The effect of 1-methylcyclopropene (1-MCP) application before and after cutting on the shelflife extension of fresh-cut tomatoes

[ENG] Exposing partially ripe fruit to 1-methylcyclopropene (1-MCP) before or after cutting may be a useful supplement to proper temperature and relative humidity management for maintaining quality of fresh-cut fruit products. In this study tomato fruits were exposed to 0.5 ppm of 1-MCP for 24 hours, while tomato slices were exposed to the same concentration of 1-MCP for 6 hours. Untreated slices were used as control. Initially and after 3, 7, and 9 days of storage at 5 °C the following quality attributes were evaluated: flesh and skin color, firmness, total soluble solids content, titratable acidity and weight loss. In addition, respiration rate and ethylene production were measured. Fresh-cut tomato slices treated with 0.5 ppm of 1-MCP before cutting showed higher firmness retention than untreated slices, while slices treated after cutting showed an intermediate firmness value. Color development was delayed in both 1-MCP treated samples, which presented higher skin and flesh hue angle value compared with untreated slices. The initial decrease in skin hue angle value was reduced in slices treated either before or after cutting, while those treated after cutting showed the highest value of hue angle of the flesh. Application of 1-MCP did not affect the respiration rate, but slowed down C2H4 production in slices treated after cutting, compared to slices from untreated tomatoes. No significant effect of 1-MCP treatment was observed on titratable acidity, while for soluble solids content slices treated after cutting showed a value significantly higher than untreated slices. Application before processing resulted most effective for firmness retention, while all other effects were more visible when application followed cutting. [SPA] La exposición de tomate parcialmente maduro a 1-metilciclopropeno (1-MCP) antes o después del corte puede representar un método útil, sumado al control de la temperatura y humedad, para mantener la calidad del producto mínimamente procesado. En este estudio los tomates enteros han sido expuestos a 0,5 ppm de 1-MCP durante 24 horas, mientras las rodajas se expusieron a la misma concentración durante 6 horas. Como control se utilizaron rodajas de tomate no tratadas. Inicialmente y tras 3, 7, y 9 días de conservación a 5 ºC se evaluaron los siguientes atributos cualitativos: color (piel y pulpa), firmeza, contenido en sólidos solubles, acidez titulable y pérdida de peso. Además, se midieron la tasa respiratoria y de producción de etileno. Las rodajas de tomate tratadas con 0,5 ppm de 1-MCP antes del corte mostraron mayor mantenimiento de firmeza que las no tratadas, mientras que las rodajas tratadas después del corte han mostrado un valor intermedio de firmeza. El desarrollo del color se ha ralentizado en los dos tratamientos realizados con 1-MCP; las rodajas tratadas han mostrado un mayor valor del ángulo de tinta de la piel y de la pulpa respecto al control. La disminución inicial del ángulo de tinta de la piel se redujo en los dos tratamientos, mientras las rodajas tratadas después del corte han mostrado un mayor valor del ángulo de tinta de la pulpa. La aplicación del 1-MCP no ha afectado a la actividad respiratoria pero ha disminuido la emisión de etileno en rodajas tratadas después del corte respecto a las rodajas no tratadas con 1-MCP. No se ha observado efecto significativo del tratamiento sobre la acidez titulable, el contenido de sólidos solubles de las rodajas tratadas después del corte se ha mostrado más alto que el control. La aplicación del 1-MCP antes y después del procesado ha resultado más efectiva para el mantenimiento de la firmeza y del color, respectivamente

Automatic optimization of array queries

Non-trivial scientific applications often involve complex computations on large multi-dimensional datasets. Using relational database technology for these datasets is cumbersome since expressing the computations in terms of relational queries is difficult and time-consuming. Moreover, query optimization strategies successful in classical relational domains may not suffice when applied to the multi-dimensional array domain. The RAM (Relational Array Mapping) system hides these issues by providing a transparent mapping between the scientific problem specification and the underlying database system. This paper focuses on the RAM query optimizer which is specifically tuned to exploit the characteristics of the array paradigm. We detail how an intermediate array-algebra and several equivalence rules are used to create efficient query plans and how, with minor extensions, the optimizer can automatically parallelize array operation

