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Article

Assessing Honey Quality: A Focus on Some Physicochemical Parameters of Honey from Iasi County (Romania)

by
Aida Albu
1,†,
Florin Dumitru Bora
2,3,*,
Simona-Maria Cucu-Man
4,
Vasile Stoleru
5,*,
Cătălin-Emilian Nistor
1,
Ioan Sebastian Brumă
6 and
Oana-Raluca Rusu
7,†
1
Department of Control, Expertise and Services, Faculty of Food and Animal Sciences, “Ion Ionescu de la Brad” University of Life Sciences, 8 Mihail Sadoveanu Alley, 700489 Iasi, Romania
2
Viticulture and Oenology Department, Advanced Horticultural Research Institute of Transylvania, Faculty of Horticulture and Business in Rural Development, University of Agricultural Sciences and Veterinary Medicine Cluj-Napoca, 3-5 Mănăștur Street, 400372 Cluj-Napoca, Romania
3
Laboratory of Chromatography, Advanced Horticultural Research Institute of Transylvania, Faculty of Horticulture and Business for Rural Development, University of Agricultural Sciences and Veterinary Medicine, 3-5 Mănăștur Street, 400372 Cluj-Napoca, Romania
4
Department of Chemistry, Faculty of Chemistry, “Alexandru Ioan Cuza” University of Iasi, 11 Carol I Blvd, 700506 Iasi, Romania
5
Department of Horticulture Technologies, Faculty of Horticulture, “Ion Ionescu de la Brad” University of Life Sciences, 3 Mihail Sadoveanu Alley, 700490 Iasi, Romania
6
Romanian Academy, Iasi Branch, “Gh. Zane” Institute for Economic and Social Research, 2 Codrescu Street, 700481 Iasi, Romania
7
Department of Public Health, Faculty of Veterinary Medicine, “Ion Ionescu de la Brad” University of Life Sciences, 8 Mihail Sadoveanu Alley, 700449 Iasi, Romania
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Agriculture 2025, 15(3), 333; https://doi.org/10.3390/agriculture15030333 (registering DOI)
Submission received: 30 December 2024 / Revised: 27 January 2025 / Accepted: 30 January 2025 / Published: 3 February 2025
(This article belongs to the Special Issue Quality Assessment and Processing of Farm Animal Products)
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Versions Notes

Abstract

:
The study of honey in Iasi County reveals its ecological, economic and health importance, emphasizing its unique properties, role in biodiversity and value in promoting sustainable beekeeping and regional identity. This study aimed to investigate the characteristics of honey from Iasi County, Romania, analyzing 27 samples collected in 2020 and 2021. The samples include tilia (8 raw, 7 commercial), acacia (2 raw, 2 commercial), rapeseed (3 raw), sunflower (3 raw) and lavender (2 raw) honey. Analyses were carried out under Romanian/EU standards, assessing parameters such as color, electrical conductivity, moisture, total soluble solids (TSS), acidity (free, lactone, total), pH, hydroxymethylfurfural (HMF), ash and mineral composition (Na, K, Ca, Mg, Cu, Zn, Mn and Fe). The results revealed significant differences between raw and commercial honeys. Notably, in commercial tilia honey, higher values were found for color (38.58 mm Pfund vs. 24.14 mm Pfund), total acidity (25.93 meq·kg−1 vs. 17.36 meq·kg−1) and HMF levels (8.84 mg·kg−1 vs. 3.68 mg·kg−1). Conversely, water-insoluble solids (0.08% vs. 0.15%) and ash content (0.21% vs. 0.30%) were lower in commercial samples. Potassium was the most abundant mineral detected, while copper and zinc levels were the lowest. Significant correlations were observed between several parameters, including ash with electrical conductivity and HMF with acidity. This study underscores the impact of processing on honey quality and highlights the importance of understanding honey composition for consumers and producers alike.

Graphical Abstract

1. Introduction

Honey, a natural product made by bees from the nectar of flowers, is known for its health benefits, nutritional value and unique flavors. In Romania, honey has great cultural and agricultural importance. The diverse types of honey produced in the region reflect not only the country’s rich floral biodiversity, unique local climate and long-standing beekeeping traditions but also the profound and harmonious connection between nature and humans [1,2].
The floral origin of honey is a key determinant of its unique properties and quality. A thorough understanding of its botanical source requires an integrated approach, combining melissopalynological, sensorial and physicochemical analyses. When combined, these methods give a clear and reliable understanding of the honey’s origin and authenticity. Melissopalynological analysis is particularly important because it identifies pollen types about the geographical and environmental origin of honey. The analysis of pollen through melissopalynology is crucial for understanding how the characteristics of honey are linked to the specific regional biodiversity where it originates [3,4].
Scientific interest in honey characterization and quality assessment has increased due to the need to ensure both authenticity and health benefits of honey consumed by the public. Honey’s composition varies with the nectar source, bee species, geographic landforms, soil characteristics, potential environmental contaminants, beekeeping practices and honey-processing methods influencing its flavor, color and nutritional properties. As a result, honey differs in its physicochemical properties, which significantly influence its quality, purity and suitability for commercial use [2,5,6].
Honey quality is assessed through physicochemical parameters like color, moisture content, specific gravity and total soluble solids, which are key indicators of its composition and the standards of its production [7,8,9].
Factors such as free acidity, lactone content, total acidity, pH, ash content and electrical conductivity are essential for the assessment of honey purity, authenticity and environmental influences [10,11,12].
Honey’s color varies according to its nectar source, offering important information about its botanical origin; excess moisture content in honey can cause fermentation, leading to a significant reduction in the product’s shelf life; the level of acidity affects tastes and preservation of honey [13,14,15]. pH influences its stability and flavor profile, while ash content and electrical conductivity reflect the mineral content and are used to differentiate floral origins of honey; hydroxymethylfurfural (HMF) indicates honey quality and freshness [16,17,18].
The mineral elements present in honey, such as potassium (K), sodium (Na), calcium (Ca), magnesium (Mg), manganese (Mn), copper (Cu), iron (Fe) and zinc (Zn), contribute significantly to its nutritional and health-promoting properties [15,19,20].
Their concentrations, which reflect the environmental and geographic characteristics of the region, are influenced by the botanical source of the nectar and the soil composition [21,22]. In addition, these elements provide valuable insights into environmental conditions, acting as bioindicators of pollution and ecological health [23,24].
To ensure quality and authenticity, European regulations specify minimum pollen content limits for monofloral honey characterization. Melissopalynological analysis is commonly used to identify the botanical origin, pollen types and other characteristics of honey samples. According to European Union Council Directive 2001/110/EC, honey must meet specific physicochemical criteria to be deemed compliant; these include the following limits: water-insoluble solids: ≤0.1% (≤0.5% for pressed honey); moisture content: ≤20%; free acidity: ≤50 meq/kg; electrical conductivity: ≤0.8 mS/cm for nectar honeys (one of exceptions is tilia honey, which can exceed this threshold) [25].
Despite its widespread use, knowledge regarding the chemical composition of raw and commercial honey remains crucial for ensuring its quality, authenticity and safety for consumers.
The differences in composition between raw and commercially processed honey, especially regarding nutrient retention or loss, underline the importance of conducting thorough scientific analysis. The variation in chemical properties offers insights into the honey’s origins, production processes as well as its preservation potential during storage [26].
Understanding the chemical composition and physicochemical properties of honey is essential for maintaining quality standards in the honey industry. The analysis of moisture, color, pH, total soluble solids, acidity, ash content, electrical conductivity, HMF and mineral elements provides valuable data for assessing honey’s quality and ensuring that it meets both commercial and regulatory standards [27,28]. This article provides a comprehensive analysis of the diversity found in Romanian raw and commercial honey, focusing on its chemical composition. By evaluating the key physicochemical parameters, the study highlights the importance of understanding honey’s chemical composition to ensure consumer safety and protect the integrity of the honey market. The findings demonstrate the significant impact of processing methods on honey quality, emphasizing the need for both consumers and producers to understand honey’s composition to make informed choices and maintain product authenticity.

2. Materials and Methods

2.1. Honey Test Samples

Twenty-seven raw and commercial honey samples of Apis mellifera were collected from Iasi County, a northeastern region of Romania, during 2020 and 2021 (Figure 1). The sample set comprised various types of honey: tilia (Tilia spp.) (T), with 8 samples of raw honey (T-R) and 7 samples of commercial honey (T-C); acacia (Robinia pseudoacacia) (A), with 2 samples of raw honey (A-R) and 2 samples of commercial honey (A-C); rapeseed (Brassica napus) (R), with 3 raw honey samples (A-R); sunflower (Helianthus annuus) (SF), with 3 raw honey samples (SF-R); and lavender (Lavandula spp.) (L), with 2 raw honey samples (L-R).
Raw (R) honey samples were from artisanal beekeepers (T1, T3, T4, T5, T6, T9, T12, T15, A17, A19, R20, R21, R22, SF23, SF24, SF25, L26, L27). All commercial (C) honey samples were purchased from Iasi stores (T2, T7, T8, T10, T11, T13, T14, A16, A18). In Table 1, the list of sample codes, harvest locations and years is presented. The samples were stored in airtight glass containers in a dark environment, with the temperature maintained between 20 °C and 25 °C, until the chemical analyses were carried out.

2.2. Melissopalynological Analysis

The botanical origin of the 27 investigated monofloral honey samples was identified through the melissopalynological method proposed by Louvreaux et al. (1978) [29], with some modifications to the sample dissolution process and to the centrifugation step (optimizing the time and speed) for improved pollen separation. Ten grams of the sample were dissolved in 20 mL of distilled water at 40–45 °C and mix thoroughly to dissolve the sugar crystals. The solution was centrifuged twice at 3500 rpm for 30 min. The supernatant liquid was removed, and a solution of 5‰ sulfuric acid (Merck KGaA, Darmstadt, Germany) was added to the sediment and then centrifuged twice at the same time and speed using a UNIVERSAL 320 HETTICH centrifuge (Hettich GMBH, Tuttlingen, Germany). The sediment was transferred to a glass slide in two drops, covered with a lamella and examined by counting at least 800 pollen grains using an optical microscope (Optika, Italy) with 40× and 100× objectives [30,31].

2.3. Pfund Value and Color

The mm Pfund of honey samples were spectrophotometrically measured using the Shimadzu UV-1700 Pharma Spec instrument (manufacturer: Shimadzu Corporation, Analytical Instruments Division, Kyoto, Japan). A 50% (w/v) honey aqueous solution of honey sample was centrifuged at 3200 rpm for 5 min at UNIVERSAL 320 HETTICH centrifuge (Hettich GMBH—Tuttlingen, Germany) and measured at 635 nm wavelength. The absorbance values were converted to mm Pfund using a formula [17]:
Pfund [mm] = −38.7 + 371.39 × Abs
where Pfund = the honey color value on the Pfund scale [mm]; Abs = the absorbance value measured at 635 nm

2.4. Water-Insoluble Solids (WIS)

Ten grams of honey sample were weighed on a PI-214 DENVER analytical balance (Denver Instrument GMBH—Gottingen, Germany) and dissolved in distilled water, filtered through pre-weighed filter paper and washing several times. The sample was then dried in an ESAC 100 oven at 105 °C (SC Electronic April Aparatura Electronica Speciala S.R.L—Cluj-Napoca, Romania). The content of water-insoluble solids (WIS) was calculated by subtracting the weight of the filter paper from weight of the filter paper with the insoluble solids, result expressed as a percentage [31].

