Hailiang Zhang

Linear and nonlinear multivariate regressions for determination sugar content of intact Gannan navel orange by Vis-NIR diffuse reflectance spectroscopy

Linear and nonlinear multivariate regressions were implemented to estimate sugar content of intact Gannan navel orange based on Vis-NIR diffuse reflectance spectroscopy in the wavelength range of 450-1750 nm. Four pre-processing methods, including average smoothing, multiplicative scatter correction (MSC), first and second derivatives, were applied to improve the predictive ability of the models. The models were developed by MLR, PCR, PLS, Poly-PLS and Spline-PLS with MSC pretreatment. Except MLR, the predictive results were insignificant among PCR, PLS, Poly-PLS and Spline-PLS by analysis of variance test at 5% level. The Spline-PLS model was superior to others with R of 0.87, RMSEP of 0.47^@?Brix and SDR=2.34. The results illustrated Spline-PLS could be applied to deal with nonlinear problem, and Vis-NIR spectroscopy in combination with it, could determine sugar content of intact Gannan navel orange precisely.

Food Safety and Technological Implications of Food Traceability Systems

Food safety has become an important food quality attribute.Both food industry and authorities need to be able to trace back and to authenticate food products and raw materials used for food production to comply with legislation and to meet the food safety and food quality requirements. Traceability is increasingly becoming a necessary task in the food industry which is mainly driven by recent food crises and the consequent demands for transparency in the food chain. This is leading to the development of traceability concepts and technologies adapted to different food industry needs. The content of this paper include several aspects such as overseas food traceability system present conditions and development, food traceability system present conditions, problems and prospect in China, put forward the main measures of pushing on food traceability system of china.

Improving the optimization efficiency and precision of least squares support vector regression (LSSVR) for pear property prediction

In this study, Visible/near-infrared (Vis/NIR) diffuse reflectance spectroscopy at 530-1560 nm region was investigated for the analysis of the soluble solids content (SSC) and color of pear. Least squares support vector regression (LSSVR) has been proven to be a powerful tool for modeling complex samples through the use of adapted kernel functions. However, one of the major drawbacks of LSSVR is that the optimization of the regularization and kernel meta-parameters is time-consuming during training the model, and the modeling results are sensitive to spectral noise. Wavelet compression pretreatment is an effective method for spectral information extraction and noise elimination. The calibration set was composed of 75 pear samples and 32 pear samples were used as the validation set. The raw and pretreated spectra by wavelet compression were modeled using LSSVR, It was shown that wavelet compression procedure not only shortened the modeling time, but also improved the predictive precision. The correlation coefficient (r) was improved from 0.78 to 0.93 for SSC, and from 0.95 to 0.96 for color, respectively. The root mean square error of prediction (RMSEP), optimization time and calibration variables were reduced from 0.68, 0.33s and 1031 to 0.41, 0.03s and 24 for SSC, while from 1.10, 0.33s and 1031 to 1.07, 0.03s and 40 for color. The results indicated that Vis/NIR spectroscopy combined with wavelet compression procedure and LSSVR is a reliable approach for predicting the SSC and color of pear.

A portable Vis-NIR spectrometer to determine soluble solids content in Gannan navel orange by LS-SVM and EWs selection

The objective of this paper was to determine soluble solids content (SSC) of intact Gannan navel orange by a portable near-infrared (NIR) spectrometer with the optical fiber in the wavelength range of 551~950nm. The effective wavelength regions (EWs) were chosen from the spectra pro-processed by second derivative by interval partial least square (iPLS) and backward interval partial least square (Bipls). Then the partial least square (PLS) and least square support vector machine (LS-SVM) models were developed with EWs. 60 unknown samples were used to evaluate the performance of them. The LS-SVM model was better than others with EWs chosen by Bilps. The correlation coefficient (R) and root mean square error of prediction (RMSEP) for LS-SVM (Bipls) were 0.86 and 0.55°Brix. The results showed that the portable NIR combination with LS-SVM was a feasible method to determine SSC of intact Gannan navel orange nondestructively.

NIR sensitive wavelength selection based on different methods

Near infrared (NIR) sensitive wavelengths of soluble solids content (SSC) were selected based on genetic algorithms (GAs) and interval partial least square (iPLS) in Nanfeng mandarin fruits, the better result was obtained with sensitive wavelengths of 642–787nm, 861–1152nm and 1225–1368nm by iPLS. Then 30 unknown samples were used to evaluate performance of the model built with sensitive wavelengths. The correlation coefficient (R) and root mean square error of prediction (RMSEP) were 0.9500 and 0.5354, respectively. It was demonstrated NIR spectroscopy combined with sensitive wavelengths selection algorithm was a powerful tool to rapidly determine SSC of Nanfeng mandarin fruits nondestructively.

Nondestructive measurement soluble solids content of apple by portable and online near infrared spectroscopy

Two different near infrared spectrometric systems were used to determine soluble solids content (SSC) of intact apple, including a portable near infrared (NIR) spectrometer and an online NIR system. The pretreatment methods were applied to improve the predictive results. The moving average smoothing was significant. The effective wavelength regions were chosen by interval partial least squares (iPLS) and backward iPLS (Bipls). Then the models were developed by partial least square regression (PLSR) and least square support machine (LS-SVM). Performance comparisons were made in the context of 30 unknown samples prediction. The LS-SVM models were better than others with correlation coefficient (R) and root mean square error of prediction (RMSEP) of (0.88, 0.80ºBrix) and (0.82, 1.01ºBrix) for portable and online measurement mode, respectively. The results demonstrated that the online measurement mode was not as well as the portable.