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Evaluation of pollen tube growth ability in Petunia species having different style lengths.

Pollen tube growth is essential for the fertilization process in angiosperms. When pollen grains arrive on the stigma, they germinate, and the pollen tubes elongate through the styles of the pistils to deliver sperm cells into the ovules to produce the seeds. The relationship between the growth rate and style length remains unclear. In previous studies, we developed a liquid pollen germination medium for observing pollen tube growth. In this study, using this medium, we examined the pollen tube growth ability in Petunia axillaris subsp. axillaris, P. axillaris subsp. parodii, P. integrifolia, and P. occidentalis, which have different style lengths. Petunia occidentalis had the longest pollen tubes after 6 h of culture but had a relatively shorter style. Conversely, the pollination experiments revealed that P. axillaris subsp. parodii, which had the longest style, produced the longest pollen tubes in vivo. The results revealed no clear relationship between the style lengths and the growth rate of pollen tubes in vitro. Interspecific pollinations indicated that the styles affected pollen tube growth. We concluded that, in vitro, the pollen tubes grow without being affected by the styles, whereas, in vivo, the styles significantly affected pollen tube growth. Furthermore, interspecific pollination experiments implied that the pollen tube growth tended to be suppressed in the styles of self-incompatibility species. Finally, we discussed the pollen tube growth ability in relation to style lengths.

Introduction
In plant breeding, interspecific hybridization is a useful way to produce attractive genotypes by incorporating novel traits of other species; it involves crossing barriers between species as well as between genera. As these barriers contribute to speciation, a knowledge of cross-incompatibility can lead to an understanding of plant evolution. Plant speciation is closely related to morphological changes. Understanding these changes is important because the differences express adaptation to the environment (Barrett 2002). In particular, differences in floral structures as reproductive organs are related to reproductive isolation (Cuevas et al. 2018;Kulcheski et al. 2006). In Narcissus, the relationship between style length and the type of pollinators has been investigated (Pérez- Barrales et al. 2006Barrales et al. , 2014. However, the relationship between the style length and pollen tube growth rate is not well understood. To investigate ways to surmount the barriers and facilitate breeding, our study focuses on pollen tube growth ability in some Petunia species having different style lengths. Williams and Rouse (1988) suggested that the ability to produce hybrids could be related to comparative flower sizes of the parental species in Rhododendron. In the comprehensive research for genus Rhododendron, the rate of pollen tube growth was faster in the species having longer styles (Williams and Rouse 1990). In the present study, we selected the genus Petunia as a model plant, which has several species with apparent different style lengths.
Petunia originates in South America (Lorenz-Lemke et al. 2010). The flower shapes vary among species (Galliot et al. 2006). Previously, an expedition to collect the wild genetic resources of Petunia was conducted (Ando and Hashimoto 1993;Ando et al. 1995aAndo et al. , 1995bAndo et al. , 2001Kokubun et al. 1998Kokubun et al. , 2006; phylogenetic analyses were conducted (Ando et al. 2005;Chen et al. 2007;Kulcheski et al. 2006;Zhang et al. 2008), and cross-compatibility was examined (Watanabe et al. , 1996(Watanabe et al. , 1997. Petunia exhibits gametophytic self-incompatibility (Linskens 1975), but a degree of variation in self-incompatibility response occurs in the wild genetic resources . The current study evaluating pollen tube growth will contribute to the research on the mechanisms of incongruity in the styles to produce hybrid plants.
In our previous studies, pollen behavior was examined by developing a liquid in vitro pollen culture system that induces normal pollen germination and tube growth in various genera such as Alstroemeria (Hirano and Hoshino 2009), Cyrtanthus (Hirano and Hoshino 2010) and Petunia (Hoshino et al. 2016). The medium is characterized by the addition of a low concentration of yeast extract, which encourages normal and high pollen germination. As an example of the application of pollen culture, assessment of gamma-irradiated pollen grains was conducted for haploid production by inducing parthenogenesis in Juglans regia (Grouh et al. 2011). In the present study, we evaluated pollen tube growth ability using the pollen culture medium developed previously. In the previous study (Hoshino et al. 2016) and the preliminary experiments, the pollen gemination ability was confirmed in several Petunia species used in this study. Then, we examined the pollen tube growth rate in the styles of several species having different style lengths. Moreover, the effects of the pollen tube growth ability on the pollen tube lengths in the styles were analyzed. In total, we discussed the pollen tube growth ability in vitro, and pollen tube lengths in the styles after crosspollination or interspecific pollination.

Plant material
The following seven accessions of Petunia species were used in this study (Table 1) Figure 1D). All these flowers had different style lengths ( Figure 1E). The plants were grown from seeds, and eight plants per accession were cultivated as potted plants and grown in a glasshouse at the Experiment Farms, Hokkaido University, Japan. During the flowering season, a liquid fertilizer (Hyponex) was applied in the appropriate concentration and at appropriate intervals.
Style length indicates the length the pollen tube must attain to reach the ovule from the stigma in each species. To analyze the relationship between style length and pollen tube growth ability, the following experiments were designed. Initially, pollen tube growth ability was evaluated in vitro.

Measurements of the floral organs
Pistil length, stamen length, flower length, corolla width, and ovary length were measured in at least 20 flowers immediately after flowering. The pistil and stamen lengths were measured using a caliper (CD-S15C, Mitutoyo, Kawasaki, Japan) after cutting the flowers. The style lengths were calculated from the data of the pistil and ovary lengths; the pistil length−the ovary length= the style length+ stigma length. In this study, this value was used as the style length because the stigma length was minute. The ovary length data of P. occidentalis were obtained  Ando et al. (1995c) Self-compatibility (SC), Self-incompatibility (SI). Copyright © 2022 Japanese Society for Plant Biotechnology from Ando et al. (1995c).

Measurement of pollen tube length in vitro
The pollen culture was conducted according to the procedure of Hirano and Hoshino (2009) with minor modifications. Briefly, anthers were harvested immediately after flowering, and each anther was cut into quarters. One of the quarters was put into a 3.5-cm Petri dish containing 1.5 ml of a liquid medium, and pollen grains were released from the anther piece by gentle shaking. The Petri dishes were kept at 22°C. For each Petri dish, the frequency of pollen germination was calculated from five fields of view under an inverted microscope (Axiovert 200, Carl Zeiss, Jena, Germany). Pollen tube lengths were also measured using a digital camera scale (DS-Fi2, Nikon Corporation, Tokyo, Japan) with a camera control unit (DS-L3, Nikon Corporation, Tokyo, Japan). The samples showing more than 70% pollen germination frequencies after 6 h were treated as analytical data to delete data from abnormal pollen grains. Evaluation of pollen germination frequencies and measurement of pollen tube length were performed 1, 3, and 6 h after culture. At least three replicates were examined in each experiment. The ten longest pollen tubes were used for data analysis.
To detect changes in the speed of pollen tube growth for characterizing each species, pollen tube growth rate was calculated at each time point by the following formulas; (average pollen tube length 1 h after culture)/1 (h) for 0 to 1 h, [(average pollen tube length 3 h after culture)−(average pollen tube length 1 h after culture)]/(3−1) (h) for 1 to 3 h, and [(average pollen tube length 6 h after culture)−(average pollen tube length 3 h after culture)]/(6−3) (h) for 3 to 6 h.

Measurement of the pollen tube growth rate in the styles
Intraspecific and interspecific pollinations were conducted to measure the degree of pollen tube growth in the styles. For interspecific pollinations, a compatible combination reported by Watanabe et al. (2001Watanabe et al. ( , 1996 was used as a reference. Approximately two days before anthesis, the floral buds were opened, and the stamens were removed. Then, the flower buds were pollinated with fresh pollen grains according to the method of Watanabe et al. (1996) for cross-pollination in Petunia species. After 6 h, the flower buds were fixed in Farmer's solution at room temperature for 24 h. The pollen tubes in the styles were stained with aniline blue according to the protocol set out by Hoshino et al. (2006). Briefly, the flower buds were hydrolyzed in 0.1 M NaOH at 60°C for 7 min. After washing with distilled water, pistils were observed under an inverted epifluorescence microscope (Axiovert 200, Carl Zeiss, Jena, Germany). Pollen tubes were detected using the filter set no. 01 (Carl Zeiss, Jena, Germany). The images were captured by a digital camera (DS-Fi2, Nikon Corporation, Tokyo, Japan) with a camera control unit (DS-L3, Nikon Corporation, Tokyo, Japan). The three most elongated pollen tubes were measured in the styles using the camera control unit function.
The results of the intraspecific pollination were used for comparison with the data of in vitro pollen development. The results of interspecific pollination were used for comparing that of the intraspecific pollination to evaluate factors affected by pollen tube and style interactions.

Statistical analysis
The data were analyzed by R Tukey's Honestly Significant Difference (HSD) test (p<0.05) with R software.

Measurement of floral organs in Petunia species
The sizes of the floral organs (pistil length, stamen length, flower length, corolla width, and ovary length) were measured, and the style lengths were calculated (Table 2). Petunia axillaris subsp. parodii (A165) had a maximum style length at an average of 59.0 mm, whereas P. integrifolia had the shortest style length at an average of 11.1 mm. Pistil lengths of P. axillaris subsp. axillaris and P. axillaris subsp. parodii were longer than the stamen lengths of these species, meaning that the stigmas were positioned above the anthers in both species. In P. integrifolia and P. occidentalis, the stigmas were positioned below the anthers.
Evaluation of the pollen tube length as "the pollen tube growth ability" using in vitro pollen culture system We used an in vitro pollen culture system to evaluate Data were obtained from at least 20 flowers. 1) The value in Ando et al. (1995c) was used.
2) The style length was calculated by the pistil length−the ovary length. In this study, this value included stigma length. the development of the pollen tube. In the preliminary experiments, we measured pollen tube length after culture periods of 6, 9, 12, or 24 h. The growing pollen tubes became entwined after a 9 h culture, making measurement difficult. Therefore, we used a 6 h culture period (Figure 2) to evaluate the speed of pollen tube growth by measuring pollen tube length in time.
The pollen tube lengths were compared in each species 6 h after the in vitro pollen culture ( Figure 3A). As a result, their pollen tube growth ability differed in each species. For instance, P. occidentalis, which has a shorter style length, had maximum pollen tube length. The data indicate that pollen tube growth ability is species-specific, independent of style lengths. Thus, the pollen tube growth in vitro did not correlate with the style lengths of the flowers in these Petunia species ( Figure 3A).
Temporal changes in pollen tube growth rates were detected between 0 to 1 h, 1 to 3 h, and 3 to 6 h in the culture medium ( Figure 3B). There was no common pattern of pollen tube growth among all the species; in particular, P. occidentalis showed a remarkable pollen tube growth rate between 3 to 6 h in the culture.
In vitro pollen culture was not affected by the styles, which are the in vivo environmental factors. To examine pollen tube growth in the styles, cross-pollination experiments were conducted.