2.5. Moisture (M), Total Soluble Solids (TSS), Specific Gravity (SG)

Moisture content (M) in honey was determined using the refractive index (RI) measurements using an ABBÉ Kruss AR 2008 refractometer (Kruss Scientific GMBH, Hamburg, Germany). Honey samples were gently heated to a temperature around 45 °C to ensure that all the crystals were dissolved. The temperature of the honey samples was established using Heidolph EKT Hei-Con temperature sensor (Heidolph Scientific Products GmbH, Schwabach, Germany) and the refractive index was adjusted by adding 0.00023 for each degree Celsius above 20 °C. Knowing the corrected refractive index, the moisture content was determined using the IR-M conversion table, expressed as a percentage [31].
The total soluble solids (TSS), which represent the total soluble sugars, expressed in degrees Brix (1 °Brix corresponding to 1% sugar at 20 °C) were determined using a refractive index-to-Brix conversion table at 20 °C [32].
The specific gravity was determined using the gravimetric method with a pycnometer, the obtained values were reported in g/cm3 [33].

2.6. pH, Free Acidity (FA), Lactonic Acidity (LA) and Total Acidity (TA)

The pH values of honey solutions were measured by dissolving 10 g of sample in 75 mL of distilled water. The measurements were performed using the MULTI 3320 multiparameter (WTW GMBH, Weilheim, Germany) [33]. To assess the free acidity, the honey solutions were titrated with 0.05 N NaOH (Chemical Company, Romania) solution to a pH of 8.5. The lactonic acidity was determined by immediately adding 10 mL 0.05 N NaOH and titrated with 0.05 N HCl (Merck KGaA, Darmstadt, Germany) solution to 8.3 pH value. The total acidity of the honey samples was calculated by summing the free and lactonic acidity values, with the results expressed in meq kg−1 [34].

2.7. Ash and Electrical Conductivity (EC)

The ash content of the honey was determined in two steps: burning and calcination in a furnace (Nabertherm B180, Nabertherm GMBH, Lilienthal, Germany); the ash content was expressed in g/100 g [31].
Electrical conductivity was measured using a MULTI 3320 multiparameter instrument (WTW GMBH, Weilheim, Germany) in a 20% solution of the sample (based on dry weight) prepared with ultrapure water (Barnstead EASY PURE II, Thermo Fisher Scientific Co. Ltd., Iowa City, IA, USA). The result was reported in mS·cm−1 [33].

2.8. Hydroxymethylfurfural (HMF)

The spectrophotometric method described by White was used to quantify the hydroxymethylfurfural (HMF) content in honey. Five grams of honey were dissolved in 25 mL of distilled water and transferred into a 50 mL volumetric flask. A volume of 0.5 mL each of each Carrez solution I and Carrez solution II (Merck KGaA, Darmstadt, Germany) was added to the honey solution. and the final volume was adjusted to a volume of 50 mL with distilled water. The resulting solution was filtered through filter paper. The first 10 mL of the filtrate were eliminated and the next 5 mL of each aliquots of the filtered solution were transferred into two separate test tubes. The sample solution was prepared by adding distilled water to the mark in one test tube and the reference solution was prepared by adding 5 mL of a 0.2% sodium bisulfite solution (Acros Organics, Geel, Belgium) to the other test tube. A Shimadzu UV-1700 Pharma Spec spectrophotometer (Shimadzu Corporation, Analytical Instruments Division, Kyoto, Japan) was used to measure the absorbance of both solutions at 284 nm and 336 nm. The HMF concentration in the honey samples was then calculated using a specific formula and expressed in mg·kg−1 [35].

2.9. Mineral Elements

For analysis, the honey samples were dissolved in deionized water produced by Milli-Q Integral Ultrapure Water-Type 1 at 18.2 MW × cm−1 (Merck, Darmstadt, Germany) at 65 °C. The obtained solutions were subsequently mineralized (Milestone START D Microwave Digestion System (Sorisole, Italy). All analytical grade chemicals and reagents are procured from recognized sources (Merck and Sigma Aldrich, Darmstadt, Germany) to ensure precision and reliability. Calibration curves were constructed using a high-purity ICP multi-element standard solution (Merck, Darmstadt, Germany).
The concentrations of mineral elements in honey samples were determined by inductively coupled plasma mass spectrometry (ICP-MS) (iCAP Q ICP-MS, Thermo Fisher Scientific, Waltham, MA, USA). Standard solution for calibration were prepared by dilution from a ICP Multi-Element Standard Solution (Merck, Darmstadt, Germany).
All operations, steps and procedures for determining the elements were carried out in accordance with those described by Bora et al. (2024), including extraction conditions, instrumental analyses (calibration and operating parameters), quality control measures and analytical performance parameters [19].

2.10. Statistical Analyses

The results were statistically processed using the software package STATISTICA 12.0 (StatSoft Inc., Hamburg, Germany). Pearson’s correlation coefficient was used to test for possible significant correlations between the parameters examined. Factor analysis (FA, Varimax-normalized) was applied to obtain additional information on the contributions of the investigated parameters.

3. Results

3.1. Melissopalynological Analysis

Melissopalynological analysis confirmed the botanical origin of all the studied samples, honey samples reported by the beekeepers as well as honey samples collected from stores.
All honey samples were classified as monofloral based on the predominant pollen type observed. The most abundant plant families detected in the examined honey samples are presented in Figure 2.
According to the spectrum of these grains, the presence of pollen types from different plant species in honey contributes to its unique properties. The main types of pollen from plant species found in honey samples are listed in Table 2.

3.2. Pfund Value, Color, Water-Insoluble Solids (WIS), Moisture (M), Total Soluble Solids (TSS) and Specific Gravity (SG)

Table 3 presents the analysis results of the monofloral honey samples, detailing key parameters such as water-insoluble solids (WIS), moisture (M), total soluble solids (TSS) and specific gravity (SG).

3.3. pH, Free Acidity (FA), Lactonic Acidity (LA), Total Acidity (TA), Ash, Electrical Conductivity (EC) and Hydroxymethylfurfural (HMF)

Table 4 presents the results of important physicochemical parameters—pH, free acidity (FA), lactonic acidity (LA), total acidity (TA), ash, electrical conductivity (EC) and hydroxymethylfurfural (HMF)—analyzed in five types of honey.

3.4. Mineral Elements (Na, K, Ca, Mg, Cu, Zn, Mn and Fe)

Table 5 presents the mineral content of eight elements (Na, K, Ca, Mg, Cu, Zn, Mn and Fe) determined in the five types of studied honey samples: tilia, acacia, rapeseed, sunflower and lavender.

3.5. Correlation and Multivariate Statistical Analysis

The Pearson correlation coefficients, commonly used to measure the degree of association between specific data sets, were calculated to determine the correlations between the analyzed parameters of raw tilia honey samples sourced directly from beekeepers (Table 6).
The correlations identified between the physical and chemical parameters studied specifically for commercial Tilia honey samples are presented in Table 7. Table 8 presents the results of the ANOVA test, which was performed to evaluate the differences between raw and commercial honey samples.
In Table 9, Pearson correlation coefficients are calculated to determine the relationships between the parameters of the five types of honey studied.
Table 10 and Table 11 summarize the results of factor analysis (FA): Table 10 presents the factor loadings (Varimax-normalized) and the explained variance (%) for the extracted principal components, where marked loadings exceed 0.700 and Table 11 provides the factor scores (Varimax-normalized) for the extracted factors.

4. Discussion

4.1. Melissopalynological Analysis

Honey produced by Apis mellifera L. often reflects the botanical sources from which nectar and pollen are predominantly collected, resulting distinct types of monofloral honey. Each type is characterized by unique aroma and flavor profiles as well as specific pollen signatures, as described by Louveaux et al. (1978) [29]. To define monofloral honey, minimum pollen content thresholds have been standardized internationally, ensuring these honeys retain their distinctive physical, chemical and therapeutic properties.
Melissopalynological analysis categorizes pollen frequencies into four classes for identifying honey’s botanical origins with precision: predominant pollen (PP) > 45%, secondary pollen (SP) 16–45%, important minor pollen (IMP) 3–15% and minor pollen (MP) < 3% [10,36].
The minimum pollen percentage required to classify monofloral honeys varies among European countries: acacia honey (Robinia pseudoacacia) (Italy—15%, Germany, Croatia and Serbia—20%, Romania—25%); tilia (Tilia spp.) (Germany—20%, Croatia and Serbia—25%, Romania—30%); sunflower (Helianthus annuus) (Romania—40%, Greece—20%, Germany—50%, Serbia—40%); rapeseed (Brassica napus) (Croatia—60%, Germany—80%); lavender (Lavandula spp.) (Germany—15%, Croatia—10%, Serbia—25%, Romania—30%) [37,38,39]. In Latvia, the minimum pollen percentages needed to classify monofloral honeys are 70% for rapeseed (Brassica napus), 17% for tilia (Tilia spp.) and 17% for lavender (Lavandula spp.). For other monofloral honeys, at least 45% of pollen from the same plant species is required, in accordance with national legislation [40].
The melissopalynological analysis conducted on the 27 honey samples confirmed their monofloral origin and revealed the presence of a diverse range of plant species. The examination not only validated the primary floral source but also emphasized the secondary pollen contributions, offering a deeper understanding of the botanical diversity within the foraging areas of the bees. Furthermore, the findings align with the requirements of Romanian legislation and similar international regulations, ensuring the authenticity and labeling accuracy of monofloral honey [38]. Figure 2 illustrates the distribution of the most abundant plant families identified in the analyzed honey samples, providing an overview of the dominant botanical sources. Various pollen families were identified in honey samples: Apiaceae in 24 samples (<1–5%), Asteraceae in all samples (2–75%), Boraginaceae in 10 samples (<1–11%), Brassicaceae in 25 samples (2–74%), Cyperaceae in 7 samples (<1–2%), Fabaceae in 27 samples (4–43%), Fagaceae in 14 samples (<1–10%), Lamiaceae in 26 samples (<1–42%), Malvaceae in 21 samples (<1–70%), Plantaginaceae in 12 samples (<1–3%), Poaceae in 18 samples (<1–6%), Rosaceae in 22 samples (<1–16%) and Salicaceae in 23 samples (<1–7%) (Table 2).
The predominant pollen of Tilia spp. was found between 49% and 70% in raw tilia honey samples and between 45% and 66% in commercial tilia honey samples, Iasi County being known like Tilia County because of the tilia forests. In commercial acacia honey samples, the predominant pollen (Robinia pseudoacacia) accounted for 35% and 37%, which were lower compared to the percentages found in raw acacia honey samples, 42% and 43%. All acacia honey samples had the minimum pollen percentage required to classify monofloral honeys. Brassica napus was the dominant pollen in all rapeseed honey samples (70–74%). The amount of Asteraceae (Helianthus annuus) in sunflower honey was found in the range of 69–75%. In the two lavender honey samples, the highest pollen percentage was from the Lamiaceae family (Lavandula spp.)—39% and 42%, respectively—both below the established threshold of 45%. Some honey is still considered monofloral lavender honey because certain plant species, like Lavandula spp., Citrus spp., Rosmarinus officinalis and Arbutus unedo, are known to be underrepresented in honey even when they are dominant nectar sources. Lavandula spp. honey can be classified as monofloral with as little as 15% pollen content due to its unique floral characteristics [21].
Lavender honey and tilia honey display significant variation in pollen composition across different regions due to environmental and floral diversity factors. In Portugal, lavender honey has been studied: Estevinho et al. (2016) reported Lavandula spp. pollen levels ranging between 33.17% and 36.27%, and Anjos et al. (2021) found a range of 15% to 59% of pollen, reflecting variability across regions [41,42]. In Turkey, lavender honey from the Isparta region showed even greater variation in pollen content, ranging from as low as 4% to as high as 76%. Specific studies found levels between 46% and 53% [7], 41.85% [43], and from 4% to 8% in the studied samples [44].
Tilia honey also exhibits regional differences in pollen composition. In Turkey, pollen levels typically range from 45% to 47%, although monofloral classification is possible even with lower levels. For example, Silici and Ülgen (2019) found tilia honey to be monofloral with pollen levels of 41.43% [43]. In Poland, tilia honey shows pollen concentrations between 20.4% and 41.4%, influenced by regional floral diversity [12]. In Hungary, tilia honey contains 45.89% Tilia spp. pollen, aligning more closely with higher thresholds for monofloral classification [45] but in the Czech Republic had a significantly lower pollen content of 22.8% [46]. In comparison to other studies, the pollen percentages for tilia honeys in some regions are lower than those found in our analyzed samples.
Robinia honey, also known as acacia honey, exhibits variability in pollen levels across different regions. In Turkey, pollen levels were found to range between 45% and 54% [7]. In Hungary, Robinia pseudoacacia pollen contributed 45.27% in acacia honey [45]. However, lower levels were observed in the Czech Republic, with an average pollen content of only 12.7% [46]. Other types of honey also show notable differences in pollen composition. In Hungary, sunflower honey contained 47.44% Helianthus annuus pollen [45], lower content compared to the content obtained in our samples. Rapeseed honey from the Czech Republic had a significantly higher pollen content, averaging 79.5% [46], and Brassica napus in the examined Poland rapeseed honeys ranged from 47.6% to 94.3% [47].
Accurately identifying honey’s botanical origin is crucial for quality control and market authentication. In monofloral honey samples, overrepresented pollen types, such as Brassica napus (>60%), contrast with underrepresented types like Tilia spp. (1–56%), Robinia sp. (7–60%) and Lavandula spp. (1–42%) [48]. Pollen analysis, combined with sensory evaluation, physicochemical analysis and plant compound quantification, ensures accurate classification of honey. This process also connects the floral composition of honey with its nutritional value and potential therapeutic benefits [30]. Melissopalynological analysis is essential for classifying honey samples according to their botanical origin, as it provides a direct method of identifying the plant species responsible for the pollen deposition in the honey. This analysis is particularly valuable for distinguishing monofloral honeys such as tilia, rapeseed, acacia, sunflower and lavender. In addition, melissopalynological data, combined with other parameters such as color, HMF and mineral composition, can also provide information on the geographical origin of honey. By correlating pollen profiles with regional flora, it is possible to trace the origin of honey to specific geographical areas.

