title: [Application of some different modeling algorithms to pear MT-firmness detection using NIR spectra].
abstract: Near infrared (NIR) spectroscopy is an instrumental method, which was widely studied and used for rapid and nondestructive detection of internal qualities of agricultural products. Statistical modeling is a very important and difficult process in NIR detection to establish the relationship between nondestructive NIR spectral data and interested quality index of the products. Classical multivariate calibration methods such as partial least square regression (PLSR), principle component regression (PCR), stepwise multilinear regression (SMLR) were often used for modeling. In the present study, besides these algorithms, another mixed algorithm was adopted for establishing a nonlinear model of NIR spectra and Magness Taylor(MT) firmness of "Xueqing" pears. The mixed algorithm was combined with SMLR and artificial neural network (ANN). NIR diffuse reflectance spectra of intact pears were measured in the spectral range of 800-2630 nm using InGaAs detector. However, only spectral information between 800 and 2500 nm was used for modeling because of the low signal to noise ratio beyond 2500 nm. Comparing the classical multivariate calibration methods of PLSR, PCR and SMLR, the modeling results using PLSR method were much better than the other two methods. Moreover, models based on original spectra turned out better results than models based on derivative spectra for all the three methods. The best results were r = 0.87, RMSEC = 3.88 N of calibration and r = 0.84, and RMSEP = 4.26 N of validation by using PLSR method based on original spectra. The mixed algorithm also performed better than SMLR and PCR, but was a bit worse than PLSR: r = 0.85, RMSEC = 4.15 N of calibration and r = 0.82, and RMSEP = 4.67 N of validation. The results indicated that fruit NIR spectra could be used for MT-firmness prediction when a proper algorithm was chosen, however, further study on statistic modeling is still necessary to improve the predicting performance.
authors.affiliation: College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310029, China.
authors.forename: Xia-ping
authors.initials: XP
authors.lastname: Fu
authors.forename: Yi-bin
authors.initials: YB
authors.lastname: Ying
authors.forename: Hui-shan
authors.initials: HS
authors.lastname: Lu
authors.forename: Hai-yan
authors.initials: HY
authors.lastname: Yu
authors.forename: Hui-rong
authors.initials: HR
authors.lastname: Xu
mesh_terms: D000465:Algorithms; D002138:Calibration; D004058:Diffusion; D008954:Models, Biological; D011312:Pressure; D031989:Pyrus / Q000737:chemistry* / Q000254:growth & development*; D019265:Spectroscopy, Near-Infrared / Q000295:instrumentation / Q000379:methods*; D013314:Stress, Mechanical