Free amino acid determination by GC-MS combined with a chemometric approach for geographical classification of bracatinga honeydew honey (Mimosa scabrella Bentham)

Accepted Manuscript Free amino acid determination by GC-MS combined with a chemometric approach for geographical classification of bracatinga honeydew honey (Mimosa scabrella Bentham) Mônia Stremel Azevedo, Siluana Katia Tischer Seraglio, Gabriela Rocha, Claudia Berenice Balderas Arroyo, Marcel Piovezan, Luciano V. Gonzaga, Daniel de Barcellos Falkenberg, Roseane Fett, Marcone Augusto Leal de Oliveira, Ana Carolina Oliveira Costa PII:

S0956-7135(17)30128-7

DOI:

10.1016/j.foodcont.2017.03.008

Reference:

JFCO 5509

To appear in:

Food Control

Received Date: 14 January 2017 Revised Date:

8 March 2017

Accepted Date: 9 March 2017

Please cite this article as: Azevedo M.S., Seraglio S.K.T., Rocha G., Balderas Arroyo C.B., Piovezan M., Gonzaga L.V., de Barcellos Falkenberg D., Fett R., de Oliveira M.A.L. & Costa A.C.O., Free amino acid determination by GC-MS combined with a chemometric approach for geographical classification of bracatinga honeydew honey (Mimosa scabrella Bentham), Food Control (2017), doi: 10.1016/ j.foodcont.2017.03.008. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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Free amino acid determination by GC-MS combined with a chemometric

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approach for geographical classification of bracatinga honeydew honey (Mimosa

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scabrella Bentham)

4 Mônia Stremel Azevedo1, Siluana Katia Tischer Seraglio1, Gabriela Rocha1, Claudia

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Berenice Balderas Arroyo1, Marcel Piovezan1, Luciano V. Gonzaga1, Daniel de

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Barcellos Falkenberg2, Roseane Fett1, Marcone Augusto Leal de Oliveira3, Ana

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Carolina Oliveira Costa1*

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*Corresponding author: Tel. +55 48 3721 2975 / Fax: +55 48 3721 9943

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E-mail: [email protected]

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Federal University of Santa Catarina, Department of Sciences and Food Technology, Rodovia Admar Gonzaga, 1346, Itacorubi, 88040-900, Florianopolis, SC, Brazil

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Federal University of Santa Catarina, Department of Botany, Campus Reitor João

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David Ferreira Lima, s/n, Trindade, 88040-900, Florianopolis, SC, Brazil

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Federal University of Juiz de Fora, Department of Chemistry, Rua José Lourenço

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Kelmer, s/n - Campus Universitário Bairro São Pedro, 36036-900 - Juiz de Fora, MG,

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Brazil

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Abstract

24 Honeydew honey is increasingly being valued by consumers and the food industry

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worldwide, particularly bracatinga honeydew honey (Bhh) obtained from honeydew of

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plant-sucking insects (Tachardiella sp.) that infest the tree species bracatinga (Mimosa

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scabrella Bentham) from Santa Catarina State (SC), Brazil. Due to mixture between

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honeys, authentication is an important aspect of quality control and its regard with the

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origin guarantee in terms of source and geographical documentation needs to be

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determined. We therefore determined the free amino acids (FAA) by GC-MS to

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elucidate the contribution of plant-sucking insects (Tachardiella sp.) and Apis mellifera

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bees to the Bhh in order to classify this honey from five different geographic areas of

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Santa Catarina, using chemometric approach. The results showed that proline is

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provided exclusively by Apis mellifera bees, and this honey could be differentiated into

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geographic regions based on the FAA profile. Principal component analysis identified

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the main FAA responsible for clustering of the samples in these regions (the sum of the

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first 2 principal components account for 82% of the total variance) and provided a

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similar discrimination of the geographical location map, particularly with regard to the

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northern and southern geographical orientations. This method is therefore a reliable

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analytical strategy for the authentication of this honey.

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Keywords: Brazilian honeydew honey, honeydew honey authentication, honeydew

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honey discrimination, cluster analysis, principal component analysis.

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1. Introduction

46 Honeydew honey is obtained from the secretions of trees and living plants or from the

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excretions of plant-sucking insects (European Commission, 2002a). In the case of

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excretions, the plant-sucking insects pierce the leaf or other plant parts, feed on the sap,

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and excrete the unused surplus as droplets of honeydew, which are then gathered by

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bees (Chamorro, Nates-Parra, & Kondo, 2013). Honeydew honey is increasingly being

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valued by consumers and the food industry due to its strong and characteristic flavor

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which differs from that of floral honeys. Furthermore, the rising demand for honeydew

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honey in many European countries requires its differentiation from other types of honey

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as a response to the consumer market. In Brazil, honeydew honey is obtained from the

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bracatinga plant Mimosa scabrella Bentham, with natural occurrence that covers the

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regions from Minas Gerais until Rio Grande do Sul, which can produce this honeydew

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honey. However, Santa Catarina region was the largest exporter of honey in 2015

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(ABEMEL, 2016) and its quality was recognized with World Beekeeping Award in

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Apimondia International Apiculture Congress in 2013 among 37 countries

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(APIMONDIA, 2016), being important its differentiation. Every two years,

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corresponding to the lifecycle of the plant-sucking insects (Tachardiella sp.), the plant

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is infested, producing honeydew from January to June, which is gathered by Apis

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mellifera bees (Mazuchowski, Rech, & Toresan, 2014). Fig. 1 shows the M. scabrella

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B. species and bees collecting honeydew.

