ANN-based method for olive Ripening Index automatic prediction

Furferi, Rocco; Governi, Lapo; Volpe, Yary

doi:10.1016/j.jfoodeng.2010.07.016

Cited by 45 publications

(33 citation statements)

References 18 publications

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“…Some studies in the literature have used the correlation between images and degree of ripeness to characterize the ripening process and define appropriate threshold values for various cultivars in order to evaluate possible applications in field and postharvest practice (Bodria et al 2002;Choong et al 2006). Computer vision has also been applied to table olives to detect external damage (Diaz et al, 2000(Diaz et al, , 2004Riquelme et al 2008, Furferi et al 2010) and has become a widely used technology in the production of table olives at industrial level.…”

Section: Introductionmentioning

confidence: 99%

Determination of the olive maturity index of intact fruits using image analysis

et al. 2013

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In this work, the maturity index of different samples of olives was objectively assessed by image analysis obtained through machine vision, in which algorithms of color-based segmentation and operators to detect edges were used. This method allows a fast, automatic and objective prediction of olive maturity index. This prediction value was compared to maturity index (MI), generally used by olive oil industry, based on the subjective visual determination of color of fruit skin and flesh. Machine vision was also applied to the automatic estimation of size and weight of olive fruits. The proposed system was tested to obtain a good performance in the classification of the fruit in batches. When applied to several olive samples, the maturity index predicted by machine vision was in close agreement with the maturity index of fruits visually estimated, values that are currently used as standards. The evaluation of weight of fruit also provided good results (R 2 =0.91). These results obtained by image analysis can be used as a useful method for the classification of olives at the reception in olive mill, allowing a better quality control of the production process.

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Section: Introductionmentioning

confidence: 99%

Determination of the olive maturity index of intact fruits using image analysis

et al. 2013

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“…However, there have been very few works with which to compare this study. Several authors have studied different ways to determine rapidly some of the EVOO characteristics, although they could not be comparable to working with intact olives in many cases(Cayuela, Yousfi, Carmen Martínez, & García, ; Furferi, Carfagni, & Daou, ; Furferi, Governi, & Volpe, ; Salguero‐Chaparro, Baeten, Fernández‐Pierna, & Peña‐Rodríguez, ; Salguero‐Chaparro & Peña‐Rodriguez, ). Other acceptable predictive models were developed by means of PLS analysis and an NIR system to measure on‐line the pigment levels contained in EVOO at the VC outlet (Jiménez Marquez, ).…”

Section: Resultsmentioning

confidence: 99%

Predictive ANN models for the optimization of extra virgin olive oil clarification by means of vertical centrifugation

Funes

Allouche

Beltrán

et al. 2017

J Food Process Engineering

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Eight artificial neural networks were developed as predictive models for regulated and nutritional parameters of oils obtained from vertical centrifugation. The networks were designed considering the NIR spectra of oily must at its exit from the horizontal decanter centrifuge, and the flows and temperatures of the oil and of the addition water. The results obtained in all the networks designed indicated the good creative capacity of the neural models through their quality indicators (RER and RPD). The correlations between the real data and those predicted by the networks were within the range of 0.76–0.99, the UV absorption networks (K232 and K270) being those which gave lower correlations. For polyphenols, tocopherols and carotene and chlorophyll pigments, the te‐Student external validation test makes a reliable prediction of these parameters. With predictive models, the vertical centrifuge can be modeled and regulated as well as monitored on‐line to control and optimize the clarification phase within the extraction process of extra virgin olive oil (EVOO). Practical applications The predictive ANN can use to regulate and model the vertical centrifuge to obtain an EVOO of a maximum regulated and nutritional quality possible, reducing water and energy consumption. Besides, with on‐line prediction of oil characterization, it will be possible to perform a correct classification prior to its storage in the cellar.

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“…A non-contact ultrasonic system already has been developed for tomato quality inspection [18]. The same experimental setup has been used with some of the needed updates for firmness and stiffness extraction (Fig.…”

Section: Freshness Analysis Using Stiffness and Firmness Analysismentioning

confidence: 99%

Quality assessment of commercial bread samples based on breadcrumb features and freshness analysis using an ultrasonic machine vision (UVS) system

2015

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This paper presents the use of a in situ developed ultrasonic machine vision system for quality parameter extraction of breadcrumb features and freshness. An image processing technique has been used for breadcrumb analysis on collected digital images of various bread samples while an ultrasonic assessment technique has been used for quantification of the freshness of various bread samples. Various threshold methods (isodata, Otsu, minimum error, moment preserving and fuzzy method) have been implemented and compared with the proposed method to segment breadcrumbs from collected digital bread images. Threshold performance was assessed by two important criteria such as uniformity and busyness (arrangement of a pixel to its neighborhood pixels) of the binary versions of input breadcrumb sample images. Quality parameters were computed for each optimal threshold on 500 digital images of bread slices. Other important quality parameter of bread is the outline of its brown color section, which corresponds to the appropriate baking stage. Slight variations in threshold lead to substantial variations in crumb feature values, with cell uniformity, void fraction, intensity and entropy calculation showing more sensitivity than others. Propagation delay and attenuation in the received acoustic signal have been calculated for stiffness and firmness evaluation. A second order relationship has been observed between the storage time and stiffness of the various bread samples. The proposed method is very efficient in the sense of quality parameter calculations. Although some of the previously reported methods showed a relatively higher amount of busyness than other methods, the reported method performs well on images with large void areas.

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ANN-based method for olive Ripening Index automatic prediction

Cited by 45 publications

References 18 publications

Determination of the olive maturity index of intact fruits using image analysis

Determination of the olive maturity index of intact fruits using image analysis

Predictive ANN models for the optimization of extra virgin olive oil clarification by means of vertical centrifugation

Quality assessment of commercial bread samples based on breadcrumb features and freshness analysis using an ultrasonic machine vision (UVS) system

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