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Article

Comparison of Two Derivative Methods for the Quantification of Amino Acids in PM2.5 Using GC-MS/MS

by
Jungmin Jo
1,
Na Rae Choi
2,
Eunjin Lee
1,
Ji Yi Lee
1 and
Yun Gyong Ahn
3,*
1
Department of Environmental Science & Engineering, Ewha Womans University, Seoul 03760, Republic of Korea
2
Department of Environmental Engineering, Kangwon National University, Chuncheon 24341, Republic of Korea
3
Metropolitan Seoul Center, Korea Basic Science Institute (KBSI), University-Industry Cooperation Building, 150 Bugahyeon-ro, Seodaemun-gu, Seoul 03759, Republic of Korea
*
Author to whom correspondence should be addressed.
Chemosensors 2025, 13(8), 292; https://doi.org/10.3390/chemosensors13080292
Submission received: 11 June 2025 / Revised: 25 July 2025 / Accepted: 31 July 2025 / Published: 7 August 2025
(This article belongs to the Special Issue GC, MS and GC-MS Analytical Methods: Opportunities and Challenges (Third Edition))
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Abstract

Amino acids (AAs), a type of nitrogen-based organic compounds in the atmosphere, are directly and indirectly related to climate change, and as their link to allergic diseases becomes more known, the need for quantitative analysis of ultrafine dust (PM2.5) will become increasingly necessary. When sensing water-soluble AAs using a gas chromatograph combined with a tandem mass spectrometer (GC-MS/MS), derivatization should be considered to increase the volatility and sensitivity of target analytes. In this study, two methods were used to compare and evaluate 13 AA derivatives in PM2.5 samples: N-tert-butyldimethylsilyl-N-methyltrifluoroacetamide with 1% tert-butyldimethylchlorosilane (MTBSTFA w/1% t-BDMCS), which is preferred for silylation, and ethyl chloroformate (ECF) with methanol (MeOH) for chloroformate derivatization. The most appropriate reaction conditions for these two derivative methods, such as temperature and time, and the analytical conditions of GC-MS/MS for the qualitative and quantitative analysis of AAs were optimized. Furthermore, the calibration curve, detection limit, and recovery of both methods for validating the quantification were determined. The two derivative methods were applied to 23 actual PM2.5 samples to detect and quantify target AAs. The statistical significances between pairwise measurements of individual AAs detected by both methods were evaluated. This study will help in selecting and utilizing appropriate derivative methods for the quantification of individual AAs in PM2.5 samples.

1. Introduction

Amino acids (AAs) are organic compounds containing the basic amino groups (-NH2) and carboxyl groups (-COOH) [1,2], and their quantitative analysis has been attempted in various sample matrices such as plants [3,4,5], foods [6,7], tissues [8,9,10], biological fluids [11,12,13], and drugs [14,15]. As organic compounds containing atmospheric nitrogen, AAs mainly come from various sources such as primary biological organisms (bacteria, fungi, pollen, etc.) [16], biomass combustion [17,18,19], and the ocean [20,21,22]. AAs have been recognized to contribute to hygroscopic growth and cloud condensation nucleus (CCN) activity in the atmosphere, which may play an important role in climate change by being associated with radiative forcing at the Earth’s surface [17,22]. From a health perspective, proteinaceous AAs have been suggested to act as ice nuclei (IN) [23] and have allergenicity and deleterious effects on humans [9,15,24,25]. In addition to these aspects, the demand for atmospheric AA measurement is also increasing from the viewpoint of the oceanic biogeochemical cycle of nitrogen [26] as well as long-range transport [27,28,29]. However, only a few measurements of organic nitrogen compounds have been conducted due to analytical difficulties despite their need. Gas chromatography (GC) or liquid chromatography (LC) can be used to separate individual AAs, but due to their polar functional groups, demanding analytical conditions are needed. Due to the high water solubility of AAs, it is difficult to analyze them in an underivatized AA form using commonly used reversed-phase LC, and a hydrophilic interaction chromatography (HILIC) column needs to be used [30,31,32,33]. From the perspective of separating derivatized AAs, GC is superior to LC because it can use capillary columns with higher separation efficiency [34,35,36]. The derivative reactions that can be utilized in GC analysis include silylation, alkylation, or acylation, which typically change the analyte properties for better separation as well as enhanced method sensitivity [37,38,39]. There are a variety of detectors that can be used in combination with GC, but non-selective detectors such as the thermal conductivity detector (TCD), flame ionization detector (FID), and nitrogen–phosphorus detector (NPD) are not recommended for the analysis of derivatives [40]. By contrast, as a detector, mass spectrometers (MSs) provide significant sensitivity for this analysis and the exact identification of a solute eluted from a chromatographic column [41,42]. A tandem mass spectrometer (MS/MS) coupled with a gas chromatograph, which has recently been increasingly utilized in various matrices due to its superior sensitivity, efficiency, and reproducibility, was used as a detector for quantitative analysis [43,44,45].
There are more than 500 AAs known in nature that have multiple functional groups that can be derivatized, and because of their diverse structures and polarities, it is difficult to specify a single advantageous derivative method. Silylation reagents have been used for this derivatization up until now because of their high versatility [40,46,47,48]. They exhibit excellent sensitivity to derivatives of the analyte, and the mass spectra for various silyl derivatives (trimethylsilyl and tert butyldimethylsilyl derivatives) have also already been extensively constructed because of their high usability. However, a major point is that these reactions are moisture-sensitive and should be carried out in an anhydrous, or water-free, environment. This requires an additional drying step of the extracts [49,50]. The 13 targeted amino acids in PM2.5 in this study are also compounds with diverse structures and polarities (Table S1). The only derivatization used for AA analysis in atmospheric samples so far has been silyl derivative methods; despite the many benefits mentioned, the disadvantage of this method is that it takes a lot of time to dry the aqueous extract because of the strict drying conditions required. Therefore, this method is time-constrained for studies that require a large number of samples to be monitored. Unlike silylation agents which only work in the nonaqueous phase, alkyl chloroformate derivatization is known to be highly reactive in aqueous medium, less expensive, and less time-consuming during the process [51,52,53]. Therefore, alkyl chloroformate, which has not been used so far, was applied in this study for the quantitative analysis of 13 AAs present in atmospheric PM2.5 and was compared with the silylation method. However, this method requires a more extensive sample preparation including liquid extraction (LLE) and the reconstructed mass spectra for the derivatives of target AAs.
The aim of this study was to establish the procedures of sample preparation and GC-MS/MS conditions for two derivative methods and to verify the validity of these methods. By comparing the quantitative results of both methods for the actual samples, AAs with differences in quantification were identified and the preferred analytical methods of the corresponding AAs were suggested. This study is the first attempt to compare two derivative methods for the quantification of AAs in PM2.5 samples, and this attempt will be helpful in finding an appropriate method for analyzing various types of AAs and atmospheric AA measurement.

