Background: Online team-based learning is a crucial teaching method to successfully increase students’ engagement during the pandemic. This study provides a report on online team-based learning during a traditional medicine course attended by undergraduate students from different higher education institutions in Indonesia and overseas.    Methods: A questionnaire was administered to determine the active participation of team members and to carry out a course evaluation   Results: The learning outcome was successfully achieved as 96% of the groups scored above the passing grade for the team-based projects. Students from various institutions had generally positive opinions on the course, especially on the course design, course material and the speakers, and the collaborative working with students from diverse backgrounds.    Conclusion: Language barriers and technical difficulties were some factors that hindered the students from gaining full benefit from the course, and thus should be mitigated in the implementation of online team-based learning. Additionally, a well-designed pre-test and post-test survey should be conducted to aid the assessment of students’ comprehension of the course.

Diabetes is a major public health burden whose prevalence has been steadily increasing over the past decades. Glycated hemoglobin (HbA1c) is currently the gold standard for diagnostics and monitoring glycemic control in diabetes patients. HbA1c biosensors are often considered to be cost-effective alternatives for smaller testing laboratories or clinics unable to access other reference methods. Many of these sensors deploy nanomaterials as recognition elements, detection labels, and/or transducers for achieving sensitive and selective detection of HbA1c. Nanomaterials have emerged as important sensor components due to their excellent optical and electrical properties, tunable morphologies, and easy integration into multiple sensing platforms. In this review, we discuss the advantages of using nanomaterials to construct HbA1c sensors and various sensing strategies for HbA1c measurements. Key gaps between the current technologies with what is needed moving forward are also summarized.

Tea, derived from Camellia sinensis L., is considered as the most popular beverages in the world. The quality of teas may vary depending on harvesting location and geographical origins, thus the traceability of teas according to their origins is very essential to assure tea’s quality. Due to economic reasons, high quality tea products may be added with foreign materials or adulterated with low quality ones, as a consequence, some analytical methods have been proposed and developed to quality control of tea. During the recent years, the application of molecular spectroscopic techniques (UV-Vis, Fluorescence, Near Infrared, Mid Infrared, Raman) in combination with multivariate data analysis has emerged as rapid and reliable analytical tool in the quality control of food, including food authentication. The objective of this review is to update the application of molecular spectroscopy (UV-Vis, fluorescence, infrared and Raman) for the quality control and authentication of tea products either geographical origins issue or detection of potential adulterants. The variables obtained during molecular spectral measurement involve hundreds or thousands of data, which make data analysis rather complex. Fortunately, the specific chemometrics tools can solve the problems arising from big data coming from analyte signals, spectral interferences and overlapping peaks. This review paper provides an overview of the recently developed approaches and latest research carried out in molecular spectroscopic techniques in combination with chemometrics for the quality control and for authentication of teas

Ultraviolet (UV)-visible and Fourier-transformed infrared (FTIR) spectroscopy are two of the most popular and readily available laboratory instruments. Fingerprinting analysis of the UV-visible and FTIR spectra has been applied for food classification and authentication studies. In this study, the UV-visible and FTIR spectra of brewed tea, and their data fusion data sets, were used to build models for the classification of tea based on tea types and origins. The study included black and green tea samples from several provinces in Sumatra and Java Islands (Indonesia). Chemometric models of principal component analysis (PCA), k-nearest neighbor (kNN), and logistic regression were developed for classification purposes. All PCA models were able to well-separate the tea groups. kNN and logistic regression models based on UV-visible spectra successfully classified green and black tea with >0.8 classification accuracy. The kNN model of FTIR spectra had good accuracy (0.903) for classifying tea based on its origin. ReliefF algorithm was employed to select the best features among the data fusion data sets of UV-visible and FTIR spectra. The data fusion data sets of UV-visible and FTIR spectra demonstrated good separation of tea types and origins with a high area under the ROC curve (>0.8) and moderate accuracy (0.548). Therefore, UV-visible and FTIR spectroscopy may provide complementary information for tea classification based on tea types and origins.

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