Importance of derivatization in cholesterol analysis using GC-FID for bakery products containing plant and animal lipid

doi:10.21203/rs.3.rs-2480458/v1

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Importance of derivatization in cholesterol analysis using GC-FID for bakery products containing plant and animal lipid

https://doi.org/10.21203/rs.3.rs-2480458/v1

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Cholesterol can be analyzed using derivatization and non-derivatization techniques. Analysis without derivation is a simple and reproducible method for some samples. However, cholesterol and α-tocopherol could be eluted together during gas chromatographic analysis if the sample contains both analytes. In this study, the importance of derivatization in cholesterol analysis in bakery products was investigated. Derivatized and non-derivatized cholesterol and α-tocopherol standards were eluted on HP-5 capillary column in GC-FID. The cholesterol and α-tocopherol standard resolution (R_s) using derivatization technique was 3.1 which is considered as well separated (> 1.5) based on AOAC guidelines. However, cholesterol and α-tocopherol were coeluted by GC-FID without derivatization. The GC-FID cholesterol analytical method was validated and applied for cholesterol determination in bakery samples. Method validation results were effective to be used for bakery products: limits of detection (0.03 mg/100 g) and quantification (0.08 mg/100 g), accuracy (102.1% recovery) and precision (repeatability: relative standard deviation (RSD) 1.5%; reproducibility: RSD 1.9%). The cholesterol content of bakery products range from 2.76 ± 0.06 mg/100 g (castella) to 114.26 ± 4.72 mg/100 g (chrysanthemum bread) depending on the ingredients used. This study shows that derivatization step is essential in cholesterol analysis of bakery products containing both cholesterol and α-tocopherol to prevent possible overestimation of cholesterol.

GC-FID

cholesterol

tocopherol

bakery products

method validation

trimethylsilyl derivatization

Cholesterol is a kind of sterol having multiple hydrocarbon rings in its structure. It is the precursor of bile acids, steroid hormones, and provitamin D₃and plays a significant role in making the membrane structure of different cells of our body. Cholesterol is essential for our body, but a high intake of dietary cholesterol is not recommended. High consumption of cholesterol increases low-density lipoprotein cholesterol levels in the blood, which is the main reason for cardiovascular diseases and causes premature death (Hu et al. 1999; Connor and Connor 2002; Dinh et al. 2011). In addition, high consumption of cholesterol could induce cerebral inflammatory markers, and promote colorectal, and breast cancer (Thirumangalakudi et al. 2008; Ullrich et al. 2010; Du et al. 2016; Li et al. 2016). Nowadays, cardiovascular diseases are the main reason for premature death all over the world. According to the WHO, it is projected that every year more than 23 million people will die from cardiovascular diseases by 2030 (WHO, 2013). As a result, people are becoming conscious and interested in low-cholesterol diets rather than cholesterol-rich foods. So, it is important to know the cholesterol profile of frequently consumed foods to evaluate the personalized diet.

Red meat, poultry, fish, eggs, and dairy products are the most common sources of cholesterol (mostly found in animal-originated foods). However, there are other sources of cholesterol that most people are not always aware of. Bread, cake, and various kinds of cookies are good examples of these foods. Although it only contains low levels of cholesterol, high-volume consumption can impose undesirable effects. Bread (cream bread, buttercream bread, and croissant), cakes, and cookies are made of milk, butter, and eggs together with wheat flour. Most often people consume more than 2/3 pieces of this type of bread, cake, and cookie at a time. According to the USDA database, the cholesterol contents of milk, butter, and egg are 48 mg/100 g, 235 mg/100 g, and 400 mg/100 g, respectively (USDA, 2015). Therefore, the cholesterol content of bread, cake, or cookies made with 200 g butter or 100 g egg would contain more than one’s daily recommended (300 mg/day) intake of cholesterol (WHO, 2013). As a result, the cholesterol content of these bakery products could no longer remain negligible and thereby creates a significant impact to raise blood serum-cholesterol levels in our body. However, information on cholesterol in highly consumed bakery products is very limited.

