Application of visible-near infrared spectroscopy in tandem with multivariate analysis for the rapid evaluation of matcha physicochemical indicators

• Published in: Food chemistry 2023-09, Vol.421, p.136185-136185, Article 136185
• Main Authors: Wu, Jizhong, Zareef, Muhammad, Chen, Quansheng, Ouyang, Qin
• Format: Article
• Language: English
• Subjects: Algorithms; Amino Acids; Chemometrics; Fine tea powder; Least-Squares Analysis; Multivariate Analysis; Physicochemical information; Polyphenols - analysis; Spectroscopy, Near-Infrared - methods; Variable selection; Visible-near infrared spectroscopy
• ISSN: 0308-8146; 1873-7072
• DOI: 10.1016/j.foodchem.2023.136185

•The particle size and free amino acids of matcha were evaluated using Vis-NIR.•Different strategies for variable selection of Vis-NIR spectra were compared.•The ICPA-CARS-PLS evaluation models exhibited the optimum performance. Consumer preference for matcha is heavily influenced by its physicochemical properties. The visible-near infrared (Vis-NIR) spectroscopy technology coupled with multivariate analysis was investigated for rapid and non-invasive evaluation of particle size and the ratio of tea polyphenols to free amino acids (P/F ratio) of matcha. The multivariate selection algorithms such as synergy interval (Si), variable combination population analysis (VCPA), competitive adaptive reweighted sampling (CARS), and interval combination population analysis (ICPA) were compared, and eventually, the variable selection strategy of ICPA and CARS hybridization was firstly proposed for selecting the characteristic wavelengths from Vis-NIR spectra to build partial least squares (PLS) models. Results indicated that the ICPA-CARS-PLS models achieved satisfactory performance for the evaluation of matcha particle size (Rp = 0.9376) and P/F ratio (Rp = 0.9283). Hence the rapid, effectual, and nondestructive online monitoring, Vis-NIR reflectance spectroscopy in tandem with chemometric models is significant for the industrial production of matcha.