2. Materials and Methods

2.1. Chemical and Reagents

All individual AAs, except for L-aspartic acid (Asp), DL-isoleucine (Ile), L-methionine (Met), and L-threonine (Thr), were purchased from Daejung Chemical and Metals Co. (Siheung, Korea). Asp and Met were purchased from Junsei Chemical Co. (Tokyo, Japan), Ile from Sigma–Aldrich Co. (St. Louis, MO, USA), and Thr from Samchun Chemical Co. (Korea). All AAs were present in the L form in the reagent except isoleucine, which was present in the racemic mixture. Information on the 13 target AAs, including their abbreviations, classifications, physicochemical properties, and chemical structures, is summarized in Table S1. Standard analytes were prepared by dissolving each in distilled water (DW) at a concentration of 1000 mg/L, except for L-tyrosine (Tyr), which was dissolved in 0.1 M HCl to prepare the stock solution. The standard mixture of AAs was prepared by diluting the stock solutions with methanol (MeOH). The DW used was ultrapure water (18.2 MΩ·cm) obtained directly from a Millipore Milli-Q system (Merck Millipore, Darmstadt, Germany). An internal standard (IS) mixture containing L-alanine-d4 (Ala-d4), L-methionine-d3 (Met-d3), and L-phenylalanine-13C6 (Phe-13C6) (Cambridge Isotope Laboratories, Inc., Andover, MA, USA), each at 0.5 ng/μL in DW, was used. The derivatization reagents, N-tert-butyldimethylsilyl-N-methyltrifluoroacetamide with 1% tert-butyldimethylchlorosilane (MTBSTFA w/1% t-BDMCS) and ethyl chloroformate (ECF), were purchased from Sigma-Aldrich (St. Louis, MO, USA). Pyridine was obtained from Daejung Chemical and Metals Co. (Siheung, Korea). MeOH (Fisher Scientific, Raleigh, NC, USA) and chloroform (JT Baker, Phillipsburg, NJ, USA) of high-purity HPLC-grade were used.

2.2. Sampling Site

In this study, AAs were analyzed using PM2.5 samples collected at the Gyeonggi-do Air Quality Research Center, located in Ansan, Gyeonggi-do, Republic of Korea (37°32′ N, 126°83′ E). The sampling site is shown in Figure S1. The sampling campaign was conducted during the winter period, from 4 February to 29 February 2024. PM2.5 samples were collected daily over a 23 h period, from 6:00 p.m. to 5:00 p.m. the following day. PM2.5 samples were collected at 1.0 m3/min using a high-volume air sampler (HV-1700RW, Shibata, Tokyo, Japan) with quartz fiber filters (QFFs, 203 × 254 mm, Whatman 1851-865, UK). QFFs were pre-baked at 550 °C for 12 h prior to sampling to remove organic contaminants. After sampling, all filters were wrapped in pre-baked aluminum foil and stored at −18 °C until analysis.