Cholesterol has been analyzed using different methods like gravimetry, colorimetry, fluorimetry, and chromatography (Albuquerque et al. 2016). Among them, gas chromatography is the most suitable and reliable method for cholesterol identification and quantification because it could separate and quantify cholesterol from other similar phytosterol compounds (Daneshfar et al. 2009). Many researchers performed derivatization to analyze cholesterol and cholesterol oxidation products (Xu et al., 2015; Chiu et al., 2018) while some researchers did not use derivatization in cholesterol analysis (Chen et al. 2015; Sharmin et al. 2017; Höring et al., 2019; Naviglio et al., 2018). Derivatization is the process of conversion of an analyte into another compound. The derivative product of an analyte is more volatile than its original one which facilitates the gas chromatographic analysis. More importantly, the derivatization reaction increases the chromatographic resolution and specificity of the analyte. However, derivatization makes cholesterol analysis time-consuming, complex, and costly. As a result, researchers are interested in comparatively short and simple cholesterol analysis methods without derivatization. There were some researchers who already developed and validated the cholesterol analysis method successfully without derivatization (Chen et al. 2015; Sharmin et al. 2017; Höring et al., 2019; Naviglio et al., 2018). Though GC columns are effective for cholesterol separations using FID, it has been reported that α-tocopherol could partially or fully co-elute with cholesterol (Fenton, 1992; Dinh et al., 2011) during cholesterol analysis. This problem could be eliminated by using GC-MS which is very expensive and not available in most of the laboratory. Based on the above discussion, choosing an appropriate cholesterol analysis method is important to get accurate cholesterol data rather than inappropriate value. More importantly, when the sample contains only α-tocopherol or cholesterol and α-tocopherol both simultaneously then cholesterol analysis process should be chosen more carefully otherwise α-tocopherol could be quantified as cholesterol.

The plant-originated foods ingredients in bakery products such as vegetable oil and flour are ampoule sources of α-tocopherol. Bakery products such as bread, cake, and cookies are made of milk, butter, eggs, and oil, which provides cholesterol from animal ingredients as well as α-tocopherol from plant and animal sources. Therefore, we should carefully choose the right analytical procedure to analyze cholesterol from bakery products to prevent the misinterpretation of cholesterol due to the presence of α-tocopherol. In this study, cholesterol analysis was investigated using both derivatization and without derivatization techniques to check whether derivatization is essential for cholesterol analysis of bakery products or not. The developed method was then validated and tested on analyzing cholesterol composition of commercially produced and frequently consumed bakery products including bread, cookies, and cake.

2.1. Reagents and materials

Butylated hydroxytoluene (BHT), heptane (99.0%), cholesterol standard (99.0%), 5α-cholestane (100%), N, N-dimethyl formamide (99.8%), hexamethyldisilazane (99.99%), and cholrotrimethylsilane (99.98%) were purchased from Sigma–Aldrich Chemical Co. (St. Louis, MO). Potassium hydroxide (85.0%) and pyrogallol were obtained from Daejung Chemicals & Metals Co. (Siheung-si, Korea) and Tokyo Chemical Industry Co. (Tokyo, Japan) respectively. All reagents used in this study were of GC grade. Standard reference material SRM-1849 (infant/adult nutritional formula) was obtained from the National Institute of Standards and Technology (NIST, Gaithersburg, MD, USA).

