Fast chromatographic determination of free amino acids in bee pollen

Citation: Martín-Gómez, B.; Salahange, L.; Tapia, J.A.; Martín, M.T.; Ares, A.M.; Bernal, J. Fast Chromatographic Determination of Free Amino Acids in Bee Pollen. Foods 2022,11, 4013. https:// doi.org/10.3390/foods11244013 Academic Editor: Fatih Oz Received: 18 November 2022 Accepted: 9 December 2022 Published: 12 December 2022 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). foods Article Fast Chromatographic Determination of Free Amino Acids in Bee Pollen Beatriz Martín-Gómez 1, Laura Salahange 1, Jesús A. Tapia 1,2 , María T. Martín1, Ana M. Ares 1 and JoséBernal 1,* 1Analytical Chemistry Group (TESEA), I. U. CINQUIMA, Faculty of Sciences, University of Valladolid, 47011 Valladolid, Spain 2Department of Statistics and Operations Research, Faculty of Sciences, University of Valladolid, 47011 Valladolid, Spain *Correspondence: [email protected]; Tel.: +34-983186347 Abstract: The consumption of bee pollen has increased in the last few years due to its nutritional and health-promoting properties, which are directly related to its bioactive constituents, such as amino acids. Currently, there is great interest in understanding the role of these in bee products as it provides relevant information, e.g., regarding nutritional value or geographical and botanical origins. In the present study, two fast chromatographic methods were adapted based on commercial EZ:faast ™ kits for gas chromatography-mass spectrometry and liquid chromatography–mass spectrometry for determining free amino acids in bee pollen. Both methods involved the extraction of amino acids with water, followed by a solid phase extraction to eliminate interfering compounds, and a derivatization of the amino acids prior to their chromatographic separation. The best results in terms of run time ( <7 min ), matrix effect, and limits of quantification (3–75 mg/kg) were obtained when gas chromatography– mass spectrometry was employed. This latter methodology was applied to analyze several bee pollen samples obtained from local markets and experimental apiaries. The findings obtained from a statistical examination based on principal component analysis showed that bee pollen samples from commercial or experimental apiaries were different in their amino acid composition. Keywords: authentication; bee pollen; bioactive compounds; food analysis; food quality; free amino acids; GC-MS; LC-MS; markers; principal component analysis 1. Introduction Bee products, such as honey, royal jelly, or bee pollen, have been consumed since ancient times for their nutritional value and health promoting effects (of an antioxidant, anti-inflammatory, anti-cancer, analgesic, anti-fungal or anti-viral nature) [ 1 – 4 ]. The consumption of bee products has been increasing in interest in the last few years, and this is particularly relevant in the case of bee pollen [ 5 , 6 ]. However, the production of bee products cannot undergo a rapid growth in the short term, and this may result in fraudulent practice in the form of adulteration [ 7 ], which is causing significant damage to the beekeeping industry. Therefore, the authentication of bee products, especially honey and bee pollen, in terms of botanical and geographical origins is essential to protect consumer health and to avoid fraudulence [ 8 ]. One of the strategies employed to authenticate the origin of bee pollen is the study of its composition, as it is well-known that it is mainly dependent on the type of plant and the geographical origin [ 5 , 9 , 10 ]. Consequently, in the last few years, different families of compounds (lipids, phenolic compounds, betaines, glucosinolates, minerals and amino acids) have been examined in bee pollen not only to determine their nutritional or bioactive properties, but also regarding their function