2.3. Sample Preparation of AAs in PM2.5 Samples

The analytes collected on QFF (9 cm2) were extracted by cutting them into four pieces, placing them into a sterilized 50 mL conical tube, and ultrasonically extracting them with 15 mL of DW for 30 min. Prior to extraction, 25 ng of the IS mixture was spiked into the sample. Extraction was repeated three times, and the combined extracts were filtered through a 0.45 μm polyethersulfone (PES) syringe filter. The filtrate was evaporated to dryness using a rotary evaporator at 40 °C. The residue was reconstituted in 5 mL of a 3:2 (v/v) DW/MeOH mixture, which was used as the final extract for subsequent derivatization using two different methods: Hereafter, two derivative names were conveniently used for each derivatizing reagent: MTBSTFA w/1% t-BDMCS and ECF-MeOH.
For the MTBSTFA w/1% t-BDMCS derivative method, 200 μL of the extract was transferred to an e-tube and evaporated to dryness under a stream of nitrogen (N2) gas at 40 °C. It took approximately 1–2 h to dry completely. After complete drying, 70 μL of MTBSTFA w/1% t-BDMCS and 30 μL of pyridine were added to the e-tube and mixed thoroughly. The mixture was then transferred to a vial and sealed. MTBSTFA w/1% t-BDMCS derivatization was performed by incubating the sealed vial for 2 h in a heat block (Thermolyne Type 17,600 Dri-Bath, Dubuque, IA, USA) preheated to 90 °C. Following the derivatization, the vial was cooled to room temperature for at least 30 min before GC-MS/MS analysis. For the ECF-MeOH derivative method, 1 mL of the extract was mixed with 1 mL of a 3:2 (v/v) DW/MeOH solution in a polypropylene test tube. Subsequently, 200 μL of pyridine and 100 μL of ECF were added, and the mixture was ultrasonicated for 1 min to accelerate the reaction.
The AA derivatives by ECF-MeOH derivatization were extracted with 500 μL of chloroform containing 1% ECF and vortexed for 30 s. The mixture was allowed to stand for a few minutes to achieve phase separation. The organic layer (bottom layer) was transferred to a separate test tube, and a small amount of anhydrous sodium sulfate was added to remove residual moisture. The derivatized solution was transferred to a vial containing an insert and analyzed by GC-MS/MS.
The concentration of IS in the final sample solution was 0.01 ng/μL for both derivative methods. The prepared samples were analyzed in triplicate, and all analyses were completed within three days. The mean value for the blank filters was subtracted in all analyses.
The analytical procedures for the extraction and derivatization of AAs in PM2.5 samples are shown in Figure 1.

2.4. GC-MS/MS Analysis

AAs were analyzed using a gas chromatograph–tandem mass spectrometer (GC-MS/MS) system from Agilent Technologies (USA), consisting of a 7890B gas chromatograph coupled with a 7010 triple quadrupole (QqQ) mass selective detector (MSD) (KBSI Instrument code, SD301). A DB-5MS UI capillary column (5% diphenyl, 95% dimethylsiloxane phase, 30 m × 0.25 mm × 0.25 μm) from J&W Scientific (Folsom, CA, USA) was used. High-purity helium was used as the carrier gas and N2 as the collision gas, with flow rates of 1.0 and 1.5 mL/min, respectively. An amount of 1 μL of the sample was injected in splitless mode at 280 °C. The oven temperature was initially set to 60 °C and held for 2 min, ramped to 310 °C at 10 °C/min, and held for 10 min. Target analytes were analyzed in multiple reaction monitoring (MRM) mode, with optimal precursor and product ions selected by applying appropriate collision energies (CEs). The molecular characteristics (molecular weights, formulas, and retention times) of AA derivatives obtained by two derivatization methods, as well as the analytical parameters used in the optimized MRM modes, are summarized in Table S2.

2.5. Method Validation

To validate the analytical methods, the linearity of the calibration curve, the limits of detection and quantification, and recovery were determined and compared for both derivative methods. The linearity of the calibration curve was verified by evaluating the coefficient of determination (R2) and the relative standard deviation (RSD) of the relative response factor (RRF) [45]. The acceptable range for the RSD of the RRF was set within ±25% [54,55,56]. The limits of detection were calculated and evaluated based on the method detection limit (MDL) and method quantification limit (MQL). The MDL was determined in accordance with the Eurachem guideline [57] by repeatedly analyzing the lowest concentration of actual matrix-based samples (spiked filters). This approach was adopted to reflect matrix effects and account for variability throughout the analytical process. The MDL was determined by analyzing seven replicates of the lowest-concentration filter samples and calculated by multiplying the standard deviation by 3.14 (t-value for 6 degrees of freedom) [58]. The MQL was calculated by multiplying the MDL value by 3 [59].
Recovery was evaluated by spiking blank filters with low (2 ng) and high (20 ng) concentrations of AA standard solutions. Triplicate measurements were performed at each level, and the average recovery was calculated to assess accuracy. Precision was evaluated based on the RSD of the triplicate measurements.

2.6. Statistics Analysis

A statistical comparison was conducted on the sample analysis results obtained from the two derivatization methods for AAs. The Shapiro–Wilk test was used to assess data normality, and either a paired t-test or the Wilcoxon signed-rank test was applied depending on the results. All statistical analyses were performed using IBM SPSS Statistics (version 25), with significance levels set at p < 0.05 and <0.01.

