Physicochemical parameters constitute the cornerstone of honey quality evaluation. Moisture content determines fermentation risk; hydroxymethylfurfural (HMF) and diastase activity reflect thermal history and storage conditions with HMF additionally serving as a key indicator of honey quality, freshness, and heat treatment exposure; and free acidity, pH, electrical conductivity, sugar composition, and proline content collectively characterise botanical origin, maturity, and authenticity ^1,3,4. Despite its inherent antimicrobial properties, honey is not a sterile product ⁵. Contamination may occur through primary sources such as pollen and soil or through secondary routes during harvesting and processing. In microbiological assessments of honey, total mesophilic aerobic bacteria (TMAB), yeasts and moulds, coliform bacteria, sulphite-reducing anaerobic bacteria, and pathogen indicators constitute the primary targets of evaluation ^4-6.

Integrated evaluation, combining physicochemical and microbiological data within a single analytical framework, provides a more realistic picture of honey quality and safety than either dimension alone ^4,7. Nevertheless, many regional studies still address these aspects separately, limiting their interpretive value for food safety management ⁸.

Türkiye is among the world's leading honey producers, endowed with a rich diversity of honey varieties owing to its varied climate, vegetation, and geographical conditions. The Western Black Sea region, and Kastamonu province in particular, supports the production of botanically distinct honey types, including chestnut, pine, and flower varieties, owing to its diverse forested flora ^9-11. Prior studies from this locality have addressed palynological characterisation, selected physicochemical parameters, and mineral or heavy metal profiles ^10,11, but no study has systematically combined physicochemical and microbiological quality assessment within a single framework. The present study addresses this gap by providing integrated, region-specific data on chestnut, pine, and flower honeys from Kastamonu province, with the aim of establishing a physicochemical and microbiological quality baseline and contributing to evidence-based monitoring of honey produced in this region.

Physicochemical Analyses: Analyses followed the Turkish Food Codex Honey Communiqué (TGK 2020/7) and IHC harmonised methods ^3,12. Moisture content was determined refractometrically (Atago PAL-22S; Wedmore table) and dry matter calculated as the complement. pH was measured at 20°C with a calibrated pH meter (two-point calibration, pH 4.0 and 7.0) on a 10 g/75 mL honey–water solution; free acidity was determined by potentiometric titration of the same solution with 0.1 N NaOH to pH 8.3 and expressed as meq/kg. Electrical conductivity was measured in a 20% (w/v) solution in ultrapure water at 20°C ^3,4,9. Glucose, fructose, sucrose, and maltose were quantified by HPLC-RID (Agilent 1260 Infinity II; Aminex HPX-87C column, 300×7.8 mm, 80°C; ultrapure water mobile phase, 0.6 mL/min; injection volume 20 µL) after filtration through a 0.45 µm nylon membrane. HMF was determined by HPLC-UV (Phenomenex Kinetex C18, 150 × 4.6 mm; 95:5 water–acetonitrile with 0.1% formic acid; 1.0 mL/min; 284 nm) following Carrez clarification. Both analytes were quantified against five-point external calibration curves (r²>0.999; Sigma-Aldrich standards, purity ≥ 99%) ^1,3. Diastase activity was determined by the Phadebas method at 40°C/30 min and expressed as diastase number (DN). Proline content was quantified colorimetrically by the ninhydrin method at 505 nm. Water-insoluble matter was determined gravimetrically after filtration through a 0.45 µm glass-fibre filter ^3,4,9.

