Flesh Browning Assessment in 'Cripps Pink' Apples Using Vis-Nir Spectroscopy

Torres, Carolina A.; Sánchez-Contreras, Javier; Hernández, Omar Hernández; León, Leinis

doi:10.17660/actahortic.2015.1079.53

Cited by 9 publications

(1 citation statement)

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“…In calibration, a discrimination accuracy of 89% was noted, while in validation, accuracy decreased to 81%. Torres et al (2015) reported use of PCA and PLS-DA methods with reflectance spectra (392-442, 542-592 and 642-692 nm) to discriminate apple fruit with high levels of diffuse browning (in which the defect is present close to the skin) from sound fruit, but not fruit with moderate browning from sound fruit, nor fruit with radial browning (in which the defect is present deeper in the mesocarp). Other discrimination techniques are also relevant to this application,e.g.…”

Section: Introductionmentioning

confidence: 99%

Assessment of internal flesh browning in intact apple using visible-short wave near infrared spectroscopy

Khatiwada

Subedi

Hayes

et al. 2016

Postharvest Biology and Technology

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Section: Introductionmentioning

confidence: 99%

Assessment of internal flesh browning in intact apple using visible-short wave near infrared spectroscopy

Khatiwada

Subedi

Hayes

et al. 2016

Postharvest Biology and Technology

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Advancing mango quality assurance: Non‐destructive detection of spongy tissue using visible near‐infrared spectroscopy and machine learning classification

Kiran,

Yeasin,

Aradwad

et al. 2024

Future Postharvest and Food

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The renowned Alphonso mango, celebrated in India for its exquisite taste, saffron hue, pleasing texture, and prolonged shelf life, holds significant global commercial appeal. Unfortunately, the widespread issue of spongy tissue (ST) disorder in Alphonso mangoes results in a soft, corky texture, affecting up to 30% of mangoes in a single batch. This challenge leads to the outright rejection of affected mangoes during export due to delayed disorder identification. The current evaluation method involves destructive cutting, causing substantial fruit loss, and lacks assurance for the overall batch quality. This study addresses these challenges by focusing on the utilization of visible near‐infrared spectroscopy as a non‐invasive method to assess the internal quality of mangoes. Additionally, it introduces innovative classification models for automated binary categorization (Healthy vs. ST affected). Through preprocessing and principal component analysis of spectral reflectance data for feature extraction and wavelength optimization, successful wavelength ranges of 650–970 nm were identified, effectively distinguishing between healthy and damaged mangoes. Various machine learning models used notably, linear discriminant analysis, support vector machine, and logistic regression exhibited strong discriminative capabilities with higher accuracy reaching 99%. This non‐destructive approach addresses critical challenges in the mango export industry, offering early detection of internal disorders and minimizing postharvest losses.

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Nondestructive evaluation: detection of external and internal attributes frequently associated with quality and damage

Nicolaı̈

Ketelaere

Dizon

et al. 2022

Postharvest Handling

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Flesh Browning Assessment in 'Cripps Pink' Apples Using Vis-Nir Spectroscopy

Cited by 9 publications

References 0 publications

Assessment of internal flesh browning in intact apple using visible-short wave near infrared spectroscopy

Assessment of internal flesh browning in intact apple using visible-short wave near infrared spectroscopy

Advancing mango quality assurance: Non‐destructive detection of spongy tissue using visible near‐infrared spectroscopy and machine learning classification

Nondestructive evaluation: detection of external and internal attributes frequently associated with quality and damage

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