Varietal Differences in the Physical and Engineering Attributes of Underutilized Pigmented and Non-pigmented Paddy and Rice Landraces

Abstract

Traditional rice landraces cannot be processed using standard machinery or unit operations. Traditional rice, on the other hand, is becoming more popular as its purported health benefits spread. Cracked rice grains, broken rice, and subpar milled rice kernels result from using non-standard equipment on landraces. So, studying the physical characteristics of rice and paddy is crucial for modifying milling equipment to lessen post-harvest losses. To preserve and familiarize the underutilized landraces, a comparative study of three pigmented and three non-pigmented landraces were assessed for their physical, gravimetric, and engineering traits using standard analytical protocols. The analysis demonstrated substantial variation among the evaluated properties. The majority of the traditional landraces were classified as long-bold and long-slender grains. The landraces exhibited variations in the equivalent diameter ranging from 0.29 to 0.46 mm and 2.54 to 3.38 mm for rice and paddy samples respectively. The major differences in other attributes like sphericity, aspect ratio, and surface area were also observed. Gravimetric properties of paddy and rice samples of Madumuzhungi (MMP) showed significantly lower values in contrast to other landraces. The highest porosity (51.43%), moisture content (14.97%) and water activity (0.88) was exhibited by the Madumuzhungi paddy sample. The non-pigmented rice samples displayed lower values for thousand kernal weight (TKW), grain volume and surface area. Almost all the variants except White mappillai samba paddy were under 30 g for TKW, indicating the differences between pigmented and non-pigmented landraces. Moisture content significantly affected the gravimetric and engineering properties of the landraces. The correlations between the dimensions and engineering qualities were investigated using principal component analysis (PCA). Five principal components, which account for 100% of the total variance, were used via PCA to reduce the dimensionality of the data.