HS‐GC‐IMS detection of volatile organic compounds in Acacia honey powders under vacuum belt drying at different temperatures

Abstract Honey is a commodity of great nutritional value, but deep‐processed honey products are uncommon. Herein, we used vacuum belt dryer to dry Acacia honey at 60°C, 70°C, and 80°C, prepared it into powder, and analyzed its volatile compound differences. We established HS‐GC‐IMS method to detect the volatile organic compounds (VOCs) of these three Acacia honey powders (AHPs). In total, 77 peaks were detected, and 23 volatile compounds were identified, including eight aldehydes, six ketones, three furans, one alcohol, one phenol, one lactone, one ester, one acid, and one nitrile. Moreover, principal component analysis (PCA) and fingerprint similarity analysis based on the Euclidean distance distinguished the three heating temperature treatments. Clearly, it was concluded that there are significant differences in volatile substances at different tested temperatures, and when the AHP was incubated at 80°C, more volatile compounds were detected.

The abbreviation of Acacia honey powder under different processes are as follows: VBD-AHP1 (after 60℃ vacuum belt drying of Acacia honey powder); VBD-AHP2 (after 70°C vacuum belt drying of Acacia honey powder); and VBD-AHP3 (after 80°C vacuum belt drying of Acacia honey powder).

| HS-GC-IMS system
Headspace sampling conditions were set as follows: 2.0 g sample was placed in a 20 mL headspace bottle and incubated at 80°C for 20 min. The centrifuge speed was 500 rpm, and the temperature of inject needle was 85°C, and 500 μL sample was injected.

| Statistical analysis
Statistical data analysis was performed by Laboratory Analytical Viewer (LAV) and GC-IMS Library Search software from different angles.

| HS-GC-IMS plots of different treatments of honey powders
In this study, HS-GC-IMS was used to analyze the VOCs of Acacia honey powders under different temperatures during vacuum belt drying processing. From Figure 1, the ordinate represents the retention time of the gas chromatography, and the abscissa represents drift time. When the drift time is between 7.92 and 7.93 ms, there is a reaction ion peak (RIP). Moreover, when the drift time is between 8.3 and 8.8 ms, there is an ethanol peak marked separately, with a higher signal response intensity.
In order to compare the differences between this three samples in more detail, the different comparison modes (Figure 2): Select the spectrum of VBD-AHP1 as the reference, and subtract the reference from the spectra of the two other samples. If the two VOCs are the same, the deducted background is white. And red means the concentration of the substance is higher than the reference, while blue means lower. The brighter the color, the higher the content, and vice versa. It can be observed from Figure 2 that there was little difference between VBD-AHP1 and VBD-AHP2. When the drift time is | 4087 FENG Et al.
between 8.0 and 9.5, the content of VOCs in VBD-AHP2 was lower than VBD-AHP1, and later, when the drift time is 10.0, VBD-AHP2 was higher, but the difference was not significant. For VBD-AHP3, the contents of volatile substances were more complicated than the former two, so further judgment was needed. However, more volatile compounds were found in VBD-AHP3, maybe that high temperature promoted some fewer volatile compounds (Plutowska et al., 2011).