as markers of its origin [11–17]. Amino acids are responsible for a large part of the biological activity of bee pollen. They play an important role in human nutrition (e.g., in metabolism, reducing excessive body Foods 2022,11, 4013. https://doi.org/10.3390/foods11244013 https://www.mdpi.com/journal/foods Foods 2022,11, 4013 2 of 16 fat) [ 18 ]. They have been extensively studied in bee pollen in the last few years [5,9,17–22] , with the primary objective of characterizing bee pollen as regards its botanical or geographical origin, or to evaluate its nutritional value. It should be mentioned that we have recently demonstrated the potential of amino acids as markers of the apiary of origin and harvesting period [ 5 ], which represents a significant advance in the authentication of this product. However, the overall analysis time per sample, including sample treatment (solvent extraction and on-line derivatization) and chromatographic analysis (HPLC with fluorescence detector), is very high (>1 h), which could affect its applicability for analyzing many samples. The relatively long period of time required for both sample preparation and analysis is a common problem when determining amino acids by chromatographic techniques [ 23 ]. Fortunately, this procedure could be expedited by using some commercial kits (EZ:faast TM , Phenomenex, Torrance, CA, USA). Simple solid-phase extraction (SPE) and rapid derivatization of the amino acids combine to shorten preparation time considerably, and analysis time varies between 7 (gas chromatography-mass spectrometry, GC-MS) and 17 min (high performance liquid chromatography-mass spectrometry, HPLC-MS). These kits have previously been used in different food matrices [ 23 ], including honey [ 24 ], but to our knowledge they have never been used in bee pollen. Thus, the aim of the present study was to evaluate for the first time the potential of the EZ:faast TM GC-MS and HPLC-MS kits for determining free amino acid analysis in bee pollen. Additionally, the analytical performances of both methods were compared to choose the best option in terms of overall analysis time, sensitivity (limits of quantification, LOQ), matrix effect, and precision. Further aims of this work concerned determining the free amino acid content in bee pollen samples from different origins (commercial and experimental apiaries), and comparing these by means of chemometrics, or, more specifically, principal component analysis (PCA). 2. Materials and Methods 2.1. Chemical and Materials Solutions with mixing standards prepared from analytical grade standards at a concentration of 200 nmol/mL (see Tables 1and 2), reagents and organic solvents (see Section 2.3.2) were supplied in the EZ:faast TM GC-MS and LC-MS kits for free amino acid analysis (Phenomenex, Torrance, CA, USA). Ammonium formate (purity ≥ 97%) was supplied by Sigma-Aldrich Chemie Gbmh (Steinheim, Germany), while methanol (HPLC-grade) was obtained from LabScan Ltd. (Dublin, Ireland). Syringe filters (17 mm, Nylon 0.45 µ m) were purchased from Nalgene (Rochester, NY, USA), and ultrapure water was obtained from Millipore Milli-RO plus and Milli-Q systems (Bedford, MA, USA). An Eppendorf Centrifuge 5810R (Hamburg, Germany), a Moulinette chopper device from Moulinex (Paris, France), as well as a Vibromatic mechanical shaker, a Vortex device, and a drying oven from J.P. Selecta S.A. (Barcelona, Spain) were used for the sample treatment. Table 1. GC-MS data and limits of quantification for the studied free amino acids. Amino Acid (Abbreviation) Retention Time (min) Ions (m/z) LOQ (mg/kg) Alanine (ALA) 1.36 130 Q,C, 158 C, 88 C5 Glycine (GLY) 1.47 116 Q,C , 162 C , 102 C7 Valine (VAL) 1.69 158 Q,C, 116 C, 72 C4 Norvaline (NVAL; IS) 1.82 158 Q,C, 116 C, 72 CNE Leucine (LEU) 1.92 172 Q,C, 130 C, 86 C3 Isoleucine (ILE) 1.98 172 Q,C , 130 C , 101 C5 Threonine (THR) 2.21 101 Q,C, 160 C, 74 C7 γ-Amino-n-butyric acid (GABA) 2.23 144 Q,C , 172 C , 130 C18 Serine (SER) 2.26 146 Q,C, 203 C, 60 C8 Proline (PRO) 2.33 156 Q,C, 243 C, 70 C15 Asparagine (ASN) 2.42 69 Q,C, 155 C, 141 C75 Foods 2022,11, 4013 3 of 16 Table 1. Cont. Amino Acid (Abbreviation) Retention Time (min) Ions (m/z) LOQ (mg/kg) Aspartic acid (ASP) 3.00 216 Q,C, 130 C, 88 C5 Methionine (MET) 3.03 101 Q,C , 277 C , 203 C5 Hydroxyproline (HYP) 3.17 172 Q,C, 86 C, 68 C15 Glutamic acid (GLU) 3.37 230 Q,C, 170 C, 84 C25 Phenylalanine (PHE) 3.40 148 Q,C , 206 C , 190 C3 Glutamine (GLN) 4.08 84 Q,C, 187 C, 112 C11 Lysine (LYS) 4.73 170 Q,C , 153 C , 128 C4 Histidine (HIS) 4.92 81 Q,C, 282 C, 168 C40 Tyrosine (TYR) 5.22 107 Q,C , 206 C , 164 C3 Tryptophan (TRP) 5.52 130 Q,C , 332 C , 229 C19 QQuantification ions; CConfirmation ions; IS, internal standard; NE, not evaluated. Table 2. HPLC-MS data and limits of quantification for the studied free amino acids. Amino Acid (Abbreviation) Retention Time (min) Ions (m/z) LOQ (mg/kg) Arginine (ARG) 3.09 303 Q,C, 70 C, 156 C270 Homoarginine (HARG; IS) 3.25 317 Q,C, 128 C, 84 CNE Glutamine (GLN) 3.22 275 Q,C, 172 C, 84 C130 Serine (SER) 3.60 234 Q,C , 174 C , 146 C20 Asparagine (ASN) 3.72 243 Q,C , 157 C , 115 C210 Hydroxyproline (HYP) 3.90 260 Q,C , 172 C , 157 C160 Glycine (GLY) 4.10 204 Q,C , 248 C , 144 C40 Threonine (THR) 4.20 248 Q,C , 188 C , 160 C100 Alanine (ALA) 5.07 218 Q,C , 158 C , 130 C9 γ-Amino-n-butyric acid (GABA) 5.49 232 Q,C , 172 C , 130 C10 Sarcosine (SAR) 5.70 218 Q,C, 158 C, 88 C40 Ornithine (ORN) 6.50 347 Q,C , 287 C , 156 C15 Methionine-d3 (MET-d3; IS) 6.80 281 Q,C , 221 C , 193 CNE Methionine (MET) 6.88 278 Q,C , 218 C , 190 C50 Proline (PRO) 6.95 244 Q,C , 184 C , 156 C8 Lysine (LYS) 7.55 361 Q,C , 301 C , 170 C65 Aspartic acid (ASP) 7.57 304 Q,C , 216 C , 130 C35 Histidine (HIS) 7.60 370 Q,C , 196 C , 110 C17 Valine (VAL) 7.96 246 Q,C , 158 C , 116 C80 Glutamic acid (GLU) 8.06 318 Q,C , 258 C , 172 C16 Tryptophan (TRP) 8.46 333 Q,C , 273 C , 245 C20 Leucine (LEU) 9.50 260 Q,C, 172 C, 74 C16 Phenylalanine (PHE) 9.68 294 Q,C , 206 C , 120 C45 Isoleucine (ILE) 9.95 260 Q,C, 172 C, 74 C96 Tyrosine (TYR) 12.25 396 Q,C , 308 C , 136 C48 QQuantification ions; CConfirmation ions; IS, internal standard; NE, not evaluated. 2.2. Standards Standard in solvent solutions were prepared as indicated in the corresponding GCMS and HPLC-MS EZ:faast TM kits. Briefly, different volumes of the amino acid mixtures supplied in the kits (200 nmol/mL) were mixed with the internal standard solution to obtain five different calibration levels (LOQ (see Tables 1and 2), 20, 50, 100, and 200 nmol/mL). It should be mentioned that the standard mixtures of amino acid standards were prepared following the sample treatment described in Section 2.3.2, and that the concentration of each internal standard (IS; homoarginine (HARG) and methionine-d3 (MET-d3), HPLCMS; norvaline (NVAL), GC-MS) should be of 200 nmol/mL). On the other hand, matrixmatched standards were prepared to evaluate the analytical performance of the method (see Section 3.1.2). The only difference in relation to the standard in solvent solutions Foods 2022,11, 4013 4 of 16 was the use of bee pollen samples (0.05 g (HPLC-MS) or 0.10 g (GC-MS)) which were spiked after the extraction with ultrapure water with different volumes of the free amino acid standards (LOQ-200 nmol/mL) and the internal standards at the same concentration (200 nmol/mL). It should be noted that all the bee pollen samples analyzed contained endogenous free amino acids. Thus, to calculate the signal for the spiked bee pollen samples, the areas corresponding to endogenous levels had to be determined. These areas were subtracted from the total area obtained for the spiked samples. Stock amino acid solutions provided in the kits should be placed in the freezer after use. Meanwhile, calibration solutions were stored in glass containers in darkness at +4 ◦C. All solutions remained stable for over 2 weeks. 