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Near Infrared Spectroscopy in Fuling mustard in the application of quality inspection

Author: LiuBing
Tutor: YangJiDong
School: Southwestern University
Course: Analytical Chemistry
Keywords: Near Infrared Spectroscopy Fuling mustard Simultaneous determination Quality Indicators Oral active ingredient
CLC: TS255.7
Type: Master's thesis
Year: 2011
Downloads: 224
Quote: 3
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Abstract

NIR (Near Infrared, referred NIR) is developing very rapidly in recent years, a quick and easy method of analysis, its greatest feature is the fast, non-destructive and simultaneous determination. NIR located at the mid-infrared region of the spectrum visible region of the spectrum of electromagnetic waves between, usually near-infrared spectral region is defined as the range of 780-2526nm (12820-3959cm-1), mainly from the spectral information of the frequency of internal vibration molecule Hop frequency absorption, and mainly reflect the molecule CH, NH, OH and SH groups such octave harmony frequency vibration absorption, and its chemical informative quite rich. Since most of organic compounds containing such groups are, therefore, suitable for near-infrared spectroscopy of the material types and applications are very widely used, can be used for non-destructive measurement, on-line analysis. Because NIR analytical speed, sample preparation is simple, can be determined simultaneously, does not destroy the sample, environmental pollution, etc., so that near-infrared spectroscopy is widely used in agriculture, petrochemical, pharmaceutical and other fields. Especially in the food processing industry, NIR spectroscopy has been applied to food lipids, proteins, carbohydrates, salt and other flavor compounds and food processing testing, grading and other properties of the assessment. GB currently detected by the various components of Fuling mustard needs were detected, a very tedious, time consuming and not environmentally friendly, such as near-infrared spectroscopy technology into the production of mustard Fuling, Fuling mustard can be determined simultaneously in a variety of ingredients, which can effectively improve the mustard production efficiency, online testing and process control, and promote modernization of mustard production. Based on the scientific and technological project of Chongqing Science and Technology Commission (CSTC2008EA5008) under the auspices of the Near Infrared Spectroscopy in Fuling mustard quality testing in the application: 1. Near Infrared Spectroscopy Fuling mustard rapid detection of moisture, total acid and amino-state nitrogen content of the main component of Fuling mustard were detected with high accuracy, to meet production quality testing for Fuling mustard accuracy requirements; 2. Near Infrared Spectrometry Fuling mustard and total pectin sugar content, the application of near infrared involving mustard taste the ingredients were tested, results are accurate and reliable; 3.PLS rapid detection of cluster analysis in the salt content of Fuling mustard to study near-infrared determination of the feasibility of inorganic salt content in food ; 4 Rapid Identification of near infrared spectroscopy study Fuling mustard brand, the model can better identify different brands of mustard; 5. Near Infrared Spectrometry Astragalus Astragalus polysaccharides and oral astragaloside content The main ingredient for oral predict better, can be used for online production; an infrared spectroscopy method for rapid detection of Fuling mustard in the water, the total nitrogen content of amino acids as well as application of near infrared spectroscopy combined with partial least squares ( PLS), in order to establish the material Fuling mustard to evaluate the quality of the quantitative analysis model. Experimental determination of the 58 samples were Fuling mustard near-infrared spectral data, through pretreatment methods to eliminate the noise, and finally through partial least squares regression model was established. Thus obtained to evaluate the quality of the water, total acid and amino acid content of NIR quantitative model, the coefficient of determination (R2) respectively 95.78,97.54,95.04, cross-validation mean square error (RMSECV) were 0.256,0.0347,0.0363. The model with 18 parts not involved in modeling mustard external validation sample, its water content, total acid and amino external validation coefficient of determination (R2) were 95.62,95.39,94.6, standard deviation prediction set (RMSEP) was 0.107 , 0.0168,0.0388. Internal cross-validation and external validation have proved near-infrared quantitative analysis of a relatively high degree of accuracy, to meet production