Rapid Detection of Dragon Fruit Peel Powder Adulteration by Vis-NIR and SW-NIR Spectroscopy with PLSR Model
Abstract
An important factor in choosing a food product is its quality and safety. Meanwhile, visual aspects are a benchmark for product acceptance. Dragon fruit peel powder (DFP) has excellent potential as a natural food coloring. This study aims to detect adulteration in dragon fruit peel powder using two spectroscopy techniques: Visible-Near Infrared (Vis-NIR) and Shortwave-Near Infrared (SW-NIR) spectroscopy. The adulterants include purple sweet potato flour (PP), erythrosine dye powder (ER), and remazol textile dye powder (TX) with varying concentrations of 0%, 0.5%, 1%, 5%, 10%, 20%, 30%, 40%, 50%, and 100%. Partial least squares regression (PLSR) with ten spectral preprocessing methods was used to analyze data and assess model performance. The results show that combining of spectroscopy with the PLSR model significantly improves accuracy, achieving R²P values above 0.92 for all adulterants. These findings highlight Vis-NIR and SW-NIR spectroscopy combined with PLSR modeling, as rapid, non-destructive tools. Vis-NIR spectroscopy proved superior to SW-NIR spectroscopy in detecting food colorant adulteration because of its sensitivity to color pigments.
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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, 31(1), 63–75. https://doi.org/10.11598/BTB.2024.31.1.2054
Amanah, H.Z., Joshi, R., Masithoh, R.E., Choung, M.-G., Kim, K.-H., Kim, G., & Cho, B.-K. (2020). Nondestructive measurement of anthocyanin in intact soybean seed using Fourier Transform Near-Infrared (FT-NIR) and Fourier Transform Infrared (FT-IR) spectroscopy. Infrared Physics & Technology, 111, 103477. https://doi.org/10.1016/j.infrared.2020.103477
Ambrose, A., & Cho, B.-K. (2014). A review of technologies for detection and measurement of adulterants in cereals and cereal products. Journal of Biosystems Engineering, 39(4), 357-365. https://doi.org/10.5307/JBE.2014.39.4.357
Batistela, V.R., Pellosi, D.S., de Souza, F.D., da Costa, W.F., Santin, S.M.O., de Souza, V.R., Caetano, W., de Oliveira, H.P.M., Scarminio, I.S., & Hioka, N. (2011). pKa determinations of xanthene derivates in aqueous solutions by multivariate analysis applied to UV–Vis spectrophotometric data. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 79(5), 889–897. https://doi.org/10.1016/J.SAA.2011.03.027
Beć, K.B., Grabska, J., & Czarnecki, M.A. (2018). Spectra-structure correlations in NIR region: Spectroscopic and anharmonic DFT study of n-hexanol, cyclohexanol and phenol. Spectrochimica Acta - Part A: Molecular and Biomolecular Spectroscopy, 197, 176–184. https://doi.org/10.1016/j.saa.2018.01.041
Chen, R., Li, S., Cao, H., Xu, T., Bai, Y., Li, Z., Leng, X., & Huang, Y. (2024). Rapid quality evaluation and geographical origin recognition of ginger powder by portable NIRS in tandem with chemometrics. Food Chemistry, 438, 137931. https://doi.org/10.1016/J.FOODCHEM.2023.137931
Dharmawan, A., & Masithoh, R.E. (2025). Authentication of robusta coffee origins by shortwave NIR spectroscopy coupled with dimensionality reduction and neural networks. International Journal of Agriculture and Biosciences, 14(6), 1291–1301. https://doi.org/10.47278/journal.ijab/2025.110
ElMasry, G., Sun, D.W., & Allen, P. (2011). Non-destructive determination of water-holding capacity in fresh beef by using NIR hyperspectral imaging. Food Research International, 44(9), 2624-2633. https://doi.org/10.1016/j.foodres.2011.05.001
Eveline, & Audina, M. (2019). Utilization of super red dragon fruit peel (Hylocereus Costaricensis (F.A.C. Weber) Britton & Rose) in the making of fermented beverage. IOP Conference Series: Earth and Environmental Science, 292(1), 012037. https://doi.org/10.1088/1755-1315/292/1/012037
Fernández-Novales, J., López, M. I., Sánchez, M. T., Morales, J., & González-Caballero, V. (2009). Shortwave-near infrared spectroscopy for determination of reducing sugar content during grape ripening, winemaking, and aging of white and red wines. Food Research International, 42(2), 285-291. https://doi.org/10.1016/j.foodres.2008.11.008
Guo, Z., Huang, W., Peng, Y., Chen, Q., Ouyang, Q., & Zhao, J. (2016). Color compensation and comparison of shortwave near infrared and long wave near infrared spectroscopy for determination of soluble solids content of “Fuji” apple. Postharvest Biology and Technology, 115, 81-90. https://doi.org/10.1016/j.postharvbio.2015.12.027
