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

Anisur Rahman,Moon Kim,Hoonsoo Lee,Changyeun Mo,Byoung-Kwan Cho,Lalit Kandpal,Santosh Lohumi

doi:10.3390/app7010109

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.

Highlights

Tomatoes are the most important fruit grown worldwide with approximately 170.8 million tons produced in 2014, which are widely consumed in either fresh or processed form [1]
The specific objectives of this study were to acquire the hyperspectral images of intact tomatoes; secondly, to recognize the important region of interests (ROIs) and extract the corresponding spectral data within the acquired hyperspectral images; and, to establish the quantitative analysis model using partial least squares (PLS) regression and create chemical images by applying regression coefficients resulting from the best PLS regression model
The near-infrared region (NIR) was sensitive to the concentrations of organic materials, which involved the response of molecular bonds C–H, O–H, and N–H [35]

Summary

Introduction

Tomatoes are the most important fruit grown worldwide with approximately 170.8 million tons produced in 2014, which are widely consumed in either fresh or processed form [1]. They are considered healthy because they contain high levels of lycopene, which is a natural antioxidant, as well as β-carotene, vitamin C, and vitamin E [2]. Sci. 2017, 7, 109 safety of finished products [3,4]. It is necessary to develop a nondestructive measurement technique to obtain information on these quality attributes from whole samples, which will be very useful for producers, processors and distributors to ascertain the quality of tomatoes

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Methods

Results

Conclusion