4.2. Pfund Value and Color

Color greatly influences the perception of honey quality. Some people prefer light-colored honey, while others prefer dark-colored honey. These preferences may have a significant impact on the product’s price, sometimes outweighing the importance of its composition and therapeutic quality [5]. The variation in honey color is influenced by factors including phenolic compounds, carotenoids, sugars, minerals, pollen, water content, floral source, geographical origin and processing conditions [2,49].
In our study, the color of raw tilia honey ranged from 4.25 to 39.17 mm Pfund, while commercial honey exhibited a darker range of 12.94 to 55.48 mm Pfund (Table 3). This increased darkness in commercial honey may not solely result from climatic, geographical, or floral factors but could also be influenced by heating during processing, the technological steps involved, or extended storage durations.
The floral origin of tilia and specific processing conditions contribute to the darker color observed in some regions, resulting in tilia honey colors covering almost the entire Pfund scale. Other studies in Romania reported color ranges of 23.1–41.8 mm Pfund [2,10,49]. In Republic of Moldova, similar values of 22.00–38.00 mm Pfund were recorded [14], aligning with our findings. Notably, tilia honey from Hungary stands out for its darker color, with a broad range of 38.27–139.48 mm Pfund, approaching the upper limit of the Pfund scale [50].
The color of acacia honey varies widely according to the region and harvesting time, often measured in Pfund scale values. In Romania, this study found raw samples with values less 0.1 mm Pfund (<0.1 to 0.14 mm Pfund) and commercial honey ranging from −0.77 to 0.01 mm Pfund. Other studies in Romania reported values such as 0.1–0.2 mm Pfund [2], 12.84 mm Pfund [10] and 11.00–45.00 mm Pfund [49].
Studies on acacia honey conducted in other countries have shown a large variation in color, as follows: in Hungary, Bodor et al. observed exceptionally light values of −7.26 mm Pfund, falling outside the typical Pfund range to 20.92 mm Pfund [50]; in Serbia, Srećković et al. reported values averaging −4.30 ± 3.70 mm Pfund [51]. In Poland, studies showed a range of 0.1–49.8 mm Pfund [48] and −5.7 mm Pfund [52]. In the Czech Republic, Pospiech et al. (2021) found darker acacia honey with a Pfund value of 47.4 mm [46], and in Croatia, Flanjak et al. (2016) reported a range of 1.00–8.00 mm Pfund [9]. These differences in color reflect regional and processing variations in acacia honey.
In our study, the color of rapeseed honey ranged from 48.09 to 59.25 mm Pfund. In Romania, previous studies reported values between 45.2 and 73.7 mm Pfund [2]. Pauliuc et al. (2022) observed a lower value of 36.14 mm Pfund [10], and in their study, Ratiu et al. (2020) noted a higher value of rapeseed honey of 94.13 mm Pfund [17].
In various studies from European countries, the color range of rapeseed honey has been reported from as low as 2.08 mm Pfund to as high as 138.56 mm Pfund. In the Czech Republic, Pospiech et al. (2021) recorded a value of 73.3 mm Pfund [46]. A study from Poland found a range from 33.8 mm to 114.07 mm Pfund [17,48,52]. The rapeseed honey harvested from Hungary was characterized by a wide color variation, ranging from 2.08 mm to 138.56 mm Pfund [50].
Sunflower honey varies in color from white yellow to light amber, influenced by its origin, sunflower variety and processing methods. In our study, Romanian sunflower honey showed a color range of 11.64–13.07 mm Pfund, other findings reporting 15.5–53.9 mm Pfund [2], 33.66 mm Pfund [10] and 79.00–83.00 mm Pfund [49]. In Republic of Moldova, Chirsanova et al. (2021) a range of 39.00–41.00 mm Pfund [14]. Hungarian sunflower honey displayed much higher values, from 96.3 mm Pfund up to 198.91 mm Pfund, exceeding the typical Pfund scale [50]. Sunflower Polish honey samples ranged from 62.07 to 114.44 mm Pfund [17]; Portuguese sunflower honey was darker, with a value of 97.60 mm Pfund [53]; in Spain, the range of the sunflower honey color was 38–72 mm Pfund [54]. These variations highlight the influence of geographical origin and local factors on the color of sunflower honey.
The color of lavender honey samples ranged from 16.08 to 17.39 mm Pfund. In Romania, Pauliuc et al. (2022) reported a darker value of 63.86 mm Pfund [10]. Other studies highlight significant variations in lavender honey color across different regions. In Turkey, the color ranged from 25 mm Pfund to 35 mm Pfund [55]. Portuguese lavender honey showed values between 39.4 mm Pfund and 49.4 mm Pfund [53], while Moroccan samples exhibited a much wider range, from 71 mm Pfund to 150 mm Pfund [56]. In Spain, lavender honey color was reported to vary from 6 mm Pfund to 91 mm Pfund [54] and from 24.21 mm Pfund to 61.48 mm Pfund [8]. In contrast, darker samples were observed in a study by Puścion-Jakubik et al. (2022) with values ranging from 92.2 mm Pfund to 187.6 mm Pfund [48].
Honey color variation is primarily determined by factors such as the floral source, phenolic compounds, carotenoids, sugars, minerals, harvest time and processing conditions. Each type of honey exhibits unique color profiles shaped by these influences [1,50].

4.3. Water-Insoluble Solids (WIS), Moisture (M), Total Soluble Solids (TSS), Specific Gravity (SG)

Water-insoluble solids (WIS) in honey consist of various impurities such as bee body parts, plant remnants, pollen grains, wax fragments, even dust particles [57]. All these impurities can influence the clarity of honey, a parameter valued by consumer’s acceptance. According to established honey quality standards, the permissible WIS content is limited to 0.1% for commercial-grade honey and up to 0.5% for pressed honey [25,38]. In this case, the water-insoluble solids (WIS) content in honey samples varied across different floral sources and processing levels. For raw honey, the WIS content ranged from 0.073 to 0.193% in tilia honey, 0.123 to 0.153% in rapeseed honey, 0.150 to 0.233% in sunflower honey and 0.143 to 0.160% in lavender honey. In raw acacia honey, we obtained lower WIS values, between 0.043% and 0.060%. For commercial honey, WIS content ranged from 0.043 to 0.113% in tilia honey and 0.087–0.103% in acacia honey. When comparing raw and commercial, raw honey samples generally contained higher WIS values than commercial honey samples. In this research, commercial acacia honey samples contained higher WIS values of 0.103% than raw acacia honey samples. According to established quality legislation all raw honey samples remained within the legal limit for unprocessed honey, certain commercial honey samples, such as tilia and acacia, occasionally approached or slightly exceeded the 0.1% threshold. Studies made on the blossom honey samples, from the Czech Republic market showed a content of water-insoluble solids under 0.1% (0.002–0.070%) [58]
The processes through which honey undergoes to meet commercial standards can influence its quality both positively and negatively. For example, the filtration process used to clarify honey can positively affect its clarity and shelf-life, but it may also negatively impact its nutritional and therapeutic properties by removing beneficial components such as pollen [59].
The moisture content of honey is a critical parameter affecting its quality, stability and shelf-life. An increase in the water content of honey can initiate fermentation, leading to the degradation of the product. This process not only compromises the honey’s quality but also results in undesirable changes to its sensorial, nutritional and physicochemical properties [60].
In this investigation, the moisture content of tilia honey ranged from 16.8% to 19.7%, with a slightly higher value observed in commercial honey compared to raw honey, aligning with the maximum allowable limit of 20% set by honey quality legislation (Table 3). Comparisons with data from other studies reveal variations based on geographic and climatic conditions, thus, in Romania, the moisture content of tilia honey ranges from 14.50% to 18.80% [23,61,62]. Lower moisture content values of 14.62% and of 16.75% have been recorded in the studies conducted by Vijan et al. (2023) and Pauliuc et al. (2022), respectively [10,63]. In the Republic of Moldova, values were slightly higher, ranging from 18.70% to 19.89% [14]. Studies in Serbia reported moisture levels between 15.9 and 17.3% [64], 16.00 and 19.09% [18], and 15.8 and 17.1% [11]. In Poland, the reported values were 15.9–19.0% [12] and 19.05–20.50% [26]. Research from Turkey indicated a moisture content of 19.80% [7], while in the Czech Republic, it was 16.2% [46]; in Croatia, the moisture content of tilia honey ranged from 15.9 to 20.0% [9]. Importantly, all measured values in our study comply with the legal threshold of 20%, ensuring the quality and safety of the tilia honey analyzed.
The moisture content of acacia honey ranged from 18.2% to 20.0%, the maximum value registered at commercial honey, the maximum limit set by honey quality legislation (Table 3).
In Romania, the moisture content of acacia honey ranges from 15.40% to 18.60% [61,62], while other studies reported higher values, up to 22.8% [23], surpassing the maximum limit established by legislation. Lower moisture content values of 15.96% and of 14.36% have been recorded in the studies conducted by Vijan et al. (2023) and Pauliuc et al. (2022), respectively [10,63].
Studies from countries such as Serbia (13.63–19.01%) [11,18,64], Czech Republic, Croatia and Germany (14.6–19.9%) [9,46,65] show that the moisture content of acacia honey generally falls below the 20% limit, indicating consistent compliance with quality standards in these regions. Can et al. (2015) reported a moisture content value for Turkish honey exceeding 20.80%, which surpasses the maximum limit established by legislation [7].
The moisture content of rapeseed honey in this study ranged from 17.5% to 19.9% (Table 3). All research conducted in Romania [10,61,63], as well as in other European countries, reported moisture content values below the 20% limit, ranging from 14.41% to 19.89% [11,46,64].
The sunflower honey samples analyzed in this study exhibited moisture levels close to the legislative limit of 20%, ranging from 19.6% to 19.9%. The moisture content of honey samples analyzed across various European countries generally ranged from 14.6% to 19.3%, with the exception of rapeseed honey from Spain, which recorded a maximum value of 21.2% [11,53,54,64].
For lavender honey, the moisture content was found to range from 18.2% to 19.3%, values that ensure long-term quality (Table 3). Studies carried out on honey from Serbia, Spain, Portugal and Morocco obtained values ranging between 14.53% and 19.40% [8,11,53,56]; In Saudi Arabia and Turkey moisture content values in lavender honey were found from 15.5% to 17.17% [44,55,65].
Values exceeding the maximum limit established by legislation, which could lead to adverse effects on the product, were recorded at 20.80% and at 25.2%; these values were reported by Can et al. for lavender honey from Turkey and by Juan-Borrás et al. (2015) for lavender honey from Spain [7,54].
Generally, total soluble solids (most of them consist of sugar) content is 80% or higher. According to Nyau and Mwanza (2013), honey above 80 °Brix (<20% water) is qualitative and more stable during storage as moisture declines with increasing total soluble solids [32]. This classification is especially related to the additional insurance of water content (TSS is inversely related to water and to safety during storage for a longer period). Honey research in this study found TSS ranging from 78.6% in sunflower honey to 81.7% in raw tilia honey (Table 3).
The specific gravity of honey is inversely proportional to its humidity: the denser the honey, the less moisture it contains. It is also typically inversely related to total sugars (especially glucose and fructose) and total solids. As sugar concentration increases, specific gravity tends to increase. In the same way, specific gravity can be influenced by mineral content and impurities, with higher levels of these components generally resulting in a higher specific gravity.
This parameter is important to know if honey is fake and is of practical importance to monitor honey stock. The minimum average value of the specific gravity of 1.418 g/cm3 was found in the samples of sunflower honey and the maximum average value of 1.429 g/cm3 in the samples of raw tilia honey (Table 3).

