A rapid and nondestructive method to determine the distribution map of protein, carbohydrate and sialic acid on Edible bird's nest by hyper-spectral imaging and chemometrics. (15th August 2017)

• Record Type: Journal Article
• Authors: Shi, Jiyong; Hu, Xuetao; Zou, Xiaobo; Zhao, Jiewen; Zhang, Wen; Holmes, Mel; Huang, Xiaowei; Zhu, Yaodi; Li, Zhihua; Shen, Tingting; Zhang, Xiaolei
• Is Part Of: Food chemistry. Volume 229(2017)
• Page Start: 235
• Page End: 241
• Publication Date: 2017-08-15
• Subjects: Edible bird's nest -- Distribution map -- Hyper-spectral imaging -- Nondestructive; Food -- Analysis -- Periodicals; Food -- Composition -- Periodicals; 664
• DOI: 10.1016/j.foodchem.2017.02.075
• Languages: English
• ISSNs: 0308-8146

Abstract

Highlights: A rapid and nondestructive method for determining the distribution of EBN components were first proposed. Distribution maps of three EBN components content (PC, CC and SAC) were determined. Distribution tendency of PC, CC and SAC on the whole EBN sample was first analyzed. Abstract: Edible bird's nest (EBN) is a precious functional food in Southeast Asia. A rapid and nondestructive method for determining the distribution map of protein content (PC), carbohydrate content (CC) and sialic acid content (SAC) on EBN sample was proposed. Firstly, 60 EBNs were used for hyperspectral image acquisition, and components content (PC, CC and SAC) were determined by chemical analytical methods. Secondly, the spectral signals of EBN hyperspectral image and EBN components content were used to build calibration models. Thirdly, spectra of each pixel in EBN hyperspectral image were extracted, and these spectra were substituted in the calibration models to predict the PC, CC and SAC of each pixel in the EBN image, so the visual distribution maps of PC, CC and SAC on the whole EBN were obtained. It is the first time to show the distribution tendency of PC, CC and SAC on the whole EBN sample.