The pollen tube growth in the styles after pollination
The pollen tube growth in the styles was evaluated by measuring the lengths detected with aniline blue staining after intraspecific pollinations. In the preliminary experiment, the measurements took place 6 and 18 h after intraspecific pollination. The pollen tubes arrived at the near end of the style 18 h after intraspecific pollination in P. axillaris subsp. parodii ( Figure 4A). To detect differences between the species and compare the pollen tube lengths in vitro for the same time period, 6 h after pollination was selected as the appropriate time. The pollen tube lengths were measured in the styles 6 h after intraspecific pollinations ( Figure 4B). Consequently, the style lengths in each species were correlated with the pollen tube lengths in the pistils (Supplementary Figure  S1). Among them, P. axillaris subsp. parodii, which had the longest styles, also had the longest pollen tubes, approximately 9.2 mm on average. In P. occidentalis, which had the greatest pollen tube growth rate in vitro ( Figure 3B), the pollen tube length was the shortest, approximately 3.1 mm on average ( Figure 4B). The pollen tube length in P. axillaris subsp. parodii was significantly greater than that in the other species.
The pollen tube lengths in the styles ( Figure 4B) were compared as the ratio of the pollen tube length to style length ( Figure 4C). The ratios, indicating the degree of pollen tube achievement in the styles, showed that the values of P. axillaris subsp. parodii and P. occidentalis were similar, and there was no obvious difference in this experiment.
To compare the pollen tube growth in the styles of other species, the pollen tube lengths were measured in the styles after interspecific pollinations. Through this test, the effects of the pollen grains from another species on the ovary parent became apparent ( Figure  5A). It was found that pollen grains of P. occidentalis had different pollen tube lengths in different species and showed a longer pollen tube length in the styles of P. axillaris subsp. parodii (interspecific pollination) than in P. occidentalis (intraspecific pollination). Similarly, the pollen tubes of P. integrifolia became longer in the styles of P. axillaris subsp. parodii (interspecific pollination) than that of P. integrifolia (intraspecific pollination). In the styles of P. axillaris subsp. parodii, the pollen grains from the other three species could grow pollen tubes similar to those they would grow in styles of their own species. In the styles of P. integrifolia, which harbors self-incompatibility, the pollen tube growth seemed to be suppressed. In the styles of P. occidentalis, the pollen tubes of P. axillaris subsp. parodii were longer than that of P. occidentalis (intraspecific pollination). Thus, the pollen tube lengths were altered by the styles of different species.
To analyze the results of cross-pollination experiments, the data of interspecific pollinations in Figure 5A were aligned in the order of the pollen tube lengths ( Figure  5B). This exercise showed that the styles in the species showing self-incompatibility suppressed pollen tube growth in the interspecific cross-combinations.
The pollen tube growth ability in vitro was not related to pollen tube lengths in the styles after intraspecific pollinations. Also, it was not related to the style lengths of their own species in the genus Petunia.

Discussion
Our results indicated that the pollen tube growth ability in vitro was not a factor controlling the pollen tube growth rate in the styles. This study suggests that pollen tube lengths in the styles were not determined by the pollen tube growth ability but rather by the control mechanism of pollen tube growth in cooperation with the styles. The results of this study implied the following  Figure 4B is compared to that of Table 2, showing the style lengths. The ratio of the pollen tube length in the styles is expressed.
points: 1) a species retains a species-specific pollen tube growth ability in vitro, 2) the pollen tube growth ability does not have a direct relationship to the style length of the species, and 3) the pollen tube growth changed in different-species ovary parents indicating the effects of the styles on the speed of pollen tube growth.
Accounts of the interaction between pollen tube growth and styles have been published in research about gametophytic self-incompatibility (Meng et al. 2011;Sun et al. 2018). In comparison with the present study, we should note a case study of heteromorphic self-incompatibility showing that flowers of different style lengths (pin and thrum) are compatible with each other. In this heteromorphic self-incompatibility, the pollen grains from the thrum flowers are compatible with the pin flowers and vice versa. In the genus Fagopyrum (buckwheat) showing heteromorphic selfincompatibility, Hirose et al. (1994) reported that the pollen tubes from the thrum flowers could grow longer in various interspecific pollinations. Morris (1951) observed that the pollen tubes from thrum flowers stopped on the thrum flower stigmas, whereas the pollen tubes from the pin flowers stopped on the pin flower styles. Thus, pistil styles have a significant effect on pollen tube growth. In the present study, the pollen tube length was positively correlated with the style length in the same species within the genus Petunia. However, the pollen tube growth ability evaluated using in vitro culture did not correlate with style length. Brewbaker and Kwack (1963) noted the importance of calcium for pollen germination and proposed a useful culture medium. Based on the composition of this medium, we developed a liquid pollen culture medium containing yeast extract, which can be used for a wide range of plant species such as Alstroemeria (Hirano and Hoshino 2009), Cyrtanthus (Hirano and Hoshino 2010), and Petunia (Hoshino et al. 2016). In these studies, we focused on binucleate pollen species and analyzed the timing of sperm cell formation (the division of generative cells) during pollen tube development using flow cytometry. In the pollen germination medium, the pollen grains of these species showed sperm cell formation with timing unaffected by extrinsic factors, suggesting that this culture system would be suitable for investigating pollen tube growth ability. In the previous study, the addition of pistil extracts into the culture medium promoted pollen tube growth in Nicotiana (Wu et al. 2000). In the present study, the suppressive effect of the styles on pollen tube growth in some species was examined by comparing the pollen tube growth ability in vitro and the pollen tube length in the styles. To explain the suppressive effect, we propose two hypotheses: 1) the influence of self-incompatibility by the styles, and 2) coordination of pollen tube growth by the styles to control sperm cell formation, which is required for normal fertilization.
In the self-incompatibility of the genus Petunia (Solanaceae), S-RNase as an inhibitory agent affects pollen tubes in the styles (Kubo et al. 2015). Incompatible pollen tubes cannot decompose S-RNase, and the compatible pollen tubes need to grow while decomposing the S-RNase in the pistils with self-incompatibility. In Lilium, self-incompatible stylar extracts inhibited pollen tube growth in vitro (Dickinson et al. 1982). In the present study, the pollen tubes may grow slowly in the styles with self-incompatibility ( Figure 5B). Conversely, the pistils of self-compatible species affected the pollen tube growth promotively even in interspecific pollination when P. occidentalis was used as an ovary parent ( Figure  5A). Notably, the pollen tubes of P. axillaris subsp. parodii were the longest in P. occidentalis ovaries suggesting the presence of compatible and promotive combinations between pollen grains and styles.
Regarding sperm cell formation, we showed that the timing of generative cell division was 12-15 h in a Petunia cultivar using the same pollen culture system as in the present study by flow cytometry (Hoshino et al. 2016). This indicates that the division of the generative cells occurs independently without the assistance of the styles. However, the pollen tubes have to stay in the styles for an appropriate time to form sperm cells before fertilization. For example, P. occidentalis, which has small pistils, grew the longest pollen tubes in vitro but had the shortest pollen tubes in the styles. Interestingly, pollen tube growth rates changed as faster or slower when compared to the self-styles after interspecific pollinations in Rhododendron (Williams and Rouse 1990). We concluded that the styles control pollen tube growth to ensure sperm cell formation followed by normal fertilization in the ovules.
This study revealed differences between pollen tube growth ability and the pollen tube length in styles of different lengths after intraspecific or interspecific pollinations. Although the medium has high availability on different species, the results of this study should be considered for the differences in the medium and the species affinity. A further study is required to optimize the culture medium for each species. In addition, we will focus on the analysis of the relationship between pollen tube behaviors and self-incompatibility on a molecular level.


s2fieldsofstudy:
Agricultural And Food Sciences
Biology

provenance: AgriculturalAndFoodSciences,2022-2022-Agricultural And Food Sciences-2022.jsonl.gz.gz

text:
Transcriptome analysis of the growth-promoting effect of volatile organic compounds produced by Microbacterium aurantiacum GX14001 on tobacco (Nicotiana benthamiana)

Background Plant growth-promoting rhizobacteria (PGPR) release volatile organic compounds (VOCs), which promote plant growth. Results A potential PGPR strain GX14001 was isolated from marine samples, and the VOCs produced by GX14001 significantly promoted tobacco (Nicotiana benthamiana) growth in a plate experiment. Based on 16S rRNA sequence alignment and physiological and biochemical characterization, GX14001 was identified as Microbacterium aurantiacum. Comparative transcriptome analysis was conducted between GX14001 VOCs-treated tobacco and the control; it was found that 1286 genes were upregulated and 1088 genes were downregulated. Gene ontology (GO) analysis showed that upregulated genes were involved in three biological processes: polysaccharide metabolic, polysaccharide catabolic and carbohydrate metabolic. The downregulated genes were involved in six biological processes, namely cell redox homeostasis, cellular homeostasis, carbohydrate metabolic process, homeostatic process, obsolete electron transport, and regulation of biological quality. Kyoto encyclopedia of genes and genomes (KEGG) pathway analysis showed that 190 upregulated differentially expressed genes were mainly involved in plant hormone signal transduction, phenylpropyl biosynthesis, plant–pathogen interaction, and flavonoid biosynthesis. The 148 downregulated differentially expressed genes were mainly involved in plant hormone signal transduction and the metabolism of ascorbic, aldehyde, and pyruvate acids. Further analysis revealed that many genes were differentially expressed in the metabolic pathways of plant hormone signals, which were speculated to be the main reason why GX14001 VOCs promoted tobacco growth. To further study its regulatory mechanism, we found that GX14001 promoted plant growth through auxin, salicylic acid, and gibberellin in Arabidopsis mutant experiments. Conclusion The VOCs produced by Microbacterium aurantiacum GX14001 may promote the growth of tobacco through the auxin, salicylic acid and gibberellin pathways. Supplementary Information The online version contains supplementary material available at 10.1186/s12870-022-03591-z.

