Organic and non-organic soybean flours, although visually indifferent, have a significant difference in price and nutrition content. Therefore, the accurate authentication detection of organic soybean flour is necessary. Visible-near-infrared (Vis-NIR) spectroscopy coupled with chemometric methods is a non-destructive technique applied to detect authentic or adulterated organic soybean flour. The spectra of organic, adulterated organic, and non-organic soybean flours were captured using a Vis-NIR spectrometer at 350–1000 nm. The spectra were analyzed using partial least squares (PLS), principal component analysis (PCA), and the combination of these two with discriminant analysis (DA). The results showed that PCA using PC1 and PC2 could differentiate organic and non-organic soybean flours, whereas PC1 and PC4 can detect pure and adulterated organic soybean flours. The PCA–linear DA models showed 98.5% accuracy (Acc) for predicting pure organic and adulterated soybean flours and 100% Acc for predicting organic and non-organic flours. Moreover, PLS regression models resulted in a high R^² of >95% for predicting organic and non-organic flours and pure and adulterated soybean flours. In addition, the PLS-DA models can differentiate organic from non-organic soybean flour and distinguish pure and adulterated soybean flours with 100% Acc and reliability.

• Berrueta, L. A., Alonso-Salces, R. M., & Héberger, K. (2007). Supervised pattern recognition in food analysis. Journal of Chromatography A, 1158(1-2), 196-214. https://doi.org/10.1016/j.chroma.2007.05.024

• Bøhn, T., Cuhra, M., Traavik, T., Sanden, M., Fagan, J., & Primicerio, R. (2014). Compositional differences in soybeans on the market: Glyphosate accumulates in Roundup Ready GM soybeans. Food Chemistry, 153, 207-215. https://doi.org/10.1016/j.foodchem.2013.12.054

• Chen, J. Y., Chen, X. W., Lin, Y. Y., Yen, G. C., & Lin, J. A. (2021). Authentication of dark brown sugars from different processing using three-dimensional fluorescence spectroscopy. LWT, 150, Article 111959. https://doi.org/10.1016/j.lwt.2021.111959

• de Almeida, V. E., de Sousa Fernandes, D. D., Diniz, P. H. G. D., de Araújo Gomes, A., Véras, G., Galvão, R. K. H., & Araujo, M. C. U. (2021). Scores selection via Fisher’s discriminant power in PCA-LDA to improve the classification of food data. Food Chemistry, 363, Article 130296. https://doi.org/10.1016/j.foodchem.2021.130296

• Dixit, A., Antony, J. I., Sharma, N. K., & Tiwari, R. K. (2011). Soybean constituents and their functional benefits. Research Signpost, 661(2), 367-383.

• Esteki, M., Shahsavari, Z., & Simal-Gandara, J. (2018). Use of spectroscopic methods in combination with linear discriminant analysis for authentication of food products. Food Control, 91, 100-112. https://doi.org/10.1016/j.foodcont.2018.03.031

• Esteki, M., Simal-gandara, J., Shahsavari, Z., Zandbaaf, S., & Dashtaki, E. (2018). A review on the application of chromatographic methods, coupled to chemometrics, for food authentication. Food Control, 93(April), 165-182. https://doi.org/10.1016/j.foodcont.2018.06.015

• Gomiero, T. (2018). Food quality assessment in organic vs. conventional agricultural produce: Findings and issues. Applied Soil Ecology, 123(February), 714-728. https://doi.org/10.1016/j.apsoil.2017.10.014

• 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

• Hartman, G. L., Pawlowski, M. L., Herman, T. K., & Eastburn, D. (2016). Organically grown soybean production in the USA: Constraints and management of pathogens and insect pests. Agronomy, 6(1), Article 16. https://doi.org/10.3390/agronomy6010016

• Jawaid, S., Talpur, F. N., Sherazi, S. T. H., Nizamani, S. M., & Khaskheli, A. A. (2013). Rapid detection of melamine adulteration in dairy milk by SB-ATR-Fourier transform infrared spectroscopy. Food Chemistry, 141(3), 3066-3071. https://doi.org/10.1016/j.foodchem.2013.05.106