3. Results and Discussion

3.1. Optimization of Two Derivatization Methods

Chemical derivatization is indispensable for AA measurement by GC/MS due to their polar, zwitterionic, and nonvolatile properties. Up until now, the most widely used derivatization procedure for AA measurement in fine aerosol particles is silylation [21,60,61,62]. Among them, the MTBSTFA reagent is used more than trimethylsilyl-N-methyl trifluoroacetamide (MSTFA) in silylation reactions due to the stability of the derivatives, which is a reaction product [63,64]. The silylation reagent is moisture-sensitive, and silyl derivatization techniques have the main drawback of being a time-consuming sample preparation process because of the long-term drying of extracts of water-soluble AAs. Chloroformate is known to have a more rapid derivatization than silylation and can be processed under mild temperature and humidity conditions; accordingly, both methods were investigated for AA analysis in fine aerosol particles in this study. For tert-butyldimethylsilyl (TBDMS) derivatives of 13 AAs, parameters such as the temperature, time, and added volumes of the derivatization reagent and pyridine were optimized.
The optimal conditions for temperature and reaction time with only the MTBSTFA w/1% t-BDMCS derivative reagent were investigated using a 0.5 ng/μL AA standard solution as shown in Figure 2a,b. When comparing the sum of peak abundances of 13 AA derivatives, the derivatization temperature was highest at 90 °C (Figure 2a), and a reaction time of 2 h showed higher efficiency (Figure 2b) compared to other times not only for the sum of peak abundances for AAs but also for individual AAs at 90 °C. Basic pyridine could react with the acid produced in the silylation to increase the reaction speed [65]. Figure 2c shows the effect of the addition of pyridine on the derivative reaction with the final volume set to 100 μL, without or with an increase of 10 μL of pyridine. When the volume of pyridine increased to 30 µL (adding volumes of 70 + 30 µL for MTBSTFA w/1% t-BDMCS: pyridine) at 90 °C for 0.5 h, the total peak area of the AA derivative was highest. When more pyridine was added, the AA derivatives tended to decrease. An amount of 30 μL of pyridine was added in 70 µL of MTBSTFA w/1% t-BDMCS, and derivatization was performed while increasing the reaction time to 0.5, 1, and 2 h. As a result, the highest AA-derivatized products were measured at 2 h (Figure 2d). Figure 2e shows the results of comparing the total and individual AA derivatives in the reactions at 90 °C for 2 h without pyridine addition, with only 100 µL of MTBSTFA w/1% t-BDMCS, or with 30 µL of pyridine added to 70 μL of MTBSTFA w/1% t-BDMCS. The total AA derivative that was produced increased by a factor of about 1.5 in the silyl reaction by adding pyridine compared to without, and the results for the individual AA derivatives were the same, except for Trp. Accordingly, the best result of the derivative condition for AA was obtained by carrying out the reaction at 90 °C for 2 h, with 30 μL of pyridine added to 70 μL of MTBSTFA w/1% t-BDMCS.
The AA derivative method using chloroformate has been mainly applied in biological media, and the main reagent has been ECF. Based on the results of previous studies presented in Supplementary Table S3, the reaction conditions for the derivatization of AA with ECF were determined. The specific conditions are described in Section 2.3, and the results from adding a volume of the derivatizing reagent and the extraction conditions of AA derivatives from an aqueous sample are shown in Figure 3.
The optimal addition amount of ECF was investigated by adding 200 μL of pyridine to 2 mL of an aqueous sample containing 0.5 ng/μL of AA standard solution. When the reaction efficiency of total AA derivatives was compared by peak areas according to the amounts of ECF added (10, 30, 50, 80, and 100 μL), the addition of 100 μL of ECF showed the highest efficiency. In particular, the Asn among the 13 target AAs increased dramatically according to the amount of ECF added, as shown in Figure 3a. The AA derivative products by ECF were extracted from an aqueous sample with chloroform, and three extraction conditions were compared based on the extracted added volume of chloroform, the extraction efficiency in this solvent extraction, and the number of extractions, as shown in Figure 3b. As a result of comparing the extraction efficiency of AA derivatives based on the Ea condition, it was found to increase approximately 2- and 2.5-fold under Eb and Ec conditions, respectively. Therefore, the amount of ECF added and the solvent extraction of AA derivatives with chloroform were finally determined to be 100 μL followed by one extraction with 500 μL of chloroform, respectively.