Background
In recent years, due to the excessive use of chemical fertilizers and pesticides, the earth's ecological environment and agricultural soil have undergone severe changes. Therefore, reducing the application of chemical fertilizers and pesticides, and using biological fertilizers and biological control to improve production efficiency and environmental safety are required for sustainable agriculture and development [1]. The concept of plant growthpromoting rhizobacteria (PGPR) was first proposed by the American scientist J.W. Kloepper; initially, it only referred to plant growth-promoting bacteria, but in the course of research and development, the notion of PGPR has been enriched and expanded. As such, now PGPR is a group of beneficial microorganisms that can promote plant growth through various mechanisms, such as nitrogen fixation, phosphorus solubilization, disease resistance, increasing plant uptake of nutrients, adjusting phytohormone concentrations, and producing volatile organic compounds (VOCs). The microbial production of VOCs is an important growth-promoting mechanism for plants. Microorganisms, unlike promoters of plant growth, can play a role in plant growth without having direct contact with plants by producing VOCs. Microbial VOCs can promote plant growth by regulating plant hormones synthesis/transport, and inducing systemic resistance and tolerance.

VOCs produced by PGPR regulate the synthesis/transport of plant hormones
As early as 2003, Ryu et al. found that VOCs produced by Bacillus subtilis GB03 could significantly promote plant growth through the cytokinin pathway [2]. Zhang et al. (2007) used "oligonucleotide microarrays" to detect the RNA transcription level of Arabidopsis thaliana seedlings exposed to Bacillus subtilis and found that the volatiles of rhizosphere bacteria GB03 could promote the growth of A. thaliana by regulating the homeostasis of auxin [3]. Huang et al. (2015) screened PGPR from the soil of a tropical rain forest and identified it as Bacillus subtilis. Studies have found that volatile substances of these strains can regulate the expression of 3-Indoleacetic acid (IAA)-related genes to promote the growth of A. thaliana [4]. Zhang et al. (2008) found that soil bacteria could enhance photosynthesis in A. thaliana by reducing glucose induction and abscisic acid (ABA) levels in plants. The levels of ABA biosynthetic transcripts and downstream metabolites were reduced in A. thaliana by GB03 (Bacillus subtilis) treatment [5]. Xie et al. (2016) found that VOCs produced by Bacillus subtilis JC01 and JC03 had obvious promoting effects on plants. Subsequent studies found that the VOCs produced by them have a significant upregulation effect on the genes related to the IAA signaling pathway, and the VOCs produced by JC01 have a downregulation effect on the genes related to the ABA signaling pathway, indicating that JC01 and JC03 promote plant growth mainly by regulating IAA and ABA signaling pathways [6]. Tahir et al. (2017) investigated the effects of VOCs produced by the plant growth-promoting rhizobacterium Bacillus subtilis SYST2 on hormone regulation and growth promotion in tomato (Lycopersicum esculentum) plants. Volatiles from SYST2 caused differential expression of genes related to growth and increased the auxin, gibberellin, and cytokinin content, thus promoting tomato (Lycopersicum esculentum) growth [7].

Systemic resistance induced by VOCs produced by PGPR
VOCs produced by microorganisms can promote plant growth through induced systemic resistance (ISR) [8]. Ryu et al. (2004) found that Bacillus subtilis strain GB03 and Bacillus amyloquefaciens strain IN937a produced volatile compounds capable of stimulating the production of ISR in A. thaliana seedlings against Ervinia carotovora subsp. carotovora infection [9]. Ping et al. (2004) found that VOCs, such as butanediol and acetone, produced by certain microorganisms can induce ISR triggering in plants, and further studies on Arabidopsis mutants found that this growth-promoting response was related to the production of cytokinin and ethylene [10]. Rudrappa et al. (2010) showed through analysis of Bacillus subtilis FB17 and A. thaliana defense-impaired mutants that exogenous application of Bacillus subtilis to induce ethylene coupling triggers ISR and protects plants against Pseudomonas syringae pv. tomato DC3000, a process that requires the involvement of salicylic acid (SA)/ ethylene (ET) and is not dependent on the jasmonic acid (JA) pathway [11]. Lee et al. (2012) found that Bacillus polymyxa E681 produced tridecane, which significantly induced systemic resistance (ISR) in plants [12]. Wonglom et al. (2020) found that the volatile organic compounds produced by Trichoderma asperellum showed antifungal activity and increased the activity of cell wall degrading enzymes, thereby promoting lettuce (Lactuca sativa) growth [13]. Choudoir et al. (2019) reported that VOCs produced by Actinomycetes were able to stimulate Keywords: Plant growth promoting rhizobacteria (PGPR), Volatile organic compounds (VOCs), Plant growth promotion, Promotional mechanisms, Transcriptome analysis the growth of beneficial symbionts representing plants, and inhibiting plant pathogenic bacteria at the same time [14]. Lee et al. (2019) found that several stress-related genes were downregulated after RNA sequencing analysis of plants treated with the terpene 1-decene [15].

System tolerance induced by VOCs produced by PGPR
Unlike ISR, which means that plants can develop systemic resistance to pathogenic microorganisms, IST refers to the ability of the strain to help plants tolerate abiotic stresses [16]. Recently, a greater number of studies have shown that PGPR and its VOCs can induce plant tolerance to abiotic stresses, including drought stress, salt stress, and nutrient deficiency, by a mechanism known as induced systemic tolerance (IST) [17]. Cho et al. (2008) found that 2R,3R-butanediol produced by Pseudominas chlororaphis O6 was able to induce drought tolerance in A. thaliana through a salicylic acid (SA) signaling pathway-dependent mechanism by regulating stomatal opening and closing in plants [18]. Vaishnav et al. (2015) reported that a PGPR strain Pseudomonas simiae AU produced VOCs that triggered tolerance to salt stress in soybean (Glycine max) [19]. The role of bacterial VOCs in drought tolerance in maize (Zea mays) was first reported by Yasmin et al. (2021), who found that VOCs produced by Pseudomonas pseudoalcaligenes induced tolerance in maize plants under 7-day drought stress [20]. Cappellari and Banchio (2019) found that VOCs produced by Bacillus amyloliquefaciens GB03 were able to promote the growth of Mentha piperita under salt stress. Mentha piperita grown in a salt-containing culture medium exposed to VOCs had better morphological characteristics and higher chlorophyll content [21]. VOCs produced by GB03 (Bacillus subtilis) can regulate the sodium transporter AtHKT1, so that the sodium ion level is upregulated in plant stems and downregulated in roots, resulting in low accumulation in plants, thus enabling them to survive and thrive under salt stress [5]. VOCs from Bacillus subtilis GB03 can increase the mRNA levels of iron absorption genes in plants and cause acidification of the plant rhizosphere, increasing the mobility of iron in plants and helping them to increase iron absorption [22].
Over the years, the studies on plant growth promotion by VOCs produced by PGPR mainly focus on Bacillus, Pseudomonas, Actinomyces and Trichoderma, but the studies on Microbacterium aurantiacum are few. In this study, we screened a strain of Microbacterium aurantiacum, named GX14001, which could significantly promote plant growth by VOCs. We performed transcriptome analysis of tobacco treated with GX14001 to analyze the growth-promoting pathway. In addition, combined with Arabidopsis mutants, the signal pathway and mechanism of GX14001 VOCs promoting tobacco growth were discussed.

VOCs from GX14001 promote tobacco growth
To detect whether GX14001 VOCs affect plant growth, tobacco seedlings were co-cultured with GX14001 on a plate divided into two sections (I-plate) at 25 °C for two weeks. Compared with the control, the fresh weight, root length, lateral root number, leaf length and leaf width of tobacco plants treated with GX14001 VOCs showed significant growth-promoter effects. As shown in Fig. 1, the experimental results showed that the VOCs produced by GX14001 significantly promoted the growth of tobacco (P < 0.01).

Physiological-biochemical identification of Microbacterium aurantiacum
This strain was Gram stain positive, oxidase and peroxidase positive, aerobic, non-motile, and non-sporeforming. After 2 d of growth at 30 °C, the colonies on the plate were round, convex, and orange. It can be grown at a pH of 6-8 and 30-37 °C and cannot be grown at ≥ 5% NaCl. The optimum pH for growth is 7, and the optimum temperature is 30 °C. Hydrolysis can produce pyruvate, assimilate arabinose, mannitol, and propionate, and use arabinose, glucose, mannose, raffinose, and sucrose to produce acid. The physiological and biochemical details are shown in Table S2.

Identification of Microbacterium aurantiacum 16S rRNA
The 16S rRNA gene sequences obtained from the sequencing of GX14001 were compared and analyzed on the Ezbiocloud website, and a phylogenetic tree was constructed using MEGA6 [23] (Fig. 2). The outgroup in the phylogenetic tree was selected with reference to the commonly selected outgroup in the Microbacterium spp. phylogeny [24]. The results showed that GX14001 had the highest homology and closest genetic relationship with Microbacterium aurantiacum; therefore, GX14001 was identified as Microbacterium aurantiacum. The sequence of the GX14001 strain was uploaded to NCBI to obtain its GenBank accession number MZ489467.

Data processing and feasibility analysis
Reads from the down sequencing machine were filtered to obtain high-quality reads. After data filtering, some basic statistics were performed on the data, and the statistics are shown in Table S3. According to Table S3, the Q30s of the downstream data are higher than 93% and the data are available. As shown in Fig. S1, the biological repetition correlation R 2 values of the control treatment (CK) and GX14001 VOCs treatment (N) were all greater Fig. 1 A The I-plate was used to co-culture tobacco seedlings and sterile water culture without physical contact; B The I-plate was used to co-culture tobacco seedlings and Microbacterium aurantiacum culture without physical contact; C Growth promoting effect of GX14001-VOCs on tobacco. CK was non-treated with bacteria and GX14001 was bacterial-treated with GX14001-VOCs. "*" refers to significant difference between treatments, P < 0.05, "**" refers to P < 0.01. The unit of leaf length and width and root length is cm; the unit of fresh weight is g; the unit of lateral root number is number Fig. 2 The Neighbor-Joining tree based on the 16S rRNA gene sequences showing the phylogenetic relationship of GX14001 with related taxa. The evolutionary distances were computed using the p-distance method and are in the units of the number of base differences per site. The sequence of Agrococcus jenensis DAM9580 T (X92492) was used as an outgroup. Bootstrap support values were calculated from 1000 replicates and only values above 50% are shown. Bar, 0.005 substitutions per nucleotide position. Evolutionary analyses were conducted in MEGA6 [23] than 0.8, which indicates a high similarity of expression patterns between samples among treatments and good replication of samples.

