-Fluorenylmethoxycarbonyl chloride (FMOC-Cl) (derivatization) Organic matter (interferents)        Waiman et al. (2012)
UV–Vis spectroscopy    Aqueous solution    –    3200 Tungsten halogen lamp coupled to the cuvette holder by a 500 μm core diameter optical fiber            De Góes et al. (2017)
FS-AU/DNA    Distilled water    Pesticides and target materials containing carboxyl groups    10    2 h (analysis time)        Lee et al. (2010)
SERS        Tomato juice    –    16.9 × 10-12 (2.48) 90% (ARA)    4-Mercaptophenylboronic acid (derivatization)        Torul et al. (2010)
SERS        Water Spiked beer    –    0.1 (water), 0.01 (spiled beer) Thiocholine-induced aggregation of OsCO-Au NPS        Tan et al. (2017)
SERS        Aqueous solution    –    900    He–Ne laser 632.8 nm Silver nanoparticles    De Góes et al. (2017)
SPR-pd    Buffer solution    –    98    Glycine, thiacloprid, and imidacloprid (no interferents) Ding and Yang (2013)
NMR     31P, Biological fluids and tissue digest    –    1 × 10    6    1 min (analysis time)    Just an enzymic digestion of the liver (pretreatment)            Dickson et al. (1988)
NMR 1H 31P    Biological fluids Salicylate, alcohol/glycol    33,814  10–20 min (analysis time)    Cartigny et al. (2004)
Colorimetric assay    Water and food  –    2.9    2-Mercapto-5-nitrobenzimidazole-capped silver nanopar-ticles adding  Mg2+        Rawat et al. (2016)
Colorimetric sensor    Drinkable, lake and ground water    –    169    20 min (analysis time) H2PO4 2−,   HPO4 −,   SO4 2−,  C2O4 2−,    CO3 2−,   F−,   Cl−,  NO3−, chloride salts of ion  Na+,   K+,   Ca2+,   Ba2+ and  Mg2+,   KNO3, KBr, and Pb(NO3)2, dicamba, AMPA, acetochlor (at 4  μg mL−1), atrazine (at 4 μg mL−1) (not interferents)Concentrations can be differ-entiated by naked eyes         Chang et al. (2016a)
Strip colorimetric cdPVA    Environmental water    –    100    1–3 s (analysis time)AMPA and glycine (no interferents) 30 μL (sample volume) 20 days (stability)        De Almeida et al. (2015)
Strip colorimetric test    River water    AChE inhibitors    100    30–60 min (analysis time)        Liu et al. (2015)
CS-AuNPs    Tap water with chloro–1    0.1 mM of GLU, AMPA, dicamba, acetochlor, atrazine, and trifluralin (not interferents) SO4 2−, Al(III) and Cu(II) (interferents)        Zheng et al. (2013)
TR-FRET    Water    –    131.9    Lanthanide (Ln 3+)-doped nanoparticles    Wang et al. (2016d)
FRET        Apple    –    9.8 × 10−3    Vitamin C, Vitamin B2, AMPA and GLU (not interferents) 2–15 min (analysis time) pH 7.0        Guo et al. (2014)
OC-IIDA    Water, salted water    –    200    12 simultaneous tests        Minami et al. (2014)
CL-MIS-MIMs    Foodstuff, water    –    46 (4.68% for n= 11) 96 independent measurements sequentially in 10 min (analysis time)        Zhao et al. (2011)
Oscillo-polarographic    Formulations and soil    –    96 (1.7) N-nitroso-N-(phosphonomethyl) glycine     (derivatization) Sun et al. (2007)
DPP (differential pulse polarography)    Crops, soil, and water    –    500–1000    > 60% (ARA) aminomethylphosphonic acid (not interferents)        Friestad and Brønstad (1985)
Amp-HRP    Corn GLU    0.1    60 × (reusability)        Songa et al. (2009c)
DIPN-GNPs-PGE    Water    GLU    0.34 (0.13%)    97.8–102.3% (ARA) N-nitroso (derivatization) 20 × (reusability) 60 days (stability)        Prasad et al. (2014)
DIPN-GNPs-PGE    Soil    GLU    0.35 (0.48%)    98.6–102.8% (ARA) N-nitroso (derivatization) 20 × (reusability) 60 days (stability)        Prasad et al. (2014)
DIPN-GNPs-PGE    Human serum GLU    0.35 (5.49%)    98.1–110.2% (ARA) N-nitroso (derivatization) 50-fold dilution (Pretreatment) 20 × (reusability) 60 days (stability)        Prasad et al. (2014)
ES-Atemoya    Environmental water    –    30 (5.5%)    94.9–108.9% (ARA) 8 weeks (Stability)    Oliveira et al. (2012)
ES-HRP        Doubly distilled water    –    1.7    20 min (analysis time) Unknown (interferents)    Songa et al. (2009b)
