دورية أكاديمية
Evaluation of Postharvest Senescence of Broccoli via Hyperspectral Imaging.
العنوان: | Evaluation of Postharvest Senescence of Broccoli via Hyperspectral Imaging. |
---|---|
المؤلفون: | Guo X; University of Florida, Department of Electrical and Computer Engineering, Gainesville, Florida, USA., Ahlawat YK; University of Florida, Horticultural Sciences Department, Gainesville, Florida, USA., Liu T; University of Florida, Horticultural Sciences Department, Gainesville, Florida, USA., Zare A; University of Florida, Department of Electrical and Computer Engineering, Gainesville, Florida, USA. |
المصدر: | Plant phenomics (Washington, D.C.) [Plant Phenomics] 2022 May 09; Vol. 2022, pp. 9761095. Date of Electronic Publication: 2022 May 09 (Print Publication: 2022). |
نوع المنشور: | Journal Article |
اللغة: | English |
بيانات الدورية: | Publisher: Science Partner Journals Country of Publication: United States NLM ID: 101769942 Publication Model: eCollection Cited Medium: Internet ISSN: 2643-6515 (Electronic) Linking ISSN: 26436515 NLM ISO Abbreviation: Plant Phenomics Subsets: PubMed not MEDLINE |
أسماء مطبوعة: | Original Publication: [Washington, DC] : Science Partner Journals, [2019]- |
مستخلص: | Fresh fruit and vegetables are invaluable for human health; however, their quality often deteriorates before reaching consumers due to ongoing biochemical processes and compositional changes. We currently lack any objective indices which indicate the freshness of fruit or vegetables resulting in limited capacity to improve product quality eventually leading to food loss and waste. In this conducted study, we hypothesized that certain proteins and compounds, such as glucosinolates, could be used as one potential indicator to monitor the freshness of broccoli following harvest. To support our study, glucosinolate contents in broccoli based on HPLC measurement and transcript expression of glucosinolate biosynthetic genes in response to postharvest stresses were evaluated. We found that the glucosinolate biosynthetic pathway coincided with the progression of senescence in postharvest broccoli during storage. Additionally, we applied machine learning-based hyperspectral image (HSI) analysis, unmixing, and subpixel target detection approaches to evaluate glucosinolate level to detect postharvest senescence in broccoli. This study provides an accessible approach to precisely estimate freshness in broccoli through machine learning-based hyperspectral image analysis. Such a tool would further allow significant advancement in postharvest logistics and bolster the availability of high-quality, nutritious fresh produce. Competing Interests: The authors declare no competing and no potential conflict of interest. (Copyright © 2022 Xiaolei Guo et al.) |
References: | Crit Rev Food Sci Nutr. 2012;52(11):1039-58. (PMID: 22823350) Food Res Int. 2020 Oct;136:109529. (PMID: 32846593) Food Chem. 2017 Oct 15;233:60-68. (PMID: 28530612) IEEE Trans Pattern Anal Mach Intell. 2018 Oct;40(10):2342-2354. (PMID: 28961102) Front Plant Sci. 2016 Feb 10;7:45. (PMID: 26904036) |
تواريخ الأحداث: | Date Created: 20220527 Latest Revision: 20220716 |
رمز التحديث: | 20231215 |
مُعرف محوري في PubMed: | PMC9115666 |
DOI: | 10.34133/2022/9761095 |
PMID: | 35620399 |
قاعدة البيانات: | MEDLINE |
تدمد: | 2643-6515 |
---|---|
DOI: | 10.34133/2022/9761095 |