Multiple regression models and Computer Vision Systems to predict antioxidant activity and total phenols in pigmented carrots

Pace, Bernardo; Cefola, Maria; Renna, Floriana; Renna, Massimiliano; Serio, Francesco; Attolico, Giovanni

doi:10.1016/j.jfoodeng.2013.02.005

Cited by 33 publications

(16 citation statements)

References 33 publications

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“…The analysis of correlation highlights a positive correlation between β-carotene and lycopene content and chromaticity, with a value close to 1, and hence indicates to be a strong correlation. Similar results were also found in a study correlating colorimetric values and carotenoids in sows [30]. In this study, the correlation of color changes with carotenoid content suggests that the more intense the color, greater is the content of these bioactive compounds.…”

Section: Resultssupporting

confidence: 90%

Chemical Composition and Antioxidant Activity of <i>Bunchosia glandulifera</i> Fruit at Different Ripening Stages

Blank¹,

Justen²,

Fraga³

et al. 2018

FNS

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Bunchosia glandulifera belongs to the family Malpighiaceae and is popularly known as "caferana" in the city of Santo Antonio da Patrulha. The fruits have a high economic value and their inclusion in the diet has health benefits. They are natural sources of antioxidants because of the presence of bioactive compounds. However, their chemical composition can be influenced by the maturation process. This study aims to evaluate the changes in the bioactive antioxidant compounds in the pulp and seeds of B. glandulifera fruit during different stages of ripening. The fruits, were analyzed for caffeine, carotenoids, flavonoids, vitamin C, and total phenolic content as well as for antioxidant activity, at four different stages of ripening. An increase in bioactive compounds was observed from the second maturation stage to the ripened fruit, and the ripened fruit had the highest bioactive compound content. Phenolic compound and carotenoid levels were found to be highest in the pulp. Carotenoid content showed a positive correlation with the antioxidant activity. Colorimetric analysis also showed a high correlation between the levels of carotenoids and color change during the ripening stages.

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Section: Resultssupporting

confidence: 90%

Chemical Composition and Antioxidant Activity of <i>Bunchosia glandulifera</i> Fruit at Different Ripening Stages

Blank¹,

Justen²,

Fraga³

et al. 2018

FNS

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show abstract

“…In another study with bananas the stage of fruit maturity is estimated by analyzing the color, stains and texture in the image [14]. Pace et al [4] proposed a method of Computer Vision System to estimate the antioxidant and phenol content on carrots based on the fruit's surface color. The color is determined by the center of mass of the two-dimensional histogram considering channels a and b (CIE Lab color space.…”

Section: Consideration Of Size Shape Color and Chemical Properties mentioning

confidence: 99%

“…A spectral analysis of bananas obtained under white and ultra-violet has been performed [3]. A Computer Vision System to estimate the antioxidant and phenol content on carrots based on the fruit's surface color was accomplished [4]. An index of the tomatoes ripeness is proposed, which allows classifying the fresh fruit into 6 classes, according to the USDA international standard [5].…”

Section: Introductionmentioning

confidence: 99%

Digital Measurement of Maturity Indices of Mangoes using Selected Image Features

Fmim¹,

Smd²,

Fmd³

et al. 2017

J Food Process Technol

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Maturity is the most important factor to determine the storage-life and quality of fruits like mangoes. Fruit maturity can be recognized by different attributes and among them skin color is the most significant criteria for judging maturity. Typically, human experts visually detect the fruit color to identify the maturity stages which is very prone to error. In this paper, a method of digital image processing has been proposed to classify mangoes into six maturity stages according to the United States department of agriculture (USDA) standard classification. The experimentation considers sample images of more than 100 mangoes of different stages. A total of 24 image features are extracted and then correlation based and information gain based evaluation has been performed in order to select the most informative feature sets. Categorization is done using the decision tree which provides up to 96% classification accuracy. Several computer vision based methods have been developed to determine the state of the maturity of different fruits. A spectral analysis of bananas obtained under white and ultra-violet has been performed [3]. A Computer Vision System to estimate the antioxidant and phenol content on carrots based on the fruit's surface color was accomplished [4]. An index of the tomatoes ripeness is proposed, which allows classifying the fresh fruit into 6 classes, according to the USDA international standard [5]. A Computer Vision System to discriminate varieties of French olives is developed where Pit images (frontal and profile) were used and characteristics such as the histograms of the RGB model and form descriptors (area, perimeter, length, width, etc.) were computed [6]. Liming and Yanchao [7] developed an automated strawberry grading system using image processing technique and graded the strawberry adopting one or two or three indices among shape, color and size. Digital Measurement of Maturity Indices of Mangoes Using Selected Image FeaturesA method to classify mango fruits into their maturity stages was developed using the fuzzy logic and RGB color sensor model. The advantage of fuzzy approaches is, approximate inference can be performed by fuzzy IF-THEN [8]. Another method to categorize mangoes according to their maturity indices was proposed using their color and size features and histogram analysis. The advantage of histogram analysis is low computational complexity [9].As it was seen, a large number of works to estimate the maturity

