Optimization for spray drying process parameters of nutritionally rich honey powder using response surface methodology

The purpose of the present work was to study the effect of inlet temperature (160–180°C), feed rate (0.08–0.13 ml/s), concentration of gum Arabic (35–45%), aonla extract (6–8%), and basil extract (6–8%) on the product properties (bulk density, hygroscopicity, total phenolic content (TPC), antioxidant activity (AOA), and vitamin C content) of spray-dried nutritionally rich honey powder using response surface methodology. Higher inlet air temperatures led to lower bulk density and hygroscopicity, whereas addition of aonla and basil extracts led to higher TPC, AOA, and vitamin C content which were encapsulated by gum Arabic. Statistical analysis showed that independent variables significantly affected all the responses (p < 0.0001). Perturbation and 3D surface plots were drawn for each of the responses from the mathematical models. Second-order polynomial models with high R2 (0.97–0.99) values were constructed for each powder physicochemical properties namely bulk density, hygroscopicity, TPC, AOA, and vitamin C content. Desirable nutritionally rich honey powder was obtained at inlet temperature of 170°C, 0.11 ml/s feed rate, 45% gum Arabic, 8% aonla extract, and 6% basil extract

Discrimination of high altitude Indian honey by chemometric approach according to their antioxidant properties and macro minerals

The study was intended to characterize three honeys (acacia, pine honeydew and multifloral) from high altitude Kashmir valley of India according to their macro minerals (K, Ca, Na and P), antioxidant properties and sugar parameters. The result for total phenolic content (22.68–59.84 mg GAE/100 g) and total flavonoid content (6.10–8.12 mg QE/100 g), revealed that honeys from Kashmir valley have high antioxidant activity. Principal component analysis (PCA), explained more than 81% of the variance. Four sugars were identified and quantified by HPLC, which include monosaccharides and disaccharides. Chemometric methods such as principal component analysis and linear discriminate techniques were applied on the data in order to differentiate the honeys. PCA explained more than 81% of the variance with the first two PC variables with minerals and antioxidant properties having highest discriminating power while LDA successfully classified all the unifloral honey samples. Keywords: Honey, Antioxidant, Minerals, PCA, LD