2.3. Sample Procurement and Treatment 2.3.1. Samples Bee pollen samples were obtained from four apiaries with homogeneous colonies of Apis mellifera iberiensis (one representative sample per apiary, n= 4; A1–A4) and from local markets in Valladolid (Spain; n= 8; C1–C8). It must be remarked that all the commercial samples were labelled as multifloral, and the specific geographical origin was not provided, as it was only stated that were produced in Spain. Moreover, it should be mentioned that a representative sample from each apiary (Pistacho, Fuentelahiguera, Tío Natalio, and Monte), located on the province of Guadalajara (Spain; see Supplementary Materials, Figure S1), was analyzed according to the results summarized in our recent study [5]. In addition, bee pollen samples were collected using pollen traps placed at the entrance of the hive. Every two weeks, the pollen trap grid was closed for a period of 24 h in the different hives. In the present study, samples were collected in June (2018). The pollen stored in the collection drawer during this period was collected, immediately sealed, identified, and taken to the laboratory where it was frozen until analysis. 2.3.2. Sample Treatment Bee pollen samples were mixed, ground, and pooled for optimum sample homogeneity. Next, the pollen was dried until the mass stabilized. Subsequently, it was stored in the dark at − 20 ◦ C until analysis. Samples were treated according to the procedures described in the EZ:faast TM GC-MS and LC-MS kits, and the only differences were the amount of bee pollen and the solvent used in the final reconstitution step. Figure 1outlines the steps of the sample treatment study. 2.4. Chromatographic Systems Chromatographic conditions were adapted from those recommended in the EZ:faast ™ GC-MS and HPLC-MS kits (Phenomenex) for free amino acid analysis. 2.4.1. GC-MS Conditions An Agilent Technologies (Palo Alto, CA, USA) 6890 GC coupled to an Agilent Technologies 5973 MS equipped with an ALS 7863 autosampler and MS ChemStation E 01.00.237 software (Agilent Technologies) was employed. The chromatographic column was a Zebron ZB-AAA (10 m × 0.25 mm × 0.25 µ m) from Phenomenex. Separation and detection conditions are summarized in Table 3. It should be mentioned that scan mode (50–450 m/z) was used for data acquisition to identify possible compounds characteristic of the locations. Meanwhile, quantification was performed in selected ion monitoring (SIM) mode, with one target/quantification and two qualifier ions for each analyte (see Table 1). Foods 2022,11, 4013 5 of 16 Foods 2022, 11, x FOR PEER REVIEW 5 of 16 Figure 1. Scheme of the proposed sample treatment. 2.4. Chromatographic Systems Chromatographic conditions were adapted from those recommended in the EZ:faast™ GC-MS and HPLC-MS kits (Phenomenex) for free amino acid analysis. 2.4.1. GC-MS Conditions An Agilent Technologies (Palo Alto, CA, USA) 6890 GC coupled to an Agilent Technologies 5973 MS equipped with an ALS 7863 autosampler and MS ChemStation E 01.00.237 software (Agilent Technologies) was employed. The chromatographic column was a Zebron ZB-AAA (10 m × 0.25 mm × 0.25 μm) from Phenomenex. Separation and detection conditions are summarized in Table 3. It should be mentioned that scan mode (50–450 m/z) was used for data acquisition to identify possible compounds characteristic of the locations. Meanwhile, quantification was performed in selected ion monitoring (SIM) mode, with one target/quantification and two qualifier ions for each analyte (see Table 1). Figure 1. Scheme of the proposed sample treatment. Table 3. GC-MS and HPLC-MS conditions. GC-MS Parameter Final Setting