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{INSERT FIGURE 1}

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ACCEPTED MANUSCRIPT The geographical classification of honey is important for monitoring the features

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of this product. For this purpose, specific parameters or chemical markers are selected,

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such as volatile organic compounds (Yang et al., 2012), multielement stable isotope

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ratios (Schellenberg et al., 2010), mineral content (Rizelio et al., 2012), and

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carbohydrates (Consonni, Cagliani, & Cogliati, 2013), to evaluate pollen grain

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morphology in honey samples (melissopalynologic analysis) (Corvucci, Nobili,

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Melucci, & Grillenzoni, 2015) and, especially, it has been reported that concentration

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profiles and ratios of amino acids are characteristic from source (nectar or honeydew),

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regions and countries (Kivrak, 2015; Patzold & Bruckner, 2006). Among the techniques

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used for amino acid determination in honeys, gas chromatography (GC) and liquid

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chromatography (LC) have been widely used for geographical and botanical origin

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classification. Patterns and ratios of amino acids can characterize specific regions and

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mass spectrometry (MS) is a selective and sensitive detector which allows structural

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elucidation of the analyzed compounds (Kivrak, 2015; Nozal, Bernal, Toribio, Diego, &

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Ruiz, 2004; Patzold & Bruckner, 2006).

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Although the content of amino acids can arise from different sources, for

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example, nectar, honey bee and pollen (Cometto, Faye & Naranjo, 2003), or to be

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primarily related to the presence of pollen in the honey (Pinheiro, Torres, Raimundo &

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Santos, 2015), in bracatinga honeydew honey, amino acids can come from M. scabrella

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B. phloem, which feeds the plant-sucking insects, belonging to the plant family

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Leguminosae, a specie with capacity to fix nitrogen that may contain up to 0.4% of

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nitrogenous substances (proteins, amino acids and amides) (Camargo-Ricalde,

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Montano, Reyes-Jaramillo, Jimenez-Gonzalez & Dhillion, 2010); Mazuchowski et al.,

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2014), suggesting a larger free amino acids (FAA) number and/or greater FAA

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concentrations in bracatinga honeydew honey, even after plant sucking insects

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processing. Within this context the present work determined, for the first time, free amino

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acids (FAA) via gas chromatography with mass spectrometry detection (GC-MS) in

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bracatinga honeydew honey (Bhh), rich in nitrogenous substances, and in honeydew (H)

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from plant-sucking insects (Tachardiella sp.), aiming to elucidate the contribution of

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Apis mellifera bees in the processing of this honey. Furthermore, we propose a

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geographic classification among honeydew honeys from the same species and

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mesoregions using a chemometric approach.

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2. Material and methods2.1 Botanical identification of the Mimosa scabrella Bentham

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species

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Through the technical support of the Agronomist from the Empresa de Pesquisa

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Agropecuária e Extensão Rural de Santa Catarina (EPAGRI), were selected bracatinga

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honeydew honey producers with apiaries in specific areas, close to the bracatinga

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populations of the regions of interest. Aiming to ensure the bracatinga honeydew honey

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authenticity, the botanical identification of the Mimosa scabrella Bentham species was

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confirmed by Professor Daniel de Barcellos Falkenberg, by comparison to voucher

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specimen deposited in the Herbarium of the Departament of Botany of the Federal

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University of Santa Catarina, under identification number FLOR 8578. Samples were

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collected in their natural habitats and the identification was carried out using the

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traditional taxonomic methodology (Stuessy, 2009).

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2.2 Sample preparation

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ACCEPTED MANUSCRIPT 118 Samples were collected from five areas in the mountainous plateau region of

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Santa Catarina State (SC), southern Brazil and identified using the acronyms shown in

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Table 1. Fig. 2 shows the sampling locations in the mountain plateau region of SC,

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southern Brazil. A total of 21 Bhh honeycomb samples were collected from three

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randomly selected beehives at each apiary and a total of 9 honeydew samples were

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collected from a bracatinga population located near to the apiaries that were used in the

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collection of Bhh from February to June 2014 (harvest period, specifically of this honey

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in Brazil).

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{INSERT TABLE 1}

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The honeydew samples from Tachardiella sp. were collected manually directly

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from the honeydew droplet excreted by the aphid in polypropylene microtubes of 1 mL.

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transported under refrigeration (5 ± 2ºC) and were kept in a freezer (-18 ± 2ºC) until

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analysis. The Bhh samples were transported under refrigeration (5 ± 2ºC), drained into a

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glass funnel, homogenized with a glass rod, and centrifuged at 2,000 rpm for 10 min

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(Fanem, 280R, São Paulo, Brazil). Both samples (Bhh and H) were kept in a freezer (-

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18 ± 2ºC) until analysis (a maximum of 2 months). Derivatization of amino acids was

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performed using a 20 to 100 mg mL-1 honey solution in water to ensure that the

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concentration of each amino acid present in the sample as within the working range of

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the standard curve. The sample treatment procedure is described in the kit EZ:faast GC-

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MS for free amino acid analysis and shown in Fig. 3.