3.2. Optimization of GC-MS/MS in MRM Mode

The conditions of GC-MS/MS were optimized to enable the quantitative analysis of 13 target AAs without the sample cleanup procedure under two derivative methods. The two derivatizing reagents (MTBSTFA w/1% t-BDMCS and ECF-MeOH) can generate single or more derivatives by substitutions on the polar function groups of AA.
Figure 4 presents the representative EI mass spectra measured in full scan mode of alanine derivatives generated by the reaction with two derivatizing reagents. In the reaction of Ala with the MTBSTFA w/1% t-BDMCS reagent, the main product was an ether containing two TBDMS (+2 TBDMS), which was generated by substituting with hydrogens on amine and acid groups in the molecule of Ala. In the case of other MTBSTFA w/1% t-BDMCS derivatizations of AAs, the ethers containing two TBDMS were the main products in most cases, and there were five AAs (Ser, Thr, Asp, Asn, and Tyr) that produced ethers containing three TBDMS (+3 TBDMS) (see Table S2). The ECF-MeOH derivatization procedure of AAs enables the conversion of a polar AA into a nonpolar volatile product by the simultaneous blocking of both the amino and carboxylic acid groups, and the main reaction product is N-ethoxycarbonyl amino acid methyl ester (ECME) [66,67]. The major products generated by the two different derivatization reactions of AA exhibit quite characteristic fragment ions in each EI mass spectrum, allowing for the determination of the quantifier and quantitative ions. Despite the low abundance of molecular ions by EI ionization, each TBDMS derivative of AA was clearly identified in the mass spectrum due to the cleavage of its characteristic fragment ion [M-57]+, tert-butyl, from the molecular ion. While the MS information of TBDMS derivatives for AAs are open-access in the NIST library, ECF-MeOH derivatives for AAs are less well known.
Figure 5 shows the chromatograms by comparing the results of two derivatives of AAs obtained from MRM conditions that allow for high selectivity by isolating target AAs from interferences, while clearly identifying them. The derivatized AAs with both derivatizing reagents had different retention times on the DB-5MS GC column because their molecular weights differ depending on the number of functional groups involved in each reaction. A feature in the separation of isomers such as isoleucine (D- and L-forms) was that, while partial separation was achieved for TBDMS derivatives (at retention times from 16.0 to 16.2 min in Figure 5a), complete separation was possible for ECF-MeOH derivatives (at retention times from 12.4 to 12.6 min in Figure 5b). According to these results, the use of the ECF-MeOH derivatizing reagent could be preferred over MSTFA in derivatization in GC/MS analysis for separating the D- and L-forms of isoleucine. However, MTBSTFA w/1% t-BDMCS derivatization could be preferred in the case of Ser and Thr because their ECF-MeOH derivatives were eluted closer together (at retention times from 12.5 to 12.6 min in Figure 5b) from the column compared to TBDMS derivatives of Ser and Thr (at retention times from 18.7 to 19.1 min in Figure 5a).

3.3. Method Validation of Two Derivative Methods

Method validations of the two derivative methods for the application to PM2.5 samples were performed to evaluate whether two optimized methods could provide accurate and reliable analytical results. Table 1 shows the linearities of the calibration curves, MDLs, MQLs, accuracies, and precisions for 13 AAs, which were obtained by the two methods.
Linearities were assessed using each calibration curve of the 13 AAs that were constructed with at least five concentration levels, and proper coefficients of determination (R2) greater than 0.99 were obtained for all analytes. The MDL and MQL of AAs were in the ranges of 0.094–2.336 and 0.281–7.009 ng/m3 obtained by the MTBSTFA w/1% t-BDMCS derivative methods, and in the range of 0.016–0.411 and 0.048–1.234 ng/m3 by the ECF-MeOH derivative method, respectively. Overall, the MDLs and MQLs of AAs by ECF-MeOH derivatization showed lower detection limits and, in the case of Phe, showed the lowest MDL level by the ECF-MeOH derivative method, which was 6 times lower than that by the MTBSTFA w/1% t-BDMCS derivative method. To validate the accuracy and precision of the two derivative methods, three replicate spiking experiments with AAs for the two levels (0.02 and 0.2 ng) were performed. The accuracy and precision were assessed by the average recovery and percentage RSD of three results at each concentration, as shown in Table 1. The acceptable recoveries in the range of 80–120% were obtained for all AAs by both derivative methods, except for Trp in the case of the MTBSTFA w/1% t-BDMCS derivative method. Precision was evaluated as an acceptable standard within 25% RSD [68,69,70], considering the influence of the matrix, since sample preparation was performed without a cleanup procedure after extraction and all AAs met this criterion. The recovery of AAs by the MTBSTFA w/1% t-BDMCS derivative method, which could be denatured by high temperature or specific chemical reactions [47,71], was slightly lower than that of the ECF-MeOH derivative method. Importantly, the average recovery and relative standard deviation of Trp by means of MTBSTFA w/1% t-BDMCS derivatization were 6.3% with 16.4% RSD at low concentration, and 43.7% with 6.7% RSD at high concentration, respectively. Due to the poor recovery, the MDL and MQL of Trp could not be measured by the MTBSTFA w/1% t-BDMCS derivative method. These results are in agreement with the previous studies and they have been reported to be due to the adsorption on glassware or other surfaces during the derivatization [62,72]. Unlike the TBDMS derivative method based on a post-extraction derivatization, the recovery of Trp in the ECF-MeOH derivatization method via the derivatization–extraction procedure was within an acceptable range.