A Case Study on Array Query Optimisation

The development of applications involving multi-dimensional data sets on top of a RDBMS raises several difficulties that are not directly related to the scientific problem being addressed. In particular, an additional effort is needed to solve the mismatch existing between the array-based data model typical for such computations and the set-based data model provided by the RDMBS. The RAM (Relational Array Mapping) system fills this gap, silently providing a mapping layer between the two data models. As expected though, a naive implementation of such an automatic translation cannot compete with the efficiency of queries written by an experienced programmer. In order to make RAM a valid alternative to expensive and time-consuming hand-written solutions, this performance gap should be reduced. We study a real-world application aimed at the ranking of multimedia collections to assess the impact of different implementation strategies. The result of this study provides an illustrative outlook for the development of generally applicable optimisation techniques

A case study on array query optimisation

textabstractThe development of applications involving multi-dimensional data sets on top of a RDBMS raises several difficulties that are not directly related to the scientific problem being addressed. In particular, an additional effort is needed to solve the mismatch existing between the array-based data model typical for such computations and the set-based data model provided by the RDMBS. The RAM (Relational Array Mapping) system fills this gap, silently providing a mapping layer between the two data models. As expected though, a naive implementation of such an automatic translation cannot compete with the efficiency of queries written by an experienced programmer. In order to make RAM a valid alternative to expensive and time-consuming hand-written solutions, this performance gap should be reduced. We study a real-world application aimed at the ranking of multimedia collections to assess the impact of different implementation strategies. The result of this study provides an illustrative outlook for the development of generally applicable optimisation techniques

Flexible and efficient IR using array databases

textabstractThe Matrix Framework is a recent proposal by IR researchers to flexibly represent all important information retrieval models in a single multi-dimensional array framework. Computational support for exactly this framework is provided by the array database system SRAM (Sparse Relational Array Mapping) that works on top of a DBMS. Information retrieval models can be specified in its comprehension-based array query language, in a way that directly corresponds to the underlying mathematical formulas. SRAM efficiently stores sparse arrays in (compressed) relational tables and translates and optimizes array queries into relational queries. In this work, we describe a number of array query optimization rules and demonstrate their effect on text retrieval in the TREC TeraByte track (TREC-TB) efficiency task, using the Okapi BM25 model as our example. It turns out that these optimization rules enable SRAM to automatically translate the BM25 array queries into the relational equivalent of inverted list processing including compression, score materialization and quantization, such as employed by custom-built IR systems. The use of the high-performance MonetDB/X100 relational backend, that provides transparent database compression, allows the system to achieve very fast response times with good precision and low resource usage

Distribution Rules for Array Database Queries

Non-trivial retrieval applications involve complex computations on large multi-dimensional datasets. These should, in principle, benefit from the use of relational database technology. However, expressing such problems in terms of relational queries is difficult and timeconsuming. Even more discouraging is the efficiency issue: query optimization strategies successful in classical relational domains may not suffice when applied to the multi-dimensional array domain. The RAM (Relational Array Mapping) system hides these difficulties by providing a transparent mapping between the scientific problem specification and the underlying database system. In addition, its optimizer is specifically tuned to exploit the characteristics of the array paradigm and to allow for automatic balanced work-load distribution. Using an example taken from the multimedia domain, this paper shows how a distributed realword application can be efficiently implemented, using the RAM system, without user intervention

Flexible and efficient IR using array databases

The Matrix Framework is a recent proposal by IR researchers to flexibly represent all important information retrieval models in a single multi-dimensional array framework. Computational support for exactly this framework is provided by the array database system SRAM (Sparse Relational Array Mapping) that works on top of a DBMS. Information retrieval models can be specified in its comprehension-based array query language, in a way that directly corresponds to the underlying mathematical formulas. SRAM efficiently stores sparse arrays in (compressed) relational tables and translates and optimizes array queries into relational queries. In this work, we describe a number of array query optimization rules and demonstrate their effect on text retrieval in the TREC TeraByte track (TREC-TB) efficiency task, using the Okapi BM25 model as our example. It turns out that these optimization rules enable SRAM to automatically translate the BM25 array queries into the relational equivalent of inverted list processing including compression, score materialization and quantization, such as employed by custom-built IR systems. The use of the high-performance MonetDB/X100 relational backend, that provides transparent database compression, allows the system to achieve very fast response times with good precision and low resource usage