Programmed temperature conditions from 110 ◦C to 320 ◦C (0 min), at 30 ◦C/min Carrier gas Helium Flow-rate (mL/min) 1.1 Injector temperature 250 Injection volume (L) 2 Injection mode Splitless MS operating mode Electron impact Scan range (m/z) 50–450 MS temperatures ion source 240 ◦C, quadrupole 180 ◦C, and auxiliary 310 ◦C Foods 2022,11, 4013 6 of 16 Table 3. Cont. HPLC-MS parameter Final setting Gradient elution mode Ammonium formate (10 mM) in water (A) and ammonium formate (10 mM) in methanol (B): (i) 0.00 min (A:B, 32:68, v/v); (ii) 13.00 min (A:B, 17:83, v/v); (iii) 13.01 min (A:B, 32:68, v/v); (iv) 17.00 min (A:B, 32:68, v/v) Flow-rate (mL/min) 0.5 Injection volume ((L) 5 Temperature (◦C) 35 MS Ionization source ESI Scan range (m/z) 60–600 Capillary voltage (V) 3500 Fragmentor voltage (V) 60 Drying gas (N2) flow (L/min) 8 Drying gas (N2) temperature (◦C) 325 Nebulizer gas pressure (psi) 40 As can be seen in the total ion chromatogram (TIC; Figure 2), under optimal GC-MS conditions, all compounds eluted in less than 6 min. It should be mentioned that PRO peak is not complete in Figure 2due to the fact that the chromatogram was amplified in order to show the minor amino acids, not because it was saturated. Foods 2022, 11, x FOR PEER REVIEW 6 of 16 Table 3. GC-MS and HPLC-MS conditions. GC-MS Parameter Final Setting Programmed temperature conditions from 110 °C to 320 °C (0 min), at 30 °C/min Carrier gas Helium Flow-rate (mL/min) 1.1 Injector temperature 250 Injection volume (L) 2 Injection mode Splitless MS operating mode Electron impact Scan range (m/z) 50–450 MS temperatures ion source 240 °C, quadrupole 180 °C, and auxiliary 310 °C HPLC-MS parameter Final setting Gradient elution mode Ammonium formate (10 mM) in water (A) and ammonium formate (10 mM) in methanol (B): (i) 0.00 min (A:B, 32:68, v/v); (ii) 13.00 min (A:B, 17:83, v/v); (iii) 13.01 min (A:B, 32:68, v/v); (iv) 17.00 min (A:B, 32:68, v/v) Flow-rate (mL/min) 0.5 Injection volume ((L) 5 Temperature (°C) 35 MS Ionization source ESI Scan range (m/z) 60–600 Capillary voltage (V) 3500 Fragmentor voltage (V) 60 Drying gas (N2) flow (L/min) 8 Drying gas (N2) temperature (°C) 325 Nebulizer gas pressure (psi) 40 As can be seen in the total ion chromatogram (TIC; Figure 2), under optimal GC-MS conditions, all compounds eluted in less than 6 min. It should be mentioned that PRO peak is not complete in Figure 2 due to the fact that the chromatogram was amplified in order to show the minor amino acids, not because it was saturated. Figure 2. Representative chromatogram (total ion chromatogram (TIC) mode using the quantification ions; see Table 1) obtained from a standard in solvent mixture of free amino acids. The GC-MS conditions are summarized in Section 2.4.1 and Table 1. 1, ALA; 2, GLY; 3, VAL; 4, NVAL (IS); 5, LEU; 6, ILE; 7, THR; 8, GABA; 9, SER; 10, PRO; 11, ASN; 12, ASP; 13, MET; 14, MET-d3; 15, GLU; 16, PHE; 17, GLN; 18, LYS; 19, HIS; 20, TYR; 21, TRP. Figure 2. Representative chromatogram (total ion chromatogram (TIC) mode using the quantification ions; see Table 1) obtained from a standard in solvent mixture of free amino acids. The GC-MS conditions are summarized in Section 2.4.1 and Table 1. 1, ALA; 2, GLY; 3, VAL; 4, NVAL (IS); 5, LEU; 6, ILE; 7, THR; 8, GABA; 9, SER; 10, PRO; 11, ASN; 12, ASP; 13, MET; 14, MET-d3; 15, GLU; 16, PHE; 17, GLN; 18, LYS; 19, HIS; 20, TYR; 21, TRP. 2.4.2. HPLC-MS Conditions An Agilent Technologies 1100 HPLC coupled to a MS detector (single quadrupole) equipped with an electrospray ionization (ESI) source was selected to perform the analyses. An EZ:faast ™ AAA-MS (250 × 3.0 mm, 4 µ m; Phenomenex) analytical column was used for separation of the amino acids. Separation and detection conditions are summarized in Table 3. Full-scan spectra were obtained by scanning from m/z60 to 600, and quantification