scholarly journals AO–MW–PLS method applied to rapid quantification of teicoplanin with near-infrared spectroscopy

2017 ◽  
Vol 10 (01) ◽  
pp. 1650029 ◽  
Author(s):  
Jiemei Chen ◽  
Tian Ai ◽  
Tao Pan ◽  
Lijun Yao ◽  
Fenggeng Xia
Keyword(s):  
Fermentation Process ◽  
Near Infrared ◽  
Fermentation Broth ◽  
Nir Spectroscopy ◽  
The Public ◽  
Model Set ◽  
Leave One Out ◽  
Mixture Samples ◽  
Pls Method ◽  
Rapid Quantification

Teicoplanin (TCP) is an important lipoglycopeptide antibiotic produced by fermenting Actinoplanes teichomyceticus. The change in TCP concentration is important to measure in the fermentation process. In this study, a reagent-free and rapid quantification method for TCP in the TCP–Tris–HCl mixture samples was developed using near-infrared (NIR) spectroscopy by focusing our attention on the fermentation process for TCP. The absorbance optimization (AO) partial least squares (PLS) was proposed and integrated with the moving window (MW) PLS, which is called AO–MW–PLS method, to select appropriate wavebands. A model set that includes various wavebands that were equivalent to the optimal AO–MW–PLS waveband was proposed based on statistical considerations. The public region of all equivalent wavebands was just one of the equivalent wavebands. The obtained public regions were 1540–1868[Formula: see text]nm for TCP and 1114–1310[Formula: see text]nm for Tris. The root-mean-square error and correlation coefficient for leave-one-out cross validation were 0.046[Formula: see text]mg mL[Formula: see text] and 0.9998[Formula: see text]mg mL[Formula: see text] for TCP, and 0.235[Formula: see text]mg mL[Formula: see text] and 0.9986[Formula: see text]mg mL[Formula: see text] for Tris, respectively. All the models achieved highly accurate prediction effects, and the selected wavebands provided valuable references for designing specialized spectrometers. This study provided a valuable reference for further application of the proposed methods to TCP fermentation broth and to other spectroscopic analysis fields.

scholarly journals Rapid determination of hyaluronic acid concentration in fermentation broth with near-infrared spectroscopy

2014 ◽  
Vol 07 (06) ◽  
pp. 1450012 ◽  
Author(s):  
Qin Dong ◽  
Hengchang Zang ◽  
Lixuan Zang ◽  
Aihua Liu ◽  
Yanli Shi ◽  
...  
Keyword(s):  
Hyaluronic Acid ◽  
Fermentation Process ◽  
Near Infrared ◽  
Fermentation Broth ◽  
Rapid Determination ◽  
Nir Spectroscopy ◽  
Promising Tool ◽  
Calibration Models ◽  
Order Polynomial

Hyaluronic acid (HA) concentration is an important parameter in fermentation process. Currently, carbazole assay is widely used for HA content determination in routine analysis. However, this method is time-consuming, environment polluting and has the risk of microbial contamination, as well as the results lag behind fermentation process. This paper attempted the feasibility to predict the concentration of HA in fermentation broth by using near infrared (NIR) spectroscopy in transmission mode. In this work, a total of 56 samples of fermentation broth from 7 batches were analyzed, which contained HA in the range of 2.35–9.69 g/L. Different data preprocessing methods were applied to construct calibration models. The final optimal model was obtained with first derivative using Savitzky–Golay smoothing (9 points window, second-order polynomial) and partial least squares (PLS) regression with leave-one-block-out cross validation. The correlation coefficient and Root Mean Square Error of prediction set is 0.98 and 0.43 g/L, respectively, which show the possibility of NIR as a rapid method for microanalysis and to be a promising tool for a rapid assay in HA fermentation.