3.4. Comparative Analysis of Atmospheric PM2.5 Samples

The determination of AAs in PM2.5 samples has only been studied using MTBSTFA w/1% t-BDMCS derivatization up until now [21,62]. Two validated methods were applied to 23 actual field samples and paired measurements of AAs were compared between the two paired results in this study. Table 2 shows the average concentrations of the detected AAs in 23 PM2.5 samples and the relative difference (%) between two derivative methods.
The relative differences in individual AA concentrations obtained by the two Tmethods were evaluated based on MTBSTFA w/1% t-BDMCS derivatization, considered a traditional method. Since the MDL of Trp could not be obtained by MTBSTFA w/1% t-BDMCS derivatization, it was not quantified in PM2.5 samples. AAs were detected in all PM2.5 samples in a diverse range of concentrations, and their relative differences between both methods ranged from 2.1 to −95.8%. The analytes had relative differences in concentrations between the two methods exceeding 25% for Ser, Asn, Asp, and Tyr. Among them, Ser exhibited the largest difference between the two methods, with differences of −95.8%.
A statistical normality test was performed to determine the quantitative difference between the two derivatives of individual AAs. For those meeting the assumption of a normal distribution, paired t-tests were applied, while Wilcoxon’s signed-rank tests were used for non-normally distributed data. Figure 6 shows the comparative statistical results.
A significance test was performed at a 99% confidence level for Leu. A total of 10 AAs showed no statistically significant differences between the two derivative methods at a 95% confidence level except Ser and Leu. It is common in statistical analysis to use a 95% confidence level [73], but p-values were measured including a 99% confidence level in this study. All AAs except Ser exhibited p-values ranging from 0.048 to 0.605, indicating no statistically significant differences between the two derivative methods at the 95 or 99% confidence levels. By contrast, Ser showed a p-value of 0.00006 (p < 0.05), indicating a statistically significant difference in concentration between two derivative methods. The comparison between the two derivatives of Ser and Thr revealed clear differences in the aspect of its resolution of adjacent peaks, which were identified as key contributors to the observed statistical difference. To determine the possibility of co-elution or matrix interference with Ser, spiking experiments using 0.4 ng/μL of pure standard were performed on the same extracted sample with different concentrations obtained by the two methods. The recovery of Ser obtained by the MTBSTFA w/1% t-BDMCS derivative method was 139%, while that obtained by the ECF-MeOH derivative method was 43%. For the matrix effect, it was calculated as 39% (moderate matrix effect) for the MTBSTFA w/1% t-BDMCS derivative method and –57% (strong matrix effect) for the ECF-MeOH derivative method. The procedures and results of the matrix spiking experiments are presented in Supplementary Table S4. These results indicate that matrix interferences affected the detection of Ser derivatives in both methods and likely contributed to the observed statistical difference in the quantification of Ser. To remove matrix interferences of each method, additional cleanup procedures should be considered after extraction.

4. Conclusions

For the quantification of 13 amino acids in PM2.5 samples using GC–MS/MS, the traditional MTBSTFA w/1% t-BDMCS derivative method and a newly attempted ECF–MeOH derivative method were compared in this study. Since the aqueous extracts from the PM2.5 sample were derivatized in two ways without clean-up procedures, the essential conditions such as reaction temperature, time, and volume of reagents required for each method were optimized. The ECF-MeOH derivative method could drastically reduce the sample preparation time compared to MTBSTFA w/1% t-BDMCS derivatization, which requires complete drying of the aqueous sample extracts and a reaction time of 2 h. The sample preparation time of the ECF-MeOH derivative method was about half that of MTBSTFA w/1% t-BDMCS derivatization, but an extensive procedure such as LLE was required. Furthermore, the appropriate MRM conditions were established to minimize the effects of interferences and increase the sensitivities of AA derivatives by two methods. The individual mass spectra of AA derivatives using the ECF-MeOH derivatizing reagent were built for their identification. To determine the applicability of the two derivative methods for the quantification of AAs in actual PM2.5 samples, method validation was performed including the linearity of the calibration curve, detection limit, and recovery test. The validation of the two methods for the quantification of all AAs in PM2.5 samples was confirmed except Trp, whose recovery and MDL were not verified by MTBSTFA w/1% t-BDMCS derivatization. Therefore, it was confirmed that the quantification of Trp in PM2.5 by the MTBSTFA w/1% t-BDMCS derivatization was not in favor. The results of the pairwise measurements of AAs in 23 actual field samples showed that there were no statistically significant differences between two methods at a 99% confidence except Ser. The statistical result for Ser was confirmed by the spiking experiments using a pure standard solution, indicating that matrix interferences affected the detection of Ser derivatives in both methods. Since the derivatives produced in the two derivatization reactions are in completely different forms, interferences in actual samples are also expected to be different. Contrary to the MTBSTFA w/1% t-BDMCS derivatization procedure which requires a long time and high-temperature heating, ECF could be effectively applied to PM2.5 samples with mild temperature and a rapid derivatization time in this study. As many derivative methods have not been applied so far for the quantification of AAs in PM2.5 samples using GC/MS, the attempt in this study will help in finding the appropriate derivative methods for individual AAs. In addition, further research is needed on the post-extraction cleanup procedures to eliminate interferences and matrix effects present in actual samples.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/chemosensors13080292/s1. Figure S1: Map of sampling location (Ansan, Republic of Korea); Table S1: List of target AAs and their information; Table S2: Instrumental conditions for target AAs and their corresponding IS derivatized by (a) MTBSTFA w/1% t-BDMCS and (b) ECF-MeOH; Table S3: The derivative conditions of ethyl chloroformate for AA analysis from the various sample matrices in previous studies; Table S4: Results of matrix spikes for serine obtained by two derivative methods.

