Differentiation of Chinese rice wines from different wineries based on mineral elemental fingerprinting

Shen, Fei; Wu, Jian; Li, Bobin; Jiang, Tao

doi:10.1016/j.foodchem.2013.06.119

Cited by 27 publications

(23 citation statements)

References 21 publications

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“…Mo, Fe, Mn and Cu concentration were recorded highest in RR2 (Table 2). The mean values for most elements were consistent and similar to the result published previously (Shen et al 2013).…”

Section: Mineral Profilesupporting

confidence: 90%

Evaluation of Physical, Thermal, Pasting Characteristics and Mineral Profile of Pigmented and Nonpigmented Rice Cultivars

Samyor

Deka

Das

2015

Journal of Food Processing and Preservation

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Physical, thermal and pasting properties of pigmented and nonpigmented rice cultivars were investigated in the present study. The mineral profiles for 11 minerals were also investigated. Physical characterization of the rice cultivars was analyzed by measurement of axial dimension, angle of repose, etc. In pasting properties, RR 2 (Umling ame red rice) showed higher pasting temperature (90.5C), final viscosity (3,477 cP) and set back viscosity (1,876 cP) than RR1 (Lingkang taker ame red rice) and WR (Pungpo taker ame white rice). Texture profile characterization of rice cultivars was also carried out. The thermal properties of rice samples were analyzed using differential scanning calorimetry. The thermal properties showed that RR 2 had the highest onset (To, 77.12C), endset (Tp, 104.61C) and peak value (140C). This study provides the knowledge of physical, thermal and pasting properties of grain and flour of rice cultivars that would be relevant for the postharvest processing. PRACTICAL APPLICATIONSMost of the indigenous rice cultivars available in north-east India are underutilized. Effective processing and development of new food products can result in a significant increase in utilization of the indigenous rice cultivars. Hence, it is needed to know the physical, pasting and thermal properties of rice cultivars. The present study provides information on physical, pasting and thermal properties of indigenous underutilized rice cultivars.

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“…Mo, Fe, Mn and Cu concentration were recorded highest in RR2 (Table 2). The mean values for most elements were consistent and similar to the result published previously (Shen et al 2013).…”

Section: Mineral Profilesupporting

confidence: 90%

Evaluation of Physical, Thermal, Pasting Characteristics and Mineral Profile of Pigmented and Nonpigmented Rice Cultivars

Samyor

Deka

Das

2015

Journal of Food Processing and Preservation

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“…Previous research has found that multielement analysis is an effective method of verifying the geographical origins of foods (Tormen and others ; Martin and others ; Kruzlicova and others ; Shen and others ). Uncovering a significant correlation between plant and soil was difficult because of distractions in topsoil from industrial wastewater irrigation, aerial deposition, and usage of fertilizers and pesticides (Mench and others ; Ullrich and others ).…”

Section: Resultsmentioning

confidence: 99%

Identification of the Geographical Origins of Pomelos Using Multielement Fingerprinting

Yan

Liu

Xiong

et al. 2015

Journal of Food Science

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Eighty pomelo samples and 80 soil samples were examined using a multielement component test to predict the geographical origins of pomelos produced in 4 regions (Sichuan, Chongqing, Fujian, and Guangxi Provinces) of China. The concentrations of 8 elements were determined by atomic absorption spectrometry. Ca, K, and Na were the most abundant elements. Principal component analysis (PCA) and linear discriminant analysis (LDA) were applied to reduce the dimensionality of the multielement data from 8 to 2 while retaining the highest possible variance. Using PCA and LDA, 69.66% and 91.30%, respectively, of the pomelo origins were classified correctly using multielement variables, along with 67.06% and 83.40% for soil multielement analysis. Results indicated that the LDA method was more effective for geographical origin classification than PCA. The results of the multielement component test demonstrated its capability to screen pomelo origins rapidly.

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“…PCA is often the first step of data analysis to detect patterns or outliers between samples . This procedure was used mainly to achieve a reduction of dimensionality to permit a primary evaluation of the between‐category similarity .…”

Section: Methodsmentioning

confidence: 99%

“…In order to classify the 112 wine samples according to their geographical origins, several pattern recognition techniques such as principal component analysis (PCA), soft independent modelling of class analogy (SIMCA), linear discriminate analysis (LDA), discriminant partial least squares (PLS-DA) and support vector machine (SVM) were used as multivariate tools. PCA is often the first step of data analysis to detect patterns or outliers between samples (10,31). This procedure was used mainly to achieve a reduction of dimensionality to permit a primary evaluation of the between-category similarity (3).…”

Section: Multivariate Data Analysismentioning

confidence: 99%

Discrimination of Chinese rice wines of different geographical origins by UV-vis spectroscopy and chemometrics

Long

et al. 2015

J. Inst. Brew.

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Discrimination of Chinese rice wines from four geographical origins ('Zhejiang', 'Jiangsu', 'Shanghai' and "Fujian") in China was investigated according to their UV-vis spectra. The UV-vis absorption spectra of 112 samples in the wavelength range of 190-800 nm were collected. Then the absorption data was subjected to principal component analysis to reveal differences between samples and the potential possibility of using multivariate methods to distinguish differences. Classification models were developed by soft independent modelling of class analogy, linear discriminate analysis (LDA), discriminant partial least squares and support vector machine. Seven mathematical pre-treatments were applied to improve the performance of the multivariate classification models. Among the seven pre-treatments, standard normal transformation (SNV) was superior to the other six methods. The results showed that, compared with other models, SNV-treated LDA models achieved better performances with an average correct classification rate of 98.96% in the training set and 100% in the testing set. The results demonstrate that UV spectroscopy combined with chemometric data analysis, as a rapid method to classify Chinese rice wines according to their geographical origins, is feasible.

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Differentiation of Chinese rice wines from different wineries based on mineral elemental fingerprinting

Cited by 27 publications

References 21 publications

Evaluation of Physical, Thermal, Pasting Characteristics and Mineral Profile of Pigmented and Nonpigmented Rice Cultivars

Evaluation of Physical, Thermal, Pasting Characteristics and Mineral Profile of Pigmented and Nonpigmented Rice Cultivars

Identification of the Geographical Origins of Pomelos Using Multielement Fingerprinting

Discrimination of Chinese rice wines of different geographical origins by UV-vis spectroscopy and chemometrics

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