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ARS Home » Southeast Area » Athens, Georgia » U.S. National Poultry Research Center » Quality & Safety Assessment Research » Research » Publications at this Location » Publication #367704

Research Project: Develop Rapid Optical Detection Methods for Food Hazards

Location: Quality & Safety Assessment Research

Title: Assessment of matcha sensory quality using hyperspectral microscope imaging technology

Author
item QUYANG, QIN - Jiangsu University
item WANG, LI - Jiangsu University
item Park, Bosoon
item KANG, RUI - Nanjing Agricultural University
item WANG, ZHEN - Jiangsu University
item CHEN, QUANSHENG - Jiangsu University
item GUO, ZHIMING - Jiangsu University

Submitted to: LWT - Food Science and Technology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 3/6/2020
Publication Date: 3/7/2020
Citation: Quyang, Q., Wang, L., Park, B., Kang, R., Wang, Z., Chen, Q., Guo, Z. 2020. Assessment of matcha sensory quality using hyperspectral microscope imaging technology. LWT - Food Science and Technology. https://doi.org/10.1016/j.lwt.2020.109254.
DOI: https://doi.org/10.1016/j.lwt.2020.109254

Interpretive Summary: Macha, a fine green tea powder, is rapidly becoming the new drink of choice for many people around the world. Usually, high quality matcha possesses vibrant green color, and nice balance between natural sweetness, slightly bitter and vegetal tea flavor. On the contrary, lower quality matcha has a more yellowish/brownish hue with a coarse and gritty texture. Thus, sensory attributes are of great importance to assess their quality. In general, human sensory panels assign scores to tea samples considering their appearance, aroma and sensory taste characteristics. However, the results from human sensory panel test are susceptible to subjectivity, fatigue, and physical/mental health of experts. Hyperspectral microscope imaging (HMI) technology, coupled with chemometrics, was developed for estimating the sensory quality of matcha. The characteristic spectra were extracted from the optimized Region of Interest followed by selecting key spectral variables, which were used as the original data of artificial neural networks (ANN) models, by a competitive adaptive reweighted sampling (CARS) algorithm. The CARS-ANN models showed better than 82% predictive performance for all quality attributes which exceeds traditional chemometric models. Thus, HMI technology is a promising tool in estimating the quality of matcha powder as a rapid, objective and highly efficient method.

Technical Abstract: Hyperspectral microscope imaging (HMI) technology, coupled with chemometrics, was attempted to mimic the human panel test for estimating the sensory quality of matcha in this study. The hypercubes from HMI system contained spatial and spectral information related to the tea samples. Models were established based on the spectral information and the sensory scores from the human panel evaluations for sensory attributes including appearance, infusion color, aroma, taste and overall quality. The characteristic spectra were first averaged and extracted from all the pixels in the optimized regions of interest (ROI). Then, the key spectral variables were selected by competitive adaptive reweighted sampling and used for building artificial neural networks models (namely CARS-ANN models). Results from the CARS-ANN models demonstrated that the key variables only accounted for between 2% and 7% of the original variables of each sensory attribute. The CARS-ANN models achieved great performances, with 11 variables and 0.891 Rp (correlation coefficient in prediction set) for the appearance, 8 variables and 0.847 Rp for the infusion color, 21 variables and 0.821 Rp for the aroma, 6 variables and 0.883 Rp for the taste, 7 variables and 0.882 Rp for the overall quality. Results indicated that HMI technology has the potential for a rapid, objective and accurate tool for estimating the quality of matcha powder.