Yande Liu

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.

Determination of Pesticide Residues on the Surface of Fruits Using Micro-Raman Spectroscopy

A simple, rapid and environmentally friendly method was developed for micro-Raman spectroscopy determination of pesticide residues on the surface of fruits. Raman spectra of fruits, pesticides and pericarps sprayed by pesticide solutions were acquired using a laser power of 14 mW at excitation wavelength of 780 nm. From the Raman spectra, the residual pesticides could be distinguished and determined through the characteristic Raman peaks. The overall results indicted that micro-Raman spectroscopy is a potential tool to determine the pesticide residues on the surface of fruits for fruit quality and safety control.

Determination of dimethoate and phosmet pesticides with surface-enhanced Raman spectroscopy

ABSTRACT Surface-enhanced Raman spectroscopy spectra of dimethoate and phosmet pesticides were recorded using a Klarite substrate. Significant enhancements were achieved with dimethoate over a concentration range of 0.5–10 µg mL−1 and phosmet over a concentration range of 0.1–10 µg mL−1. The best prediction model for dimethoate pesticide was achieved with a correlation coefficient of 0.940 and a root mean square error of prediction of 0.864 µg mL−1, with the first derivative and standard normalized variate data preprocessing, and the best prediction model of phosmet pesticide was achieved with a correlation coefficient of 0.949 and a root mean square error of prediction of 0.741 µg mL−1 with the first derivative data preprocessing. Our study shows that pesticides, including dimethoate and phosmet, could be quantitatively measured at as low as 0.5 µg mL−1 level using surface-enhanced Raman technology coupled with a Klarite substrate and the results indicated that surface-enhanced Raman spectroscopy with a Klarite substrate has potential for the analysis of dimethoate and phosmet residues.

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.

Determination of Copper and Zinc Pollutants in Ludwigia prostrata Roxb Using Near-Infrared Reflectance Spectroscopy (NIRS)

The feasibility of using near-infrared reflectance spectroscopy (NIRS) to determine the concentrations of copper (Cu) and zinc (Zn) in Ludwigia prostrata Roxb plants was investigated. Ludwigia prostrata Roxb plants were grown over a full growth cycle under controlled laboratory conditions in soils contaminated with heavy metals. The Cu and Zn concentrations in 72 L. prostrata Roxb samples were analyzed using fame atomic absorption spectrometry, and NIRS spectra were collected in the 1099–2500 nm range. Five mathematical treatments of the spectral data were compared prior to developing the calibration models (n = 48) using partial least squares regression methods. The two calibration models for Cu and Zn concentrations were evaluated according to the correlation coefficient of cross-validation (Rcv) and root mean squares error of cross-validation. The highest Rcv and the lowest RMSECV were obtained for Cu (0.9 and 7.24 mg kg−1) and Zn (0.94 and 19.17 mg kg−1), respectively. The results showed that near-infrared diffuse refectance spectroscopy can be used for the rapid determination of Cu and Zn in leaves of L. prostrata Roxb plants.

Potable NIR spectroscopy predicting soluble solids content of pears based on LEDs

A portable near-infrared (NIR) instrument was developed for predicting soluble solids content (SSC) of pears equipped with light emitting diodes (LEDs). NIR spectra were collected on the calibration and prediction sets (145:45). Relationships between spectra and SSC were developed by multivariate linear regression (MLR), partial least squares (PLS) and artificial neural networks (ANNs) in the calibration set. The 45 unknown pears were applied to evaluate the performance of them in terms of root mean square errors of prediction (RMSEP) and correlation coefficients (r). The best result was obtained by PLS with RMSEP of 0.62°Brix and r of 0.82. The results showed that the SSC of pears could be predicted by the portable NIR instrument.

Least square support vector machine for citrus greening by use of near infrared spectroscopy

Citrus greening or Huanglongbing (HLB) is one of most serious citrus diseases in the world. Once a tree is infected, there is no cure. The feasibility was investigated for discriminating citrus greening by use of near infrared (NIR) spectroscopy and least square support vector machine (LS-SVM). The spectra of sound and citrus greening samples were recorded in the wavenumber range of 4000-9000 cm-1. The preprocessing method of second derivative with a gap of seven was adapted to eliminate spectral baseline. The spectral variables were optimized by principal component analysis (PCA) and (UVE) algorithms. The unknown samples were used to access the performance of the models. Compared to the PLS-DA model, the LS-SVM was better with the input vector of the first 15 principal components and linear kernel function. The regularization factor (γ) of linear kernel function was 1.8756, and the operation time of LS-SVM model was 0.86s. The recognition error of the LS-SVM model was zero. The results showed that the combination of LS-SVM and NIR spectroscopy could detect citrus greening nondestructively and rapidly.

The Classification of Pavement Crack Image Based on Beamlet Algorithm

Pavement distress, the various defects such as holes and cracks, represent a significant engineering and economic concern. This paper based on Beamlet algorithm using MATLAB software to process the pavement crack images and classify the different cracks into four types: horizontal, vertical, alligator, and block types. Experiment results show that the proposed method can effectively detect and classify of the pavement cracks with a high success rate, in which transverse crack and longitudinal crack detection rate reach to 100%, and alligator crack and block crack reach more than 85%.

FT-NIR and Confocal Microscope Raman Spectroscopic Studies of Sesame Oil Adulteration

Adulteration of edible vegetable oils is a very serious problem affecting its commercial value and customers’ health. FT-NIR and Confocal Micro-Raman have been explored for discriminating adulteration of sesame oils. The soybean, corn, and peanut oil were mixed into sesame oils in the range of 5~20% (v/v),respectively,and their Confocal Micro-Raman and FT-NIR spectra were collected. Principal component analysis (PCA) and Partial least squares discriminant analysis (PLSDA) were used for the discrimination and classification of adulteration in sesame oil based on spectral data. In addition, the spectra were subjected to the pretreatements (eg, Savitzky-Golay smoothing, Multiplicatnive Scatter Correction, first derivatives and second derivatives) before developing PCA and PLS-DA models. FT-NIR was found to give better efficient in classification of adulteration oils by using PLS-DA with ca.100% classification accuracy, and Raman also gave 100% classification accuracy. The results demonstrated that FT-NIR and Confocal Micro-Raman both can be used to determine the authenticity of edible oils rapidlly.

On-line NIR predicting soluble solids content of intact pears combination with wavelet transform and support vector regression

The objective of this paper was to predicting soluble solids content of intact pears using on-line near-infrared spectroscopy (NIRS) combination with wavelet transform (WT) and least squares-support vector machine (LS-SVM). Spectra of 200 pears were collected in the wavelength range of 840∼950nm at the speed of 5 pears per second. All samples were divided into two sets: calibration set (n=150) and validation set (n=50). The spectra were pretreated with the preprocessing method of multiplicative scatter correction (MSC), first derivative (1st D), second derivative (2nd D) and wavelet transform (WT). Partial least squares (PLS) and LS-SVM models were developed with the treated spectra. By comparison the LS-SVM model was super to PLS ones with r of 0.87 and RMSEP of 0. 43oBrix using WT treated spectra. The results indicated that LS-SVM combined with WT could be utilized as a precision method in predicting SSC of intact pears.