2.2 Cholesterol extraction

Cream puff, croissant, butter bread, pizza bread, doughnut, chrysanthemum bread, chocolate conch bread, mocha bread, soboro bread, mammoth bread, plain bread cheese ball, jam bread, macaroni, apple pie, dacquoise, and castella were obtained from the Ministry of Food and Drug Safety (MFDS), the Republic of Korea. All the samples were procured in a homogenized form into a screw-capped plastic container. Samples were transported in an icebox and stored at -70˚C until assay. Cholesterol was extracted and determined using the Korean Food Standards Codex (2010) and Chun et al. (2006) methods with some minor modifications. At first, about 5 g of samples was placed in an extraction glass tube with a glass stopper and added with 10 mL of ethanol containing pyrogallol (6%, w/v). The mixture was well mixed by a vortex (Vortex-Genie 2, Scientific Industries Inc., Bohemia, NY, USA) for 10 sec and sonicated for 10 min. Next, 8 mL of 60% (w/v) potassium hydroxide was added to the mixture and vortexed. The glass tube was then flushed with nitrogen gas, connected with an air condenser tightly, and placed in a shaking water bath (Han-Baek Scientific, Bucheon, Korea) at 75˚C and 100 rpm for 60 min. Samples were then immediately cooled down by running tap water and added with 20 mL of (2%, w/v) sodium chloride solution and mixed thoroughly. Each sample was added with 15 mL of extraction solution (hexane: ethyl acetate = 85:15, 0.01% BHT) and mixed vigorously for 2 min. The solution was left undisturbed for 10 min to facilitate phase separation. The upper unsaponifiable layer was collected in a 50 mL-volumetric flask through an anhydrous sodium sulfate layer. The sample was then re-extracted two more times with the same extraction solution and all of the collected upper layer was combined into the 50 mL-volumetric flask. The accumulated sample extract (unsaponifiable layer) was adjusted to 50 mL with extraction solvent and mixed thoroughly. An aliquot (10 mL) of the extract was dried under N₂ gas. The residue was dissolved in 3 mL of acetone and dried out under N₂ gas.

2.3 Trimethylsilyl derivatization

Extracted bakery sample and standards (cholesterol and α-tocopherol) were derivatized. Briefly, three mL of N, N-dimethyl formamide (DMF) was added to the dried residues and was dissolved properly. One mL of DMF was then derivatized with 200 µL of HMDS and 100 µL of trimethylchlorosilanein (TMCS) at room temperature. The mixture was mixed properly and kept undisturbed for 15 min. Later, 1 mL of 0.1 mg/mL 5α-cholestane (internal standard) and 10 mL deionized distilled water was added to the derivatized products and vortexed vigorously. The phase separation was obtained within 10 min. The separated upper layer was then collected in a GC vial through anhydrous sodium sulfate salt for gas chromatographic separation.

2.4 Without derivatization

Cholesterol and α-tocopherol standards were mixed and filtered into a GC vial through anhydrous sodium sulfate salt for gas chromatographic separation.

2.5 Cholesterol analysis by GC

Gas chromatographic separation of cholesterol derivatives was conducted by using a Hewlett Packard 5890 Series II GC system (Palo Alto, CA, USA) equipped with a 7673A auto-sampler, a Hewlett Packard-5 capillary column (30 m × 0.32 mm × 0.25 µm; Agilent J&W Scientific Inc., Technologies, Folsom, CA, USA), and a flame ionizing detector (FID). Both injection and detection temperatures were 300°C. The initial oven temperature was set at 250°C. The oven temperature was increased at 15°C/min up to 300°C in two stages. First, the temperature was ramped up to 285°C and maintained for 18 min. Then, it was increased up to 300°C at the same increasing rate and was maintained for 2 min. The oven temperature was then decreased at 15°C/min, and the total run time of the applied gas chromatographic method was 32.47 min. Hydrogen and air were used as fuel gases (H₂: 30 mL/min and O₂: 300 mL/min), while nitrogen was used as carrier gas (N₂: 3 mL/min). The split ratio was 50:1. All analyses were performed in triplicates.

2.6 Peak Identification and quantification

Cholesterol peak identification was performed by using retention time (RT) and relative retention time of the cholesterol standard (RRT). RRT is the ratio of the retention time of internal standard and the retention time of cholesterol standard. The retention time and relative retention time of the derivatized cholesterol standard were 12.15 min and 0.68 respectively. RRT is determined by using the following equation (i).

