Abstract Overall objective of the study was assessment for freshness parameters of gilthead seabream (Sparus aurata). The freshness was determined by two conventional method (i.e. Minolta color measurement method) was compared with a novel machine vision technology (i.e image color analysis method). The quality characteristics related to the freshness of the fresh fish are traditionally based on the color measurements of the whole fish by using Minolta color measurement method. This study aimed to compare the use of the image analysis method to see whether the freshness of seafood can be measured more accurately. Our analysis proved more exact and reliable results in the freshness evaluation of the Sparus aurata based on the color changes in the eyes, gill and skin of the fish. This study revealed that image analysis can be successfully used for the assessment of fish freshness by measuring the color parameters of images captured from skin, gill and eye of the fish during cold storage. It is easy and practical to use image color analysis method in the quality control evaluation of fish freshness measurement. This analysis method can be suitable for use in continuous process at the seafood processing factories (e.g. before packaging to sort out fresh vs old fish automatically).

Frutos de tomate (Lycopersicon esculentum Mill.), cv. Solar Set, foram colhidos no estádio verde-maduro (100% da superfície com coloração verde) e tratados com 100 µL.L-1 de etileno gasoso a 20°C. Após atingirem o estádio verde-rosado (menos de 10% da superfície do fruto com coloração vermelha ou amarelo-tanino), parte dos frutos foram submetidos a uma queda de 0,40 m sobre uma superfície plana e lisa. Posteriormente, os frutos submetidos ao impacto e os não-submetidos foram armazenados a 20°C e 85-95% de umidade relativa até estarem completamente amadurecidos. Os frutos com e sem injúrias mecânicas foram então colocados individualmente no frasco de amostragem do "nariz eletrônico" e os doze sensores iniciaram a detecção dos compostos emanados pelos frutos. Os dados foram submetidos à análise discriminante multivariada. O grau de dissimilaridade entre os tratamentos foi definido utilizando-se a distância de Mahalanobis. As diferenças encontradas nos frutos com e sem injúria mecânica foram significativas (P<0,0041). A distância de Mahalanobis entre grupos (28,19 unidades) foi um indicativo dramático das diferenças encontradas entre os dois grupos de frutos. A análise do desempenho do "nariz eletrônico" demonstrou que o equipamento é uma ferramenta útil para classificar, não-destrutivamente, tomates expostos a condições extremas de manuseio pós-colheita, como injúrias mecânicas.

Tomato (Lycopersicon esculentum Mill) fruits, 'Solar Set', were harvested at the mature-green stage (green color in 100% of the fruit surface) and gassed with 100mL.L-1 of ethylene at 20°C. At the breaker stage (less than 10% of the fruit surface is red or tannin-yellowish), fruit were dropped from a 40 cm height onto a smooth surface. Following impact, fruits were stored at 20°C and 85-95% relative humidity until table-ripe stage. Bruised and unbruised fruit were then placed individually inside the electronic nose-sampling vessel and the twelve conducting polymer sensors were lowered into the vessel and exposed to the volatile given off by the fruit. Data were analyzed employing multivariate discriminant analysis (MVDA), which maximizes the variance between treatments. The degree of dissimilarity was defined using the Mahalanobis distance The differences found between bruised and unbruised fruit were highly significant (P<0.0041). The Mahalanobis distance between groupings (28.19 units) was a dramatic indicative of the differences between the two treatments. The electronic nose proved to be a useful tool to nondestructively identify and classify tomato fruit exposed to harmful post-harvest practices such as mechanical injuries.