Comparison of Visible-Near Infrared and Short Wave Infrared hyperspectral imaging for the evaluation of rainbow trout freshness
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- 作者：Mostafa Khojastehnazh ; Mohammad Hadi Khoshtaghaza ; Barat Mojaradi ; Masoud Rezaei ; Mohammad Goodarzi ; Wouter Saeys
- 关键词：Hyperspectral imaging ; Vis&ndash ; NIR ; SWIR ; Freshness ; Rainbow trout
- 刊名：Food Research International
- 出版年：February, 2014
- 年：2014
- 卷：56
- 期：Complete
- 页码：25-34
- 全文大小：2103 K
文摘
The freshness of rainbow trout is one of the most important quality parameters to attract customers. Common methods to detect fish freshness are usually subjective to the skill of a quality evaluator and are time consuming and destructive. Therefore, an automatic, nondestructive, accurate and quick method is needed. Hyperspectral imaging has demonstrated its efficiency in the meat and fish industries for quality control purposes. This method is nondestructive, fast and automatic. In this study, two setups for hyperspectral imaging named 鈥淰isible-Near Infrared鈥?(Vis-NIR) and 鈥淪hort Wave Infrared鈥?(SWIR) are used to determine fish freshness. Eighty fresh rainbow trouts were divided into four batches which were separately preserved in ice for 1, 3, 5 and 7 days, respectively. Principle Component Analysis (PCA) and Partial Least Squares-Discriminate Analysis (PLS-DA) were used as unsupervised and supervised techniques for the evaluation of rainbow trout freshness. Results obtained by PCA technique indicated that four classes of samples can be detected using the Vis-NIR mean spectrum by applying a second derivative (D2) preprocessing method. The RCV2 and RPre with D2 preprocessing were 0.97 and 0.98 for Vis-NIR and 0.84 and 0.67 for SWIR, respectively. The corresponding values of RMSECV and RMSEPre were 0.16 and 0.14 in Vis-NIR and 0.44 and 0.76 in SWIR, respectively. Classification model achieved an overall correct classification of 100% and 75% for Vis-NIR and SWIR, respectively. The obtained results using both PCA and PLS-DA methods indicated that the Vis-NIR imaging system performs better than SWIR. Among all applied preprocessing techniques, the second derivative preprocessing achieved the best performance.