RRT = retention time of internal standard/retention time of cholesterol …… (i)

RRT has no unit, as it is the ratio of two retention times. The identified peak was then quantified by using equation (ii)

Cholesterol content (mg/100 g) = 100{X(V₃/V₄)/[W(V₂/V₁)]} …… (ii)

Where, X = cholesterol concentration in a sample

V₁ = total extract volume

V₂ = taken extract volume to dry under nitrogen

V₃ = added total N, N-dimethyl formamide (DMF) volume

V₄ = taken DMF volume for derivatization

Cholesterol concentration was determined from a standard curve equation. The vertical axis of that standard curve represented the ratio of the peak area of cholesterol and internal standard, while the horizontal axis of the standard curve represented cholesterol concentration. Six different cholesterols to internal standard ratios were plotted against six different cholesterol standard concentrations to obtain a standard curve. The standard equation used in this study was y = 10.3467x + 0.0150 where; ‘y’ is the ratio of cholesterol and internal standard peak area. Cholesterol concentration was represented by ‘x’ and 0.0150 was the intercept. The R² value of the standard curve was 1.0000.

2.7 Method validation

Cholesterol analysis was validated according to the AOAC single laboratory method validation guideline (AOAC, 2002). The applied method was validated by different method validation parameters: limits of detection (LOD), limits of quantification (LOQ), linearity, accuracy, and precision. LOD and LOQ of cholesterol were determined based on the signal-to-noise ratio of 3:1 and 10:1, respectively (Academy, 1996; AOAC, 2002). The linearity of the cholesterol standard curve was checked at six different concentrations (0.0025-0.2000 mg/mL).

The accuracy of cholesterol assay was determined by analyzing standard reference material SRM-1869 (infant/adult nutritional formula II). Two different levels of precision, repeatability, and reproducibility were investigated. Repeatability was determined by repeated cholesterol analysis of a commercially produced milk powder five times a day. On the other hand, reproducibility precision was determined by analyzing the milk powder sample once a day for 5 days. Nowadays, HorRat value has also become one of the acceptable criteria for many of the recently adopted chemical methods of analysis performed by different international authorities as like as AOAC International, the European Union, and other European organizations that deal with food analysis (European Committee for Standardization and Nordic Analytical Committee). HorRat was calculated by the formula developed by Horwitz & Albert (2006).

2.8 Analytical quality control

Commercial infant formula (Maeil, Seoul, Korea) was used as quality control (QC) sample in this study. QC Sample was purchased from a local market (Seoul, Korea) and transported in an airtight poly bag, and stored at -20°C until analysis. Cholesterol contents of commercial infant formula were analyzed during every assay to develop an analytical quality control chart. Control and action lines (mean ± 2SD and ± 3SD, respectively) of the control chart were used to control the cholesterol analysis method.

3.1 Peak elution of derivatized-/non-derivatized- cholesterol and α-tocopherol

Derivatized-/non-derivatized- cholesterol and α-tocopherol standards were eluted on a HP-5 GC column and their GC chromatograms were provided in supplement Fig. 1. The retention time of derivatized cholesterol and α-tocopherol standard were 12.15 min and 11.80 min, respectively whereas the retention time of non-derivatized cholesterol and α-tocopherol were 11.62 min and 11.60 min, respectively (Fig. 1). Non-derivatized retention time of cholesterol and α-tocopherol peaks were remarkably close to each other. As a result, when cholesterol and α-tocopherol co-exist together in a sample during that time, non-derivatized cholesterol and α-tocopherol could not elute separately but instead elute together. A representative gas chromatogram of separated (derivatized) cholesterol and α-tocopherol and a chromatogram of co-eluted (non-derivatized) cholesterol and α-tocopherol of jam bread were presented in Fig. 2. Peak resolution value of cholesterol and α-tocopherol after derivatization was 3.11 (Fig. 2). According to AOAC, (2002) for effective separation of two analytes, the resolution of two adjacent peaks must be over 1.5. In this study, the resolution of derivatized cholesterol and α-tocopherol peaks showed an excellent separation.