Microbiological Analyses: For enumeration assays, 10 g of each sample was homogenised with 90 mL of 0.1% peptone water (BagMixer 400, Interscience, France) for 1 min to yield the initial 10-1 suspension; serial dilutions (10-2 to 10-4) were prepared accordingly ¹³. The analytical detection limit for all enumeration methods was 10 CFU/g (1.0 log CFU/g). Total mesophilic aerobic bacteria (TMAB) were enumerated on Plate Count Agar (PCA; Merck) at 30±1 °C for 72 h ¹⁴. Total coliform bacteria were determined on Violet Red Bile Agar (VRBA; Merck) by double-layer pour plate at 30±1°C for 24 h (ISO 4832:2006) ¹⁵. Sulphite-reducing anaerobic bacteria were enumerated on SPS agar (Merck) under anaerobic conditions (AnaeroGen, Oxoid) at 35±2 °C for 18–48 h ¹⁶. Yeasts and moulds were counted on acidified PDA (pH 3.5±0.1; Merck) at 22±1 °C for 5–7 days ¹⁷. Results below the detection limit are reported as <1.0 log CFU/g or 'not detected in 1 g'.

Salmonella spp. detection followed ISO 6579-1:2017 using a 25 g test portion ¹⁸. Pre-enrichment was conducted in buffered peptone water (BPW; 37±1°C, 18 h), followed by selective enrichment in Rappaport–Vassiliadis soy broth (RVS; 41.5±1°C, 24 h) and Muller–Kauffmann tetrathionate/novobiocin broth (MKTTn; 37±1°C, 24 h). Isolates were plated onto XLD and Salmonella chromogenic agar (CAS; bioMérieux) and incubated at 37±1°C for 24–48 h. Suspected colonies were confirmed biochemically by API 20E (bioMérieux). Results are reported as 'Salmonella spp. detected' or 'Salmonella spp. not detected in 25 g'.

Statistical Analysis: Normality and variance homogeneity were assessed by Shapiro–Wilk and Levene's tests, respectively. Parametric data were analysed by one-way ANOVA with Tukey HSD post-hoc; non-parametric data were analysed by Kruskal–Wallis with Dunn–Bonferroni post-hoc. Effect sizes were reported as partial η²p (ANOVA) and ε² (Kruskal–Wallis), interpreted as small (<0.06), medium (0.06–0.13), or large (≥0.14). Spearman correlation and principal component analysis (PCA) on autoscaled data were used to explore multivariate structure. All analyses were performed in IBM SPSS Statistics v29.0 (IBM Corp., USA) at p<0.05.

Post-hoc statistical power analysis was conducted for all one-way ANOVA comparisons using Cohen's f as the effect size measure, calculated from partial eta-squared values (η²p) as f= √[η²p / (1 − η²p)], with a harmonic mean group size of n̄= 13.26 (derived from group sizes of n= 14, 14, and 12) and α= 0.05. For twelve of the fourteen significant physicochemical parameters, observed power (1 − β) exceeded 0.90 and reached 1.000 for HMF (η²p=0.931) and diastase activity (η²p=0.877), confirming that the study was well-powered for the detection of large effects. Free acidity, while yielding a statistically significant result (p<0.001), returned a moderate observed power (1−β=0.531, f=0.735), reflecting a medium effect size (η²p=0.351); the significant outcome nonetheless remains valid and is not subject to Type II error. For the two non-significant parameters, water-insoluble matter (η²p=0.014, f=0.119, 1−β=0.061) and total mesophilic aerobic bacteria (η²p=0.050, f=0.229, 1−β=0.092), the low observed power reflects near-zero effect sizes rather than inadequate sample size and is consistent with the biological interpretation that these parameters do not differ meaningfully among honey types.

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Table 1: Physicochemical parameters of chestnut, pine, and flower honeys from Kastamonu, Türkiye

Moisture, pH, Free Acidity, and Electrical Conductivity: Pine honey had the lowest moisture content; chestnut and flower honeys were statistically comparable (Table 1, Figure 1). Chestnut and pine honeys did not differ in pH, but both were significantly higher than flower honey (η²p=0.676), indicating a more acidic profile in the latter. Free acidity was lowest in chestnut honey, with pine and flower forming a common group. The electrical conductivity of chestnut and pine honeys significantly exceeded that of flower honey, consistent with the higher mineral load of honeydew-derived products.

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Figure 1: Distribution of physicochemical parameters (moisture, pH, free acidity, electrical conductivity, F/G ratio, and HMF) in Kastamonu honey types. Boxes show IQR with median; whiskers extend to 1.5 × IQR; dots represent individual observations. Different letters indicate significant differences (Tukey HSD or Dunn–Bonferroni, p<0.05).