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2.3 Chemicals and solutions

146 Solutions with mixing standards prepared from analytical grade standards at a

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concentration of 200 nmol mL-1 (Ala, Sar, Gly, ABA, Val, β-AiB, Nva (Internal

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Standard, IS), Leu, aILE, Ile, Thr, Ser, Pro, Asn, Tpr, Asp, Met, Hyp, Glu, Phe, Aaa,

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Apa, Gln, Orn, Gpr, Lys, His, Hly, Tyr, Php, Trp, Cth, Cys), reagents and organic

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solvents (0.33 mol L-1 sodium hydroxide, 20 mmol L-1 hydrochloric acid, 20% 3-

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picoline, n-propanol, propyl chloroformate, isooctane and chloroform) and a Zebron

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ZB-AAA GC chromatographic column were supplied in the kit EZ:faast GC-MS for

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free amino acid analysis (Phenomemex, Torrance, CA, USA). High purity water was

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generated using a Milli-Q Simplicity® UV system from Millipore Corporation (Saverne,

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Alsace, France).

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2.4 GC-MS instrumentation

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The GC-MS conditions applied were those recommended in the kit EZ:faast

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GC-MS for free amino acid analysis. The analysis was performed using a 7890 gas

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chromatograph coupled to a mass spectrometer model 5975C (Agilent Technologies,

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Palo Alto, CA, USA). Carrier gas (He, purity 99.999%, Air Liquid Brasil Ltda,

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Florianópolis, Brazil) flow was kept constant at 1.1 mL min-1. The oven temperature

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program was as follows: initial temperature 110ºC, 30ºC min-1 ramp to 320ºC. The

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temperature of the injection port was 250ºC. The MS temperatures were as follows: ion

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source 240ºC, quadrupole 180ºC, and auxiliary 310ºC. The scan range was 45–450 m/z

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(3.5 scans s-1); the scan mode was used for data acquisition to identify possible

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compounds characteristic of the locations. Under these conditions, a 1.5 µL sample was

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injected in split mode 1:15.

171 2.5 Quality assurance and quality control systems (QA/QC) and greenness evaluation of

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analytical method: Analytical Eco-Scale.

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QA/QC are two of the main activities that are required to ensure the quality of

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analytical measurements (Konieczka & Namiesnik, 2010). Since some validation

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parameters of the method proposed in the kit EZ:faast GC-MS for free amino acid

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analysis were evaluated for a honey matrix by Nozal et. al. (2004), in this study the

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linearity, matrix effect, precision, accuracy, detection and quantification limits

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(Eurachem, 2014) and uncertainty measurement parameters (EURACHEM/CITAC,

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2012) were evaluated (European Commission, 2002b) along with the system suitability.

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The procedures used for evaluation of the validation parameters and greenness of the

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analytical method were inserted in Supplementary Material.

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2.6 Statistical analysis

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The statistical software package Statistica 13 Ultimate Academic® (StatSoft Inc.,

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Tulsa, OK, USA) was used to compare measurement duplicates of the analyzed FAA in

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the H samples (n = 18) from the mountain plateau region of SC; the normality of the

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data obtained was verified applying the Shapiro-Wilk W-test (Shapiro & Wilk, 1965).

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In cases where there was significant evidence of non-normality, non-parametric Mann-

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Whitney U-test was used to identify differences between two independent groups

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ACCEPTED MANUSCRIPT (regions) of data obtained by FAA amount and to determine the statistical significance.

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Wilcoxon's matched pairs test was used to compare two variables (FAA from Bhh and

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H) (Burke, 2001). In measurement duplicates of the analyzed FAA in the Bhh samples

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(n = 42), in cases where there was significant evidence of non-normality, Kruskal-

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Wallis non-parametric test was used to identify significant differences between regions.

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Cluster analysis (CA) and principal component analysis (PCA) were used to investigate

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partner behavior of the data set in order to identify a geographical classification of the

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analyzed regions. Differences between the means at the 95% (p < 0.05) confidence level

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were considered statistically significant. Data were expressed as mean ± expanded

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uncertainty (U).

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3. Results and discussion

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3.1 Derivatization reaction and FAA identification

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The derivatization procedure consisted of a solid-phase extraction clean-up,

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followed by a derivatization step using an organic phase with an alkyl chloroformate

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reagent which reacts with both the carbonyl and amine groups, producing derivatives

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which are stable at room temperature; in addition, a liquid/liquid extraction was carried

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out. The GC-MS parameters set allowed the identification of up to 32 FAA with good

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resolution, as can be seen in Fig. 4(a). The application of the method revealed the

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predominant presence of the FAA Ser, Pro, Asn, Asp, and Glu in the samples (Fig.

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4(b)). Thus, the verification of the analytical performance for quantification purposes

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was applied only to these analytes.

