Application of Non-Destructive Rapid Determination of Piperine in Piper nigrum L. (Black Pepper) Using NIR and Multivariate Statistical Analysis: A Promising Quality Control Tool

• Published in: Foods Vol. 9; no. 10; p. 1437
• Main Authors: Park, Jong-Rak, Kang, Hyun-Hee, Cho, Jong-Ku, Moon, Kwang-Deog, Kim, Young-Jun
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 11.10.2020; MDPI
• Subjects: Calibration; chemometric modeling; Chemometrics; Chromatography; eco-friendly analysis; Flavor; Food science; High performance liquid chromatography; Infrared spectroscopy; Liquid chromatography; Methods; Multivariate statistical analysis; Near infrared radiation; near infrared spectroscopy; Piper nigrum; Piper nigrum L; Piperine; Predictions; Quality control; Regression models; Software; Solvents; Spectrum analysis; Standard error; Statistical analysis; Statistical methods; United States > US; Vietnam; Germany; piperine; eco-friendly analysis; near infrared spectroscopy; chemometric modeling; Piper nigrum L
• ISSN: 2304-8158; 2304-8158
• DOI: 10.3390/foods9101437

Piperine is a bioactive alkaloid compound which provides a unique spicy flavor derived from plants of the L. Black pepper ( = 160) collected from Vietnam was studied using non-destructive near infrared spectroscopy (NIRS). The spectral acquisition ranged from 1100 to 2500 nm, and a chemometrics analysis program was performed to quantify the piperine contents. High performance liquid chromatography (HPLC) analysis was carried out to develop a chemometric model based on reference values. The black pepper samples were divided into two groups used for calibration ( = 120) and prediction ( = 40) sets. The optimum calibration model was developed by pretreatment of the spectra. The analyses results based on the prediction samples included a coefficient of determination ( ) of 0.914, a root mean square error of prediction (RMSEP) and a standard error of prediction (SEP) of about 0.220 g/100 g, and a ratio performance to deviation (RPD) value of 3.378 regarding the partial least square (PLS) regression model, and an of 0.921, an RMSEP and SEP of 0.210 g/100 g, and an RPD of 3.571, with respect to the principal components (PC) regression model. These results indicate that NIRS can be applicable as a control, or as an alternative rapid and effective method to quantify piperine in L.