Application of Direct Thermal Desorption–Gas Chromatography–Mass Spectrometry for Determination of Volatile and Semi-Volatile Organosulfur Compounds in Onions: A Novel Analytical Approach

The population is now more aware of their diets due to the connection between food and general health. Onions (Allium cepa L.), common vegetables that are minimally processed and grown locally, are known for their health-promoting properties. The organosulfur compounds present in onions have powerful antioxidant properties and may decrease the likelihood of developing certain disorders. It is vital to employ an optimum approach with the best qualities for studying the target compounds to undertake a thorough analysis of these compounds. In this study, the use of a direct thermal desorption–gas chromatography–mass spectrometry method with a Box–Behnken design and multi-response optimization is proposed. Direct thermal desorption is an environmentally friendly technique that eliminates the use of solvents and requires no prior preparation of the sample. To the author’s knowledge, this methodology has not been previously used to study the organosulfur compounds in onions. Likewise, the optimal conditions for pre-extraction and post-analysis of organosulfur compounds were as follows: 46 mg of onion in the tube, a desorption heat of 205 °C for 960 s, and a trap heat of 267 °C for 180 s. The repeatability and intermediate precision of the method were evaluated by conducting 27 tests over three consecutive days. The results obtained for all compounds studied revealed CV values ranging from 1.8% to 9.9%. The major compound reported in onions was 2,4-dimethyl-thiophene, representing 19.4% of the total area of sulfur compounds. The propanethial S-oxide, the principal compound responsible for the tear factor, accounted for 4.5% of the total area.


Introduction
The Allium genus includes the onion, a vegetable that holds significant value both in terms of its economic and nutritional contributions [1]. The use of onions in food, including as seasoning in many dishes, has been prevalent in most countries for hundreds of years [2]. However, onions are not only used in culinary contexts but also for medical purposes [3]. Many studies have reported that onion consumption helps to prevent the occurrence of several illnesses, such as inflammatory diseases [4], cancer [5], diabetes [6], and neurological disorders [7]. These biological effects are largely associated with the chemical components of onions, especially organosulfur and phenolic compounds [8]. Regarding the latter, different studies carried out by our research group have demonstrated the high levels of flavonols and anthocyanins in onion bulbs [9][10][11]. In addition, these compounds are responsible for the color of the bulb and have strong antioxidant activity. On the other hand, organosulfur compounds can make up as much as 5% of an onion's dry weight. ture [32]. Additionally, this technique is renowned for its higher final sensitivity and lower detection limits [33,34]. However, for onions, the use of DTD or TD coupled with GC-MS has not been previously explored to analyze the profile of their organosulfur compounds.
Although DTD is a versatile preconcentration technique for GC-MS, various parameters such as the sample amount, the desorption tube temperature, or the trap heat temperature could affect the extraction. Therefore, it is crucial that the DTD method has the most suitable characteristics for the compounds to be analyzed. The use of experimental designs (DOEs) combined with the response surface methodology (RSM) is a widely adopted strategy for optimizing methodologies for the analysis of bioactive compounds. This approach enables the determination of the optimal values of the factors that minimize or maximize the response or achieve a specific goal [35].
Therefore, this work aims to develop and validate a direct thermal desorption-gas chromatography-mass spectrometry (DTD-GC-MS) method for the simultaneous determination of organosulfur compounds in onions using both a BBD and an RSM. This combination could allow the relationship between factors and their response to be fully understood, as well as methods to be systematically and effectively optimized. Likewise, the DTD-GC-MS method proposed in this study could be used by laboratories, researchers, and companies to better understand the organosulfur onion profile and select onion cultivars with the best nutraceutical value or sensory characteristics.

