Singular spectrum analysis for improving hyperspectral imaging based beef eating quality evaluation

被引：34

作者：

Qiao, Tong ^{[1
]}

Ren, Jinchang ^{[1
]}

Craigie, Cameron ^{[2
]}

Zabalza, Jaime ^{[1
]}

Maltin, Charlotte ^{[2
]}

Marshall, Stephen ^{[1
]}

机构：

[1] Univ Strathclyde, Dept Elect & Elect Engn, Ctr Excellence Signal & Image Proc CeSIP, Glasgow G1 1XW, Lanark, Scotland

[2] Qual Meat Scotland, Rural Ctr, Ingliston EH28 8NZ, Newbridge, Scotland

来源：

COMPUTERS AND ELECTRONICS IN AGRICULTURE | 2015年 / 115卷

关键词：

Hyperspectral imaging; Beef quality prediction; Singular spectrum analysis; Principal component analysis; Support vector machine; PRINCIPAL COMPONENT ANALYSIS; EFFECTIVE FEATURE-EXTRACTION; NEAR-INFRARED SPECTROSCOPY; DATA REDUCTION; PREDICTION; IMAGERY;

D O I：

10.1016/j.compag.2015.05.007

中图分类号：

S [农业科学];

学科分类号：

09 ;

摘要：

Detecting beef eating quality in a non-destructive way has been popular in recent years. Among various non-destructive assessing methods, the feasibility of hyperspectral imaging (HSI) system was investigated in this paper. Hyperspectral images of beef samples were collected in an abattoir production line and used for predicting the beef tenderness and pH value. Support vector machine (SVM) was applied to construct the prediction equation. Before utilizing the original HSI spectral profiles directly, we propose to use singular spectrum analysis (SSA) as a pre-processing approach, where SSA has been proven to be an effective technique for time-series analysis in diverse applications. The results indicate that SSA can remove the instrumental noise of HSI system effectively and therefore improve the prediction performance. (C) 2015 Elsevier B.V. All rights reserved.

引用

页码：21 / 25

页数：5

共 28 条

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