Mid-Infrared Spectroscopy Analysis Combined with Support Vector Machine for Rapid Discrimination of Botanical Origin of Honey

2018 ◽  
Vol 55 (6) ◽  
pp. 063003
Author(s):  
徐天扬 Xu Tianyang ◽  
杨娟 Yang Juan ◽  
孙晓荣 Sun Xiaorong ◽  
刘翠玲 Liu Cuiling ◽  
李熠 Li Yi ◽  
...  
Cartilage ◽  
2021 ◽  
pp. 194760352199322
Author(s):  
Vesa Virtanen ◽  
Ervin Nippolainen ◽  
Rubina Shaikh ◽  
Isaac O. Afara ◽  
Juha Töyräs ◽  
...  

Objective Joint injuries may lead to degeneration of cartilage tissue and initiate development of posttraumatic osteoarthritis. Arthroscopic surgeries can be used to treat joint injuries, but arthroscopic evaluation of articular cartilage quality is subjective. Fourier transform infrared spectroscopy combined with fiber optics and attenuated total reflectance crystal could be used for the assessment of tissue quality during arthroscopy. We hypothesize that fiber-optic mid-infrared spectroscopy can detect enzymatically and mechanically induced damage similar to changes occurring during progression of osteoarthritis. Design Bovine patellar cartilage plugs were extracted and degraded enzymatically and mechanically. Adjacent untreated samples were utilized as controls. Enzymatic degradation was done using collagenase and trypsin enzymes. Mechanical damage was induced by (1) dropping a weight impactor on the cartilage plugs and (2) abrading the cartilage surface with a rotating sandpaper. Fiber-optic mid-infrared spectroscopic measurements were conducted before and after treatments, and spectral changes were assessed with random forest, partial least squares discriminant analysis, and support vector machine classifiers. Results All models had excellent classification performance for detecting the different enzymatic and mechanical damage on cartilage matrix. Random forest models achieved accuracies between 90.3% and 77.8%, while partial least squares model accuracies ranged from 95.8% to 84.7%, and support vector machine accuracies from 91.7% to 80.6%. Conclusions The results suggest that fiber-optic Fourier transform infrared spectroscopy attenuated total reflectance spectroscopy is a viable way to detect minor and major degeneration of articular cartilage. Objective measures provided by fiber-optic spectroscopic methods could improve arthroscopic evaluation of cartilage damage.


2014 ◽  
Vol T162 ◽  
pp. 014042 ◽  
Author(s):  
L Lenhardt ◽  
I Zeković ◽  
T Dramićanin ◽  
Ž Tešić ◽  
D Milojković-Opsenica ◽  
...  

Sensors ◽  
2019 ◽  
Vol 19 (13) ◽  
pp. 2934 ◽  
Author(s):  
Lei Feng ◽  
Susu Zhu ◽  
Shuangshuang Chen ◽  
Yidan Bao ◽  
Yong He

Adulteration is one of the major concerns among all the quality problems of milk powder. Soybean flour and rice flour are harmless adulterations in the milk powder. In this study, mid-infrared spectroscopy was used to detect the milk powder adulterated with rice flour or soybean flour and simultaneously determine the adulterations content. Partial least squares (PLS), support vector machine (SVM) and extreme learning machine (ELM) were used to establish classification and regression models using full spectra and optimal wavenumbers. ELM models using the optimal wavenumbers selected by principal component analysis (PCA) loadings obtained good results with all the sensitivity and specificity over 90%. Regression models using the full spectra and the optimal wavenumbers selected by successive projections algorithm (SPA) obtained good results, with coefficient of determination (R2) of calibration and prediction all over 0.9 and the predictive residual deviation (RPD) over 3. The classification results of ELM models and the determination results of adulterations content indicated that the mid-infrared spectroscopy was an effective technique to detect the rice flour and soybean flour adulteration in the milk powder. This study would help to apply mid-infrared spectroscopy to the detection of adulterations such as rice flour and soybean flour in real-world conditions.


2017 ◽  
Vol 243 (8) ◽  
pp. 1447-1457 ◽  
Author(s):  
Jorge Leonardo Sanchez ◽  
Sérgio Benedito Gonçalves Pereira ◽  
Patrícia Casarin de Lima ◽  
Gabriela Possebon ◽  
Augusto Tanamati ◽  
...  

Author(s):  
Omar Elhamdaoui ◽  
Aimen El Orche ◽  
Amine Cheikh ◽  
Khalid Karrouchi ◽  
Khalid Laarej ◽  
...  

Abstract Background Morocco is an important world producer and consumer of several varieties of date palm. In fact, the discrimination between varieties remains difficult and requires the use of complex and high-cost techniques. Objective We evaluated in this work the potential of mid-infrared spectroscopy (MIR) and chemometric models to discriminate eight date palm varieties. Methods Four chemometric models were applied for the analysis of the spectral data, including principal component analysis (PCA), support vector machine discriminant analysis (SVM-DA), linear discriminant analysis (LDA) and partial least squares (PLS). MIR spectroscopic data were recorded from the wavenumber range 4000 – 600 cm−1, with a spectral resolution of 4 cm−1. Results The discriminant analysis was performed by LDA and SVM-DA with a 100% correct classification rate for the date mesocarp. Partial least-squares was applied as a complementary chemometric tool aimed at quantifying moisture content, the validation of this model shows a good predictive capacity with a regression coefficient of 84% and a root mean square error of cross-validation of 0.50. Conclusions The present study clearly demonstrates that MIR spectroscopy combined with chemometric approaches constitutes a promising analytical method to classify date palms according to their varietal origin and to establish a regression model for predicting moisture content. Highlights Alternative analytical method to discriminate of date palm cultivars by FTIR-ATR spectroscopy coupled with chemometric approaches.


2015 ◽  
Vol 98 (9) ◽  
pp. 6620-6629 ◽  
Author(s):  
G. Visentin ◽  
A. McDermott ◽  
S. McParland ◽  
D.P. Berry ◽  
O.A. Kenny ◽  
...  

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