Author Contributions

Conceptualization, J.J. and Y.G.A.; methodology, N.R.C. and J.Y.L.; software, J.J. and N.R.C.; validation, J.J. and E.L.; formal analysis, N.R.C. and E.L.; investigation, J.Y.L. and N.R.C.; resources, N.R.C. and E.L.; data curation, J.J. and E.L.; writing—original draft preparation, J.J. and Y.G.A.; writing—review and editing, J.Y.L., N.R.C., and Y.G.A.; visualization, J.J.; supervision, Y.G.A.; project administration, J.Y.L. and N.R.C.; funding acquisition, N.R.C. and Y.G.A. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by a KBSI research grant (C512210) and by a grant from the National Institute of Environmental Research (NIER), funded by the Ministry of Environment (ME) of the Republic of Korea (NIER-2025-04-02-029).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data presented in this study are available on request from the corresponding author.

Conflicts of Interest

The authors declare that they have no competing interests.

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Figure 1. Analytical procedures for the quantification of AAs in PM2.5 samples according to two derivative methods (MTBSTFA w/1% t-BDMCS and ECF-MeOH).
Figure 1. Analytical procedures for the quantification of AAs in PM2.5 samples according to two derivative methods (MTBSTFA w/1% t-BDMCS and ECF-MeOH).
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Figure 2. Optimization of the derivatization with (a) temperature, (b) reaction time at 90 °C, (c) the addition of (MTBSTFA w/1% t-BDMCS: pyridine) at 90 °C, and (d) reaction time with the addition of 70 + 30 µL (MTBSTFA w/1% t-BDMCS: pyridine) at 90 °C. (e) The effect of pyridine addition under the optimized condition at 90 °C for 2 h.
Figure 2. Optimization of the derivatization with (a) temperature, (b) reaction time at 90 °C, (c) the addition of (MTBSTFA w/1% t-BDMCS: pyridine) at 90 °C, and (d) reaction time with the addition of 70 + 30 µL (MTBSTFA w/1% t-BDMCS: pyridine) at 90 °C. (e) The effect of pyridine addition under the optimized condition at 90 °C for 2 h.
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Figure 3. The effect of (a) the amount of ECF derivatizing reagent before solvent extraction and (b) the total amount of solvent used to extract AA derivatives from an aqueous sample on the extraction efficiency. Ea, Eb, and Ec represent the following extraction conditions: Ea, one extraction with 200 μL of chloroform; Eb, a repeat of Ea twice; Ec, one extraction with 500 μL of chloroform.
Figure 3. The effect of (a) the amount of ECF derivatizing reagent before solvent extraction and (b) the total amount of solvent used to extract AA derivatives from an aqueous sample on the extraction efficiency. Ea, Eb, and Ec represent the following extraction conditions: Ea, one extraction with 200 μL of chloroform; Eb, a repeat of Ea twice; Ec, one extraction with 500 μL of chloroform.
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Figure 4. EI mass spectra of alanine derivatives generated by chemical reaction with (a) MTBSTFA w/1% t-BDMCS and (b) ECF-MeOH.
Figure 4. EI mass spectra of alanine derivatives generated by chemical reaction with (a) MTBSTFA w/1% t-BDMCS and (b) ECF-MeOH.
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Figure 5. MRM chromatograms of target AA derivatized with (a) MTBSTFA w/1% t-BDMCS and (b) ECF-MeOH using a standard solution at 0.1 ng/μL. Each peak of 13 separated AA is indicated by a different color line along with their abbreviation.
Figure 5. MRM chromatograms of target AA derivatized with (a) MTBSTFA w/1% t-BDMCS and (b) ECF-MeOH using a standard solution at 0.1 ng/μL. Each peak of 13 separated AA is indicated by a different color line along with their abbreviation.
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Figure 6. Comparison of the quantitative difference and statistical significance of individual AAs detected by MTBSTFA w/1% t-BDMCS (Mₐ) and ECF-MeOH (Mb) derivative methods in 23 PM2.5 samples.
Figure 6. Comparison of the quantitative difference and statistical significance of individual AAs detected by MTBSTFA w/1% t-BDMCS (Mₐ) and ECF-MeOH (Mb) derivative methods in 23 PM2.5 samples.
