Milk adulteration identification using hyperspectral imaging and machine learning. Journal of Dairy Science, 108(2), 1301-1314. https://doi.org/10.3168/jds.2024-25635
Callao, M. P., & Ruisánchez, I. (2018). An overview of multivariate qualitative methods for food fraud detection. Food Control, 86, 283-293. https://doi.org/10.1016/j.foodcont.2017.11.034
Cevoli, C., Mingrone, M., & Fabbri, A. (2026). Hyperspectral imaging and linear and nonlinear machine learning for tracing the geographical origin of pistachios. Food Control, 184, 112048. https://doi.org/10.1016/j.foodcont.2026.112048
Çinarer, G., Doğan, N., Kılıç, K., & Doğan, C. (2024). Rapid detection of adulteration in pistachio based on deep learning methodologies and affordable system. Multimedia Tools and Applications, 83(5), 14797-14820. https://doi.org/10.1007/s11042-023-16172-5
Cortés, V., Blasco, J., Aleixos, N., Cubero, S., & Talens, P. (2019). Monitoring strategies for quality control of agricultural products using visible and near-infrared spectroscopy: A review. Trends in Food Science & Technology, 85, 138-148. https://doi.org/10.1016/j.tifs.2019.01.015
Doğan, C., Şehirli, E., Doğan, N., & Buran, İ. (2023). Non-targeted approach to detect pistachio authenticity based on digital image processing and hybrid machine learning model. Journal of Food Measurement and Characterization, 17(2), 1693-1702. https://doi.org/10.1007/s11694-022-01671-0
Eksi-Kocak, H., Mentes-Yilmaz, O., & Boyaci, I. H. (2016). Detection of green pea adulteration in pistachio nut granules by using Raman hyperspectral imaging. European Food Research and Technology, 242(2), 271-277. https://doi.org/10.1007/s00217-015-2538-3
Faqeerzada, M. A., Lohumi, S., Kim, G., Joshi, R., Lee, H., Kim, M. S., & Cho, B. K. (2020). Hyperspectral shortwave infrared image analysis for detection of adulterants in almond powder with one-class classification method. Sensors, 20(20), 5855.
Genis, H. E., Durna, S., & Boyaci, I. H. (2021). Determination of green pea and spinach adulteration in pistachio nuts using NIR spectroscopy. Lwt, 136, 110008. https://doi.org/10.1016/j.lwt.2020.110008
He, H. J., Chen, Y., Li, G., Wang, Y., Ou, X., & Guo, J. (2023). Hyperspectral imaging combined with chemometrics for rapid detection of talcum powder adulterated in wheat flour. Food Control, 144, 109378. https://doi.org/10.1016/j.foodcont.2022.109378
Horn, B., Esslinger, S., Pfister, M., Fauhl-Hassek, C. and Riedl, J. 2018. Non-targeted detection of paprika adulteration using mid-infrared spectroscopy and one-class classification–Is it data preprocessing that makes the performance? Food chemistry, 257, 112-119. https://doi.org/10.1016/j.foodchem.2018.03.007
Innamorato, V., Longobardi, F., Lippolis, V., Cortese, M., Logrieco, A.F., Catucci, L., Agostiano, A. and De Girolamo, A. (2019). Tracing the Geographical Origin of Lentils (Lens culinaris Medik.) by Infrared Spectroscopy and Chemometrics. Food Analytical Methods, 12(3), 773-779. https://doi.org/10.1007/s12161-018-1406-8
Jamshidi, B., (2012). Non-destructive Taste Characterization and Classification of Oranges Using Vis/NIR Spectroscopy, Doctoral Dissertation, Tarbiat Modares University. (In Persian).
Khadivi, A., Nikoogoftar-Sedghi, M., & Tunç, Y. (2025). Agronomic characteristics, mineral nutrient content, antioxidant capacity, biochemical composition, and fatty acid profile of Iranian pistachio (Pistacia vera L.) cultivars. BMC Plant Biology, 25(1), 1-21. https://doi.org/10.1186/s12870-025-06094-9
Kheiralipour, K., Ahmadi, H., Rajabipour, A., Rafiee, S., Javan-Nikkhah, M., Jayas, D. S., & Siliveru, K. (2016). Detection of fungal infection in pistachio kernel by long-wave near-infrared hyperspectral imaging technique. Quality Assurance and Safety of Crops & Foods, 8(1), 129-135.