Qualitative Analyses
To have an overview of the profile of compounds present in onions, qualitative analyses were performed under unoptimized conditions as follows: 40 mg of onion sample; tube heating at 150 • C for 600 s; a trap temperature heating at 265 • C for 180 s. Table 1 shows the compounds divided into families. A total of fifty-one VOCs and SVOCs were tentatively identified, according to the Wiley Library, with a matching factor greater than 80%. Among these compounds, twenty were organosulfur compounds, and the remaining were eight aldehydes, three carboxylic acids, six alcohol, five ketones, five esters, two furans, one alkane, and one carbon dioxide. The distribution of the VOCs and SVOC in the red onion is graphically represented by a pie chart (Figure 1).
A total of fifty-one VOCs and SVOCs were tentatively identified, according to the Wiley Library, with a matching factor greater than 80%. Among these compounds, twenty were organosulfur compounds, and the remaining were eight aldehydes, three carboxylic acids, six alcohol, five ketones, five esters, two furans, one alkane, and one carbon dioxide. The distribution of the VOCs and SVOC in the red onion is graphically represented by a pie chart (Figure 1).

Figure 1.
Distribution of the fifty-one compounds. Each color shows the percentage composition of the fifty-one VOCs and SVOCs following the normalization method of the chromatographic peak areas.
Carboxylic acids (35.7%), organosulfur (28.1%), and aldehydes (13.9%) accounted for more than 77.6% of the composition of red onion. This information agreed with the data reported by other authors who indicated that red onion is mainly characterized by these three families of compounds [27].
As for aldehydes, 2-methyl-2-pentenal was produced by the sequential transformation of 1-propenyl sulfenic acid into thiopropanal-S-oxide [36,37]. In addition, the condensation of propanal and acetaldehyde could produce (E)-2-methyl-2-butenal, which in turn could be reduced to 2-methyl-butanal [38]. Moreover, some aldehydes characteristics of heated onions were detected because of the temperatures applied during DTD. Likewise, acetaldehyde, propanal, 2-methyl-butanal, and 2-methyl-propanal have been recognized as by-products of the Maillard process, resulting from the Strecker degradation of the respective amino acids [39]. As for carboxylic acids, acetic, propanoic, and hexanoic acids have already been described in the volatile composition of roasted onions [36].
This study focused on the twenty organosulfur compounds identified in onions (Table 2) and aimed to analyze the impact of DTD conditions on their extraction and analysis. Figure 2 shows their chemical structures, and the characteristic mass spectral ions are shown in the Supplementary Materials (Table S1). Carboxylic acids (35.7%), organosulfur (28.1%), and aldehydes (13.9%) accounted for more than 77.6% of the composition of red onion. This information agreed with the data reported by other authors who indicated that red onion is mainly characterized by these three families of compounds [27].
As for aldehydes, 2-methyl-2-pentenal was produced by the sequential transformation of 1-propenyl sulfenic acid into thiopropanal-S-oxide [36,37]. In addition, the condensation of propanal and acetaldehyde could produce (E)-2-methyl-2-butenal, which in turn could be reduced to 2-methyl-butanal [38]. Moreover, some aldehydes characteristics of heated onions were detected because of the temperatures applied during DTD. Likewise, acetaldehyde, propanal, 2-methyl-butanal, and 2-methyl-propanal have been recognized as by-products of the Maillard process, resulting from the Strecker degradation of the respective amino acids [39]. As for carboxylic acids, acetic, propanoic, and hexanoic acids have already been described in the volatile composition of roasted onions [36].
This study focused on the twenty organosulfur compounds identified in onions ( Table 2) and aimed to analyze the impact of DTD conditions on their extraction and analysis. Figure 2 shows their chemical structures, and the characteristic mass spectral ions are shown in the Supplementary Materials (Table S1).

Individual Box-Behnken Designs and Analysis of Variances
Two Box-Behnken designs were carried out for the development and optimisation of the DTD-GC-MS method for the pre-extraction and post-analysis of both the total sulfur compounds area and the area of the propanethial S-oxide. The ANOVA was applied to each experimental matrix, and the results are shown in Table 2 and Table 3.

