Microstructure analysis and detection of mealiness in ‘Forelle’ pear ( Pyrus communis L.) by means of X-ray computed tomography

Muziri, Tavagwisa; Theron, Karen I.; Cantré, Dennis; Wang, Zi; Verboven, Pieter; Nicolaı̈, Bart; Crouch, E.M.

doi:10.1016/j.postharvbio.2016.06.006

Cited by 37 publications

(6 citation statements)

References 28 publications

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“…In regions with severe internal disorder development, cells broke down completely and cavities were observed. This is in line with observations for pear made by (Lammertyn et al, 2003a;Muziri et al, 2016;van Dael et al, 2017).…”

Section: Internal Variability Must Be Measured To Separate 'Defective' From 'Healthy' Pear Fruitsupporting

confidence: 93%

“…X-ray CT delivers 3D images of the X-ray absorption contrast, thereby resolving shape and volume of the fruit while providing better spatial information on the internal fruit structure and disorders than in radiographies. CT has been successfully used in research to analyze the internal quality of fruit and vegetables (Donis-González et al, 2016a, 2016bHerremans et al, 2014Herremans et al, , 2013Jarolmasjed et al, 2016;Lammertyn et al, 2003aLammertyn et al, , 2003bMuziri et al, 2016;Si and Sankaran, 2016). Yet, it is still challenging to use CT efficiently for high-throughput quality inspection, which limits its usage in industrial applications to the inspection of high value products at a relatively slow rate, or to its usage in research and development stages (Buratti et al, 2018;Wevers et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

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Nondestructive internal quality inspection of pear fruit by X-ray CT using machine learning

et al. 2020

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To preserve the quality of fresh pear fruit after harvest and deliver quality fruit year-round a controlled supply chain and long-term storage are applied. During storage, however, internal disorders can develop due to suboptimal storage conditions that may not cause externally visible symptoms. This makes them impossible to be detected by current commercial quality grading systems in a reliable and non-destructive way. A combination of a Support Vector Machine coupled with a feature extraction algorithm and X-ray Computed Tomography is proposed to successfully detect internal disorders in 'Conference' and 'Cepuna' pear fruit nondestructively. Classifiers were able to distinguish defective from sound fruit with classification accuracies ranging between 90.2 and 95.1 % depending on the cultivar and number of used features. Moreover, low false positive and negative rates were obtained, respectively ranging between 0.0 and 6.7 %, and 5.7 and 13.3 %. Classifiers trained on 'Conference' data were transferred effectively to the 'Cepuna' cultivar, suggesting generalizability to other cultivars as well. With continuing developments in both hardware and software to increase inspection speed and reduce equipment costs, the method can be implemented in industrial applications, e.g., inline translational X-ray CT.

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Section: Internal Variability Must Be Measured To Separate 'Defective' From 'Healthy' Pear Fruitsupporting

confidence: 93%

Section: Introductionmentioning

confidence: 99%

Nondestructive internal quality inspection of pear fruit by X-ray CT using machine learning

et al. 2020

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“…X-ray CT scanning, which is notably a non-destructive method, has long been developed and validated for the analysis of agricultural food texture during both preharvest and postharvest periods (Wang et al, 2018). However, the application of Xray CT scanning for the diagnosis and analysis of pear fruit physiological disorders is quite limited (Lammertyn et al, 2003;Muziri et al, 2016;Duan et al, 2020). This study is the first to systematically analyze the cork spot distribution, porous characteristics, network skeleton of different areas, and fruit core integrity and structure in both healthy and cork spotted pear fruit using the high-resolution X-ray CT scanning to explore the causal factors of cork spot disorder in pear.…”

Section: Discussionmentioning

confidence: 99%

“…Herremans et al (2015) characterized 3D tissue anatomy at the level of single cells and intercellular spaces in pear using the X-ray CT analysis, demonstrating significant differences between genotypes in void and cell networks that relate to differences in aeration properties of tissues. X-ray CT was also applied to analyze and detect the mealiness in "Forelle" (Pyrus communis) (Muziri et al, 2016). In addition to the abovementioned studies, X-ray CT was also used to visualize the subsurface microstructure and void network connectivity in other various products, such as mango (Cantre et al, 2014b), pomegranate (Magwaza and Opara, 2014), kiwifruit (Cantre et al, 2014a), and cucumber (Kuroki et al, 2004).…”