Henn, R., Kirchler, C.G., Grossgut, M.E., & Huck, C.W. (2017). Comparison of sensitivity to artificial spectral errors and multivariate LOD in NIR spectroscopy – Determining the performance of miniaturizations on melamine in milk powder. Talanta, 166, 109–118. https://doi.org/10.1016/J.TALANTA.2017.01.035
Hernanda, R.A.P., Kim, J., Kim, J., Faqeerzada, M.A., Amanah, H.Z., Cho, B.-K., Kim, M. S., & Lee, H. (2024). Spectral analysis for Protaetia brevitarsis seulensis powder adulterated with chickpea (Cicer arietinum) and soybean (Glycine max) flour by pointed near-infrared spectroscopy. Korean Journal of Agricultural Science, 51(4), 451–464. https://doi.org/10.7744/kjoas.510404
Huang, C.J., Han, L.J., Liu, X., & Ma, L.J. (2009). Rapid measurement for moisture and calorific value of straw based on near infrared spectroscopy and local algorithm. Hongwai Yu Haomibo Xuebao/Journal of Infrared and Millimeter Waves, 28(3), 184-187. https://doi.org/10.3724/SP.J.1010.2009.00184
Lanjewar, M.G., Asolkar, S., Parab, J.S., & Morajkar, P.P. (2024). Detecting starch-adulterated turmeric using Vis-NIR spectroscopy and multispectral imaging with machine learning. Journal of Food Composition and Analysis, 136, 106700. https://doi.org/10.1016/J.JFCA.2024.106700
Lohumi, S., Lee, S., Lee, W.H., Kim, M.S., Mo, C., Bae, H., & Cho, B.K. (2014). Detection of starch adulteration in onion powder by FT-NIR and FT-IR spectroscopy. Journal of Agricultural and Food Chemistry, 62(38), 9246–9251. https://doi.org/10.1021/jf500574m
Lúcia, M., Simeone, F., Parrella, R.A.C., Schaffert, R.E., Damasceno, C.M.B., Leal, M.C.B., & Pasquini, C. (2017). Near infrared spectroscopy determination of sucrose, glucose and fructose in sweet sorghum juice. Microchemical Journal, 134, 125–130. https://doi.org/10.1016/j.microc.2017.05.020
Lukacs, M., Bazar, G., Pollner, B., Henn, R., Kirchler, C.G., Huck, C.W., & Kovacs, Z. (2018). Near infrared spectroscopy as an alternative quick method for simultaneous detection of multiple adulterants in whey protein-based sports supplement. Food Control, 94, 331–340. https://doi.org/10.1016/j.foodcont.2018.07.004
Lukacs, M., Vitalis, F., Bardos, A., Tormási, J., Bec, K.B., Grabska, J., Gillay, Z., Tömösközi-Farkas, R.A., Abrankó, L., Albanese, D., Malvano, F., Huck, C.W., & Kovacs, Z. (2024a). Comparison of multiple NIR instruments for the quantitative evaluation of grape seed and other polyphenolic extracts with high chemical similarities. Foods, 13(24), 4164. https://doi.org/10.3390/FOODS13244164
Lukacs, M., Zaukuu, J.-L.Z., Bazar, G., Pollner, B., Fodor, M., & Kovacs, Z. (2024b). Comparison of multiple NIR spectrometers for detecting low-concentration nitrogen-based adulteration in protein powders. Molecules, 29(4), 781. https://doi.org/10.3390/molecules29040781
Macavilca, E.A., & Condezo-Hoyos, L. (2020). Assessment of total antioxidant capacity of altiplano colored quinoa (Chenopodium quinoa willd) by visible and near-infrared diffuse reflectance spectroscopy and chemometrics. LWT, 134, 110182. https://doi.org/10.1016/J.LWT.2020.110182
Masithoh, R.E., Hernanda, R.A.P., Amanah, H.Z., Cho, B.K., & Rohman, A. (2025). Nondestructive quantification of betalain secondary metabolites in red beet (Beta vulgaris) powder using near- and mid-infrared spectroscopy. Food Biophysics, 20(1), 26. https://doi.org/10.1007/s11483-024-09915-w
Masithoh, R.E., Pahlawan, M.F.R., Arifani, E.N., Amanah, H.Z., & Cho, B.K. (2024). Determination of the betacyanin and betaxanthin contents of red beet (Beta Vulgaris) powder using partial least square regression based on visible-near infrared spectra. Trends in Sciences, 21(5), 7639. https://doi.org/10.48048/tis.2024.7639
Merzlyak, M.N., Solovchenko, A.E., & Gitelson, A.A. (2003). Reflectance spectral features and non-destructive estimation of chlorophyll, carotenoid and anthocyanin content in apple fruit. Postharvest Biology and Technology, 27(2), 197–211. https://doi.org/10.1016/S0925-5214(02)00066-2
Monago-Maraña, O., Eskildsen, C.E., Galeano-Díaz, T., Muñoz de la Peña, A., & Wold, J.P. (2021). Untargeted classification for paprika powder authentication using visible – Near infrared spectroscopy (VIS-NIRS). Food Control, 121, 107564. https://doi.org/10.1016/j.foodcont.2020.107564
