Physicochemical and Microbiological Changes in Non-Sulfitted Dried Apricots as Affected by Storage Condition

Abstract

This study was conducted to determine the changes in the physicochemical and microbiological qualities of the non-sulfited sun dried apricots (NSDAs) at three different moisture contents (MCs, 13.7, 23.5 and 27.0%) and at four different storage temperatures (4, 10, 20, and 30 °C) for 12 months in bulk and packaged samples. NSDAs were subjected to physical (moisture, water activity, pH and reflectance colour values), chemical (browning formation, β-carotene, and acidity), and microbial (counts for total aerobic mesophilic and psycrofilic bacteria, yeast and mould, xerofilic mould and yeast, osmophilic yeast, lactic acid bacteria, Enterobacteriaceae, Staphylococcus-Micrococcus spp.) analyses at one-month time intervals. Results indicated that while the fastest brown colour formation occurred in the samples containing 23.5% MC and stored at 30 °C, the browning decelerated below 10 °C. No significant change was observed in β-carotene contents of NSDAs during storage (P>0.05). After rehydration, osmophilic yeast count of NSDAs increased 3 by 2.5 log colony-forming units (cfu/g). Significant reductions were observed in the microbial loads of the samples at 23.5 and 27.0% MCs with the decreasing water activity (P<0.05) during storage and increasing storage temperatures. However, yeast and mould counts exceeded 5.0 log cfu/g of the samples containing 27.0% MC after 2-months storage at 20 °C. Overall, we suggest that MC and storage temperature for NSDAs should be below 23.5% and 20 °C to achieve high microbial and physicochemical qualities for a year, respectively.

Impact of germination pre-treatments on buckwheat and Quinoa: Mitigation of anti-nutrient content and enhancement of antioxidant properties

“…The measurements were recorded L* (Lightness), +a* (redness), +b* (yellowness), C* (Chroma), and h° (hue angle) color coordinates. The total color changes (ΔE) were calculated using L*, a* and b* coordinates according to the following equations ( Güzel et al, 2022 ). ΔΕ = [(Δa) 2 + (Δb) 2 + (ΔL) 2 ] 0.5 …”

Using Machine Learning Algorithms to Predict the Sweetness of Bananas at Different Drying Times

“…Therefore, the color of dried fruits changes due to the formation of brown color, which is affected by temperature and relative humidity. Research shows that the formation of brown color in dried fruits increases with increasing temperature and decreasing humidity (Güzel et al, 2022). Baini and Langrish (2009) dried bananas at 70 degrees Celsius and relative humidity from 15% to 40% and found that the change in redness of the sample dried at 15% relative humidity was 67% higher than that of the sample dried at 40% relative humidity.…”