ES-HRP        Phosphate buffer solution (0.1 M, pH 6.10) AMPA    0.16    20 min (analysis time) Unknown (interferents) 3 × (reusability)        Songa et al. (2009b)
NiAl-LDH    Deionised water    GLU    169    20 s (analysis time)        Khenifi et al. (2009)
VET        Aqueous environments    –    16,905    Humic acids and  Ca2+ (not interferents)    Bataller et al. (2012)
Spectroelectrochemical Electrochemical    Double-distilled water AMPA    16,905    Screen-printed electrode (SPE) Signal of [Ru(bpy)3]2+ (ECL)        Habekost (2017)
DPV (differential pulse voltammetry)    Nature water    –    59    Copper electrode Phosphate buffer 0.05 mol L−1 and pH 7.3        Garcia and do Carmo Rollemberg (2007)
DPV    GLY 99.9% purediluted        –    2.02    Copper phthalocyanine/multiwalled carbon nanotube film-modified glassy carbon        Moraes et al. (2010)
SWV (square wave voltammetry)    GLY 99.9% purediluted    –    25    Mercury drop electrode N-nitroso (derivatization)    Teófilo et al. (2004)
SWV        Soil    –    25    Carbon fiber microelectrode    Phosphate buffer 0.2 mol L−1 and pH 5.3 (pretreatment)     88.5–102.3% (ARA)         Tapsoba et al. (2012)
PEC        Orange juice    –    0.004 (2.9–3.6%)        94.5–114.9% (ARA) pH and time dependent (pre-treatment) Starch, carbendazim, PMG, vitamin B1, glucose, sulfluramid, blank, sucrose and acetochlor (not interferents)
Li et al. (2016)
CE        Marijuana Paraquat and AMPA    8000    Indirect UV/VIS detection        Sharma et al. (2012)
CE        Natural waters AMPA    85  (< 6%)    84–87% (ARA) p-toluenesulfonyl chloride (derivatization) Salts in water (interferents)        Corbera et al. (2005)
CE-C(4)D    Drinking water    GLU, AMPA    0.1–2.2 (10%)    Not informed        See et al. (2010)
CE/LIF        River water, broccoli, soybean GLU    0.34    84.0–101.0% (ARA)    Wei et al. (2013)
CE/MS/mESI    Wheat GLU, AMPA, 3-MPPA    169.07 (1–2%)    –    Goodwin et al. (2003)
CE/UV–Vis    Ground and lake water GLU, AMPA    16.9    9-Fluorenylmethyl chloroformate (derivatization)
Table 1 (continued)
Method    Samples    Simultaneous identification    LOD ng mL−1 or ng g−1  (RSD) Other analytical characteristics        References
CI-ELISA    Water        –     76            Glyphosine, AMPA (interferents)    Clegg et al. (1999)
L’ELISA     Surface and ground waters    –    0.1 (0.2%)     Succinic anhydride (derivatization)     Lee et al. (2002)
Cyan-sensor     Water, soil, environmental     –     450 Minutes till some days (analysis time) Other herbicides, heavy metals copper and zinc and a representative volatile organic 3,5-DCP (interferents) Shao et al. (2002)
BERA         Distilled water        –0.1 3–5 min (analysis time)         Kintzios et al. (2001)
CRMS Water for treatment, Atrazine, lead nitrate, cadmium nitrate 2000 1 min (analysis time) Che and Liu (2014)
LOD lower detection limit,
RSD relative standard deviations

The authors are grateful for the financial support of the state funding agencies, Fundação de Amparo à Pesquisa do Estado de Minas Gerais (FAPEMIG-Process: 01/17 CEX APQ 02633/17), Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) and Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES).
Compliance with ethical standards
Conflict of interest 
The authors declare that they have no conflict of interest.
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A. L. Valle1· F. C. C. Mello2· R. P. AlvesBalvedi3· L. P. Rodrigues4· L. R. Goulart1*
  L. P. Rodrigues *L. R. Goulart
1 Institute of Genetics and Biochemistry, Federal University of Uberlândia, Av. Amazonas s/n, Campus Umarama, Bl. 2E, Sl. 248, Uberlândia, MG CEP: 38400-902, Brazil
2 Institute of Health Sciences, Federal University of Uberlândia, Av. Pará 1720, Uberlândia, Brazil
3 Federal University of Triângulo Mineiro, Av. Paranaiba 1295, Unid II, Iturama, MG, Brazil
4 Institute of Engineering, Science and Technology, Federal University of Jequitinhonha and Mucuri’s Valleys,
Av. Manoel Bandeira 460, Janaúba, MG CEP: 39.440-000, Brazil

(6) (PDF) Glyphosate detection: methods, needs and challenges. Available from: [accessed Oct 09 2019].