Individual Box-Behnken Designs and Analysis of Variances
Two Box-Behnken designs were carried out for the development and optimisation of the DTD-GC-MS method for the pre-extraction and post-analysis of both the total sulfur compounds area and the area of the propanethial S-oxide. The ANOVA was applied to each experimental matrix, and the results are shown in Tables 2 and 3. The models effectively described the observed data for both responses, as well as the total area (Y TA ) and the area of the propanethial S-oxide (Y C3H6OS ), explaining 84.84% and 76.35% of their variability, respectively. Additionally, both models fit well as lack-of-fit tests with p-values greater than 0.05 (0.0577 and 0.0586, respectively). So, 2 s-order equations can be constructed to predict each response value as a function of the independent variables (Equations (1) and (2)).
Likewise, ANOVA showed the significance of each factor and its interaction with the response variable. Only variables and interactions with a p-value less than 0.05 were significantly affected at a 95% level of significance. This statistical information was graphically represented using the Pareto chart ( Figure 3). Likewise, ANOVA showed the significance of each factor and its interaction wit response variable. Only variables and interactions with a p-value less than 0.05 wer nificantly affected at a 95% level of significance. This statistical information was g ically represented using the Pareto chart ( Figure 3). Onion sample weight (X1) significantly impacted the pre-extraction and post-ana of the organosulfur compounds of the red onions (p-value < 0.05). The onion sa amount used negatively affected the total area of the organosulfur compounds (b1 = x 10 8 ) and the area of the propanethial S-oxide area (b1 = − 1.16 x 108).
The tube desorption temperature positively affected the total area of the organos compounds because greater temperatures increased the efficiency of the pre-extra process (b2 = 9.83 × 10 8 ). However, it is worth noting that, if the desorption temperat too high, the peak area could show a negative trend due to the degradation of the pounds [38]. This could be observed by the effect of the desorption temperature o area of the propanethial S-oxide-the effect was not significant, but negative. The c tions in the tube oven during the desorption stage were crucial for the effective extra of the compounds [37].
Finally, for a clear understanding of the interactive and main effects, 3D surface were represented using the designed model. Figure 4a-d show the combined impa the onion sample-tube desorption temperature, the tube desorption temperature-tub sorption time, and the tube desorption temperature-trap heat temperature, on th sponse variables. Onion sample weight (X 1 ) significantly impacted the pre-extraction and post-analysis of the organosulfur compounds of the red onions (p-value < 0.05). The onion sample amount used negatively affected the total area of the organosulfur compounds (b 1 = −5.97 × 10 8 ) and the area of the propanethial S-oxide area (b 1 = − 1.16 × 108).
The tube desorption temperature positively affected the total area of the organosulfur compounds because greater temperatures increased the efficiency of the pre-extraction process (b 2 = 9.83 × 10 8 ). However, it is worth noting that, if the desorption temperature is too high, the peak area could show a negative trend due to the degradation of the compounds [38]. This could be observed by the effect of the desorption temperature on the area of the propanethial S-oxide-the effect was not significant, but negative. The conditions in the tube oven during the desorption stage were crucial for the effective extraction of the compounds [37].
Finally, for a clear understanding of the interactive and main effects, 3D surface plots were represented using the designed model. Figure 4a-d show the combined impact of the onion sample-tube desorption temperature, the tube desorption temperature-tube desorption time, and the tube desorption temperature-trap heat temperature, on the response variables.

Multi-Response Optimization
Finally, RSM provided details on the optimal values that each factor should assume to achieve maximum response. Specifically, the values required to optimize the extraction of total organosulfur and the extraction of propanethial S-oxide are included in Table 4. On the other hand, to identify the best conditions not only for the total area but also for the area of the propanethial S-oxide, the MRO was applied. As Table 4 shows, the optimal conditions obtained through both individual experiments and MRO achieved a desirable value of 84.8%. The precision of the MRO method was also validated through conducting 27 experiments carried out on 3 consecutive days. The approach exhibited desirable reproducibility and intermediate precision, as evidenced by a CV below 10% for all organosulfur compounds (Supplementary Material, Table S2). These results were considered acceptable, as the CVs were below the commonly accepted threshold of 10% [37].

