[Study on the Rapid Evaluation of Total Volatile Basic Nitrogen (TVB-N) of Mutton by Hyperspectral Imaging Technique].

Total Volatile Basic Nitrogen (TVB-N) was usually taken as the physicochemical reference value to evaluate the mutton freshness. In order to explore the feasibility of hyperspectral (HSI) imaging technique to detect mutton freshness, 71 representative mutton samples were collected and scanned using a diffuse reflectance hyperspectral imaging (HSI) system in the Visible-Near infrared (NIR) spectral region (400-1 000 nm), and the chemical values of TVB-N content were determined using the semimicro Kjeldahl method according to the modified Chinese national standard. The representative spectra of mutton samples were extracted and obtained after selection of the region of interests (ROIs). The samples of calibration set and prediction set were divided at the ratio of 3 : 1 according to the content gradient method. Optimum HSI calibration models of the mutton (TVB-N) were established and evaluated by comparing different spectral preprocessing methods and modeling methods, which included Stepwise Multiple Linear Regression (SMLR), Partial Least Squares Regression (PLSR) and Principal Component Regression (PCR) methods. The results are that through the utilization of Multiplicative Scatter Correction (MSC), first derivative, Savitzky-Golay (S-G) smoothing and mean-centering together, both PLSR and PCR were able to achieve quantitative detection of mutton TVB-N. As for the PLSR model of mutton TVB-N established, the spectral pretreatment methods chosen included MSC, first derivative, S-G (15,2) smoothing and mean-centering, and the latent variables (LVs) number used was 11. As for the calibration set of PLSR model of mutton TVB-N, the correlation coefficient (r) and root mean square error of calibration (RMSEC) were 0.92 and 3.00 mg x (100 g)(-1), respectively. As for the prediction set of PLSR model of mutton TVB-N, the correlation coefficient (r), Root Mean Square Error of Prediction (RMSEP), and ratio of standard deviation to standard error of prediction (RPD) were 0.92, 3.46 mg x (100 g)(-1) and 2.35, respectively. The study demonstrated that the rapid and accurate analysis of TVB-N, the key freshness attribute, could be implemented by using the hyperspectral imaging (HSI) technique. The study provides the basis for further rapid and non-destructive detection of other mutton freshness attributes by using the hyperspectral imaging (HSI) technique, the improvement of current modeling effect of TVB-N content and the application involved of the technique in the practical production.

[Effects of Regions of Interest (ROIs) on Detection Models of Mutton pH Based on Hyperspectral Imaging].

Selection of Regions of interest (ROIs) and subsequent spectral extraction was a key step of non-destructive detection and analysis based on hyperspectral imaging (HSI). For the rapid and accurate detection of mutton pH, the study on the effects of 2 different ROIs on mutton pH models was carried out in the visible-near infrared region of 473～1 000 nm. 2 ROIs methods of Rectangle Regions (RR) and Image Segmentation (IS) were adopted to extract 122 corresponding representative spectra respectively. The influence of different preprocessing methods and ROIs methods on 3 pH models, including stepwise multiple linear regression (SMLR), principal component regression (PCR) and partial least squares regression (PLSR), was compared and analyzed. The results indicated that SMLR and PLSR model performance was optimal in 3 models established with spectral data extracted from Rectangle Regions (RR) and Image Segmentation (IS) respectively. As for the SMLR model, corresponding to the RR ROIs method, the correlation coefficient (Rcal) and root mean square error (RMSEC) of calibration set was 0.85 and 0.085 respectively, and the correlation coefficient (Rp) and root mean square error (RMSEP) of prediction set was 0.82 and 0.097 respectively. As for the PLSR model, corresponding to the IS ROIs method, the correlation coefficient(Rcal) and root mean square error (RMSEC) of calibration set was 0.95 and 0.050 respectively, and the correlation coefficient (Rp) and root mean square error (RMSEP) of prediction set was 0.91 and 0.071 respectively. By comparing the modeling results of spectral data extracted from 2 ROIs methods, the modeling performances of Image Segmentation (IS) were always better than Rectangle Regions (RR) in all the 3 modeling methods. The study shows that it is feasible to apply hyperspectral imaging technology combined with the ROIs method of Image Segmentation (IS) to accurate, fast and non-destructive detection of mutton pH.

[Study on Characteristic Bands Selection of Lamb pH Value Based on Hyperspectral Imaging and Partial Least Squares(PLS)].

