Study on the quantitative measurement of firmness distribution maps at the pixel level inside peach pulp

Zhu, Nan; Lin, Menghua; Nie, Yating; Wu, Di

doi:10.1016/j.compag.2016.09.018

Cited by 27 publications

(13 citation statements)

References 34 publications

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“…1d , phytic acid, Na-alginate and vitamin C could efficiently enhance preservation. The firmness results in phytic acid, Na-alginate and composite vitamin C-treated groups (3 N on average) were generally higher than those in single-treated groups, among which the 0.1% phytic acid and 1% Na-alginate treatments had the best effect at 3.12 N and 3.21 N. The firmness of the experimental cultivar ‘Baihua’ is relatively lower than normal peach species, which have firmness levels of 8–40 N 13 , 19 , 20 , and this cultivar is more vulnerable to mechanical damage during transportation.…”

Section: Resultsmentioning

confidence: 88%

Determination of Quality Changes in Peaches Wrapped in Active Paper and Stored at Ambient Temperature in Summer

Zhou

et al. 2017

Sci Rep

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Peaches are known for their palatable flavor and abundant nutrients. However, peaches are perishable, and the existing preservation techniques for peaches are still immature. To further extend the shelf life and prevent nutrient loss of perishable peaches under ambient temperature in summer (approximately 25–32 °C), we conducted experiments wrapping peaches (Prunus persica cv ‘Baihua’) in single- and composite-treated vegetal fibrous papers that contained calcium carbonate, phytic acid, Na-alginate and vitamin C. The pathogenic fungi that primarily caused peach decay during storage belonged to the genera of Penicillium, Botrytis, Aspergillus, Alternaria, and Rhizopus. After analyzing quality attributes, including weight loss, firmness, soluble sugar content, respiration rate, relative electric conductivity, malonaldehyde content, peroxidase activity and the decay index, we proved that vitamin C within the preservative paper greatly contributes to peach preservation. Combined with phytic acid and Na-alginate, the composite vitamin C preservative papers played significant roles in delaying fruit senescence, and 0.4% (w/v) vitamin C preservative paper with 1% Na-alginate could maintain quality and extend shelf life with the best effect. This preservation technique significantly postponed the respiration peak by 2–3 days and is a significant contribution to contemporary commercial production.

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Section: Resultsmentioning

confidence: 88%

Determination of Quality Changes in Peaches Wrapped in Active Paper and Stored at Ambient Temperature in Summer

Zhou

et al. 2017

Sci Rep

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“…Removing these redundant variables proves to improve the model accuracy and robustness in some applications. Successive projections algorithm (SPA) [ 22 , 23 ], uninformation variable elimination (UVE) [ 24 , 25 ], UVE-SPA [ 26 , 27 ], and competitive adaptive reweighted sampling (CARS) [ 28 , 29 ] methods, which are commonly used in the selection of near-infrared spectroscopy variables, were used to select wavelengths that could discriminate insects and damage from the leaves. Also, there were eight types of models based on the selected variables, which are the PLS-DA model based on variables selected by SPA (SPA-PLS-DA model); the LS-SVM model based on variables selected by SPA (SPA-LS-SVM model); the PLS-DA model based on variables selected by UVE (UVE-PLS-DA model); the LS-SVM model based on variables selected by UVE (UVE-LS-SVM model); the PLS-DA model based on variables selected by UVE-SPA (UVE-SPA-PLS-DA model); the LS-SVM model based on variables selected by UVE-SPA (UVE-SPA-LS-SVM model); the PLS-DA model based on variables selected by CARS (CARS-PLS-DA model); and the LS-SVM model based on variables selected by CARS (CARS-LS-SVM model).…”

Section: Methodsmentioning

confidence: 99%

Potential of Visible and Near-Infrared Hyperspectral Imaging for Detection of Diaphania pyloalis Larvae and Damage on Mulberry Leaves

Huang

Yang

Meng

et al. 2018

Sensors

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Mulberry trees are an important crop for sericulture. Pests can affect the yield and quality of mulberry leaves. This study aims to develop a hyperspectral imaging system in visible and near-infrared (NIR) region (400–1700 nm) for the rapid identification of Diaphania pyloalis larvae and its damage. The extracted spectra of five region of interests (ROI), namely leaf vein, healthy mesophyll, slight damage, serious damage, and Diaphania pyloalis larva at 400–1000 nm (visible range) and 900–1700 nm (NIR range), were used to establish a partial least squares discriminant analysis (PLS-DA) and least-squares support vector machines (LS-SVM) models. Successive projections algorithm (SPA), uninformation variable elimination (UVE), UVE-SPA, and competitive adaptive reweighted sampling were used for variable selection. The best models in distinguishing between leaf vein, healthy mesophyll, slight damage and serious damage, leaf vein, healthy mesophyll, and larva, slight damage, serious damage, and larva were all the SPA-LS-SVM models, based on the NIR range data, and their correct rate of prediction (CRP) were all 100.00%. The best model for the identification of all five ROIs was the UVE-SPA-LS-SVM model, based on visible range data, which had the CRP value of 97.30%. In summary, visible and near infrared hyperspectral imaging could distinguish Diaphania pyloalis larvae and their damage from leaf vein and healthy mesophyll in a rapid and non-destructive way.

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“…Firmness is related to the maturity of the fruit and can be an indicator of product's shelf life, and as such is a key factor for consumers when purchasing fruit in deciding whether the product is fresh and of high quality. Zhu et al [194] applied linear and non-linear methods calibration to establish firmness of peaches using PLS and SVM approaches. In this study, the linear method with variable selection by competitive adaptive reweighted sampling (CARS) algorithm showed better results than SVM model.…”

Section: Fruitsmentioning

confidence: 99%

Comparison of Chemometric Problems in Food Analysis using Non-Linear Methods

2020

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Food analysis is a challenging analytical problem, often addressed using sophisticated laboratory methods that produce large data sets. Linear and non-linear multivariate methods can be used to process these types of datasets and to answer questions such as whether product origin is accurately labeled or whether a product is safe to eat. In this review, we present the application of non-linear methods such as artificial neural networks, support vector machines, self-organizing maps, and multi-layer artificial neural networks in the field of chemometrics related to food analysis. We discuss criteria to determine when non-linear methods are better suited for use instead of traditional methods. The principles of algorithms are described, and examples are presented for solving the problems of exploratory analysis, classification, and prediction.

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Study on the quantitative measurement of firmness distribution maps at the pixel level inside peach pulp

Cited by 27 publications

References 34 publications

Determination of Quality Changes in Peaches Wrapped in Active Paper and Stored at Ambient Temperature in Summer

Determination of Quality Changes in Peaches Wrapped in Active Paper and Stored at Ambient Temperature in Summer

Potential of Visible and Near-Infrared Hyperspectral Imaging for Detection of Diaphania pyloalis Larvae and Damage on Mulberry Leaves

Comparison of Chemometric Problems in Food Analysis using Non-Linear Methods

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