Sugar Composition: Glucose was greatest in flower honey, inversely reflected by the F/G ratio, which was highest in chestnut honey. Pine honey was distinguished by markedly elevated maltose relative to both chestnut and flower honeys, a recognised biochemical signature of honeydew origin (Table 1, Figure 1). These patterns are reflected in the Spearman correlation matrix (Figure 2) and PCA loading directions (Figure 3).

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Figure 2: Spearman rank correlation matrix of physicochemical parameters of Kastamonu honey types. Colour intensity reflects the magnitude of rs; blue = positive, red = negative. Significance levels: * p<0.05, ** p<0.01, *** p<0.001.

HMF, Diastase Activity, and Proline: HMF yielded the largest effect size (η²p=0.931), with all three honey types forming statistically distinct groups: flower > chestnut > pine (Table 1). Effect sizes across all physicochemical parameters are illustrated in Figure 4, ranging from negligible for water-insoluble matter (η²p=0.014) to very large for HMF and diastase activity (η²p=0.877). Diastase activity and proline content both decreased in the order chestnut > pine > flower, each group significantly different from the others. All samples remained above the legal diastase minimum (8 DN) and substantially exceeded the proline authenticity threshold (180 mg/kg), confirming the absence of sugar adulteration.

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Figure 4: Effect sizes (partial eta-squared, η²p) for physicochemical parameters of Kastamonu honey types (chestnut, pine, and flower). Parameters are ranked in descending order. Dashed lines indicate conventional thresholds for medium (ε²≥0.06) and large (ε²≥0.14) effects.

Multivariate Structure: PCA achieved clear three-way botanical separation (Figure 3). PC1 was driven by HMF, diastase activity, and F/G ratio; PC2 discriminated along the maltose–EC axis. The group-level heatmap (Figure 5) consolidates these fingerprints: pine honey by elevated maltose, chestnut by high EC and F/G ratio, and flower honey by elevated HMF and glucose alongside suppressed diastase activity.

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Figure 3: PCA biplot of physicochemical parameters of Kastamonu honey types. Scores are coloured by honey type; loading vectors indicate variable contributions to PC1 and PC2. Data were autoscaled prior to analysis.

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Figure 5: Heatmap of z-score normalised group means for physicochemical parameters of Kastamonu honey types. Colour gradient: blue=below group mean; red=above group mean.

Microbiological Quality: Microbiological results are summarised in Table 2. TMAB counts were uniformly low across all honey types, with no statistically significant difference among groups (F(2.37) = 0.974, p=0.387, η²p=0.050). Yeasts and moulds were below the analytical detection limit (<1.0 log CFU/g) in all 40 samples. Coliform bacteria, sulphite-reducing anaerobic bacteria, and Salmonella spp. were not detected in any sample examined.

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Table 2: Microbiological quality of chestnut, pine, and flower honeys from Kastamonu, Türkiye (log CFU/g)

Moisture, Dry Matter, and Electrical Conductivity: Pine honey's lower moisture content is consistent with the prolonged evaporation associated with honeydew-based production ²⁵. Elevated EC in chestnut and pine honeys relative to flower honey reflects the higher mineral load of honeydew-derived and chestnut-origin products, corroborating findings from comparable Turkish localities. Demir Kanbur et al. ²⁶ reported the highest electrical conductivity values in chestnut honey from Senoz Valley, Rize (a river valley located in the Eastern Black Sea region of Türkiye) compared with highland honeys from the same area, and Saral ²⁷ documented EC values of 0.56–1.12 mS/cm in Artvin chestnut honeys, which is consistent with the 1.249±0.164 mS/cm observed here. That pine honey's EC was statistically indistinguishable from chestnut honey in this dataset may reflect the specific mineralogical profile of the Kastamonu locality ²⁵.