was performed in selected ion monitoring (SIM) mode (see Table 2). Under optimal HPLCMS conditions, all compounds eluted in less than 14 min (see Figure 3). Foods 2022,11, 4013 7 of 16 Foods 2022, 11, x FOR PEER REVIEW 7 of 16 2.4.2. HPLC-MS Conditions An Agilent Technologies 1100 HPLC coupled to a MS detector (single quadrupole) equipped with an electrospray ionization (ESI) source was selected to perform the analyses. An EZ:faast™ AAA-MS (250 × 3.0 mm, 4 μm; Phenomenex) analytical column was used for separation of the amino acids. Separation and detection conditions are summarized in Table 3. Full-scan spectra were obtained by scanning from m/z 60 to 600, and quantification was performed in selected ion monitoring (SIM) mode (see Table 2). Under optimal HPLC-MS conditions, all compounds eluted in less than 14 min (see Figure 3). Figure 3. Representative chromatograms (SIM mode using the quantification ions; see Table 2) obtained from a standard in solvent mixture of free amino acids. The HPLC-MS conditions are summarized in Section 2.4.2 and Table 2. 2.5. Statistical Analysis Statistical analysis was performed by means of SAS PROC PRINCOMP and SAS PROC DISCRIM (version 9.4; SAS Institute Inc., Cary, NC, USA). Firstly, a principal component analysis (PCA) was employed. This is a multivariate technique to summarize data by reducing the number of quantitative variables, and to detect the principal components as linear relationships between the original variables [25]. PCA calculates so many components as quantitative variables have been measured in the sample, and all the PCAs should explain the entire original variability of the data. To determine how many principal components must be used in the discriminant analysis to classify each bee pollen Figure 3. Representative chromatograms (SIM mode using the quantification ions; see Table 2) obtained from a standard in solvent mixture of free amino acids. The HPLC-MS conditions are summarized in Section 2.4.2 and Table 2. 2.5. Statistical Analysis Statistical analysis was performed by means of SAS PROC PRINCOMP and SAS PROC DISCRIM (version 9.4; SAS Institute Inc., Cary, NC, USA). Firstly, a principal component analysis (PCA) was employed. This is a multivariate technique to summarize data by reducing the number of quantitative variables, and to detect the principal components as linear relationships between the original variables [ 25 ]. PCA calculates so many components as quantitative variables have been measured in the sample, and all the PCAs should explain the entire original variability of the data. To determine how many principal components must be used in the discriminant analysis to classify each bee pollen sample in one group, it is important to consider the proportion of accumulated variability explained by the components; if possible, at least 90%. Meanwhile, for a data set containing a classification variable defining groups of observations, the discriminant procedure obtains a criterion to classify each observation into one of the groups. The discriminant function obtained with the PROC DISCRIM program is quadratic when normality is assumed and the homogeneity of covariances is tested. Foods 2022,11, 4013 8 of 16 3. Results and Discussion 3.1. Chromatographic Methods 3.1.1. Optimization of the Methods As has already been mentioned in previous sections of this study, the general working conditions of the kits are specified by their manufacturer. Therefore, the conditions selected do not vary largely from those specified in the kits. However, we decided to carry out tests to verify the influence of certain parameters when determining amino acids in bee pollen samples, since, logically speaking, the proposed conditions are general and not adapted to a particular matrix. Firstly, the extraction step of the sample treatment was