Huirong Xu

Application Fourier transform near infrared spectrometer in rapid estimation of soluble solids content of intact citrus fruits

Nondestructive method of measuring soluble solids content (SSC) of citrus fruits was developed using Fourier transform near infrared reflectance (FT-NIR) measurements collected through optics fiber. The models describing the relationship between SSC and the NIR spectra of citrus fruits were developed and evaluated. Different spectra correction algorithms (standard normal variate (SNV), multiplicative signal correction (MSC)) were used in this study. The relationship between laboratory SSC and FT-NIR spectra of citrus fruits was analyzed via principle component regression (PCR) and partial least squares (PLS) regression method. Models based on the different spectral ranges were compared in this research. The first derivative and second derivative were applied to all spectra to reduce the effects of sample size, light scattering, instrument noise, etc. Different baseline correction methods were applied to improve the spectral data quality. Among them the second derivative method after baseline correction produced best noise removing capability and yielded optimal calibration models. A total of 170 NIR spectra were acquired; 135 NIR spectra were used to develop the calibration model; the remaining spectra were used to validate the model. The developed PLS model describing the relationship between SSC and NIR reflectance spectra could predict SSC of 35 samples with correlation coefficient of 0.995 and RMSEP of 0.79 °Brix.

Prediction of Enological Parameters and Discrimination of Rice Wine Age Using Least-Squares Support Vector Machines and Near Infrared Spectroscopy

The use of least-squares support vector machines (LS-SVM) combined with near-infrared (NIR) spectra for prediction of enological parameters and discrimination of rice wine age is proposed. The scores of the first ten principal components (PCs) derived from PC analysis (PCA) and radial basis function (RBF) were used as input feature subset and kernel function of LS-SVM models, respectively. The optimal parameters, the relative weight of the regression error gamma and the kernel parameter sigma 2, were found from grid search and leave-one-out cross-validation. As compared to partial least-squares (PLS) regression, the performance of LS-SVM was slightly better, with higher determination coefficients for validation ( Rval2) and lower root-mean-square error of validation (RMSEP) for alcohol content, titratable acidity, and pH, respectively. When used to discriminate rice wine age, LS-SVM gave better results than discriminant analysis (DA). On the basis of the results, it was concluded that LS-SVM together with NIR spectroscopy was a reliable and accurate method for rice wine quality estimation.

Application of NIR spectroscopy for firmness evaluation of peaches

The use of near infrared (NIR) spectroscopy was proved to be a useful tool for quality analysis of fruits. A bifurcated fiber type NIR spectrometer, with a detection range of 800∼2500 nm by InGaAs detector, was used to evaluate the firmness of peaches. Anisotropy of NIR spectra and firmness of peaches in relation to detecting positions of different parts (including three latitudes and three longitudes) were investigated. Both spectra absorbency and firmness of peach were influenced by longitudes (i, ii, iii) and latitudes (A, B, C). For modeling, two thirds of the samples were used as the calibration set and the remaining one third were used as the validation or prediction set. Partial least square regression (PLSR) models for different longitude and latitude spectra and for the whole fruit show that collecting several NIR spectra from different longitudes and latitudes of a fruit for NIR calibration modeling can improve the modeling performance. In addition, proper spectra pretreatments like scattering correction or derivative also can enhance the modeling performance. The best results obtained in this study were from the holistic model with multiplicative scattering correction (MSC) pretreatment, with correlation coefficient of cross-validation rcv=0.864, root mean square error of cross-validation RMSECV=6.71 N, correlation coefficient of calibration r=0.948, root mean square error of calibration RMSEC=4.21 N and root mean square error of prediction RMSEP=5.42 N. The results of this study are useful for further research and application that when applying NIR spectroscopy for objectives with anisotropic differences, spectra and quality indices are necessarily measured from several parts of each object to improve the modeling performance.

