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This study was designed to characterise the cultured and wild populations of Anabas testudineus in Malaysia using morphological parameters. Fish samples from the East and West coast of the country were obtained from fishermen (wild samples) and well-recognised climbing perch farmers in Kedah, Kelantan, Johor and Selangor. The Truss network method was applied to obtain necessary data and analysed to examine phenotypic variation between the cultured and wild stocks. Results obtained suggest that each hatchery population belonged to a distinct stock as revealed by their separate clustering into individual unique groups. However, an extensive overlap was observed in the wild population suggesting similarity of origin. The most important morphological parameters for the discrimination of the two populations are the homologous landmark B (i.e., snout to insertion of the pelvic fin) and C (i.e., above the eye to insertion of the pelvic fin). Genetic characterisation of the A. testudineus is needed to complement the findings of this study and establish a baseline for the development of a selective breeding programme for the fish species in Malaysia. [ABSTRACT FROM AUTHOR]

• Tri-step FT-IR fingerprints of Chuan-Mutong and Guan-Mutong are presented. • Machine learning classifiers were developed to distinguish between Chuan-Mutong and Guan-Mutong. • Comparison of machine learning classifier with PCA and PLS-DA was performed. • The proposed FT-IR with PLS-DA and machine learning classifier method was characterized by being simple, fast and reliable. Chuan-Mutong (Clemetis spp.) is a precious medicinal herb in traditional Chinese medicine that possesses various therapeutic effects especially well known for its diuretic effect and widely used in Malaysia. However, there were several reported Chinese herb nephropathy cases due to the adulteration of Aristolochia spp. found in combinational herbal regimen. Guan-Mutong (Aristolochia manshuriensis), which looks similar in appearance and has similar therapeutic effects as Chuan-Mutong, has the possibility to substitute the Chuan-Mutong. Therefore, there is a necessity to differentiate the types of Mutong using analytical authentication methods. In this paper, a rapid and accurate method is proposed to discriminate Chuan-Mutong from Guan-Mutong by using tri-step fourier transform infrared spectroscopy (FT-IR) identification approaches. The method involves the deployment of FT-IR, second derivative infrared spectra (SD-IR), and two-dimensional correlation infrared spectra (2D-IR). In our approach, FT-IR spectra of Chuan-Mutong and Guan-Mutong were subjected to discrimination using principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), and machine learning classifiers (ML). Chuan-Mutong and Guan-Mutong can be clearly classified or discriminated against each other by ML, PLS-DA and PCA. The sensitivity, accuracy and specificity of ML were >90%, while the sensitivity, accuracy and specificity of PLS-DA were 100%. It is hence demonstrated that the infrared spectroscopic identification approach using PCA, PLS-DA and ML can be effectively used to differentiate Chuan-Mutong and Guan-Mutong. PLS-DA and ML provide a simple, fast, and high accuracy prediction to differentiate Chuan-Mutong and Guan-Mutong. [ABSTRACT FROM AUTHOR]