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Open AccessArticle

Penetration Depth Measurement of Near-Infrared Hyperspectral Imaging Light for Milk Powder

Key Laboratory of Advanced Process Control for Light Industry, Ministry of Education, Jiangnan University, Wuxi 214122, China
Environmental Microbial and Food Safety Laboratory, Agricultural Research Service, USDA, Beltsville, MD 20705, USA
National Institute of Agricultural Science, Rural Development Administration, 310 Nongsaengmyeong-ro, Wansan-gu, Jueonju-si, Jeollabuk-do 54875, Korea
School of Biosystems and Food Engineering, University College Dublin, Dublin 4, Ireland
Food Quality, Agricultural Research Service, USDA, Beltsville, MD 20705, USA
Author to whom correspondence should be addressed.
Academic Editor: Gonzalo Pajares Martinsanz
Sensors 2016, 16(4), 441;
Received: 1 September 2015 / Revised: 21 March 2016 / Accepted: 22 March 2016 / Published: 25 March 2016
(This article belongs to the Special Issue Imaging: Sensors and Technologies)
The increasingly common application of the near-infrared (NIR) hyperspectral imaging technique to the analysis of food powders has led to the need for optical characterization of samples. This study was aimed at exploring the feasibility of quantifying penetration depth of NIR hyperspectral imaging light for milk powder. Hyperspectral NIR reflectance images were collected for eight different milk powder products that included five brands of non-fat milk powder and three brands of whole milk powder. For each milk powder, five different powder depths ranging from 1 mm–5 mm were prepared on the top of a base layer of melamine, to test spectral-based detection of the melamine through the milk. A relationship was established between the NIR reflectance spectra (937.5–1653.7 nm) and the penetration depth was investigated by means of the partial least squares-discriminant analysis (PLS-DA) technique to classify pixels as being milk-only or a mixture of milk and melamine. With increasing milk depth, classification model accuracy was gradually decreased. The results from the 1-mm, 2-mm and 3-mm models showed that the average classification accuracy of the validation set for milk-melamine samples was reduced from 99.86% down to 94.93% as the milk depth increased from 1 mm–3 mm. As the milk depth increased to 4 mm and 5 mm, model performance deteriorated further to accuracies as low as 81.83% and 58.26%, respectively. The results suggest that a 2-mm sample depth is recommended for the screening/evaluation of milk powders using an online NIR hyperspectral imaging system similar to that used in this study. View Full-Text
Keywords: penetration depth; hyperspectral imaging; milk powder; PLS-DA penetration depth; hyperspectral imaging; milk powder; PLS-DA
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Huang, M.; Kim, M.S.; Chao, K.; Qin, J.; Mo, C.; Esquerre, C.; Delwiche, S.; Zhu, Q. Penetration Depth Measurement of Near-Infrared Hyperspectral Imaging Light for Milk Powder. Sensors 2016, 16, 441.

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