A New Wireless Device for Real-Time Mechanical Impact Evaluation in a Citrus Packing Line

2020 ◽  
Vol 63 (1) ◽  
pp. 1-9
Author(s):  
Mariangela Vallone ◽  
Maria Alleri ◽  
Filippa Bono ◽  
Pietro Catania
Keyword(s):  
Real Time ◽  
Fruit Quality ◽  
Critical Points ◽  
Variance Component ◽  
Sensor Node ◽  
Micro Electro Mechanical System ◽  
Component Model ◽  
Variance Component Model ◽  
Total Acceleration ◽  
The Impact

Abstract. Postharvest handling of fresh fruit is a potential source of bruising and damage, with significant consequences for fruit quality and marketability. In the last 30 years, different types of impact-recording devices (also called electronic fruits or pseudo-fruits) have been developed with the aim of measuring the impacts experienced by fruits during postharvest operations. The aim of this study was to develop and test a novel wireless instrumented sphere to study the critical points in a citrus packing line by measuring the impacts experienced by fruits in real-time. The non-commercial device was based on a MEMS (micro-electro-mechanical system) sensor node with a sensing range from ±1×g to ±400×g (g = 9.8 m s-2), a ferroelectric RAM (FRAM) memory, a radio frequency (RF) transmitter, a microcontroller, and a 75 mAh lithium battery. The sensor node was placed inside a plastic ellipsoid case with a total weight of 100 g to represent a ‘Tardivo di Ciaculli’ mandarin. An FR receiver allowed real-time transmission of the measured data. Tests were performed in the Consorzio del Mandarino Tardivo di Ciaculli packing line (Palermo, Italy). Total acceleration values, representing the stresses experienced by fruit in the packing line, were studied using a variance component model. The results showed that total acceleration remained below 20×g in most of the measurements, but considerably higher values, up to 80×g, were obtained between the brushing and waxing machines. In particular, waxing was identified as the most critical operation based on the impact transmitted to the fruit. Our system proved to be effective for immediate on-line assessment of the accelerations experienced by fruits, allowing prompt intervention to guarantee fruit quality in postharvest operations.HighlightsA novel, wirelessly instrumented sphere was developed and tested to study the critical points in a fruit packing line.The total acceleration experienced by the fruits was studied using a variance component model.The system was proven effective in online assessment of the accelerations experienced by fruits. Keywords: Acceleration, Damage, Instrumented sphere, Mandarin, Postharvest.

scholarly journals On the Best Quadratic Minimum Bias Non-Negative Estimator of a Two-Variance Component Model

2011 ◽  
Vol 1 (3) ◽  
pp. 280-285 ◽  
Author(s):  
Lars Sjöberg
Keyword(s):  
Variance Components ◽  
Variance Component ◽  
Component Model ◽  
Variance Component Model ◽  
Minimum Bias ◽  
Unbiased Estimators ◽  
Independent Observations ◽  
Special Case

On the Best Quadratic Minimum Bias Non-Negative Estimator of a Two-Variance Component ModelVariance components (VCs) in linear adjustment models are usually successfully computed by unbiased estimators. However, for many unbiased VC techniques estimated variance components might be negative, a result that cannot be tolerated by the user. This is, for example, the case with the simple additive VC model aσ2/1 + bσ2/2 with known coefficients a and b, where either of the unbiasedly estimated variance components σ2/1 + σ2/2 may frequently come out negative. This fact calls for so-called non-negative VC estimators. Here the Best Quadratic Minimum Bias Non-negative Estimator (BQMBNE) of a two-variance component model is derived. A special case with independent observations is explicitly presented.

scholarly journals Two variance component model improves genetic prediction in family data sets

2015 ◽  
Author(s):  
George Tucker ◽  
Po-Ru Loh ◽  
Iona M MacLeod ◽  
Ben J Hayes ◽  
Michael E Goddard ◽  
...  
Keyword(s):  
Variance Component ◽  
Mixed Model ◽  
Component Model ◽  
Pedigree Information ◽  
Variance Component Model ◽  
Data Sets ◽  
Sample Sizes ◽  
Pedigree Structure ◽  
Genetic Prediction ◽  
Current Sample

Genetic prediction based on either identity by state (IBS) sharing or pedigree information has been investigated extensively using Best Linear Unbiased Prediction (BLUP) methods. Such methods were pioneered in the plant and animal breeding literature and have since been applied to predict human traits with the aim of eventual clinical utility. However, methods to combine IBS sharing and pedigree information for genetic prediction in humans have not been explored. We introduce a two variance component model for genetic prediction: one component for IBS sharing and one for approximate pedigree structure, both estimated using genetic markers. In simulations using real genotypes from CARe and FHS family cohorts, we demonstrate that the two variance component model achieves gains in prediction r2 over standard BLUP at current sample sizes, and we project based on simulations that these gains will continue to hold at larger sample sizes. Accordingly, in analyses of four quantitative phenotypes from CARe and two quantitative phenotypes from FHS, the two variance component model significantly improves prediction r2 in each case, with up to a 20% relative improvement. We also find that standard mixed model association tests can produce inflated test statistics in data sets with related individuals, whereas the two variance component model corrects for inflation.

scholarly journals Genetic analysis of personality traits and alcoholism using a mixed discrete continuous trait variance component model

1999 ◽  
Vol 17 (S1) ◽  
pp. S121-S126 ◽  
Author(s):  
Stefan A. Czerwinski ◽  
Michael C. Mahaney ◽  
Jeff T. Williams ◽  
Laura Almasy ◽  
John Blangero
Keyword(s):  
Genetic Analysis ◽  
Personality Traits ◽  
Variance Component ◽  
Component Model ◽  
Variance Component Model ◽  
Continuous Trait ◽  
Trait Variance
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