Rapid identification and quantification of Panax notoginseng with its adulterants by near infrared spectroscopy combined with chemometrics

Liu, Peng; Wang, Jing; Li, Qian; Gao, Jun; Tan, Xiaoyao; Bian, Xihui

doi:10.1016/j.saa.2018.07.094

Cited by 56 publications

(26 citation statements)

References 39 publications

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“…Secara umum, fungsi metode transformasi spektra adalah memperbaiki kualitas data spektra dengan cara (Liu et al 2019a). Sumber noise lain yang mungkin muncul adalah perbedaan baseline spektra.…”

Section: Analisis Dataunclassified

“…Hanya saja, kelemahan spektrometer UVvisible secara umum adalah memiliki nilai signal to noise ratio atau SNR yang lebih rendah. Moving average smoothing merupakan salah satu metode yang efektif untuk menghilangkan noise spektra dan meningkatkan nilai SNR(Liu et al 2019a). Sumber noise lain yang mungkin muncul adalah efek hamburan dari perbedaan partikel dalam sistem sampel madu.…”

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Klasifikasi Madu Berdasarkan Jenis Lebah (Apis dorsata versus Apis mellifera) Menggunakan Spektroskopi Ultraviolet dan Kemometrika

Suhandy

Yulia

Kusumiyati

2020

J. Ilmu. Pertan. Indones.

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In this research, spectral data in UV region (200-400 nm) alongside PCA and SIMCA chemometrics were used to classify two types of honey obtained from different honeybees (Apis dorsata versus Apis mellifera). A total of 200 Durian monofloral honey samples from Apis dorsata and 120 samples for Longan monofloral honey from Apis mellifera were prepared. Therefore, spectral data were recorded based on the following parameters: range of acquisition 200-400 nm, transmittance mode, and interval 1 nm. In addition, the original spectra were transformed using three different algorithms: moving average smoothing with 11 segments, standard normal variate (SNV), and Savitzky-Golay 1st derivative with 11 segments and 2 ordos. The result of PCA using transformed spectra in the range of 250-400 nm explained the possibility of clearly separating Durian and Longan honey along the PC1 axis, with 98% variance, while the SIMCA showed a 100% proper classification rate for all prediction samples. In addition, several important wavelengths were identified alongside high x-loadings values at 270 and 300 nm. These results were closely related to the absorbance of important phenolic compounds in honey, including benzoic, salicylic, and aryl-alyphatic acids. The results demonstrate a probability to establish simple and low-cost honey authentication systems, using UV spectroscopy and chemometrics on free-chemical in sample preparations. Keywords: authentication, Apis dorsata, Apis mellifera, SIMCA, UV spectroscopy

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Section: Analisis Dataunclassified

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Klasifikasi Madu Berdasarkan Jenis Lebah (Apis dorsata versus Apis mellifera) Menggunakan Spektroskopi Ultraviolet dan Kemometrika

Suhandy

Yulia

Kusumiyati

2020

J. Ilmu. Pertan. Indones.

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“…Among them, near-infrared (NIR) and mid-infrared (MIR) spectroscopy are probably the most publicized technologies [27][28][29][30]. These two technologies can provide detailed structural information on sample properties and composition at the molecular level [31][32][33][34]. Like human fingerprints, the infrared spectrum of any substance has to be unique [31].…”

Section: Introductionmentioning

confidence: 99%

“…Like human fingerprints, the infrared spectrum of any substance has to be unique [31]. This is the reason for NIR and MIR spectral fingerprints can be applied to identify or classify different samples [31][32][33][34]. In the case of medicinal plants, chemical constituents and their ratios of biochemicals of different species can vary substantially [35,36].…”

Section: Introductionmentioning

confidence: 99%

Discrimination of Gentiana and Its Related Species Using IR Spectroscopy Combined with Feature Selection and Stacked Generalization