Nisa, N.H., Masithoh, R.E., Pahlawan, M.F.R., & Wati, A.T. (2025). Application of machine learning and deep learning to detect adulteration in food flour based on spectroscopy data: A systematic review. BIO Web of Conferences, 192, 01005. https://doi.org/10.1051/bioconf/202519201005
Pellosi, D.S., Estevão, B.M., Freitas, C.F., Tsubone, T.M., Caetano, W., & Hioka, N. (2013). Photophysical properties of erythrosin ester derivatives in ionic and non-ionic micelles. Dyes and Pigments, 99(3), 705–712. https://doi.org/10.1016/J.DYEPIG.2013.06.026
Rinnan, Å., van den Berg, F., & Engelsen, S.B. (2009). Review of the most common pre-processing techniques for near-infrared spectra. TrAC Trends in Analytical Chemistry, 28(10), 1201–1222. https://doi.org/10.1016/J.TRAC.2009.07.007
Rismiwandira, K., Roosmayanti, F., Pahlawan, M.F.R., & Masithoh, R.E. (2021). Application of fourier transform near-infrared (FT-NIR) spectroscopy for detection of adulteration in palm sugar. IOP Conference Series: Earth and Environmental Science, 653(1), 012122. https://doi.org/10.1088/1755-1315/653/1/012122
Roosmayanti, F., Rismiwindira, K., & Masithoh, R.E. (2021). Detection of coconut (Cocos nucifera) sugar adulteration in palm (Arenga pinnata Merrill) sugar by Fourier Transform Infrared (FT-IR) spectroscopy. Food Research, 5(Suppl. 2), 31–36. https://doi.org/10.26656/fr.2017.5(S2).013
Saenjum, C., Pattananandecha, T., & Nakagawa, K. (2021). Antioxidative and anti-inflammatory phytochemicals and related stable paramagnetic species in different parts of dragon fruit. Molecules, 26(12), 3565. https://doi.org/10.3390/molecules26123565
Sans, S., Ferré, J., Boqué, R., Sabaté, J., Casals, J., & Simó, J. (2018). Determination of chemical properties in ‘calçot’ (Allium cepa L.) by near infrared spectroscopy and multivariate calibration. Food Chemistry, 262, 178-183. https://doi.org/10.1016/j.foodchem.2018.04.102
Saputri, D.A.S., Pahlawan, M.F.R., Murti, B.M.A., & Masithoh, R.E. (2022). Vis/NIR spectroscopy for non-destructive method in detecting soybean seeds viability. IOP Conference Series: Earth and Environmental Science, 1038(1), 012043. https://doi.org/10.1088/1755-1315/1038/1/012043
Singh, S., Szostak, R., & Czarnecki, M.A. (2021). Vibrational intensities and anharmonicity in MIR, NIR and Raman spectra of liquid CHCl3, CDCl3, CHBr3 and CDBr3: Spectroscopic and theoretical study. Journal of Molecular Liquids, 336, 116277. https://doi.org/10.1016/j.molliq.2021.116277
Thalhamer, B., & Buchberger, W. (2019). Adulteration of beetroot red and paprika extract based food colorant with Monascus red pigments and their detection by HPLC-QTof MS analyses. Food Control, 105, 58–63. https://doi.org/10.1016/j.foodcont.2019.05.020
Vera, W., Salvador-Reyes, R., Quispe-Santivañez, G., & Kemper, G. (2025). Detection of adulterants in powdered foods using near-infrared spectroscopy and chemometrics: Recent advances, challenges, and future perspectives. Foods, 14(18), 3195. https://doi.org/10.3390/foods14183195
Wang, X., Esquerre, C., Downey, G., Henihan, L., O’Callaghan, D., & O’Donnell, C. (2018). Assessment of infant formula quality and composition using Vis-NIR, MIR and Raman process analytical technologies. Talanta, 183, 320–328. https://doi.org/10.1016/J.TALANTA.2018.02.080
Xiaowei, H., Liuzi, D., Zhihua, L., Jin, X., Jiyong, S., Xiaodong, Z., Junjun, Z., Ning, Z., Holmes, M., & Xiaobo, Z. (2023). Fabrication and characterization of colorimetric indicator for Salmon freshness monitoring using agar/polyvinyl alcohol gel and anthocyanin from different plant sources. International Journal of Biological Macromolecules, 239, 124198. https://doi.org/10.1016/j.ijbiomac.2023.124198
Zhang, D., Xu, L., Wang, Q., Tian, X., & Li, J. (2019). The Optimal Local Model Selection for Robust and Fast Evaluation of Soluble Solid Content in Melon with Thick Peel and Large Size by Vis-NIR Spectroscopy. Food Analytical Methods, 12(1), 136–147. https://doi.org/10.1007/s12161-018-1346-3
Zhang, M., Zhao, C., Shao, Q., Yang, Z., Zhang, X., Xu, X., & Hassan, M. (2019). Determination of water content in corn stover silage using near-infrared spectroscopy. International Journal of Agricultural and Biological Engineering, 12(6), 143-148. https://doi.org/10.25165/j.ijabe.20191206.4914
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