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3.2 Figures of merit of validation

221 Calibration curves obtained via ordinary least squares method (OLSM)

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presented acceptable linearity from 50 to 200 nmol mL-1, with R2 s being higher than

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0.9910. Besides, statistical assumptions such as residues normality test (p-value ≥ 0.05

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by Shapiro-Wilk W-test) and homoscedasticity test (p-value ≥ 0.05 by Bartlett's test)

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indicated appropriate model fit. The lack-of-fit test by ANOVA found no significance

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within the 95% confidence interval (p-value ≥ 0.05), indicating absence of lack-of-fit in

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the regression model. Limit of detection (LOD) values obtained were 11.5, 6.45, 11.5,

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11.5, and 14.8 nmol mL-1 (0.84, 0.52, 1.06, 1.07, and 1.52 mg kg-1) for Ser, Pro, Asn,

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Asp, and Glu, respectively, while limit of quantification (LOQ) values obtained were

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38.5, 21.5, 38.4, 38.4, and 49.3 nmol mL-1 (2.83, 1.73, 3.55, 3.57, and 5.07 mg kg-1) for

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Ser, Pro, Asn, Asp, and Glu, respectively. The relative standard deviation (RSD) of the

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instrumental precision was ≤ 2.30% for relative peak area and ≤ 0.02% for retention

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time, showing that the instrumental system was suitable for this use. The RSD for the

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intra-day precision was ≤ 6.01%, which is in accordance with the criterion of

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acceptability of 10%. Accuracy values ranged from 80.6 to 104%, adhering to the

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criterion of acceptability of 80 to 110% (European Commission, 2002b). No matrix

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effects were detected in the studied range.

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The values calculated by the expanded uncertainty method were 18.3, 10.8, 17.8,

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14.7, and 23,8% for Ser, Pro, Asn, Asp, and Glu, respectively. The accuracy evaluation

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was the major source of uncertainty, showing agreement with works that determined

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expanded uncertainty in procedures containing SPE clean-up and extraction processes

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prior to GC-MS analysis in complex matrices, with results up to 24.3% of expanded

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uncertainty (Abreu, Caboni, Cabras, Garau, & Alves, 2006; Stepan, Hajslova,

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Kocourek, & Ticha, 2004).

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So far, only a few studies have evaluated the total concentration of FAA in

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honeydew of plant-sucking insects and their host plants. For example, Fischer, Volkl,

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Schopf, & Hoffmann (2002) assessed honeydew of the aphid species Metopeurum

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fuscoviride on Tanacetum vulgare from Germany; Dhami, Gardner-Gee, Houtte, Villas-

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Boas, & Beggs (2011) evaluated Ultracoelostoma spp. honeydew from Nothofagus

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spp., Coelostomidia wairoensis honeydew from Kunzea ericoides and Coelostomidia

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zealandica honeydew from Myoporum laetum and Pittosporum crassifolium from New

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Zeland; only one study quantified individual FAA in honeydew of Tuberculatos

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quercicola from Quercus dentata from Japan (Yao & Akimoto, 2002). The lack of

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studies in this matrix may be due to the difficulties of collecting honeydew (in µL

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quantities), which is often located at high parts of the plants. In this work, we were able

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to collect H from a bracatinga population located near the apiaries used for the

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collection de Bhh from two regions. The results for the determination of FAA in Bhh

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and H samples in these two areas in the mountainous plateau region of SC are shown in

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Table 2.

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ACCEPTED MANUSCRIPT Studies regarding the amount of FAA in honeydew, mainly in excretions from

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plant-sucking insects used for honeydew honey production, are rare. In this study, the

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predominant FAA found in H (serine, proline, asparagine, aspartic acid and glutamic

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acid) were similar to those commonly found in honeydews from other species (Dhami et

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al., 2011). Aspartic acid, serine, asparagine, glutamine, glycine and phenylalanine were

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the amino acids found in the honeydew from Metopeurum fuscoviride with asparagine

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and glutamine as predominant amino acids (Fischer et al., 2002); valine, leucine

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isoleucine, proline, phenylalanine, glutamic acid and tyrosine were detected in the

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species analyzed by Dhami et al. (2011), with proline as the most abundant amino acid.

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The FAA concentrations found in the present study were higher than those determined

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by Yao & Akimoto (2002), which obtained concentrations from, approximately, 51, 37,

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12 and 3,0 mg kg-1 for serine, proline, aspartic acid and glutamic acid, respectively.

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3.4 Statistical methods applied to Bhh and H samples

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To date, there are no studies comparing the FAA composition of excretions of

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plant-sucking insects and the final honeydew honey product, aiming to identify the

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origin of the FAA in the honey. In this study, we verified the FAA contribution from

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plant-sucking insects (Tachardiella sp.) and Apis mellifera bees to the final product

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(Bhh).

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The normality of the data obtained was verified using Shapiro-Wilk test (p ≥

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0.05). Table 2 shows that the data obtained for Pro in H and for Asp in Bhh was not

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normally distributed. We therefore used the non-parametric Mann-Whitney U-test to

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detect differences between the two geographical origins and found that only Asp in Bhh

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showed a significant difference (p < 0.05), indicating that the location where the Bhh is

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ACCEPTED MANUSCRIPT produced affects the profile. With regard to the geographical origin of the H, there were

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no significant differences in the mean concentrations of predominant FAA. By

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employing the non-parametric Wilcoxon's matched pairs test, which compares

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dependent groups, was possible to compare the FAA from Bhh and H in order to

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investigate the contribution of the bees to the final honey. As shown in Table 2, only

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Pro showed a significant difference (p < 0.05) when we compared the mean

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concentrations obtained in Bhh and H, while no significant difference was observed for

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Ser, Asn, Asp, and Glu (p ≥ 0.05). In regards to the contribution of FAA of the bees to

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the final Bhh product, an effective contribution could be determined for Pro, showing

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significantly higher values in Bhh when compared to H, while Ser, Asn, Asp and Glu

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can be supplied and/or consumed by it after plant-sucking insects processing.