pH, Free Acidity, and Their Mechanistic Relationship: Flower honey had significantly lower pH (4.027±0.275) than chestnut (5.078±0.296) and pine honeys (4.844±0.363), despite comparable free acidity values–an apparent inconsistency that can be explained by differences in buffering capacity among honey types. The higher electrical conductivity of chestnut honey, indicative of a higher mineral load, may confer greater buffering capacity, potentially maintaining a higher pH despite moderate titratable acidity; flower honey's lower mineral-associated buffering reserve may yield a lower pH at similar acidity levels ²⁸. Comparable pH differentials between chestnut and polyfloral honeys have been reported by Akgün et al. ^29, who documented higher pH in chestnut honey relative to multifloral and acacia types from Ordu province, and by Gürbüz et al. ^22, who reported a mean pH of 4.10±0.73 across mixed-botanical Turkish honeys, which is consistent with the flower honey values observed here. This distinction is functionally relevant: the more acidic environment of flower honey may enhance hydrogen peroxide generation via glucose oxidase activity, a principal antimicrobial mechanism that operates in conjunction with osmotic pressure and phenolic compounds ³⁰; however, as water activity, osmolarity, and phenolic content were not directly measured in the present study, this interpretation should be regarded as mechanistically plausible rather than conclusively demonstrated.

Sugar Composition: Botanical Differentiation and Crystallisation Behaviour: Sugar profiles reflected well-established botanical fingerprints. The highest F/G ratio in chestnut honey (1.620±0.102) indicates the lowest crystallisation tendency among the three types, consistent with Ucurum et al. ^31, who reported F/G ratios exceeding 1.3 in both chestnut and pine honeydew honeys relative to multifloral flower types and identified sugar profile and electrical conductivity as key discriminant parameters between honeydew and flower honeys. The markedly elevated maltose in pine honey (7.866±1.273 g/100 g versus 2.156±0.532 in chestnut and 3.313±0.895 in flower) is a recognised biochemical signature of honeydew origin, consistent with the enzymatic processing of oligosaccharide-rich honeydew secretions on Pinus brutia ^25,32. This pattern aligns with findings from Ucurum et al. ^25, who documented elevated maltose and higher proline and diastase activity as characteristic features of Turkish pine honeydew honeys across 373 geographically distributed samples, and with Aslan and Arık Kibar ^24, who similarly confirmed higher diastase and acidity in pine honeys relative to Turkish flower types. Cobanoglu et al. ³³ further demonstrated that sugar and phenolic profiles together constitute reliable botanical differentiation markers between Turkish pine and flower honeys. The specific honeydew-producing insect(s) active in the Kastamonu locality were not determined in the present study. The strong negative maltose–F/G correlation (rs ≈ −0.80; Figure 2) is consistent with these botanical patterns, as increasing maltose reduces the relative fructose fraction, a relationship previously documented in comparative studies of honeydew and flower honeys ^25,31,32.

HMF and Diastase Activity and Proline: HMF formation proceeds via acid-catalysed fructose dehydration, predisposing lower-pH honeys, such as flower honey, to higher accumulation irrespective of thermal history ^34,35. Conversely, the mineral-rich matrix of pine honey confers a buffering effect that suppresses HMF formation, explaining the lowest values in that type. The inverse diastase gradient (chestnut > pine > flower) reflects not only processing history but also botanical differences in enzyme input: chestnut and honeydew honeys receive higher salivary enzyme contributions than most flower types ^25-27. Despite the lowest diastase activity in flower honey, all values exceeded the 8 DN legal minimum.

Proline decreased significantly in the order chestnut > pine > flower, yet all samples substantially exceeded the 180 mg/kg authenticity threshold; chestnut honey additionally surpassed the 500 mg/kg type-specific limit ^12, consistent with the proline-rich nectars of Castanea sativa reported across Turkish Black Sea localities ^22,27.

