Application of near‑infrared spectroscopy for rapid detection of moisture content of ginger (Zingiber officinale Rosc.)
Аннотация
Background. Ginger is a horticultural commodity and a biopharmaceutical crop with many benefits and high economic value. Ginger is widely used as a raw material in the functional food, cosmetics, and herbal pharmaceutical industries. Water content is a critical parameter affecting the quality, storage stability, and activity of bioactive compounds in ginger rhizomes.
Purpose. This study aims to develop a calibration model to predict ginger rhizome moisture content using near-infrared spectroscopy and chemometric modeling for fresh, dried, and fresh-dried ginger.
Materials and methods. This study used three varieties of ginger, i.e., merah ginger, emprit ginger, and gajah ginger, obtained from the Beringharjo market in Yogyakarta, Indonesia. Two moisture-content conditions were applied: fresh ginger obtained directly from the market and dried ginger after two weeks of storage at room temperature (28°C) with a relative humidity of 76.1%. The spectroscopic instrument used in this study was a NIR spectrometer (Flame-NIR-Ocean Optics, USA) operating over the wavelength range of 950–1700 nm. The reflectance spectra of three varieties of ginger rhizomes, i.e., emprit, gajah, and merah, were measured at wavelengths of 950–1700 nm, with reference water content values measured using the thermogravimetric method. The partial least squares regression (PLSR) model was used to predict the moisture content of dried and fresh ginger, as well as a combination of fresh and dried ginger.
Results. The dried ginger model showed the highest prediction accuracy (R²P = 0.75; RPD = 2.00). The key wavelength contributing to water content prediction was identified in the 1400–1500 nm region, corresponding to the first overtone of O–H stretching vibrations. These results indicate that near-infrared spectroscopy can be used for fast and non-destructive prediction of water content in ginger rhizomes.
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Литература
Jelled, A., et al. (2015). Chemical and antioxidant parameters of dried forms of ginger rhizomes. Ind. Crop. Prod., 77, 30–35. https://doi.org/10.1016/j.indcrop.2015.08.052. EDN: https://elibrary.ru/XPQADH
Schaller, T., & Schieberle, P. (2020). Comparison of the Key Aroma Compounds in Fresh, Raw Ginger (Zingiber officinale Roscoe) from China and Roasted Ginger by Application of Aroma Extract Dilution Analysis. J. Agric. Food Chem., 68(51). https://doi.org/10.1021/acs.jafc.0c06731. EDN: https://elibrary.ru/LOCTYP
Spence, C. (2023). Ginger: The pungent spice. Int. J. Gastron. Food Sci., 33, 100793. https://doi.org/10.1016/j.ijgfs.2023.100793
Anh, N. H., et al. (2020). Ginger on Human Health: A Comprehensive Systematic Review of 109 Randomized Controlled Trials. Nutrients, 12(1), 1–28. https://doi.org/10.3390/nu12010157. EDN: https://elibrary.ru/ZTMORN
Shaukat, M. N., Nazir, A., & Fallico, B. (2023). Ginger Bioactives: A Comprehensive Review of Health Benefits and Potential Food Applications. Antioxidants, 12(11), 1–26. https://doi.org/10.3390/antiox12112015. EDN: https://elibrary.ru/MXUFXC
Malik, A., & Kumar, M. (2023). Advancements in ginger drying technologies. J. Stored Prod. Res., 100, 102058. https://doi.org/10.1016/j.jspr.2022.102058. EDN: https://elibrary.ru/VYHOUQ
Tapia, M. S., Alzamora, S. M., & Chirife, J. (2020). Effects of Water Activity (aw) on Microbial Stability as a Hurdle in Food Preservation. In Water Activity in Foods (pp. 323–355). https://doi.org/10.1002/9781118765982.ch14
Ajav, B. E. A., & Ogunlade, C. A. (2014). Physical Properties of Ginger (Zingiber Officinale). Glob. J. Sci. Front. Res. D Agric. Vet., 14(8).
Jayashree, E., Visvanathan, R., & T, J. Z. (2014). Quality of dry ginger (Zingiber officinale) by different drying methods. J. Food Sci. Technol., 51, 3190–3198. https://doi.org/10.1007/s13197-012-0823-8
Elmasry, G. (2023). Thermal Degradation of Bioactive Compounds during Drying Process of Horticultural and Agronomic Products: A Comprehensive Overview. Agronomy, 13(6).
Pawliszyn, J. (2012). Comprehensive Sampling and Sample Preparation Analytical Techniques for Scientists. Academic Press.