Identification of DEGs and functional annotation for differential expression gene analysis of transcriptome samples
GOseq and KOBAS software were used for GO functional enrichment analysis of differential gene sets and KEGG pathway enrichment analysis. Genes with an expression that changed more than twice (P ≤ 0.05) between the GX14001 VOCs treatment and the control were considered differentially expressed. In this transcriptome sequencing of tobacco, a total of 71,951 genes were detected. Among them, 2374 differentially significantly expressed genes were screened. Among them, 1286 genes (54.2%) were upregulated and 1088 genes (45.8%) were downregulated (Fig. 3-A). To obtain comprehensive gene function information, gene function annotations of seven databases were performed, namely Nr, Nt, Pfam, KOG/COG, Swiss-prot, KEGG, and GO. We selected the annotation results of five databases for display ( Fig. 3-B).

GO functional enrichment analysis of differential genes
The GO enrichment analysis showed that a total of 22,933 genes were enriched, including 1297 differential genes. There were 767 upregulated differential genes and 530 downregulated differential genes in the GO enrichment distribution. The main biological functions performed by the differentially expressed genes from the sequencing results were molecular functions, cellular components and biological processes (Fig. 4).
The sequencing results showed that the upregulated biological processes (BP) were mainly concentrated in polysaccharide metabolism and catabolic processes and carbohydrate metabolism. The upregulated cellular components (CC) were mainly concentrated in the FANCM-MHF complex, nucleosome cellular components, DNA packaging complex and protein. The molecular function (MF) enrichment was focused on hydrolase activity, catalytic activity, calcium ion binding, DNA-binding transcription factor activity, and transferase activity.
The downregulated biological processes were mainly concentrated in cellular homeostasis, carbohydrate metabolic, electron transfer and regulation of biological quality processes. The downregulated molecular functions were mainly in oxidoreductase activity and electron transfer activity. There were no differential downregulated genes in cellular components (CC).

Analysis of differential gene KEGG enrichment
To further understand the influencing factors of tobacco growth promotion under GX14001 VOCs treatment,  KEGG enrichment analysis was carried out on the data. A total of 63 KEGG upregulated entries and 61 KEGG downregulated entries were enriched. There were 338 differentially expressed genes, 190 of which were upregulated and 148 of which were downregulated (Fig. 5). The enrichment pathways of DEGs were mainly related to "plant hormone signal transduction, " "plant pathogen interaction, " "carbon fixation in photosynthetic organisms, " "stilbene, diphenylheptanone and gingerol biosynthesis, " "phenylpropanoid biosynthesis, " "pentose and glucuronolactone interconversion, " "glyceride metabolism, " "flavonoid biosynthesis" and "ascorbic acid and aldehyde metabolism. "

Study of plant hormone signaling pathways
The transcriptome sequencing results of tobacco cocultured with GX14001 VOCs for two weeks showed that the differentially expressed genes were mainly in the plant hormone signal transduction pathway (Fig. 7, which obtained by KEGG [25]). These hormone pathways included auxin, cytokinin, gibberellin, ethylene, jasmonic acid, and salicylic acid. These hormones are not only involved in the regulation of plant growth and development but can also be used as key factors mediating the plant stress response.
The results of I-plate experiments showed that treatment with GX14001 VOCs significantly promoted the growth of tobacco, with significant increases in leaf expansion, fresh weight, root length, and lateral root number (P < 0.01). Transcriptome data showed that in the auxin signaling pathway, the expression of auxin transporter gene AUX1, auxin positive regulatory response factor IAA27, auxin response factor ARF5, and the genes encoding tobacco auxin-induced protein X15 in the SAUR family were all upregulated by roughly 2.02, 2.04, 2.28, and 4.02 times, respectively. The downregulated expression of SAUR32 and SAUR50 may induce plant growth by regulating cell wall acidification, and the expression of SAUR32 and SAUR50 was downregulated

Effects of GX14001 VOCs on the growth of Arabidopsis mutants
To further investigate the mechanism of VOCs produced by strain GX14001 promoting plant growth, we used Arabidopsis mutants to explore the role of plant hormone signaling in GX14001 VOCs promoting Arabidopsis growth. The results showed that GX14001 VOCs had significant growth-promoting effects on the leaves and fresh weight of the Arabidopsis wild type and ethylene (etr1) Arabidopsis mutant (Fig. 8 A, B), while there were no significant growth-promoting effects on auxin (arf1), salicylic acid (npr1), and gibberellin (gai1) (Fig. 8 C, D, E). This indicated that the growth-promoting effect of strain GX14001 VOCs on plants mainly depended on auxin, salicylic acid, and gibberellin pathways, rather than the ethylene pathway (Table 1). C Growth-promoting effect of strain GX14001 on arf1 mutant; D Growth-promoting effect of strain GX14001 on npr1 mutant; E Growth-promoting effect of strain GX14001 on gai1 mutant. "*" refers to significant difference between treatments, P < 0.05, "**" refers to P < 0.01. The unit of leaf length and width and root length is cm; the unit of fresh weight is g

Discussion
The Microbacterium sp. has been identified in previous studies as an important class of phospholysis bacteria [26][27][28]. The application of Microbacterium sp. in agriculture can also be traced back to 2007 when Karlidag et al. (2007) found that the combination of Microbacterium FS01 and Bacillus M3 or OSU-142 had the potential to improve yield, growth, and nutrition in apple trees [29]. Sheng et al. (2008) isolated Microbacterium G16 from rape roots in heavy metal contaminated soil and found that inoculation with G16 increased rape biomass and total lead uptake, while G16 exhibited different multiple heavy metal and antibiotic resistance characteristics and increased water-soluble lead in solution and soil [30]. Cordovez et al. (2018) reported that the volatiles of Microbacterium strain EC8 promote plant growth by regulating sulfur and nitrogen metabolism through roots and found that its induction of plant growth promotion was tissue specific [31]. Therefore, Microbacterium sp. has certain prospective applications in agriculture, but no previous studies have been conducted on Microbacterium aurantiacum in this regard.
In this study, we found for the first time that VOCs produced by Microbacterium aurantiacum can significantly promote the growth of tobacco. We analyzed the transcriptome of differentially expressed genes between the treated group of Nicotiana benthamiana that had been inoculated with GX14001 VOCs and the non-inoculated group as a control contrast, and we found that its VOCs may exert their pro-growth effects mainly through the plant hormone signaling pathway. Numerous studies have shown that most plant hormones play a complex role in regulating the growth and development of plants. The same hormone can regulate multiple development processes, and the same given development process requires the synergistic action of many different hormones. As found in this study, downregulated expression of ARR-17, a two-component response regulator gene in the cytokinin pathway, promoted cell division and bud germination; and key genes in the auxin, ethylene, jasmonic acid, and salicylic acid pathways had significantly upregulated expression, promoting all aspects of tobacco growth. In addition, due to the mutual crosstalk between plant hormone signals, different hormone signals act differently on each other and thus have different effects on plant growth and development. For example, Traw et al. (2003) found that jasmonic acid, salicylic acid, and gibberellin interacted in the induction of trichomes in Arabidopsis, with jasmonate and salicylate-dependent exhibited antagonistic effects, while jasmonic acid and gibberellin interacted synergistically [32]. Kong et al. (2020) found that auxin and salicylic acid signals could antagonize each other in the lateral root development of Arabidopsis [33]. Ghorbel et al. (2021) proposed the idea that the existence of mutual antagonism between cytokinin and jasmonic acid during xylem growth and development of Arabidopsis roots [34]. In this study, the expression of AHK2_3_4, a cytokinin receptor gene encoding a regulatory histidine kinase in the cytokinin pathway, an ERF5 transcription factor in the gibberellin pathway, and NPR5-like gene encoding a regulatory protein in the salicylate pathway were all downregulated; expression of the DELLA-GAI protein gene, which negatively regulates the gibberellin synthesis pathway, was upregulated, and it is speculated that the expression of these genes may respond to multiple signaling pathways, but we are not certain of its specific mechanism at present. Subsequent experimental validation by Arabidopsis mutants revealed that the growth-promoting effect may depend mainly on three pathways, auxin, gibberellin, and salicylic acid pathways, but not on ethylene or other pathways. Finally, we found that key genes in phenylpropanoid biosynthesis, flavonoid biosynthesis, stilbenoid, diarylheptanoid, and gingerol biosynthesis pathways were differentially expressed and significantly upregulated, and it was hypothesized that the upregulated expression of these genes increased the accumulation of flavonoids, astragalus compounds, and lignin, thus promoting the growth and development of tobacco.
Our studies only expound on the growth-promoting effect and mechanism of VOCs produced by Microbacterium aurantiacum on tobacco; the VOCs produced by this strain were not explored, which will be further investigated subsequently. In addition, up to now, some volatile organic compounds are being studied or used in agriculture, for example, studies have proved that 2-nonanone, 2-undecanone, and 2-tridecanone released by solid lipid nanoparticles and nanostructured lipid carriers can stimulate the growth induction of L. sativa and Table 1 Growth promotion response of A. thaliana mutants to VOCs " + "means that the strain has plant growth promotion,"-"means that the strain has no plant growth promotion

Description
Growth promoting effect of GX14001-VOCs  22:208 S. lycopersicum by improving the development of shoot and root; Novellus (based on terpene compounds), a biological bactericide produced by Eden Research, UK, can effectively control grey mold (Botrytis cinerea) on wine grapes and fresh grapes. But on the whole, there are some difficulties in the application of volatile organic compounds in agriculture, which may be due to the following reasons. First, most of the studies on the promotion of plant growth by VOCs produced by strains have been tested in laboratory culture dishes, but have not been applied in the field. Second, in the practical application of agriculture, there are also questions as to whether the strain can colonize the soil and the stability of the VOCs produced by it to promote plant growth. Finally, the application of VOCs must consider the complexity of actual agricultural environmental conditions. Studies have shown that VOCs are extremely unstable in polluted environments [35]. Technical limitations and defects are the key problems we must solve in the future.

