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Appl. Sci. 2017, 7(1), 109; doi:10.3390/app7010109

Nondestructive Estimation of Moisture Content, pH and Soluble Solid Contents in Intact Tomatoes Using Hyperspectral Imaging

1
Department of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University, 99 Daehak-ro, Yuseong-gu, Daejeon 34134, Korea
2
Department of Farm Power and Machinery, Bangladesh Agricultural University, Mymensingh 2202, Bangladesh
3
Environmental Microbiology and Food Safety Laboratory, BARC-East, Agricultural Research Service, US Department of Agriculture, 10300 Baltimore Avenue, Beltsville, MD 20705, USA
4
National Academy of Agricultural Science, Rural Development Administration, 310 Nongsaengmyeong-ro, Wansan-gu, Jeonju, Jeollabuk-do 54875, Korea
*
Author to whom correspondence should be addressed.
Academic Editor: Hung Nguyen
Received: 3 December 2016 / Revised: 10 January 2017 / Accepted: 17 January 2017 / Published: 21 January 2017
(This article belongs to the Special Issue Applications of Hyperspectral Imaging for Food and Agriculture)
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Abstract

The objective of this study was to develop a nondestructive method to evaluate chemical components such as moisture content (MC), pH, and soluble solid content (SSC) in intact tomatoes by using hyperspectral imaging in the range of 1000–1550 nm. The mean spectra of the 95 matured tomato samples were extracted from the hyperspectral images, and multivariate calibration models were built by using partial least squares (PLS) regression with different preprocessing spectra. The results showed that the regression model developed by PLS regression based on Savitzky–Golay (S–G) first-derivative preprocessed spectra resulted in better performance for MC, pH, and the smoothing preprocessed spectra-based model resulted in better performance for SSC in intact tomatoes compared to models developed by other preprocessing methods, with correlation coefficients (rpred) of 0.81, 0.69, and 0.74 with root mean square error of prediction (RMSEP) of 0.63%, 0.06, and 0.33% Brix respectively. The full wavelengths were used to create chemical images by applying regression coefficients resulting from the best PLS regression model. These results obtained from this study clearly revealed that hyperspectral imaging, together with suitable analysis model, is a promising technology for the nondestructive prediction of chemical components in intact tomatoes. View Full-Text
Keywords: hyperspectral imaging; intact tomato; diffuser lighting; moisture content; pH; soluble solid content; partial least squares regression hyperspectral imaging; intact tomato; diffuser lighting; moisture content; pH; soluble solid content; partial least squares regression
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MDPI and ACS Style

Rahman, A.; Kandpal, L.M.; Lohumi, S.; Kim, M.S.; Lee, H.; Mo, C.; Cho, B.-K. Nondestructive Estimation of Moisture Content, pH and Soluble Solid Contents in Intact Tomatoes Using Hyperspectral Imaging. Appl. Sci. 2017, 7, 109.

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