Multivariate Structure: PCA achieved clear three-way botanical separation, with PC1 driven by HMF, diastase, and F/G ratio, and PC2 discriminating along the maltose–EC axis – a structure paralleling multivariate differentiation reported for Turkish ²⁵ and Greek ³⁶ unifloral honeys, reinforcing cross-regional validity of these parameters as botanical discriminants. The Spearman matrix (Figure 2) captured mechanistically interpretable associations: maltose–F/G (rs ≈ −0.80), EC–free acidity (joint mineral–organic acid influence), and the inverse HMF–diastase relationship. The group heatmap (Figure 5) consolidates these as coherent typological signatures.

Microbiological Quality and Its Relationship to Physicochemical Properties: Yeasts and moulds were below the analytical detection limit (<1.0 log CFU/g) in all 40 samples, while coliform bacteria, sulphite-reducing anaerobic bacteria, and Salmonella spp. were not detected in any sample examined. These findings are consistent with a growing body of evidence documenting the microbiological safety of high-quality honeys across different geographical and botanical origins. Gomes et al. ⁴ reported the complete absence of faecal coliforms, sulphite-reducing clostridia, and Salmonella in commercial Portuguese honeys, attributing this to the synergistic inhibitory effects of low water activity, acidic pH, and hydrogen peroxide generation. Tornuk et al. ⁷ similarly reported undetectable yeast and mould counts in artisanal Turkish flower honeys, and Luca et al. ⁸ comprehensively documented the antimicrobial mechanisms (including osmotic stress, low pH, and hydrogen peroxide generation) that collectively suppress microbial proliferation in honey. Kędzierska-Matysek et al. ³⁷ found that 52% of Polish artisanal varietal honeys contained fewer than 10 CFU/g of total bacteria, with fungal counts below 10 CFU/g in 81% of samples, while Pauliuc et al. ³⁸ reported satisfactory microbiological profiles with no detectable Salmonella or pathogenic Enterobacteriaceae across five botanical honey types from Bucovina, Romania. The absence of a microbiological gradient mirroring the substantial physicochemical differences observed among honey types in the present study is consistent with a threshold effect: although pH, EC, and sugar composition differed significantly, all types maintained inhibitory conditions sufficient to prevent microbial proliferation. Moisture content data (15.8–17.1%) correspond to expected a_w values of approximately 0.55–0.62, based on published reference values for comparable honey types ⁵. These values provide indirect evidence that water activity suppression was maintained throughout. Once moisture and pH fall within inhibitory ranges, botanical-origin differences in antimicrobial intensity become of limited relevance to routine microbiological safety ^8,30.

The most discriminant parameters (HMF, diastase activity, maltose, glucose, and F/G ratio) closely mirror the botanical and biochemical logic of each honey type. The markedly elevated maltose content of pine honey constitutes a robust biochemical fingerprint of honeydew origin; the identity of the honeydew-producing insect(s) active in the Kastamonu locality was not determined in the present study and warrants further investigation. The pH–free acidity paradox in flower honey is explained by differences in buffering capacity. Divergent HMF–diastase profiles reflect both post-harvest conditions and intrinsic botanical differences in enzyme input and acid-catalysed fructose dehydration, underscoring the need for type-specific rather than uniform quality interpretation.

Microbiologically, all types were free from coliforms, pathogens, and detectable yeast–mould growth, with uniformly low and statistically indistinguishable TMAB counts. This supports the view that honey's microbiological safety is determined primarily by process hygiene and moisture-mediated water activity suppression, rather than botanical origin ^5,8,30.

Collectively, the physicochemical and microbiological data generated in this study provide a comprehensive quality baseline for chestnut, pine, and flower honeys from Kastamonu province, Türkiye. The parameters examined (including moisture, HMF, diastase activity, sugar composition, electrical conductivity, free acidity, and proline) are established indicators of honey quality, maturity, and post-harvest handling integrity, and the dataset may serve as a regional reference for routine quality monitoring. It should be acknowledged, however, that the establishment of geographical indication status requires additional analytical dimensions, particularly melissopalynological profiling, phenolic fingerprinting, and stable isotope analysis. These were beyond the scope of the present study. Future research incorporating these approaches would more directly address botanical authentication and geographical indication documentation for Kastamonu honeys.

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