3.2 Derivatization and its importance in cholesterol analysis

Derivatization is the process of conversion of an analyte into another compound. Derivatization reaction eases the analytical ability by increasing the volatility of nonvolatile analyte and the specificity of the analyte. It also reduces the analyte absorption in the GC system and improves detector response. Therefore, cholesterol has been analyzed through derivatization process for many years. Among different derivatization techniques silylation is the widely used derivatization for cholesterol analysis. In TMS derivatization, an active hydrogen (-OH, -COOH, -NH, -NH₂, -SH groups) is replaced by a trimethylsilyl leaving group. Silylation reaction is driven by that leaving group. The better the leaving group, the better the silylation. A representative TMS derivatization reaction scheme of cholesterol and α-tocopherol is presented in Fig. 3.There are also a group of researchers who successfully quantified cholesterol without derivatization from wide range of samples which includes eggs, milk, vegetable oil, human serum and Korean dishes (Choong et al., 1999; Chen et al., 2015; Sharmin et al. 2017; Höring et al., 2019). Choong et al., (1999) reported high level of cholesterol (193.4- 3166.1 µg/g) in vegetable oil which may be the result of overestimation. They did not performed derivatization and could not separate cholesterol from α-tocopherol which can be the reason of overestimated cholesterol. In this study, the cholesterol and α-tocopherol content of the bakery products were presented simultaneously. Cholesterol and α-tocopherol both have an active hydrogen group (-OH) in their structure. Derivatization converts both cholesterol and α-tocopherol to corresponding identical compounds and helped them to be eluted separately rather than to be co-eluted (Fig. 2). On the other hand, the non-derivatization method applied in our study could not separate α-tocopherol from cholesterol, which could overestimate the cholesterol content of the sample (Fig. 2). Derivatization plays a vital role to separate cholesterol and α-tocopherol. If samples have only cholesterol without α-tocopherol, the cholesterol content could be accurately measured by a simple GC without derivatization. However, derivatization should be considered for cholesterol analysis by GC for the samples containing α-tocopherol.

3.3 Method validation

3.3.1 LOD, LOQ, and linearity

LOD, LOQ and linearity of cholesterol analysis were presented in Table 1. A high linear correlation (R² = 0.999) was observed between standard concentration and peak area over a range of period (0.0025 mg/mL to 0.2000 mg/mL). LOD and LOQ of cholesterol analysis were 0.03 mg/100 g and 0.08 mg/100 g, respectively. It indicates that this method can detect cholesterol if cholesterol level is ≥ 0.03 mg/100 g, and quantify cholesterol with acceptable reliability at least 0.08 mg cholesterol/100 g sample. The LOQ obtained in this study is half of the LOQs of Chen et al. (2015) and Sharmin et al. (2017), showing a good sensitivity in analysis. It indicates that the applied method can quantify cholesterol from very low cholesterol concentration samples.

Table 1

Limit of detection, limit of quantification, linearity, and precision of cholesterol analysis by saponification-trimethylsilyl derivatization coupled with GC-FID
Analyte	LOD¹⁾	LOQ²⁾	Linearity		Repeatability³⁾			Reproducibility⁴⁾
Analyte	(mg/100 g)		Linear equation	R squared	Mean ± SD⁵⁾	RSD⁶⁾ (%)	HorRat_r⁷⁾	Mean ± SD	RSD (%)	HorRat_R⁸⁾
Cholesterol	0.032	0.078	y = 10.2691x + 0.0064	0.9996	46.908 ± 0.70	1.49	0.26	47.54 ± 0.92	1.93	0.34
¹⁾ Limit of detection
²⁾ Limit of quantification
³⁾ Repeatability refers to the results of independent determinations carried out on a sample by analyzing five replicates of the sample on the same day.
⁴⁾ Reproducibility refers to the results of independent determinations carried out on a sample by analyzing five replicates of the sample (once a day for 5 day)
⁵⁾ Mean of cholesterol contents (mg/100 g) ± standard deviation
⁶⁾ Relative standard deviation,
⁷⁾ Horwitz ratio for repeatability
⁸⁾Horwitz ratio for reproducibility

3.3.2. Precision

Repeatability and reproducibility of cholesterol analysis were performed by a commercial infant formula (Table 1). The relative standard deviation (RSD) of repeatability and reproducibility of cholesterol analysis was 1.49% and 1.93% respectively. RSD of repeatability and reproducibility for the applied method was within the acceptable range (≤ 4% for repeatability and ≤ 8% for reproducibility) according to the AOAC guideline (2002). The average cholesterol content of the commercial infant formula was 47.23 ± 0.84 mg/100 g.