Pretreatment of tobacco and Arabidopsis mutants
Tobacco and A. thaliana (ecotype Col-0) were studied, which were obtained from our lab (Guangxi Key Laboratory for Polysaccharide Materials and Modifications, School of Marine Sciences and Biotechnology, Guangxi Minzu University). The phytohormone biosynthesis and metabolism-related gene mutants (etr1, arf1, npr1 and gai1) were obtained from the Arabidopsis Biological Resource Center (http:// www. arabi dopsis. org) or the European Arabidopsis Stock Centre (http:// arabi dopsis. info). The required tobacco / Arabidopsis mutant seeds were placed in a 1.5 mL Eppendorf (EP) tube. The test plant seeds were surface sterilized by soaking in 75% (V/V) ethanol for 2-3 min, followed by soaking in 1% sodium hypochlorite solution for 2-3 min, and then soaking in sterilized water 4-5 times until the disinfectants were completely removed to avoid its influence on seed development. Seeds in EP tubes were placed on sterilized filter paper and dried in an ultra-clean worktable. Seeds were plated on Murashige and Skoog Basal Medium (MS medium) [36] with sterilized bamboo sticks. After completion, the plate was sealed with sealing film and cultivated in a 25 °C intelligent artificial climate box until germination. The conditions of the intelligent artificial climate box were light 16 h (140 µmol photons m −2 /s), dark 8 h, and temperature 25 °C.

Isolation and identification of strains and fermentation broth
Strain GX14001 was isolated from the intestinal tract of fish (not live fish) by our lab and collected from Beihai, Guangxi, China. The strain GX14001 has been preserved in China General Microbiological Culture Collection Center with the preservation number of CGMCC 1.19276. In this study, the taxonomic status of isolate GX14001 was determined by phylogenetic analysis and physiological characteristics. The physiological characteristics refer to the method reported by Niste et al. [37]. The tested bacteria were used to inoculate Trypticase Soy Broth (TSB) liquid medium and cultured at 30 °C and 200 r/min for 24 h. Subsequently, bacteria were diluted with sterile water to about 1 × 10 8 CFU/mL. Special statement: The samples used in the experiment were not taken from live fish.

Co-culture experiment
The experiment was carried out on a 9 cm plate divided into two sections (I-plate). One side was used to culture tobacco, and the other side was used to culture bacteria. Due to the limitations of the I-plate, bacteria cannot contact and communicate directly with tobacco, while the VOCs produced by bacteria can communicate and interact with tobacco through the upper part of the culture plate (refer to the method reported by Zhang et al. 2007) [3]. The MS medium was used on the plant side, and Tryptose Soya Agar Medium (TSA medium) was used for the bacteria. After 5 d of tobacco culture, the tobacco seedlings with the same growth conditions were transplanted to the side of the two-cell culture plate MS, and 5 seedlings were transplanted in each culture plate to maintain the same spacing of tobacco plants. A 10 μL bacterial suspension was used to inoculate the other side of the TSA medium, and the bacterial content was about 1 × 10 8 CFU/mL. The control experiment was inoculated on TSA medium with 10 μL sterile water, and the treatment group and the control group did three replicates respectively. After that, the culture dishes were sealed with sealing parafilm and cultured in an intelligent artificial climate chamber. The culture conditions were consistent with those used previously. After two weeks of culture, the biomass of tobacco, namely leaf length and width, root length, lateral root number and fresh weight was measured or counted.

Plant transcriptome analysis
Three biological replicates (plates) of each group of control and treatment group were sent to Wekemo Tech Co., Ltd. Shenzhen China for transcriptome sequencing analysis. The treatment group was tobacco plants exposed to volatile substances of strain GX14001 for two weeks.

RNA extraction, library construction and RNA-Seq
Total RNA was extracted from the control groups CK_1, CK_2, and CK_3 and the treatment groups N_1, N_2, and N_3. The experiment was divided into two experimental groups, each with three replicates (nontreated with bacteria (control group: CK_1, CK_2, CK_3) and bacterial-treated with GX14001 VOCs (treatment group: N_1, N_2, N_3). The quality and quantity of total RNA were accurately detected by an Agilent 2100 bioanalyzer. Briefly, mRNA was purified from total RNA using poly-T oligo-attached magnetic beads. Fragmentation was carried out using divalent cations under an elevated temperature in the first strand synthesis reaction buffer (5X). First strand cDNA was synthesized using a random hexamer primer and M-MuLV reverse transcriptase (RNase H-). Second strand cDNA synthesis was subsequently performed using DNA polymerase I and RNase H. The remaining overhangs were converted into blunt ends via exonuclease/polymerase activities. After adenylation of the 3' ends of DNA fragments, adaptors with hairpin loop structures were ligated to prepare for hybridization. To select cDNA fragments of preferentially 370-420 bp in length, the library fragments were purified with the AMPure XP system (Beckman Coulter, Beverly, USA). Then, PCR was performed with Phusion High-Fidelity DNA polymerase, Universal PCR primers, and the Index (X) Primer. Finally, PCR products were purified (AMPure XP system), and library quality was assessed on the Qubit2.0 Fluorometer, Agilent Bioanalyzer 2100 systems and qRT-PCR. After the library was qualified, the cDNA library was sequenced on the Illumina Novaseq 6000 platform. Three biological replications were used for RNA-seq experiments.

Differential expression analysis
The clean data (clean reads) were obtained by removing reads containing an adapter, reads containing ploy-N, and low-quality reads from raw data. At the same time, the Q20, Q30 and GC content of the clean data were calculated. All downstream analyses were based on highquality clean data. After getting clean reads, Trinity was used to splice clean reads [38]. The transcript sequence obtained by Trinity splicing was used as a reference sequence for subsequent analysis. The longest cluster sequence was obtained after hierarchical clustering by Corset for subsequent analysis [39]. Differential expression analysis for both groups was performed using the DESeq2 R package (1.20.0). DESeq2 provides statistical procedures for determining differential expression in numerical gene expression data using a model based on a negative binomial distribution. The obtained p-values were adjusted to the control to determine the false discovery rate using the method of Benjamini and Hochberg [40]. The genes with adjusted P value < 0.05 found by DESeq2 were designated as differentially expressed genes. The GO function enrichment analysis and KEGG pathway enrichment analysis were performed by GOseq and KOBAS software.

qRT-PCR validation
To validate the RNA-seq data, expression levels of 11 DEGs selected randomly were screened by qRT-PCR, and actin and glyceraldehyde-3-phosphate dehydrogenase (GAPDH) were selected as internal control genes. As mentioned above, RNA was isolated from the control group CK_1, CK_2, and CK_3, and the treatment group, N_1, N_2, and N_3. Reverse transcription was conducted using the StarScript II First-strand cDNA Synthesis Kit (GenStar, Beijing, China). The gene primers and the internal control for qRT-PCR are listed in Table  S1. All primers were synthesized by AuGCT DNA-SYN Biotechnology Company (Wuhan, China) and tested by regular PCR. The primer concentration was 10 μM, and the DNA quantity was 200 ng/μL. The specificity of the product was checked by 2% agarose gel electrophoresis prior to the melt curve. The qRT-PCR system comprised 5 μL EvaGreen 2 × qPCR MAsterMix, 0.3 µL of each primer, 1 µL diluted cDNA, and 3.4 µL DNase/RNasefree ddH 2 O. PCR amplification was conducted with the following conditions: 1 cycle of 95℃ for 10 min, 40 cycles of 95℃ for 15 s, and 60℃ for 60 s.

Data Processing
Microsoft Excel 2016 and IBM SPSS Statistics 21 software were used for statistical analysis of the data. Oneway analysis of variance and LSD were used for the significant difference test. OriginPro 2018 was used for histogram plotting, and the relative expression levels of the DEGs were calculated using the 2 −ΔΔ Ct method [41].


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Quality Assessment of Apple and Grape Juices from Serbian and German Markets by Planar Chromatography—Chemometrics

The high consumption of plant-based foods on a global scale has increased the number of adulterations in the food industry. Along with this, analytical approaches to fraud detection need to be further developed. A nontargeted effect-directed profiling by high-performance thin-layer chromatography hyphenated with five effect-directed assays (free radical scavenging assay, Aliivibrio fischeri bioassay, and acetylcholinesterase, butyrylcholinesterase, and tyrosinase inhibition assays) and multi-imaging provided additional information on the antioxidative, antimicrobial, and enzyme inhibition activities for 18 apple and 18 grape juices from markets in Serbia and Germany. Bioactive zones of interest were eluted using an elution head-based interface and further characterized by electrospray ionization high-resolution mass spectrometry. The different profiles were evaluated chemometrically, and several compounds, which were characteristic of samples from different markets located in Serbia and Germany, were identified in apple juice (such as chlorogenic acid, phloridzin, epicatechin, and caffeic acid) and grape juice (such as chlorogenic acid, epicatechin, and quercetin). The developed rapid and simple method for the quality assessment of fruit juices coming from different (geographic) markets showed clear quality differences. Thus, it could be used to learn more about quality differences, to detect fraud in fruit juice production, and to verify the authenticity of the origin.