evaluated with the aim of identifying and quantifying the amino acids present in the pollen in the fastest possible way, while using a minimum of pollen and solvent. As chromatographic analysis was faster with GC-MS, the tests were initially performed with this technique. Free amino acids have generally been extracted from bee pollen with ultrapure water and ethanol [ 26 ]. Consequently, we tested both solvents. Results showed that twenty amino acids were identified when using ultrapure water, whilst only four of these (alanine, ALA; phenylalanine, PHE; proline, PRO; valine, VAL) were observed when ethanol was employed. The same behavior was observed with HPLC-MS, although the number of amino acids identified (twenty-three) was greater, as arginine (ARG), ornithine (ORN) and sarcosine (SAR) could now be discerned. Thus, ultrapure water was the solvent chosen to continue the experiments. Subsequently, different amounts of sample and solvent were tested (0.05–1.00 g; 2–5 mL). The best results in terms of the number of compounds detected, the proportion of amino acids extracted, the solvent, and the sample were obtained for GCMS when using 0.10 g of bee pollen and 2 mL of ultrapure water. Under these conditions twenty amino acids were identified, whereas with the more diluted options, histidine (HIS), lysine (LYS), tyrosine (TYR), and tryptophan (TRP), could not be detected. In relation to HPLC-MS analysis, a different sample amount, in this case the lowest (0.05 g), was chosen as the number of free amino acids extracted was the same as with larger amounts, and the proportion extracted was quite similar. Regarding the chromatographic conditions, optimization studies were carried out only with the injection volume and the ESI parameters for the HPLC-MS method. Meanwhile, the GC-MS conditions were the same than those specified in the kit, since the results were satisfactory when the recommended conditions were employed. An injection volume of 1 µ L is indicated in the kit for HPLC-MS, but with this value few amino acids were identified. Therefore, we decided to test larger injection volumes (5 and 10 µ L). These volumes provided better results in terms of identification and quantification, whilst the same number of amino acids were detected with these amounts. However, on quantification of the samples, a difference between the two volumes was observed, since the number of saturated peaks corresponding to certain amino acids was not the same. With 10 µ L, several saturated peaks were observed, compared with only two, namely HIS and PRO, when 5 µ L was used. Nevertheless, as these peaks also appeared saturated when an injection volume of 1 µ L was used, we finally decided to work with an injection volume of 5 µ L, bearing in mind the need to dilute the samples of bee pollen with ultrapure water (1:10, v/v) to determine HIS and PRO by HPLC-MS. This implies that samples analyzed by HPLC-MS should be injected twice if the minority amino acids were not observed in the diluted samples. Moreover, the ESI conditions were also examined, as indicated in the kit. Flow injection analyses were conducted for selecting the optimal ESI-MS parameters in the infusion mode (5 µ L/min) of standard solutions of three of the free amino acids (glutamine (GLN), PHE and PRO), the best results being obtained with the conditions detailed in Section 2.4.2. An example of the optimization procedure of fragmentor voltage for GLN is shown in Figure S2 (see Supplementary Materials). Under the chosen chromatographic conditions (see Section 2.4), all the compounds were eluted in less than 6 min (GC-MS) or 13 min (HPLC-MS; see Figures 1and 2), with an overall analysis time, including sample treatment and chromatographic analysis, of close to 30 min (GC-MS) or 50 min (HPLC-MS). It should be noted that the number of Foods 2022,11, 4013 9 of 16 compounds identified by HPLC-MS (twenty-three) was slightly larger than with GC-MS (twenty). According to the existing literature, these are not only the fastest chromatographic methods for determining amino acids in bee pollen, but also the proposals with the lowest amounts of solvents required. 