In the context of fair trade and protection of consumer rights, the aim of this study was to combat adulteration, counterfeiting, and fraud in the tea market, and rebuild the image of high-quality Taiwan teas. Experts at the Tea Research and Extension Station, Taiwan (TRES), are engaged in promotion of the systems of origin identification (AOC) and grading for authentication of Taiwan’s premium teas. From tea evaluation competitions (bottom-up quality campaign), the flavor descriptions and consumers’ perceptions were deconvoluted and characterized for the eight Taiwan specialty teas, namely, Bi-Luo-Chun, Wenshan Paochong, High-Mountain Oolong, Dongding Oolong, Tieh-Kuan-Yin, Red Oolong, Oriental Beauty, and Taiwan black tea. Then, according to the manufacturing processes, producing estates and flavor characters, the specialty teas were categorized into six sensory wheels. The flavor descriptors of the sensory wheels were also recognized in consumers’ feedback. In recent years, the performance of international trade in tea also demonstrates that the policy guidelines for authentication of specialty teas are helpful to the production and marketing. Furthermore, the development of sensory wheels of Taiwan’s specialty teas is the cornerstone to the establishment of the Taiwan-tea assortment and grading system (TAGs) for communication with the new generation consumers, enthusiasts, sellers, and producers.

Kefir, a beverage produced by the action of lactic acid bacteria (LAB), yeasts and acetic acid bacteria on milk, has a long tradition of offering health benefits. The objective of this research was to optimize the best formula for producing the kefir candy with maximum viabilities of LAB and yeasts using the response surface modeling and the sequential quadratic programming (SQP) method. In this study, milk was mixed with 5% kefir grains and incubated at 20C for 24 h. The samples were blended with lyoprotectants (galactose, skim milk powder [SMP] and sucrose), freeze‐dried and then mixed with sweeteners and compressed to form candies. The ratio of the lyoprotectants was determined using response surface modeling for constructing a response surface model and then using an SQP method to optimize the model. Optimization results indicated that kefir containing galactose, sucrose and SMP at 4.53, 7.0 and 5.03% (w/v), respectively, produced a chewable tablet with the highest viability of the microorganisms investigated. A relatively higher survival of microorganisms could be achieved by placing the kefir candy product in a glass bottle with deoxidant and desiccant at 4C. PRACTICAL APPLICATIONS A number of novel fermented dairy products have been developed and marketed under the concept of probiotic products, but few of these products were associated with confectionary goods. The kefir candy created in the present study with high viable cell counts (106–107 cfu/g) after extended storage (up to 2 months) provides a flavorful option to offer the health benefits of probiotics. This study not only provides an opportunity to resolve the difficulty of kefir commercialization due to its post‐acidity and gas production but also increases the variety of dairy products in the market.

Partially fermented tea such as oolong tea is a popular drink worldwide. Preventing fraud in partially fermented tea has become imperative to protect producers and consumers from possible economic losses. Visible/near-infrared (VIS/NIR) spectroscopy integrated with stepwise multiple linear regression (SMLR) and support vector machine (SVM) methods were used for origin discrimination of partially fermented tea from Vietnam, China, and different production areas in Taiwan using the full visible NIR wavelength range (400–2498 nm). The SMLR and SVM models achieved satisfactory results. Models using data from chemical constituents’ specific wavelength ranges exhibited a high correlation with the spectra of teas, and the SMLR analyses improved discrimination of the types and origins when performing SVM analyses. The SVM models’ identification accuracies regarding different production areas in Taiwan were effectively enhanced using a combination of the data within specific wavelength ranges of several constituents. The accuracy rates were 100% for the discrimination of types, origins, and production areas of tea in the calibration and prediction sets using the optimal SVM models integrated with the specific wavelength ranges of the constituents in tea. NIR could be an effective tool for rapid, nondestructive, and accurate inspection of types, origins, and production areas of teas.