Section: Introductionmentioning

confidence: 99%

High-Resolution Microstructure Analysis of Cork Spot Disordered Pear Fruit “Akizuki” (Pyrus pyrifolia Nakai) Using X-Ray CT

et al. 2021

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Cork spot is one of the most damaging physiological disorders in pear fruit, causing considerable economic loss every year. However, the mechanism of cork spot occurrence requires further examination. In this study, X-ray CT scanning was applied to analyze the microstructure of pear fruit “Akizuki” (Pyrus pyrifolia), a cultivar susceptible to cork spot disorder, to elucidate the fruit texture alteration between healthy and cork spotted fruit. Results showed that cork spotted fruit had much higher porosity (9.37%) than healthy fruit (3.52%). Reconstructed three-dimensional (3D) network skeleton models showed highly branched pore channels in cork spotted fruit and a low degree of pore connectivity in healthy fruit. Even in areas of disordered fruit without cork spot, the pore throat diameter, pore length, and coordinated core number (i.e., 77, 160, and 16, respectively) were much higher than that of healthy fruit. The structure analysis of fruit core showed that core deformation only occurred in cork spotted fruit. A much more highly branched network was observed in cork spotted fruit cores compared with healthy fruit cores. High-resolution observation of flesh tissue directly demonstrated that pore size in cork spotted fruit (87 μm) was four times larger than that of healthy fruit (22 μm). Altered expression of genes related to Ca2+ transport and the uneven distribution of intracellular Ca2+ were also shown to associate with the development of cork spot disorder. Our results suggest that flesh tissue damage likely occurred prior to the initiation of cork spot. The dysfunction of long-distance and transmembrane Ca2+ transport channels could be responsible for the imbalanced distribution of Ca2+ inside the fruit, thus resulting in the development of cork spot.

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“…Muziri et al. () studied microscopic analysis and tissue detection in pears by employing X‐ray computed tomography and confirmed that the tissue of crispy fruits is more porous than non‐crispy ones. They further provided evidence regarding the formation of lysigenous masses in crispy pears and indicated that these fruits are larger and more oval while non‐crispy fruit cells are more spherical.…”

Section: Introductionmentioning

confidence: 96%

The use of CT scan imaging technique to determine pear bruise level due to external loads

Azadbakht

Torshizi

Mahmoodi

2018

Food Science & Nutrition

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X‐ray computed tomography (CT) is an effective noninvasive tool to visualize fresh agricultural commodities’ internal components and quality attributes, also imaging via X‐ray CT is a non‐destructive and well‐developed method applied in measuring the internal effects of agricultural products. In the present research, 120 healthy pears with their health verification through the CT were selected. Next, 81 healthy pears were selected and subjected to quasi‐static and dynamic loading. The impact of the incoming pressures was investigated within 5, 10, and 15 days of storage. After loading and storing with the use of CT method, the total volume and the bruise volume of the pears were measured and the ratio of the bruise volume to the volume of each pear was calculated. Quasi‐static loads were pressurized over a period of two ways; the pressure of wide edge was exerted at three force levels of 70, 100, and 130 N while the pressure of the thin edge was applied at 15, 20, and 25 N. Dynamic loading was performed by utilizing a pendulum and 300, 350, and 400 g mass. The results of the experiments indicated that in the quasi‐static loading, the maximum and minimum amounts of pear bruise were 45.138% and 0.094% of the fruit, respectively. Besides, in the case of thin edge pressures, the minimum and maximum bruise levels were 0.007% and 19.88%, respectively. These values were obtained through 5 and 15 days of storage, respectively. In the dynamic loading, the maximum and minimum amounts of pear bruise were 47.36% and 0.21% of the total fruit, respectively, occurring at 400 and 300 g mass impact.

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Microstructure analysis and detection of mealiness in ‘Forelle’ pear ( Pyrus communis L.) by means of X-ray computed tomography

Cited by 37 publications

References 28 publications

Nondestructive internal quality inspection of pear fruit by X-ray CT using machine learning

Nondestructive internal quality inspection of pear fruit by X-ray CT using machine learning

High-Resolution Microstructure Analysis of Cork Spot Disordered Pear Fruit “Akizuki” (Pyrus pyrifolia Nakai) Using X-Ray CT

The use of CT scan imaging technique to determine pear bruise level due to external loads

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