Estimation of the shelf life of pezik pickles using Weibull hazard analysis

Estimating average wind speed in Thailand using confidence intervals for common mean of several Weibull distributions

The Weibull distribution has been used to analyze data from many fields, including engineering, survival and lifetime analysis, and weather forecasting, particularly wind speed data. It is useful to measure the central tendency of wind speed data in specific locations using statistical parameters for instance the mean to accurately forecast the severity of future catastrophic events. In particular, the common mean of several independent wind speed samples collected from different locations is a useful statistic. To explore wind speed data from several areas in Surat Thani province, a large province in southern Thailand, we constructed estimates of the confidence interval for the common mean of several Weibull distributions using the Bayesian equitailed confidence interval and the highest posterior density interval using the gamma prior. Their performances are compared with those of the generalized confidence interval and the adjusted method of variance estimates recovery based on their coverage probabilities and expected lengths. The results demonstrate that when the common mean is small and the sample size is large, the Bayesian highest posterior density interval performed the best since its coverage probabilities were higher than the nominal confidence level and it provided the shortest expected lengths. Moreover, the generalized confidence interval performed well in some scenarios whereas adjusted method of variance estimates recovery did not. The approaches were used to estimate the common mean of real wind speed datasets from several areas in Surat Thani province, Thailand, fitted to Weibull distributions. These results support the simulation results in that the Bayesian methods performed the best. Hence, the Bayesian highest posterior density interval is the most appropriate method for establishing the confidence interval for the common mean of several Weibull distributions.

Multiple regression models and Artificial Neural Network (ANN) as prediction tools of changes in overall quality during the storage of spreadable processed Gouda cheese

The aim of the study was to compare the ability of multiple linear regression (MLR) and Artificial Neural Network (ANN) to predict the overall quality of spreadable Gouda cheese during storage at 8 °C, 20 °C and 30 °C. The ANN used five factors selected by Principal Component Analysis, which was used as input data for the ANN calculation. The datasets were divided into three subsets: a training set, a validation set, and a test set. The multiple regression models were highly significant with high determination coefficients: R2 = 0.99, 0.87 and 0.87 for 8, 20 and 30 °C, respectively, which made them a useful tool to predict quality deterioration. Simultaneously, the artificial neural networks models with determination coefficient of R2 = 0.99, 0.96 and 0.96 for 8, 20 and 30 °C, respectively were built. The models based on ANNs with higher values of determination coefficients and lower RMSE values proved to be more accurate. The best fit of the model to the experimental data was found for processed cheese stored at 8 °C.

Greek Coffee Quality Loss During Home Storage: Modeling the Effect of Temperature and Water Activity

The aim of this study was to monitor and characterize Greek coffee staling during home storage (secondary shelf life, SSL) using sensory evaluation techniques. Storage temperature (T) and product water activity (a_w ) are considered as the major factors affecting SSL. Water sorption isotherms fitted to Guggenheim Anderson-de Boer model were used to predict product stability; coffee samples were stable at a_w < 0.52. Coffee samples equilibrated at a_w = 0.15 (the fresh sample), 0.22, 0.33, and 0.52 were stored at T = 25 °C, 35 °C, and 45 °C under simulated home storage conditions. Samples were obtained at appropriate times for each T and a_w condition and sensorially evaluated. Greek coffee brews were prepared and freshly served during sensory evaluation. The use of Weibull hazard analysis provided an effective approach to SSL determination as a function of T and a_w . SSL values ranged from 20 (Τ = 45 °C, a_w = 0.52) to 104 days (Τ = 25 °C, a_w = 0.15). Quality loss based on coffee aroma changes (aroma quality, aroma intensity, aftertaste, off-flavor) was also studied (S_i , sensory scoring using 9-point magnitude scale) and kinetically modeled. Quality loss rates (k_i ) were calculated and used to predict SSL values. Based on the results of both Weibull hazard analysis and sensory scoring of individual aroma characteristics, it was concluded that the lowest SSL was calculated for a_w = 0.52 at T = 35 °C to 45 °C. The temperature dependence of aftertaste, aroma quality loss, and off-flavor production was not statistically significant for 0.15 < a_w < 0.33 (P > 0.05); the aroma intensity was the most sensitive parameter. PRACTICAL APPLICATIONS: Secondary shelf life (SSL) represents the time after pack opening during which a food maintains an acceptable quality level. During home/catering usage, coffee is not consumed immediately after pack opening. During consumption, quality degradation reactions proceed with higher rates due to variable storage conditions mainly related to atmosphere changes in the pack, leading to the entrance of oxygen/moisture and temperature. Therefore, SSL is important, and can be used as a tool for product management during consumption reducing food waste. There is a lack of studies dealing with SSL prediction of coffee, while no studies have been carried out on Greek coffee.

Kinetic models of quality parameters of spreadable processed Gouda cheese during storage

The aim of the study was to prepare mathematical models based on the Arrhenius equation as predictive tools for the assessment of changes in quality parameters during the storage of spreadable Gouda cheese at temperatures of 8, 20 and 30 °C. The activation energy value and the chemical reaction rate constant enabled the construction of kinetic models, which helped to estimate the direction and rate of changes. Moreover, the activation energy (E_a) of the quality parameters was used to determine the sequence of their vulnerability during storage. The value of activation energy corresponding to temperature changes resulted in the following order of susceptibility of the quality parameters: ΔC > ΔE ≈ water activity > texture parameters > pH > colour > sensory parameters > rheological parameters. The research showed limited applicability of the mathematical models for estimation of quality parameters referring to spreadable processed Gouda cheese.