Martínez-Peña, R., Castillo, S., Vélez, S., & Álvarez, S. (2026). HSI and ML for non-destructive pistachio quality assessment: Influence of location and irrigation on nutrient and fat composition. Food Control, 112011. https://doi.org/10.1016/j.foodcont.2026.112011
Menevseoglu, A., Aykas, D. P., & Adal, E. (2021). Non-targeted approach to detect green pea and peanut adulteration in pistachio by using portable FT-IR, and UV–Vis spectroscopy. Journal of Food Measurement and Characterization, 15(2), 1075-1082. https://doi.org/10.1007/s11694-020-00710-y
Mishra, P., Mohd Asaari, M.S., Herrero-Langreo, A., Lohumi, S., Diezma, B., & Scheunders, P. (2017). Close range hyperspectral imaging of plants: A review. Biosystems Engineering, 164, 49-67. https://doi.org/10.1016/j.biosystemseng.2017.09.009
Mohammadi-Moghaddam, T., Razavi, S. M., Taghizadeh, M., Pradhan, B., Sazgarnia, A., & Shaker-Ardekani, A. (2018). Hyperspectral imaging as an effective tool for prediction the moisture content and textural characteristics of roasted pistachio kernels. Journal of Food Measurement and Characterization, 12(3), 1493-1502. https://doi.org/10.1007/s11694-018-9764-x
Nargesi, M. H., Amiriparian, J., Bagherpour, H., & Kheiralipour, K. (2024). Detection of different adulteration in cinnamon powder using hyperspectral imaging and artificial neural network method. Results in Chemistry, 9, 101644. https://doi.org/10.1016/j.rechem.2024.101644
Park, J. J., Cho, J. S., Lee, G., Yun, D. Y., Park, S. K., Park, K. J., & Lim, J. H. (2023). Detection of red pepper powder adulteration with allura red and red pepper seeds using hyperspectral imaging. Foods, 12(18), 3471.
Sezer, B., Apaydin, H., Bilge, G., & Boyaci, I. H. (2019). Detection of Pistacia vera adulteration by using laser induced breakdown spectroscopy. Journal of the Science of Food and Agriculture, 99(5), 2236-2242.
Taylan, O., Cebi, N., Yilmaz, M. T., Sagdic, O., Ozdemir, D., & Balubaid, M. (2021). Rapid detection of green‐pea adulteration in pistachio nuts using Raman spectroscopy and chemometrics. Journal of the Science of Food and Agriculture, 101(4), 1699-1708.
Taylan, O., Bulut, M., Cebi, N., Sagdic, O., & Celebi, Y. (2026). Explainable AI-assisted hybrid self-organising maps and deep learning algorithms for detecting pistachio adulteration with peas. Food Additives & Contaminants: Part A, 1-19. https://doi.org/10.1080/19440049.2025.2597288
Temizkan, R., Doğan, M. A., Atakan, O., Nazlım, B. A., & Ayvaz, H. (2020). Rapid detection of green pea adulteration in ground pistachio nuts using near and mid-infrared spectroscopy. International Journal of Agriculture Forestry and Life Sciences, 4(1), 71-78.
Teye, E., Amuah, C.L., McGrath, T. and Elliott, C. 2019. Innovative and rapid analysis for rice authenticity using hand-held NIR spectrometry and chemometrics. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy. https://doi.org/10.1016/j.saa.2019.03.085
Trentanni Hansen, G. J., Almonacid, J., Albertengo, L., Rodriguez, M. S., Di Anibal, C., & Delrieux, C. (2019). NIR-based Sudan I to IV and Para-Red food adulterants screening. Food Additives & Contaminants: Part A, 36(8), 1163-1172. https://doi.org/10.1080/19440049.2019.1619940
Williams, L., Shukla, P., Sheikh-Akbari, A., Mahroughi, S., & Mporas, I. (2025). Measuring the level of aflatoxin infection in pistachio nuts by applying machine learning techniques to hyperspectral images. Sensors, 25(5), 154
