Nondestructive measurement of anthocyanin in intact soybean seed using Fourier Transform Near-Infrared (FT-NIR) and Fourier Transform Infrared (FT-IR) spectroscopy

2020 ◽  
Vol 111 ◽  
pp. 103477
Author(s):  
Hanim Z. Amanah ◽  
Rahul Joshi ◽  
Rudiati Evi Masithoh ◽  
Myoung-Gun Choung ◽  
Kyung-Hwan Kim ◽  
...  
2015 ◽  
Vol 7 (2) ◽  
pp. 736-746 ◽  
Author(s):  
S. Assi ◽  
A. Guirguis ◽  
S. Halsey ◽  
S. Fergus ◽  
J. L. Stair

Three handheld spectrometers, near-infrared (NIR), Raman and attenuated total reflectance Fourier transform-infrared (ATR-FT-IR) spectroscopy, were used for the identification of ‘legal high’ model mixtures and Internet products.


2020 ◽  
Vol 20 (3) ◽  
pp. 680 ◽  
Author(s):  
Rudiati Evi Masithoh ◽  
Hanim Zuhrotul Amanah ◽  
Byoung Kwan Cho

This research aimed at providing a fast and accurate method in discriminating tuber flours having similar color by using Fourier transform near-infrared (FT-NIR) and Fourier transform infrared (FT-IR) spectroscopy in order to minimize misclassification if using human eye or avoid adulteration. Reflectance spectra of three types of tubers (consisted of Canna edulis, modified cassava, and white sweet potato) were collected to develop a multivariate model of partial least-squares discriminant analysis (PLS-DA). Several spectra preprocessing methods were applied to obtain the best calibration and prediction model, while variable importance in the projection (VIP) wavelength selection method was used to reduce variables in developing the model. The PLS-DA model achieved 100% accuracy in predicting all types of flours, both for FT-NIR and FT-IR. The model was also able to discriminate all flours with coefficient of determination (R2) of 0.99 and a standard error of prediction (SEP) of 0.03% by using 1st Savitzky Golay (SG) derivative method for the FT-NIR data, as well as R2 of 0.99 and SEP of 0.08% by using 1st Savitzky Golay (SG) derivative method for the FT-IR data. By applying the VIP method, the variables were reduced from 1738 to 608 variables with R2 of 0.99 and SEP of 0.09% for FT IR and from 1557 to 385 variables with R2 of 0.99 and SEP of 0.05% for FT NIR.


2020 ◽  
Author(s):  
Huayan Yang ◽  
Fangling Wu ◽  
Fuxin Xu ◽  
Keqi Tang ◽  
Chuanfan Ding ◽  
...  

Abstract Fourier transform infrared (FT-IR) spectroscopy is a label-free and highly sensitive technique that provides complete information on the chemical composition of biological samples. The bacterial FT-IR signals are extremely specific and highly reproducible fingerprint-like patterns, making FT-IR an efficient tool for bacterial typing and identification. Due to the low cost and high flux, FT-IR has been widely used in hospital hygiene management for infection control, epidemiological studies, and routine bacterial determination of clinical laboratory values. However, the typing and identification accuracy could be affected by many factors, and the bacterial FT-IR data from different laboratories are usually not comparable. A standard protocol is required to improve the accuracy of FT-IR-based typing and identification. Here, we detail the principles and procedures of bacterial typing and identification based on FT-IR spectroscopy, including bacterial culture, sample preparation, instrument operation, spectra collection, spectra preprocessing, and mathematical data analysis. Without bacterial culture, a typical experiment generally takes <2 h.


Sign in / Sign up

Export Citation Format

Share Document