Multi-Response Optimization
Finally, RSM provided details on the optimal values that each factor should assume to achieve maximum response. Specifically, the values required to optimize the extraction of total organosulfur and the extraction of propanethial S-oxide are included in Table 4. On the other hand, to identify the best conditions not only for the total area but also for the area of the propanethial S-oxide, the MRO was applied. As Table 4 shows, the optimal conditions obtained through both individual experiments and MRO achieved a desirable value of 84.8%. The precision of the MRO method was also validated through conducting 27 experiments carried out on 3 consecutive days. The approach exhibited desirable reproducibility and intermediate precision, as evidenced by a CV below 10% for all organosulfur compounds (Supplementary Material, Table S2). These results were considered acceptable, as the CVs were below the commonly accepted threshold of 10% [37].
The validated method was applied to the red onion sample, obtaining the results of the area shown in Table 5 and the total ion chromatogram (TIC) shown in Figure 5. Table 5. Organosulfur compounds extracted and analyzed using the DTD-GC-MS MRO optimized method. Compositional percentages were computed by the normalization method from the GC peak areas.

Code
Compound Individual Relative Area (g −1 ) Percentage Composition (%) The validated method was applied to the red onion sample, obtaining the results of the area shown in Table 5 and the total ion chromatogram (TIC) shown in Figure 5. Table 5. Organosulfur compounds extracted and analyzed using the DTD-GC-MS MRO optimized method. Compositional percentages were computed by the normalization method from the GC peak areas.

Distribution of the Organosulfur Compound in Red Onion by MRO DTD-GC-MS Method
The optimized method for analyzing the distribution of 20 sulfur compounds in a red onion sample was then used, and the results are included in Figure 6.

Distribution of the Organosulfur Compound in Red Onion by MRO DTD-GC-MS Method
The optimized method for analyzing the distribution of 20 sulfur compounds in a red onion sample was then used, and the results are included in Figure 6. Figure 6. Distribution of the twenty organosulfur compounds. Each color shows its percentage composition calculated by using the normalization method from the GC peak areas.
When alliinase and cysteine sulfoxides came together, they generated a mixture of sulfenic acids, ammonia, and pyruvate. The major onion cysteine sulfoxide, i.e., S-1-propenyl-L-cysteine sulfoxide, was transformed into the 1-propenyl sulfenic acid, which was then turned into propanethial S-oxide. This organosulfur compound, which is known as the onion lachrymatory factor (LF), accounted for 4.5% of the composition of red onions and was stable during the gas chromatography analysis, making it easy to trap and measure.
The other products of the condensation reaction, i.e., thiosulfinates, were degraded during trapping and GC analysis, thus generating most of the organosulfur compounds identified, i.e., the sulfides [39], which were involved in further transformations and showed biological activity [34]. Disulfides were observed in frozen onions, while the drying of onions increased trisulfides [40]. The resulting mixture of monosulfides, disulfides, and trisulfides accounted for 4.9%, 23.9%, and 9.6% (i.e., 38.4% of carbon sulfide CSn) of the red onion composition, respectively. Additionally, propanothiol was identified as a significant source of flavor in fresh onions [41], accounting for 5.9% of the red onion composition.
In addition, high temperatures during thermal desorption can trigger the thermolysis of alkyl-1-propenyl disulphides and di(1-alkenyl) disulphides to form thiophene [42]. This family of compounds accounted for 25.3% of the red onion composition, so it was one of the main components.