| Identification of VOCs from different treatments of Acacia honey powders
The aroma components of Acacia honey contain alcohols (14.41 ng/ ml), alkanes (4.34 ng/ml), esters (3.31 ng/ml), acids (3.06 ng/ml), aldehydes (2.45 ng/ml), furan, benzene and its derivatives (0.93 ng/ ml), and ketones (0.70 ng/ml) (Pei et al., 2014). In this research, F I G U R E 1 HS-GC-IMS plot of Acacia honey powders F I G U R E 2 HS-GC-IMS plot in difference comparison mode of Acacia honey powders HS-GC-IMS was used to detect the VOCs of Acacia honey powder with vacuum belt drying at different temperatures. The qualitative analysis of volatile components in Acacia honey powder is shown in Figure 3, in which the abscissa represents the drift time (normalized) and the ordinate represents the retention time, and the numbers correspond to the compounds in Table 1. A total of 77 peaks were detected and 23 volatile compounds were identified, including eight aldehydes, six ketones, three furans, one alcohol, one phenol, one lactone, one ester, one acid, and one nitrile. Table 1 lists the qualitative results, including the compound name, CAS number, molecular weight (MW), the Retention Index (RI), the retention time (RT), and the drift time (DT).
Heat treatment can increase aldehydes and ketones (Li et al., 2012). And aldehydes were derived from the auto-oxidation of lipid, while ketones were mainly derived from the thermal oxidation or degradation of unsaturated fatty acids (Liu et al., 2020).
Surprisingly, n-Nonanal was only found in Acacia honey, and also contained its homologues heptanal and octanal, which had sweet citrus flavor (Plutowska et al., 2011). Moreover, Escriche et al. (2009) found that heat treatment significantly changed 29 compounds, 20 of which belonged to the alcohol and aldehyde family. Among them, 2,3-butanediol was detected and its content was increased by pasteurization. 2,3-Butanediol can be prepared from sugar, molasses, malt syrup, or alcohol mother liquor as raw materials through biolog- Of note was that, in this study, a large number of benzene compounds (benzaldehyde, phenol, phenylacetic acid) were also found. In addition, Acacia honey contained a lot of esters, accounting for 32.43% of aroma components (Pei et al., 2014). But a small amount of ethyl acetate was detected in Acacia honey (Wang et al., 2021), may be derived from the interaction of alcohol and free fatty acids produced by lipid oxidation in the sample . Acacia honey also contained phenylacetaldehyde (1.96 ng/ml) (Pei et al., 2014), which was heated and oxidized to generate phenylacetic acid. The detected 3-butenenitrile belonged to the category of nitriles, which may be caused by prolonged contact between packaging materials and honey powders.

| Gallery plot of different treatments of Acacia honey powders
In order to clearly compare the specific volatile substance differ-

| Cluster analysis of Acacia honey powders
Principal component analysis (PCA) is a multivariate statistical method used to examine the correlation between multiple variables, constitutes a powerful visualization tool, provides a method to reduce the dimensionality of the data, and can eliminate unnecessary information (Chen et al., 2020). In order to analyze the problem comprehensively, PCA is applied to these variables.
Generally, when the cumulative contribution rate of PC1 and PC2 reaches 60%, PCA model is considered as the preferred separation model (Wu et al., 2015). PCA had been used to distinguish honey from different floral origins, different varieties of honey, and discrimination between conventional honey and organic honey (Schuhfried et al., 2016;Schwolow et al., 2019;Wang, Yang, et al., 2019). However, less research for the processes of honey In this study, PCA was used to distinguish VBD-AHP at different temperatures. A total cumulative contribution was 91%, of which PC1 was 74% and PC2 was 17%. In addition, it can be observed which can confirm that there was no significant difference between them. However, both of them were far away from VBD-AHP3, which can prove that the components were obviously different.
Meanwhile, the analysis of fingerprint similarity based on the Euclidean distance judged the difference in the samples. This method is a cluster analysis method based on distance discrimination, which refers to the true distance between two points in space, or the natural length of the vector (i.e., the distance from the point to the origin) (Tang, 2019), reflects the degree of intimacy between the research subjects (Li et al., 2008).  used the square Euclidean distance measurement method to cluster analysis on the gas-phase matching data of 38 honey samples from 4 different nectar sources, and found that these honeys could be clustered into one category, respectively. Figure 6 shows the fingerprint similarity based on Euclidean distance, and Table 2 shows the values of Euclidean distance between the three.
We can find that VBD-AHP1 and VBD-AHP2 were relatively close, and the average Euclidean distance between them was 2,507,274.071. Meanwhile, the average Euclidean distance between VBD-AHP1 and VBD-AHP3 was 12,494,019.43 and VBD-AHP2 and VBD-AHP3 was 11,772,169.19. So, the difference in VBD-AHP3 was more significant than the first two.

