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Kiani, Sajad, Ayyari, Mahdi. (1405). Medicinal Plants Authenticity Evaluation using an Intelligent Hyperspectral Imaging System Coupled with Biologically Inspired Unsupervised Algorithms: Nepeta Crispa Willd Case Study. , 13(2), 383-394. doi: 10.22059/ijhst.2025.384543.952
Sajad Kiani; Mahdi Ayyari. "Medicinal Plants Authenticity Evaluation using an Intelligent Hyperspectral Imaging System Coupled with Biologically Inspired Unsupervised Algorithms: Nepeta Crispa Willd Case Study". , 13, 2, 1405, 383-394. doi: 10.22059/ijhst.2025.384543.952
Kiani, Sajad, Ayyari, Mahdi. (1405). 'Medicinal Plants Authenticity Evaluation using an Intelligent Hyperspectral Imaging System Coupled with Biologically Inspired Unsupervised Algorithms: Nepeta Crispa Willd Case Study', , 13(2), pp. 383-394. doi: 10.22059/ijhst.2025.384543.952
Kiani, Sajad, Ayyari, Mahdi. Medicinal Plants Authenticity Evaluation using an Intelligent Hyperspectral Imaging System Coupled with Biologically Inspired Unsupervised Algorithms: Nepeta Crispa Willd Case Study. , 1405; 13(2): 383-394. doi: 10.22059/ijhst.2025.384543.952

Medicinal Plants Authenticity Evaluation using an Intelligent Hyperspectral Imaging System Coupled with Biologically Inspired Unsupervised Algorithms: Nepeta Crispa Willd Case Study

International Journal of Horticultural Science and Technology
دوره 13، شماره 2، تیر 2026، صفحه 383-394    اصل مقاله (638.69 K)
نوع مقاله: Research paper
شناسه دیجیتال (DOI): 10.22059/ijhst.2025.384543.952
نویسندگان
Sajad Kiani* 1؛ Mahdi Ayyari2
1Biosystems Engineering Department, Sari Agricultural Sciences and Natural Resources University, Sari, Iran
2Horticultural Science Department, Tarbiat Modares University, Tehran, Iran
چکیده
This study evaluated the efficiency of a handheld Hyperspectral Imaging (HSI) system coupled with biologically inspired unsupervised algorithms as a screening technique for the authenticity evaluation of naturally-grown Nepeta Crispa Willd (N.Crispa) samples from on-farm cultivated samples. The volatile oils of 25 samples were isolated with hydrodistillation, and then the Gas Chromatography (GC) analyses were done to determine the total Volatile Organic Compounds (VOCs) as the reference data. On the other hand, the samples' reflectance spectra were captured using the HSI camera and pre-processed using the Savitzky-Golay (SG) algorithm. Principal Component Analysis (PCA) was then applied for the visual discrimination of the samples and the data reduction. Next, two unsupervised algorithms, crisp clustering by the self-organizing Map (SOM) and an automatic clustering based on the artificial bee colony (ABC), were applied to perform the real clustering of the samples. The SOM unified distance matrices explained the changes in spectral characteristics between the N.Crispa samples and indicated the variation of the sample’s VOCs following the GC results. The automatic clustering by the ABC algorithm illustrated its capability to cluster the two main sample groups according to the sample’s spectra. The HSI system combined with the ABC algorithm will provide a novel nondestructive and rapid technique for evaluating the authenticity of naturally-grown Nepeta Crispa Willd samples.
کلیدواژه‌ها
Artificial Neural Networks؛ Clustering؛ Geographical Origins؛ Medicinal Plants
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