• Khodabakhshian, R., Bayati, M. R., & Emadi, B. (2021). An evaluation of IR spectroscopy for authentication of adulterated turmeric powder using pattern recognition. Food Chemistry, 364, Article 130406. https://doi.org/10.1016/j.foodchem.2021.130406

• Khuwijitjaru, P., Boonyapisomparn, K., & Huck, C. W. (2020). Near-infrared spectroscopy with linear discriminant analysis for green “Robusta” coffee bean sorting. International Food Research Journal, 27(2), 287-294.

• Lakshmisha, G., Singh, V. P., Shivakumar, B. G., & Arora, A. (2012). Effect of organic and inorganic nutrients on physiological traits of soybean (Glycine max (L.) Merr). Indian Journal of Plant Physiology, 17(1), 52-56.

• Lichtenthaler, H. K., & Buschmann, C. (2001). Chlorophylls and carotenoids: Measurement and characterization by UV-VIS spectroscopy. Current Protocols in Food Analytical Chemistry, 1, F4.3.1-F4.3.8. https://doi.org/https://doi.org/10.1002/0471142913.faf0403s01

• Martins, F. C. O. L., Sentanin, M. A., & De Souza, D. (2019). Analytical methods in food additives determination: Compounds with functional applications. Food Chemistry, 272(April 2018), 732-750. https://doi.org/10.1016/j.foodchem.2018.08.060

• Masithoh, R. E., Lohumi, S., Amanah, H. Z., Yoon, W. S., & Cho, B. K. (2020). Development of multi-product calibration models of various root and tuber powders by Fourier Transform Near Infra-red (FT-NIR) spectroscopy for the quantification of polysaccharide contents. Heliyon, 6(10), Article e05099. https://doi.org/10.1016/j.heliyon.2020.e05099

• Masithoh, R. E., Pahlawan, M. F. R., & Wati, R. K. (2021). Non-destructive determination of SSC and pH of banana using a modular Vis/NIR spectroscopy: comparison of partial least square (PLS) and principle component regression (PCR). In IOP Conference Series: Earth and Environmental Science (Vol. 752, No. 1, p. 012047). IOP Publishing. https://doi.org/10.1088/1755-1315/752/1/012047

• Masithoh, R. E., Rondonuwu, F., Setyabudi, F. M. C. S., & Cho, B. K. (2020). Development of calibration model for determination of sweeteners additives in Indonesia rice flour-based food by FT-NIR spectroscopy. In IOP Conference Series: Earth and Environmental Science (Vol. 542, No. 1, p. 012017). IOP Publishing. https://doi.org/10.1088/1755-1315/542/1/012017

• Masithoh, R. E., Roosmayanti, F., Rismiwandira, K., & Pahlawan, M. F. R. (2021). Detection of palm sugar adulteration by fourier transform near-infrared (FT-NIR) and fourier transform infrared (FT-IR) spectroscopy. Sugar Tech, 24(3), 920-929. https://doi.org/10.1007/s12355-021-01058-3

• Meerza, S. I. A., & Gustafson, C. R. (2019). Does prior knowledge of food fraud affect consumer behavior? Evidence from an incentivized economic experiment. PLoS ONE, 14(12), 1-14. https://doi.org/10.1371/journal.pone.0225113

• 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

• Monma, M., Terao, J., Lto, M., Saito, M., & Chikuni, K. (1994). Carotenoid components in soybean seeds varying with seed color and maturation stage. Bioscience, Biotechnology, and Biochemistry, 58(5), 926-930. https://doi.org/10.1271/bbb.58.926

• Ndlovu, P. F., Magwaza, L. S., Tesfay, S. Z., & Mphahlele, R. R. (2021). Vis-NIR spectroscopic and chemometric models for detecting contamination of premium green banana flour with wheat by quantifying resistant starch content. Journal of Food Composition and Analysis, 102, Article 104035. https://doi.org/10.1016/j.jfca.2021.104035