HorRat is another commonly used performance parameter to evaluate the acceptability of an analytical method. HorRat values which fall within 0.5 to 2 are considered fully acceptable. However, if the value is more than 2 then the test sample may not be properly homogenized before analysis or needs further optimization of the applied process or proper training (Horwitz and Albert 2006). HorRat values below 0.5 indicate excellent training and experience (Horwitz and Albert 2006). In this study, HorRat values of repeatability (HorRat_r) and reproducibility (HorRat_R) were 0.26 and 0.34 respectively, which indicated that the applied method is excellent in terms of HorRat ratio. Based on RSD and HorRat values of repeatability and reproducibility, the applied analytical method could be used as a precise method to analyze cholesterol.

3.3.3 Accuracy

The accuracy of the applied method was evaluated by analyzing SRM-1869 (Infant/Adult Nutritional Formula II) and presented in Table 2. TMS-derivatized GC chromatogram of cholesterol of SRM-1869 was presented in supplement Fig. 1 which shows that cholesterol and α-tocopherol clearly separated after derivatization. The analytical value obtained by using TMS-derivatization coupled with GC was within the range of the certified values for SRM-1849. The recovery percentage of cholesterol from SRM-1869 was 102.1%, showing excellent accuracy.

Table 2

Accuracy of cholesterol analysis by saponification-trimethylsilyl derivatization coupled with GC-FID
Sample¹⁾	Analyte	Contents (mg/g)		Recovery (%)
Sample¹⁾	Analyte	Certified value²⁾	Analytical value³⁾	Recovery (%)
SRM-1869	Cholesterol	13.02 ± 0.47	13.30 ± 0.17	102.1
¹⁾ SRM indicates standard reference material
²⁾ Certified value provided by NIST
³⁾The analytical value obtained by using GC-FID in this study

3.4 Cholesterol content of bakery products (bread, cake, and cookie)

In general, bakery products such as bread, cake, and cookies have both cholesterol and α-tocopherol derived from wheat flour, butter, milk, egg, etc. In this study, the validated derivatization-GC method was applied to analyze cholesterol from 17 kinds of bakery products and their cholesterol contents were presented in Table 3. The applied derivatization process separated cholesterol from α-tocopherol and helped to quantify cholesterol accurately rather than overestimate. Researchers who did not apply derivatization in cholesterol analysis might have overestimated the cholesterol quantification if their sample contains both cholesterol and α-tocopherol. Choong et al., (1999), Chen et al.,( 2015), and Sharmin et al. (2017) did not apply the derivatization technique in their studies but their samples (vegetable/animal fat and oil, fish, meat, etc.) contain both α-tocopherol and cholesterol. Therefore, there is a possibility of cholesterol overestimation in their study.

Table 3

The cholesterol contents of bakery products analyzed by saponification-trimethylsilyl derivatization coupled with GC-FID
Sample name	Cholesterol content (mg/100 g)
Castela	114.26 ± 4.72
Croissants	80.35 ± 1.97
Mocha bread	52.08 ± 0.38
Dacquoise	50.84 ± 0.48
Soboro bread	50.53 ± 0.65
Doughnut	47.22 ± 0.35
Pizza bread	45.60 ± 0.48
Butter bread	36.21 ± 0.55
Macaron	35.38 ± 0.58
Cream puff	26.70 ± 0.43
Cheese ball	23.23 ± 0.57
Mammoth bread	23.19 ± 0.30
Chocolate conch bread	19.61 ± 0.44
Apple pie	14.39 ± 0.26
Jam bread	3.44 ± 0.15
Plain bread	3.16 ± 0.02
Chrysanthemum bread	2.76 ± 0.06

High variation in cholesterol values with a range of 2.76 ± 0.06 mg/100 g (castella cake) to 114.26 ± 4.72 mg/100 g (chrysanthemum bread) can be attributed due to the different recipe and ingredient composition of the 17 bakery samples in this study. An intake of 270 g castella, 375 g croissants, or 600 g mocha bread/soboro bread/dacquoise singly could provide more than 300 mg of cholesterol which is the recommended daily cholesterol level of intake (WHO/FAO, 2003).

