Wide-characterization of high and low dry matter kiwifruit through spatiotemporal multi-omic approach

Valasiadis, Dimitrios; Kollaros, Marios Georgios; Michailidis, Michail; Polychroniadou, Chrysanthi; Tanou, Georgia; Bazakos, Christos; Molassiotis, Athanassios

doi:10.1016/j.postharvbio.2023.112727

Cited by 4 publications

(2 citation statements)

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“…They emphasized the importance of these factors in determining the overall eating quality of kiwifruit. Non-destructive methodologies have been widely used to determine the quality features of kiwifruit, including firmness, SSC, and DM [2,[13][14][15][16][17][18][19]. Therefore, these quality features significantly contribute to the overall quality and postharvest performance of kiwifruit.…”

Section: Discussionmentioning

confidence: 99%

“…The Vis/NIR spectroscopy technique has been utilized to evaluate the qualitative attributes of various fruits, such as apples, citrus fruits, and kiwifruits [12]. Non-destructive techniques like NIR, along with hyperspectral imaging, have been extensively employed to assess the qualitative characteristics of kiwifruit, such as firmness, pH, soluble solid content (SSC), and dry matter (DM) [2,[13][14][15][16][17][18][19][20][21][22]. The integration of Vis/NIR into machine learning (ML) algorithms enhances the effectiveness of learning, estimating, predicting, and classifying crucial quality parameters [23].…”

Section: Introductionmentioning

confidence: 99%

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Non-Destructive Quality Estimation Using a Machine Learning-Based Spectroscopic Approach in Kiwifruits

Tziotzios,

Pantazi,

Paraskevas

et al. 2024

Horticulturae

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The current study investigates the use of a non-destructive hyperspectral imaging approach for the evaluation of kiwifruit cv. “Hayward” internal quality, focusing on physiological traits such as soluble solid concentration (SSC), dry matter (DM), firmness, and tannins, widely used as quality attributes. Regression models, including partial least squares regression (PLSR), bagged trees (BTs), and three-layered neural network (TLNN), were employed for the estimation of the above-mentioned quality attributes. Experimental procedures involving the Specim IQ hyperspectral camera utilization and software were followed for data acquisition and analysis. The effectiveness of PLSR, bagged trees, and TLNN in predicting the firmness, SSC, DM, and tannins of kiwifruit was assessed via statistical metrics, including R squared (R²) values and the root mean square error (RMSE). The obtained results indicate varying degrees of efficiency for each model in predicting kiwifruit quality parameters. The study concludes that machine learning algorithms, especially neural networks, offer substantial accuracy, surpassing traditional methods for evaluating kiwifruit quality traits. Overall, the current study highlights the potential of such non-destructive techniques in revolutionizing quality assessment during postharvest by yielding rapid and reliable predictions regarding the critical quality attributes of fruits.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Non-Destructive Quality Estimation Using a Machine Learning-Based Spectroscopic Approach in Kiwifruits

Tziotzios,

Pantazi,

Paraskevas

et al. 2024

Horticulturae

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Genetic mosaic of the Mediterranean fig: comprehensive genomic insights from a gene bank collection

Bazakos,

Michailidis,

Tourvas

et al. 2024

Physiologia Plantarum

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High‐depth whole‐genome resequencing of 53 diverse fig tree genotypes yielded a rich dataset of genetic variants. We successfully identified 5,501,460 single‐nucleotide polymorphisms (SNPs) and 1,228,537 insertions and deletions (InDels), providing a high‐density and excellent‐quality genetic map of the fig tree. We also performed a detailed population structure analysis, dividing the 53 genotypes into three geographical groups and assessing their genetic diversity and divergence. Analysis of structural variants (SVs) and copy number variations (CNVs) revealed their potential functional impact, particularly in plant‐pathogen interaction and secondary metabolism. Metabolomic fingerprinting of fig genotypes uncovered extensive variation in primary metabolites and polyphenolic compounds, highlighting the influence of genotype on fruit quality traits such as nutritional content and bioactive compound composition. The genome‐wide association study (GWAS) identified critical SNPs associated with fruit quality and morphological features. The discovery of significant candidate genes, such as AGL62, GDSL, and COBRA‐like protein 4 genes, offers promising targets for marker‐assisted selection and genome editing approaches to improve fig fruit morphological and quality traits. This extensive genomic analysis of fig trees enhances our understanding of the genetic basis of important agronomic traits and provides a rich resource for future research in this economically and nutritionally significant fruit.

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Breeding of new kiwifruit (Actinidia chinensis) cultivars with yellow (golden) fleshed and superior characteristics

Asadi,

Ghasemnezhad,

Bakhshipour

et al. 2024

BMC Plant Biol

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The most widely cultivated species globally is Actinidia deliciosa cv. ‘Hayward’. However, in recent years, consumers have shown greater demand for new varieties with novel flesh colour, flavour and appearance in international markets. To meet these expectations, one breeding study was started in 2016 at the University of Guilan in Iran to develop new kiwifruit cultivars with superior characteristics. In this breeding program, 201 female and 534 male hybrid genotypes were obtained from combinations of six different parents using the controlled cross-breeding technique. The evaluations were carried out over three consecutive stages. In the first stage, 201 female hybrid genotypes and control (‘Golden’) were evaluated based on 25 phenological and pomological traits. In the second stage, hybrids passed the desired threshold values in 7 key attributes were selected. Sensory testing in the next stage evaluated these selected hybrid genotypes more. As a result of the first stage, 30 hybrid genotypes with fruit weight (90 ≥ g) were selected. Then, in the second stage, 21 hybrids that showed acceptable dry matter, hue angle, flesh firmness, soluble solid content, acidity, and ascorbic acid were selected. Finally, 13 hybrid genotypes received high scores in sensory testing, and just two hybrid genotypes represented a 100% of novelty with positive texture. These genotypes were selected as potential cultivar candidates. In future studies, fruit yield, disease/pest resistance, and post-harvest performance trials of the 13 selected candidates will be evaluated at different sites. Thus, the ones that give the best results will be introduced to kiwifruit growers.

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Wide-characterization of high and low dry matter kiwifruit through spatiotemporal multi-omic approach

Cited by 4 publications

References 59 publications

Non-Destructive Quality Estimation Using a Machine Learning-Based Spectroscopic Approach in Kiwifruits

Non-Destructive Quality Estimation Using a Machine Learning-Based Spectroscopic Approach in Kiwifruits

Genetic mosaic of the Mediterranean fig: comprehensive genomic insights from a gene bank collection

Breeding of new kiwifruit (Actinidia chinensis) cultivars with yellow (golden) fleshed and superior characteristics

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