Shen

Wang

2020

Molecules

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Gentiana, which is one of the largest genera of Gentianoideae, most of which had potential pharmaceutical value, and applied to local traditional medical treatment. Because of the phytochemical diversity and difference of bioactive compounds among species, which makes it crucial to accurately identify authentic Gentiana species. In this paper, the feasibility of using the infrared spectroscopy technique combined with chemometrics analysis to identify Gentiana and its related species was studied. A total of 180 batches of raw spectral fingerprints were obtained from 18 species of Gentiana and Tripterospermum by near-infrared (NIR: 10,000–4000 cm−1) and Fourier transform mid-infrared (MIR: 4000–600 cm−1) spectrum. Firstly, principal component analysis (PCA) was utilized to explore the natural grouping of the 180 samples. Secondly, random forests (RF), support vector machine (SVM), and K-nearest neighbors (KNN) models were built while using full spectra (including 1487 NIR variables and 1214 FT-MIR variables, respectively). The MIR-SVM model had a higher classification accuracy rate than the other models that were based on the results of the calibration sets and prediction sets. The five feature selection strategies, VIP (variable importance in the projection), Boruta, GARF (genetic algorithm combined with random forest), GASVM (genetic algorithm combined with support vector machine), and Venn diagram calculation, were used to reduce the dimensions of the data variable in order to further reduce numbers of variables for modeling. Finally, 101 NIR and 73 FT-MIR bands were selected as the feature variables, respectively. Thirdly, stacking models were built based on the optimal spectral dataset. Most of the stacking models performed better than the full spectra-based models. RF and SVM (as base learners), combined with the SVM meta-classifier, was the optimal stacked generalization strategy. For the SG-Ven-MIR-SVM model, the accuracy (ACC) of the calibration set and validation set were both 100%. Sensitivity (SE), specificity (SP), efficiency (EFF), Matthews correlation coefficient (MCC), and Cohen’s kappa coefficient (K) were all 1, which showed that the model had the optimal authenticity identification performance. Those parameters indicated that stacked generalization combined with feature selection is probably an important technique for improving the classification model predictive accuracy and avoid overfitting. The study result can provide a valuable reference for the safety and effectiveness of the clinical application of medicinal Gentiana.

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“…However, most of these methods were used to differentiate gelatin origins by combining with chemometric methods; few studies were performed to explore if the edible gelatin was adulterated with the toxic industrial gelatin. As a fast and nondestructive method, NIR spectroscopy has been widely used in the detection of adulteration identification of agricultural products and foods [18][19][20][21][22]. In this paper, NIR spectroscopy combined with four kinds of supervised pattern recognition methods was used to study the feasibility of identifying edible gelatin adulterated with industrial gelatin.…”

Section: Introductionmentioning

confidence: 99%

Near Infrared Spectroscopy Based on Supervised Pattern Recognition Methods for Rapid Identification of Adulterated Edible Gelatin

Zhang

Sun

Wang

et al. 2018

Journal of Spectroscopy

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The aim of this work is to identify the adulteration of edible gelatin using near-infrared (NIR) spectroscopy combined with supervised pattern recognition methods. The spectral data obtained from a total of 144 samples consisting of six kinds of adulterated gelatin gels with different mixture ratios were processed with multiplicative scatter correction (MSC), Savitzky–Golay (SG) smoothing, and min-max normalization. Principal component analysis (PCA) was first carried out for spectral analysis, while the six gelatin categories could not be clearly distinguished. Further, linear discriminant analysis (LDA), soft independent modelling of class analogy (SIMCA), backpropagation neural network (BPNN), and support vector machine (SVM) were introduced to establish discrimination models for identifying the adulterated gelatin gels, which gave a total correct recognition rate of 97.44%, 100%, 97.44%, and 100%, respectively. For the SIMCA model with significant level α = 0.05, sample overlapping clustering appeared; thus, the SVM model presents the best recognition ability among these four discrimination models for the classification of edible gelatin adulteration. The results demonstrate that NIR spectroscopy combined with unsupervised pattern recognition methods can quickly and accurately identify edible gelatin with different adulteration levels, providing a new possibility for the detection of industrial gelatin illegally added into food products.

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Rapid identification and quantification of Panax notoginseng with its adulterants by near infrared spectroscopy combined with chemometrics

Cited by 56 publications

References 39 publications

Klasifikasi Madu Berdasarkan Jenis Lebah (Apis dorsata versus Apis mellifera) Menggunakan Spektroskopi Ultraviolet dan Kemometrika

Klasifikasi Madu Berdasarkan Jenis Lebah (Apis dorsata versus Apis mellifera) Menggunakan Spektroskopi Ultraviolet dan Kemometrika

Discrimination of Gentiana and Its Related Species Using IR Spectroscopy Combined with Feature Selection and Stacked Generalization

Near Infrared Spectroscopy Based on Supervised Pattern Recognition Methods for Rapid Identification of Adulterated Edible Gelatin

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