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3.5 Determination of FAA in Bhh samples

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The results for the determination of FAA in Bhh samples from five areas in the

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mountainous plateau region of SC are shown in Table 3. By subjecting the data obtained

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in the FAA analysis to a Shapiro-Wilk test (p ≥ 0.05), Pro and Asn in Bhh showed non-

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normal distribution (Table 3); we therefore used the non-parametric Kruskal-Wallis test

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to verify significant differences between regions and observed that, at a significant level

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of 5%, Ser, Asp, and Glu were significantly different between BR and BS, Pro between

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BR and UB, and Asn between BR and BS regions. Although there are only a few

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studies for comparison, generally, we found higher FAA values in Bhh than other

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authors in honeydew and floral honeys (Table 4).

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3.6 Chemometric approach for geographical classification of Bhh samples

320 The CA technique (complete linkage using Euclidean distances) was used to

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examine the trend in the data set in order to discover natural groupings of Bhh samples

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of the studied regions. Predominant FAA quantification enabled determination of the

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similarity between the regions analyzed, resulting in the formation of six main clusters,

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which represent the locations BS, UB, LG, UP (two clusters), and BR (Fig. 5(a)).

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Fig. 5(b) shows the loading plot of the main PCs for the variables of the

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analyzed Bhh and the distribution responsible for clustering in the respective quadrants

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in Fig. 5(c). The PC1 (67.10%) explained most of the variability and PC2 explained

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only 14.85%. These values are considered appropriate, who established a total explained

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variance ≥ 70 % (Rizelio et al., 2012; Chudzinska & Baralkiewicz, 2010; Kaiser, 1960);

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this reliability is explained by the fact that the sum of the first 2 PCs account for 82% of

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the total variance of the data obtained. Thus, Fig. 5(c) represents the graphic distribution

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of the Bhh samples according to their factor scores and shows that these samples can

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differ according to their geographical origin in the BS, LG, BR, UB, and UP regions.

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Only one sample (UP4) from UP region showed a greater distance with respect to the

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clusters formed and, according to Fig. 5(b), it is possible to suggest a different behavior

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from Asp and Pro variables in this apiary, specifically. According to the geographical

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coordinates shown in Table 1, by quantifying Asp, Glu, and Pro, it was possible to

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ACCEPTED MANUSCRIPT achieve a good discrimination between two relatively close regions (BS and BR) when

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compared to LG, since the latter would be farther in terms of localization.

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Quantification of Asn and Ser allowed discrimination between UP and UB regions,

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which have higher altitudes in relation to other locations. However, some samples

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showed similar amounts, probably due to the close vicinity of these regions. The

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distribution of the samples in Fig. 5(c) showed similar classification to the geographical

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location map (Fig. 2) of the analyzed regions located in the mesoregion (mountainous

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plateau region) in SC, particularly with regard to the northern and southern geographical

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orientation (Fig. 6).

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4. Conclusions

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In this study, for the first time, we determined free amino acids in bracatinga

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honeydew honey (Mimosa scabrella Bentham) and plant-sucking insect honeydew

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(Tachardiella sp.), with the aim to elucidate the contribution of plant-sucking insect and

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Apis mellifera bees in honeydew honey amino acid composition and to classify these

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honeys of the same species and mesoregions, using a chemometric approach.

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We compared the results for bracatinga honeydew honey and plant-sucking

362

insect honeydew and only the amino acid proline differed significantly, which suggests

363

that bees effectively contribute to proline concentrations in the studied regions, while

364

Ser, Asn, Asp and Glu, can be supplied and/or consumed by it after plant-sucking

365

insects processing.

16

ACCEPTED MANUSCRIPT We found higher FAA concentrations in bracatinga honeydew honey than

367

previously reported for honeydew and floral honeys, that can be related to the species

368

Mimosa scabrella Bentham. By using CA and PCA techniques, we were able to

369

discriminate these honey samples according to their previously known geographical

370

origin (82% of total variance); in addition, PCA identified the main FAA responsible for

371

clustering of the samples in these regions and provided a similar discrimination of the

372

geographical location map, particularly with regard to the northern and southern

373

geographical orientation. These results demonstrate that the FAA profile is a reliable

374

analytical method for the authentication of this honey.

376

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375

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366

Acknowledgements

377

The authors wish to thank the Conselho Nacional de Desenvolvimento

379

Científico e Tecnológico (CNPq), Coordenação de Aperfeiçoamento de Pessoal de

380

Nível Superior (CAPES), Fundação de Amparo à Pesquisa do Estado de Santa Catarina

381

(FAPESC), Agronomist Saulo Luis Poffo (Empresa de Pesquisa Agropecuária e

382

Extensão Rural de Santa Catarina - EPAGRI), and the participating beekeepers from the

383

mountainous plateau region of Santa Catarina State. We also thank Msc Fabiana Della

384

Betta for her contributions to this manuscript and Mariléia Corrêa da Silva for providing

385

Figures 1 and 3.

386

The authors have no conflict of interest to declare.

