[Rapid nondestructive detection of apple quality attributes using hyperspectral scattering images].

The research discussed the prediction method of apple's internal quality such as firmness and soluble solids content with the combination of parameters getting from hyperspectral fitting scattering curve. The research compared different molding methods using the combination of the three Lorentzian fitting parameters with partial least squares (PLS), stepwise multiple linear regression (SMLR) and neural network (NN). The normalized combination parameters and original combination parameters were used to establish prediction models, respectively. The partial least squares (PLS) prediction models using the combination of three original parameters gave a better results with the correlation of calibration Rc = 0.93, the standard error of calibration SEC = 0.56, the correlation of validation R = 0.84, and the standard error of validation SEV = 0.94 for firmness of apples. The partial least squares (PLS) prediction models using combination of normalized parameters also gave a good results with Rc = 0.95, and the standard error of calibration SEC= 0. 29, the correlation of validation Rv = 0. 83, and the standard error of validation SEV = 0.63 for soluble solids content of apples. The research showed that using hyperspectral scattering curve can detect apple quality attributes at the same time.

[A rapid nondestructive measurement method for assessing the total plate count on chilled pork surface].

The present paper proposed a method based on the hyperspectral technology for rapidly, nondestructively quantify the total plate count on chilled pork surface. In the research, 50 chilled pork samples stored at 4 degrees C for 1-14 days were used to study the relationship between the total plate count on chilled pork surface and their hyperspectral images collected in 400-1 100 nm. Two models were established using MLR and PLSR methods, and the prediction showed that they can both give satisfactory results with R(v) = 0.886 and 0.863 respectively. The overall research demonstrates that the hyperspectral technology can well quantify the total plate count on chilled pork surface, and so indicates that it is a valid tool to assess the quality and safety properties of chilled pork in the future.

[Measurement of chlorophyll content in wheat leaves using hyperspectral scanning].

The objective of the present research was to evaluate the potential of hyperspectral scanning as a way for nondestructive measurement of chlorophyll content in wheat leaves, which can indicates the plant healthy status. One hundred twenty samples were randomly picked from Xiao Tangshan farm. Ninety samples were used as calibration set and others were used for verification set. After capturing hyperspectral image in the range of 400-1,000 nm, the chlorophyll contents of samples were measured immediately. Four different mathematical treatments were used in spectra processing in the wavelength range of 491-887 nm: multiplicative scatter correction (MSC), first derivative correction, and second derivative correction. Statistical models were developed using partial least square regression (PLSR), and stepwise multiple linear regression (SMLR) analysis technique. The results showed that the best calibration model was obtained by PLSR analysis, after processing spectra with MSC and second derivate, with a relatively higher coefficient of determination of calibration (0.82) and validation (0.79) respectively, a relatively lower RMSEC value (0.69), and a small difference between RMSEC (0.69) and RMSEP (0.71). The results indicate that it is feasible to use hyperspectral scanning technique for nondestructive measurement of chlorophyll content in wheat leaves.

[Study of spatially resolved hyperspectral scattering images for assessing beef quality characteristics].

Hyperspectral scattering techniques were used to predict beef pH, tenderness (i. e. WBSF: Warner-Bratzler Shear Force) and color parameters. Thirty-three fresh strip loin cuts were collected from 2-day postmortem carcass. After capturing scattering images and measuring pH values, the samples were vacuum packaged and aged to seventh day, then their color parameters (L*, a*, b*) and WBSF were measured as references. The optical scattering profiles were extracted from the hyperspectral images and fitted to the Lorentzian distribution (LD) function with three parameters. LD parameters, such as the peak height, full scattering width at half maximum (FWHM) and the scattering asymptotic were calculated at individual wavelength. Stepwise regression was used to determine optimal combinations of wavelengths for each of parameters. The optimal combinations were then used to establish multi-linear regression (MLR) models to predict the beef attributes. The full cross validation method was used to examine the performance of models. The models were able to predict beef WBSF with R(CV) = 0.86, and with the SE(CV) (the standard error of cross validation) of 11.7 N, 91% classification accuracy could be obtained. Two-day pH values with R(CV) = 0.86, SE(CV) = 0.07 and color parameters (L*, a*, b*) with R(CV) of 0.92, 0.90 and 0.88, with the SE(CV) of 0.90, 1.34 and 0.41 were obtained respectively. This research provided available technique for the development of multispectral system, which could be implemented online to determine beef steaks color and tenderness.