Nondestructive Evaluation of Anthocyanin Concentration and Soluble Solid Content at the Vine and Blossom Ends of Green Mature Mangoes during Storage by Hyperspectral Spectroscopy

Makino, Yoshio; Isami, Aiko; Suhara, Takehiro; Goto, Kenjiro; Oshita, Seiichi; Kawagoe, Yoshinori; Kuroki, Shinichiro; Purwanto, Y. Aris; Ahmad, Usman; Sutrisno, Sutrisno

doi:10.3136/fstr.21.59

Cited by 5 publications

(5 citation statements)

References 17 publications

(22 reference statements)

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“…The accuracy, however, may be affected by system devices and optical geometry. The distance between the lens and a sample in the present study was shorter by approximately 110 mm than that reported in a previous study by Makino et al [13]. The data accuracy improves with the strength of the ray.…”

Section: Discussioncontrasting

confidence: 48%

“…Figure 4 shows that CVS is used for determining anthocyanin concentrations in the peel of fully-ripenedmangoes. Makino et al [13] reported that a hyperspectral camera can be used for the same objective at a correlation coefficient accuracy of 0.94 and root mean square error of 1.31 mg•kg −1 . In the present study, the concentration of anthocyanin was more precisely assumed using CVS than using the hyperspectral camera.…”

Section: Discussionmentioning

confidence: 99%

“…Reduction in distance in the present study might improve accuracy for assuming anthocyanin concentrations. According to the study conducted by Makino et al [13], prediction accuracy for anthocyanin concentrations in the peel of green-mature fruits using a hyperspectral camera was affected by chlorophyll a concentration on the basis of the analytical results obtained on using PLSR analysis. This may be caused by the chlorophyll a compound seriously affecting the intensity of the ray reflected from the fruit.…”

Section: Discussionmentioning

confidence: 99%

“…Kang et al [12] monitored the change in maturity of "B74" mangoes on the basis of hue angle measured using a Computer Vision System (CVS). Moreover, Makino et al [13] predicted anthocyanin concentration in mango peels using hyperspectral imaging combined with PLSR analysis.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A Grading Method for Mangoes on the Basis of Peel Color Measurement Using a Computer Vision System

Makino¹,

Goto²,

Oshita³

et al. 2016

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An objective grading method using a Computer Vision System (CVS) for mangoes is proposed. Red peel was selected using two types of color space values at chroma = 22 and hue angle = 52˚. Eighteen out of 25 fully-ripened fruits were graded as "excellent," determined by the share of red area per fruit being in the range of 80%-100%. In contrast, all green-mature fruits were graded as "fair," where the share of red area per fruit was <30%. If the threshold for the share of the red area on the peel is set between 10% (maximal green-mature fruits) and 18% (minimal fully-ripened fruits), automatic removal of green-mature fruits on a grading line is feasible. CVS was effective for nondestructively assuming anthocyanin concentration. A linear relationship between the natural logarithm of the concentration and hue angle was observed (y = −0.0542x + 7.83), with a correlation coefficient accuracy of 0.94 and root mean square error of 1.31 mg•kg −1. This result may be effective for the visualization of anthocyanin distribution on mango skin. The threshold for red peel can be in the range of 131-186 mg•kg −1. This suggests that the pigment concentration is usable as a universal threshold. This value is unaffected by conditions for image acquisition or color measurement (e.g., light source, sensor, filter, and optical geometry), unlike color space values as hue angle.

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Section: Discussioncontrasting

confidence: 48%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Grading Method for Mangoes on the Basis of Peel Color Measurement Using a Computer Vision System

Makino¹,

Goto²,

Oshita³

et al. 2016

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“…The overall view and description of the parts of the hyperspectral camera system (JFE Techno-Research Corporation, Tokyo, Japan) are presented in Figure 1, the same as the previous report [28].…”

Section: Determination Of Spectral Data By the Hyperspectral Camera Smentioning

confidence: 99%

Prediction of Degreening Velocity of Broccoli Buds Using Hyperspectral Camera Combined with Artificial Neural Networks

Makino

Kousaka

2020

Foods

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Developing a noninvasive technique to estimate the degreening (loss of green color) velocity of harvested broccoli was attempted. Loss of green color on a harvested broccoli head occurs heterogeneously. Therefore, hyperspectral imaging technique that stores spectral reflectance with spatial information was used in the present research. Using artificial neural networks (ANNs), we demonstrated that the reduction velocity of chlorophyll at a site on a broccoli head was related to the second derivative of spectral reflectance data at 15 wavelengths from 405 to 960 nm. The reduction velocity was predicted using the ANNs model with a correlative coefficient of 0.995 and a standard error of prediction of 5.37 × 10−5 mg·g−1·d−1. The estimated reduction velocity was effective for predicting the chlorophyll concentration of broccoli buds until 7 d of storage, which was established as the maximum time for maintaining marketability. This technique may be useful for nondestructive prediction of the shelf life of broccoli heads.

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Determination of starch content in single kernel using near-infrared hyperspectral images from two sides of corn seeds

Liu

Huang

Yang

et al. 2020

Infrared Physics & Technology

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Nondestructive Evaluation of Anthocyanin Concentration and Soluble Solid Content at the Vine and Blossom Ends of Green Mature Mangoes during Storage by Hyperspectral Spectroscopy

Cited by 5 publications

References 17 publications

A Grading Method for Mangoes on the Basis of Peel Color Measurement Using a Computer Vision System

A Grading Method for Mangoes on the Basis of Peel Color Measurement Using a Computer Vision System

Prediction of Degreening Velocity of Broccoli Buds Using Hyperspectral Camera Combined with Artificial Neural Networks

Determination of starch content in single kernel using near-infrared hyperspectral images from two sides of corn seeds

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