Comparison of DTD Methodology with Another Extraction Techniques
Finally, the developed MRO DTD-GC-MS method was compared with other extraction techniques to highlight its advantages.
Concerning solvent extraction techniques for volatile compounds, DTD offers multiple advantages as reported by other authors [34,43]. Firstly, it is an automated extraction Figure 6. Distribution of the twenty organosulfur compounds. Each color shows its percentage composition calculated by using the normalization method from the GC peak areas.
When alliinase and cysteine sulfoxides came together, they generated a mixture of sulfenic acids, ammonia, and pyruvate. The major onion cysteine sulfoxide, i.e., S-1propenyl-L-cysteine sulfoxide, was transformed into the 1-propenyl sulfenic acid, which was then turned into propanethial S-oxide. This organosulfur compound, which is known as the onion lachrymatory factor (LF), accounted for 4.5% of the composition of red onions and was stable during the gas chromatography analysis, making it easy to trap and measure.
The other products of the condensation reaction, i.e., thiosulfinates, were degraded during trapping and GC analysis, thus generating most of the organosulfur compounds identified, i.e., the sulfides [39], which were involved in further transformations and showed biological activity [34]. Disulfides were observed in frozen onions, while the drying of onions increased trisulfides [40]. The resulting mixture of monosulfides, disulfides, and trisulfides accounted for 4.9%, 23.9%, and 9.6% (i.e., 38.4% of carbon sulfide CS n ) of the red onion composition, respectively. Additionally, propanothiol was identified as a significant source of flavor in fresh onions [41], accounting for 5.9% of the red onion composition.
In addition, high temperatures during thermal desorption can trigger the thermolysis of alkyl-1-propenyl disulphides and di(1-alkenyl) disulphides to form thiophene [42]. This family of compounds accounted for 25.3% of the red onion composition, so it was one of the main components.

Comparison of DTD Methodology with Another Extraction Techniques
Finally, the developed MRO DTD-GC-MS method was compared with other extraction techniques to highlight its advantages.
Concerning solvent extraction techniques for volatile compounds, DTD offers multiple advantages as reported by other authors [34,43]. Firstly, it is an automated extraction technique in which there is hardly any sample handling, and it does not use solvents, which means that it is a more environmentally friendly technique. In addition, the yields and the sensitivity offered by this technique are much higher than conventional solvent extractions.
However, it should be noted that it is not the only solvent-free technique currently available in the literature. SPME combined with HS has been used on several occasions for the study of sulfur compounds in onion samples [8,43]. Nonetheless, the choice of the adsorbent material is consistently a crucial aspect of the process because it must adsorb a diverse range of molecules with varying chemical properties, molecular weights, and polarities. With SPME, the fiber's surface capacity is relatively restricted, which can lead to analytes competing for adsorption sites, potentially causing a greater bias towards specific volatile compounds [18]. Overall, TD typically has a higher surface capacity than SPME due to the larger size of the trap used in TD compared to the SPME fiber. To facilitate an experimental comparison of both techniques, an analysis of the organosulfur compounds in the same onion matrix using SPME (AOC-6000 Plus Multifunctional Autosampler, Shimadzu, Kyoto, Japan) was carried out. The SPME analysis conditions used were as follows: 0.5 g of onion was pre-incubated at 150 • C with pulsed agitation for 10 min at a speed of 500 rpm. The headspace above the samples in a vial was exposed to a DVB/Carbon WR/PDMS SPME fiber (manufactured by Shimadzu, Kyoto, Japan) at a depth of 22 mm for 20 min. After extraction, the SPME fiber was withdrawn and introduced into the gas chromatograph inlet, where it was desorbed for 5 min using a split mod at a temperature of 220 • C. The results obtained showed that the number of sulfur compounds identified by SPME (14 sulfur compounds) was lower than that identified by the MRO DTD-GC-MS method (20 sulfur compounds). In addition, the extraction of these compounds also showed worse results, with a total area by SPME of 78,595,746 ± 612,8231 g −1 -lower than the total area obtained by DTD (9,389,028,511 ± 908,282,182 g −1 ). This shows that, in the case of sulfur compounds in onion, DTD correctly optimized by BBD and MRO and combined with GC-MS provides a suitable extraction and analysis method for the compounds of interest, yielding better results than other more common techniques, such as SPME.

Onion Samples
The onion samples that were the focus of this investigation were procured from a nearby marketplace located in the province of Cadiz, Spain. Specifically, this variety has been used by the research group in previous studies about the evaluation of the content of phenolic compounds [9][10][11]44]. For all the experiments carried out, the onions used were in freeze-dried form to avoid the presence of water and to have a more homogeneous onion matrix [45]. The type of onion used for our investigation is a variety of Spanish origin that is cultivated from June to December. The bulbs were characterized by their globose/conical shape with red outer skins in different shades (depending on the variety). The flesh has an intense purple color, a strong taste, and high pungency.

