Freshness measurement of eggs using near infrared (NIR) spectroscopy and multivariate data analysis

2011 ◽  
Vol 12 (2) ◽  
pp. 182-186 ◽  
Author(s):  
Hao Lin ◽  
Jiewen Zhao ◽  
Li Sun ◽  
Quansheng Chen ◽  
Fang Zhou
Keyword(s):  
Data Analysis ◽  
Multivariate Data Analysis ◽  
Near Infrared ◽  
Multivariate Data ◽  
Nir Spectroscopy

Sensing tomato’s pathogen using Visible/Near infrared (VIS/NIR) spectroscopy and multivariate data analysis (MVDA)

2015 ◽  
Vol 3 (1) ◽  
pp. 12-22
Author(s):  
Nawaf Abu-Khalaf
Keyword(s):  
Data Analysis ◽  
Multivariate Data Analysis ◽  
Near Infrared ◽  
Multivariate Data ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Sensor Technology ◽  
Support Vector ◽  
Wide Range

Quality of agricultural products is a very important issue for consumers as well as for farmers in relation to price, health and flavour. One of the factors that determine the quality is the absence of pathogens that can cause diseases for products and also for consumers. An advanced method to sense pathogens and their antagonists is the use of Visible/Near Infrared (VIS/NIR) spectroscopy. In this paper, the VIS/NIR spectroscopy, with the help of two techniques of multivariate data analysis (MVDA); namely principal component analysis (PCA) and support vector machine (SVM)-classification; showed very reliable results for sensing two artificially inoculated fungi (Fusarium oxysporum f. sp. Lycopersici and Rhizoctonia solani), and two antagonistic bacteria (Bacillus atrophaeus and Pseudomonas aeruginosa). The two fungi cause loss of quality and quantity for tomatoes. The results showed that the lowest classification rates using VIS/NIR spectroscopy for pathogens, antagonistic and their combinations were 90%, 85% and 74%, respectively. These results open a wide range for using VIS/NIR spectroscopy sensor technology for agricultural commodities quality at quality control checkpoints.

Sensing tomato’s pathogen using Visible/Near infrared (VIS/NIR) spectroscopy and multivariate data analysis (MVDA)

2015 ◽  
Vol 3 (1) ◽  
pp. 12-22
Author(s):  
Nawaf Abu-Khalaf
Keyword(s):  
Data Analysis ◽  
Multivariate Data Analysis ◽  
Near Infrared ◽  
Multivariate Data ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Sensor Technology ◽  
Support Vector ◽  
Wide Range

Quality of agricultural products is a very important issue for consumers as well as for farmers in relation to price, health and flavour. One of the factors that determine the quality is the absence of pathogens that can cause diseases for products and also for consumers. An advanced method to sense pathogens and their antagonists is the use of Visible/Near Infrared (VIS/NIR) spectroscopy. In this paper, the VIS/NIR spectroscopy, with the help of two techniques of multivariate data analysis (MVDA); namely principal component analysis (PCA) and support vector machine (SVM)-classification; showed very reliable results for sensing two artificially inoculated fungi (Fusarium oxysporum f. sp. Lycopersici and Rhizoctonia solani), and two antagonistic bacteria (Bacillus atrophaeus and Pseudomonas aeruginosa). The two fungi cause loss of quality and quantity for tomatoes. The results showed that the lowest classification rates using VIS/NIR spectroscopy for pathogens, antagonistic and their combinations were 90%, 85% and 74%, respectively. These results open a wide range for using VIS/NIR spectroscopy sensor technology for agricultural commodities quality at quality control checkpoints.

scholarly journals Spectral Characterization of Copper and Iron Sulfide Combustion: A Multivariate Data Analysis Approach for Mineral Identification on the Blend

Metals ◽  
2019 ◽  
Vol 9 (9) ◽  
pp. 1017 ◽  
Author(s):  
Walter Díaz ◽  
Carlos Toro ◽  
Eduardo Balladares ◽  
Victor Parra ◽  
Pablo Coelho ◽  
...  
Keyword(s):  
Data Analysis ◽  
Spectral Data ◽  
Multivariate Data Analysis ◽  
Near Infrared ◽  
Iron Sulfide ◽  
Multivariate Data ◽  
Copper Production ◽  
Gaussian Kernel ◽  
Support Vector ◽  
Drop Tube

