Quality Assessment of Sweet Corn Hybrids Using Near-Infrared Spectroscopy Technique
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Abstract
This research aimed to evaluate the potential of Fourier Transform Near-Infrared Spectroscopy (FT-NIRS) as a nondestructive technique for assessing the quality of fresh sweet corn hybrids Total dissolved sugar amount range: 8 Brix to 15 Brix. The study analyzed sweet corn samples within the wavelength range of 1000–2500 nm using modified partial least squares regression (PLSR) with internal validation (n=144). The developed calibration model for sweetness prediction yielded a coefficient of determination (R²) of 0.93 with a standard error of calibration (SEC) of 0.54% and a standard error of prediction (SEP) of 0.54%. The cross-validation process confirmed a high correlation (R = 0.93), indicating reliable prediction accuracy. Key chemical bonds, including C-H and O-H, were identified through spectral analysis at wavelengths of 1450, 1715, and 1928 nm. The model's accuracy improved with second-derivative spectral preprocessing, achieving an R² of 0.98 and a minimal SEC of 0.05%. These findings demonstrate that FT-NIRS can efficiently and accurately estimate sweetness levels in fresh sweet corn ears, supporting rapid screening in breeding programs aimed at developing high-quality sweet corn varieties.
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References
Office of Agricultural Economics. (2019). Details of the Agricultural Economic Situation. Available from http://http://www.oae.go.th. Accessed date: 4 February 2021. (in Thai)
Osborne, B. G., et al. (1993). Practical NIR Spectroscopy with Application in Food and Beverage Analysis. New York: Longman Scientific & Technical.
Bagchi, T. B., et al. (2016) Development of NIRS Models to Predict Protein and Amylose Content of Brown Rice and Proximate Compositions of Rice Bran. Food Chemistry, 191, 21-27.
Clevers, J. G. P. W., et al. (2008) Using Spectral Information from the NIR Water Absorption Features for the Retrieval of Canopy Water Content. International Journal of Applied Earth Observation and Geoinformation, 10(3), 388-397.
Chylinska, M., et al. (2016). Study on Dietary Fibre by Fourier Transform-Infrared Spectroscopy and Chemometric Methods. Food Chemistry, 196, 114-122.
Magwaza, L., et al. (2016) Development of NIRS Models for Rapid Quantification of Protein Content in Sweetpotato [Ipomoea batatas (L.) LAM.]. LWT - Food Science and Technology, 72, 63-70.
Pojic, M., et al. (2010). The Development of Near-Infrared Spectroscopy (NIRS) Calibration for Prediction of Ash Content in Legumes on the Basis of Two Different Reference Methods. Food Chemistry, 123(3), 800-805.
Teye, E., et al. (2015). Estimating Cocoa Bean Parameters by FT-NIRS and Chemometrics Analysis. Food Chemistry, 176, 403-410.
Hell, J., et al. (2016). A Comparison Between Near-Infrared (NIR) and Mid-Infrared (ATR -FTIR) Spectroscopy for the Multivariate Determination of Compositional Properties in Wheat Bran Samples. Food Control. 60, 365-369.
Redaelli, R., et al. (2016). Development of a NIRS Calibration for Total Antioxidant Capacity in Maize Germplasm. Talanta, 154, 164-168.
Moros, J., et al. (2008). Chemometric Determination of Arsenic and Lead in Untreated Powdered Red Paprika by Diffuse Reflectance Near-Infrared Spectroscopy. Analytica Chimica Acta, 613(2), 196-206.
Ahmed, M. R., et al. (2015). Evaluation of Sugar Content in Potatoes Using NIR Reflectance and Wavelength Selection Techniques. Postharvest Biology and Technology, 103, 17-26.
Redaelli, R., et al. (2016). Development of a NIRS Calibration for Total Antioxidant Capacity in Maize Germplasm. Talanta, 154, 164-168.
Awanthia, M. G. G., et al. (2019). Adaptation of Visible and Short Wave Near Infrared (VIS-SW-NIR) Common PLS Model for Quantifying Paddy Hardness. Journal of Cereal Science, 89, 102795.
Christian, W. H. (2015). Advances of Infrared Spectroscopy in Natural Product Research. Phytochemistry Letters, 11, 384-393.
Qiu, G., et al. (2019). Cultivar Classification of Single Sweet Corn Seed Using Fourier Transform Near-Infrared Spectroscopy Combined with Discriminant Analysis. Applied sciences, 9(8), 1530.
Rosales, A., et al. (2022). Near-Infrared Spectroscopy to Predict Provitamin A Carotenoids Content in Maize. Agronomy, 12(5), 1027.
Dongyun, X., et al. (2018) Assessment of Important Soil Properties Related to Chinese Soil Taxonomy Based On Vis-NIR Reflectance Spectroscopy. Computers and Electronics in Agriculture, 144, 1-8.
