[Partial least squares regression variable screening studies on apple soluble solids NIR spectral detection].

Abstract To improve the predictive ability and robustness of the NIR correction model of the soluble solid content (SSC) of apple, the reverse interval partial least squares method, genetic algorithm and the continuous projection method were implemented to select variables of the NIR spectroscopy of the soluble solid content (SSC) of apple, and the partial least squares regression model was established. By genetic algorithm for screening of the 141 variables of the correction model, prediction has the best effect. And compared to the full spectrum correction model, the correlation coefficient increased to 0.96 from 0.93, forecast root mean square error decreased from 0.30 degrees Brix to 0.23 degrees Brix. This experimental results show that the genetic algorithm combined with partial least squares regression method improved the detection precision of the NIR model of the soluble solid content (SSC) of apple.

[Research on optimization of model for detecting sugar content of navel orange by online near infrared spectroscopy].

The objective of the present research was to optimize the model of sugar content in navel orange for improving the detection presicion by the online near infrared spectroscopy. The reference wavelength was chosen by coefficient of variation of the different wavelengths in the calibration set in the wavelength range of 700.28 - 933.79 nm. Then the spectra were transformed into ratio specra. The absorbance and ration spectra were pretreated by different preprocessing methods. The models of sugar content were developed by partial least squares (PLS) and least squares support vector regression (LSSVR). The 30 unknown navel orange samples were applied to evaluate the performance of the models. By comparison of the predictive performances, the LSSVR model was the best among the models with the first derivative preprocessing and ration spectra. The correlation coeffiecient (R(P)) of the best model was 0.85, the root mean square error of prediction (RMSEP) was 0.41 Brix. The results suggested that it was feasible to improve the precision of online near infrared spectroscopy detecting sugar content in navel orange by the optimization of reference wavelengths, the first derivative preprocessing and LSSVR.

[Application of Raman spectroscopy technique to food quality and safety determination].

With the merits of doing without sample pretreatment, easy operation, short response time and high sensitivity, Raman spectroscopy technique can acquire samples' physical chemistry and the deep structure information. It has been widely applied in petrol chemical, biomedicine, geoarchaeology, criminal justice, gem identification, etc. Raman spectroscopy has good application prospect in food quality and safety determination, for its spectra are not extremely sensitive to polar materials such as water. The detection principle, classification and the system composition of Raman spectroscopy technique were introduced briefly. The latest research progress in food constituent analysis and pesticide residue determination by Raman spectroscopy was reviewed. Finally, its key technologies for food quality and safety determination were pointed out and the future research was prospected.

[Visible transmittance techniques for rapid analysis of sugar content of fresh navel orange juices].

The feasibility of visible spectroscopic technology for rapid quantifying sugar content (SC) of navel orange fresh juices was investigated by means of spectral transmittance technique. A total of 55 juice samples were used to develop the calibration and prediction models. Calibration models based on different spectral ranges and different spectral pretreatment were compared in the precent research. Performance of different models was investigated in terms of root mean square errors of prediction (RMSEP) and correlation coefficient (r) of validation set of samples. The correlation coefficient of calibration model for SC was 0.965, the correlation coefficient of prediction model for SC was 0.857, and the corresponding RMSEP was 0.562. The results show that visible transmittance technique is a feasible method for non-invasive estimation of fruit juice SC.

[Near infrared diffuse reflectance spectroscopy for rapid analysis of soluble solids content in navel orange].

The potential of using visible and near infrared diffuse reflectance spectroscopy to assess soluble solids content (SSC) of intact navel orange was examined. A total 40 samples were used to develop the calibration and prediction models. NIR spectral data were collected in the spectral region between 350 and 2 500 nm and their second derivative spectra were used for the present study. Different scattering correction algorithms (no preprocessing and multiplicative scattering correction (MSC)) were compared. Camibration models based on different spectral ranges, different derivatives and different kinds of statistical models including partial least square (PLS) and principal component regression (PCR) were also compared. The best results of PLS models with the second derivative spectra are r = 0.929, RMSEC = 0.517 and RMSEP = 0.592 in the wavelength range of 361-2 488 nm. The results show that this method is feasible for non destructive assessing of SSC of the navel orange.