Dataset Information

Measurement of ripening of raspberries (Rubus idaeus L) by near infrared and colorimetric imaging techniques.

ABSTRACT: This work includes the evaluation of 168 samples of raspberries 'Glen Lyon', representing whole maturation period, by colorimetric and near infrared imaging techniques, as well as the quantification of total phenols, total anthocyanins and antioxidant activity by chemical methods. Samples showed significant differences depending on the maturation stage using CIELAB colour parameters and total anthocyanins content. The application of partial least squares regression allowed predicting the chemical features from image analysis data, with coefficients of determination (R²) up to 0.75. The best prediction for total anthocyanins including colorimetric data was observed. The proposed methodology can be used as a reference method for assessing important quality attributes of raspberries. Moreover, it is useful, rapid and accurate automatic inspection method.

SUBMITTER: Rodriguez-Pulido FJ

PROVIDER: S-EPMC5583109 | biostudies-literature | 2017 Aug

REPOSITORIES: biostudies-literature

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Measurement of ripening of raspberries (<i>Rubus idaeus</i> L) by near infrared and colorimetric imaging techniques.

Rodríguez-Pulido Francisco J FJ Gil-Vicente María M Gordillo Belén B Heredia Francisco J FJ González-Miret M Lourdes ML

Journal of food science and technology 20170616 9

This work includes the evaluation of 168 samples of raspberries 'Glen Lyon', representing whole maturation period, by colorimetric and near infrared imaging techniques, as well as the quantification of total phenols, total anthocyanins and antioxidant activity by chemical methods. Samples showed significant differences depending on the maturation stage using CIELAB colour parameters and total anthocyanins content. The application of partial least squares regression allowed predicting the chemica ...[more]

PMID: 28928519

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Project description:The benefit of pollinators to crop production is normally calculated using "pollinator dependence ratios," which reflect the proportion of yield lost (here reported as a value between 0 and 1) in the absence of pollinators; these ratios are quantified experimentally using pollinator exclusion experiments. Pollinator dependence ratio estimates can vary considerably for a single crop, creating large, frequently overlooked, uncertainty in economic valuations of pollinators. The source of this variation is usually unclear. We experimentally measured the pollinator dependence ratio of two UK commercial cultivars of raspberry Rubus idaeus L., using a range of yield metrics-fruit set, marketable fruit set, fruit weight, and marketable fruit weight-over 3 years (2019-2021), to quantify the effects of yield metric, interannual variation, and cultivar on pollinator dependence ratio estimates. We found a difference in the pollinator dependence ratio for fruit set of 0.71 between 2019 and 2020, showing the importance of carrying out exclusion studies over multiple years. Averaged over multiple years and two cultivars, the dependence ratio was 0.68 measured using marketable fruit set and 0.64 using marketable fruit weight. Imposing a quality threshold (size and shape) below which fruits would not be of commercial value (marketable fruit set/weight) dramatically increased both the pollinator dependence ratio and subsequent economic valuations of pollination service derived from it. Our study shows that, for raspberry, estimates of the pollinator dependence ratio, and therefore, the economic value of insect pollinators, are highly sensitive to the choice of yield metric and can change between years and cultivars. Many economic decisions about pollinator management, at farm, regional and national scales rely on estimates of pollinator dependence. We, therefore, recommend that for estimating pollinator dependence ratios, pollinator exclusion studies are conducted over three or more years and use yield metrics that incorporate quality criteria linked to actual market values and commercial thresholds.

Dataset Information

Measurement of ripening of raspberries (Rubus idaeus L) by near infrared and colorimetric imaging techniques.

Publications

Measurement of ripening of raspberries (<i>Rubus idaeus</i> L) by near infrared and colorimetric imaging techniques.

Similar Datasets

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