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387 388 389

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17

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Nozal, M. J., Bernal, J. L., Toribio, M. L., Diego, J. C., & Ruiz, A. (2004). Rapid and

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chromatography with flame ionization or mass spectrometric detection. Journal of

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Chromatography A, 1047, 137-146. Patzold, R., & Bruckner, H. (2006). Gas chromatographic detection of D-amino acids in

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Pinheiro, L. A., Torres, L. M., Raimundo, J., & Santos, S. A. P. (2015). Effects of

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pollen, sugars and honeydew on lifespan and nutrient levels of Episyrphus

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Shapiro, S. S., & Wilk, M. B. (1965). An analysis of variance test for normality

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(complete samples). Biometrika, 52, 591-611. Silici, S., & Karaman, K. (2014). Chemometric approaches for the characterization of

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Turkish rhododendron and honeydew honeys depending on amino acid

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composition. Journal of Liquid Chromatography & Related Technologies, 37, 864-877.

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Stepan, R., Hajslova, J., Kocourek, V., & Ticha, J. (2004). Uncertainties of gas

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chromatographic measurement of troublesome pesticide residues in apples

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ACCEPTED MANUSCRIPT 487

employing conventional and mass spectrometric detectors. Analytica Chimica

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Acta, 520, 245-255.

489 490

Stuessy, T. F. (2009). Plant taxonomy: the systematic evaluation of comparative data. 2a ed. New York: Comumbia University Press. Yang, Y., Battesti, M., Paolini, J., Muselli, A., Tomi, P., & Costa, J. (2012).

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Melissopalynological origin determination and volatile composition analysis of

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Corsican ‘‘Erica arborea spring maquis’’ honeys. Food Chemistry, 134, 37-47.

RI PT

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Yao, I., & Akimoto, S. (2002). Flexibility in the composition and concentration of

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amino acids in honeydew of the drepanosiphid aphid Tuberculatus quercicola.

496

Ecological Entomology, 27, 745-752.

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Figure captions

499 500

Fig. 1. (a) Mimosa scabrella Bentham species. (b) and (c) bees collecting honeydew.

501 Fig. 2. Sampling locations in the mountainous plateau region of Santa Catarina State,

503

southern Brazil.

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504 Fig. 3. Diagram of the derivatization procedure.

SC

505 506

Fig. 4. (a) TIC of standard mix of 32 amino acids (100 nmol mL-1) and the IS (norvaline

508

at 200 nmol mL-1) obtained by GC/MS. (b) TIC of bracatinga honeydew honey sample

509

by obtained GC/MS. For analytical conditions, see GC-MS conditions section. Legend

510

peaks - 1: Ala; 2: Sar; 3: Gly; 4: ABA; 5: Val; 6: β-AiB; IS: Norvaline (200 nmol mL-1);

511

7: Leu; 8: aILE; 9: Ile; 10: Thr; 11: Ser; 12: Pro; 13: Asn; 14: Tpr; 15: Asp; 16: Met; 17:

512

Hyp; 18: Glu; 19: Phe; 20: Aaa; 21: Apa; 22: Gln; 23: Orn; 24: Gpr; 25: Lys; 26: His;

513

27: Hly; 28: Tyr; 29: Php; 30: Trp; 31: Cth; 32: Cys. UI: unidentified peak

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Fig. 5. (a) Dendogram for cases resulting from CA of the data corresponding to FAA

516

concentrations in bracatinga honeydew honey samples. (b) Loading plot resulting from

517

PCA for variables of bracatinga honeydew honey samples. (c) Score plot resulting from

518

PCA for cases of bracatinga honeydew honey samples.

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Fig. 6. Score plot resulting from PCA for cases of bracatinga honeydew honey samples

521

and mesoregion geographical map of Santa Catarina State, southern Brazil.

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HIGHLIGHTS

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Free amino acids were determined in bracatinga honeydew honeys and honeydew.

Results evidenced that proline is provided exclusively by bee to the honeydew honey.

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High concentrations of free amino acids were observed.

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Bracatinga honeydew honeys from a mesoregion were discriminated geographically.

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Free amino acids can be an analytical strategy for authentication of honeydew honey.

ACCEPTED MANUSCRIPT Table 1. Sampling of bracatinga honeydew honey (Bhh) and plant-sucking insects honeydew (H). Bhh Sampling Number of samples

Apiary identification

5

BS1; BS2; BS3; BS4; BS5.

Altitude: 870 m Latitude: 27° 48' 29" Longitude: 49° 32' 1"

3

Lages (LG)

Altitude: 930 m Latitude:27º 49' 0" Longitude: 50° 19' 35"

4

Urubici (UB)

Altitude: 1087 m Latitude: 28° 1' 39" Longitude: 49° 36' 45"

3

Urupema (UP)

Altitude: 1342 m Latitude: 28° 17' 38" Longitude: 49° 55' 54"

6

BR1; BR2; BR3.

LG1; LG2; LG3; LG4.

UB1; UB2; UB3.

M AN U

Bom Retiro (BR)

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Bocaina do Sul (BS)

Geographical coordinates Altitude: 858 m Latitude: 27° 44' 32" Longitude: 49° 56' 25"

SC

Region

UP1; UP2; UP3; UP4; UP5; UP6.

H Sampling

Bocaina do Sul (BS)

Altitude: 1342 m Latitude: 28° 17' 38" Longitude: 49° 55' 54"

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Urupema (UP)

Geographical coordinates Altitude: 858 m Latitude: 27° 44' 32" Longitude: 49° 56' 25"

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Region

Number of samples

Apiary identification

3

BS3; BS4; BS5

6

UP1; UP2; UP3; UP4; UP5; UP6.

