Non-destructive prediction of ‘Marsh’ grapefruit susceptibility to postharvest rind pitting disorder using reflectance Vis/NIR spectroscopy

Ncama, Khayelihle; Tesfay, S.Z.; Fawole, Olaniyi Amos; Opara, Umezuruike Linus; Magwaza, Lembe Samukelo

doi:10.1016/j.scienta.2017.12.028

Cited by 30 publications

(26 citation statements)

References 29 publications

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“…Despite the large market availability of the latter, presenting different levels of portability, spectral ranges, sizes, and prices, only a few studies have focused on its application to assess the quality and ripening of oranges and mandarins on-tree (Table 4), perhaps due to the complexities involved under field conditions, and the performance deterioration of calibration models, in spite of the spectral range used [63][64][65][66][67][68]. Nevertheless, the QA assessed on-tree (Table 4) comprise fruit mass and size, color parameters [63,67,68], pericarp thickness, SSC, TA, firmness, MI, juice pH and mass, and BrimA index, which measures the balance between sweetness and acidity as described by [12]. Noteworthy, the majority of the calibration models exhibited R 2 < 0:8, despite the range used, and did not include external validation, except for [65,66].…”

Section: Prediction Of Quality Attributesmentioning

confidence: 99%

“…Among the most typical physiological disorders registered through the supply chain of citrus fruit, there is the section drying, the rind breaking disorder (RBD), the rind pitting disorder (RP), freezing damage, and granulation, as reported for tangerine (Citrus tangerine Tanaka [10], 'Nules Clementine' mandarin (Citrus Â clementina) [11], 'Marsh' grapefruit (Citrus Â paradisi Macfad.) [12], sweet lemons (Citrus limettioides Tan.) [13], and 'Honey' pomelo (Citrus maxima Merr.)…”

Section: Introductionmentioning

confidence: 99%

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Nondestructive Assessment of Citrus Fruit Quality and Ripening by Visible–Near Infrared Reflectance Spectroscopy

Cavaco¹,

Passos²,

Pires³

et al. 2021

Citrus - Research, Development and Biotechnology

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As non-climacteric, citrus fruit are only harvested at their optimal edible ripening stage. The usual approach followed by producers and packinghouses to establish the internal quality and ripening of citrus fruit is to collect fruit sets throughout ripening and use them to determine the quality attributes (QA) by standard and, in many cases, destructive and time-consuming methods. However, due to the large variability within and between orchards, the number of measured fruits is seldom statistically representative of the batch, resulting in a fallible assessment of their internal QA (IQA) and a weak traceability in the citrus supply chain. Visible/near-infrared reflectance spectroscopy (Vis–NIRS) is a nondestructive method that addresses this problem, and has proved to predict many IQA of a wide number of fruit including citrus. Yet, its application on a daily basis is not straightforward, and there are still several questions to address by researchers in order to implement it routinely in the crop supply chain. This chapter reviews the application of Vis–NIRS in the assessment of the quality and ripening of citrus fruit, and makes a critical evaluation on the technique’s limiting issues that need further attention by researchers.

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Section: Prediction Of Quality Attributesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Nondestructive Assessment of Citrus Fruit Quality and Ripening by Visible–Near Infrared Reflectance Spectroscopy

Cavaco¹,

Passos²,

Pires³

et al. 2021

Citrus - Research, Development and Biotechnology

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show abstract

“…Visible and near infrared (Vis-NIR) spectroscopy has been widely used in many sectors, including the food, material, and life sciences [7][8][9]. In the field of forestry, many studies have demonstrated its potential to determine components, such as moisture, density, lignin content, and so on; detect wood preservation; and classify species [10][11][12][13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Visible-Near Infrared Spectroscopy and Chemometric Methods for Wood Density Prediction and Origin/Species Identification

