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Start Over Topic carotenoids Publication Year Range Last 10 years
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1. Evaluating the Impacts of Selected Packaging Materials on the Quality Attributes of Cassava Flour (cvs. TME 419 and UMUCASS 36).

Author

Opara UL, Caleb OJ, and Uchechukwu-Agua AD

Subjects
Bacteria growth & development, Flour, Food Storage methods, Fungi growth & development, Humans, Humidity, Paper, Plant Roots, Plastics, Polyethylene, Temperature, Vegetables, Carotenoids analysis, Color, Food Microbiology, Food Packaging methods, Food Preservation methods, Manihot chemistry
Abstract

The influence of packaging materials (plastic bucket, low density polyethylene [LDPE] bags and paper bags) on quality attributes of the flour of 2 cassava cultivars (TME 419 and UMUCASS 36) stored at 23 ± 2 °C and 60% relative humidity (RH) were investigated for 12 wk. Cassava flour from each package type was evaluated for proximate composition, physicochemical properties and microbial growth at 4-wk intervals. Total color difference (∆E) of both cassava flour cultivars increased with storage duration. Flour packed in plastic bucket had the lowest change in color (3.2 ± 0.42) for cv. "TME 419ˮ and (4.1 ± 0.87) for cv. "UMUCASS 36ˮ at the end of week 12. Total carotenoid decreased across all treatment, and after the 12 wk storage, the highest total carotenoid retention (1.7 ± 0.02 and 2.0 ± 0.05 μg/mL) was observed in flour packed in plastic bucket. However, cassava flour in paper bag had the lowest microbial count of 3.4 ± 0.03 and 3.4 ± 0.08 log cfu/g for total aerobic mesophilic bacteria and fungi, respectively., (© 2015 Institute of Food Technologists®)

Published
2016
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2. Optical biopsy identification and grading of gliomas using label-free visible resonance Raman spectroscopy

Author

Zong Rui, Yan Zhou, Xinguang Yu, Lin Zhang, Kai Wang, Binlin Wu, Ke Zhu, Mingyue Zhao, Gangge Cheng, Robert R. Alfano, Cheng-hui Liu, Chunyuan Zhang, and Lingyan Shi

Subjects
Support Vector Machine, Optical Phenomena, Optical Physics, Spectrum Analysis, Raman, 01 natural sciences, Protein Structure, Secondary, Nuclear magnetic resonance, optical biopsy, cancer grades, Principal Component Analysis, medicine.diagnostic_test, Brain Neoplasms, Chemistry, carotenoids, Margins of Excision, Glioma, Human brain, Atomic and Molecular Physics, and Optics, Electronic, Optical and Magnetic Materials, cancer margins, medicine.anatomical_structure, histopathology, Paper, medicine.medical_specialty, brain, Brain tumor, Biomedical Engineering, Nerve Tissue Proteins, label-free, resonance Raman, 010309 optics, Biomaterials, Opthalmology and Optometry, 0103 physical sciences, Biopsy, Biomarkers, Tumor, medicine, Humans, tryptophan, General, Grading (tumors), Receiver operating characteristic, glioblastoma, biomarkers, Optics, Optical Biopsy, Lipid Metabolism, medicine.disease, gliomas, Histopathology, Neoplasm Grading
Abstract

Glioma is one of the most refractory types of brain tumor. Accurate tumor boundary identification and complete resection of the tumor are essential for glioma removal during brain surgery. We present a method based on visible resonance Raman (VRR) spectroscopy to identify glioma margins and grades. A set of diagnostic spectral biomarkers features are presented based on tissue composition changes revealed by VRR. The Raman spectra include molecular vibrational fingerprints of carotenoids, tryptophan, amide I/II/III, proteins, and lipids. These basic in situ spectral biomarkers are used to identify the tissue from the interface between brain cancer and normal tissue and to evaluate glioma grades. The VRR spectra are also analyzed using principal component analysis for dimension reduction and feature detection and support vector machine for classification. The cross-validated sensitivity, specificity, and accuracy are found to be 100%, 96.3%, and 99.6% to distinguish glioma tissues from normal brain tissues, respectively. The area under the receiver operating characteristic curve for the classification is about 1.0. The accuracies to distinguish normal, low grade (grades I and II), and high grade (grades III and IV) gliomas are found to be 96.3%, 53.7%, and 84.1% for the three groups, respectively, along with a total accuracy of 75.1%. A set of criteria for differentiating normal human brain tissues from normal control tissues is proposed and used to identify brain cancer margins, yielding a diagnostic sensitivity of 100% and specificity of 71%. Our study demonstrates the potential of VRR as a label-free optical molecular histopathology method used for in situ boundary line judgment for brain surgery in the margins.

Published
2019