DTD-GC-MS Procedure and Conditions
The DTD equipment used was a TD-20 System (Shimadzu, Kyoto, Japan). Specifically, the onion sample was placed in a sample tube (with an outer diameter of 1/4 (6.35 mm) and a length of 90 mm) and was secured at both ends with silica wool to prevent leakage. The flow rate of the carrier gas (helium) was adjusted to 1 mL s −1 . The cold trap, which concentrated the desorbed compounds to a bandwidth compatible with the capillary column, was set to −15 • C. The compounds were injected into the GC module in a split mode with a split ratio of 1:50. The sample amount collected in the tube, the temperature and time of the sample-tube heating block, and the temperature and time of the trap desorption were chosen according to the results of the response surface design of experiments. A detailed outline of the process is shown in the Supplementary Materials ( Figure S1).
The GC-MS equipment used was GCMS-TQ8040 (Shimadzu, Kyoto, Japan). The chromatographic separation was performed on a Suprawax-280 capillary column (Teknokroma, Barcelona, Spain; 60 m length × 0.25 mm column I.D. × 0.25 µm film thickness). The injector was set at 25 • C. Moreover, the temperature program of the oven was as follows: 40 • C isothermal for 300 s; from 40 • C to 200 • C at a rate of 0.05 • C s −1 ; 200 • C isothermal for 300 s; from 200 • C to 270 • C at a rate of 0.67 • C s −1 ; and 270 • C isothermal for 120 s. Likewise, helium (99.999%) was used as the carrier gas at both a constant linear velocity of 35 cm s −1 and a flow rate of 0.031 mL s −1 . Regarding the mass spectrometer, the ionization mode was electron impact with a voltage of 70 eV. The mass spectrometer worked in a full-scan mode in the range of 40-400 m/z. The ion source temperature was 200 • C.
Compounds were identified by comparing their mass spectra using the Wiley library (Wiley Registry of Mass Spectral Data, 7th Edition, 2000) and the criterion of at least 80% similarity [46]. The area of the chromatographic signal produced by the largest mass fragment (base peak) was measured to determine the area of each compound. Furthermore, a normalizing approach was used to obtain the percentage composition (%) from the peak area of each compound: the area of the base peak/total area.

Box-Behnken Design
The parameters that affected the analysis of the organosulfur compounds by using DTD-GC-MS were optimized with a BBD [47]. As aforementioned, several factors were evaluated to determine their optimum levels: the onion sample amount placed in the sample tube (X 1 ), the tube desorption temperature (X 2 ), the tube desorption time (X 3 ), the trap heat temperature (X 4 ), and the trap heat time (X 5 ). In a BBD, each factor was studied at 3 levels: at a lower level (−1), at an intermediate level (0), and at a higher level (1). Due to the lack of prior studies on the application of DTD to analyze the organosulfur compounds in onions, the operating range of each factor for the BBD was selected according to the outcome of the OFAT experiments. The OFAT experiments are summarized in the Supplementary Materials (Table S3 and Figure S2). According to these results, the range for each factor was chosen to consider not only the greatest total areas but also the area of the individual propanethial S-oxide: 40-50-60 mg for X 1 ; 180-200-220 • C for X 2 ; 600-900-1200 s for X 3 ; 250-265-280 • C for X 4 ; 120-180-240 s for X 5 . Two response variables were defined: the total area (Y TA ) of the base peak of each compound, and the area of the propanethial S-oxide (Y C3H6OS ). The latter is of interest as it is obtained from the main SCs of onions and plays the role of lacrimator [48]. Both response variables were expressed as relative areas, i.e., as a function of the accurate mass of onion weighed for each experiment: the total area per gram of onion (g −1 ) and the area of the propanethial S-oxide per gram of onion (g −1 ), respectively. The study involved two separate BBD experiments, each design consisted of 46 treatments, including 6 repetitions at the center point to calculate the error. The complete matrix with the experimental and predicted values for each response variable is included in Supplementary Materials (Table S4).