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Table 1. Linearities, MDLs, MQLs, and recoveries of 13 AAs obtained by (a) MTBSTFA w/1% t-BDMCS and (b) ECF-MeOH derivative methods.
Table 1. Linearities, MDLs, MQLs, and recoveries of 13 AAs obtained by (a) MTBSTFA w/1% t-BDMCS and (b) ECF-MeOH derivative methods.
AAsLinearityLimits of Detection and QuantificationRecovery ± RSD % (n = 3)
Range
(ng/μL)		RRF a
RSD (%)		R2MDL
(ng/m3)		MQL
(ng/m3)		RSD (%)2 ng
(Low Level)		20 ng
(High Level)
(a) MTBSTFA w/1% t-BDMCS
Ala0.008–0.0221.90.99900.2290.6885.7107.0 ± 5.8104.2 ± 4.3
Asn0.005–0.54.30.99930.1890.56611.393.2 ± 11.9102.7 ± 23.5
Asp0.005–0.424.80.99990.2590.77715.698.5 ± 2.7105.4 ± 16.6
Gly0.04–0.421.00.99992.3367.00914.6114.8 ± 15.1103.7 ± 14.9
Ile0.005–0.29.90.99950.3381.01413.985.6 ± 5.293.6 ± 5.1
Leu0.005–0.111.70.99950.2640.79219.084.5 ± 7.1109.2 ± 7.4
Met0.002–0.22.70.99990.0940.2816.990.5 ± 2.0100.4 ± 4.7
Phe0.005–0.45.70.99990.0990.2975.098.5 ± 2.7104.6 ± 3.3
Ser0.01–0.217.40.99600.7782.33515.8105.3 ± 4.7108.4 ± 3.6
Thr0.005–0.117.80.99840.2950.88516.0106.3 ± 10.0119.7 ± 11.8
Trp0.005–0.118.80.9988 6.3 ± 16.443.7 ± 6.7
Tyr0.005–0.27.10.99800.1850.5557.190.7 ± 12.7109.2 ± 15.5
Val0.005–0.416.70.99990.3150.94415.590.2 ± 9.1119.3 ± 4.4
(b) ECF-MeOH
Ala0.005–0.44.90.99980.1120.3356.4115.4 ± 1.3115.8 ± 2.6
Asn0.005–0.414.70.99950.1790.53717.1116.6 ± 1.4109.6 ± 1.8
Asp0.005–0.48.70.99990.0910.2748.4118.7 ± 10.5113.7 ± 13.8
Gly0.008–0.412.00.99960.3381.01510.6117.2 ± 4.6115.5 ± 4.1
Ile0.001–0.0815.00.99980.0960.2895.481.5 ± 3.8113.6 ± 15.1
Leu0.001–0.0819.20.99990.1180.35411.280.0 ± 1.897.5 ± 12.9
Met0.001–0.089.51.00000.1040.31213.490.5 ± 2.0100.4 ± 4.7
Phe0.001–0.28.70.99990.0160.04810.5115.3 ± 9.7114.2 ± 3.6
Ser0.001–0.0523.30.99930.1450.43515.5103.1 ± 21.2103.7 ± 13.6
Thr0.002–0.115.70.99870.2250.6756.798.2 ± 16.8119.4 ± 13.2
Trp0.005–0.213.10.99910.1060.3194.1118.8 ± 0.5117.6 ± 14.1
Tyr0.005–0.211.00.99850.4111.23415.994.1 ± 11.295.6 ± 1.3
Val0.002–0.219.80.99990.0860.25910.1110.3 ± 13.299.4 ± 4.5
a RRF: relative response factor.
Table 2. Summarized results of detected AAs in PM2.5 samples and the relative difference (%) between two derivative methods.
Table 2. Summarized results of detected AAs in PM2.5 samples and the relative difference (%) between two derivative methods.
AAsMean ± SD (ng/m3, n = 23)Relative Difference (%) *
MTBSTFA w/1% t-BDMCS (Ma)ECF-MeOH (Mb)
Ala3.599 ± 3.9573.197 ± 3.070−11.2
Asn0.419 ± 0.8940.757 ± 1.36380.9
Asp3.030 ± 2.6344.348 ± 5.38843.5
Gly16.929 ± 11.87817.918 ± 10.5275.8
Ile1.346 ± 1.8071.141 ± 1.848−15.3
Leu0.903 ± 1.3010.762 ± 1.226−15.6
Met0.240 ± 0.3930.180 ± 0.325−23.5
Phe0.538 ± 0.7210.549 ± 0.8012.1
Ser11.488 ± 14.7730.485 ± 0.769−95.8
Thr5.177 ± 4.2493.883 ± 6.640−24.8
Tyr0.845 ± 1.3770.604 ± 0.961−28.5
Val1.182 ± 1.8181.045 ± 1.643−11.6
* Relative difference = (Mb−Ma)/Ma × 100 (%); Ma and Mb, the concentrations of AAs obtained by MTBSTFA with 1% t-BDMCS derivatization and ECF-MeOH derivatization, respectively.
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Jo, J.; Choi, N.R.; Lee, E.; Lee, J.Y.; Ahn, Y.G. Comparison of Two Derivative Methods for the Quantification of Amino Acids in PM2.5 Using GC-MS/MS. Chemosensors 2025, 13, 292. https://doi.org/10.3390/chemosensors13080292

AMA Style

Jo J, Choi NR, Lee E, Lee JY, Ahn YG. Comparison of Two Derivative Methods for the Quantification of Amino Acids in PM2.5 Using GC-MS/MS. Chemosensors. 2025; 13(8):292. https://doi.org/10.3390/chemosensors13080292

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Jo, Jungmin, Na Rae Choi, Eunjin Lee, Ji Yi Lee, and Yun Gyong Ahn. 2025. "Comparison of Two Derivative Methods for the Quantification of Amino Acids in PM2.5 Using GC-MS/MS" Chemosensors 13, no. 8: 292. https://doi.org/10.3390/chemosensors13080292

APA Style

Jo, J., Choi, N. R., Lee, E., Lee, J. Y., & Ahn, Y. G. (2025). Comparison of Two Derivative Methods for the Quantification of Amino Acids in PM2.5 Using GC-MS/MS. Chemosensors, 13(8), 292. https://doi.org/10.3390/chemosensors13080292

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