Nondestructive Detection of Postharvest Quality of Cherry Tomatoes Using a Portable NIR Spectrometer and Chemometric Alg
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Nondestructive Detection of Postharvest Quality of Cherry Tomatoes Using a Portable NIR Spectrometer and Chemometric Algorithms Lei Feng 1 & Min Zhang 1,2,3
&
Benu Adhikari 4 & Zhimei Guo 5
Received: 22 July 2018 / Accepted: 26 December 2018 # Springer Science+Business Media, LLC, part of Springer Nature 2019
Abstract The aim of this study was to assess the applicability of a portable NIR spectroscopy system and chemometric algorithms in intelligently detecting postharvest quality of cherry tomatoes. The postharvest quality of cherry tomatoes was evaluated in terms of firmness, soluble solids content (SSC), and pH, and a portable NIR spectrometer (950–1650 nm) was used to obtain the spectra of cherry tomatoes. Partial least square (PLS), support vector machine (SVM), and extreme learning machine (ELM) were applied to predict the postharvest quality of cherry tomatoes from their spectra. The effects of different preprocessing techniques, including Savitzky–Golay (S-G), multiplicative scattering correction (MSC), and standard normal variate (SNV) on prediction performance were also evaluated. Firmness, SSC and pH values of cherry tomatoes decreased during storage period, based on which the tomato samples could be classified into two distinct clusters. Similarly, cherry tomatoes with different storage time could also be separated by the NIR spectroscopic characteristics. The best prediction accuracy was obtained from ELM algorithms using the raw spectra with Rp2, RMSEP, and RPD values of 0.9666, 0.3141 N, and 5.6118 for firmness; 0.9179, 0.1485%, and 3.6249 for SSC; and 0.8519, 0.0164, and 2.7407 for pH, respectively. Excellent predictions for firmness and SSC (RPD value greater than 3.0), good prediction for pH (RPD value between 2.5 and 3.0) were obtained using ELM model. NIR spectroscopy is capable of intelligently detecting postharvest quality of cherry tomatoes during storage. Keywords Cherry tomato . Near infrared spectroscopy . Partial least square . Support vector machine . Extreme learning machine
Introduction Cherry tomato is one of the most popularly grown fruit in the world. It possesses a number of beneficial nutrients such as vitamins A, C, and E; lycopene; β-carotene; and other bioactive components (Du et al. 2009; Yun et al. 2015). Cherry tomato is
* Min Zhang [email protected] 1
State Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi 214122, Jiangsu, China
2
Jiangsu Province Key Laboratory of Advanced Food Manufacturing Equipment and Technology, Jiangnan University, Wuxi, China
3
School of Food Science and Technology, Jiangnan University, Wuxi 214122, Jiangsu, China
4
School of Science-RMIT University, Melbourne, VIC 3083, Australia
5
Wuxi Haihe Equipment Co., Wuxi, China
perishable due to its high moisture content. During postharvest storage period, quality of fruit changes rapidly because of respiration and metabolic activities. Firmness, soluble solids content (SSC) and pH are the primary quality attributes of cherry tomatoes which are associated with change
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