The pyrometallurgical processes for primary copper production have only off-line and time-demanding analytical techniques to characterize the in and out streams of the smelting and converting steps. Since these processes are highly exothermic, relevant process information could potentially be obtained from the visible and near-infrared radiation emitted to the environment. In this work, we apply spectral sensing and multivariate data analysis methodologies to identify and classify copper and iron sulfide minerals present in the blend from spectra measured during their combustion in a laboratory drop-tube setup, in which chemical reactions that take place in flash smelting furnaces can be reproduced. Controlled combustion experiments were conducted with two industrial concentrates and with high-grade mineral species as well, with a focus on pyrite and chalcopyrite. Exploratory analysis by means of Principal Component Analysis (PCA) applied on the spectral data depicted high correlation features among species with similar elemental compositions. Classification algorithms were tested on the spectral data, and a classification accuracy of 95.3% with a support vector machine (SVM) algorithm with a Gaussian kernel was achieved. The results obtained by the described procedures are shown to be very promising as a first step in the development of a predictive and analytical tool in search of fitting the current need for real-time control of pyrometallurgical processes.

Fungal decay of spruce and beech wood assessed by near-infrared spectroscopy in combination with uni- and multivariate data analysis

Holzforschung ◽  
2007 ◽  
Vol 61 (6) ◽  
pp. 680-687 ◽  
Author(s):  
Karin Fackler ◽  
Manfred Schwanninger ◽  
Cornelia Gradinger ◽  
Ewald Srebotnik ◽  
Barbara Hinterstoisser ◽  
...  
Keyword(s):  
Data Analysis ◽  
Multivariate Data Analysis ◽  
Near Infrared ◽  
Lignin Content ◽  
Beech Wood ◽  
Multivariate Data ◽  
Principal Component ◽  
Brown Rot ◽  
White Rot ◽  
Least Squares Regression

Abstract Wood is colonised and degraded by a variety of micro-organisms, the most efficient ones are wood-rotting basidiomycetes. Microbial decay processes cause damage to wooden constructions, but also have great potential as biotechnological tools to change the properties of wood surfaces and of sound wood. Standard methods to evaluate changes in infected wood, e.g., EN350-1 1994, are time-consuming. Rapid FT-NIR spectroscopic methods are also suitable for this purpose. In this paper, degradation experiments on surfaces of spruce (Picea abies L. Karst) and beech (Fagus silvatica L.) were carried out with white rot basidiomycetes or the ascomycete Hypoxylon fragiforme. Experiments with brown rot or soft rot caused by Chaetomium globosum were also performed. FT-NIR spectra collected from the degraded wood were subjected to principal component analysis. The lignin content and mass loss of the specimens were estimated based on univariate or multivariate data analysis (partial least squares regression).

scholarly journals Determination of Stevioside and Rebaudioside A in Stevia rebaudiana Bertoni Leaves Using near Infrared Spectroscopy and Multivariate Data Analysis

2018 ◽  
Vol 18 (4) ◽  
pp. 664 ◽  
Author(s):  
Yohanes Martono ◽  
Suryasatriya Trihandaru ◽  
Ferdy Semuel Rondonuwu
Keyword(s):  
Data Analysis ◽  
Multivariate Data Analysis ◽  
Near Infrared ◽  
Cross Validation ◽  
Multivariate Data ◽  
Stevia Rebaudiana ◽  
Partial Least Square ◽  
Steviol Glycosides ◽  
Rebaudioside A ◽  
Wavenumber Region

Rebaudioside A and stevioside are abundant steviol glycoside contained in Stevia rebaudiana leaves. These components are widely used as a natural sweetener. The objective of this study was to develop rapid determination method of stevioside, and rebaudioside A in S. rebaudiana leaves using near infrared trans-reflectance spectroscopy (NIRS) combined with multivariate analysis. The reference method used was HPLC. A prediction model was developed using partial least square (PLS) regression. Calibration parameters were calculated based on a calibration set of various stevioside, rebaudioside A from 23 samples. Performance of PLS model was assessed in term of optimum determination coefficient (R2), and minimum root mean square error of cross-validation (RMSEV). Validation of PLS model was performed using cross-validation and leave one out calibration of PLS component. Rebaudioside A has well PLS model in wavenumber region of 4100–5100 cm-1, and stevioside determination using difference wavenumber region of 4760-5016 cm-1. PLS model for total (sum of stevioside and rebaudioside A content) was exploited in wavenumber region of 4568-4928 cm-1. NIRS in combination with multivariate data analysis of PLSR can be applied as a rapid method for determining rebaudioside A and the total amount of steviol glycosides in S. rebaudiana leaves.

Sign in / Sign up

Export Citation Format

Share Document