Nondestructive measurement of internal potato defects using visible and near-infrared spectral analysis

Authors: SEMYALO, DENNIS - Chungnam National University; KIM, YENA - Chungnam National University; EMMANUEL, OMIA - Chungnam National University; ARIEF, MUJAMMAD - Chungnam National University; KIM, HAEUN - Chungnam National University; SIM, EUN-YEONG - National Institute Of Crop Science, Iksan, South Korea; Kim, Moon; Baek, Insuck; CHO, BYOUNG-KWAN - Chungnam National University

Submitted to: Agriculture
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 11/6/2024
Publication Date: 11/8/2024
Citation: Semyalo, D., Kim, Y., Emmanuel, O., Arief, M., Kim, H., Sim, E., Kim, M.S., Baek, I., Cho, B. 2024. Nondestructive measurement of internal potato defects using visible and near-infrared spectral analysis. Agriculture. 14. Article e14112014. https://doi.org/10.3390/agriculture14112014.
DOI: https://doi.org/10.3390/agriculture14112014

Interpretive Summary: Potatoes are a staple food crop consumed worldwide, with their significance extending from household kitchens to large-scale food processing industries. However, their quality can be affected by internal defects such as bruises, internal browning, internal sprouting, and dry rot. This study explored how to identify and measure these internal defects in potatoes using a nondestructive visible–near-infrared spectroscopy (VIS-NIRS). To determine types of potato defects, the spectral data were processed using partial least squares discriminant analysis (PLS-DA) and partial least squares regression (PLSR) methods. These methods successfully identified the types of internal potato defects with over 90% accuracy. This investigation demonstrates the effective use of nondestructive spectroscopy methods to characterize internal defects of potatoes where the methods can be used by industry to assure high quality potato products to consumers.

Technical Abstract: Potatoes are a staple food crop consumed worldwide, with their significance extending from household kitchens to large-scale food processing industries. Their market value and quality are often compromised by various internal defects such as pythium, bruising, internal browning, hollow heart, gangrene, blackheart, internal sprouting, and dry rot. This study aimed to classify internal-based defects and investigate the quantification of internal defective areas in potatoes using visible–near-infrared spectroscopy (VIS-NIRS). The acquisition of the spectral data of potato samples was performed in transmittance mode using a spectrometer with a wavelength range of 200-1,100 nm. The detection and classification of internal-based defects and categories was performed using partial least squares discriminant analysis (PLS-DA), while the quantification of the internal defective area was based on partial least squares regression (PLSR). The PLS-DA double cross-validation accuracy for the distinction between non-defective and all internally defective potatoes was 90.78 %. The double cross-validation classification accuracy achieved for pythium, bruising, and non-defective categories in the PLS-DA analysis was 91.03 %. The internal defective area validation model based on PLSR achieved a correlation coefficient of double cross-validation (Rv) of 0.91 and a root mean square error of double cross-validation (RMSEV) of 0.85 cm2. This study makes a valuable contribution to advancing reliable and nondestructive techniques for evaluating internal defects in potatoes. This could potentially transform the quality control procedures within the potato industry, minimizing wastage and guaranteeing the supply of high-quality potato products to consumers.