This research studied the feasibility of making rapid measurements of the soluble solids contents (SSC) and acidity of 'Fuji'apple (Malus domestica Borkh. cv. Fuji) fruit. FT-NIR spectra were recorded in the interactance mode, using fiber optics and a special sample holder. Calibration models related the FT-NIR spectra to SSC, titratable acidity (TA) and available acidity (pH) were developed based on partial least square (PLS) regression with respect to the logarithms of the reflectance reciprocal and its first and second derivative. The prediction performance of calibration models in different wavelength regions was also investigated. The best models gave a standard errors of prediction (SEP) of 0.455, 0.044 and 0.068, and correlation coefficients of 0.968, 0.728 and 0.831 for SSC, TA and pH, respectively, in the wavelength range of 812-2357 nm. Based on the results, it was concluded that FT-NIR spectrometry could be easy to facilitate, reliable, accurate and fast method for non-invasive measurements of apple SSC and acidity.

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 800nm to 2619nm 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.

This research evaluated the potential of a Fourier transform near−infrared (FT−NIR) spectrometer with a reflectance fiber optic probe for determination of total soluble solids (TSS) content and available acid (VA) in intact peaches. It also investigated the relative performance of the calibration models with different data preprocessing methods (original, first derivative, and second derivative). Reflectance spectra were collected from two opposite sides of individual peaches, followed by standard destructive methods for analyzing TSS and VA of peaches. Calibration models were developed with the use of a partial least squares (PLS) technique. The best calibration model for TSS gave good predictions of TSS, with a coefficient of determination (r2) of 0.916 and a standard error of prediction (SEP) of 0.534. The best calibration model for VA prediction yielded (r2)=0.904 and SEP=0.129. Thus, FT−NIR reflectance can be used to predict the TSS and VA of intact peaches. Based on the results, it was concluded that FT−NIR spectrometry could be an easy to facilitate, reliable, accurate, and fast method for non−invasive measurement of peach TSS and VA.

Proportional integral plus feedforward (PI+FF) control was proposed for identifying the pipe temperature in hot water heating greenhouse. To get satisfying control result, ten coefficients must be adjusted properly. The data for training and testing the radial basic function (RBF) neural-networks model of greenhouse were collected in a 1028m2 multi-span glasshouse. Based on this model, a method of coefficients adjustment is described in this article.

Huanghua pear is an important fruit in China. The shape and condition of the stems are important indices for classifying Huanghua pears. Images of Huanghua pears were acquired with a machine vision system. Using templates with different sizes, an algorithm for judging the presence of stems was developed. Meanwhile, the stem head and the joint point between the stem and the pear body were labeled. After calculating slopes of the approximate tangential lines of the stem at the head and bottom positions, the included angle of these two lines was obtained. It was found that the included angle of a broken stem was smaller than that of a good stem. Based on this feature, good stems can be distinguished from broken stems. Results of a test on 53 pear images showed that the accuracy for judging the presence and integrity of the stems reached 100% and 93%, respectively. A method for describing the irregular shapes of Huanghua pears was also studied. Fourier transformation and Fourier inverse transformation pairs were used as the shape descriptors. The first 16 harmonic components of the Fourier descriptor were found sufficient to represent the primary shapes of uanghua pears. These components were used as the inputs to an artificial neural network (ANN) to classify Huanghua pears. The classification accuracy reached 90%.