Physico-chemical, Enzymatic, Mineral and Colour Characterization of Three Different Varieties of Honeys from Kashmir Valley of India with a Multivariate Approach

Abstract

The present study was undertaken to determine the physico-chemical properties (moisture content, reducing sugars, proline content, electrical conductivity, ash content, pH, titrable acidity, HMF, water activity, total soluble solids and total solids), enzymatic activity (diastase and invertase), colour characteristics (mmpfund, ABS 450 and CIE L * a * b * ) and mineral content (Cu, Mn, Fe, Zn, Pb and Cd) of three different varieties of honeys from Kashmir valley of India (acacia honey, pine honeydew and multifl oral honey). Of the honey samples analysed, only pine honeydew were grouped in dark category of honey (L * <50) while acacia honey and multifl oral honey were confi rmed as light coloured honeys (L * >50) and possessed both red and yellow components. The concentrations of mineral content were found highest in pine honeydew followed by multi fl oral and least in acacia honey. All the physico-chemical properties and enzymatic activity indicated that all the three analysed varieties of honey met the criteria set by the International Honey Commission and revised codex standards for honey. The source of honey had a signifi cant effect (p<0.05) on physico-chemical characteristics, enzymatic activity, mineral content and colour properties. Strong and positive correlations exhibited among minerals, colour (mmpfund) and L * value indicated that dark coloured honey contained high mineral content. Multivariate analysis proved to be an effective tool in classifying the three varieties of honey based on physico-chemical characteristics, enzymatic activity, mineral content and colour properties.

Phenolic profiles, antioxidant, antimicrobial, and DNA damage inhibitory activities of chestnut honeys from Black Sea Region of Turkey

“…CH is known for its dark color like oak, heather, and pine honeys (Küçük et al, ). According to the results of a large number of studies, it is accepted that phenolic contents and antioxidant activities of dark colored honeys are higher when compared with light colored ones (Beretta et al, ; Bertoncelj, Doberšek, Jamnik, & Golob, ; Can et al, ; Holderna‐Kedzia & Kedzia, ; Nayik & Nanda, ). Various studies indicated that the color of honey could be determined by sensory and instrumental methods (Terrab, Díez, & Heredia, ).…”

The impact of different heating temperatures on physicochemical, color attributes, and antioxidant activity parameters of Greek honeys

“…Some typical examples of storage may include conventional storage at home or refrigerated storage for the design and completion of a research study, such in our case. The higher antioxidant stability of dark colored honeys during storage is owed to their much higher pigment content compared to blossom honeys (Gheldof & Engeseth, ; Karabagias et al, ; Nayik & Nanda, ), comprising thus, an additional parameter of product's nutritional character during storage under refrigeration.…”

“…The great variability in honey composition and properties is owed to numerous factors including: botanical and geographical origin, bee species, harvesting season, processing conditions, beekeepers manipulations and practices, along with storage time, etc. (Chaikham, Kemsawasd, & Apichartsrangkoon, ; Gheldof, Wang, & Engeseth, ; Karabagias, Dimitriou, Kontakos, & Kontominas, ; Louppis, Karabagias, Kontakos, Kontominas, & Papastephanou, ; Nayik et al, ; Nayik, Dar, & Nanda, ; Nayik & Nanda, 2015a, , ; Wang, Gheldof, & Engeseth, ).…”

Rheological and Thermal Properties of Selected Brazilian Honeys from Various Floral Origins

“…A principal component analysis (PCA) was conducted to evaluate the overall effect of the physicochemical (moisture content, ash content, a w , HMF, and pH) rheological (η [10C], η [15C], η [20C], η [25C], η [30C], G′ [1 Hz] and G″ [1 Hz]), and thermal (T g ) parameters, by a descriptive perspective. This methodology is applied to achieve a reduction of original data matrix while retaining the maximum amount of variability present in the data (Nayik and Nanda ). In a PCA analysis, when the components accumulate a high relative percentage of all variation (>70%), it can explain the variability shown between the samples (Mardia et al .…”