Huishan Lu

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.

Quality determination of Chinese rice wine based on Fourier transform near infrared spectroscopy

To evaluate the applicability of near infrared (NIR) spectroscopy for the determination of five enological parameters (alcoholic degree, pH value, total acid, amino acid nitrogen and °Brix) in Chinese rice wine, transmission spectra were collected in the spectral range from 800 nm to 2500 nm in a 1 mm path length rectangular quartz cuvette with air as the reference at room temperature. Five calibration equations for the enological parameters were established between the reference data and NIR spectra by partial least squares (PLS) regression, separately. The best calibration results were achieved for the determination of alcoholic degree and °Brix. The coefficients of determination in calibration (R2cal) for alcoholic degree and °Brix were 0.93 and 0.96, respectively. The predictive deviation ratio (RPD) value of the calibration for alcoholic degree was higher than 3 (3.95), which demonstrated the robustness of the calibration model. The RPD value for °Brix was 2.34. The performance of the calibration models for pH, total acid and amino acid nitrogen were not as good as that of alcoholic degree and °Brix. The RPD values for the three parameters were 1.41, 1.38 and 1.27, respectively and R2cal values were 0.97, 0.83 and 0.90, respectively. In the validation step, r2val values for alcoholic degree and °Brix were all higher than 0.9. The performance of pH and total acid were acceptable [the coefficients of determination in validation (r2val) for pH and total acid were 0.82 and 0.77, respectively]. The performance of amino acid nitrogen model was the worst, with an r2val value of 0.62. The results demonstrated that the NIR spectroscopic technique could be used to predict the concentrations of these five enological parameters in Chinese rice wine.

Measurement of Soluble Solids Content in Watermelon by Vis/NIR Diffuse Transmittance Technique

Watermelon is a popular fruit in the world with soluble solids content (SSC) being one of the major characteristics used for assessing its quality. This study was aimed at obtaining a method for nondestructive SSC detection of watermelons by means of visible/near infrared (Vis/NIR) diffuse transmittance technique. Vis/NIR transmittance spectra of intact watermelons were acquired using a low-cost commercially available spectrometer operating over the range 350–1000 nm. Spectra data were analyzed by two multivariate calibration techniques: partial least squares (PLS) and principal component regression (PCR) methods. Two experiments were designed for two varieties of watermelons [Qilin (QL), Zaochunhongyu (ZC)], which have different skin thickness range and shape dimensions. The influences of different data preprocessing and spectra treatments were also investigated. Performance of different models was assessed in terms of root mean square errors of calibration (RMSEC), root mean square errors of prediction (RMSEP) and correlation coefficient (r) between the predicted and measured parameter values. Results showed that spectra data preprocessing influenced the performance of the calibration models. The first derivative spectra showed the best results with high correlation coefficient of determination [r=0.918 (QL); r=0.954 (ZC)], low RMSEP [0.65 °Brix (QL); 0.58 °Brix (ZC)], low RMSEC [0.48 °Brix (QL); 0.34 °Brix (ZC)] and small difference between the RMSEP and the RMSEC by PLS method. The nondestructive Vis/NIR measurements provided good estimates of SSC index of watermelon, and the predicted values were highly correlated with destructively measured values for SSC. The models based on smoothing spectra (Savitzky-Golay filter smoothing method) did not enhance the performance of calibration models obviously. The results indicated the feasibility of Vis/NIR diffuse transmittance spectral analysis for predicting watermelon SSC in a nondestructive way.

Effect of biological variability on the robustness of FT-NIR models for sugar content of pears

The aim of the present study was to investigate the effect of pear fruit biological variability on robustness of FT-NIR models for sugar content of pears. The robustness of the calibration models for sugar content with respect to the three factors (orchard, season and cultivar) was tested based on FT-NIR spectroscopy and a statistical analysis was performed on a large spectral data set to analyze the effect of this three factors. Season and cultivar were responsible for a major amount of the spectral variability, whereas the influence of the orchard was low and only appeared for certain cultivars during specific seasons. It was found that the accuracy of the models increased considerably when including more variability in the calibration set. On the other hand, adding more data to the calibration set increased the chance of adding atypical data, which resulted in reduced model accuracy. Therefore, it is important to construct the calibration data set in such a way that it is representative for future measurements.

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.