Bread is considered one of the staple food products of many countries (Nahar et al. 2019). Nowadays, Asian people are changing their eating habits to bakery products rather than rice. Although the concentration of cholesterol in bakery products is lower than the cholesterol-rich foods (red meat/shrimp, etc.), it can be no longer negligible if people would consume more as their daily food. Therefore, choosing an accurate procedure for cholesterol quantification in bakery products is necessary.

3.5 Quality control of cholesterol analysis

Analytical quality control was performed to obtain the reliability of the analytical data of cholesterol levels in bakery products commonly consumed in Korea. Commercial infant formula was used as an internal quality control sample and assayed with every analysis of samples. A quality control chart of cholesterol analysis is shown in supplement Fig. 2. The average cholesterol content of the quality control sample was 47.23 ± 0.84 mg/100 g. All the assay values of the quality control sample were within the upper and lower control lines (48.90 mg/100 g and 45.55 mg/100 g, respectively) of the quality control chart, which means that the cholesterol analysis was performed as validated under control.

For reliable cholesterol analysis, the analytical method consisted of saponification (alkaline hydrolysis), consecutive trimethylsilyl-derivatization, and coupling with GC-FID was validated by evaluating analysis performance parameters. Specifically, it was focused on the effect of the derivatization step after saponification in cholesterol analysis and the validation method was applied for determining cholesterol in a total of 17 different kinds of bread, cake, and cookies distributed and consumed nationwide in Korea. This study showed that cholesterol analysis by GC without derivatization could overestimate the cholesterol content if samples contain α-tocopherol. It was found that trimethylsilyl derivatization helped to separate cholesterol from α-tocopherol on a GC column, resulting in the improvement of specificity. The saponification-trimethylsilyl derivatization coupled with GC-FID showed excellent accuracy and precision based on the AOAC guidelines (2002). LOD of the applied analytical method was enough for detecting cholesterol in samples down to 0.03 mg/100 g. In this study, reliable information on the cholesterol content of bakery products was obtained. It would be used for upgrading the national food composition table which is officially released by the Korea Ministry of Food and Drug and utilized by researchers, nutritionists, and policymakers.

CRediT author statement

Md. Atiqual Islam: Conceptualization, Methodology, Analysis, Software, Writing- Original draft preparation, Validation. Won-Ho Hong: Methodology, Analysis, Instrument control. Jiyeon Chun: Conceptualization, Writing- Reviewing and Editing, Supervision, Fund collection.

Declaration of conflict of interest

The authors declare that they have no known conflict of financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgment

This research was supported by a grant from the ministry of food and drug safety.

Funding

This research was supported by the ministry of food and drug safety, Republic of Korea (Grant number: 20162MFDS087).

Data Availability Statements

All data generated or analyzed during this study are included in this article as Tables, Figures, and supplement materials.

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Importance of derivatization in cholesterol analysis using GC-FID for bakery products containing plant and animal lipid

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Abstract

Figures

Introduction

Materials & Methods

2.1. Reagents and materials

2.2 Cholesterol extraction

2.3 Trimethylsilyl derivatization

2.4 Without derivatization

2.5 Cholesterol analysis by GC

2.6 Peak Identification and quantification

2.7 Method validation

2.8 Analytical quality control

Results And Discussion

3.1 Peak elution of derivatized-/non-derivatized- cholesterol and α-tocopherol

3.2 Derivatization and its importance in cholesterol analysis

3.3 Method validation

3.3.1 LOD, LOQ, and linearity

3.3.2. Precision

3.3.3 Accuracy

3.4 Cholesterol content of bakery products (bread, cake, and cookie)

3.5 Quality control of cholesterol analysis

Conclusion

Declarations

Data Availability Statements

References

Additional Declarations

Supplementary Files

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