The rapid determination of total polyphenols content and antioxidant activity in Dendrobium officinale using near-infrared spectroscopy

2016 ◽  
Vol 8 (23) ◽  
pp. 4584-4589 ◽  
Author(s):  
Longhui Ma ◽  
Zhimin Zhang ◽  
Xingbing Zhao ◽  
Sufeng Zhang ◽  
Hongmei Lu
Keyword(s):  
Antioxidant Activity ◽  
Infrared Spectroscopy ◽  
Near Infrared ◽  
Rapid Determination ◽  
Nir Spectroscopy ◽  
Dendrobium Officinale ◽  
Chemometric Methods ◽  
Total Polyphenols ◽  
Rapid Quantification

NIR spectroscopy coupled with chemometric methods for rapid quantification of total polyphenols content and antioxidant activity inDendrobium officinale.

scholarly journals Nondestructive Prediction of Optimal Harvest Time of Cherry Tomatoes Using VIS-NIR Spectroscopy and PLSR Calibration

2011 ◽  
Vol 1 ◽  
pp. 92-96 ◽  
Author(s):  
Hai Qing Yang
Keyword(s):  
Near Infrared ◽  
Nir Spectroscopy ◽  
Coefficient Of Determination ◽  
Harvest Time ◽  
Least Squares Regression ◽  
Calibration Models ◽  
Optimal Harvest ◽  
Leave One Out

In situ determination of optimal harvest time of tomatoes is of value for growers to optimize fruit picking schedule. This study evaluates the feasibility of using visible and near infrared (VIS-NIR) spectroscopy to make an intact estimation of harvest time of tomatoes. A mobile, fibre-type, AgroSpec VIS-NIR spectrophotometer (Tec5, Germany), with a spectral range of 350-2200 nm, was used for spectral acquisition of tomatoes in reflection mode. The harvest time of tomatoes was measured by the days before harvest. After dividing spectra into a calibration set (70%) and an independent prediction set (30%), spectra in the calibration set were subjected to a partial least-squares regression (PLSR) with leave-one-out cross validation to establish calibration models. Validation of calibration models on the independent prediction set indicates that the best model can produce excellent prediction accuracy with coefficient of determination (R2) of 0.90, root-mean-square error of prediction (RMSEP) of 2.5 days and residual prediction deviation (RPD) of 3.01. It is concluded that VIS-NIR spectroscopy coupled with PLSR models can be adopted successfully for in situ determination of optimal harvest time of tomatoes, which allows for automatic fruit harvest by a horticultural robot.

scholarly journals Use of FT-NIR Spectroscopy for Bovine Colostrum Analysis

2006 ◽  
Vol 75 (1) ◽  
pp. 57-63 ◽  
Author(s):  
P. Navrátilová ◽  
L. Hadra ◽  
M. Dračková ◽  
B. Janštová ◽  
L. Vorlová ◽  
...  
Keyword(s):  
Fourier Transformation ◽  
Near Infrared ◽  
Cross Validation ◽  
Correlation Coefficients ◽  
Nir Spectroscopy ◽  
Bovine Colostrum ◽  
Total Solids ◽  
Calibration Models ◽  
Spectral Ranges ◽  
Pls Method

Fourier transformation near infrared spectroscopy (FT-NIR) in combination with partial least squares (PLS) method were used to determine the content of total solids, fat, non-fatty solids, lactose and proteins in bovine colostrum. Spectra of 90 samples were measured in the reflectance mode with a transflectance cuvette in the 10000-4000 cm-1 spectral ranges with 100 scans. Calibration was performed and statistical values of correlation coefficients (R) and standard error of calibration values (SEC) were computed for total solids (0.986 and 0.919, respectively), fat (0.997 and 0.285, respectively), non-fatty solids (0.995 and 0.451, respectively), lactose (0.934 and 0.285, respectively) and protein (0.999 and 0.149, respectively). The calibration models developed were verified by cross validation. It follows from the study that FT-NIR spectroscopy can be used to determine the components of bovine colostrum.

scholarly journals Rapid Quantification of Melamine in Different Brands/Types of Milk Powders Using Standard Addition Net Analyte Signal and Near-Infrared Spectroscopy

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Bang-Cheng Tang ◽  
Chen-Bo Cai ◽  
Wei Shi ◽  
Lu Xu
Keyword(s):  
Near Infrared ◽  
Multivariate Calibration ◽  
Mean Squared Error ◽  
Nir Spectroscopy ◽  
Standard Addition ◽  
Additional Advantage ◽  
Net Analyte Signal ◽  
Analyte Signal ◽  
And Control ◽  
Rapid Quantification