| D ISCUSS I ON AND CON CLUS I ON
Generally, liquid Acacia honey has a thick fluid-like shape; fresh Acacia honey is colourless and the color may be deepened when it placed; moreover, it has light fragrance of sophora flower and is not easy to crystallize, and no impurities were visible with normal vision. Meanwhile, the content of glucose and fructose is more than 60 g/100 g, sucrose is less than 5 g/100 g, and maltose is less than 3 g/100 g. From the visual analysis, brownish-yellow amorphous powder of AHP at 60, 70°C, while light yellow amorphous powder of AHP at 80°C. And AHP3 is finer and less dense than those at 60°C and 70°C. Most importantly, honey powder would retain pure honey flavor and is a natural sweetener, which is more convenient to carry than liquid honey, avoiding the waste of liquid honey and environmental hygiene problems, reducing storage space, and prolonging preservation time. Therefore, the study of AHP is crucial and essential.
Because honey powder is a deep processing product of honey, and nowadays, the processing of honey powder is monotonous, mainly freeze-dried and roller cylinder-dried. So, we considered whether vacuum belt drying can be applied in the honey industry, and we looked up the literature and found that, for materials with high stickiness, easy agglomeration, thermoplastic, and thermosensitive properties, the best option was vacuum-belt dryer.
Therefore, in order to analyze its process parameters, we considered three different temperatures of vacuum belt drying, mainly to detect the differences in VOCs between the three honey powders, and to identify differences in flavor aspects that consumers pay attention to.
The HMF in Acacia honey is mainly produced by amino acids and glucose or fructose in honey undergoing the Maillard reaction under acidic conditions (Kowalski et al., 2013). Since HMF can cause irritation to human eyes, mucous membranes, skin, etc., and can cause cell and gene mutations, excessive intake can result in poisoning and even initiate cancer (Ma et al., 2019). Therefore, HMF in international trade of honey belongs to the mandatory detection indicator, which states that its content should be ≤40 mg/ kg. Our raw material acceptance criteria indicate that the HMF is less than 10 mg/kg, and the HMF content was found to be within 4 h of heating at 80°C (Lu et al., 2006). In addition, Lu et al. (2006) found that the formation rate of HMF was buckwheat honey >jujube honey >acacia honey >locust honey, and proposed that during honey thermal processing treatment, heating temperature should be controlled within 80°C to prevent excessive HMF F I G U R E 6 Fingerprint similarity based on the Euclidean distance of VBD-AHPs content. Furthermore, Wang et al. (2018) surveyed to obtain commercially available Acacia honey in Chengdu, Sichuan Province, China, the number of detected was 22, the eligibility rate was 100%, and the mean content of HMF was 5.96 ± 2.93 mg/kg, 81.82% were in the range of 0-10 mg/kg, and 18.18% were in the range of 10.1-20 mg/kg.
In this study, HS-GC-IMS was used to detect VOCs of VBD-AHP.
A total of 77 peaks were detected and 23 volatile compounds were identified, including eight aldehydes, six ketones, three furans, one alcohol, one phenol, one lactone, one ester, one acid, and one nitrile. Since GC-IMS is not able to detect alkane compounds, 43 signal peaks were not characterized in the current study, and these unknown peaks will be worthy to continue to be identified using GC-

CO N FLI C T O F I NTE R E S T
The authors declare no conflicts of interests.

AUTH O R S CO NTR I B UTI O N S
Duo Feng investigated the study, wrote the original draft, and involved in plot analysis. Jing Wang involved in formal analysis. Yue He investigated the study. Xiao-jiao Ji validated the study. Hui Tang provided resources. Yong-mei Dong visualized the data. Wen-jie Yan wrote, reviewed, and edited the manuscript, supervised the data, acquired funding. [+] VBD-AHP1