On-site variety discrimination of tomato plant using visible-near infrared reflectance spectroscopy

The use of visible-near infrared (NIR) spectroscopy was explored as a tool to discriminate two new tomato plant varieties in China (Zheza205 and Zheza207). In this study, 82 top-canopy leaves of Zheza205 and 86 top-canopy leaves of Zheza207 were measured in visible-NIR reflectance mode. Discriminant models were developed using principal component analysis (PCA), discriminant analysis (DA), and discriminant partial least squares (DPLS) regression methods. After outliers detection, the samples were randomly split into two sets, one used as a calibration set (n=82) and the remaining samples as a validation set (n=82). When predicting the variety of the samples in validation set, the classification correctness of the DPLS model after optimizing spectral pretreatment was up to 93%. The DPLS model with raw spectra after multiplicative scatter correction and Savitzky-Golay filter smoothing pretreatments had the best satisfactory calibration and prediction abilities (correlation coefficient of calibration (Rc)=0.920, root mean square errors of calibration=0.196, and root mean square errors of prediction= 0.216). The results show that visible-NIR spectroscopy might be a suitable alternative tool to discriminate tomato plant varieties on-site.

Support Vector Machines and Near Infrared Spectroscopy for Quantification of Vitamin C Content in Kiwifruit

Vitamin C is considered as an important nutrition component of fruits, especially of kiwifruit. Traditional destructive method for vitamin C measurement is very complex and fussy. This paper proposes the use of least-squares support vector machine (LS-SVM) as an alternative multivariate calibration method for the quantification of vitamin C content in “Qinmei” kiwifruit, using near infrared spectroscopy with direct measurements by diffuse reflectance in the spectral range of 800-2500 nm. The loading values for the spectral contribution of the first ten factors were used as inputs of the LS-SVM model. The best results of the LS-SVM models in this study are R2c=0.998, SEC=1.484 mg/100g for calibration and R2v=0.969, SEP=3.847 mg/100g for validation, with 10 factor used in the model. Partial least square regression (PLSR) method was also applied as a comparison. The calibration performance of the LS-SVM and PLSR models were equally well, however, the prediction performance of LS-SVM models were much better than PLSR models. It can be concluded that LS-SVM is a feasible and promising method for prediction of vitamin C content in kiwifruit from NIR spectra.

Prediction of Trace Metals in Chinese Rice Wine by Fourier Transform Near-Infrared Spectroscopy

The feasibility of near-infrared (NIR) spectroscopy for rapid determination of potassium, calcium, magnesium, zincum, and iron in Chinese rice wine was studied. The 28 samples were of different ages (1, 3, and 5 years). The content of trace metals (potassium, magnesium, zincum, and iron) was measured by atomic absorption spectroscopy (AAS) to check the accuracy of NIR determination. NIR transmission spectra were collected in rectangular quartz cuvettes of different optical path-lengths (1, 2, 5, and 10 mm). Calibration models were established by partial least squares (PLS) regression. The PLS models of the NIR spectra group of the 5 mm optical path-length gave the best calibration result. In addition, the results for potassium, calcium, and magnesium were promising. The determination coefficients of validation (R2val) for the three trace metals were 0.81, 0.79, and 0.88, and the root mean square errors of prediction (RMSEP) were 17.40, 24.80, and 2.20 mg L-1, respectively. However, the results for zincum and iron were less satisfactory. The corresponding R2val values were 0.51 and 0.52, respectively. Overall, the results of this study indicated that the NIR spectroscopic technique could offer screening capability for potassium, calcium, and magnesium in Chinese rice wine. Further research is needed using a larger number of Chinese rice wines over a wider range of wine age to refine the screening method.