Effect of wavelet transform techniques upon the estimation of sugar content in apple with near-infrared spectroscopy

Wavelet transform (WT) has proven a powerful and efficient tool for dealing with chemical data due to its characteristic of dual localization and has been widely used in analytical chemistry. This paper aims at serving three purposes: First, it gives a review of the applications of the wavelet transform in infrared spectroscopy; Second, it gives a quick summary of aspects and properties of wavelets and wavelet transforms which are needed in order to understand how to (pre-) process data from spectrometry with wavelet methods; Third, it shows on a typical example (apple NIR spectra) how wavelet transforms can be used in order to extract quantitative information. The sugar content of intact apple was measured by NIRS and analyzed by wavelet transform, which is a new development in signal treatment method in recent years. The results show that the spectra treated with wavelet transform indicate more effectively the relationship with sugar content in intact apple. Compared with original spectra, wavelet transform of three-size has the most marked relation with sugar content. The predicting precision of five-element regression is the best and the scale 3 is the best for its 0.904 correlation efficient of determination and the 0.777 in standard error of prediction which is less than that of primitive spectra. Therefore, the conclusion of improved predicting precision for quantitative detection of sugar content in intact apple with wavelet transform can be drawn.

Measurement of internal quality of watermelon by vis/NIR diffuse transmittance technique

Watermelon is a popular fruit in the world. Soluble solids content (SSC) is major characteristic used for assessing watermelon internal quality. This study was about a method for nondestructive internal quality detection of watermelons by means of visible/Near Infrared (Vis/NIR) diffuse transmittance technique. Vis/NIR transmittance spectra of intact watermelons were acquired using a low-cost commercially available spectrometer when the watermelon was in motion (1.4m/s) and in static state. Spectra data were analyzed by partial least squares (PLS) method. The influences of different data preprocessing and spectra treatments were also investigated. Performance of different models was assessed in terms of root mean square errors of calibration (RMSEC), root mean square errors of prediction (RMSEP) and correlation coefficient (r) between the predicted and measured parameter values. Results showed that spectra data preprocessing influenced the performance of the calibration models and the PLS method can provide good results. The nondestructive Vis/NIR measurements provided good estimates of SSC index of watermelon both in motion and in static state, and the predicted values were highly correlated with destructively measured values. The results indicated the feasibility of Vis/NIR diffuse transmittance spectral analysis for predicting watermelon internal quality in a nondestructive way.

Application of principal component regression and artificial neural network in FT-NIR soluble solids content determination of intact pear fruit

The artificial neural networks (ANNs) have been used successfully in applications such as pattern recognition, image processing, automation and control. However, majority of todays applications of ANNs is back-propagate feed-forward ANN (BP-ANN). In this paper, back-propagation artificial neural networks (BP-ANN) were applied for modeling soluble solid content (SSC) of intact pear from their Fourier transform near infrared (FT-NIR) spectra. One hundred and sixty-four pear samples were used to build the calibration models and evaluate the models predictive ability. The results are compared to the classical calibration approaches, i.e. principal component regression (PCR), partial least squares (PLS) and non-linear PLS (NPLS). The effects of the optimal methods of training parameters on the prediction model were also investigated. BP-ANN combine with principle component regression (PCR) resulted always better than the classical PCR, PLS and Weight-PLS methods, from the point of view of the predictive ability. Based on the results, it can be concluded that FT-NIR spectroscopy and BP-ANN models can be properly employed for rapid and nondestructive determination of fruit internal quality.

Near-infrared spectroscopy for non-destructive determination of soluble solids content of Chinese citrus

Near-infrared (NIR) spectroscopy has become a very popular technique for the non-invasive assessment of intact fruit. This work presents an application of a low-cost commercially available NIR spectrometer for the estimation of soluble solids content (SSC) of Chinese citrus. The configuration for the spectra acquisition was used (diffuse transmittance), using a custom-designed contact optical fiber probe. Samples of Chinese citrus in deferent orchard, collected over the 2005 harvest seasons, were analyzed for soluble solids content (Brix). Partial least squares calibration models, obtained from several preprocessing techniques (smoothing, multiplicative signal correction, standard normal variate, etc), were compared. Also, the short-wave (SW-NIR) spectral regions were used. Performance of different models was assessed in terms of root mean square of cross-validation, root mean square of prediction (RMSEP) and R for a validation set of samples. RMSEP of 0.538 with R = 0.896 indicate that it is possible to estimate Chinese citrus SSC (Brix value), by using a portable spectrometer.