Rapid identification of adulterated cow milk by non-linear pattern recognition methods based on near infrared spectroscopy

Zhang, Liguo; Zhang, Xin; Ni, Lei; Xue, Zhibin; Gu, Xin; Huang, Shixin

doi:10.1016/j.foodchem.2013.08.064

Cited by 97 publications

(44 citation statements)

References 25 publications

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“…Therefore, 10, 15, 20 were used as the principal component number after repeated screening, and compared the prediction accuracy of the models in the experiment. Another factor that affects the quality of the model is smoothing point [14]. The selection of smoothing points is an essential step in Savitzky-Golay filtering.…”

Section: Discussionmentioning

confidence: 99%

“…All citrus leaves were picked from Citrus reticulate Blanco orchards of Guangzhou Fuhe (23°44′N, 113°69′E), and each tree was 6-years-old and about 250-300 cm tall. Each sample used in the experiment must be tested by RT-qPCR to determine whether it carries the CLas or not, the RT-qPCR components and conditions were those described by Li et al, and the primers HLBasf/HLBasr and the probe HLBp, which were designed to amplify the 16S rDNA gene of CLas were used [14].The threshold cycle (C t ) from the PCR analysis indicates the presence or absence of CLas bacteria infected in leaves. C t >32 is considered as CLas-negative, while C t <30 is considered CLas-positive.…”

Section: Samplesmentioning

confidence: 99%

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PLS-DA Infrared Spectra Model of Citrus Leaves for the Characterization of Citrus Huanglongbing

Chen¹,

Lv²,

Wang³

et al. 2016

Proceedings of the 2016 International Conference on Biomedical and Biological Engineering

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Abstract. Citrus huanglongbing disease is an important disease of citrus species, which leads to the change of chemical composition in leaves. The infrared technique may be advantageous to characterize the chemical differences between Huanglongbing infected and healthy leaves rapidly and nondestructively. In this study, the near-infrared spectra of citrus leaves were obtained in the field by using MicroNIR 1700 Spectrometer, and then precision detection of citrus HLB-associated bacteria (Candidatus Liberibacter asiaticus, CLas) by RT-qPCR for verification. The method of Soft Independent Modeling of Class Analogy(SIMCA) and Partial Least Squares Discriminant Analysis (PLS-DA) was used to establish the qualitative discriminant models of HLB diagnosis, and using first derivative and Savitzky-Golay for further data processing. The results showed that the correct rejection rate of Principal Component Analyses (PCA) model of CLas-negative leaves was more than 80%, and correct recognition rate of CLas-positive leaves was more than 95% in different citrus orchards from the field. The correct rejection rate of PLS-DA model of CLas-negative leaves was more than 83%, and correct recognition rate of CLas-positive leaves was more than 99% in different citrus orchard from the field. Both models were well distinguished whether the citrus leaves were CLas-negative or CLas-positive(even if the citrus leaves was asymptomatic), which provided a new method for the rapid diagnosis and early warning of HLB in the field.

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Section: Discussionmentioning

confidence: 99%

Section: Samplesmentioning

confidence: 99%

PLS-DA Infrared Spectra Model of Citrus Leaves for the Characterization of Citrus Huanglongbing

Chen¹,

Lv²,

Wang³

et al. 2016

Proceedings of the 2016 International Conference on Biomedical and Biological Engineering

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“…Mid-infrared spectra have also been used for detecting and quantifying synthetic milk (preparation of vegetable oils with detergents and urea), whey, urea, and hydrogen peroxide (Santos et al, 2013). Zhang et al (2014) used NIR spectra and a modified support vector machine for identification of dextrin or starch with melamine, urea, or ammonium nitrate in fluid milk, but only levels of adulteration equal to or greater than 5% could be recognized.…”

Section: Introductionmentioning

confidence: 99%

Quantification of whey in fluid milk using confocal Raman microscopy and artificial neural network

Rocha

Paiva

Anjos

et al. 2015

Journal of Dairy Science

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In this work, we assessed the use of confocal Raman microscopy and artificial neural network as a practical method to assess and quantify adulteration of fluid milk by addition of whey. Milk samples with added whey (from 0 to 100%) were prepared, simulating different levels of fraudulent adulteration. All analyses were carried out by direct inspection at the light microscope after depositing drops from each sample on a microscope slide and drying them at room temperature. No pre- or posttreatment (e.g., sample preparation or spectral correction) was required in the analyses. Quantitative determination of adulteration was performed through a feed-forward artificial neural network (ANN). Different ANN configurations were evaluated based on their coefficient of determination (R2) and root mean square error values, which were criteria for selecting the best predictor model. In the selected model, we observed that data from both training and validation subsets presented R2>99.99%, indicating that the combination of confocal Raman microscopy and ANN is a rapid, simple, and efficient method to quantify milk adulteration by whey. Because sample preparation and postprocessing of spectra were not required, the method has potential applications in health surveillance and food quality monitoring.

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“…Near-infrared spectroscopy is further complemented by new technologies such as continuous wave functional near-infrared spectroscopy (Scholkmann et al 2014). Near infrared has been employed for oil quality (Casale and Simonetti 2014), wine characterization (Martelo-Vidal and Vazquez 2014) (Mignani et al 2014), and milk quality control (Wang et al 2014;Zhang et al 2014). Other applications include hydrothermal carbonization liquid products (Reza et al 2014) and deuterium= hydrogen exchange in liquid D 2 O (Unger, Ozaki, and Siesler 2014).…”

Section: Introductionmentioning

confidence: 99%

Determination of Fenitrothion in Water by Near Infrared Spectroscopy and Chemometric Analysis

Tang

Xiang

et al. 2015

Analytical Letters

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The feasibility of the determination of fenitrothion in water is reported using two-dimensional correlation spectroscopy to select the modeled spectral regions for near-infrared spectroscopy. Partial least squares discriminant analysis (PLSDA) and least squares support vector machines discriminant analysis (LSSVM) were used to model and predict the spectra of fenitrothion aqueous solutions. Spectral regions from 5365 to 6757 per centimeter and 6944 to 7800 per centimeter were selected according to the dynamic spectra and the two-dimensional correlation synchronous spectra. Four classification methods (random division 1, random division 2, Kennard-Stone 1, and Kennard-Stone 2) were used to divide the samples into calibration and validation sets. The classification methods impacted both PLSDA and LSSVM, but the impact was larger on PLSDA than on LSSVM. In addition, different pretreatment methods (mean center, standard normal variate transformation, and orthogonal signal correction) were applied to PLSDA; the pretreatments also affected the predictions. Both PLSDA and LSSVM models were used to characterize real water samples and yielded satisfactory results. These methods allowed the direct and rapid determination of fenitrothion in water.

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Rapid identification of adulterated cow milk by non-linear pattern recognition methods based on near infrared spectroscopy

Cited by 97 publications

References 25 publications

PLS-DA Infrared Spectra Model of Citrus Leaves for the Characterization of Citrus Huanglongbing

PLS-DA Infrared Spectra Model of Citrus Leaves for the Characterization of Citrus Huanglongbing

Quantification of whey in fluid milk using confocal Raman microscopy and artificial neural network

Determination of Fenitrothion in Water by Near Infrared Spectroscopy and Chemometric Analysis

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