Introduction
Due to the high nutritional value and bioactive potential of constituents, the fruit juice market has grown in recent years. According to the Global Food Safety Initiative, fruit products sold on the market should be authentic, which means that the declaration on the label should be consistent with the composition and origin of the food ingredients [1]. Commercial fruit juices are obtained by the industrial processing of fruits, and their quality depends on the fruit species, geographical origin, growing conditions, stage of ripeness, and technology used [2]. Fruit juices are often adulterated by misrepresenting the proper species or geographical origin of the fruit or by adding cheaper and inferior ingredients, which leads to a reduction in product quality.
The consumption of apple and grape juices is associated with various health-promoting effects (such as anticancer, antimutagenic, antiproliferative, anti-inflammatory, antimicrobial, and free radical scavenging activities) due to the high content of phenolic compounds [3][4][5]. The most common phenolic constituents in apples are chlorogenic acid, caffeic acid, catechin, epicatechin and their oligomers (proanthocyanidins), quercetin and its glycosides, and dihydrochalcones (such as phloridzin and phloretin xyloglucoside) [6]. On the one hand, phloridzin as a characteristic flavanoid for apples can be used to detect the adulteration of fruit juices [7]. On the other hand, anthocyanins (such as cyanidin, peonidin, delphinidin, petunidin, and malvidin glucosides), resveratrol, flavonols, phenolic acids, and tannins (such as catechins, epicatechins, and proanthocyanidins) can be the main phenolic compounds present in red grapes and juices [8][9][10].
Serbia and Germany are countries with a long tradition of fruit cultivation due to favorable climate conditions. Apple juice is one of the most consumed fruit juices in Serbia and Germany. According to the research on orchards conducted in Serbia and Germany in 2017, apples are in the first place in terms of production and average yield per hectare (https://www.stat.gov.rs; https://apps.fas.usda.gov, accessed on 1 June 2022). Red grape juices have a proven high phenolic content and strong antioxidant activity that make them a preferable option among other widely consumed juices [2].
Due to increasing consumption, the quality assessment of fruit juices has recently become an important issue in the food industry. The quality of fruit juices has a significant influence in terms of consumer protection and food safety. Apple and grape juices have been recognized as functional superfoods in numerous studies. Apple juice had a positive effect on plasmatic antioxidant capacity [11], the prevention of Alzheimer's disease, and the reduction in the risk of cancer and stroke [12]. It has also been reported that the consumption of grape juice alters oxidative status and may prevent cardiovascular diseases, atherosclerosis, Parkinson's disease, and cataracts [13][14][15]. Neurocognitive functions can also be improved by consuming grape juice [16]. In addition to biological activity, phenolic compounds also influence the sensory properties of fruit juices, such as color, stability, bitterness, and astringency [9,17]. Evaluation of the unique phenolic profile of fruit juices could be used as a tool for authenticity and the identification of fruit beverages [17]. Investigating the complex phytochemical profile of fruit juices requires the development of new reliable, efficient, and sensitive analytical methods or the improvement of existing ones. The phytochemical profile of geographically different juice samples could be used to build fruit juice databases, which in turn can be used to establish the authenticity and geographical origin of food products by comparing the compositions of unknown samples with reference or control samples.
The most frequently used analytical methods are targeted and focus on the detection and identification of particular compounds or class of compounds as chemical markers for authenticity assessment (such as amino acids, organic acids, saccharides). Target analyses by high-performance liquid chromatography [9] and gas chromatography-quadrupole mass spectrometry [18] are most commonly used to ensure the authenticity of fruit juices, with emphasis on the identification and quantification of specific compounds. High-performance thin-layer chromatography (HPTLC) is becoming more popular in food analysis due to the straightforward hyphenation with planar-effect-directed assays, multi-detection of the same chromatogram, highly targeted substance identification, parallel analyses under the same experimental conditions, low running costs, low solvent consumption [19][20][21], and method greenness compared to HPLC [22]. The obtained HPTLC chromatogram can be statistically analyzed by sophisticated chemometric tools to gain maximum information, such as the identification of chemical markers of geographical origin and similarity/dissimilarity between samples [22][23][24].
The aim of this study was to develop a fast and simple analytical approach for the quality assessment of commercially available apple and grape juices purchased on different markets. The juices were purchased from the local stores in Serbia and Germany and were considered as samples originating from two different market groups. It was of interest to find out if there are differences between the selected markets from Serbia (SMS) versus Germany (SMG) with regard to the juice quality. Not only the physicochemical composition, but also the biofunctional composition (bioactivity) of the fruit juices was taken into account. The developed HPTLC method was hyphenated with five different effect-directed assays, having no target analytes in mind. In this effect-directed nontarget analysis, interesting bioactive compounds were observed and further characterized by electrospray ionizationhigh-resolution mass spectrometry (ESI-HRMS). Sophisticated chemometric techniques were used to learn more about quality differences between two market groups and to identify compounds that are potential markers for the geographic discrimination of apple and grape fruit juices on the markets.

HPTLC−FLD Profiling of Fruit Juice Extracts
An HPTLC−UV/Vis/FLD method was developed to investigate the profiles of 18 apple and 18 grape juice extracts, whereby 9 samples were collected from markets, each in Serbia and Germany (Table S1). The juice samples were extracted with ethyl acetatedichloromethane 3:1, concentrated to dryness, and redissolved in methanol. For the first time, a chemical profiling of phenols in apple and grape juices was developed using ethyl acetate-toluene-formic acid-water 16:4:3:2, V/V/V/V, as mobile phase. For detection of phenolics, the derivatization with the natural product A reagent (2-aminoethyldiphenylbori nate/polyethylene glycol 400) followed. Already this simple physicochemical profiling (providing UV/Vis/FLD chromatograms) showed interesting differences. The chromatogram at fluorescence detection (FLD) 366 nm especially highlighted that the phenolics after derivatization are suited for the differentiation of samples from both markets. A characteristic blue, fluorescent band at hR F 98 was present only in the SMS samples in both apple and grape juice extracts ( Figure 1). The investigated apple juice extract numbers (nos.) 1−18 ( Figure 1a) contained characteristic blue fluorescent bands at hR F 37 and 87, as well as orange fluorescent bands at hR F 43, 48, 66, and 70. Almost all SMG apple juice samples (nos. 10−18) contained another blue fluorescent band at hR F 80, although with different signal intensities, in contrast to the SMS apple juice extracts. The latter showed a lower intensity for the blue fluorescent band at hR F 37 in general and at hR F 87 in sample nos. 2, 5, 6, and 9. The highest number of well-separated high intense bands at hR F 36, 43, 47, 60, 64, 70, and 86 was noticed in SMS sample nos. 7 and 8 and SMG sample nos. 11, 12, and 15. taken into account. The developed HPTLC method was hyphenated with five different effect-directed assays, having no target analytes in mind. In this effect-directed nontarget analysis, interesting bioactive compounds were observed and further characterized by electrospray ionization-high-resolution mass spectrometry (ESI-HRMS). Sophisticated chemometric techniques were used to learn more about quality differences between two market groups and to identify compounds that are potential markers for the geographic discrimination of apple and grape fruit juices on the markets.

HPTLC−FLD Profiling of Fruit Juice Extracts
An HPTLC−UV/Vis/FLD method was developed to investigate the profiles of 18 apple and 18 grape juice extracts, whereby 9 samples were collected from markets, each in Serbia and Germany (Table S1). The juice samples were extracted with ethyl acetate-dichloromethane 3:1, concentrated to dryness, and redissolved in methanol. For the first time, a chemical profiling of phenols in apple and grape juices was developed using ethyl acetate-toluene-formic acid-water 16:4:3:2, V/V/V/V, as mobile phase. For detection of phenolics, the derivatization with the natural product A reagent (2-aminoethyldiphenylborinate/polyethylene glycol 400) followed. Already this simple physicochemical profiling (providing UV/Vis/FLD chromatograms) showed interesting differences. The chromatogram at fluorescence detection (FLD) 366 nm especially highlighted that the phenolics after derivatization are suited for the differentiation of samples from both markets. A characteristic blue, fluorescent band at hRF 98 was present only in the SMS samples in both apple and grape juice extracts ( Figure 1). The investigated apple juice extract numbers (nos.   In the case of the 18 grape juice extract nos. 1−18 (Figure 1b), two blue fluorescent bands at hR F 43 and 55 as well as one turquoise fluorescent band at hR F 85 were present in almost all analyzed extracts, but with different signal intensities. In addition, an intense orange fluorescent band at hR F 48 and further ones were detected in grape juice extract nos. 8−13 and 16−18. The highest number of well-separated high-intensity bands was noticed in sample nos. 8 and 9 from the SMS and 10, 11, and 13 from the SMG.

Planar Free Radical Scavenging Assay
HPTLC chromatograms were dipped in the DPPH • assay solution to detect compounds with free radical scavenging activity as yellow bands on a purple plate background. Both apple and grape juice extracts possessed DPPH • scavenging activity (Figures 2a and 3a), whereby nos. 12 and 16 were the most active ones, respectively.

Planar Enzyme Inhibition Assays
The AChE is the main catalytic enzyme found in synaptic clefts of the central nervous system, erythrocytes, and brain, while the BChE is a nonspecific enzyme, mainly present in the plasma, liver, and muscle tissue [25]. Both are involved in the termination of impulse transmission at cholinergic synapses and related to different neurological disorders, such as Alzheimer disease, ataxia, senile dementia, and myasthenia gravis [26]. Thus, the role of AChE inhibitors is to enhance muscle contraction and strengthen cholinergic neurotransmission. In the HPTLC−AChE/BChE inhibition autograms, AChE (Figures 2b and 3b) and BChE inhibiting compounds (Figures 2c and 3c) were evident as colorless or bright bands against a purple plate background in all analyzed apple and grape juice samples, but their signal intensities were different. Apple samples ( Figure 2b) showed a high inhibiting potential with strong AChE inhibiting bands at hR F 35, 46 (in no. 5), and 87 (in nos. 17 and 18). In contrast, grape juices had less intensive active bands at hR F 39, 52, 84, and 90 ( Figure 3b). The characteristic AChE inhibiting band at hR F 39 was noticed in almost all juice extracts.
Characteristic BChE inhibiting bands were observed in all investigated juice extracts. The most active bands among the apple juice extracts were found at hR F 35, 80, and 87 (Figure 2c), while the overall highest inhibiting potential was noticed for sample nos. 11, 12, 17, and 18, followed by sample nos. 5, 7-9, 11, and 13. All grape juice extracts contained BChE inhibiting bands at hR F 42 and 85, but their intensity varied. The most active band was noticed at hR F 96 in juice sample nos. 1-9 from the SMS (Figure 3c).
Tyrosinase is a polyphenol oxidase enzyme, involved in the synthesis of melanin of humans and responsible for the enzymatic browning of fruits and vegetables. Its inhibition is of interest to the food industry to prevent the undesirable browning of foods, and to medicine and cosmetics for the treatment of dermatological problems [27]. In the HPTLCtyrosinase autograms, tyrosinase inhibitors were evident as colorless or bright bands against a gray background. Among the apple juice extracts (Figure 2d), a similar tyrosinase inhibiting potential was observed at hR F 38, 52, 62, 79, and 86. However, nos. 5, 11, 12, 17, and 18 were highlighted due to their overall higher tyrosinase response. In the case of the grape juice extracts (Figure 3d), the already noticed inhibiting band originating from a compound at hR F 82 was found in sample nos. 8−18, while samples from the SMS (nos. 1−6) revealed a very strong inhibiting effect for the compound band at hR F 95.

Planar Antimicrobial Bioassay
The marine A. fischeri bacteria are nonpathogenic and manageable microorganisms for testing the bioactivity of samples [28]. Bioactive compounds with a negative impact on the A. fischeri metabolism were detected as dark zones against a bioluminescent background (Figures 2e and 3e, depicted as greyscale image). In all investigated apple juice extracts, compounds at hR F 95 and 88 showed the most intense, darkest bands. Apple juice extract no. 18 showed the highest response against A. fischeri bacteria, followed by sample nos. 1, 3, 12, 15, 16, and 17. In the analyzed grape juice extracts, sample nos. 8−18 showed a moderate to strong antibacterial activity for bands at hR F 90 and 58. The grape juice extracts had stronger antimicrobial activities compared to the apple juice extracts.

