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Quality Analysis of Stored Bell Peppers Using Near-Infrared Hyperspectral Imaging
Rahman, Anisur,Faqeerzada, Mohammad Akbar,Joshi, Rahul,Lohumi, Santosh,Kandpal, Lalit Mohan,Lee, Hoonsoo,Mo, Changyeun,Kim, Moon Sung,Cho, Byoung-Kwan American Society of Agricultural and Biological En 2018 Transactions of the ASABE Vol.61 No.4
<P>Abstract. The objective of this study was to predict the moisture content (MC), soluble solids content (SSC), and titratable acidity (TA) content in bell peppers during storage (18°C, 85% relative humidity) over 12 days, based on near-infrared hyperspectral imaging (NIR-HSI) in the 1000-1500 nm wavelength range. The mean spectra of 148 mature bell peppers were extracted from the hyperspectral images, and multivariate calibration models were built using partial least squares (PLS) regression with different preprocessing spectra techniques. The most effective wavelengths were selected using the variable importance in projection (VIP) technique, which selected optimal variables for the target quality parameters of bell peppers from a full set of variables. Subsequently the selected variables were used to develop a PLS-VIP model for simplifying the prediction model. The MC, SSC, and TA content in bell peppers during storage changed from 90.7% to 93.0%, from 6.1%Brix to 7.3%Brix, and from 0.222% to 0.334%, respectively. The PLS regression model with MC, SSC, and TA content resulted in coefficients of determination (R<SUP>2</SUP>pred) of 0.83, 0.85, and 0.7, with standard errors of prediction (SEP) of 0.08%, 0.075%Brix, and 0.013%, respectively, using SNV preprocessed spectra for MC and TA content and Savitzky-Golay (S-G) second-order derivatives preprocessed spectra for SSC of bell peppers. By contrast, the prediction results yielded R<SUP>2</SUP>pred of 0.69, 0.75, and 0.68, respectively, with SEP values of 0.103%, 0.107%Brix, and 0.011% when the PLS-VIP model was employed. The PLS-VIP model simplified the calibration model by selecting the most important variables in terms of their responsiveness to bell pepper quality properties. The results revealed that HSI coupled with multivariate analysis can be used successfully to predict the MC, SSC, and TA content in bell peppers. Keywords: Fruit quality, Hyperspectral imagery, Image analysis, Spectral analysis, Stored bell pepper.</P>
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