Quality analysis of stored bell peppers using near-infrared hyperspectral imaging

Authors: RAHMAN, ANISUR - Chungnam National University; FAQEERZADA, MOHAMMAD - Chungnam National University; JOSH, RAHUL - Chungnam National University; LOHUMI, SANTOSH - Chunghnam National University; KANDAL, LALIT - Chungnam National University; LEE, HOONSOO - Us Forest Service (FS); MO, CHANGYEUN - Korean Rural Development Administration; Kim, Moon; CHO, BYOUNG-KWAN - Chungnam National University

Submitted to: Transactions of the ASABE
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 6/14/2018
Publication Date: 8/21/2018
Citation: Rahman, A., Faqeerzada, M., Josh, R., Lohumi, S., Kandal, L., Lee, H., Mo, C., Kim, M.S., Cho, B. 2018. Quality analysis of stored bell peppers using near-infrared hyperspectral imaging. Transactions of the ASABE. 61(4):1-9. https://doi.org/10.13031/trans.12482.
DOI: https://doi.org/10.13031/trans.12482

Interpretive Summary: Fresh bell peppers continue to ripen after harvesting, which means that during post-harvest storage and distribution, physiological changes continue to occur that affect quality attributes such as fruit maturity, shape, color, moisture content (MC), total soluble solids content (SSC), and titratable acidity (TA). Traditional analytical laboratory methods to assess MC, SSC, and TA usually involve sample-destructive measurements of portions of individual fruits, and are also expensive and time-consuming. Thus, there exists a great demand for alternative means to rapidly and nondestructively assess whole-fruit quality and to do so without complex procedures or hazardous chemicals. Using a 12-day storage period at 18°C, 85% relative humidity, this study acquired hyperspectral images in the 1000 nm to 1500 nm near-infrared (NIR) region and quality attribute measurements for whole bell peppers removed from storage at 0, 4, 8, and 12 days to develop prediction models for MC, SSC, and TA. Analysis of the hyperspectral images and the quality measurements found that selected near-infrared wavebands of light could be effectively used to predict and visualize the attributes in whole bell peppers. The results suggest that NIR hyperspectral imaging has potential for development and implementation for rapid and non-destructive quality inspection of bell peppers and other fresh produce to help producers and processors minimize quality losses when making decisions on storage, distribution, and marketing of their products to consumers.

Technical 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 (RH)) over 12 days, based on hyperspectral imaging (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 to predict MC, SSC, and TA content in bell peppers with different preprocessing 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%, 6.1%Brix to 7.3% Brix, and 0.222% to 0.334%, respectively. The PLS regression model with MC, SSC, and TA content resulted in coefficients of determination (R2pred) 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 R2pred of 0.82, 0.82, and 0.68, with SEP values of 0.082%, 0.083%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 selected optimum wavelengths were used to create distribution maps for MC, SSC, and TA content of bell peppers. The results revealed that HSI coupled with multivariate analysis can be used successfully to predict the MC, SSC, and TA content in bell peppers.