Profiling multiple static and transient puff-pastry characteristics with a robust-and-intelligent processor

Besseris, George J.

doi:10.1016/j.jfoodeng.2015.04.011

Cited by 7 publications

(7 citation statements)

References 49 publications

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“…Spitzbarth improved the plastic behavior of puff pastry fats by enhancing heat transfer during crystallization [23]. Furthermore, Besseris and Renzetti et al found that the fat consistency and number of laminations are the primary determinants of controlling puff pastry overall quality [24,25].…”

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confidence: 99%

Physicochemical and Rheological Characterization of Roll‐in Shortenings

Macias‐Rodriguez

Marangoni

2016

J Americ Oil Chem Soc

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confidence: 99%

Physicochemical and Rheological Characterization of Roll‐in Shortenings

Macias‐Rodriguez

Marangoni

2016

J Americ Oil Chem Soc

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“…Once the number of replicates was settled, the response dataset for each characteristic was condensed using ranking operations [14][15][16][17][18]. Both monitored characteristics followed the "smaller-is-better" optimization direction based on Taguchi categorization [13].…”

Section: Discussionmentioning

confidence: 99%

“…Although as seen by the coefficient of determination (R 2 = 99.6 %) for the perfect slope value of 1.00, several points were on or beyond the two confidence interval boundaries. This means that the complication of messiness was present in this type of response and thus it justified the decision to proceed to the data conversion step utilizing distribution-free stochastics [18]. At this stage, no additional experiments were required.…”

Section: Machinabilitymentioning

confidence: 99%

“…The CW614N performed best in minimizing chip morphology. To provide a statistical support for this outcome, the nonparametric conversion of the CM dataset from Table 5 is tabulated in Table 7 following the published rank transformation procedures for multifactorial OA schemes [16][17][18]. The rank transformed replicates, CM1 and CM2 from Table 5, were cast respectively to the rank ordered responses rCM1 and rCM2 which upon row addition created the sum of ranks of CM (srCM) response.…”

Section: Chip Morphologymentioning

confidence: 99%

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Machinability evaluation and screening of leaded and lead-free brasses using a non-linear robust multifactorial profiler

Toulfatzis

Pantazopoulos²,

Besseris

et al. 2016

Int J Adv Manuf Technol

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The aim of this work was the evaluation of the machinability of leaded brass namely CuZn39Pb3 (CW614N) in comparison to three lead-free brasses alloys namely CuZn42 (CW510L), CuZn38As (CW511L), and CuZn36 (C27450). The machinability of the studied alloys was investigated, based on chip morphology and power consumption, as quality characteristics. Microstructure examination and hardness testing was employed for the characterization of the selected alloys. Design of experiments (DOE) was employed in a multi-non-parametric study in order to identify the critical-to-machinability parameters and obtain their optimum values for high performance machining. The attempted joint screening using a four-level orthogonal array revealed that the depth of cut and the alloy type were the two statistically predominant effects. The chip morphology and the power consumption in a balanced concurrent optimization effort were optimal when the depth of cut was set at 0.5 mm for the alloy type CW614N chip morphology optimization. The optimal chip morphology response was split in equal chances to produce needle or arc chips. The predicted power consumption was confined in a range of values with an upper boundary at 65 W. The findings of the statistical evaluation were experimentally confirmed, validating the DOE approach. Keywords Machinable brass . Lead-free brasses . Machinability . Design of experiments (DOE) Nomenclature ANOVA Analysis of variance ER Error contribution HV final The chip average hardness (Vickers hardness) HV initial The initial surface hardness (Vickers hardness) MANOVA Multivariate analysis of variance M CM G Grand median value of CM data M P G Grand median value of P data mCM DC o Median value of CM for the optimal setting of DC effect mCM M o Median value of CM for the optimal setting of M effect mP DC o Median value of P for the optimal setting of DC effect mP M o Median value of P for the optimal setting of M effect MR Master response (ranked values of ssRS) OA Orthogonal array R 2 Coefficient of determination rCM i (i = 1,2) Ranked values of the response of the chip Morphology replicates rP i (i = 1,2) Ranked values of the response of the power consumption replicates srCM rSM 1 + rSM 2 srP rP 1 + rP 2 rCM Ranked values of srCM rP Ranked values of srP ssRS rCM 2 + rP 2 WH

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“…OAs are also flexible by facilitating the simultaneous testing of numerical and categorical inputs. In general, Taguchi-type OA samplers have been well accepted in food engineering applications ( Besseris, 2015 ; Das Mohapatra et al., 2009 ; Oztop et al., 2007 ; Pouliou and Besseris, 2013 ; Sharif et al., 2014 ; Tasirin et al., 2007 ). However, the subsequent task of data conversion requires intensive and careful manipulation when addressing to complex materials like breads.…”

Section: Introductionmentioning

confidence: 99%

Taguchi-generalized regression neural network micro-screening for physical and sensory characteristics of bread

Besseris

2018

Heliyon

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Generalized regression neural networks (GRNN) may act as crowdsourcing cognitive agents to screen small, dense and complex datasets. The concurrent screening and optimization of several complex physical and sensory traits of bread is developed using a structured Taguchi-type micro-mining technique. A novel product outlook is offered to industrial operations to cover separate aspects of smart product design, engineering and marketing. Four controlling factors were selected to be modulated directly on a modern production line: 1) the dough weight, 2) the proofing time, 3) the baking time, and 4) the oven zone temperatures. Concentrated experimental recipes were programmed using the Taguchi-type L9(34) OA-sampler to detect potentially non-linear multi-response tendencies. The fused behavior of the master-ranked bread characteristics behavior was smart sampled with GRNN-crowdsourcing and robust analysis. It was found that the combination of the oven zone temperatures to play a highly influential role in all investigated scenarios. Moreover, the oven zone temperatures and the dough weight appeared to be instrumental when attempting to synchronously adjusting all four physical characteristics. The optimal oven-zone temperature setting for concurrent screening-and-optimization was found to be 270–240 °C. The optimized (median) responses for loaf weight, moisture, height, width, color, flavor, crumb structure, softness, and elasticity are: 782 g, 34.8 %, 9.36 cm, 10.41 cm, 6.6, 7.2, 7.6, 7.3, and 7.0, respectively.

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Profiling multiple static and transient puff-pastry characteristics with a robust-and-intelligent processor

Cited by 7 publications

References 49 publications

Physicochemical and Rheological Characterization of Roll‐in Shortenings

Physicochemical and Rheological Characterization of Roll‐in Shortenings

Machinability evaluation and screening of leaded and lead-free brasses using a non-linear robust multifactorial profiler

Taguchi-generalized regression neural network micro-screening for physical and sensory characteristics of bread

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