Browsing by Author "Rivera S"

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    Blueberry firmness - A review of the textural and mechanical properties used in quality evaluations
    (Elsevier B.V, 2022-10-01) Rivera S; Giongo L; Cappai F; Kerckhoffs H; Sofkova-Bobcheva S; Hutchins D; East A
    Firmness is an important parameter for fresh blueberries as it influences the quality perceived by consumers and postharvest storage potential. However, the blueberry research community has not yet identified a universal standard method that can evaluate firmness for quality purposes. Different mechanical tests have been considered, offering different perspectives on this quality trait. This review summarises the most common methods previously used to evaluate textural and mechanical properties of fresh blueberries as influenced by pre- and postharvest factors. In addition, this review intends to assist the blueberry research community and commercial supply chain when selecting suitable methods to measure blueberry firmness as a fruit quality response. Different research initiatives to develop, optimize or standardise instrumental methods to assess blueberry firmness and relate to consumer sensory perception are reviewed. Mechanical parameters obtained by compression tests are the most previously used techniques to evaluate the influence of genotype, maturity, calcium, and postharvest management on blueberry firmness or to relate to sensory descriptors. However, standardising operational settings (e.g., compression distance, loading speed, and calculation procedures) is required to make results comparable across data collection conditions. Whether other mechanical test methods such as penetration or a combination of tests can better characterise blueberry quality or the relationship with consumer acceptance remains unknown and is worth studying.
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    Data of texture profile analysis performed by different input settings on stored ‘Nui’ and ‘Rahi’ blueberries
    (Elsevier Inc, 2021-10) Rivera S; Kerckhoffs H; Sofkova-Bobcheva S; Hutchins D; East A
    Texture Profile Analysis is a well-established method for assessing mechanical properties of horticultural food products and consists of two compression cycles on a repeated motion to a given strain using a flat surface probe (i.e., compression plate). Input settings of target deformation (strain%) and duration (s) between compression cycles utilized for Texture Profile Analysis could influence output mechanical properties. The article provides data related to the ability of different Texture Profile Analysis operational settings to enable the separation of blueberries with variable mechanical properties. To create variable mechanical parameters of ‘Nui’ and ‘Rahi’ blueberries, fruit was stored in four relative humidity for 21 d at 4°C. For each storage humidity, mechanical properties of hardness (BH, N), hardness slope (BHS, kN m−1), apparent modulus of elasticity (E, MPa), and resilience (BR, -) were determined by utilizing two strain (15% or 30% of berry equatorial height). Meanwhile, mechanical parameters of cohesiveness (BCo, -), and springiness (BSp, -) were obtained by utilizing the combination of two strain (15% or 30%) and two duration between cycles (2 s and 10 s) as TPA operational settings. The statistical evaluation was conducted by one-way ANOVA, and the means of each storage humidity were separated according to the Tukey-HSD test (P = 0.05). The data presented in this article was used to select the Texture Profile Analysis operational settings utilized in the article entitled “Influence of water loss on mechanical properties of stored blueberries” Rivera et al. [1].
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    Influence of water loss on mechanical properties of stored blueberries
    (Elsevier, 27/02/2021) Rivera S; Kerckhoffs H; Sofkova-Bobcheva S; Hutchins D; East A
    Moisture loss is considered a main cause of blueberry softening during postharvest storage. However, the causal relationship between softening and water loss has only previously been described by force to 1 mm compression. This study was performed to identify suitable instrumental tests that allow the separation of blueberries with different water loss values during storage. Mechanical properties were measured by double compression (Texture Profile Analysis) and puncture test. Variability on blueberry mechanical properties was created by regulating storage humidity and consequently water loss. As water loss increases during storage, hardness slope (slope of a straight line drawn between the trigger force of 0.06 N and the force at 15 % strain) obtained by the compression test reduces, and the displacement at berry skin break obtained by puncture test, increases. Therefore, these parameters can be potentially used to quantify mechanical changes in stored blueberries.
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    Non-destructive firmness assessment of ‘SunGold’ kiwifruit a three-year study
    (Taylor and Francis Group on behalf of the Royal Society of New Zealand, 2024-02-14) Sneddon T; Rivera S; Li M; Heyes J; East A; Golding J
    Kiwifruit (Actinidia chinensis var. chinensis) firmness is routinely measured in a destructive manner for decision-making purposes. Thus, a population’s quality is inferred by measuring a sample from that population. Consequently, studies have investigated non-destructive techniques for measuring fruit firmness. However, most of these studies have been restricted to a single season or focused on performance over long-term storage. This work compared non-destructive compression (1 mm deformation) and acoustic stiffness with flesh firmness measured with a penetrometer across three seasons. ‘SunGold’ kiwifruit were harvested from 11, 9 and 3 orchards on multiple occasions in 2020, 2021 and 2022, respectively. Kiwifruit was freighted to Palmerston North and assessed on arrival. Thirty fruit per orchard were measured on lab arrival, whilst 24 fruit per orchard were stored for two weeks at 0°C prior to assessment. The non-destructive methods had a strong (r2 > 0.89–0.92) segmented correlation with flesh firmness (0.52–10 kgf). Flesh firmness could be adequately estimated with the non-destructive methods within a season. However, segmented regression performance was reduced when predicting for a season outside of the training population. Nonetheless, these non-destructive methods may be useful for estimating flesh firmness at harvest and after short-term storage (2 weeks at 0 °C).