Non-invasive measurement of soluble solid content and pH in Kyoho grapes using a computer vision technique

Xia, Zhengyan; Wu, Di; Nie, Ping; He, Yong

doi:10.1039/c5ay02694f

Cited by 16 publications

(14 citation statements)

References 31 publications

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“…Partial least squares regression is a linear regression method for multivariate calibration and has been widely applied in fruit and vegetable analysis, with favorable results. 10,13,31–33 This method involves finding a quantitative relationship between two matrices, X , a matrix of predictors for instance spectral data, and Y , a matrix of responses of interest. 34 The PLSR can be expressed as where B is the matrix of regression coefficients and F is the matrix of Y residuals.…”

Section: Methodsmentioning

confidence: 99%

Noninvasive Determination of Firmness and Dry Matter Content of Stored Onion Bulbs Using Shortwave Infrared Imaging with Whole Spectra and Selected Wavelengths

et al. 2018

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A firm texture of dry onions is important for consumer acceptance. Both the texture and dry matter content decline during storage, influencing the market value of onions. The main goal of this study was to develop predictive models that in future might form the basis for automated sorting of onions for firmness and dry matter content in the industry. Hyperspectral scanning was conducted in reflectance mode for six commercial batches of onions that were monitored three times during storage. Mean spectra from the region of interest were extracted and partial least squares regression (PLSR) models were constructed. Feature wavelengths were identified using variable selection techniques resulting from interval partial least squares and recursive partial least squares analyses. The PLSR model for firmness gave a root mean square error of cross-validation (RMSECV) of 0.84 N, and a root mean square error of prediction (RMSEP) of 0.73 N, with coefficients of determination ( R) of 0.72 and 0.83, respectively. The RMSECV and RMSEP of the PLSR model for dry matter content were 0.10% and 0.08%, respectively, with a R of 0.58 and 0.79, respectively. The whole wavelength range and selected wavelengths showed nearly similar results for both dry matter content and firmness. The results obtained from this study clearly reveal that hyperspectral imaging of onion bulbs with selected wavelengths, coupled with chemometric modeling, can be used for the noninvasive determination of the firmness and dry matter content of stored onion bulbs.

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Section: Methodsmentioning

confidence: 99%

Noninvasive Determination of Firmness and Dry Matter Content of Stored Onion Bulbs Using Shortwave Infrared Imaging with Whole Spectra and Selected Wavelengths

et al. 2018

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“…The CVS is widely applied as a quality assurance technique for food products nowadays. More specifically, the CVS has been extensively studied over the decades in rapidly examining a series of interior and exterior quality metrics such as the varieties, defects and maturities of fruits in grapes (Xia, Wu, Nie, & He, ), bananas (Mendoza & Aguilera, ), watermelons (Koc, ), and rapeseeds (Kurtulmus & Unal, ). It is foreseeable that the CVS can be applied in larger fields of industrial applications.…”

Section: Introductionmentioning

confidence: 99%

Rapid identification of chrysanthemum teas by computer vision and deep learning

Gao

et al. 2020

Food Science & Nutrition

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Seven commercial Chinese chrysanthemum tea products were classified by computer vision combined with machine learning algorithms. Without the need of building any specific hardware, the image acquisition was achieved in two computer vision approaches. In the first approach, a series of multivariate classification models were built after morphological feature extraction of the image. The best prediction accuracies when classifying flowering stages and tea types were respectively 90% and 63%. In comparison, the deep neural network was applied directly on the raw image, yielded 96% and 89% correct identifications when classifying flowering stage and tea type, respectively. The model can be applied for rapid and automatic quality determination of teas and other related foods. The result indicated that computer vision, especially when combined with deep learning or other machine learning techniques can be a convenient and versatile method in the evaluation of food quality. K E Y W O R D S chrysanthemum tea, computer vision classification, deep neural network, morphological feature

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“…Objective and quantitative color evaluation methods are desired in various agricultural fields 7 . Several studies using RGB (red‐green‐blue) images to evaluate the quality of fruits have been conducted, such as measuring the soluble solid content (SSC) and moisture content of the seedless watermelon, 22 measuring the soluble solid content and pH of Kyoho grapes, 23 and predicting three different ripening stages of papaya fruit 24 and oil palm fruit bunch 25 . Each R, G, or B component of a digital color image is stored using eight bits per color component per pixel in the range between 0 and 255 26 .…”

Section: Introductionmentioning

confidence: 99%

Correlating the natural color of tropical fruit juice with its pH

Zahir

Yahaya

Omar

2020

Color Research & Application

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Color analysis is a critical approach used to analyze the quality properties of fruits, particularly in tracing the ripening stage through the peel color of the fruits. In this study, the color features of natural and untreated tropical fruit juice images (ie, B10 Averrhoa carambola L. and Sala mango) were correlated with the pH of the juice samples. The CIELAB color characteristics of the fruits showed that the mango juice had darker features than the carambola juice. The cause may be due to the higher soluble solid content and denser yellow and red pigments in the mango juice. For carambola juice, multiple linear regression (MLR) performed on red‐blue (RB) components generated the best prediction of pH with the highest coefficient of determination and lowest root mean square of error. For mango juice, MLR performed on normalized red‐green‐blue (RGB) components generated the best prediction of pH.

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Non-invasive measurement of soluble solid content and pH in Kyoho grapes using a computer vision technique

Abstract: The determination of SSC and pH in grapes could be well performed on the basis of RGB color features.

Cited by 16 publications

References 31 publications

Noninvasive Determination of Firmness and Dry Matter Content of Stored Onion Bulbs Using Shortwave Infrared Imaging with Whole Spectra and Selected Wavelengths

Noninvasive Determination of Firmness and Dry Matter Content of Stored Onion Bulbs Using Shortwave Infrared Imaging with Whole Spectra and Selected Wavelengths

Rapid identification of chrysanthemum teas by computer vision and deep learning

Correlating the natural color of tropical fruit juice with its pH

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