4.4. pH, Free Acidity (FA), Lactonic Acidity (LA) and Total Acidity (TA)

Usually, the pH of honey is between 3.5 and 5.5. Between 3.9 and 5.8 were the average pH values of the analyzed honey samples (Table 4). Concentration of organic acids lowers honey pH, giving it acidic character. Results in the same range were found by other investigators; for example, the pH value of the Romanian honey has been reported to vary between 3.65 and 5.36 [2,10,23,62,63,66].
Honey’s free acidity refers to its natural organic acids, which influence pH and taste, while lactone acidity represents bound acids that can release more acid when hydrolyzed. Total acidity is the sum of both and reflects the total acidity of the honey. Since high acidity may indicate fermentation or spoilage, these parameters are important in assessing the freshness, quality and safety of honey [1,62,66]. In order, to prevent degradation processes in honey, European legislation has a maximum recommendation for free acidity of 50 meq·kg−1 [25].
Analyzing of the 27 tested honey samples indicated that that the free acidity values are under the maximum recommended for free acidity of 50 meq·kg−1: raw tilia honey samples (5.1–19.6 meq·kg−1) had lower acidity than commercial tilia honey samples (12.7–25.0 meq·kg−1), probably due to processing, particularly, heating (pasteurization), can increase honey’s acidity by breaking down sugars into organic acids as well prolonged heating or improper storage further accelerates acid formation. Acacia honey, with raw (10.2–10.6 meq·kg−1) slightly higher than commercial (6.4–7.9 meq·kg−1) is the least acidic and most stable. Rapeseed honey has moderate acidity (8.3–14.4 meq·kg−1), while sunflower honey acidity is consistently higher (15.0–17.9 meq·kg−1). Lavender honey has a moderate range of acidity, which varies from 10.8 to 12.4 meq kg−1 (Table 4). Raw honey generally has a wider and more natural acidity range, while processing tends to make it more uniform.
Studies on the same types of honey have shown that acacia honey has the lowest free acidity (in Romania 3.86–25.40 meq·kg−1 [1,2,10,61,62,63], in other countries 5.46–24.62 meq·kg−1 [11,17,46,64]), while sunflower honey has the highest (in Romania 14.40–50.10 meq·kg−1 [61,62,63,66], in other countries 21.00–45.75 meq·kg−1) [11,17,53,64]. Higher free acidity values were also found in tilia honey (in Romania 3.40–38.60 meq·kg−1, in other countries 7.30–45.60 meq·kg−1) and rapeseed honey (in Romania 5.25–46.60 meq·kg−1) [2,10,61,63], in other countries 12.80–36.75 meq·kg−1) [17,46,64]. Regarding free acidity, values obtained for lavender honey from Serbia, Spain, Portugal, Morocco and Turkey (14.40–38.90 meq·kg−1) were higher compared to those found in our research [8,11,41,53,55,56].
Sunflower honey samples have lower average values of 6.5 meq·kg−1 of lactonic acid and 22.9 meq·kg−1 of total acidity, comparable to previous studies such as Chiş and Purcărea (2015) who reported lactones of 9.98 meq·kg−1 and total acidity of 32.25 meq·kg−1 in sunflower honey from Bihor County, Romania [66]. Similarly, Acquarone et al. observed lactones of 1.80 meq·kg−1 and total acidity of 29.3 meq·kg−1 in sunflower honey from different geographical origins [13]. Aazaza et al. (2013) measured lactones and total acidity in sunflower honey from Portugal and reported values of 13.83 meq·kg−1 and 39.33 meq·kg−1, respectively [53]. These results illustrate the variability in the physicochemical characteristics of sunflower honey in different regions.

4.5. Ash and Electrical Conductivity (EC)

Characteristics used to distinguish blossom honey from honeydew honey are ash and its positively correlated conductivity. The ash content reflects the macro- and micro-elements in honey; due to factors such as atmospheric conditions, soil mineral content, plant physiology and influenced by the composition of the collected nectar, ash content varies. Electrical conductivity of honey depends on ashes and acidity, since increased levels of ion, organic acid and protein increase conductivity [5,11,60].
In this report, the ash values content in honey samples were various: for raw honey, the ash content ranged from 0.091 to 0.401% in tilia honey, 0.054 to 0.084% in rapeseed honey, 0.070 to 0.235% in sunflower honey and 0.079 to 0.083% in lavender honey. In raw acacia honey we obtained lower ash content values, between 0.044% and 0.053%. For commercial honey, ash content ranged from 0.156 to 0.322% in tilia honey and 0.034 to 0.037% in acacia honey (Table 4). Processing methods such as filtration, which remove impurities such as pollen and wax, are the reason that commercial honey has a lower ash content than raw honey. These impurities contribute to the ash content, so their removal results in a lower value in commercial honey.
Ash content in honey varies by region and flower origin, therefore, the researchers published the following data: for tilia honey, the ash content ranges between 0.11 and 0.20% in Romania, 0.21 and 0.50% [2,61,63] in the Republic of Moldova, and 0.23 and 0.46% [14] in Serbia [64]. Acacia honey shows values from 0.07 to 0.19% in Romania and from 0.02 to 0.09% [2,62,63] in Serbia [64]. Rapeseed honey has an ash content of 0.09% in Romania and 0.05–0.14% in Serbia [63,64]. Sunflower honey ranges from 0.11 to 0.50% [62,63,67] in Romania and 0.15 to 0.29% in Serbia [64]. The ash content obtained by Aazza et al. ranges from 0.11 to 0.18% in lavender honey from Portugal [53].
The average electrical conductivity (EC) values of the raw honey samples under investigation were: 0.667 mS·cm−1 for tilia, 0.187 mS·cm−1 for rapeseed, 0.360 mS·cm−1 for sunflower and 0.219 mS·cm−1 for lavender. The raw acacia honey had a lower average EC of 0.167 mS·cm−1, while the commercial honey had an EC of 0.494 mS·cm−1 for the tilia honey and 0.141 mS·cm−1 for the acacia honey (Table 4). Electrical conductivity, strongly correlated with ash content, was easily measured and integrated into updated honey quality standards. Current legislation sets a maximum limit of 0.8 mS·cm−1 for blossom honey. However, higher EC values, often exceeding this limit, may be found in certain types of honey, including tilia honey (Tilia spp.) [25].
In our study, four samples of raw tilia honey exceeded the maximum permissible value of 0.8 mS·cm−1, with values ranging from 0.817 to 0.841 mS·cm−1. The average EC for raw tilia honey was higher (0.667 mS·cm−1) compared to the average EC of 0.494 mS·cm−1 for commercial tilia honey samples.
Other studies conducted in Romanian highlight as honey from different regions reported lower EC values compared to our findings, with a maximum of 0.437 mS·cm−1 for tilia honey [2,10,23,63] and 0.268 mS·cm−1 for acacia honey [23,63,67]. Electrical conductivity values obtained from rapeseed and sunflower honeys, fall within the same range as those observed in our study [10,23,63,66].
Studies carried out on honey from other countries (Serbia, Croatia, Poland, Czech Republic, Portugal, Spain, Turkey) to determine electrical conductivity have shown values similar to those obtained in our study. Specifically, for tilia honey, the EC values ranged from 0.23 to 0.81 mS·cm−1 [7,8,11,12], for acacia honey from 0.08 to 0.60 mS·cm−1, for rapeseed honey from 0.05 mS·cm−1 to 0.28 mS·cm−1 [17,18,44,46] and for sunflower honey from 0.23 mS·cm−1 to 0.46 mS·cm−1 [53,55,64,67]. For lavender honey from Morocco, Chakir et al. (2016) reported higher EC values ranging from 0.319 mS·cm−1 to 0.547 mS·cm−1 [56].

4.6. Hydroxymethylfurfural (HMF)

Hydroxymethylfurfural (HMF) is a significant indicator of thermal processing and the degradation state of honey, reflecting its freshness and quality. High HMF levels, caused by heating or prolonged storage, can reduce honey’s nutritional value and safety. The European Union has set a maximum limit of 40 mg·kg−1 to ensure honey quality [11,25,38]. The HMF content in fresh honey is typically around 10 mg·kg−1 [11]. Chiș and Purcărea (2011) demonstrated that heating honey increases its HMF content. They found that heating tilia honey at 70 °C for up to 12 h did not exceed the European limit of 40 mg·kg−1. However, the HMF content increased significantly from 1.041 mg·kg−1 after one hour to 1301.116 mg·kg−1 after 24 h [67]. In our analysis, the HMF levels in the studied honey samples were below the legal limit with values situated between 1.3 mg·kg−1 for sunflower honey and 14.7 mg·kg−1 for commercial tilia honey. However, commercial tilia and acacia honeys had higher values of 8.8 mg·kg−1 and of 5.1 mg·kg−1, respectively, compared to values of 3.7 mg·kg−1 and of 3.8 mg·kg−1 in raw honey (Table 4).
Analysis of honey from various countries has shown a wide range of HMF values. In Spain, HMF values ranged from <0.5 to 37.4 mg·kg−1 [8,54]; in Serbia, from 0.77 to 28.20 mg·kg−1 [11,18]; in Poland, from 0.50 to 23.23 mg·kg−1 [12,26]; in the Czech Republic, HMF values fell within the 1.7–2.5 mg·kg−1 range [46]; in Portugal, from 0.60 to 12.80 mg·kg−1 [53]; in Croatia, values were found between 0.00 and 2.40 mg·kg−1 [9]; in Turkey, values were found to range from 2.51 to 12.56 mg·kg−1 [7,55]. In a sample of acacia honey from Germany, a low HMF value of 6.08 mg·kg−1 was reported [65].
Lower HMF values generally indicate better preservation and quality, while higher values suggest possible degradation due to excessive heat exposure or improper storage. Proper handling and storage practices are crucial to preserving honey quality and minimizing HMF formation.

