Non-destructive evaluation of avocado fruit maturity using near infrared spectroscopy and PLS regression models

Olarewaju, Olaoluwa Omoniyi; Bertling, I.; Magwaza, Lembe Samukelo

doi:10.1016/j.scienta.2015.12.047

Cited by 74 publications

(44 citation statements)

References 46 publications

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“…Savitzky–Golay (SG) is the most frequently used digital data smoothing filter [ 42 , 49 , 65 , 91 , 138 , 139 ], which applies the Linear Least Squares method to fit low-degree polynomial data [ 140 ]. However, SG has contrasting effects on the performance of multivariate statistical models [ 141 ].…”

Section: Visible and Infrared Spectroscopymentioning

confidence: 99%

“…But Herrera et al showed that SG filters using a second-order polynomial performed better than other scattering correction methods for the prediction of wine grape Brix [ 137 ]. Standard Normal Variate (SNV) [ 72 , 78 , 142 , 143 ] and Multiple Scattering Correction (MSC) [ 42 , 67 , 137 , 139 , 144 , 145 ] are the two most frequently used techniques for scattering correction. MSC is used to eliminate the nonlinear scattering due to the non-uniform travel distance of light by linearizing each spectrum to a reference spectrum, which is the always the mean spectrum [ 67 ].…”

Section: Visible and Infrared Spectroscopymentioning

confidence: 99%

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Advances in Non-Destructive Early Assessment of Fruit Ripeness towards Defining Optimal Time of Harvest and Yield Prediction—A Review

Lecourt

Bishop

2018

Plants

139

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Global food security for the increasing world population not only requires increased sustainable production of food but a significant reduction in pre- and post-harvest waste. The timing of when a fruit is harvested is critical for reducing waste along the supply chain and increasing fruit quality for consumers. The early in-field assessment of fruit ripeness and prediction of the harvest date and yield by non-destructive technologies have the potential to revolutionize farming practices and enable the consumer to eat the tastiest and freshest fruit possible. A variety of non-destructive techniques have been applied to estimate the ripeness or maturity but not all of them are applicable for in situ (field or glasshouse) assessment. This review focuses on the non-destructive methods which are promising for, or have already been applied to, the pre-harvest in-field measurements including colorimetry, visible imaging, spectroscopy and spectroscopic imaging. Machine learning and regression models used in assessing ripeness are also discussed.

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Section: Visible and Infrared Spectroscopymentioning

confidence: 99%

Section: Visible and Infrared Spectroscopymentioning

confidence: 99%

Advances in Non-Destructive Early Assessment of Fruit Ripeness towards Defining Optimal Time of Harvest and Yield Prediction—A Review

Lecourt

Bishop

2018

Plants

139

View full text Add to dashboard Cite

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“…Indirect predictions are commonly used, especially for minerals [7,59,60]. In contrast, oil content and fatty acid composition can be assessed directly because molecular bonds such as O-H and C-H cause reflectance [61,62]. The concentrations of six mineral nutrients (B, Ca, Mg, Mn, Na, P) were correlated between flesh and skin samples whereas the concentrations of four mineral nutrients (Fe, K, S, Zn) were not correlated between flesh and skin samples, despite developing acceptable models using both flesh and skin images.…”

Section: Discussionmentioning

confidence: 99%

Rapid Determination of Nutrient Concentrations in Hass Avocado Fruit by Vis/NIR Hyperspectral Imaging of Flesh or Skin

et al. 2020

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Fatty acid composition and mineral nutrient concentrations can affect the nutritional and postharvest properties of fruit and so assessing the chemistry of fresh produce is important for guaranteeing consistent quality throughout the value chain. Current laboratory methods for assessing fruit quality are time-consuming and often destructive. Non-destructive technologies are emerging that predict fruit quality and can minimise postharvest losses, but it may be difficult to develop such technologies for fruit with thick skin. This study aimed to develop laboratory-based hyperspectral imaging methods (400–1000 nm) for predicting proportions of six fatty acids, ratios of saturated and unsaturated fatty acids, and the concentrations of 14 mineral nutrients in Hass avocado fruit from 219 flesh and 194 skin images. Partial least squares regression (PLSR) models predicted the ratio of unsaturated to saturated fatty acids in avocado fruit from both flesh images (R2 = 0.79, ratio of prediction to deviation (RPD) = 2.06) and skin images (R2 = 0.62, RPD = 1.48). The best-fit models predicted parameters that affect postharvest processing such as the ratio of oleic:linoleic acid from flesh images (R2 = 0.67, RPD = 1.63) and the concentrations of boron (B) and calcium (Ca) from flesh images (B: R2 = 0.61, RPD = 1.51; Ca: R2 = 0.53, RPD = 1.71) and skin images (B: R2 = 0.60, RPD = 1.55; Ca: R2 = 0.68, RPD = 1.57). Many quality parameters predicted from flesh images could also be predicted from skin images. Hyperspectral imaging represents a promising tool to reduce postharvest losses of avocado fruit by determining internal fruit quality of individual fruit quickly from flesh or skin images.

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“…Moreover, the prediction of the DM content of ‘Hass’ avocado using a spectrometer on the fruit skin has been reported in several studies 16‐22 . These studies have used multiple linear regression, principal component regression, and partial least squares regression models.…”

Section: Introductionmentioning

confidence: 99%

Prediction of dry matter content of recently harvested ‘Hass’ avocado fruits using hyperspectral imaging

2020

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BACKGROUND‘Hass’ avocado consumption is increasing due to its organoleptic properties, so it is necessary to develop new technologies to guarantee export quality. Avocado fruits do not ripen on the tree, and the visual classification of its maturity is not accurate. The most commonly used fruit maturity indicator is the percentage of dry matter (DM). The aim of this research was to investigate a non‐destructive method with hyperspectral images to predict the percentage of DM of fruits across the spectral range of 400–1000 nm.RESULTSNo correlation between fruit weight and color with the percentage of DM was found in the study area. Cross‐validation efficiency of different data sources, including the spectrum extraction zone (the center, a line from the peduncle to the base, and the whole fruit) and the average of one or two fruit faces, was compared. Four linear regression models were compared. Data of the whole fruit and average of both sides per fruit using a support vector machine regression were selected for the prediction test. Following the cross‐validation concept, five sets of calibration and test data were selected and optimized for calibration. The best test prediction set comprised an R2 = 0.9, a root‐mean‐square error of 2.6 g kg−1 DM, a Pearson correlation of 0.95, and a ratio of prediction to deviation of 3.2.CONCLUSIONSThe results of the study indicate that hyperspectral images allow classifying export fruits and making harvesting decisions. © 2020 Society of Chemical Industry

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Non-destructive evaluation of avocado fruit maturity using near infrared spectroscopy and PLS regression models

Cited by 74 publications

References 46 publications

Advances in Non-Destructive Early Assessment of Fruit Ripeness towards Defining Optimal Time of Harvest and Yield Prediction—A Review

Advances in Non-Destructive Early Assessment of Fruit Ripeness towards Defining Optimal Time of Harvest and Yield Prediction—A Review

Rapid Determination of Nutrient Concentrations in Hass Avocado Fruit by Vis/NIR Hyperspectral Imaging of Flesh or Skin

Prediction of dry matter content of recently harvested ‘Hass’ avocado fruits using hyperspectral imaging

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