Ageing related states of complex network formation in areca nuts

TL;DR

This study introduces a novel, low-cost method using smartphone imaging and quantum mechanical concepts to analyze and differentiate the internal ageing patterns of areca nuts, revealing intrinsic similarities and diversity in ageing.

Contribution

The paper presents the first application of density of states (DOS) in analyzing complex internal patterns of areca nuts for age classification, combining biophysical mathematics with accessible imaging.

Findings

DOS provides a unique age-related coefficient.

Aged and less aged nuts show distinct average DOS values.

The method identifies intrinsic ageing similarities without complex algorithms.

Abstract

Complex pattern formation is an essential characteristic of plants and their ageing, growth, and evolution. Perception of these patterns is an intrinsic nature of plant-dependent animals for coexistence. Areca nut consisting of complex patterns is considered to be addictive for humans and has increased adverse health effects. However, no critical study is performed on the complex pattern of the areca nut. A large number of areca nuts has been studied since 2017 to develop a low-cost tool for the LMICs to categorise areca nuts. We present the first finding to identify similarities among complex networks of differently aged areca nuts by investigating the internal patterns of randomly chosen nuts from the same age group. We developed a smartphone camera-based high-resolution measurement with comprehensive biophysical mathematics and a quantum mechanical concept called density of states (DOS). We found that the DOS can provide a unique coefficient to represent age and ageing together using a single number. The average of these single numbers for less aged nuts and highly aged nuts are 4.9 and 3.8, respectively. If fruit looks aged from its external morphology as well as internal morphology, our method identifies the intrinsic similarities among the ageing networks without implementing any computationally expensive search algorithm. We show clear evidence of the diversity of ageing from relative and absolute colour vision perspectives. We have also conducted further analyses of local DOS, Fourier decomposition, correlation study, spectral decomposition, and structural similarity index.