Xiaping Fu

Detection of melamine in milk powders using near-infrared hyperspectral imaging combined with regression coefficient of partial least square regression model

Illegal use of nitrogen-rich melamine (C3H6N6) to boost perceived protein content of food products such as milk, infant formula, frozen yogurt, pet food, biscuits, and coffee drinks has caused serious food safety problems. Conventional methods to detect melamine in foods, such as Enzyme-linked immunosorbent assay (ELISA), High-performance liquid chromatography (HPLC), and Gas chromatography-mass spectrometry (GC-MS), are sensitive but they are time-consuming, expensive, and labor-intensive. In this research, near-infrared (NIR) hyperspectral imaging technique combined with regression coefficient of partial least squares regression (PLSR) model was used to detect melamine particles in milk powders easily and quickly. NIR hyperspectral reflectance imaging data in the spectral range of 990-1700nm were acquired from melamine-milk powder mixture samples prepared at various concentrations ranging from 0.02% to 1%. PLSR models were developed to correlate the spectral data (independent variables) with melamine concentration (dependent variables) in melamine-milk powder mixture samples. PLSR models applying various pretreatment methods were used to reconstruct the two-dimensional PLS images. PLS images were converted to the binary images to detect the suspected melamine pixels in milk powder. As the melamine concentration was increased, the numbers of suspected melamine pixels of binary images were also increased. These results suggested that NIR hyperspectral imaging technique and the PLSR model can be regarded as an effective tool to detect melamine particles in milk powders.

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.

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.

Determination of soluble solid content and acidity of loquats based on FT-NIR spectroscopy

The near infrared (NIR) spectroscopy technique has been applied in many fields because of its advantages of simple preparation, fast response, and non-destructiveness. We investigated the potential of NIR spectroscopy in diffuse reflectance mode for determining the soluble solid content (SSC) and acidity (pH) of intact loquats. Two cultivars of loquats (Dahongpao and Jiajiaozhong) harvested from two orchards (Tangxi and Chun’an, Zhejiang, China) were used for the measurement of NIR spectra between 800 and 2500 nm. A total of 400 loquats (100 samples of each cultivar from each orchard) were used in this study. Relationships between NIR spectra and SSC and acidity of loquats were evaluated using partial least square (PLS) method. Spectra preprocessing options included the first and second derivatives, multiple scatter correction (MSC), and the standard normal variate (SNV). Three separate spectral windows identified as full NIR (800∼2500 nm), short NIR (800∼1100 nm), and long NIR (1100∼2500 nm) were studied in factorial combination with the preprocessing options. The models gave relatively good predictions of the SSC of loquats, with root mean square error of prediction (RMSEP) values of 1.21, 1.00, 0.965, and 1.16 °Brix for Tangxi-Dahongpao, Tangxi-Jiajiaozhong, Chun’an-Dahongpao, and Chun’an-Jiajiaozhong, respectively. The acidity prediction was not satisfactory, with the RMSEP of 0.382, 0.194, 0.388, and 0.361 for the above four loquats, respectively. The results indicate that NIR diffuse reflectance spectroscopy can be used to predict the SSC and acidity of loquat fruit.

Application of near-infrared spectroscopy with fiber optics for detecting interior quality in peaches

Fourier transform near infrared (FT-NIR) spectroscopy was tested as a non-destructive method to assess the sugar content (SC) and the valid acidity of intact peaches. Calibration models were created from spectral and constituent measurements. Data recorded from two sides of individual peach served as the calibration and the validation sets. Partial least squares (PLS) technique used to develop the prediction models by different data preprocessing. The best model for SC had a high correlation efficient (0.956), a low SEP (0.532), a low SEC (0.542), a SDR value of 3.34(>3.00), and also a small difference between SEP and SEC. The best model for valid acidity had a high correlation coefficient (0.948), a relatively low SEP (0.129), a relatively low SEC (0.124) and a SDR value of 2.68 (<3.00), and also a small difference between SEC and SEP. The results of this study suggest that FT-NIR method can be feasible to detect sugar content rapidly. However, the low acid content in the fruit might have caused the relative insensitivity for prediction valid acidity. Further work is required to optimize and implement this technique.

Investigation of absorption and scattering characteristics of kiwifruit tissue using a single integrating sphere system

For a quantitative understanding of light interaction with fruit tissue, it is critical to obtain two fundamental parameters: the absorption coefficient and the scattering coefficient of the tissue. This study was to investigate the optical properties of kiwifruit tissue at the wavelength of 632.8 nm. The total reflectance and total transmittance of kiwifruit tissue from three parts (including the flesh part, the seed part, and the seed-base part) were measured using a single integrating sphere system. Based on the measured spectral signals, the absorption coefficient μa and the reduced scattering coefficient μs′ of kiwifruit tissue were calculated using the inverse adding-doubling (IAD) method. Phantoms made from Intralipid 20% and India ink as well as a Biomimic solid phantom were used for system validation. The mean values of μa and μs′ of different parts of the kiwifruit were 0.031–0.308 mm−1 and 0.120–0.946 mm−1, respectively. The results showed significant differences among the μa and μs′ of the three parts of the kiwifruit. The results of this study confirmed the importance of studying the optical properties for a quantitative understanding of light interaction with fruit tissue. Further investigation of fruit optical properties will be extended to a broader spectral region and different kinds of fruits.中文概要目 的开展水果组织的光传输特性检测分析, 以猕猴桃组织为例探讨其在632.8 nm 波长的光传输特性。创新点开发基于单积分球的水果组织光传输特性自动检测系统, 验证其可靠性并应用于水果组织的分析, 并获得猕猴桃组织不同部位在632.8 nm 的吸收和散射系数。方 法利用所搭建的单积分球系统, 获取三个不同部位(果肉、种子和种子基座)组织的全反射和全透射信息, 测量各个组织切片的厚度及折射率。运用逆倍增算法计算得到各组织样本的吸收系数μa和约化散射系数μs′, 并根据计算所得结果对猕猴桃不同组织的光传输特性进行比较分析。结 论本实验结果测得猕猴桃不同部位组织的吸收系数和散射系数分别为0.031∼0.308 mm−1 和0.120∼0.946 mm−1, 并显示猕猴桃不同部位组织的吸收和散射特性具有显著差异。种子部位受随机分散的种子的影响较大, 组织较均匀的果肉和种子基座部位结果证实散射系数大于吸收系数, 符合生物组织高散射介质特性。这些结果说明了定量测定组织的光传输特性参数对深入研究光与水果组织相互作用的重要性。

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.

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.

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.

Investigation of NIR hyperspectral imaging for discriminating melamine in milk powder

Melamine (2,4,6-triamino-1,3,5-triazine) contamination of food has become an urgent and broadly recognized issue for which rapid and accurate identification methods are needed by the food industry. In this study, the feasibility and effectiveness of near-infrared (NIR) hyperspectral imaging was investigated for detecting melamine in milk powder. Hyperspectral NIR images (144 bands spanning from 990 to 1700 nm) were acquired for Petri dishes containing samples of milk powder mixed with melamine at various concentrations (0.02% to 1%). Spectral bands that showed the most significant differences between pure milk and pure melamine were selected, and two-band difference analysis was applied to the spectrum of each pixel in the sample images to identify melamine particles in milk powders. The resultant images effectively allowed visualization of melamine particle distributions in the samples. The study demonstrated that NIR hyperspectral imaging techniques can qualitatively and quantitatively identify melamine adulteration in milk powders.