Hyperspectral guidance for summer tea processing: Enhancing taste and aroma through short‐term cycled heaping

Abstract

To reduce the unpleasant flavor in tea infusion made from summer fresh leaves and lessen tea resource wastage, a short‐term cycled heaping method, addressing the temperature/moisture variations during prolonged heaping, was introduced to produce high‐quality yellow tea with significantly enhanced taste and aroma. Furthermore, a hyperspectral approach was developed to anticipate such improvements. Specifically, short‐term cycled heaping reduced summer tea's astringency and bitterness while increasing sweetness. Using near‐infrared spectroscopy, the multiplicative scatter correction (MSC)–support vector machine (SVM) model predicted tea umami with 66.25% precision and richness with 77.64% accuracy via standard normal variate (SNV)–SVM, based on epigallocatechin (EGC), epicatechin (EC), and gallocatechin gallate (GCG) variations. Aroma enhancement was forecasted by a hyperspectral–electronic nose regression model, achieving over 81.50% prediction accuracy for summer tea aromas in the visible spectrum and surpassing 73.45% in the near‐infrared domain, primarily attributed to pentanal, propanal, toluene, ethyl propionate, and 2,3‐pentanedione variations. Overall, hyperspectral‐guided summer tea processing, particularly for summer‐harvested leaves, shows potential in offering a valuable screening tool, enhancing quality, and efficiency in the tea industry and promoting sustainable development.