Fodor, M., Matkovits, A., Benes, E. L., & Jókai, Z. (2024). The Role of Near-Infrared Spectroscopy in Food Quality Assurance: A Review of the Past Two Decades. Foods, 13(21). https://doi.org/10.3390/foods13213501. EDN: https://elibrary.ru/TOINJY
Wu, Z., Ouyang, G., Shi, X., Ma, Q., Wan, G., & Qiao, Y. (2014). Absorption and Quantitative Characteristics of C-H Bond and O-H Bond of NIR. Spectrosc. Atoms Mol., 117(5), 703–709. https://doi.org/10.1134/S0030400X1411023X. EDN: https://elibrary.ru/OXSNOA
Manley, M. (2014). Near-infrared spectroscopy and hyperspectral imaging: Non-destructive analysis of biological materials. Chem. Soc. Rev., 43(24), 8200–8214. https://doi.org/10.1039/c4cs00062e
Yan, C. (2025). A review on spectral data preprocessing techniques for machine learning and quantitative analysis. iScience, 28(7), 112759. https://doi.org/10.1016/j.isci.2025.112759. EDN: https://elibrary.ru/CRYTWQ
Baeten, V., Lesnoff, M., Deryck, A., & Antonio, J. (2026). A step-by-step tutorial to local partial least squares analyses for near-infrared spectroscopic data analysis. Anal. Chim. Acta, 1401. https://doi.org/10.1016/j.aca.2026.345242
Castro, W., Oblitas, J., Rojas, E. E., & Avila-George, H. (2020). Partial Least Square Regression for Food Analysis — Basis and Example. In S. Sevda & A. Singh (Eds.), Mathematical and Statistical Applications in Food Engineering. CRC Press.
Priambodo, D. C., Saputro, D., Pahlawan, M. F. R., Saputro, A. D., & Masithoh, R. E. (2022). Determination of Acid Level (pH) and Moisture Content of Cocoa Beans at Various Fermentation Level Using Visible Near-Infrared (Vis-NIR) Spectroscopy. IOP Conf. Ser. Earth Environ. Sci., 985(1). https://doi.org/10.1088/1755-1315/985/1/012045. EDN: https://elibrary.ru/NJELDK
Amanah, H. Z., Pratiwi, E. Z. D., Rahmi, D. N., Pahlawan, M. F. R., & Masithoh, R. E. (2024). Non-destructive determination of water content in fruits using Vis-NIR spectroscopy. Food Res., 8, 9–14. https://doi.org/10.26656/fr.2017.8(S2).14. EDN: https://elibrary.ru/XGXYES
López, A., Arazuri, S., García, I., Mangado, J., & Jarén, C. (2013). A review of the application of near-infrared spectroscopy for the analysis of potatoes. J. Agric. Food Chem., 61(23), 5413–5424. https://doi.org/10.1021/jf401292j
Jamshidi, B., Jamshidi, J., & Yazdanfar, N. (2026). Development of a visible/near-infrared spectroscopic system with unique software for rapid and non-destructive detection of nitrate-contaminated leafy and fruity vegetables. Meas. Food, 21, 100274. https://doi.org/10.1016/j.meafoo.2025.100274. EDN: https://elibrary.ru/RCEVIO
Li, J., Xue, L., Liu, M., Lv, P., & Yan, L. (2011). Determination of moisture content in ginger using PSO combined with Vis/NIR. Adv. Mater. Res., 320.
Wang, T., et al. (2025). Rapid Multi-Indicator Quality Evaluation of Ginger Using Genetic Algorithm and Near-Infrared Spectroscopy. J. Chemom., 39(12), e70090. https://doi.org/10.1002/cem.70090. EDN: https://elibrary.ru/VFJBDU
Wang, H., Xu, Y., He, G., Wang, X., & Liu, H. (2026). Multi-parameter characterization of postharvest quality changes of potatoes: insights from texture, dielectric spectroscopy and optical propagation. J. Stored Prod. Res., 115, 102852. https://doi.org/10.1016/j.jspr.2025.102852. EDN: https://elibrary.ru/ICLTKY
Pahlawan, M. F. R., Wati, R. K., & Masithoh, R. E. (2021). Development of a low-cost modular VIS/NIR spectroscopy for predicting soluble solid content of banana. In IOP Conference Series: Earth and Environmental Science. https://doi.org/10.1088/1755-1315/644/1/012047. EDN: https://elibrary.ru/MRAONC
Pratiwi, E. Z. D., Pahlawan, M. F. R., Rahmi, D. N., Amanah, H. Z., & Masithoh, R. E. (2023). Non-destructive evaluation of soluble solid content in fruits with various skin thicknesses using visible-shortwave near-infrared spectroscopy. Open Agric., 8(1). https://doi.org/10.1515/opag-2022-0183. EDN: https://elibrary.ru/ZCMRRC