• Nobari-Moghaddam, H., Tamiji, Z., Akbari-Lakeh, M., Khoshayand, M. R., & Haji-Mahmoodi, M. (2021). Multivariate analysis of food fraud: A review of NIR based instruments in tandem with chemometrics. Journal of Food Composition and Analysis, 107, Article 104343. https://doi.org/10.1016/j.jfca.2021.104343

• Pahlawan, M. F. R., Murti, B. M. A., & Masithoh, R. E. (2022). The potency of Vis/NIR spectroscopy for classification of soybean based of colour. In IOP Conference Series: Earth and Environmental Science (Vol. 1018, No. 1, p. 012015). IOP Publishing.

• Pahlawan, M. F. R., Wati, R. K., & Masithoh, R. E. (2021). Development of a low-cost modular VIS / NIR spe ctroscopy for predicting soluble solid content of banana. In IOP Conference Series: Earth and Environmental Science (Vol. 644, No. 1, p. 012047). IOP Publishing. https://doi.org/10.1088/1755-1315/644/1/012047

• Pandiselvam, R., Mahanti, N. K., Manikantan, M. R., Kothakota, A., Chakraborty, S. K., Ramesh, S. V., & Beegum, P. P. S. (2022). Rapid detection of adulteration in desiccated coconut powder: vis-NIR spectroscopy and chemometric approach. Food Control, 133, Article 108588. https://doi.org/10.1016/j.foodcont.2021.108588

• Pereira, S. N. G., De Lima, A. B. S., Oliveira, T. D. F., Batista, A. S., Jesus, J. C. De, Ferrão, S. P. B., & Santos, L. S. (2022). Non-destructive detection of soybean oil addition in babassu oil by MIR spectroscopy and chemometrics. LWT, 154, Article 112857. https://doi.org/10.1016/j.lwt.2021.112857

• Rismiwandira, K., Roosmayanti, F., Pahlawan, M. F. R., & Masithoh, R. E. (2020). Application of fourier transform near-infrared (FT-NIR) spectroscopy for detection of adulteration in palm sugar. In IOP Conference Series: Earth and Environmental Science (Vol. 653, No. 1, p. 012122). IOP Publishing. https://doi.org/10.1088/1755-1315/653/1/012122

• Roosmayanti, F., Rismiwindira, K., & Masithoh, R. E. (2021). Detection of coconut (Cocos nucivera) sugar adulteration in palm (Arenga pinnata Merrill) sugar by fourier transform infrared (FT-IR) spectroscopy. Food Research, 5, 31-36.

• Sørensen, K. M., Khakimov, B., & Engelsen, S. B. (2016). The use of rapid spectroscopic screening methods to detect adulteration of food raw materials and ingredients. Current Opinion in Food Science, 10, 45-51. https://doi.org/10.1016/j.cofs.2016.08.001

• Vieira, L. S., Assis, C., de Queiroz, M. E. L. R., Neves, A. A., & de Oliveira, A. F. (2021). Building robust models for identification of adulteration in olive oil using FT-NIR, PLS-DA and variable selection. Food Chemistry, 345, Article 128866. https://doi.org/10.1016/j.foodchem.2020.128866

• Walkowiak, A., Ledziński, Ł., Zapadka, M., & Kupcewicz, B. (2019). Detection of adulterants in dietary supplements with Ginkgo biloba extract by attenuated total reflectance Fourier transform infrared spectroscopy and multivariate methods PLS-DA and PCA. Spectrochimica Acta - Part A: Molecular and Biomolecular Spectroscopy, 208, 222-228. https://doi.org/10.1016/j.saa.2018.10.008

• Zhang, Q., Li, Q., & Zhang, G. (2012). Rapid determination of leaf water content using VIS/NIR spectroscopy analysis with wavelength selection. Spectroscopy, 27(2), 93-105. https://doi.org/10.1155/2012/276795