Support vector machine regression on selected wavelength regions for quantitative analysis of caffeine in tea leaves by near infrared spectroscopy

2019 ◽  
Vol 33 (10) ◽  
pp. e3172 ◽  
Author(s):  
Somdeb Chanda ◽  
Ajanto Kumar Hazarika ◽  
Navnil Choudhury ◽  
Sk Anarul Islam ◽  
Rishabh Manna ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Infrared Spectroscopy ◽  
Quantitative Analysis ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Support Vector ◽  
Tea Leaves ◽  
Support Vector Machine Regression

Fourier Transform Near-Infrared Spectroscopy and Chemometrics To Predict Zygosacchromyces rouxii in Apple and Kiwi Fruit Juices

2018 ◽  
Vol 81 (8) ◽  
pp. 1379-1385 ◽  
Author(s):  
CHEN NIU ◽  
HONG GUO ◽  
JIANPING WEI ◽  
MARINA SAJID ◽  
YAHONG YUAN ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Fruit Juices ◽  
Support Vector ◽  
Support Vector Machine Regression ◽  
Limit Of Quantification ◽  
Kiwi Fruit ◽  
Single Variety

ABSTRACT This study investigated the capability of near-infrared spectroscopy (NIRS) to predict the concentration of Zygosaccharomyces rouxii in apple and kiwi fruit juices. The yeast was inoculated in fresh kiwi fruit juice (n = 68), reconstituted kiwi juice (n = 85), and reconstituted apple juice (n = 64), followed by NIR spectra collection and plate counting. A principal component analysis indicated direct orthogonal signal correction preprocessing was suitable to separate spectral samples. Parameter optimization algorithms increased the performance of support vector machine regression models developed in a single variety juice system and a multiple variety juice system. Single variety juice models achieved accurate prediction of Z. rouxii concentrations, with the limit of quantification at 3 to 15 CFU/mL (R2 = 0.997 to 0.999), and the method was also feasible for Hanseniaspora uvarum and Candida tropicalis. The best multiple variety juice model obtained had a limit of quantification of 237 CFU/mL (R2 = 0.961) for Z. rouxii. A Bland-Altman analysis indicated good agreement between the support vector machine regression model and the plate counting method. It suggests that NIRS can be a high-throughput method for prediction of Z. rouxii counts in kiwi fruit and apple juices.

scholarly journals Food powders classification using handheld Near-Infrared Spectroscopy and Support Vector Machine

2019 ◽  
Vol 1367 ◽  
pp. 012029 ◽  
Author(s):  
Mohamed Yasser Mohamed ◽  
Mahmud Iwan Solihin ◽  
Winda Astuti ◽  
Chun Kit Ang ◽  
Wan Zailah
Keyword(s):  
Support Vector Machine ◽  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Support Vector ◽  
Food Powders
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