相似性(几何)
认证(法律)
二进制数
MATLAB语言
计算机科学
编码(集合论)
二进制代码
质谱
模式识别(心理学)
化学
人工智能
数据库
色谱法
数学
质谱法
计算机安全
图像(数学)
算术
操作系统
程序设计语言
集合(抽象数据类型)
作者
Qian Meng,Jianqing Zhang,Xiaolan Li,Yun Li,Xuanjing Shen,Ziqing Li,Meng Xu,Changliang Yao,Pengfei Chu,Yajun Cui,De‐an Guo
出处
期刊:Food Chemistry
[Elsevier]
日期:2023-10-15
卷期号:436: 137776-137776
被引量:3
标识
DOI:10.1016/j.foodchem.2023.137776
摘要
This is the first report to use Atmospheric Pressure Solids Analysis Probe (ASAP) for rapid and intelligent authentication of 78 edible flowers. Mass spectra of 451 batches were collected, with each run for 1-2 min. Experimental raw data was automatically extracted and aligned to create a MS database, based on which flowers were identified by MS similarity scores and rankings. To avoid background interference, top 25 ions of each flower were screened and gathered into an m/z pool containing 292 ions (+) and 399 ions (-). Binary sequence IDs were then generated by automatically assigning "1″ for presence and "0″ for absence, resulting in 78 binary codes. Binary code similarity with 78 IDs was used for authentication. Above two approaches were automatically performed by MATLAB, and compared to k-nearest neighbor model, and samples were all successfully identified (100 %). The proposed method provides a high-throughput authentication approach for large-scale food samples.
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