Estimation of the sensory properties of black tea samples using non-destructive near-infrared spectroscopy sensors. (December 2022)

• Record Type: Journal Article
• Authors: Turgut, Sebahattin Serhat; Entrenas, José Antonio; Taşkın, Emre; Garrido-Varo, Ana; Pérez-Marín, Dolores
• Is Part Of: Food control. Volume 142(2022)
• Publication Date: 2022-12
• Subjects: Cupping test -- NIR chemometric Models -- Python -- Non-destructive sensors -- PCR -- PLSR; Food -- Quality -- Periodicals; Food -- Analysis -- Periodicals; Food handling -- Periodicals; Food industry and trade -- Quality control -- Periodicals; Aliments -- Industrie et commerce -- Qualité -- Contrôle -- Périodiques; Aliments -- Qualité -- Périodiques; Aliments -- Analyse -- Périodiques; Hygiène alimentaire -- Périodiques; Food -- Analysis; Food handling; Food -- Quality; Periodicals; Electronic journals; 664.07
• DOI: 10.1016/j.foodcont.2022.109260
• Languages: English
• ISSNs: 0956-7135

Abstract

Abstract: The quality characteristics of black tea are routinely assessed before it is purchased, blended and marketed to ensure its quality and value. Although some of these quality characteristics can be measured analytically, others need to be determined as sensory scores following cupping tests conducted by tea experts. However, most of these analyses (especially the sensory ones) require high training and expertise, are time-consuming and prone to human error. Therefore, in this study, non-destructive spectral sensors were combined with chemometric methods to rapidly measure the results of the cupping test (appearance, body, colour and overall quality) and some other important sensory quality attributes (bulk density, cellulose, water extract and moisture) of black tea samples. A total of 54 black tea samples from Türkiye were analysed in three different NIRS (near-infrared spectroscopy) devices (MicroNIR™ 1700, Matrix-F FT-NIR and NIRS DS 2500). Partial Least Squares Regression (PLSR) and Principal Components Regression (PCR) with stepwise variable elimination were used as regression algorithms to develop the NIR calibrations. As a result, PLSR provided slightly superior estimates with R c v 2 between 0.83 and 0.97 and R P D c v between 2.47 and 5.79 for sensory traits. For analytical traits, model statistics for PLSR ranged between 0.66-0.89 and 1.72–3.08 for R c v 2 and R P D c v, respectively. These results suggest that PLSR combined with FT-NIR technology may be promising for rapid and economical evaluation of sensory (cupping test) scores and related properties for its use in the tea industry. Highlights: NIRS was used for rapid measurement of cupping test scotes of black tea samples. Partial Least Squares and Principal Components Regression methods were applied. Three NIRS equipment (portable, benchtop, and in-situ analytical) were evaluated. Sensory properties of black teas successfully estimated without any pretreatment. FT-NIR instruments may be used for online measurement of dry black tea properties.