3.1.2. Analytical Performance of the Methods Method selectivity was evaluated by injecting a set of extracts of bee pollen samples ( n= 6 ) onto the chromatographic systems, the results being compared with those obtained for the individual standards of the amino acids under study. It was observed that the retention times coincided perfectly in all cases and that there was a great similarity between the MS spectra of the amino acids in standard and bee pollen samples (see Supplementary Materials, Figures S3 and S4). The limits of quantification (LOQs) were determined experimentally as ten times the standard deviation of the intercept for the calibration curve (matrix-matched) divided by the slope [ 27 ]. As can be seen in Tables 1and 2, the values were lower in all cases when GC-MS was employed. In addition, the LOQs obtained were like those reported in previous studies [5,17,26]. Calibration curves were constructed by plotting the signal on the y-axis (analyte peak area/internal standard area) against analyte concentration on the x-axis, and calibration standards were prepared as described in Section 2.2. The graphs obtained in all the calibration curves were straight lines, with the coefficient of the determination values (R 2 ) above 0.99 in all cases. Working range was verified by examining the deviation of back-calculation concentration from actual concentration (<15%). To evaluate whether or not there was a significant matrix effect for each amino acid, the confidence intervals of the slopes were compared on standard in solvent and matrix-matched calibration curves. In the case of overlap, the slopes were significantly the same at a confidence level of 95%, whereby no matrix effect was considered to have been present. The results are summarized in Table 4, where we see eight amino acids with a significant matrix effect when using GC-MS, and thirteen for HPLC-MS. These results were confirmed when calculating the matrix effect with the following equation: 100 × [1 − (standard in solvent-slope/matrix-matched standard-slope)]. Values higher than 20 mean that a significant matrix effect was observed, which depending on the sign provoked a signal suppression (negative) or enhancement (positive). Therefore, standard in solvent calibration curves could be used for measuring amino acids that were not affected by the matrix effect, while matrix-matched standard calibration curves should be used for the other amino acids. Table 4. Calibration curve data (n= 3). Amino Acid GC-MS HPLC-MS SCI (SS) SCI (MMS) ME * SCI (SS) SCI (MMS) ME * ALA 0.073–0.078 0.058–0.078 −11 0.005–0.008 0.020–0.047 80 ARG NE NE NE 0.001–0.015 0.002–0.017 16 ASN 0.024–0.028 0.019–0.025 −18 0.001–0.003 0.001–0.003 4 ASP 0.039–0.044 0.042–0.045 5 0.023–0.032 0.021–0.032 4 GABA 0.003–0.004 0.002–0.004 −16 0.001–0.009 0.001–0.011 16 GLN 0.008–0.011 0.017–0.020 48 0.001–0.019 0.001–0.024 19 GLU 0.006–0.010 0.006–0.012 11 0.001–0.021 0.001–0.025 16 GLY 0.048–0.064 0.048–0.059 −4 0.006–0.011 0.012–0.036 64 HIS 0.006–0.007 0.007–0.010 23 0.071–0.087 0.061–0.069 −22 HYP 0.043–0.047 0.037–0.044 −11 0.001–0.004 0.001–0.005 16 ILE 0.016–0.023 0.011–0.014 −56 0.035–0.038 0.018–0.019 −97 LEU 0.059–0.073 0.034–0.041 −76 0.077–0.079 0.055–0.069 −26 LYS 0.010–0.023 0.024–0.026 34 0.001–0.024 0.041–0.046 71 MET 0.017–0.019 0.014–0.018 −12 0.027–0.040 0.024–0.027 −31 ORN NE NE NE 0.002–0.022 0.053–0.093 84 PHE 0.006–0.007 0.019–0.026 71 0.025–0.036 0.029–0.037 9 Foods 2022,11, 4013 16 of 16 18. 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