This review covers current analytical techniques, instruments and methodologies used in the analysis of carotenoids in foods and human samples. We also cover the importance of carotenoids for human health, carotenoid content in foods, bioavailability of carotenoids and evaluation of human intake of carotenoids. There are a wide variety of extraction methods and analytical techniques for determination of carotenoids. Recent advances in analytical instruments and the discovery of unknown metabolites of carotenoids widened the scope of carotenoid studies, especially through the application of metabolomics tools. Omics instruments and statistical methods perform untargeted and targeted profiling of carotenoids in foods and human samples, thus advancing knowledge of the composition of food containing carotenoids and their role in human health. Aimed at collating valuable information about recent analytical methodologies for carotenoids, this review mainly focuses on studies released in the past five years (2009–13).

Review of methods for analysis of carotenoids

Improving vegetable quality in controlled environments

Grafting has become an imperative for intensive vegetable production since chlorofluorocarbon-based soil fumigants were banned from use on grounds of environmental protection. Compelled by this development, research into rootstock-scion interaction has broadened the potential applications of grafting in the vegetable industry beyond aspects of soil phytopathology. Grafting has been increasingly tapped for cultivation under adverse environs posing abiotic and biotic stresses to vegetable crops, thus enabling expansion of commercial production onto otherwise under-exploited land. Vigorous rootstocks have been employed not only in the open field but also under protected cultivation where increase in productivity improves distribution of infrastructural and energy costs. Applications of grafting have expanded mainly in two families: the Cucurbitaceae and the Solanaceae, both of which comprise major vegetable crops. As the main drives behind the expansion of vegetable grafting have been the resistance to soilborne pathogens, tolerance to abiotic stresses and increase in yields, rootstock selection and breeding have accordingly conformed to the prevailing demand for improving productivity, arguably at the expense of fruit quality. It is however compelling to assess the qualitative implications of this growing agronomic practice for human nutrition. Problems of impaired vegetable fruit quality have not infrequently been associated with the practice of grafting. Accordingly, the aim of the current review is to reassess how the practice of grafting and the prevalence of particular types of commercial rootstocks influence vegetable fruit quality and, partly, storability. Physical, sensorial and bioactive aspects of quality are examined with respect to grafting for watermelon, melon, cucumber, tomato, eggplant, and pepper. The physiological mechanisms at play which mediate rootstock effects on scion performance are discussed in interpreting the implications of grafting for the configuration of vegetable fruit physicochemical quality and nutritive value.

/pdf/vegetable-grafting-the-implications-of-a-growing-agronomic-2dcj9tgqk4.pdf

Vegetable Grafting: The Implications of a Growing Agronomic Imperative for Vegetable Fruit Quality and Nutritive Value.

Seedlings of tomato (Lycopersicon esculentum Mill.) cv. ‘Big Red’ were used as scion and rootstock (self-grafted) and non-grafted control, while two hybrid tomatoes ‘Heman’ and ‘Primavera’ were used as rootstocks. Grafted and non-grafted plants were grown in the greenhouse and in the open-field. Grafted plants (BH and BP) were more vigorous than the non-grafted ones in the greenhouse as well as in the open-field. Plants grafted onto ‘Heman’ and ‘Primavera’ produced 32.5, 12.8% and 11.0 and 11.1% more fruit than the control (B) in the greenhouse and the open-field, respectively, whereas self-grafted plants BB had a lower yield in both cultivation conditions. However, the self-rooted plants B presented earliness in their performance, probably due to the lack of stress that followed the grafting operation. Quality and qualitative fruit characteristics were not affected by grafting.

Effect of grafting on growth and yield of tomato (Lycopersicon esculentum Mill.) in greenhouse and open-tield

Turhan A., Ozmen N., Serbeci M.S., Seniz V., 2011. Effects of grafting on different rootstocks on tomato fruit yield and quality . Hort. Sci. (Prague), 38: 142–149. The aim of the study was to find effects of tomato grafting on another cultivar. The tomato cultivars used as scions were Yeni Talya, Swanson and Beril. Cultivars used as rootstocks were Beaufort and Arnold. Cleft grafting methods were applied. The following characteristics of grafted and nongrafted plants were recorded: fruit index, number of fruits/truss, fruit weight, fruit yield, dry matter, pH, concentration of soluble solids, titratable acidity, total sugar and lycopene and vitamin C. The results showed that fruit yield and fruit index, number of fruits/truss and fruit weights were improved by grafting. Fruit quality, measured in terms of dry matter, concentration of soluble solids, total sugar, and vitamin C content, was lower in the fruits of grafted plants than in nongrafted ones. No significant difference in lycopene and pH content was found. Titratable acid content was improved by grafting. A positive effect of grafting was recorded when Beaufort was used as rootstock. These results showed that grafting could be an advantageous alterna tive in tomato production.

/pdf/effects-of-grafting-on-different-rootstocks-on-tomato-fruit-11u3p8wj6m.pdf

Effects of grafting on different rootstocks on tomato fruit yield and quality

Effects of different rootstocks on aroma volatile compounds and carotenoid content of melon fruits

Effects of grafting on tomato and eggplant

Multimedia data manipulation and forgery has never been easier than today, thanks to the power of Artificial Intelligence (AI). AI-generated fake content, commonly called Deepfakes, have been raising new issues and concerns, but also new challenges for the research community. The Deepfake detection task has become widely addressed, but unfortunately, approaches in the literature suffer from generalization issues. In this paper, the Face Deepfake Detection and Reconstruction Challenge is described. Two different tasks were proposed to the participants: (i) creating a Deepfake detector capable of working in an “in the wild” scenario; (ii) creating a method capable of reconstructing original images from Deepfakes. Real images from CelebA and FFHQ and Deepfake images created by StarGAN, StarGAN-v2, StyleGAN, StyleGAN2, AttGAN and GDWCT were collected for the competition. The winning teams were chosen with respect to the highest classification accuracy value (Task I) and “minimum average distance to Manhattan” (Task II). Deep Learning algorithms, particularly those based on the EfficientNet architecture, achieved the best results in Task I. No winners were proclaimed for Task II. A detailed discussion of teams’ proposed methods with corresponding ranking is presented in this paper.

/pdf/the-face-deepfake-detection-challenge-1guy0ax6.pdf

The Face Deepfake Detection Challenge

Plant density and container cell volume on solanaceous seedling growth

The influence of shading levels on foliage plant growth and quality

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

A. Paratore

Author Tools

Chat about Author