4.7. Mineral Elements (Na, K, Ca, Mg, Cu, Zn, Mn and Fe)

Reflecting the distinct characteristics of the plants from which bees gather nectar, the mineral composition of honey varies based on its floral origin. Rich in essential minerals such as potassium with the highest content in honey, calcium, magnesium, sodium, phosphorus, iron, manganese, zinc, copper and other mineral elements, honey offers both nutritional benefits and distinctive flavor and color profiles [19].
Some mineral elements found in honey play an important role in several biochemical processes. Potassium and sodium in honey act as electrolytes, supporting fluid balance, heart and muscle function, kidney health, blood pressure regulation and nerve activity. Calcium is essential for bone health, fracture healing, preventing osteoporosis and proper musculoskeletal, nervous and cardiac function. Along with phosphorus, it is responsible for the reduction of the cariogenic potential of honey. Magnesium is essential for muscle contraction, neuronal signaling, bone support and antioxidant enzyme activity. It also mitigates aging and related disorders [22,68].
The four mineral elements, Na, K, Ca and Mg, were found in all analyzed honey samples. The average sodium content ranged from 1.7 mg·kg−1 to 38.3 mg·kg−1, with the lowest values observed in sunflower honey samples and the highest in commercial acacia honey samples. Potassium content varied between 34.1 mg·kg−1 in commercial acacia honey samples and 1256.9 mg·kg−1 in rapeseed honey samples. Potassium is known to be present in higher quantities compared to other elements. In this study, the average potassium content was lower than sodium in commercial acacia honey samples. The highest calcium content was found in raw tilia honey samples at 114.8 mg·kg−1, while the lowest, of 12.7 mg·kg−1, was recorded in raw acacia honey samples. The average magnesium values ranged from 2.0 mg·kg−1 to 30.4 mg·kg−1, with the maximum content observed in lavender honey. In the samples analyzed in our research, K was the predominant element, followed by Ca, Na and Mg (Table 5).
The mineral element content (Na, K, Ca and Mg) reported in various studies is summarized in Table 12.
Studies on the four mineral elements (Na, K, Ca, Mg) in the same type of honey from various countries reveal significant environmental differences, as reflected in the broad range of concentrations. In Romania, the recorded ranges were Na (3.02–47.70 mg·kg−1), K (45.35–1284.66 mg·kg−1), Ca (17.69–213.28 mg·kg−1) and Mg (2.17–84.45 mg·kg−1) [19,23]. In Latvia, the intervals were Na (5.10–7.00 mg·kg−1), K (320–1100 mg·kg−1), Ca (28–39 mg·kg−1) and Mg (13.50–13.80 mg·kg−1) [69].
In Serbia, Hungary, Slovenia and Poland, the concentrations of these elements fall within the following intervals: Na (2.06–160.04 mg·kg−1), K (46.35–2284.44 mg·kg−1), Ca (4.31–245.8 mg·kg−1) and Mg (3.12–72.31 mg·kg−1) [18,20,45,64,70,71]. The concentrations of these four elements in honey samples from Bulgaria and Portugal align with those reported in Serbia (Table 12) [53,72].
Honey contains a variety of minerals, which is why it is often referred to as a complete food and a natural healing product. The other four mineral elements quantitatively evaluated from the five types of honey studied are copper, zinc, manganese and iron. These trace elements are essential for various biological functions and are required by the body in small amounts [22,68].
Due to their low concentrations, these trace elements were undetectable in some honey samples, with concentration levels below the detection limit. Specifically, copper was not detected in seven tilia honey samples, including two commercial honey samples and two lavender honey samples. Zinc was undetectable in four tilia and three acacia honey samples, while manganese was quantifiable in four tilia honey samples, two rapeseed honey samples and one sunflower honey sample.
The results of the quantification of these elements show their heterogeneous distribution in the samples, reflecting the differences in environmental conditions and floral sources. Sunflower honey had the highest average copper concentration of 0.61 mg·kg−1, while lavender honey had the highest zinc concentration (1.14 mg·kg−1). The mean concentration of manganese varied between 0.74 mg·kg−1 and 3.38 mg·kg−1. In lavender honey, the highest concentration of iron was found at 111.01 mg·kg−1, while the lowest average concentration was observed in sunflower honey, with a value of 3.91 mg·kg−1.
In the samples analyzed in our research, iron had the highest average concentration among the four trace elements, followed by manganese, zinc and copper (Table 5).
The trace minerals concentration (Cu, Zn, Mn and Fe) recorded from various studies is summarized in Table 13.
As shown by the wide range of concentrations, studies of the four trace minerals in the same type of honey from different countries reveal significant environmental differences (Table 13).
In Romania, the reported ranges for trace elements were: Cu (LoQ–2.40 mg·kg−1), Zn (LoQ–4.80 mg·kg−1), Mn (LoQ–7.17 mg·kg−1) and Fe (LoQ–75.91 mg·kg−1) [19,23]. In Latvia, the intervals were: Mn (0.29–0.37 mg·kg−1), Fe (<0.075–1.40 mg·kg−1) and of <0.075 mg·kg−1 for Cu [69].
In Serbia, Hungary, Slovenia, Poland and Bulgaria, the concentrations of these elements are within the following ranges: Cu (LoQ–257.30 mg·kg−1), Zn (LoQ–7.59 mg·kg−1), Mn (0.08–4.72 mg·kg−1) and Fe (<0.005–154.39 mg·kg−1) [18,20,45,53,64,70,71,72] (Table 13).

4.8. Correlations Between the Investigated Variables

Performing a Pearson correlation analysis on honey parameters is crucial for evaluating the strength and nature of the relationships between variables. It provides valuable insights into how environmental factors and compositional attributes interact to influence the physicochemical and mineral properties of honey.
In Table 6 are presented the Pearson coefficients for the correlation between raw tilia honey parameters. One can observe very strong correlations between moisture and total soluble solids (r = −1.00 ***) and specific gravity (r = −1.00 ***); total soluble solids and specific gravity (r = −1.00 ***); pH and free acidity (r = 0.94 ***); total acidity and pH (r = −0.86 **) and free acidity (r = 0.85 **), copper (r = 0.81 **) and zinc (r = 0.77 *); ash content and pH (r = 0.77 *); electrical conductivity and pH (r = 0.81 *), total acidity (r = −0.76 *) and ash (r = 0.99 ***); calcium and sodium (r = 0.73 *); zinc and copper (r = 0.97 ***) and iron (r = 0.74 *); free acidity and manganese (r = −0.75 *); iron and HMF (r = 0.81 *).
In raw tilia honey, strong positive or negative correlations were observed between several parameters, with Pearson coefficients ranging from 0.50 to 0.70 (0.50 ≤ r ≤ 0.70). Thus, correlations were noticed between mm Pfund and ash, electrical conductivity and magnesium; WIS with ash, electrical conductivity and calcium; potassium with moisture, TSS and specific gravity; pH with copper and manganese; free acidity with ash and electrical conductivity; HMF with copper and zinc; lactone acidity with zinc and iron, calcium with manganese and copper with iron.
In Table 7 are presented the Pearson coefficients for the correlations between commercial tilia honey parameters. We observed strong correlation between mm Pfund with some parameters: pH (r = −0.85 *), FA (r = 0.86 *), TA (r = 0.81 *), Cu (r = −0.66); medium correlations for ash and moisture (r = −0.53), TSS (r = 0.54) and specific gravity (r = 0.53), and between mm Pfund and the parameters WIS (r = −0.55), LA (r = 0.58), HMF (r = 0.51), K (r = −0.56) and Fe (r = −0.51). Free, lactone and total acidity have medium correlations with several parameters (HMF, Na, K, Cu and Zn). Higher levels of HMF, often resulting from prolonged storage or heating, can contribute to increased acidity; minerals such as Na and K can influence the pH and buffer capacity of honey, indirectly affecting its acidity. Higher acidity in honey can enhance the dissolution and bioavailability of minerals like copper and zinc. Acidic conditions make metal ions more available and can influence enzymatic reactions, such as the oxidation process that leads to HMF formation. It is noteworthy other important correlations: ash with electrical conductivity (r = 0.90 **); a higher ash content (more minerals) leads to a higher electrical conductivity, as more minerals (ions) improve the conductivity; calcium and magnesium (r = −0.81 *); copper with zinc (r = 0.85 *), a strong positive correlation, probably from similar sources and roles in enzymatic reactions, both metals tend to occur together in honey; and iron with copper (r = 0.78 *); and zinc (r = 0.92 **).
Table 8 presented ANOVA test for the differences between raw and commercial honeys. The differences between raw and commercial honey are evident in parameters such as the mm Pfund scale, water-insoluble solids and hydroxymethylfurfural. Reflecting its natural, unprocessed state, raw honey tends to have a more varied color profile on the mm Pfund scale. Insoluble solids such as pollen and beeswax increase viscosity by creating physical barriers to fluid flow. Honey becomes thicker and more viscous because of this resistance to flow. In contrast, commercial honey is often subjected to filtration and heating treatment, which results in lower WIS and higher HMF levels, as HMF increases with heat and storage, indicating a potential for degradation. These factors illustrate the differences in quality and processing between raw and commercial honey.
Table 9 presents the Pearson coefficients for the correlations between all the parameters of the studied honey samples. Connections are observed between the mm Pfund scale, moisture, total soluble solids, acidity, ash content, electrical conductivity and others. Furthermore, a lot of correlations were found between the mineral elements and other parameters, as well as among the mineral elements.
The correlations between the analyzed parameters show that honey has an interrelated composition. Changes in one factor are related to changes in others, reflecting its overall quality and characteristics.
Table 10 summarizes the results of factor analysis. In Table 11 the scores of the extracted factors are given.
Factor analysis gives an overview of all the data set and indicates the contributions from the determined quality parameters. Seven factors were selected with eigenvalues higher than 1.00, that explain about 90% of the total variance. The loadings and corresponding eigenvalues and variances are presented in Table 10.
Factor 1 is characterized by moisture, total soluble solids and specific gravity as parameters with significant loadings and can be attributed to some fundamental stability characteristics of the product. The corresponding scores (Table 11) indicate that this factor does not highlight a particular honey type, but all the main contributing honeys are raw. Significant contributions are given by tilia (T3, T5, T9) and rapeseed (R21) and moderate contributions from tilia (T12), rapeseed (R22), acacia (A19) and sunflower (SF23, SF25).
The second factor, Factor 2, explains 18.73% of variance and reflects the acidity, including free and total acidity as dominant parameters. The scores for this factor highlight the tilia samples.
Factor 3 with high loadings of WIS has the main contributions from acacia and rapeseed honeys.
K and color are interestingly grouped in Factor 4, explaining about 13% of the variance, with main contribution from rapeseed honeys.
Factor 5 includes as dominant parameters pH, ash, electrical conductivity, Mg and Cu, defining a mineral factor, with dominant contributions from tilia T3 and T4 honeys. Factor 6 indicates the contribution of Fe from the lavender honey samples. In Factor 7 only the lactone acidity has a significant loading, given mainly by tilia honeys.

5. Conclusions

The diversity of mono- and polyfloral honeys is due to the overlapping flowering periods of different plants. Melissopalynological analysis is important to confirm floral origin, maintain quality and discover the health benefits of specific plants.
This analysis helps to identify the main nectar sources used by bees and verifies the botanical origin of the honey. The wide variety of pollen types shows how environmental factors and plant diversity positively affect the composition of honey.
All parameters tested were within legal limits, indicating that both raw and commercial honey are of good quality. Although there are significant differences between certain quality parameters, such as color, water-insoluble solids and hydroxymethylfurfural, the honey quality proves that beekeepers and honey processors in the region have improved production methods.
Differences in mineral concentrations in honey reflect both plant diversity and the surrounding environment, including water, air and soil. The composition of the environment is reflected in raw honey, proving that bees are reliable bioindicators of environmental health.
The investigated quality parameters and the relationships between them indicate a good environmental quality, valuable natural resources and appropriate honey-processing techniques in the region.