ACCEPTED MANUSCRIPT

Table 2. Mean concentration (mg kg-1 ± U) of FAA found in bracatinga honeydew honey and honeydew samples of the mountainous plateau region of the Santa Catarina State. Pro

Asn

Asp

H

Bhh

H

Bhh

H

BS3

180 ± 30

280 ± 50

470 ± 50

110 ± 10

220 ± 40

280 ± 50

BS4

240 ± 40

170 ± 30

300 ± 30

30 ± 3.0

240 ± 40

340 ± 60

BS5 Mean of the apiaries

290 ± 50 230

130 ± 20 190

490 ± 50 420

< LOQ 50

550 ± 100 340

300 ± 50 310

UP1

330 ± 60

210 ± 40

510 ± 50

< LOQ

610 ± 110

< LOQ

UP2

160 ± 30

310 ± 60

570 ± 60

< LOQ

280 ± 50

320 ± 60

UP3

140 ± 30

380 ± 70

440 ± 50

< LOQ

350 ± 60

UP4

320 ± 60

490 ± 90

600 ± 60

170 ± 20

UP5

310 ± 60

140 ± 30

450 ± 50

UP6

280 ± 50 260

240 ± 40 290

480 ± 50 510

Glu

Bhh

H

Bhh

H

300 ± 40

220 ± 30

790 ± 190

1220 ± 290

330 ± 50

20 ± 3.0

870 ± 210

30 ± 7.0

400 ± 60 340b


890 ± 210 850

240 ± 60 500

180 ± 30

70 ± 10

850 ± 200

460 ± 110

150 ± 20

290 ± 40

490 ± 120

750 ±180

360 ± 60

180 ± 30

350 ± 50

910 ± 220

840 ± 200

510 ± 90

650 ± 120

150 ± 20

140 ± 20

720 ± 170

620 ± 150

50 ± 5.0

450 ± 80

130 ± 20

140 ± 20


810 ± 190

350 ± 80

< LOQ 108e

630 ± 110 470


190 ± 30 160c


800 ± 190 760

460 ± 110 580

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Ser Region

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Mean of the apiaries Wilcoxon's test 0.85 0.01 0.37 0.13 0.11 a p value Shapiro-Wilk test 0.10 0.29 0.09 < 0.00 0.18 0.36 0.04 0.08 0.05 0.96 a p value Mann-Whitney test 0.60 0.20 0.30 0.57 0.20 0.80 0.02 0.60 0.44 0.44 a p value Ser (serina); Pro (proline); Asn (asparagine); Asp (aspartic acid); Glu (glutamic acid); Bhh (bracatinga honeydew honey); H (bracatinga plant-sucking insects honeydew); BS (Bocaina do Sul); UP (Urupema); 1, 2, 3, 4, 5 and 6 (Different apiaries); LOQ (limit of quantification): Pro (1.73 mg kg-1), Asn (3.55 mg kg-1), and Asp (3.57 mg kg-1); apvalue: significance level at 5%; b,c Different letters in the same column indicate significant differences between two regions according to Mann-Whitney U test (p < 0.05). d,e Different letters in the same line indicate significant differences between two variables (Bhh and H) for each FAA (Ser, Pro, Asn, Asp and Glu) according to Wilcoxon's matched pairs test (p < 0.05).

ACCEPTED MANUSCRIPT Table 3. Mean concentration (mg kg-1 ± U) of FAA found in bracatinga honeydew honey samples of the mountainous plateau region of the Santa Catarina state, Brazil. Pro

