基于主成分分析-Fisher判别分析的食品类塑料瓶物证差分拉曼光谱分类
Differential Raman Spectral Inspection of Food Grade Plastic Bottles Based on Principal Component Analysis and Fisher Discriminant Analysis
姜红 1陈壮 2郝小辉 3倪婷婷4
作者信息
- 1. 甘肃警察职业学院刑事侦查系 兰州 730046;食品药品安全防控山西省重点实验室 太原 030000
- 2. 甘肃政法大学司法警察学院(公安分院) 兰州 730070
- 3. 甘肃警察职业学院刑事侦查系 兰州 730046
- 4. 南京简智仪器设备有限公司 南京 210049
- 折叠
摘要
食品类塑料瓶物证携带许多潜在证据信息,目前针对此类物证的检验研究尚处于探索阶段.利用差分拉曼光谱对46个食品类塑料瓶样品进行检验,依据样品材质及光谱特征峰可将样品分为三类.利用主成分分析(PCA)-Fisher判别分析,绘制主成分得分图,构建判别函数,建立分类模型.结果表明,食品类塑料瓶样品具有明显的聚类关系,原始分类与交叉验证分类准确率达到100％.差分拉曼光谱结合PCA-Fisher判别分析检验鉴别食品类塑料瓶物证具有一定的科学性.
Abstract
The physical evidence of plastic food grade bottles carries many potential evidentiary information,and the current research on the inspection of such physical evidence is still in the exploratory stage.Using differential Raman spectroscopy,46 food grade plastic bottle samples were examined.These samples can be divided into three categories based on their physical and spectral characteristic peaks.Principal component analysis(PCA)-Fisher discriminant analysis was used to draw a principal component score map,construct a discriminant function,and establish a classification model.The results showed that the food grade plastic bottle samples had a significant clustering relationship,and the accuracy rate of the original classification and cross validation classification reached 100％.Differential Raman spectroscopy combined with PCA-Fisher discriminant analysis has certain scientific significance in testing and identifying the physical evidence of food grade plastic bottles.
关键词
差分拉曼光谱/主成分分析/Fisher判别分析/食品类塑料瓶Key words
Differential Raman spectroscopy/Principal component analysis/Fisher discriminant analysis/Food grade plastic bottles引用本文复制引用
基金项目
食品药品安全防控山西省重点实验室开放基金(202204010931006)
出版年
2024
NETL
NSTL
维普
万方数据