title: [Detection of onion soluble solids content based on the near-infrared reflectance spectra].
abstract: Onion soluble solids content (SSC) was detected using near-infrared (924-1720 nm) reflectance spectra. Three cultivars of onions, harvested at different period, were selected for experiment and the total number of samples is 268. SSC reference value of onion juice was determined using the temperature compensated refractometer. Some pre-processing methods, such as S-G smoothing, scatter correction, and derivation, were compared to establish a statistical model based on partial least squares regression (PLSR) method. The results show that the avitzky-Golay smoothing with window 32 and span 10 is more efficient. The determination correlation coefficient of prediction R2 is 0.87 and root mean square error (RMSEP) is 2.42 degrees Brix. Compared to the 2nd derivation, the 1st derivation got better prediction result, but the spectra scatter correction is the best (R2 = 0.88, RMSEP of = 2.31 degrees Brix). The optimal prediction (R2 = 0.90, RMSEP = 1.84 degrees Brix and RPD = 3) was built based on crossing validation modeling, which shows that infrared reflectance spectroscopy with scatter correction pre-processing is feasible for onions soluble solids detection.
authors.affiliation: Key Laboratory on Modern Precision Agriculture System Integration Research of MOE, China Agricultural University, Beijing 100083, China. wanghaihua@cau.edu.cn
authors.forename: Hai-hua
authors.initials: HH
authors.lastname: Wang
authors.affiliation: Department of Biological and Agricultural Engineering, University of Georgia, Georgia 30602, USA.
authors.forename: Chang-ying
authors.initials: CY
authors.lastname: Li
authors.affiliation: Key Laboratory on Modern Precision Agriculture System Integration Research of MOE, China Agricultural University, Beijing 100083, China.
authors.forename: Min-zan
authors.initials: MZ
authors.lastname: Li
mesh_terms: D016018:Least-Squares Analysis; D015233:Models, Statistical; D019697:Onions; D012031:Refractometry; D012044:Regression Analysis; D019265:Spectroscopy, Near-Infrared