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| PELOIA,PAULO R.; RODRIGUES,LUIZ H. A.. |
| ABSTRACT In order to achieve more efficient agricultural production systems, studies relating to the patterns of influence factors on commercial blocks of outstanding performance can be performed to assist management practices. The performance is considered to be the difference between the yield of a given block and the average yield of the homogeneous group that it belongs to. The methods available to identify these outstanding blocks are usually subjective. The aim of this study was to propose an objective and repeatable approach to identify outstanding performance blocks. The proposed approach consisted of performance determination, using regression trees, and the classification of these blocks by k-means clustering. This approach was illustrated using... |
| Tipo: Info:eu-repo/semantics/article |
Palavras-chave: Clustering; Regression tree; Yield variability. |
| Ano: 2016 |
URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-69162016000500895 |
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| Cardinal, Mireille; Chaussy, Marianne; Donnay-moreno, Claire; Cornet, Josiane; Rannou, Cecile; Fillonneau, Catherine; Prost, Carole; Baron, Regis; Courcoux, Philippe. |
| To use salmon protein hydrolysates as food ingredients and to mask the fish odor, Maillard reactions were associated with enzymatic production of hydrolysates. The study explored an original approach based on regression trees (RT) and random forest (RF) methodologies to predict hydrolysate odor profiles from volatile compounds. An experimental design with four factors: enzyme/substrate ratio, quantity of xylose, hydrolysis and cooking times was used to create a range of enzymatic hydrolysates. Twenty samples were submitted to a trained panel for sensory descriptions of odor. Hydrolysate volatile compounds were extracted by means of Headspace Solid Phase MicroExtraction (HS-SPME) and analyzed using gas chromatography/mass spectrometry (GC-MS). The results... |
| Tipo: Text |
Palavras-chave: Sensory characteristics; Volatile compounds; HS-SPME/GC-MS; Regression tree; Random forest; Hydrolysate; Maillard reactions. |
| Ano: 2020 |
URL: https://archimer.ifremer.fr/doc/00624/73590/73024.pdf |
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