Multivariate calibration (MVC) and near-infrared (NIR) spectroscopy have demonstrated potential for rapid analysis of melamine in various dairy products. However, the practical application of ordinary MVC can be largely restricted because the prediction of a new sample from an uncalibrated batch would be subject to a significant bias due to matrix effect. In this study, the feasibility of using NIR spectroscopy and the standard addition (SA) net analyte signal (NAS) method (SANAS) for rapid quantification of melamine in different brands/types of milk powders was investigated. In SANAS, the NAS vector of melamine in an unknown sample as well as in a series of samples added with melamine standards was calculated and then the Euclidean norms of series standards were used to build a straightforward univariate regression model. The analysis results of 10 different brands/types of milk powders with melamine levels 0~0.12% (w/w) indicate that SANAS obtained accurate results with the root mean squared error of prediction (RMSEP) values ranging from 0.0012 to 0.0029. An additional advantage of NAS is to visualize and control the possible unwanted variations during standard addition. The proposed method will provide a practically useful tool for rapid and nondestructive quantification of melamine in different brands/types of milk powders.

scholarly journals Application of near-infrared spectroscopy technology in the complex fermentation system to achieve high-efficiency production

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Chen Yang ◽  
Chen Lingli ◽  
Guo Meijin ◽  
Li Xu ◽  
Liu jinsong ◽  
...  
Keyword(s):  
Real Time ◽  
Fermentation Process ◽  
Near Infrared ◽  
High Efficiency ◽  
Prediction Models ◽  
Nir Spectroscopy ◽  
Environmental Parameters ◽  
Inhomogeneous System ◽  
Least Squares Regression ◽  
Experiment Platform

AbstractThe fermentation process is dynamically changing, and the metabolic status can be grasped through real-time monitoring of environmental parameters. In this study, a real-time and on-line monitoring experiment platform for substrates and products detection was developed based on non-contact type near-infrared (NIR) spectroscopy technology. The prediction models for monitoring the fermentation process of lactic acid, sophorolipids (SLs) and sodium gluconate (SG) were established based on partial least-squares regression and internal cross-validation methods. Through fermentation verification, the accuracy and precision of the NIR model for the complex fermentation environments, different rheological properties (uniform system and multi-phase inhomogeneous system) and different parameter types (substrate, product and nutrients) have good applicability, and R2 was greater than 0.98, exhibiting a good linear relationship. The root mean square error of prediction shows that the model has high credibility. Through the control of appropriate glucose concentration in SG fermentation as well as glucose and oil concentrations SLs fermentation by NIR model, the titers of SG and SLs were increased to 11.8% and 26.8%, respectively. Although high cost of NIR spectrometer is a key issue for its wide application in an industrial scale. This work provides a basis for the application of NIR spectroscopy in complex fermentation systems.

scholarly journals Size Estimation of Tomato Fruits Based on Spectroscopic Analysis

2011 ◽  
Vol 225-226 ◽  
pp. 1254-1257 ◽  
Author(s):  
Hai Qing Yang ◽  
Bo Yan Kuang ◽  
Abdul M. Mouazen
Keyword(s):  
Latent Variables ◽  
Near Infrared ◽  
Back Propagation ◽  
Nir Spectroscopy ◽  
Coefficient Of Determination ◽  
Least Squares Regression ◽  
Size Estimation ◽  
Tomato Fruits ◽  
Leave One Out ◽  
Plsr Model

This study used visible and near-infrared (VIS-NIR) spectroscopy for size estimation of tomato fruits of three cultivars. A mobile, fibre-type, VIS-NIR spectrophotometer (AgroSpec, Tec 5, Germany) with spectral range of 350-2200 nm, was used to measure reflectance spectra of on-vine tomatoes growing from July to September 2010. Spectra were divided into a calibration set (75%) and an independent validation set (25%). A partial least squares regression (PLSR) with leave-one-out cross validation was adopted to establish calibration models between fruit diameter and spectra. Furthermore, the latent variables (LVs) obtained from PLS regression was used as input to back-propagation artificial neural network (BPANN) analysis. Result shows that the prediction of PLSR model can produce good performance with coefficient of determination (R2) of 0.82, root-mean-square error of prediction (RMSEP) of 4.87 mm and residual prediction deviation (RPD) of 2.35. Compared to the PLSR model, the PLS-BPANN model provides considerably higher prediction performance withR2of 0.88, RMSEP of 3.98 mm and RPD of 2.89. It is concluded that VIS-NIR spectroscopy coupled with PLS-BPANN can be adopted successfully for size estimation of tomato fruits.

