Dynamic monitoring oxidation process of nut oils through Raman technology combined with PLSR and RF-PLSR model. (July 2021)

• Record Type: Journal Article
• Authors: Wang, Cheng; Sun, Yingying; Zhou, Yanyu; Cui, Yiwen; Yao, Weirong; Yu, Hang; Guo, Yahui; Xie, Yunfei
• Is Part Of: Lebensmittel-Wissenschaft + Technologie =. Volume 146(2021)
• Publication Date: 2021-07
• Subjects: Nut oils -- Peroxide value -- Partial-least-square regression -- Raman spectroscopy; Food industry and trade -- Periodicals; Food -- Composition -- Periodicals; Microbiology -- Periodicals; Nutrition -- Periodicals; 664.005
• DOI: 10.1016/j.lwt.2021.111290
• Languages: English
• ISSNs: 0023-6438

Abstract

Abstract: During preservation of nuts, nut oils are easily oxidized; hence, peroxide value (PV) is an important evaluation index. In this study, a novel method for the determination of the PV of nuts based on partial-least-square regression (PLSR) and Forest random PLSR (RF-PLSR) model was established. Meanwhile, the Raman spectrum was processed by 24 spectral pretreatment methods to transform the whole Raman band, and the best band was selected by RF method. Among the whole bands, 36 wavenumbers were selected to establish the PLSR model. The R-square of the correction set (R 2 c) and prediction set (R 2 p) of the optimal Standard normal variate transformation + first Derivative-PLSR model and RF-PLSR were 0.9552, 0.8672, 0.8048, and 0.7927, while the root-mean-square error of calibration (RMSEC) and prediction (RMSEP) were 0.067, 0.1100, 0.1514, and 0.1547, respectively. These results showed that Raman spectroscopy combined with chemometrics could be used to establish a rapid, nondestructive, and precise method for the determination of oil oxidation index. Highlights: Dynamic monitoring of nut oil oxidation was done through Raman technology. Comparison was done on the stability and precision of different models established by different pretreatments. PLSR modeling was performed by the Random Forest algorithm to quantify oil peroxide values.