Author Contributions

Conceptualization, A.A., O.-R.R. and V.S.; methodology, A.A., O.-R.R., F.D.B. and V.S.; software, A.A., F.D.B. and S.-M.C.-M.; validation, A.A., O.-R.R., F.D.B., V.S. and S.-M.C.-M.; formal analysis, A.A., O.-R.R., F.D.B. and S.-M.C.-M.; investigation, A.A., O.-R.R., V.S., C.-E.N. and I.S.B.; resources, A.A., O.-R.R., V.S., C.-E.N. and I.S.B.; data curation, A.A., O.-R.R., V.S. and S.-M.C.-M.; writing—original draft preparation, A.A., O.-R.R., V.S., S.-M.C.-M., C.-E.N. and I.S.B.; writing—review and editing, A.A., O.-R.R., V.S. and S.-M.C.-M.; visualization, A.A., O.-R.R. and I.S.B.; supervision, A.A., O.-R.R. and V.S.; project administration, A.A., O.-R.R. and V.S. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Data Availability Statement

The original data of the paper can be obtained from the corresponding authors.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. The harvested areas from Iasi County.
Figure 1. The harvested areas from Iasi County.
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Figure 2. The distribution of the most abundant plant families identified in the examined honey samples.
Figure 2. The distribution of the most abundant plant families identified in the examined honey samples.
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Table 1. Identification code of samples, harvest locations and the corresponding years of collection.
Table 1. Identification code of samples, harvest locations and the corresponding years of collection.
SampleLocationGPS CoordinatesYearSampleLocationGPS CoordinatesYear
T1Pascani47.2513° N, 26.7226° E2020T15Dobrovat47.0092° N, 27.7208° E2021
T2Iasi47.1517° N, 27.5875° E2021A16Iasi47.1517° N, 27.5875° E2020
T3Dobrovat47.0092° N, 27.7208° E2020A17Dobrovat47.0092° N, 27.7208° E2021
T4Dobrovat47.0092° N, 27.7208° E2021A18Iasi47.1517° N, 27.5875° E2020
T5Domnita47.2589° N, 27.6198° E2020A19Vanatori47.2816° N, 27.4587° E2020
T6Comarna47.0330° N, 27.8933° E2020R20Comarna47.0330° N, 27.8933° E2020
T7Iasi47.1517° N, 27.5875° E2021R21Vladeni47.5006° N, 27.3839° E2020
T8Iasi47.1517° N, 27.5875° E2021R22Vladeni47.5006° N, 27.3839° E2021
T9Dobrovat47.0092° N, 27.7208° E2020SF23Comarna47.0330° N, 27.8933° E2020
T10Iasi47.1517° N, 27.5875° E2021SF24Tabara47.3190° N, 27.0294° E2020
T11Iasi47.1517° N, 27.5875° E2020SF25Tabara47.3190° N, 27.0294° E2021
T12Al. I. Cuza47.2761° N, 26.9739° E2020L26Movileni47.3373° N, 27.1722° E2020
T13Iasi47.1517° N, 27.5875° E2021L27Movileni47.3373° N, 27.1722° E2021
T14Iasi47.1517° N, 27.5875° E2021
Table 2. Principal pollen types from plant species found in honey samples.
Table 2. Principal pollen types from plant species found in honey samples.
SamplePredominant Pollen (>45%)Secondary Pollen (16–45%)Important Minor Pollen (3–15%)Minor Pollen (<3%)
T1Tilia spp. Achillea spp., Taraxacum spp., Helianthus annuus, Brassicaceae spp., Trifolium spp., Vicia spp., Prunus spp., Crataegus spp., Salix spp.Coriandrum sativum, Carex spp., Quercus spp., Mentha spp., Salvia spp., Plantago spp., Festuca spp.
T2Tilia spp. Achillea spp., Taraxacum spp., Helianthus annuus, Trifolium spp., Vicia spp., Prunus spp., Crataegus spp., Salix spp.Coriandrum sativum, Carex spp., Quercus spp., Mentha spp., Salvia spp., Plantago spp., Festuca spp.
T3Tilia spp. Achillea spp., Taraxacum spp., Helianthus annuus, Trifolium spp., Vicia spp., Quercus spp., Festuca spp., Prunus spp. Crataegus spp.Coriandrum sativum, Symphytum spp., Brassicaceae spp., Mentha spp., Salvia spp., Salix spp.
T4Tilia spp. Taraxacum spp., Helianthus annuus, Trifolium spp., Vicia spp., Quercus spp., Festuca spp.Coriandrum sativum, Brassicaceae spp., Mentha spp., Salvia spp., Prunus spp., Crataegus spp., Salix spp.
T5Tilia spp. Taraxacum spp., Helianthus annuus, Trifolium spp., Vicia spp., Quercus spp., Festuca spp., Prunus spp., Crataegus spp.Coriandrum sativum, Symphytum spp., Brassicaceae spp., Mentha spp., Salvia spp., Salix spp.
T6Tilia spp. Coriandrum sativum, Taraxacum spp., Trifolium spp., Vicia spp., Quercus spp., Prunus spp., Crataegus spp.Brassicaceae spp., Mentha spp., Salvia spp., Plantago spp., Salix spp.
T7Tilia spp.Brassicaceae spp.Taraxacum spp., Helianthus annuus, Trifolium spp., Vicia spp., Quercus spp., Prunus spp., Crataegus spp,. Salix spp.Coriandrum sativum, Carex spp., Mentha spp., Salvia spp., Plantago spp., Festuca spp.
T8Tilia spp.Brassicaceae spp.Taraxacum spp., Helianthus annuus, Trifolium spp., Vicia spp.Coriandrum sativum, Carex spp., Quercus spp., Mentha spp., Salvia spp., Plantago spp., Festuca spp., Prunus spp., Crataegus spp., Salix spp.
T9Tilia spp. Trifolium spp., Vicia spp.Coriandrum sativum, Taraxacum spp., Helianthus annuus, Symphytum spp., Brassicaceae spp., Mentha spp., Salvia spp., Prunus spp., Crataegus spp., Salix spp.
T10Tilia spp.Brassicaceae spp.Taraxacum spp. Helianthus annuus Trifolium spp. Vicia spp. Salix spp.Coriandrum sativum, Mentha spp., Salvia spp., Festuca spp., Prunus spp., Crataegus spp.
T11Tilia spp.Brassicaceae spp.Taraxacum spp., Helianthus annuus, Trifolium spp., Vicia spp., Salix spp.Coriandrum sativum, Mentha spp., Salvia spp. Festuca spp.
T12Tilia spp. Coriandrum sativum, Taraxacum spp., Helianthus annuus, Brassicaceae spp., Trifolium spp., Vicia spp., Prunus spp., Crataegus spp., Salix spp.Carex spp., Quercus spp., Mentha spp., Salvia spp., Plantago spp., Festuca spp.
T13Tilia spp.Brassicaceae spp.Taraxacum spp., Helianthus annuus, Trifolium spp., Vicia spp., Salix spp.Coriandrum sativum, Carex spp., Quercus spp., Mentha spp., Salvia spp.
T14Tilia spp.Brassicaceae spp.Taraxacum spp., Trifolium spp., Vicia spp.Coriandrum sativum, Mentha spp., Salvia spp., Salix spp.
T15Tilia spp. Taraxacum spp., Helianthus annuus, Trifolium spp., Vicia spp., Quercus spp., Festuca spp.Coriandrum sativum, Symphytum spp., Brassicaceae spp., Carex spp., Mentha spp., Salvia spp., Plantago spp., Prunus spp., Crataegus spp., Salix spp.
A16Robinia pseudoacaciaBrassicaceae spp.Helianthus annuus, Taraxacum spp., Mentha spp., Salvia spp., Tilia spp., Plantago spp., Festuca spp., Prunus spp., Crataegus spp., Salix spp.Coriandrum sativum, Symphytum spp.
A17Robinia pseudoacaciaPrunus spp., Crataegus spp.Helianthus annuus, Taraxacum spp., Mentha spp., Salvia spp., Plantago spp., Festuca spp., Salix spp.Coriandrum sativum, Symphytum spp., Brassicaceae spp.
A18Robinia pseudoacaciaBrassicaceae spp.Coriandrum sativum, Taraxacum spp., Tilia spp., Plantago spp., Festuca spp., Prunus spp., Crataegus spp., Salix spp.Symphytum spp., Mentha spp., Salvia spp.
A19Robinia pseudoacacia Helianthus annuus, Plantago spp., Brassicaceae spp., Mentha spp., Salvia spp., Festuca spp., Prunus spp., Crataegus spp., Salix spp.Coriandrum sativum, Symphytum spp., Tilia spp., Plantago spp.
R20Brassica napus Helianthus annuus, Trifolium spp., Vicia spp., Festuca spp., Prunus spp., Crataegus spp.Coriandrum sativum, Quercus spp., Mentha spp., Salvia spp.
R21Brassica napus Helianthus annuus, Taraxacum spp., Trifolium spp., Vicia spp., Prunus spp., Crataegus spp.Coriandrum sativum, Mentha spp., Salvia spp., Plantago spp., Festuca spp.
R22Brassica napus Trifolium spp., Vicia spp., Prunus spp., Crataegus spp.Coriandrum sativum, Mentha spp., Salvia spp., Festuca spp., Salix spp.
SF23Helianthus annuusTrifolium spp. Vicia spp.Brassicaceae spp., Mentha spp., Salvia spp.Tilia spp.
SF24Helianthus annuus Brassicaceae spp., Trifolium spp., Vicia spp., Tilia spp., Salix spp.Mentha spp., Salvia spp., Prunus spp., Crataegus spp.
SF25Helianthus annuusTrifolium spp. Vicia spp.Brassicaceae spp., Tilia spp.Mentha spp., Salvia spp.
L26 Lavandula spp. Trifolium spp. Vicia spp.Coriandrum sativum, Helianthus annuus, Symphytum spp., Trifolium spp., Prunus spp., Crataegus spp.Quercus spp., Salix spp.
L27 Lavandula spp.Coriandrum sativum, Helianthus annuus, Symphytum spp., Trifolium spp., Vicia spp., Prunus spp., Crataegus spp., Salix spp.
Table 3. Analysis results of monofloral honey samples, including mm Pfund (color), water-insoluble solids (WIS), moisture (M), total soluble solids (TSS) and specific gravity (SG).
Table 3. Analysis results of monofloral honey samples, including mm Pfund (color), water-insoluble solids (WIS), moisture (M), total soluble solids (TSS) and specific gravity (SG).
TypeDescriptive StatisticsParameter
mm PfundColorWIS
%		M
%		TSS
%		SG
g/cm3
T-Rmean ± SD19.50 ± 10.95Water white to extra light amber0.154 ± 0.0418.2 ± 0.9580.3 ± 0.941.429 ± 0.01
min–max4.25–39.170.073–0.19316.8–19.579.0–81.71.420–1.439
T-Cmean ± SD38.58 ± 15.47Extra white to light amber0.084 ± 0.0318.6 ± 0.5479.9 ± 0.541.426 ± 0.00
min–max12.94–55.480.043–0.11318.0–19.778.8–80.51.419–1.431
A-Rmean ± SD0.04 ± 0.14Water white0.052 ± 0.0119.1 ± 1.2779.5 ± 1.181.423 ± 0.01
min–max<0.01–0.10.043–0.06018.2–20.078.6–80.31.417–1.429
A-Cmean ± SD<0.01 ± 0.55Water white0.095 ± 0.0118.4 ± 0.0980.1 ± 0.091.428 ± 0.00
min–max<0.01–0.010.087–0.10318.3–18.580.1–80.21.427–1.428
R-Rmean ± SD53.47 ± 5.59Extra light amber to light amber0.138 ± 0.0218.9 ± 1.2679.6 ± 1.241.424 ± 0.01
min–max48.09–59.250.123–0.15317.5–19.978.6–81.01.418–1.434
SF-Rmean ± SD12.22 ± 0.76Extra white0.193 ± 0.0419.8 ± 0.1878.7 ± 0.181.418 ± 0.00
min–max11.64–13.070.150–0.23319.6–19.978.6–78.91.417–1.420
L-Rmean ± SD16.74 ± 0.93Extra white to white0.152 ± 0.0118.8 ± 0.7879.8 ± 0.781.425 ± 0.01
min–max16.08–17.390.143–0.16018.2–19.379.2–80.31.422–1.429
SD—standard deviation.
Table 4. Analysis results of monofloral honey samples, including pH, free acidity (FA), lactonic acidity (LA), total acidity (TA), ash, electrical conductivity (EC) and hydroxymethylfurfural (HMF).
Table 4. Analysis results of monofloral honey samples, including pH, free acidity (FA), lactonic acidity (LA), total acidity (TA), ash, electrical conductivity (EC) and hydroxymethylfurfural (HMF).
TypeDescriptive StatisticsParameter
pHFA
meq kg−1		LA
meq kg−1		TA
meq kg−1		Ash
%		EC
mS cm−1		HMF
mg kg−1
T-Rmean ± SD4.7 ± 0.6113.8 ± 4.673.6 ± 2.4317.4 ± 3.860.296 ± 0.130.667 ± 0.233.7 ± 1.80
min–max3.9–5.85.1–19.60.5–7.212.2–22.40.091–0.4010.271–0.8411.9–7.4
T-Cmean ± SD4.2 ± 0.2319.8 ± 5.216.2 ± 3.0525.9 ± 7.730.211 ± 0.050.494 ± 0.098.8 ± 3.67
min–max3.9–4.512.7–25.03.3–11.816.2–36.30.156–0.3220.398–0.6674.7–14.7
A-Rmean ± SD3.93 ± 0.0010.4 ± 0.286.0 ± 0.9816.4 ± 1.260.048 ± 0.010.167 ± 0.013.8 ± 1.11
min–max3.9–3.910.2–10.65.3–6.715.5–17.30.044–0.0530.159–0.1743.0–4.6
A-Cmean ± SD4.1 ± 0.017.2 ± 1.114.5 ± 2.3011.7 ± 1.190.035 ± 0.000.141 ± 0.005.1 ± 2.70
min–max4.0–4.16.4–7.92.9–6.110.8–12.50.034–0.0370.140–0.1423.2–7.0
R-Rmean ± SD4.2 ± 0.2311.0 ± 3.126.6 ± 0.2317.6 ± 3.210.070 ± 0.020.187 ± 0.046.1 ± 2.37
min–max4.0–4.48.3–14.46.3–6.715.0–21.20.054–0.0840.142–0.2163.3–7.7
SF-Rmean ± SD4.0 ± 0.2916.4 ± 1.476.5 ± 1.5722.9 ± 1.780.137 ± 0.090.360 ± 0.194.6 ± 3.54
min–max3.8–4.315.0–17.94.8–7.821.2–24.70.070–0.2350.253–0.5761.3–8.3
L-Rmean ± SD4.1 ± 0.1111.6 ± 1.114.6 ± 0.3116.2 ± 0.800.081 ± 0.000.219 ± 0.007.3 ± 0.82
min–max4.0–4.110.8–12.44.4–4.815.6–16.80.079–0.0830.216–0.2226.7–7.8
SD—standard deviation.
Table 5. Analysis results of mineral elements (Na, K, Ca, Mg, Cu, Zn, Mn and Fe) in monofloral honey samples.
Table 5. Analysis results of mineral elements (Na, K, Ca, Mg, Cu, Zn, Mn and Fe) in monofloral honey samples.
TypeDescriptive StatisticsParameter
Na
mg kg−1		K
mg kg−1		Ca
mg kg−1		Mg
mg kg−1		Cu
mg kg−1		Zn
mg kg−1		Mn
mg kg−1		Fe
mg kg−1
T-Rmean ± SD3.1 ± 1.76297.1 ± 248.45114.8 ± 92.8128.1 ± 7.690.08 ± 0.130.28 ± 0.440.79 ± 0.7316.6 ± 12.73
min–max1.6–6.842.8–776.211.4–253.816.1–35.4BDL–0.29BDL–1.07BDL–2.121.4–43.3
T-Cmean ± SD7.6 ± 4.00411.3 ± 205.8197.2 ± 59.1724.2 ± 6.710.18 ± 0.150.39 ± 0.351.27 ± 1.2915.7 ± 11.82