Principal Components-Artificial Neural Networks for Predicting SSC and Firmness of Fruits based on Near Infrared Spectroscopy

The use of near infrared (NIR) spectroscopy was proved to be a useful tool for components analysis of many materials. Principal component analysis (PCA) and artificial neural networks using back-propagation algorithm (BP-ANN) were used to establish nonlinear model for the prediction of soluble solid content (SSC) and firmness of peach and loquat fruits from NIR spectral data. The first ten principal components extracted from original spectra and spectra after multiplicative scattering correction (MSC) were used as input nodes of BP-ANN. TANSIG and LOGSIG transfer functions and two to nine neurons were considered for the hidden layer of the network. For peaches, the best results were R train=0.940 and R test= 0.900 for SSC; R train=0.701 and R test =0.453 for firmness. For loquats, the best results were R train=0.962 and R test= 0.893 for SSC; R train=0.812 and R test =0.624 for firmness. The results of this study show that combination of PCA and BP-ANN is feasible for predicting fruit quality based on NIRS. For further researches, factors such as the number of neurons for input layer, the number of hidden layers, other learning algorithms and so on could be studied to improve modeling performance and predicting accuracy.

Design and validation of software for real-time soluble solids content evaluation of peach by near-infrared spectroscopy

Visible/near infrared spectroscopy on-line determination had been widely used in agricultural products and food samples non-destructive internal quality determination. This research proposed to design real-time determination software in order to estimate soluble solids content (SSC) of fruit on line. Functions of the software included real-time spectroscopy pre-processing, real-time spectroscopy viewing, model building, SSC estimating, etc. In addition, Fenghua juicy peaches were used to validate the practicability and the real-time capability. And SSCs of peach samples were predicted by the software on line. The research provided some help to the real-time non-destructive internal quality determination of the fruit. As the important part of the real-time determination, the determination method and technology were fully accordance with the need at real-time and models precision.

Application FT-NIR in rapid estimation of soluble solids content of intact kiwifruits by reflectance mode

Nondestructive method of measuring soluble solids content (SSC) of kiwifruit was developed by Fourier transform near infrared (FT-NIR) reflectance and fiber optics. Also, the models describing the relationship between SSC and the NIR spectra of the fruit were developed and evaluated. To develop the models several different NIR reflectance spectra were acquired for each fruit from a commercial supermarket. Different spectra correction algorithms (standard normal variate (SNV), multiplicative signal correction (MSC)) were used in this work. The relationship between laboratory SSC and FT-NIR spectra of kiwifruits were analyzed via principle component regression (PCR) and partial least squares (PLS) regression method using TQ 6.2.1 quantitative software (Thermo Nicolet Co., USA). Models based on the different spectral ranges were compared in this research. The first derivative and second derivative were applied to all measured spectra to reduce the effects of sample size, light scattering, noise of instrument, etc. Different baseline correction methods were applied to improve the spectral data quality. Among them the second derivative method after baseline correction produced best noise removing capability and to obtain optimal calibration models. Total 480 NIR spectra were acquired from 120 kiwifruits and 90 samples were used to develop the calibration model, the rest samples were used to validate the model. Developed PLS model, which describes the relationship between SSC and NIR spectra, could predict SSC of 84 unknown samples with correlation coefficient of 0.9828 and SEP of 0.679 Brix.

Determination of toxigenic fungi and aflatoxins in nuts and dried fruits using imaging and spectroscopic techniques

Nuts and dried fruits contain rich nutrients and are thus highly vulnerable to contamination with toxigenic fungi and aflatoxins because of poor weather, processing and storage conditions. Imaging and spectroscopic techniques have proven to be potential alternative tools to wet chemistry methods for efficient and non-destructive determination of contamination with fungi and toxins. Thus, this review provides an overview of the current developments and applications in frequently used food safety testing techniques, including near infrared spectroscopy (NIRS), mid-infrared spectroscopy (MIRS), conventional imaging techniques (colour imaging (CI) and hyperspectral imaging (HSI)), and fluorescence spectroscopy and imaging (FS/FI). Interesting classification and determination results can be found in both static and on/in-line real-time detection for contaminated nuts and dried fruits. Although these techniques offer many benefits over conventional methods, challenges remain in terms of heterogeneous distribution of toxins, background constituent interference, model robustness, detection limits, sorting efficiency, as well as instrument development.