quality testing for Fuling mustard requirements. (2) Near Infrared Spectrometry Fuling mustard in pectin and total sugar content of the experimental use of Fourier transform infrared diffuse reflectance spectroscopy to Fuling mustard established for the material relevant to their taste and total sugar pectin quantitative analysis model. Measured 50 parts Fuling mustard NIR data, the original spectrum by pretreatment methods to eliminate the noise, and finally through partial least squares (PLS) regression model was established. Fuling mustard to get the total sugar content of pectin and near-infrared quantitative analysis model, the coefficient of determination (R2) were 98.31,98.35, cross-validation mean square error (RMSECV) were 0.513,0.0531. The model with 18 parts not involved in modeling mustard samples were external validation, and its total sugar pectin and external validation coefficient of determination (R2) were 96.69,95.63, the standard deviation of prediction set (RMSEP) were 0.572,0.0671 . This method can effectively meet the production of the Fuling mustard in the simultaneous determination of total sugar, pectin and precision requirements for the production and circulation areas, with development potential and value in use. 3.PLS cluster analysis in the rapid detection of Fuling mustard Fuling mustard salt content in this paper as raw material by near infrared spectroscopy to establish a rapid detection method of its salt content. Fuling mustard measured sample of 46 near-infrared spectral data, get the original spectrum by eliminating the constant offset method to eliminate noise, using partial least squares (PLS) method was established to detect the salt content of the model. The model coefficient of determination of salt (R2) was 99.25% and the internal cross-validation mean square error (RMSECV) is 0.0723. The model was not involved in the modeling of the 12 samples were Fuling mustard external validation, to get its chemical and predicted values of the coefficient of determination was 98.55%, mean square prediction set (RMSEP) was 0.0800. Through internal cross-validation and external validation certificate, near infrared quantitative analysis method has high accuracy, to meet production of the Fuling mustard in salt content detection accuracy. Meanwhile, the use of cluster analysis in a sample of modeling done qualitative discrimination, the sample is divided into two categories according to the level of salinity. Therefore, NIR can be used for the production of quality Fuling mustard fast real-time monitoring. 4 Rapid Identification of near infrared spectroscopy study Fuling mustard brand presents a near-infrared diffuse reflectance spectroscopy rapid identification of Fuling mustard brand approach. Application of near infrared diffuse reflectance analysis technology on the market, six kinds of typical spectra collected Fuling mustard brand, and to obtain the raw spectral data were smoothed spectra, first derivative and vector normalization after pretreatment such spectra, using factor method calculate the distance between the spectra between samples by ward's algorithm for cluster analysis method. And with the discriminant analysis were compared with experimental results showed that cluster analysis and discriminant analysis on the six kinds of Fuling mustard brands were well identification. 5 Near Infrared Spectrometry Astragalus Astragalus polysaccharides and oral astragaloside content Fourier transform near infrared spectrometer transmittance spectroscopy techniques Astragalus Astragalus Polysaccharide Extract Oral Liquid (APS) and astragaloside (Astragaloside Ⅳ) of content were detected and analyzed using partial least squares regression (PLS) was established Astragalus Astragalus polysaccharides and oral astragaloside mathematical NIR calibration models. Through internal cross-validation, the model determines the optimal number of variables, to obtain the optimum parameters of the model building. And through the prediction set for external validation of the model. APS correlation coefficient of 0.9797, cross-validation mean square error (RMSECV) of 0.153, mean square prediction set (RMSEP) was 0.178, astragaloside correlation coefficient of 0.9412, cross-validation mean square error is 0.0365, mean square deviation of the prediction set 0.0458. T test by the model, at a given significance level of 0.05 under the conditions of the measurement with the standard method of comparing the measurement results, there was no significant difference. The results showed that NIR spectroscopy can quickly and accurately determine the APS and the content of astragaloside expected for Astragalus Extract Oral Liquid on line.

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