Discrimination of Ganoderma lucidum according to geographical origin with near infrared diffuse reflectance spectroscopy and pattern recognition techniques

Chen, Yi; Xie, Mingyong; Yan, Yan; Zhu, Shang-Bin; Nie, Shaoping; Li, Chang; Wang, Yuanxing; Gong, Xiaofeng

doi:10.1016/j.aca.2008.04.055

Cited by 106 publications

(61 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Leverage diagnostic was applied first to detect the outlier sample and provide information on how much influence each sample had on the method standards (Chen et al 2008). Once the outlier detection was performed, DPLS model was established for the qualitative analysis of rice wine samples.…”

Section: Discriminant Partial Least-squaresmentioning

confidence: 99%

Discrimination Between Shaoxing Wines and Other Chinese Rice Wines by Near-Infrared Spectroscopy and Chemometrics

Shen

Yang

Ying

et al. 2010

Food Bioprocess Technol

View full text Add to dashboard Cite

Shaoxing rice wine (also called Shaoxing wine) is the most well-known Chinese rice wine in China. The common fraudulent practice in the commercialization of Chinese rice wine is to sell wines from different geographical origins under the denomination of Shaoxing rice wine. In this study, the use of near-infrared (NIR) spectroscopy combined with chemometrics as a rapid tool for the discrimination of Chinese rice wine from three geographical origins ("Fujian", "non-Shaoxing", "Shaoxing") has been preliminarily investigated. NIR spectra were collected in transmission mode in the wavelength range of 800-2,500 nm. Discriminant models were developed by principal component analysis (PCA), discriminant analysis (DA), and discriminant partial least-squares analysis (DPLS). The chemical properties of Chinese rice wine were also investigated to find out the difference between samples from three varied origins. The results showed that good classification could be obtained after spectral pre-treatment. The percentage of samples correctly classified by both DA and DPLS methods in calibration and validation set was 97.2% and 100%, respectively. The results demonstrated that NIR could be used as a simple and rapid technique to distinguish Shaoxing wines from non-Shaoxing wines and Fujian wines. To further validate the ability of NIR spectroscopy, more samples should be incorporated to build a more robust model.

show abstract

Section: Discriminant Partial Least-squaresmentioning

confidence: 99%

Discrimination Between Shaoxing Wines and Other Chinese Rice Wines by Near-Infrared Spectroscopy and Chemometrics

Shen

Yang

Ying

et al. 2010

Food Bioprocess Technol

View full text Add to dashboard Cite

show abstract

“…Commonly, the most representative ranges of spectra are chosen for further analysis [3,6,11,17]. The method of choosing these ranges is often not described, or the ranges are selected by using the characteristic ranges for C-H, C --O, N-H and amide bonds.…”

Section: Informative Rangementioning

confidence: 99%

“…An IR spectrum is usually composed of several thousand data points (variables); therefore, a pre-selection must be performed to obtain the most informative data sample used in subsequent analysis. Some common pre-processing techniques are differentiation [1,2,5,6,[9][10][11][12]15,16], smoothing [3,5,6,9,10,14], normalization [1,5,[7][8][9][10]13,18,20] and outlier detection [2,3]. Most commonly, pre-selection of variables is carried out either by defining the 'representative' sections of spectrum [3,6,11,17] or by simply analyzing the whole spectrum (i.e.…”

Section: Introductionmentioning

confidence: 99%

“…The pre-processed dataset (or the whole spectrum) is then analyzed using multivariate statistical methods. Such methods include cluster analysis (CA) [2,5,9,11,12,17,18], discriminant analysis (DA) [1,3,4,6,7,9,20,21], principal component analysis (PCA) [1,3,[5][6][7][8][14][15][16][17]20,21], partial least square analysis (PLS) [3,6,10,15,16,18,19] or a combination of these methods.…”

Section: Introductionmentioning

confidence: 99%

“…It may also be applied to the identification of plant, fungal and bacterial species from leaf, pollen and bacterial colony samples [1][2][3][4][5][6][7][8][9][10][11][12][13]. FTIR enables identification of green tea varieties [14], and it has been used to determine the geographical origin [6,15,16] of biological samples and essential oils. It has also been used for chemical characterization [17][18][19] and quality control of vegetable and fruit products [20][21][22][23].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Comparison and improvement of commonly applied statistical approaches for identification of plant species from IR spectra

Kokalj

Krajšek

Bratuša

et al. 2010

Journal of Chemometrics

View full text Add to dashboard Cite

a Fourier transform infrared spectroscopy (FTIR) has been studied many times in the context of identification of plant, fungal and bacterial species. Infrared spectra are commonly analyzed using multivariate statistical methods such as cluster analysis (CA), principal component analysis (PCA), partial least squares analysis (PLS) and discriminant analysis (DA). In this study, a univariate statistical method for analysis of variance (ANOVA) was used to reduce the number of variables before applying the multivariate methods. Analyzing variables using ANOVA or a combination of ANOVA with CA produced better results. Here, experiments were carried out by performing ANOVA using the first derivative of the spectra instead of the original spectra or its second derivative because using the first-derivative variables led to improved distinction between species. Different results were obtained by applying different validation methods. The leave-one-out validation method gave higher results than the validation-with-training and validation sample sets, thus indicating the non-objectivity of the leave-one-out validation method.

show abstract

Near‐Infrared Spectroscopy in Natural Product Research

Huck

2014

Encyclopedia of Analytical Chemistry

View full text Add to dashboard Cite

The properties of natural products are related to certain classes of compounds such as alkaloids, flavonoids, and essential oils. Traditionally, separation techniques including thin‐layer chromatography (TLC), liquid chromatography (LC), gas chromatography (GC), and capillary electrophoresis (CE) even hyphenated to mass spectrometry (MS) were used for the elucidation and qualitative and quantitative analyses of individual compounds. In the food industry, spectroscopic investigations using infrared radiation have been used to monitor and evaluate the composition and quality since the early 1960s. During the last four decades, near‐infrared (NIR) spectroscopy (800–2500 nm; 12,500–4000 cm −1 ) has become one of the most attractive and used methods for analysis for the following reasons: It represents a noninvasive analytical tool allowing a fast and simultaneous qualitative and quantitative characterization of natural products and their constituents. In this contribution, the principle, technique, and methodology are described followed by a discussion of quantitative and qualitative application possibilities. Finally, some hints to helpful regulatory issues and advantages and limitations are critically evaluated.

show abstract

Discrimination of Ganoderma lucidum according to geographical origin with near infrared diffuse reflectance spectroscopy and pattern recognition techniques

Cited by 106 publications

References 30 publications

Discrimination Between Shaoxing Wines and Other Chinese Rice Wines by Near-Infrared Spectroscopy and Chemometrics

Discrimination Between Shaoxing Wines and Other Chinese Rice Wines by Near-Infrared Spectroscopy and Chemometrics

Comparison and improvement of commonly applied statistical approaches for identification of plant species from IR spectra

Near‐Infrared Spectroscopy in Natural Product Research

Contact Info

Product

Resources

About