Abadi, F. R., Masithoh, R. E., Sutiarso, L., & Rahayoe, S. (2024). Evaluation of Indonesian local soybean based on chemical characteristics and visible-near infrared spectra with chemometrics. Biotropia (Bogor)., 31(1), 1–13. https://doi.org/10.11598/btb.2024.31.1.2054
Prasetyo, E. W., Amanah, H. Z., Farras, I., Pahlawan, M. F. R., & Masithoh, R. E. (2024). Detection of Fusarium spp. infection in potato (Solanum tuberosum L.) during postharvest storage through visible-near-infrared and shortwave-near-infrared reflectance spectroscopy. Open Agric., 9(1). https://doi.org/10.1515/opag-2022-0295. EDN: https://elibrary.ru/QMJBSS
Zhang, Y., Zhang, Y., Sun, F., Sun, Y., Wang, S., & Meng, J. (2022). Comparison of benchtop near infrared and micro near infrared spectrometer for quality control of dried ginger and its different degrees of processed products. Spectrosc. Lett., 55(8), 514–526. https://doi.org/10.1080/00387010.2022.2116457. EDN: https://elibrary.ru/SUFURS
Chen, R., et al. (2024). Rapid quality evaluation and geographical origin recognition of ginger powder by portable NIRS in tandem with chemometrics. Food Chem., 438, 137931. https://doi.org/10.1016/j.foodchem.2023.137931. EDN: https://elibrary.ru/ZCUETE
Schoot, M., et al. (2020). Investigating the need for preprocessing of near-infrared spectroscopic data as a function of sample size. Chemom. Intell. Lab. Syst., 204, 104105. https://doi.org/10.1016/j.chemolab.2020.104105. EDN: https://elibrary.ru/SPAHSK
Rinnan, Å., van den Berg, F., & Engelsen, S. B. (2009). Review of the most common pre-processing techniques for near-infrared spectra. TrAC — Trends Anal. Chem., 28(10), 1201–1222. https://doi.org/10.1016/j.trac.2009.07.007. EDN: https://elibrary.ru/NWQCZV
Yu, D., et al. (2022). Rapid detection of adulteration in powder of ginger (Zingiber officinale Roscoe) by FT-NIR spectroscopy combined with chemometrics. Food Chem. X, 15, 100450. https://doi.org/10.1016/j.fochx.2022.100450
Ren, Y., Lin, X., Lei, T., & Sun, D. (2021). Recent developments in vibrational spectral analyses for dynamically assessing and monitoring food dehydration processes. Crit. Rev. Food Sci. Nutr., 0(0), 1–27. https://doi.org/10.1080/10408398.2021.1947773. EDN: https://elibrary.ru/WGQJSV
Inchun, X. L. I., Iu, J. I. L., Iang, L. X. U., Anyang, H. X. U., Ang, Y. U. W., & Hang, Y. U. X. I. Z. (2024). Baseline correction for infrared spectra using relative absorbance-based independent component analysis. Opt. Express, 32(26), 47137–47153. https://doi.org/10.1364/OE.545196
Wang, P., Sun, M., Xu, H., Zhang, M., Liu, R., & Xie, Y. (2026). Application of Near-Infrared Spectroscopy in Moisture Detection of Carrot Slices During Freeze-Drying. Foods, 15(7), 1–16. https://doi.org/10.3390/foods15071256. EDN: https://elibrary.ru/DFVJNY
Li, W., Zhao, L., Guo, J., Liu, J., Jin, Z., & Li, Q. (2025). Near-infrared spectral quantitative analysis with reduced moisture interference using a spectral decomposition optimization algorithm: A case study on Amadori compounds in tobacco leaves. J. Agric. Food Res., 23, 102261. https://doi.org/10.1016/j.jafr.2025.102261. EDN: https://elibrary.ru/ZOEHRF
Kang, L., Luo, H., Ma, X., Yu, J., & Liu, H. (2026). Study on the effect of moisture content on the spectral detection of soluble solids in apricot. Sci. Rep., 16, 1–11.
Alander, J. T., Bochko, V., Martinkauppi, B., Saranwong, S., & Mantere, T. (2013). A review of optical nondestructive visual and near-infrared methods for food quality and safety. Int. J. Spectrosc., 2013, 1–36. https://doi.org/10.1155/2013/341402
Arruda, A., et al. (2021). Determination of soluble solid content in market tomatoes using near-infrared spectroscopy. Food Control, 126. https://doi.org/10.1016/j.foodcont.2021.108068
Li, T., Wei, W., Xing, S., Min, W., Zhang, C., & Jiang, S. (2023). Deep Learning-Based Near-Infrared Hyperspectral Imaging for Food Nutrition Estimation. Foods, 12(17), 1–15. https://doi.org/10.3390/foods12173145. EDN: https://elibrary.ru/XFPNFW
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