|WU, JIZHONG - Jiangsu University|
|OUYANG, QIN - Jiangsu University|
|KANG, RUI - Jiangsu Academy Agricultural Sciences|
|WANG, ZHEN - Jiangsu University|
|WANG, LI - Jiangsu University|
|CHEN, QUANSHENG - Jiangsu University|
Submitted to: Food Chemistry
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 9/6/2021
Publication Date: 9/8/2021
Citation: Wu, J., Ouyang, Q., Park, B., Kang, R., Wang, Z., Wang, L., Chen, Q. 2021. Physicochemical indicators coupled with multivariate analysis for comprehensive evaluation of matcha sensory quality. Food Chemistry. https://doi.org/10.1016/j.foodchem.2021.131100.
Interpretive Summary: Matcha is finely ground powder of green tea leaves with special planting and processing. Recently, it has gained popularity as a flavoring additive or dietary supplement in drinks, ice cream, baked goods, etc. High grade matcha depends on better sensory quality, thus the sensory quality of matcha is a pivotal factor to determine consumer acceptance. However, human sensory panel tests are difficult to popularize by virtue of professional requirements and inability to evaluate large samples. Additionally, the sensory properties (appearance, aroma, infusion color and taste) of matcha are organoleptic and influenced by physicochemical properties. In this study, we measured physicochemical indexes including colors, chemicals, and particle sizes of various matcha and correlated these with sensory scores along with polyphenols, amino acids, and soluble sugar content. To develop a multivariate model to predict matcha quality, we obtained human sensory scores, quantified physicochemical indictors, and correlated physicochemical indicators to sensory attributes of matcha. The model with physicochemical indicators enabled us to predict matcha quality more reliably and robustly than with sensory panel evaluation.
Technical Abstract: The physicochemical indicators were significantly related to sensory attributes of matcha. In this study, twenty-four physicochemical indexes including 6 colors, 9 chemicals, and 9 particle sizes of matcha samples were collected and correlated with sensory attributes. To simplify models, the eight key physicochemical indicators selected by Least Absolute Shrinkage and Selection Operator (LASSO), including a* value, hue angle, chroma, saturation, tea polyphenols, ratio of tea polyphenols to free amino acids, soluble sugar content, median diameter of particle size distribution were evaluated. The dimensions of physicochemical indicators were further reduced with principal components analysis (PCA), and then weight coefficients of key indicators selected by LASSO were calculated in accordance with eigenvalues selected by PCA. The LASSO-PCA model of matcha sensory attributes was finally established. The comprehensive score was significantly negatively correlated with overall sensory quality (r = -0.886, P < 0.01). Results showed LASSO-PCA model simplified data collection and exhibited acceptable performance of matcha sensory quality evaluation by virtue of the optimization of key physicochemical information. Specifically, results demonstrated that the PCA comprehensive evaluation model was significantly correlated with appearance (r = -0.832), infusion color (r = -0.790), aroma (r = -0.818), taste (r = -0.853), and overall quality (r = -0.886), suggesting that LASSO-PCA model has the potential in predicting matcha sensory quality.