Nondestructive Identification of Internal Potato Defects Using Visible and Short-Wavelength Near-Infrared Spectral Analysis

Dennis Semyalo, Yena Kim, Emmanuel Omia, Muhammad Akbar Andi Arief, Haeun Kim, Eun-Yeong Sim, Moon S. Kim, Insuck Baek and Byoung-Kwan Cho ()
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Dennis Semyalo: Department of Smart Agriculture Systems, College of Agricultural and Life Science, Chungnam National University, Yuseong-gu, Daejeon 34134, Republic of Korea
Yena Kim: Department of Agricultural Machinery Engineering, College of Agricultural and Life Science, Chungnam National University, Yuseong-gu, Daejeon 34134, Republic of Korea
Emmanuel Omia: Department of Agricultural Machinery Engineering, College of Agricultural and Life Science, Chungnam National University, Yuseong-gu, Daejeon 34134, Republic of Korea
Muhammad Akbar Andi Arief: Department of Smart Agriculture Systems, College of Agricultural and Life Science, Chungnam National University, Yuseong-gu, Daejeon 34134, Republic of Korea
Haeun Kim: Department of Agricultural Machinery Engineering, College of Agricultural and Life Science, Chungnam National University, Yuseong-gu, Daejeon 34134, Republic of Korea
Eun-Yeong Sim: Department of Central Area Crop Science, National Institute of Crop Science, Suwon 16429, Republic of Korea
Moon S. Kim: Environmental Microbial and Food Safety Laboratory, Agricultural Research Service, United States Department of Agriculture, Beltsville, MD 20705, USA
Insuck Baek: Environmental Microbial and Food Safety Laboratory, Agricultural Research Service, United States Department of Agriculture, Beltsville, MD 20705, USA
Byoung-Kwan Cho: Department of Smart Agriculture Systems, College of Agricultural and Life Science, Chungnam National University, Yuseong-gu, Daejeon 34134, Republic of Korea

Agriculture, 2024, vol. 14, issue 11, 1-17

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 and short-wavelength near-infrared spectroscopy. The acquisition of the spectral data of potato tubers was performed using a spectrometer with a wavelength range of 400–1100 nm. The classification of internal-based defects 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 was 91.03%. The internal defective area model based on PLSR achieved a correlation coefficient of double cross-validation of 0.91 and a root mean square error of double cross-validation of 0.85 cm 2 . This study makes a valuable contribution to advancing nondestructive techniques for evaluating internal defects in potatoes.

Keywords: internal defects; nondestructive detection; spectral analysis; partial least squares analysis; quality evaluation (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2024
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