Next Article in Journal
Isolation of Chavibetol and Methyleugenol from Essential Oil of Pimenta pseudocaryophyllus by High Performance Liquid Chromatography
Next Article in Special Issue
Identification of Maize Kernel Vigor under Different Accelerated Aging Times Using Hyperspectral Imaging
Previous Article in Journal
Co-Delivery of Gemcitabine and Paclitaxel in cRGD-Modified Long Circulating Nanoparticles with Asymmetric Lipid Layers for Breast Cancer Treatment
Previous Article in Special Issue
Insight into Rapid DNA-Specific Identification of Animal Origin Based on FTIR Analysis: A Case Study
Article Menu
Issue 11 (November) cover image

Export Article

Open AccessArticle
Molecules 2018, 23(11), 2907;

Variety Identification of Raisins Using Near-Infrared Hyperspectral Imaging

1,2, 1,2, 1,2, 1,2, 3,* and 1,2,4,*
College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, China
College of Information Science and Technology, Shihezi University, Shihezi 832000, China
State Key Laboratory of Modern Optical Instrumentation, Zhejiang University, Hangzhou 310058, China
Authors to whom correspondence should be addressed.
Academic Editors: Christian Huck and Krzysztof B. Bec
Received: 12 October 2018 / Revised: 6 November 2018 / Accepted: 7 November 2018 / Published: 8 November 2018
PDF [2754 KB, uploaded 8 November 2018]


Different varieties of raisins have different nutritional properties and vary in commercial value. An identification method of raisin varieties using hyperspectral imaging was explored. Hyperspectral images of two different varieties of raisins (Wuhebai and Xiangfei) at spectral range of 874–1734 nm were acquired, and each variety contained three grades. Pixel-wise spectra were extracted and preprocessed by wavelet transform and standard normal variate, and object-wise spectra (sample average spectra) were calculated. Principal component analysis (PCA) and independent component analysis (ICA) of object-wise spectra and pixel-wise spectra were conducted to select effective wavelengths. Pixel-wise PCA scores images indicated differences between two varieties and among different grades. SVM (Support Vector Machine), k-NN (k-nearest Neighbors Algorithm), and RBFNN (Radial Basis Function Neural Network) models were built to discriminate two varieties of raisins. Results indicated that both SVM and RBFNN models based on object-wise spectra using optimal wavelengths selected by PCA could be used for raisin variety identification. The visualization maps verified the effectiveness of using hyperspectral imaging to identify raisin varieties. View Full-Text
Keywords: near-infrared hyperspectral imaging; raisins; support vector machine; pixel-wise; object-wise near-infrared hyperspectral imaging; raisins; support vector machine; pixel-wise; object-wise

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Feng, L.; Zhu, S.; Zhang, C.; Bao, Y.; Gao, P.; He, Y. Variety Identification of Raisins Using Near-Infrared Hyperspectral Imaging. Molecules 2018, 23, 2907.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Molecules EISSN 1420-3049 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top