The estimation of ripeness is a significant section of quality determination since maturity at harvest can affect sensory and storage properties of fruits. A possible tactic for defining the grade of ripeness is sensing the aromatic volatiles released by fruit using electronic nose (e‐nose). For detection of the five ripeness grades of berries (whiteberry and blackberry), the e‐nose machine was designed and fabricated. Artificial neural networks (ANN), principal components analysis (PCA), and linear discriminant analysis (LDA) were applied for pattern recognition of array sensors. The best structure (10–11‐5) can classify the samples in five classes in ANN analysis with a precision of 100% and 88.3% for blackberry and whiteberry, respectively. Also, PCA analysis characterized 97% and 93% variance in the blackberry and whiteberry, respectively. The least correct classification for whiteberry was observed in the LDA method.

No abstract

SummaryThe objective of the experiments was to study some physical properties of potato tubers, such as dimensions, weight, projected area, sliding and rolling friction properties, in order to determine the best post-harvest options. Mean values of weight, length, width, thickness and CPA were 136.69 g, 78.99 mm, 57.12 mm, 50.44 mm, and 33.12 cm 2 , respectively. The lowest values of the coefficients of rolling and sliding friction were obtained for sheet glass. Tuber mass was predicted based on the dimensions, projected area and volume. Linear models and nonlinear models were investigated. The results indicated that best model for predicting tuber mass was based on projected area with R 2 = 0.99.

Energy consumption index is one of the most important criteria for judging about new, and emerging drying technologies. One of such novel and promising alternative of drying process is called electrohydrodynamic (EHD) drying. In this work, a solar energy was used to maintain required energy of EHD drying process. Moreover, response surface methodology (RSM) was used to build a predictive model in order to investigate the combined effects of independent variables such as applied voltage, field strength, number of discharge electrode (needle), and air velocity on moisture ratio, energy efficiency, and energy consumption as responses of EHD drying process. Three-levels and four-factor Box–Behnken design was employed to evaluate the effects of independent variables on system responses. A stepwise approach was followed to build up a model that can map the entire response surface. The interior relationships between parameters were well defined by RSM.

No abstract