Differentiation of avocados according to their botanical variety using liquid chromatographic fingerprinting and multivariate classification tree

Chemometric-Guided Approaches for Profiling and Authenticating Botanical Materials

Botanical supplements with broad traditional and medicinal uses represent an area of growing importance for American health management; 25% of U.S. adults use dietary supplements daily and collectively spent over $9. 5 billion in 2019 in herbal and botanical supplements alone. To understand how natural products benefit human health and determine potential safety concerns, careful in vitro, in vivo, and clinical studies are required. However, botanicals are innately complex systems, with complicated compositions that defy many standard analytical approaches and fluctuate based upon a plethora of factors, including genetics, growth conditions, and harvesting/processing procedures. Robust studies rely upon accurate identification of the plant material, and botanicals' increasing economic and health importance demand reproducible sourcing, as well as assessment of contamination or adulteration. These quality control needs for botanical products remain a significant problem plaguing researchers in academia as well as the supplement industry, thus posing a risk to consumers and possibly rendering clinical data irreproducible and/or irrelevant. Chemometric approaches that analyze the small molecule composition of materials provide a reliable and high-throughput avenue for botanical authentication. This review emphasizes the need for consistent material and provides insight into the roles of various modern chemometric analyses in evaluating and authenticating botanicals, focusing on advanced methodologies, including targeted and untargeted metabolite analysis, as well as the role of multivariate statistical modeling and machine learning in phytochemical characterization. Furthermore, we will discuss how chemometric approaches can be integrated with orthogonal techniques to provide a more robust approach to authentication, and provide directions for future research.

Simultaneous determination of six main components in Bushen Huoxue prescription by HPLC-CAD

BACKGROUND

The Bushen Huoxue prescription is a traditional Chinese medicine formula treating diabetic retinopathy, which was developed by our research group. Catalpol, puerarin, salvianolic acid B, ginsenoside Rg1, ginsenoside Rb1 and ginsenoside Rd are six of main effective components, which could be partly representative of this prescription. The corona charged aerosol detector (corona CAD) is one kind of universal detectors equipped with the high performance liquid chromatography (HPLC). The CAD has many advantages for the analysis of complex mixtures, but too few applications in traditional Chinese medicine compounds.

OBJECTIVE

The aims of this study are to establish a method for the determination of six components in Bushen Huoxue prescription, and to increase the use of the CAD in traditional Chinese medicine compounds.

METHODS

HPLC-CAD analysis was performed on an Inertsil ODS-SP (4.6 mm × 250 mm, 5 μm) with a mobile phase consisting of 0.5 % formic acid solution(A)-acetonitrile(B) at a flow rate of 1 mL/min (0-7 min, 1 % B; 7-12 min, 1 %-12 % B; 12-22 min, 12 %-19 % B; 22-40 min, 19 %-28 % B; 40-43 min, 28 %-33 % B; 43-50 min, 33 % B; 50-65 min, 33 %-42 % B). The column temperature maintained at 30 ℃, the injection volume was 20 μL, the atomization temperature mode was LOW, the filtration constant (filter) was 3.6 and data collection rate was 10 Hz. The methodology was examined and the linearity of regression of different functions was compared. Sixteen batches of samples were prepared and their contents were determined.

RESULTS

The six compounds showed a better linearity (R² > 0.9990) in their concentration ranges when using the linear function. The average recoveries were 99.18 %-101.30 %. Although the RSD value of puerarin and ginsenoside Rg1 was slightly out of 3 % during the average recovery investigation, all the other methodological investigations of the six components were within 3 %. The precision, stability and repeatability of the method were good. In sixteen batches of Bushen Huoxue prescription samples, the contents of six components were 0.3138 %-0.6042 % for catalpol, 0.8095 %-1.2917 % for puerarin, 0.7416 %-1.1189 % for salvianolic acid B, 0.0231 %-0.0418 % for ginsenoside Rg1, 0.0702 %-0.1724 % for ginsenoside Rb1, 0.0384 %-0.1196 % for ginsenoside Rd.

CONCLUSION

In this experiment, a method for the determination of six components in Bushen Huoxue prescription based on HPLC-CAD was established with high accuracy, good repeatability and simple operation, and it can provide references for the improvement of quality standard of the Bushen Huoxue prescription. It is reasonable and accessible for the CAD application in the determination of traditional Chinese medicine compound prescriptions.

PLS-DA vs sparse PLS-DA in food traceability. A case study: Authentication of avocado samples

Conventional and sparse partial least squares-discriminant analysis (PLS-DA and sPLS-DA) have been successfully tested in order to authenticate avocado samples in terms of three different geographical origins and six kinds of cultivar. For this, lipid chromatographic fingerprints of different avocado fruits have been acquired using gas chromatography coupled with flame ionization detector (GC-FID) and employed for building classification models. In addition, classification models concatenating strategy has been applied, which has proved to be successful to resolve multiclass problems in food authentication. Finally, fine performance metrics around of 0.95 were obtained for both multivariate classification methods.

Classification of Different Blueberry Cultivars by Analysis of Physical Factors, Chemical and Nutritional Ingredients, and Antioxidant Capacities

Blueberry fruits of different cultivars are featured with different quality indices. In this work, three types of quality factors, including 6 physical parameters, 12 chemical and nutritional components, and 3 antioxidant indices, were measured to compare and classify blueberry fruits from 12 different cultivars in China. Using the autoscaled data of quality factors, unsupervised principal component analysis was performed for exploratory analysis of intercultivar differences and the influences of quality factors. A supervised classification method, partial least squares discriminant analysis (PLSDA), was combined with the global particle swarm optimization algorithm (PSO) and two multiclass strategies, one-versus-rest (OVR) and one-versus-one (OVO), to select discriminative quality factors and develop classification models of the 12 cultivars. As a result, OVO-PLSDA with 8 quality factors could achieve the classification accuracy of 0.915. This study will provide new insights into the quality variations and key factors among different blueberry cultivars.