Abstract Agaricus bisporus was washed with 1 g/L sodium chlorite (NaClO2) (SC) solution for 30, 60 and 120 s, packed with a non-perforated polyvinyl chloride (PVC) film and stored at 4 °C for 12 days. Washing the mushrooms with the SC solution and packaging with the PVC film slowed down the change in colour and firmness of the mushrooms, but treating the mushrooms with the SC solution longer than 60 s adversely affected the mushroom texture. After 12 days of storage, the weight loss of the mushrooms packed with the PVC film was less than 10% compared to the control. Headspace O2 concentration decreased while CO2 concentration increased throughout 12 days of storage and reached about 15% and 4%, respectively. Pseudomonas spp. counts for unwashed and unpacked mushrooms reached 10.30 log10 CFU/g after 12 days of storage while the mushrooms washed with the SC solution and packaged with the PVC film had Pseudomonas spp. counts as low as 6.62 log10 CFU/g. Treating the mushrooms with the SC solution for 60 s and packaging with the PVC film were found to be the most appropriate treatment to minimize the change on the postharvest quality of white button mushroom.

Abstract The aim of this study was to investigate the effect of hot air drying on quality characteristics, physicochemical properties, morphological structure and organoleptic characteristics of bee pollen, and compute the effective moisture diffusivity and activation energy during hot air drying of bee pollen. Bee pollen samples were dried at 40, 45, 50, 55 and 60 °C. Effective moisture diffusivity (Deff) values ranged from 1.38 × 10 −10 to 4.00 × 10−10 m2/s, and the activation energy (Ea) was found to be 42.96 kJ/mol. Protein, fat, total carbohydrates and vitamin C of bee pollen were affected by the drying temperature. Dried bee pollen samples had high solubility index, and had lower L* and b* values as compared with those of the fresh bee pollen. Total color difference (Δ E) was the lowest for the bee pollen dried at 40 °C. Morphological changes on dried bee pollen surfaces increased with increasing the drying temperature. Bee pollen dried at 40 °C took the highest sensory scores and retained its quality attributes better than the bee pollen samples dried at 45, 50, 55 and 60 °C. Hot air drying at 40 °C is recommended for the drying of bee pollen.

Abstract The colour of food is one of the most important factors affecting consumers’ purchasing decision. Although there are many colour spaces, the most widely used colour space in the food industry is L*a*b* colour space. Conventionally, the colour of foods is analysed with a colorimeter that measures small and non-representative areas of the food and the measurements usually vary depending on the point where the measurement is taken. This leads to the development of alternative colour analysis techniques. In this work, a simple and alternative method to measure the colour of foods known as “computer vision system” is presented and justified. With the aid of the computer vision system, foods that are homogenous and uniform in colour and shape could be classified with regard to their colours in a fast, inexpensive and simple way. This system could also be used to distinguish the defectives from the non-defectives. Quality parameters of meat and dairy products could be monitored without any physical contact, which causes contamination during sampling.

Abstract The colour assessment ability of the computer vision system is investigated and the data are compared with colour measurements taken by a conventional colorimeter. Linear and quadratic models are built to improve currently used methodology for the conversion of RGB colour units to L * a * b * colour space. For this purpose, two innovative ideas are proposed and tested. First, substantial amount of colour tones is generated to cover as many points in the colour space as possible. Secondly, the colour space is calibrated separately, whereas in previous research in the literature, the colour space is calibrated simultaneously. It is found that the RGB colour units to L * a * b * colour space transformation approach proposed in this study is more logical and more accurate, and the prediction performance of the quadratic model is superior over the linear model.