Response Surface Methodology
In combination with BBD, RSM comprises a series of statistical and mathematical methods utilized to develop and optimize processes. RSM allows for the modelling of the curvature relationship between factors and their response by employing a second-order polynomial equation (Equation (3)) [49].
where Y represents the predicted responses (i.e., Y TA and Y C3H6OS ); X i and X j the factors involved; X i X j the interactions between factors; X i 2 the quadratic interaction between factors; β 0 the intercept; β i the linear coefficient; β ij, the coefficient of interaction between factors; β ii the quadratic coefficient; and r the random error.
Utilizing the Statgraphics Centurion version XVI software (Warrenton, VA, USA) and Design Expert software (Version 13, Stat-Ease Inc., Minneapolis, MN, USA), an analysis of variance (ANOVA) was executed.
After optimizing each of the two response variables separately, a multi-response optimization (MRO) was performed to determine their optimal conditions simultaneously. The desirability optimization methodology was employed, which combines the desirability function analysis with the design of experiments [50]. Each response was assigned a desirability score (d i ) ranging from 0 to 1, where 0 indicated an unacceptable response, and 1 indicated an ideal response. The individual desirability scores were then geometrically averaged to obtain an overall desirability score (D). Ultimately, the multi-optimization process aimed to maximize the value of D.

Conclusions
Among all vegetables, onions have perhaps the biggest market niche not only because of their great use in cooking but also because of their sulfur compounds, which can have enormously beneficial properties for one's health. Particularly for the latter reason, it is necessary to develop methods of analysis and extraction that allow for the study of these sulfur compounds in an efficient way. In this work, a DTD-GC-MS methodology has been developed for the pre-extraction and subsequent determination of the organosulfur compounds present in onions. A BBD, together with MRO, was used for optimization, considering the total area (sum of the individual area of each of the 20 identified sulfur compounds) and the individual area of the propanethial S-oxide as response variables. The MRO conditions were as follows: 46 mg of onion in the tube, a desorption heat of 205 • C for 960 s, and a trap heat of 267 • C for 180 s. In addition, the efficacy of the technique has been confirmed by demonstrating that all organosulfur compounds exhibit high levels of repeatability and intermediate precision, with coefficients of variation (CV) lower than 10%. The distribution of the twenty organosulfur compounds showed 25.3% of thiophenes, 38.4% of carbon sulfides (mixture of monosulfides, disulfides, and trisulfides), and 4.5% of propanethial S-oxide. Overall, the optimized DTD-GC-MS method has significant practical implications for laboratories, researchers, and companies hoping to determine the organosulfur content accurately and reliably in onions. To evaluate the limitations of this study, the developed method was compared with SPME, the most widely used pre-extraction technique, to study these compounds in onions. The developed method showed better extractions, with a higher amount of extracted sulfur compounds. In the future, high-resolution analytical techniques will allow us to know more about the content of these compounds in different types of onions, which could be useful for assessing how factors such as variety, origin, and cultivation method affect the composition and activity of these compounds.

Supplementary Materials:
The following supporting information can be downloaded at: https: //www.mdpi.com/article/10.3390/ph16050715/s1, Figure S1: Schematic diagram of the DTD system; Figure S2: OFAT experiments concerning the effect of the factor: (a) onion sample amount placed in the sample tube (X 1 ), (b) tube desorption temperature (X 2 ), (c) tube desorption time (X 3 ), (d) trap heat temperature (X 4 ), and (e) trap heat time (X 5 ), on both the relative total area (g −1 ) and the relative area of the propanethial S-oxide (g −1 ); Table S1: Characteristic mass spectral ions of the organosulfur compounds identified in red onion; Table S2: Validation of the developed DTD-GC-MS MRO method; Table S3: OFAT experiments to choose the range of BBD (n = 3); Table S4: BBD for the total area and the area of the propanethial S-oxide. The results corresponded to experimental and predicted values.