Via

Young

et al. 2019

Forests

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This study aimed to rapidly and accurately identify geographical origin, tree species, and model wood density using visible and near infrared (Vis-NIR) spectroscopy coupled with chemometric methods. A total of 280 samples with two origins (Jilin and Heilongjiang province, China), and three species, Dahurian larch (Larix gmelinii (Rupr.) Rupr.), Japanese elm (Ulmus davidiana Planch. var. japonica Nakai), and Chinese white poplar (Populus tomentosa carriere), were collected for classification and prediction analysis. The spectral data were de-noised using lifting wavelet transform (LWT) and linear and nonlinear models were built from the de-noised spectra using partial least squares (PLS) and particle swarm optimization (PSO)-support vector machine (SVM) methods, respectively. The response surface methodology (RSM) was applied to analyze the best combined parameters of PSO-SVM. The PSO-SVM model was employed for discrimination of origin and species. The identification accuracy for tree species using wavelet coefficients were better than models developed using raw spectra, and the accuracy of geographical origin and species was greater than 98% for the prediction dataset. The prediction accuracy of density using wavelet coefficients was better than that of constructed spectra. The PSO-SVM models optimized by RSM obtained the best results with coefficients of determination of the calibration set of 0.953, 0.974, 0.959, and 0.837 for Dahurian larch, Japanese elm, Chinese white poplar (Jilin), and Chinese white poplar (Heilongjiang), respectively. The results showed the feasibility of Vis-NIR spectroscopy coupled with chemometric methods for determining wood property and geographical origin with simple, rapid, and non-destructive advantages.

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“…Due to changes in the color of the fruit during the ripening stages as well as the change in the amount of chlorophyll a during this period, we believe that the non-destructive estimation of chlorophyll a is very useful, since it is possible to estimate the stage of fruit ripening, specifically Red Delicious apple cultivar (Costa et al 2009;Amoriello et al 2018). Destructive measurement of chlorophyll a content was done by the method used in Ncama et al (2017).…”

Section: Measuring True Chlorophyll a Contentmentioning

confidence: 99%

Non-destructive Estimation of Chlorophyll a Content in Red Delicious Apple Cultivar Based on Spectral and Color Data

Abbaspour‐Gilandeh¹,

Sabzi²,

Azadshahraki³

et al. 2020

Tarım Bilimleri Dergisi

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Non-destructive estimation of the chemical properties of fruit is an important goal of researchers in the food industry, since online operations, such as fruit packaging based on the amount of different chemical properties and determining different stages of handling, are done based on these estimations. In this study, chlorophyll a content in Red Delicious apple cultivar is predicted as a chemical property that is altered by apple ripening stage, using nondestructive spectral and color methods combined. Two artificial intelligence methods based on hybrid Multilayer Perceptron Neural Network-Artificial Bee Colony Algorithm (ANN-ABC) and Partial least squares regression (PLSR) were used in order to obtain a non-destructive estimation of chlorophyll a content. In application of the PLSR method, various pre-processing algorithms were used. In order to statistically properly validate the hybrid ANN-ABC predictive method, 20 runs were performed. Results showed that the best regression coefficient of the PLSR method in predicting chlorophyll a content using spectral data alone was 0.918. At the same time, the average determination coefficient over 20 repetitions in hybrid ANN-ABC in the estimation of chlorophyll a content, using spectral data and color features were higher than 0.92±0.040 and 0.89±0.045, respectively, which to our knowledge is a remarkable non-intrusive estimation result.

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Non-destructive prediction of ‘Marsh’ grapefruit susceptibility to postharvest rind pitting disorder using reflectance Vis/NIR spectroscopy

Cited by 30 publications

References 29 publications

Nondestructive Assessment of Citrus Fruit Quality and Ripening by Visible–Near Infrared Reflectance Spectroscopy

Nondestructive Assessment of Citrus Fruit Quality and Ripening by Visible–Near Infrared Reflectance Spectroscopy

Visible-Near Infrared Spectroscopy and Chemometric Methods for Wood Density Prediction and Origin/Species Identification

Non-destructive Estimation of Chlorophyll a Content in Red Delicious Apple Cultivar Based on Spectral and Color Data

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