This work aimed to evaluate the effect of Atmospheric Cold Plasma (ACP) on the quality of mango flour noodles (NMF). ACP treatment of 5 minutes duration on the surface of the noodles strands were performed and evaluated during three days of storage by monitoring parameters related to colour, water activity, antioxidant activity and total phenolic content. The lightness value (L*) was higher for untreated samples (NMF (U)) than for treated samples (NMF (T)), while a greater increased in the redness (a*) and yellowness (b*) values were observed for the NMF (T). The changes in aw, antioxidant activity and total phenolic content (TPC) were negligible. However the NMF (T) showed significant different (p
Seed purity is a crucial seed quality parameter in the Malaysian rice seed standard. The use of
high quality cultivated rice seed, free of any foreign seeds, is the prerequisite to sustaining high
yield in rice production. The presence of foreign seeds such as weedy rice in the cultivated rice
seeds used by the farmers can adversely affect growth and yield as it competes for space and
nutrients with the cultivated rice varieties in the field. Being the most dominant and competitive
element compared to the cultivated rice seeds, the Malaysian seed standard prescribed that the
maximum allowable of weed seeds in a 20-kilogram certified rice seed bag produced by local
rice seed processors is 10 weed seeds per kilogram. The current cleaning processes that rely
mostly on the difference in physical traits do not guarantee effective separation of weedy rice
seeds from the lots. Seed bags found to contain more than 10 weed seeds upon inspection by
the enforcing agency will not be approved for distribution to farmers. The paper describes a
study carried out to explore the use of machine vision approach to separate weedy rice seed
from cultivated rice seeds as a potential cleaning technique for the rice seed industry. The mean
classification accuracies levels of the extracted morphological feature model were achieved at
95.8% and 96.0% for training and testing data sets respectively.