Asn

Asp

Glu

BS1

320 ± 60

570 ± 60

530 ± 90

410 ± 60

1,340 ± 320

BS2

330 ± 60

490 ± 50

1,070 ± 190

450 ± 70

1,450 ± 340

BS3

180 ± 30

470 ± 50

220 ± 40

300 ± 40

790 ± 190

BS4

240 ± 40

300 ± 30

240 ± 40

330 ± 50

870 ± 200

BS5 Mean of the apiaries

290 ± 50 270b

490 ± 50 460a

550 ± 100 520a,b

400 ± 60 380b

890 ± 210 1,060b

UP1

330 ± 60

510 ± 60

610 ± 110

180 ± 30

850 ± 200

UP2

160 ± 30

570 ± 60

280 ± 50

150 ± 20

490 ± 120

UP3

140 ± 30

440 ± 50

350 ± 60

180 ± 30

910 ± 220

UP4

320 ± 60

600 ± 70

510 ± 90

150 ± 20

720 ± 170

UP5

310 ± 60

450 ± 50

450 ± 80

140 ± 20

810 ± 190

UP6 Mean of the apiaries

280 ± 50

480 ± 50

630 ± 110

LG1

140 ± 30

LG2

160 ± 30

670 ± 70

280 ± 50

280 ± 40

600 ± 140

LG3

170 ± 30

590 ± 60

210 ± 40

340 ± 50

880 ± 210

LG4 Mean of the apiaries

180 ± 30 160a,b

580 ± 60 680a,b

190 ± 30 230a,b

340 ± 50 290a,b

660 ± 160 670a,b

BR1

90 ± 20

780 ± 80

100 ± 20

140 ± 20

300 ± 70

BR2

60 ± 10

1,070 ± 120

70 ± 10

80 ± 10

110 ± 30

BR3

80 ± 20 80a

930 ± 100 920b

120 ± 20 100a

160 ± 20 130a

180 ± 40 200a

UB1

860 ± 90

b

470

240 ± 40

SC

500

a,b

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Mean of the apiaries

260

a,b

RI PT

Ser

190 ± 30 160

a,b

200 ± 30

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Region

800 ± 190 760a,b

550 ± 130

190 ± 40

420 ± 50

310 ± 60

172 ± 25.3

798 ± 189

UB2

193 ± 35.4

511 ± 55.2

283 ± 50.1

171 ± 25.2

583 ± 134

UB3

209 ± 38.2 198a,b

335 ± 36.2 422a

336 ± 59.4 301a,b

234 ± 34.5 193a,b

651 ± 155 677a,b

0.21

0.03

0.01

0.05

0.20

0.03

0.01

0.01

<0.01

0.02

EP

Mean of the apiaries Shapiro-Wilk test c p value Kruskal-Wallis test c p value

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Ser (serine); Pro (proline); Asn (asparagine); Asp (aspartic acid); Glu (glutamic acid); BS (Bocaina do Sul); UP (Urupema); LG (Lages); BR (Bom Retiro); UB (Urubici); 1, 2, 3, 4, 5 and 6 (Different apiaries); a,bValues mean of the apiaries with different letters in a column are significantly different by Kruskal-Wallis test (p < 0.05); cSignificance (p ≥ 0.05).

ACCEPTED MANUSCRIPT

Table 4. Results observed by other authors in honeydew and floral honeys (mg kg-1)

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Authors Honey Origin Ser Pro Asn *Iglesias et al. (2004) Varied species honeydew honey Spain 71.2 905 239 *Iglesias et al. (2006) Floral honey from different botanical sources Spain 299 674 80.1 Janiszewska et al. (2012) Undefined species honeydew honey Poland 9.58 263 NI Janiszewska et al. (2012) Floral honey from different botanical sources Poland 6.20 to 15.7 189 to 292 3.23 to 28.5 Kivrak. (2015) Undefined species honeydew honey Turkey ND 943 4.89 Nozal et al. (2004) Floral honey from different botanical sources Spain 11.7 to 12.8 243 to 467 20.1 to 93.9 Silici and Karaman (2014) Pinus spp. honeydew honey Turkey 4.7 207 NI In this study Mimosa scabrella Bentham honeydew honey Brazil 56.7 to 333 303 to 1067 66.0 to 1072 *Results in dry matter; Ser: serine; Pro: proline; Asn: Asparagine; Asp: aspartic acid; Glu: Glutamic acid; ND: not detected; NI: not informed.

Asp 267 47.9 12.2 4.28 to 30.7 ND 17.4 to 126 39.8 81.7 to 453

Glu 389 78.3 11.4 5.65 to 13.6 85.1 16.9 to 192 42.4 111 to 1447

ACCEPTED MANUSCRIPT

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Figure 1

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Figure 2

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Figure 3

Figure credits: Mariléia Corrêa da Silva

ACCEPTED MANUSCRIPT Figure 5 BS1-A BS1-B BS2-A BS2-B BS3-A BS3-B LG3-A LG3-B UP4-A UP4-B UP2-A UP2-B UB2-A UB2-B LG2-A LG2-B LG4-A LG4-B BS4-A BS4-B UP3-A UP3-B UB1-A UB1-B UB3-A UB3-B BS5-A BS5-B UP1-A UP1-B UP6-A UP6-B UP5-A UP5-B LG1-A LG1-B BR1-A BR1-B BR2-A BR2-B BR3-A BR3-B

0

500

1000

RI PT

(a)

1500

2000

Linkage Distance

(b)

1.5

SC

3.0 1.0

1.0

BS1-B BS1-A

2.5 2.0

M AN U

0.5

PC 2: 14.85%

Glu Ser

Asn

LG3-A LG4-B LG3-B LG4-A

BS5-A BS5-B

LG2-A LG2-B BS3-A LG1-B LG1-A BS4-ABS3-B BS4-B

0.5

BS2-A BS2-B

0.0

BR3-A BR3-B BR2-B BR2-A

BR1-B BR1-A

UB3-B UB2-A UB3-A UB2-B UP2-B UP3-B UP3-A UP2-A UB1-B UB1-A

-0.5

-0.5

UP6-A UP1-B UP6-B UP1-A UP5-B UP5-A

-1.0 -1.5

-1.0 0.0 PC 1: 67.10%

0.5

1.0

-2.0 FAA

-6

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-0.5

EP

-1.0

AC C

PC 2: 14.85%

Pro

0.0

(c)

UP4-A UP4-B

Asp

-4

-2

0 PC 1: 67.10%

2

4

6

Regions

ACCEPTED MANUSCRIPT Figure 4

Abundance

(a)

1800000

1400000

RI PT

1600000

19

31 32

1200000

28

12 5

800000

4

16 8 9 IS 7

15 10

14 24 25 23

17

600000

6

13

13

21

18

11

20

400000

26

2

1.50

2.00

2.50

3.00

Abundance 1000000 900000 800000

IS

600000 500000

3.50

4.00

4.50

5.00

5.50

6.00

6.50 Time (min)

(b)

12

18

400000

15

EP

300000

29

TE D

700000

27

M AN U

22

200000

SC

1000000

30

13

200000

11

AC C

100000

1.50

2.00

2.50

3.00

3.50

4.00

4.50

5.00

5.50

6.00

6.50

Time (min)

ACCEPTED MANUSCRIPT

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Figure 6