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“…It captures images of target object by camera and then conducts further analysis of the acquired images. Because of the fast, nondestructive and objective reflection of shape, size, color, texture of the object detected, computer vision is more and more widely applied in agricultural automation field, such as maturation evaluation (Rodríguez‐Pulido et al ; Vélez‐Rivera et al ), freshness determination (Huang et al ; Pace et al ), grade sorting (Elmasry et al ; Wang, Wang, et al 2012), species distinction (Wang, Liu, et al 2012; Shafiee et al ), defect detection (Kim et al ; Hassankhani et al ), process monitoring (Hosseinpour et al ; Sampson et al ), and quantitative prediction (Matiacevich et al ; Pace et al ). Fang et al () used diameter variation of fruit to identify tomatoes with physiological diseases by genetic algorithm and artificial neural network, and the accuracy rate was up to 100%.…”

Section: Introductionmentioning

confidence: 99%

Evaluating quality of tomato during storage using fusion information of computer vision and electronic nose

Huang

Pan

Sun

et al. 2018

J Food Process Engineering

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A nondestructive method for evaluation of tomato quality was proposed to recognize the different ripening stages of tomatoes and to predict their hardness. Computer vision and electronic nose were used, and data fusion technology was used to establish the decision model of multi‐attribute information fusion. Fisher discriminant analysis (FDA) model and support vector classifier (SVC) model for tomato grading were established. Partial least squares (PLS) model and support vector regression (SVR) model were used for predicting the hardness. Comparing with the single detection technique, the combined system which fused computer vision and electronic nose, achieved better results. In the combined system: with respect to the SVC model, classification accuracy was 96.38% for the training set, 94.20% for the prediction set; while to the FDA model, classification accuracy was 93.48% for the training set, 85.51% for the prediction set; to the PLS model, the correlation coefficient of prediction (Rp) was 94.09% and the root mean square error of prediction (RMSEP) was 2.33 N; to the SVR model, the Rp was 95.14% and RMSEP was 0.03 N. It was observed that more robust and better prediction performance on tomato detection were achieved by using fusion information. Practical applications Computer vision, electronic nose, data fusion, and pattern recognition were used for the evaluation of tomato ripeness and hardness. Computer vision captured the color information of tomatoes and electronic nose responded to the smell of tomatoes. After data fusion, information became more abundant and the discrimination models also got better performance. It proved the possibility of nondestructive method for the evaluation of tomatoes quality during storage. So it is feasible to apply this method to detect tomato quality in factory.

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Multiple regression models and Computer Vision Systems to predict antioxidant activity and total phenols in pigmented carrots

Cited by 33 publications

References 33 publications

Chemical Composition and Antioxidant Activity of <i>Bunchosia glandulifera</i> Fruit at Different Ripening Stages

Chemical Composition and Antioxidant Activity of <i>Bunchosia glandulifera</i> Fruit at Different Ripening Stages

Digital Measurement of Maturity Indices of Mangoes using Selected Image Features

Evaluating quality of tomato during storage using fusion information of computer vision and electronic nose

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Multiple regression models and Computer Vision Systems to predict antioxidant activity and total phenols in pigmented carrots

Cited by 33 publications

References 33 publications

Chemical Composition and Antioxidant Activity of &lt;i&gt;Bunchosia glandulifera&lt;/i&gt; Fruit at Different Ripening Stages

Chemical Composition and Antioxidant Activity of &lt;i&gt;Bunchosia glandulifera&lt;/i&gt; Fruit at Different Ripening Stages

Digital Measurement of Maturity Indices of Mangoes using Selected Image Features

Evaluating quality of tomato during storage using fusion information of computer vision and electronic nose

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Product

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Chemical Composition and Antioxidant Activity of <i>Bunchosia glandulifera</i> Fruit at Different Ripening Stages

Chemical Composition and Antioxidant Activity of <i>Bunchosia glandulifera</i> Fruit at Different Ripening Stages