Mechanical Properties and Chemical Composition of Peanuts as Affected by Harvest Date and Maturity

Abstract

Harvest date and maturity (by the Hull Scrape method) were compared to mechanical properties and chemical composition of peanuts. In general, later-harvested or mature peanuts exhibited higher cutting and shear-compression forces, shear-compression energy and slope of forcedeformation curve than earlier-harvested or immature peanuts. Mesocarp color of peanuts could be used as an index to group peanuts with similar shear-compression energy (sensory crunchiness). Harvest date did not affect dry basis chemical composition except carbohydrates content decreased slightly as digging was delayed. Oil content increased and carbohydrates decreased as peanuts matured. Total unsaturated fatty acids content increased with maturity.

A comprehensive look at the effect of processing on peanut (Arachis spp.) texture

“…However, the effect of the material is still present, as samples made from a given material are not entirely randomly dispersed in the texture space. This is not surprising, as textural differences between different market types and maturity levels have been reported and can be attributed to the significant compositional differences between peanut types (as shown in Table ): for instance, Spanish type peanuts consist, on average, of 10% more dietary fiber compared to Virginia type peanuts, while the sugar content of peanuts is known to vary based on genetics and growing conditions . The sensory attributes were further modeled against the large‐deformation instrumental attributes using partial least squares regression, and the results can be found in the supporting information.…”

Multivariate relationships between instrumental attributes, microstructure, sensory profiles and consumer preference in roasted peanuts (Arachis spp)

“…However, the effect of the material is still present, as samples made from a given material are not entirely randomly dispersed in the texture space. This is not surprising, as textural differences between different market types and maturity levels have been reported (Kim & Hung, 1991;, and can be attributed to the significant compositional differences between peanut types: for instance, Spanish type peanuts consist on average of 10% more dietary fibre compared to Virginia type peanuts (USDA, 2015b), while the sugar content of peanuts is known to vary based on genetics and growing conditions . The sensory attributes were further modelled against the large deformation instrumental attributes using Partial Least Square regression, and the results can be found in the supplementary material.…”

“…As a consequence of reaching anthesis on different days, each pod within a plant will reach maturity at a different time, and so at the time of harvest there will be a distribution of pod maturity levels. This has large implications on the kernel quality and organoleptic characteristics (Kim & Hung, 1991;Sanders, 1989;Sanders, Vercellotti, Blankenship, Crippen, & Civille, 1989;.…”

“…Some researchers have attempted to standardize the shape of nuts (pecans) by trimming with blades (Anzaldua- Morales & Brusewitz, 1999;Surjadinata, Brusewitz, & Bellmer, 2001), but in the current study some of the crunchiest samples would crumble if a cut was attempted. Finally, since a distribution of different maturity levels in the sample is unavoidable , and since maturity level is known to affect microstructure and therefore texture (Kim & Hung, 1991;, enough repetitions had to be made to minimize the signal to noise ratio (Vivar & Brennan, 1980). The selected probes (VBJ and P/2) were shown to fit the sample geometry and provided repeatable results.…”

“…Such examples can be found, but often the primary focus of the research lies on characterization of a particular process (Idrus & Yang, 2012; or raw material (Kim & Hung, 1991), and thus the correlation was developed for a restricted sample set. In other cases, the primary focus was on storage changes and so the lexica and instrumental methods were optimized to resolve smaller stimuli ranges (Lee & Resurreccion, 2004.…”