Characteristic bands method selection and subsequent spectral extraction has a great influence on the hyperspectral model performance. For rapid and accurate detection of mutton pH value, the effects of 2 band-selection methods on PLS models of mutton pH based on HSI technique were carried out and discussed. Initially, the preprocessing method of second derivative (2D), multiplicative scatter correction (MSC) and mean-centering together was implemented on the representative spectra of mutton muscle portion. Then, 2 methods of synergy interval partial least square (siPLS) and the combination of synergy interval partial least squares with genetic algorithm (siPLS-GA) were used to extract the characteristic bands in the spectral range of 473～1 000 nm. Finally, 2 PLS models of lamb pH value were established with the corresponding characteristic bands, and were also compared with the effect of full-band PLS model. The results indicated that the effect of siPLS-GA-PLS model was the best. As for the siPLS-GA-PLS model, 56 characteristic wavelength points were chosen, the correlation coefficient(Rcal) and root mean square error(RMSEC) of calibration set was 0.96 and 0.043 respectively, and the correlation coefficient(Rp) and root mean square error(RMSEP) of prediction set was 0.96 and 0.048 respectively. Spectral variables were reduced and model accuracy was improved. It can be concluded that characteristic bands selection and rapid and accurate detection of lamb pH can be achieved using hyperspectral imaging technique combined with siPLS-GA method.

[Nondestructive measurement of sugar content of Hami melon based on diffuse reflectance hyperspectral imaging technique].

The research on nondestructive test for detecting the sugar content of Hami melon by the technology of hyperspectral imaging was put forward. The research used the hyperspectral imaging system to get the diffuse reflective spectrum information (400 - 1 000 nm) of anilox class Hami melon sugar content, chose effective whole wavelength (500 - 820 nm)to do the modeling regression analysis the sugar content of Hami melon. The research compared the correction method of MSC and SNV, and also compared the influence of accuracy of modeling in terms of the spectrum pretreatment methods of original spectrum, first order differential, second order differential; Using the methods of PLS, SMLR and PCR, the comparative analysis of sugar content detection model effect with skin Hami melon and peel Hami melon was conducted. The results showed that after the original spectrum being processed by MSC and first order differential spectrum, modeling effect could be very good using the method of PLS and SMLR. Synthesizing correction set correlation coefficient and forecast modeling effect, it's feasible to detect the sugar content of skin Hami melon by the PLS method, with a correction sample correlation coefficient (R(c)) of 0.861 and the lower root mean square errors of correction (RMSEC) of 0.627, and a prediction sample correlation coefficient (R(p)) of 0.706 and root mean square errors of prediction (RMSEP) of 0.873. The best effect to detecti the sugar content of peel Hami melon was obtained by the SMLR method with a correction sample correlation coefficient (R(c)) of 0.928 and the lower root mean square errors of correction (RMSEC) of 0.458, with a Prediction sample correlation coefficient (R(p)) of 0.818 and root mean square errors of prediction (RMSEP) of 0.727. The results of this study indicate that the technology of hyperspectral imaging can be used to predict the sugar content of Hami melon.

[Advance in nondestructive detection of fruit internal quality based on hyperspectral imaging].

The present paper reviews the development in the field of hyperspectral imaging technology for nondestructive detection of fruit internal quality in recent years up to the year 2007. With the increasing maturity of hyperspectral imaging technology, decline of cost for its hardware and software, and improvement in hyperspectral image data processing algorithms, hyperspectral imaging technology for fruit quality nondestructive detection has become a hot research topic. In order to track the latest research developments at home and abroad, the fruit internal quality (maturity, firmness, soluble solid content, water content) detection with hyperspectral imaging was reviewed, which would provide reference for Chinese researchers.

[Qualitative analysis of fragrant pear class based on near infrared diffuse reflectance spectroscopy].

A method was developed to automatically discriminate the persistent calyx fruit and fruit without calyx of fragrant pear by means of near infrared spectroscopy (NIRS). The prediction performance of different band regions range, different principal component numbers and different preprocessing methods of the spectra (multiplicative signal correction, standard normal variate, and derivative spectra) together with discriminant analysis (DA) was also investigated, and The calibration model was established to classify the different kinds of fragrant pear. The research results for the fragrant pear classification showed that DA calibration models using these parameters with band regions between 9 091 and 4 000 cm(-1) and original spectra are optimal, with the percentage of correct sample classification being 100% and 95% for the calibration and validation set, respectively.