Tentative Assignment of Bioactive Compounds in Juice Extracts by HPTLC-ESI-HRMS
As an example for further characterization of bioactive bands in the juice extracts, HPTLC-ESI-HRMS was employed, eluting the bioactive bands via an elution head-based interface into the HRMS. Apple juice sample no. 12 and grape sample no. 14 were selected based on the HPTLC-EDA results. The structural assignment was performed based on the obtained exact masses, isotopes, mass error, and molecular fragments. The strongly blue fluorescent bioactive band at hR F 40 (Figure 1a Figure S1a) [29]. Chlorogenic acid was found especially in all apple juice extracts and most grape juice extracts.
The orange fluorescent (after derivatization) bioactive band at hR F 50 (Figure 1a) was tentatively assigned to the most prominent apple polyphenol phloridzin (phloretin  [30] based on the HPTLC-ESI-HRMS spectrum ( Figure S1b). Its principal biological functions are the regulation of glucose absorption and excretion, inhibition of lipid peroxidation, as well as inhibition of cancer cell growth [31]. Europeans consume an average of 0.7-7.5 mg/day of phloridzin, whose main source are apples and apple juice [32]. The deprotonated molecule at m/z 435.1299 [M−H] − led to the characteristic phloretin aglycon fragment ion at m/z 273.0856 ( Figure S1b) [33,34].
The blue fluorescent (after derivatization) bioactive band at hR F 80 both in the apple and grape juices extracts (Figure 1) showed the deprotonated molecule at m/z 289.0721 [M−H] − ( Figure S1c) and was tentatively assigned as epicatechin. This flavan-3-ol is commonly present in tea plant leaves, but also in fruits, especially grapes and apples. Flavan-3ols provide a wide range of health beneficial effects by acting as antioxidant, anticarcinogen, cardiopreventive, antimicrobial, anti-viral, and neuro-protective agents [5,35,36].
The blue fluorescent bioactive band at hR F 90 (Figure 1a Figure S1e), which was tentatively assigned to be quercetin.

Multivariate Analysis of HPTLC-FLD and HPTLC-EDA Fingerprints
Principal component analysis (PCA) is the most used pattern recognition technique that allows visualization of the data structure and identification of variables with the highest influence on the classification and differentiation of objects. It was applied on datasets obtained by image analysis of the HPTLC chromatograms to obtain basic insight into the specific grouping patterns and to distinguish apple and grape juice samples from the SMG versus SMS. The obtained PCA models are presented in Table S2.

Apple Juices
For the apple juice extracts, the PCA model applied on the HPTLC−FLD chromatograms at 366 nm after derivatization with the natural product A reagent (highlighting phenolic compounds) displayed a good discrimination between samples collected in Serbia and Germany according to the PC1 axis. Samples from the SMG were positioned at the lower right side of the PCA score plot, while the SMS samples were scattered at the left side of the plot (Figure 4). Within the SMS group two subgroups can be noted along the PC2 direction. The obtained loading plot showed the influence of phenolic compounds with characteristic hR F values on the classification of samples according to the geographical market. The compounds at hR F 39, 72, and 87 affected PC1 in a positive manner, indicating a higher content of these phenolic compounds in apple juices collected from the SMG, while the compound at hR F 36 significantly affected PC1 in a negative manner and is responsible for the differentiation of SMS apple juices. Variables that had the highest positive impact on the differentiation of samples along the PC2 axis were compounds at hR F 35, 66, and 88, while compounds at hR F 30 and 38 had a negative impact.
In the case of the HPTLC−DPPH • autograms ( Figure S2a), two groups of objects could be noticed (SMG and SMS samples). Variables at hR F 48 and 75 (along PC1) as well as at hR F 45 and 50 (along PC2) had the highest impact on sample grouping.
The PCA model obtained for the HPTLC−AChE autograms ( Figure S2b) confirmed the differentiation of the samples according to their geographical market. Two samples from the SMS formed a subgroup between clusters belonging to the SMS and SMG apple juices. The loading plots revealed that the compounds at hR F 27, 82, and 93 had the most positive impact on the PC1 direction and were responsible for the separation of German apple juices, while the compound at hR F 44 mainly affected the differentiation of apple juices from SMS along the PC1 axis. The compounds at hR F 25, 53, and 96 had the highest negative impact on separation along PC2, while the compounds at hR F 42 and 90 had a strong positive impact.  In the case of the HPTLC−DPPH • autograms ( Figure S2a), two groups of objects could be noticed (SMG and SMS samples). Variables at hRF 48 and 75 (along PC1) as well as at hRF 45 and 50 (along PC2) had the highest impact on sample grouping.
The PCA model obtained for the HPTLC−AChE autograms ( Figure S2b) confirmed the differentiation of the samples according to their geographical market. Two samples from the SMS formed a subgroup between clusters belonging to the SMS and SMG apple juices. The loading plots revealed that the compounds at hRF 27, 82, and 93 had the most positive impact on the PC1 direction and were responsible for the separation of German apple juices, while the compound at hRF 44 mainly affected the differentiation of apple juices from SMS along the PC1 axis. The compounds at hRF 25, 53, and 96 had the highest negative impact on separation along PC2, while the compounds at hRF 42 and 90 had a strong positive impact.
In the case of the HPTLC−BChE autograms ( Figure S2c), the score plot showed that juice extracts from SMS formed a cluster at the upper side of the plot, while those from SMG were scattered at the lower side of the plot. The highest positive contribution to separation along the PC1 axis had the compound at hRF 85, while the compounds at hRF 42, 73, and 95 had the highest negative impact. The differentiation of samples along the PC2 axis was influenced by variables at hRF 73, 78, and 96 (in positive manner) and at hRF 38 In the case of the HPTLC−BChE autograms ( Figure S2c), the score plot showed that juice extracts from SMS formed a cluster at the upper side of the plot, while those from SMG were scattered at the lower side of the plot. The highest positive contribution to separation along the PC1 axis had the compound at hR F 85, while the compounds at hR F 42, 73, and 95 had the highest negative impact. The differentiation of samples along the PC2 axis was influenced by variables at hR F 73, 78, and 96 (in positive manner) and at hR F 38 and 92 (in negative manner).
The score plot for the HPTLC−tyrosinase autograms ( Figure S2d) revealed a difference in the inhibition potential of SMS and SMG apple juice extracts. SMS samples were grouped at the lower side of the plot, while SMG samples formed the second cluster at the upper side of the plot, along the PC2 axis (except for the two samples that showed a different tyrosinase inhibiting potential from all other samples). Variables at hR F 42, 52, and 84 positively affected the separation of SMG samples along the PC2 axis, while compounds at hR F 34, 60, and 92 had the highest negative impact on the differentiation of samples from SMS. Additionally, phenolic compounds at hR F 45 and 90 significantly affected the PC1 in a positive manner and were responsible for the separation of two German apple juices from all the other analyzed samples.
The PCA model obtained for the HPTLC−A. fischeri bioautograms ( Figure S2e) indicated a partial overlapping of SMS and SMG samples, according to the antimicrobial activity. The most influential variables on the separation along the PC1 axis were compounds at hR F 84 and 97.

Grape Juices
For the grape juice extracts, the PCA model applied on the HPTLC-FLD chromatogram at 366 nm after derivatization with the natural product A reagent displayed a good discrimination between samples from the SMS versus SMG along the PC1 axis ( Figure 4). Two samples from SMS formed a subgroup between these separated clusters. Variables at hR F 42 and 90 affected PC1 in a positive manner and were responsible for the grouping of the SMG samples at the right side of the plot. The compounds at hR F 36 and 85 had the highest negative contribution to the positioning of the objects at the left side of the plot (SMS juices).
The score plot for the HPTLC−DPPH • autograms ( Figure S3a) suggested the existence of two distinctive clusters along PC1, belonging to grape juice samples from SMG (at the right side of the plot) and SMS (left side of the plot), while two grape juices from SMS overlapped with the samples from SMG because of their similar DPPH • response. The loading plot showed that the highest positive contribution to the positioning of the objects on the score plot for PC1 had compounds at hR F 30, 76, and 80, while the compounds at hR F 47, 90, and 96 had negative contributions. Compounds at hR F 26 and 67 had the highest positive impact on separation along PC2, while the compounds at hR F 33, 78, and 85 had a strong negative impact.
The PCA model for the HPTLC−AChE autograms ( Figure S3b) suggested along the PC2 direction the existence of two distinctive groups belonging to SMG and SMS samples. Two samples from SMG showed a more similar AChE inhibiting potential as well as DPPH • response to juice samples from SMG. The most influential compound for discriminating juice extracts from SMS was at hR F 73, while the compound at hR F 90 mostly affected the separation of juices from SMG.
In the case of the HPTLC−BChE autograms ( Figure S3c), samples from SMS were scattered, while juice samples from SMG formed a compact cluster at the right side of the plot, along the PC1 direction. The loading plot revealed that compounds at hR F 85 and 95 had the highest positive effect on the PC1 component, indicating a high content in SMG samples. Additionally, compounds at hR F 31 and 84 significantly affected the PC2 in a positive manner, while the compounds at hR F 43, 90, and 97 had a strong negative impact on separating SMG samples.
The score plot for the HPTLC−tyrosinase autograms ( Figure S3d) suggested the existence of two distinctive clusters, along the PC1 axis. Three samples from SMS showed different inhibition properties from all the other samples. The compound at hR F 87 had the most influence on the differentiation of SMG juices along the PC1 axis, while the compound at hR F 97 was discriminating the Serbian extracts. The compound at hR F 92 had the highest positive impact on separation along the PC2 axis.
The PCA model for the HPTLC−A. fischeri bioautograms ( Figure S3e) confirmed a different antimicrobial potential of SMG and SMS grape samples, except for two SMS samples, which showed more similar characteristics to SMG samples. The compounds at hR F 32, 50, 60, and 90 were the most important for the differentiation of grape juices along the PC1 axis.

