title: Prediction of valid acidity in intact apples with Fourier transform near infrared spectroscopy.
abstract: To develop nondestructive acidity prediction for intact Fuji apples, the potential of Fourier transform near infrared (FT-NIR) method with fiber optics in interactance mode was investigated. Interactance in the 800 nm to 2619 nm region was measured for intact apples, harvested from early to late maturity stages. Spectral data were analyzed by two multivariate calibration techniques including partial least squares (PLS) and principal component regression (PCR) methods. A total of 120 Fuji apples were tested and 80 of them were used to form a calibration data set. The influences of different data preprocessing and spectra treatments were also quantified. Calibration models based on smoothing spectra were slightly worse than that based on derivative spectra, and the best result was obtained when the segment length was 5 nm and the gap size was 10 points. Depending on data preprocessing and PLS method, the best prediction model yielded correlation coefficient of determination (r2) of 0.759, low root mean square error of prediction (RMSEP) of 0.0677, low root mean square error of calibration (RMSEC) of 0.0562. The results indicated the feasibility of FT-NIR spectral analysis for predicting apple valid acidity in a nondestructive way.
authors.affiliation: School of Biosystems Engineering & Food Science, Zhejiang University, Hangzhou 310029, China.
authors.forename: Yan-De
authors.initials: YD
authors.lastname: Liu
authors.forename: Yi-Bin
authors.initials: YB
authors.lastname: Ying
authors.forename: Xia-Ping
authors.initials: XP
authors.lastname: Fu
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D005504:Food Analysis / Q000379:methods*; D005638:Fruit / Q000737:chemistry* / Q000145:classification; D006863:Hydrogen-Ion Concentration; D016018:Least-Squares Analysis; D027845:Malus / Q000737:chemistry* / Q000145:classification; D008956:Models, Chemical; D015233:Models, Statistical; D009716:Numerical Analysis, Computer-Assisted; D025341:Principal Component Analysis; D012044:Regression Analysis; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D017550:Spectroscopy, Fourier Transform Infrared / Q000379:methods*