Open AccessRapid non‐destructive analysis of lignin using NIR spectroscopy and chemo‐metrics
Author(s) -
Wu Xin,
Li Guanglin,
Liu Xuwen,
He Fengyun
Publication year - 2021
Publication title -
food and energy security
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.253
H-Index - 25
ISSN - 2048-3694
DOI - 10.1002/fes3.289
Subject(s) - boss , bootstrapping (finance) , partial least squares regression , mathematics , feature selection , biological system , smoothing , statistics , mean squared error , linear regression , chemistry , analytical chemistry (journal) , computer science , chromatography , materials science , artificial intelligence , econometrics , metallurgy , biology
Lignin plays an important role in the formation of stone cells in pears. However, the accumulation of lignin had adverse impact on the flavor and quality of the fruit. A rapid and accurate method for measuring the lignin content of pears is therefore required. An improved variables selection method called ‘the bootstrapping soft shrinkage combined with frequency and regression coefficient of variables (FRCBOSS)’ was therefore developed based on ‘the bootstrapping soft shrinkage (BOSS)’technique, to identify the characteristic wavelengths of near‐infrared (NIR) spectra for non‐destructive and rapid analysis of lignin. Sub‐models were generated by weighted bootstrap sampling (WBS) in the FRCBOSS method. For the BOSS method, the new weights of variables were determined only as the absolute values of regression coefficients of variables in each iteration. In contrast, the FRCBOSS method also considers the frequency of variables in variable space. Moreover, the FRCBOSS algorithm overcomes the disadvantage of BOSS in selecting variables, which could incorporate useful wavelengths that would otherwise be removed by the BOSS method. In addition, a range of different pre‐treatment methods were used for comparison in the detection of lignin in the Snow pears. These include Savitzky–Golay Smoothing (SG), Normalization (NORM), Standard Normal Variate (SNV), and 1st Derivative (D1), as well as a combination of these methods and the different variables selection method (SiPLS, SiPLS‐SPA, SiPLS‐CARS, SiPLS‐BOSS, and SiPLS‐FRCBOSS). The number of variables selected by FRCBOSS was a little larger than that selected by BOSS. The partial least square regression (PLSR) model based on the 19 variables selected by SiPLS‐FRCBOSS method had the best prediction ability, with a Rp value of prediction of 0.880 and a RMSEP value of 1.004%. We conclude that NIR diffuse reflectance spectroscopy technology combined with FRC‐BOSS is an accurate and useful tool for the non‐destructive and rapid determination of pear lignin contents.