min–max3.0–13.8201.5–715.222.1–200.417.1–38.2BDL–0.34BDL–0.79BDL–2.891.2–30.2
A-Rmean ± SD25.9 ± 4.7264.3 ± 15.6812.7 ± 7.673.7 ± 0.490.47 ± 0.06BDL2.17 ± 0.1634.2 ± 28.83
min–max22.5–29.253.3–75.47.3–18.13.4–4.10.43–0.512.06–2.2813.8–54.6
A-Cmean ± SD38.3 ± 11.3334.1 ± 12.7619.1 ± 12.576.0 ± 2.140.28 ± 0.020.40 ± 0.563.38 ± 0.3419.8 ± 1.18
min–max30.3–46.325.1–43.110.2–28.04.5–7.50.26–0.29BDL–0.793.13–3.6219.0–20.6
R-Rmean ± SD3.3 ± 0.601256.9 ± 108.0041.5 ± 2.2410.7 ± 0.850.57 ± 0.041.02 ± 0.140.74 ± 1.295.1 ± 4.64
min–max2.8–3.91178.4–1380.040.0–44.110.0–11.60.54–0.620.87–1.16BDL–2.232.2–10.5
SF-Rmean ± SD1.7 ± 0.0756.6 ± 20.0817.6 ± 11.312.0 ± 0.330.61 ± 0.120.92 ± 0.311.16 ± 1.093.9 ± 3.87
min–max1.6–1.835.2–75.07.5–29.81.7–2.20.48–0.700.57–1.15BDL–2.161.3–8.4
L-Rmean ± SD26.1 ± 6.72959.0 ± 125.0128.6 ± 2.5430.4 ± 1.75BDL1.14 ± 0.242.09 ± 0.98111 ± 18.08
min–max21.4–30.9870.6–1047.426.8–30.429.1–31.60.98–1.311.40–2.7898.2–123.8
SD—standard deviation; BDL—below detection limit; limit of detection (LoD) and quantification (LoQ) for the determined elements (based on the standard deviation of blank samples): Na—LoD 3.991 μg·L−1, LoQ 13.232 μg·L−1; K—LoD 2.847 μg·L−1, LoQ 7.321 μg·L−1; Ca—LoD 5.384 μg·L−1, LoQ 17.986 μg·L−1; Mg—LoD 2.054 μg·L−1, LoQ 9.003 μg·L−1; Cu—LoD 0.035 μg·L−1, LoQ 0.139 μg·L−1; Zn—LoD 0.079 μg·L−1, LoQ 1.203 μg·L−1; Mn—LoD 0.012 μg·L−1, LoQ 0.039 μg·L−1; Fe—LoD 5.232 μg·L−1, LoQ 17.574 μg·L−1.
Table 6. Pearson coefficients for the correlation between raw tilia honey parameters (significant at p < 0.05 (*), p < 0.01 (**), p < 0.001 (***)).
Table 6. Pearson coefficients for the correlation between raw tilia honey parameters (significant at p < 0.05 (*), p < 0.01 (**), p < 0.001 (***)).
mm PfundWISMTSSSGpHFALATAASHECHMFNaKCaMgCuZnMnFe
mm Pfund1.00
WIS−0.381.00
M0.090.051.00
TSS−0.09−0.06−1.00 ***1.00
SG−0.08−0.06−1.00 ***1.00 ***1.00
pH−0.330.42−0.030.040.031.00
FA0.38−0.37−0.270.260.27−0.94 ***1.00
LA−0.070.330.31−0.31−0.320.43−0.561.00
TA0.42−0.24−0.130.120.12−0.86 **0.85 **−0.051.00
ASH−0.600.61−0.400.410.400.77 *−0.610.06−0.701.00
EC−0.530.58−0.320.320.310.81 *−0.660.05−0.76 *0.99 ***1.00
HMF0.350.31−0.130.120.13−0.090.120.520.47−0.13−0.181.00
Na−0.38−0.41−0.420.430.420.37−0.300.00−0.360.370.37−0.441.00
K0.01−0.110.55−0.56−0.57−0.350.170.010.21−0.31−0.25−0.22−0.011.00
Ca0.28−0.63−0.270.280.270.35−0.280.08−0.280.030.09−0.180.73 *−0.151.00
Mg0.530.09−0.060.060.060.38−0.230.06−0.240.250.360.100.080.140.441.00
Cu0.050.11−0.020.010.00−0.500.450.430.81 *−0.41−0.490.53−0.220.26−0.35−0.291.00
Zn0.120.180.17−0.17−0.17−0.460.360.520.77 *−0.47−0.540.59−0.380.25−0.42−0.280.97 ***1.00
Mn−0.05−0.140.44−0.43−0.440.63−0.75 *0.47−0.610.180.28−0.330.490.310.590.42−0.33−0.291.00
Fe0.410.480.03−0.04−0.03−0.050.100.580.49−0.13−0.150.81 *−0.500.01−0.270.310.670.74 *−0.171.00
Table 7. Pearson coefficients for the correlation between commercial tilia honey parameters (significant at p < 0.05 (*), p < 0.01 (**), p < 0.001 (***)).
Table 7. Pearson coefficients for the correlation between commercial tilia honey parameters (significant at p < 0.05 (*), p < 0.01 (**), p < 0.001 (***)).
mm PfundWISMTSSSGpHFALATAASHECHMFNaKCaMgCuZnMnFe
mm Pfund1.00
WIS−0.551.00
M0.13−0.451.00
TSS−0.130.45−1.00 ***1.00
SG−0.140.44−1.00 ***1.00 ***1.00
pH−0.85 *0.13−0.100.090.111.00
FA0.86 *−0.10−0.140.150.13−0.94 **1.00
LA0.580.08−0.170.170.17−0.77 *0.731.00
TA0.81 *−0.04−0.160.170.15−0.94 **0.96 ***0.89 **1.00
ASH0.18−0.12−0.530.540.530.170.13−0.040.071.00
EC0.39−0.28−0.480.480.49−0.010.240.240.260.90 **1.00
HMF0.510.010.23−0.22−0.22−0.78 *0.610.89 **0.76 *−0.45−0.151.00
Na−0.27−0.31−0.260.250.280.66−0.59−0.41−0.560.450.53−0.571.00
K−0.560.010.03−0.03−0.020.47−0.58−0.52−0.60−0.56−0.60−0.310.221.00
Ca0.060.45−0.330.340.31−0.390.460.070.34−0.21−0.420.06−0.660.171.00
Mg−0.110.32−0.490.490.460.020.21−0.280.030.27−0.09−0.43−0.300.120.81 *1.00
Cu−0.660.080.01−0.02−0.000.83 *−0.77 *−0.39−0.680.300.22−0.450.570.04−0.68−0.281.00
Zn−0.480.18−0.070.060.090.50−0.500.12−0.290.160.250.040.38−0.10−0.67−0.490.85 *1.00
Mn0.24−0.120.23−0.23−0.240.020.04−0.24−0.070.480.39−0.310.15−0.63−0.31−0.120.13−0.131.00
Fe−0.510.36−0.430.420.440.51−0.390.13−0.210.370.37−0.090.37−0.12−0.41−0.150.78 *0.92 **−0.201.00
Table 8. ANOVA test for the differences between raw and commercial honeys (significant at: p < 0.05 (*), p < 0.01 (**); ns—not significant).
Table 8. ANOVA test for the differences between raw and commercial honeys (significant at: p < 0.05 (*), p < 0.01 (**); ns—not significant).
Type HoneyParameters
mm PfundWISMTSSSGpHFALATAAshECHMFNaKCaMgCuZnMnFe
TR-TC***nsnsnsns*ns*nsns***nsnsnsnsnsnsns
AR-ACnsnsnsnsns**nsnsnsnsnsnsnsnsnsns*ns*ns
TR—raw tilia; TC—commercial tilia; AR—raw acacia; AC—commercial acacia.
Table 9. Pearson coefficients for the correlation between honey parameters (significant at p < 0.05 (*), p < 0.01 (**), p < 0.001 (***)).
Table 9. Pearson coefficients for the correlation between honey parameters (significant at p < 0.05 (*), p < 0.01 (**), p < 0.001 (***)).
mm PfundWISMTSSSGpHFALATAAshECHMFNaKCaMgCuZnMnFe
mm
Pfund		1.00
WIS−0.171.00
M0.000.061.00
TSS−0.01−0.07−1.00 ***1.00
SG0.00−0.07−1.00 ***1.00 ***1.00
pH−0.090.34−0.320.320.321.00
FA0.49 **−0.18−0.020.010.01−0.40 *1.00
LA0.32−0.090.32−0.32−0.32−0.200.201.00
TA0.54 **−0.180.11−0.11−0.11−0.42 *0.93 ***0.55 **1.00
ASH0.010.31−0.42 *0.42 *0.41 *0.77 ***0.15−0.200.051.00
EC0.010.29−0.370.370.370.76 ***0.18−0.180.090.99 ***1.00
HMF0.50 **−0.27−0.010.000.01−0.310.46 *0.60 ***0.62 ***−0.20−0.181.00
Na−0.48 *−0.40 *−0.010.020.02−0.27−0.46 *−0.02−0.40 *−0.47 *−0.49 *0.021.00
K0.53 **0.060.09−0.10−0.09−0.08−0.140.04−0.10−0.27−0.290.16−0.131.00
Ca0.25−0.13−0.40 *0.40 *0.40 *0.46 *0.22−0.120.150.45 *0.47 *−0.01−0.34−0.051.00
Mg0.260.06−0.43 *0.43 *0.43 *0.48 *0.22−0.320.070.58 **0.58 **0.07−0.250.190.64 ***1.00
Cu−0.020.120.44 *−0.44 *−0.45 *−0.35−0.180.32−0.03−0.47 *−0.50 **−0.13−0.040.08−0.53 **−0.79 ***1.00
Zn0.160.320.27−0.28−0.27−0.31−0.060.300.06−0.38 *−0.39 *0.17−0.100.47 *−0.45 *−0.230.47 *1.00
Mn−0.29−0.280.18−0.18−0.18−0.18−0.27−0.01−0.23−0.29−0.30−0.100.61 ***−0.21−0.18−0.20−0.01−0.131.00
Fe−0.220.03−0.040.050.05−0.12−0.21−0.01−0.18−0.20−0.220.150.49 *0.23−0.220.28−0.260.340.231.00
Table 10. Factor loadings (Varimax-normalized) and explained variance (%) for the extracted principal components (marked loadings are >0.700).
Table 10. Factor loadings (Varimax-normalized) and explained variance (%) for the extracted principal components (marked loadings are >0.700).
VariableFactor
1		Factor
2		Factor
3		Factor
4		Factor
5		Factor
6		Factor
7
mm Pfund0.008−0.423−0.0270.7240.1240.2370.307
WIS−0.1070.1890.845−0.0840.121−0.080−0.085
M−0.967−0.0190.0130.015−0.2280.0230.069
TSS0.9670.023−0.025−0.0230.230−0.024−0.067
SG0.9680.021−0.021−0.0160.226−0.028−0.066
pH0.1580.5170.245−0.0230.7720.1550.047
FA0.013−0.9790.0100.0060.1070.0970.063
LA−0.229−0.1600.0170.026−0.1460.0570.911
Ta−0.077−0.8940.0150.0150.0350.1040.402
ASH0.241−0.0290.345−0.2270.8210.155−0.025
EC0.189−0.0620.325−0.2330.8450.163−0.015
HMF0.091−0.475−0.1660.233−0.067−0.2220.682
Na0.0770.387−0.571−0.275−0.347−0.4830.053
K−0.0610.1330.1220.912−0.126−0.171−0.001
Ca0.252−0.132−0.2350.2210.6920.228−0.060
Mg0.250−0.1800.0130.3120.778−0.350−0.186
Cu−0.2720.1840.263−0.008−0.7110.4080.173
Zn−0.1500.0090.5410.345−0.501−0.3110.214
Mn−0.2280.273−0.569−0.307−0.121−0.2820.026
Fe0.0420.1140.0100.069−0.066−0.9440.034
Eigenvalue5.823.752.612.041.531.231.01
Total variance (%)29.1218.7313.0410.227.646.175.03
Cumulative variance (%)29.1247.8560.8971.1078.7484.9289.95
Table 11. Factor scores (Varimax-normalized) for the extracted factors.
Table 11. Factor scores (Varimax-normalized) for the extracted factors.
SampleFactor
1		Factor
2		Factor
3		Factor
4		Factor
5		Factor
6		Factor
7
T10.461.24−1.280.980.23−0.61−2.40
T20.001.25−0.880.480.93−0.25−0.91
T31.99−0.280.14−0.580.80−0.83−0.88
T4−0.45−2.19−0.360.002.78−0.451.38
T5−1.56−0.820.350.211.910.16−0.93
T60.34−0.540.93−1.020.98−0.27−0.11
T7−0.281.81−1.22−0.240.66−0.161.38
T8−0.111.55−1.210.880.56−0.570.39
T91.470.421.54−0.100.360.690.57
T100.571.870.27−0.200.200.352.71
T110.93−0.370.270.53−0.030.03−0.14
T121.160.251.31−0.900.82−0.03−1.50
T130.310.35−0.39−0.440.310.020.32
T14−1.11−0.25−0.500.410.10−0.13−0.20
T15−0.600.780.930.04−0.780.25−0.83
A160.43−1.01−0.97−0.80−1.190.26−0.73
A170.77−0.62−1.22−1.02−1.25−0.65−0.28
A180.61−1.44−1.75−1.33−0.940.330.83
A19−1.30−0.52−1.42−1.09−0.730.270.19
R20−0.50−1.060.642.50−0.88−1.000.55
R211.77−0.930.611.87−1.22−1.030.87
R22−1.39−0.21−0.051.53−0.66−0.73−0.11
SF23−1.32−0.021.91−1.16−0.38−0.960.35
SF24−0.730.870.81−0.88−1.20−0.280.40
SF25−1.190.321.14−1.09−0.98−0.53−0.53
L26−0.72−0.120.130.610.012.90−0.29
L270.49−0.340.250.81−0.413.25−0.12
Table 12. Mineral elements (Na, K, Ca, Mg) from several sources.
Table 12. Mineral elements (Na, K, Ca, Mg) from several sources.
Na (mg·kg−1)K (mg·kg−1)Ca (mg·kg−1)Mg (mg·kg−1)CountrySources
Tilia
8.61–23.4176.17–697.6417.69–81.4213.52–35.78Romania[19]
-225–33934–59-Romania[23]
5.10110028.0013.50Latvia[69]
3.88–35.89626.50–227847.18–163.0016.74–30.32Serbia[18]
8.38–29.32107.08–401.16-13.85–27.23Serbia[64]
32.601327.50111.9022.40Hungary[70]
9.291278.0867.8516.51Hungary[45]
2.18–6.141413.47–2284.4413.74–17.4912.32–36.79Slovenia[20]
-1062.91–1453.3282.08–85.6120.92–26.91Poland[71]
7.50792.0077.0021.00Bulgaria[72]
Acacia
5.30–23.3583.40–736.6118.50–33.522.17–30.95Romania[19]
-169–48645–98-Romania[23]
2.35–36.49236.30–566.0010.33–245.803.12–25.34Serbia[18]
6.75–12.5246.35–63.49-5.71–12.04Serbia[64]
23.50327.9028.1010.40Hungary[70]
5.99226.5612.395.24Hungary[45]
2.06–19.95247.79–571.904.31–6.6012.39–14.49Slovenia[20]
8.11126326.0Bulgaria[72]
Rapeseed
3.11–14.111012.05–1284.6631.37–42.8713.69–20.83Romania[19]
-207–22146–51-Romania[23]
7.0032039.0013.80Latvia[69]
9.25–160.0447.70–90.22-16.22–72.31Serbia[64]
22.80399.4068.6019.20Hungary[70]
8.491054611Bulgaria[72]
Sunflower
3.02–47.7045.35–1216.8322.28–213.2812.47–76.42Romania[19]
-163–19249–51-Romania[23]
7.77–18.76113.47–133.39-33.01–39.16Serbia[64]
55.10847.40217.7049.70Hungary[70]
13.23758.95126.3733.26Hungary[45]
87.93276.8624.9268.18Portugal[53]
7.582477114Bulgaria[72]
Lavender
25.661004.1627.8184.45Romania[19]
41.47–62.5378.09–173.176.84–14.4617.88–32.43Portugal[53]
LoQ—Limit of Quantification.
Table 13. Mineral elements (Cu, Zn, Mn, Fe) from several studies.
Table 13. Mineral elements (Cu, Zn, Mn, Fe) from several studies.
Cu (mg·kg−1)Zn (mg·kg−1)Mn (mg·kg−1)Fe (mg·kg−1)CountrySources
Tilia
LoQ–0.86LoQ–0.912.65–7.17LoQ–61.65Romania[19]
0.20–1.421.80–3.70-6.20–7.20Romania[23]
<0.15-0.29<0.075Latvia[69]
LoD–0.711.06–1.430.34–1.122.25–5.99Serbia[64]
114.60–217.400.25–7.590.53–2.140.79–3.44Serbia[18]
-1.701.30-Hungary[70]
0.120.151.01<0.05Hungary[45]
0.078–1.0430.05–2.080.37–4.721.38–70.20Slovenia[20]
0.38–0.570.16–1.523.70–4.711.22–1.68Poland[71]
0.121.042.451.62Bulgaria[72]
Acacia
LoQ–2.01LoQ–0.36LoQ–2.01LoQ–32.05Romania[19]
0.10–1.420.90–4.30-1.30–7.90Romania[23]
LoQ0.38–1.080.54–0.663.16–3.91Serbia[64]
43.76–257.300.44–6.850.08–0.490.38–4.13Serbia[18]
<0.100.150.12<0.05Hungary[45]
0.109–0.6260.22–2.100.17–0.870.21–134.59Slovenia[20]
<0.01–0.150.220.110.83Bulgaria[72]
Rapeseed
LoQ0.63–1.290.89–2.121.65–3.55Romania[19]
0.38–0.394.60–4.80-9.80–11.50Romania[23]
<0.15-0.371.40Latvia[69]
-2.300.90-Hungary[70]
<0.01–0.020.250.171.01Bulgaria[72]
Sunflower
LoQ–4.31LoQ–3.43LoQ–1.92LoQ–10.52Romania[19]
0.17–0.262.70–4.10-9.80–11.00Romania[23]
LoD2.46–3.140.25–1.172.83–3.09Serbia[64]
-5.500.70-Hungary[70]
<0.104.870.450.75Hungary[45]
<0.01–0.070.610.361.93Bulgaria[72]
Lavender
2.40LoQLoQ75.91Romania[19]
LoQ—Limit of quantification.
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MDPI and ACS Style

