Near-Infrared Spectroscopy and Principal Components Regression for the Quality Analysis of Glass/Epoxy Prepreg

2011 ◽  
Vol 19 (1) ◽  
pp. 15-20
Author(s):  
Wei Li ◽  
Wei Jia Gao ◽  
Ping Chen ◽  
Bao Lei Sun
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Principal Components ◽  
Near Infrared ◽  
Quality Analysis ◽  
Principal Components Regression

Identification of Liquor Brands Based on near Infrared Spectroscopy

2013 ◽  
Vol 834-836 ◽  
pp. 935-938
Author(s):  
Lian Shun Zhang ◽  
Chao Guo ◽  
Bao Quan Wang
Keyword(s):  
Principal Component Analysis ◽  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Principal Components ◽  
Near Infrared ◽  
Scatter Correction ◽  
Principal Component ◽  
Component Analysis ◽  
Correction Method ◽  
Variance Contribution

In this paper, the liquor brands were identified based on the near infrared spectroscopy method and the principal component analysis. 60 samples of 6 different brands liquor were measured by the spectrometer of USB4000. Then, in order to eliminate the noise caused by the external factors, the smoothing method and the multiplicative scatter correction method were used. After the preprocessing, we got the revised spectra of the 60 samples. The difference of the spectrum shape of different brands is not much enough to classify them. So the principal component analysis was applied for further analysis. The results showed that the first two principal components variance contribution rate had reached 99.06%, which can effectively represent the information of the spectrums after preprocessing. From the scatter plot of the two principal components, the 6 different brands of liquor were identified more accurate and easier than the spectra curves.

Predicting the nutritional quality of feed ingredients for pigs using near-infrared spectroscopy (NIRS) and chemical analysis

2018 ◽  
Vol 58 (4) ◽  
pp. 709 ◽  
Author(s):  
R. J. van Barneveld ◽  
H. Graham ◽  
S. Diffey
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Nutritional Quality ◽  
Near Infrared ◽  
Quality Analysis ◽  
Energy Concentration ◽  
Feed Ingredients ◽  
Pork Production ◽  
Digestible Energy

Capacity to routinely, accurately and cost-effectively measure variation in the nutritional quality of feed ingredients before diet formulation represents a fundamental pillar of sustainable pork production worldwide. Factors driving sustainable pork production include pork price, feed cost, utilisation of co-products and downgraded raw materials and variation in pork production, with all being related to the definition and ultimate nutritional quality of feed ingredients. The present paper defines rapid measures of nutritional quality in feed ingredients for pigs and demonstrates the range that can exist in these parameters, specifically digestible energy of cereal grains and the reactive-lysine concentration of oilseed meals. It provides an overview of the development of near-infrared spectroscopy (NIRS) calibrations for key nutritional-quality parameters and how they are being applied by the pork industry. Adjunct ways to measure nutritional quality of feed ingredients for pigs such as the glucose-release index and how these can be used in conjunction with NIRS are reviewed. The paper reports advanced correlation analysis between chemical components and digestible-energy concentration of cereals, and how these could be used for screening of NIRS outliers, and discusses future opportunities for application of nutritional-quality analysis using NIRS calibrations, including feed intake and portable solutions. Using advanced NIRS calibrations for digestible energy in cereals and reactive lysine in oilseed meals, pork producers will ensure that they make best use of limited resources and, as a consequence, pork will remain a nutritionally accretive food source for increasingly discerning consumers worldwide.

scholarly journals Use of Near-Infrared Spectroscopy for On-Line Monitoring the Quality of Prepreg Cloth

2008 ◽  
Vol 17 (2) ◽  
pp. 096369350801700 ◽  
Author(s):  
Wei Li ◽  
Yu Dong Huang ◽  
Ping Chen
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Resin Content ◽  
Quality Analysis ◽  
Volatile Content ◽  
On Line ◽  
Spectral Ranges ◽  
Major Emphasis

On-line monitoring the prepreg quality in the polymer composite industry continues to be a major emphasis, it will reduce the amount of prepreg manufactured under “outside specifications”. A near-infrared spectroscopy has been implemented for measurement of resin content, pre-curing degree of resin and volatile content during the manufacture of glass/phenolic resin prepreg cloth. The regression method employed was partial least squares (PLS). The optimum models were obtained by selecting different spectral pretreatments and spectral ranges. The determination coefficients (R2) for the resin content, pre-curing degree and volatile content were 98.36, 98.76 and 98.33, respectively. The root mean squares of cross-validation (RMSECV) were 0.364, 0.145 and 0.182 respectively for the resin content, pre-curing degree and volatile content, respectively. The NIR method was applied to monitor the quality of prepreg cloth on line. The results showed that the monitored values were closed to the actual values. The study indicates that the on-line quality analysis of prepreg cloth was realised by using NIR technique.

scholarly journals A Review of the Discriminant Analysis Methods for Food Quality Based on Near-Infrared Spectroscopy and Pattern Recognition

Molecules ◽  
2021 ◽  
Vol 26 (3) ◽  
pp. 749
Author(s):  
Jian Zeng ◽  
Yuan Guo ◽  
Yanqing Han ◽  
Zhanming Li ◽  
Zhixin Yang ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Food Quality ◽  
Near Infrared ◽  
Basic Structure ◽  
Quality Analysis ◽  
Future Research ◽  
Pattern Recognition Methods ◽  
Analysis Methods

Near-infrared spectroscopy (NIRS) combined with pattern recognition technique has become an important type of non-destructive discriminant method. This review first introduces the basic structure of the qualitative analysis process based on near-infrared spectroscopy. Then, the main pretreatment methods of NIRS data processing are investigated. Principles and recent developments of traditional pattern recognition methods based on NIRS are introduced, including some shallow learning machines and clustering analysis methods. Moreover, the newly developed deep learning methods and their applications of food quality analysis are surveyed, including convolutional neural network (CNN), one-dimensional CNN, and two-dimensional CNN. Finally, several applications of these pattern recognition techniques based on NIRS are compared. The deficiencies of the existing pattern recognition methods and future research directions are also reviewed.

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