Open AccessQuantification of potassium concentration with Vis–SWNIR spectroscopy in fresh lettuce
Author(s) -
Yujuan Xiong,
Shintaroh Ohashi,
K. Nakano,
Weizhong Jiang,
Kenji Takizawa,
Kazuhiro Iijima,
Phonkrit Maniwara
Publication year - 2020
Publication title -
journal of innovative optical health sciences/journal of innovation in optical health science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.421
H-Index - 24
eISSN - 1793-5458
pISSN - 1793-7205
DOI - 10.1142/s1793545820500297
Subject(s) - mathematics , support vector machine , statistics , residual , partial least squares regression , potassium , multivariate statistics , artificial intelligence , funnel plot , mean squared error , chemistry , analytical chemistry (journal) , computer science , chromatography , algorithm , confidence interval , organic chemistry , publication bias
Chronic kidney disease (CKD) is becoming a major public health problem worldwide, and excessive potassium intake is a health threat to patients with CKD. In this study, visible–short-wave near-infrared (Vis–SWNIR) spectroscopy and chemometric algorithms were investigated as nondestructive methods for assessing the potassium concentration in fresh lettuce to benefit the CKD patients’ health. Interactance and transmittance measurements were performed and the competencies were compared based on the multivariate methods of partial least-square regression (PLS) and support vector machine regression (SVR). Meanwhile, several preprocessing methods [first- and second-order derivatives in combination with standard normal variate (SNV)] and wavelength selection method of competitive adaptive reweighted sampling (CARS) were applied to eliminate noise and highlight the spectral characteristics. The PLS models yielded better prediction than the SVR models with higher correlation coefficients ([Formula: see text]) and residual predictive deviation (RPD), and lower root-mean-square error of prediction (RMSEP). Excellent prediction of green leaves was obtained by the interactance measurement with [Formula: see text], [Formula: see text][Formula: see text]mg/100[Formula: see text]g, and [Formula: see text]; while the transmittance spectra of petioles provided optimal prediction with [Formula: see text], [Formula: see text][Formula: see text]mg/100[Formula: see text]g, and RPD[Formula: see text]=[Formula: see text]3.34, respectively. Therefore, the results indicated that Vis–SWNIR spectroscopy is capable of intelligently detecting potassium concentration in fresh lettuce to benefit CKD patients around the world in maintaining and enhancing their health.