The objective of this study was to obtain data on the distribution of alkaloids in kratom plants grown in Thailand. Two collections were performed, covering the southern, central, and northern regions of Thailand and different seasons. The contents of alkaloids, including mitragynine (MG), paynantheine (PAY), and speciogynine (SG), were determined using the validated HPLC method. The 134 samples in the first collection were collected from Nam Phu subdistrict, Ban Na San, Surat Thani, Thailand, during June and October 2019 and January 2020. The maximum mitragynine content was 4.94% w/w in June (late summer), and the minimum content was 0.74% w/w in October (rainy season). To expand the study area after kratom decriminalization, 611 samples were collected in June–August 2021, October–December 2021, and January–April 2022. The accumulation of MG ranged from 0.35 to 3.46% w/w, 0.31 to 2.54% w/w, and 0.48 to 2.81% w/w, respectively. The meteorological data supported the climate’s effect on alkaloid production. Soil analysis revealed the importance of Ca and Mg in promoting alkaloid production. Geographical locations played a role in the variation of MG in kratom leaves, but did not affect the color of leaf veins. In conclusion, the present study suggested that the alkaloid content in kratom diverges based on seasonal and geographical origin.

Kratom (KT) typically exerts antidepressant (AD) effects. However, evaluating which form of KT extracts possesses AD properties similar to the standard AD fluoxetine (flu) remained challenging. Here, we adopted an autoencoder (AE)-based anomaly detector called ANet to measure the similarity of mice's local field potential (LFP) features that responded to KT leave extracts and AD flu. The features that responded to KT syrup had the highest similarity to those that responded to the AD flu at 87.11 $\pm$ 0.25%. This finding presents the higher feasibility of using KT syrup as an alternative substance for depressant therapy than KT alkaloids and KT aqueous, which are the other candidates in this study. Apart from the similarity measurement, we utilized ANet as a multi-task AE and evaluated the performance in discriminating multi-class LFP responses corresponding to the effect of different KT extracts and AD flu simultaneously. Furthermore, we visualized learned latent features among LFP responses qualitatively and quantitatively as t -SNE projection and maximum mean discrepancy distance, respectively. The classification results reported the accuracy and F1-score of 90.11 $\pm$ 0.11% and 90.08 $\pm$ 0.00%. In summary, the outcomes of this research might help therapeutic design devices for an alternative substance profile evaluation, such as Kratom-based form, in real-world applications.