Discussion
Fingerprinting methods highlight a number of unique chromatographic signals to enable sample recognition. The visual examination of the chromatograms revealed differences in the profiles among the apple juice extracts originating from SMS and SMG. All analyzed extracts were characterized via a vast number of blue and orange fluorescent bands at hR F 36, 44, 47, 65, 77, and 86. The recording of HPTLC−ESI−HRMS spectra allowed the further characterization of interesting zones. In the case of the apple juices, compounds at hR F 40, 50, 80, and 90 were assigned as chlorogenic acid (5-caffeoylquinic acid), phloridzin, epicatechin, and caffeic acid, respectively. This is in accordance with published data for German juice made of dessert and cider apples, where chlorogenic acid, quercetin glycosides, procyanidins, and dihydrochalcones, such as phloridzin (phloretin-Oglucoside) and phloretin-O-xyloglucoside, were found as dominant constituents [6]. In the case of the grape juices, blue fluorescent bands at hR F 43, 55, 85, and 90, as well as an orange fluorescent band at hR F 48, were present in all analyzed extracts. The bands at hR F 43, 85, and 90 were identified as chlorogenic acid, epicatechin, and quercetin, respectively. The characterization of grape juices produced in Brazil showed that anthocyanins and tannins were predominant phenolic compounds [9].
The effect-directed analysis of the investigated fruit juices showed a moderate to strong antioxidant potential as well as AChE, BChE, and tyrosinase inhibitory activities. In the literature [37], similar results were obtained for apple juice from a Chinese market, which confirmed the strong antioxidant activity of 5-O-caffeoylquinic acid, quercetin, and phloretin that significantly increased after juice fermentation with Lactobacillus plantarum. The analysis of the antioxidant and antimicrobial melatonin in 18 apple cultivars and juices [38] indicated that the highest melatonin level was detected in the peel, while the melatonin content in the juice was comparable to that of its flesh. Examination of the antioxidant effects of grape skin anthocyanins using various in vitro and in vivo methods confirmed a strong antioxidant and AChE inhibition potential [39]. Antimicrobials against Gram-negative A. fischeri were detected in all analyzed juices, but grape juice extracts showed stronger antimicrobial activities compared to apple juices. This is contrary to results obtained by the evaluation of the antibacterial activity of apple, pomegranate, and grape juices on clinical endodontic bacterial strains, where the highest antibacterial activity was observed in apple fruit juice [40]. However, this contradiction may be explained by varying antimicrobial pesticide residues. Evaluation of the antimicrobial effect of white grape juice extract (Trapani, Italy) against a range of Gram-positive and Gram-negative bacteria confirmed that Staphylococcus aureus was the most sensitive strain among the tested Gram-positive bacteria, while Escherichia coli was the only susceptible strain in the case of Gram-negative bacteria [41].
The proposed untargeted effect-directed profiling of phenolic compounds in fruit juice extracts could be considered as a promising analytical tool in the quality control of fruit juices, but it must be taken into account that fruit variety, ripening stage, environmental conditions, and processing technology also affect the phenolic content of fruit juices [42]. The PCA score plots reveal that the best separation between apple juices of SMG and SMS was achieved by using the HPTLC−FLD chromatogram at 366 nm after derivatization, which was the simplest protocol among the tested detection options. The effect-directed profiles (DPPH • , AChE, BChE, and tyrosinase inhibition) lead to a comparatively lower degree of separation between the samples of different geographic markets, whereby the BChE inhibition assay was the worst. However, it was still possible to separate the samples sufficiently. A similar degree of separation of apples from five geographic areas (Alpine, Dinaric, Mediterranina, Panonian, and Submetiranian) was achieved using linear discriminant analysis as the classification method and the content of primary metabolites, as well as polyphenols as input [43]. Our excellent separation of samples from two market groups (SMG and SMS) obtained by physicochemical HPTLC profiling can also be compared with the results obtained for five apple cultivars collected from five regions in China (Liquan, Xunyi, Yongshou, Sanyuan, and Luochua) using linear discriminant analysis as the classification method and the content of polyphenols as the input [44]. All this might suggest that the chemical fingerprint related to the polyphenolic content plays a significant role in the discrimination of apple juices according to geographic markets.
In the case of the grape juice samples, the PCA score plots revealed that the HPTLC fingerprints and HPTLC−DPPH • profiles provided enough information to completely distinguish samples of German and Serbian markets. The separation of different geographic samples based on the HPTLC−tyrosinase inhibition profiles was satisfactory. However, HPTLC−AChE and BChE inhibition profiles did not provide a sufficient separation. The information obtained from the physicochemical composition had a clear advantage over the effect-directed data, regarding the discrimination of apple and grape juice extracts from two selected market groups. Effect-directed data reveal the presence of important bioactive compounds, which may vary more strongly, but provide strong arguments for the valorization of food. Hence, further in-depth effect-directed studies are needed especially for plant-based food.

Sample Preparation
Fruit juice samples (18 apple and 18 grape juices) were collected from local markets in Belgrade, Serbia (Table S1, nos. 1−9) and Giessen, Germany (nos. 10−18). Therefore, the total number of analyzed samples was 36 (9 samples × 2 market groups × 2 juice types). Each of the 9 samples was considered as belonging to the same population. The fruit juices (100 mL each) were concentrated to 20 mL (basic rotary evaporator RV 05, IKA-Werke, Staufen, Germany) under reduced pressure at 35 • C. Extraction was performed with 20 mL of ethyl acetate-dichloromethane 3:1, V/V, under agitation for 3 min (Basic Vortex Mixer, Thermo Fisher Scientific, Dreieich, Germany) and repeated once. Both pooled organic phases were evaporated to dryness and each residue was dissolved in 2 mL of methanol prior to storage at −20 • C. In the final experiments, no replicate measurements were performed.

HPTLC-UV/Vis/FLD and HPTLC-EDA-UV/Vis/FLD Analysis
On the HPTLC plate silica gel 60 F 254 (20 cm × 10 cm, Art. 105641, Merck), aliquots of each fruit juice extract (3 µL for FLD 366 nm, 1 µL for DPPH • assay, 5 µL for A. fischeri bioassay and AChE inhibition assay, and 10 µL for BChE and tyrosinase inhibition assays) were applied as an 8 mm band (Automatic TLC sampler 4). No replicates were performed in the final experiments. HPTLC instruments were from CAMAG, Muttenz, Switzerland and controlled by winCATS software. Development was performed with ethyl acetatetoluene−formic acid−water 16:4:3:2, V/V/V/V [45], up to a migration distance of 70 mm in a 10 min saturated twin trough chamber (with filter paper) followed by drying under a cold flow of air (hair dryer) and heating at 100 • C for 3 min (TLC Plate Heater III). Images of the chromatograms were captured at UV/Vis/FLD (DigiStore 2). Respective positive controls were applied as described [36].
For derivatization with the natural product A reagent detecting phenolics as fluorescent bands, HPTLC chromatograms were immediately (still hot) immersed (immersion time 2 s, immersion speed 3.5 cm/s, Chromatogram Immersion Device III, if not stated otherwise) in a 0.5% solution of 2-aminoethyldiphenylborinate in ethyl acetate and then a 5% solution of polyethylene glycol 400 in dichloromethane. The images captured at FLD 366 nm were saved as TIF files.
For the DPPH • assay detecting radical scavengers as a yellow band against a violet background, HPTLC chromatograms were dipped into a 0.05% methanolic DPPH • solution [46], dried (60 • C, 1 min), and recorded under white light illumination in the reflectance mode. The images were captured again on the next day, as the signal intensity increased over time.
For the A. fischeri bioassay detecting antimicrobials as dark zones against a light grey background (bioluminescence depicted as greyscale image, BioLuminizer), HPTLC chromatograms were immersed into the luminescent Gram-negative A. fischeri bacteria suspension, prepared according to [28] and ready for use when a brilliant green-blue bioluminescent light has been emitted when the flask is shaken in the dark. Images were instantly captured with an exposure time of 30 s over a period of 20 min, which allowed time-dependent changes to be monitored.
For the AChE and BChE inhibition assays detecting enzyme inhibitors as colorless or bright bands against a purple background [47], the HPTLC chromatograms were prewetted with 1 mL of Tris-HCl buffer solution (pH 7.8, 0.05 M), immersed in the buffered enzyme solutions (AChE 666 units or BChE 334 units, plus 100 mg of BSA and 100 mL of 0.05 M TRIS buffer, pH 7.8), placed in a moistened plastic box covered with wet filter papers, and incubated at 37 • C for 30 min. Then, plates were immersed into the 1:1 solution of substrate (2.5 mg/mL of ethanolic α-naphthyl acetate solution) and chromogenic reagent (2.5 mg/mL of aqueous Fast Blue Salt B solution), dried, and recorded under white light illumination in the reflectance mode.
For the tyrosinase inhibition assay detecting enzyme inhibitors as colorless or bright bands against a purple background [48], the neutralized HPTLC chromatograms were first sprayed (Derivatizer) with 1 mL of substrate solution (45 mg of levodopa, 25 mg of CHAPS, and 75 mg of PEG 8000 were dissolved in 10 mL of phosphate buffer, consisting of 1.4 mg/mL of dipotassium hydrogen phosphate trihydrate and 1.68 mg/mL of disodium hydrogen phosphate, pH 6.8), then after drying, sprayed with 1 mL of enzyme solution (400 U/mL in phosphate buffer) and incubated in a humid box for 20 min. Images were recorded under white light illumination in reflectance mode.

Characterization of Bioactive Compounds by HPTLC-ESI-HRMS
The bioactive compounds were marked with a soft pencil on the HPTLC plate illuminated at 366 nm and transferred with an elution head-based interface (oval elution head of 4 mm × 2 mm, TLC-MS Interface, CAMAG) with 100% methanol at a flow rate of 0.1 mL/min to the heated ESI source.

Data Acquisition and Statistical Analysis
The open-source software rTLC v1.0 (http://shinyapps.ernaehrung.uni-giessen.de/ rtlc/ accessed on 1 June 2022) was used for image processing and multivariate analysis of HPTLC chromatograms. The median filter function with a two-pixel width filter was used for denoising of the images. Normalization of the images was performed by Standard Normal Variate (SNV) transformation, which removes scatter effects by centering each individual variable. The correlation-optimized warping (COW) algorithm was applied in order to remove the negative impact of the band shifts caused by fluctuations in experimental conditions, analyst errors, and instrumental variations. All data were mean-centered to transform variables in the same unit, prior to multivariate analysis.

Conclusions
The combination of nontarget chemical/biological/biochemical profiling with advanced chemometrics has proven to be an effective tool to learn more about fruit juice quality differences. It is also useful in detecting fraud in fruit juice production (due to the multi-imaging/detection feature and parallel analysis) and in authenticating the geographical origin of apple and grape juices. The bioactivity assessment revealed several characteristic bioactive compounds of apple and grape juices, of which some were tentatively assigned. The examined samples showed moderate antioxidant activity, strong AChE, BChE, and tyrosinase inhibiting potential and good response against A. fischeri bacteria. Multivariate data analysis applied on physicochemical and effect-directed profiles clearly showed that there are fruit juice quality differences between countries. Information on the bioactivity of fruit juices obtained through effect-directed profiling expands and improves knowledge about the juice product quality. Hence, this fast and cost-effective analytical approach could find application in the quality assessment of other fruit juices and further fruit products, such as jam and marmalade.