Albu, A.; Bora, F.D.; Cucu-Man, S.-M.; Stoleru, V.; Nistor, C.-E.; Brumă, I.S.; Rusu, O.-R. Assessing Honey Quality: A Focus on Some Physicochemical Parameters of Honey from Iasi County (Romania). Agriculture 2025, 15, 333. https://doi.org/10.3390/agriculture15030333

AMA Style

Albu A, Bora FD, Cucu-Man S-M, Stoleru V, Nistor C-E, Brumă IS, Rusu O-R. Assessing Honey Quality: A Focus on Some Physicochemical Parameters of Honey from Iasi County (Romania). Agriculture. 2025; 15(3):333. https://doi.org/10.3390/agriculture15030333

Chicago/Turabian Style

Albu, Aida, Florin Dumitru Bora, Simona-Maria Cucu-Man, Vasile Stoleru, Cătălin-Emilian Nistor, Ioan Sebastian Brumă, and Oana-Raluca Rusu. 2025. "Assessing Honey Quality: A Focus on Some Physicochemical Parameters of Honey from Iasi County (Romania)" Agriculture 15, no. 3: 333. https://doi.org/10.3390/agriculture15030333

APA Style

Albu, A., Bora, F. D., Cucu-Man, S.-M., Stoleru, V., Nistor, C.-E., Brumă, I. S., & Rusu, O.-R. (2025). Assessing Honey Quality: A Focus on Some Physicochemical Parameters of Honey from Iasi County (Romania). Agriculture, 15(3), 333. https://doi.org/10.3390/agriculture15030333

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