scholarly journals Real-time and on-line parallel detection of key fermentation process parameters by near-infrared spectroscopy in different environments

2021 ◽  
Author(s):  
Yang Chen ◽  
Lingli Chen ◽  
Meijin Guo ◽  
Xu Li ◽  
Jinsong Liu ◽  
...  
Keyword(s):  
Real Time ◽  
Fermentation Process ◽  
Near Infrared ◽  
Prediction Models ◽  
Nir Spectroscopy ◽  
Environmental Parameters ◽  
Inhomogeneous System ◽  
Least Squares Regression ◽  
Experiment Platform ◽  
On Line

Abstract The fermentation process is dynamically changing, and the metabolic status can be grasped through real-time monitoring of environmental parameters. In this study, a real-time and on-line monitoring experiment platform for substrates and products detection was developed based on non-contact type near-infrared (NIR) spectroscopy technology. The prediction models for monitoring the fermentation process of lactic acid, sophorolipids and sodium gluconate were established based on partial least-squares regression and internal cross-validation methods. Through fermentation verification, the accuracy and precision of the NIR model for the complex fermentation environments, different rheological properties (uniform system and multi-phase inhomogeneous system) and different parameter types (substrate, product and nutrients) have good applicability, and R2 is greater than 0.90, exhibiting a good linear relationship. The root mean square error shows that the model has high credibility. This research provides a basis for the application of NIR spectroscopy in complex fermentation systems.

scholarly journals Discriminant analysis of pyrrolizidine alkaloid contamination in bee pollen based on near-infrared data from lab-stationary and portable spectrometers

2020 ◽  
Vol 246 (12) ◽  
pp. 2471-2483
Author(s):  
Luciana De Jesus Inacio ◽  
Ilaria Lanza ◽  
Roberta Merlanti ◽  
Barbara Contiero ◽  
Lorena Lucatello ◽  
...  
Keyword(s):  
Discriminant Analysis ◽  
Near Infrared ◽  
Pyrrolizidine Alkaloid ◽  
Nir Spectroscopy ◽  
Misclassification Rate ◽  
Bee Pollen ◽  
Chemical Structures ◽  
Main Challenge ◽  
Portable Spectrometers ◽  
Leave One Out

AbstractBee pollen may be contaminated with pyrrolizidine alkaloids (PAs) and their N-oxides (PANOs), which are mainly detected by liquid chromatography coupled to tandem mass spectrometry (LC–MS/MS), even though the use of fast near-infrared (NIR) spectroscopy is an ongoing alternative. Therefore, the main challenge of this study was to assess the feasibility of both a lab-stationary (Foss) and a portable (Polispec) NIR spectrometer in 60 dehydrated bee pollen samples. After an ANOVA-feature selection of the most informative NIR spectral data, canonical discriminant analysis (CDA) was performed to distinguish three quantitative PA/PANO classes (µg/kg): < LOQ (0.4), low; 0.4–400, moderate; > 400, high. According to the LC–MS/MS analysis, 77% of the samples were contaminated with PAs/PANOs and the sum content of the 17 target analytes was higher than 400 µg/kg in 28% of the samples. CDA was carried out on a pool of 18 (Foss) and 22 (Polispec) selected spectral variables and allowed accurate classification of samples from the low class as confirmed by the high values of Matthews correlation coefficient (≥ 0.91) for both NIR spectrometers. Leave-one-out cross-validation highlighted precise recognition of samples characterised by a high PA/PANO content with a low misclassification rate (0.02) as false negatives. The most informative wavelengths were within the < 1000, 1000–1660 and > 2400 nm regions for Foss and > 1500 nm for Polispec that could be associated with cyclic amines, and epoxide chemical structures of PAs/PANOs. In sum, both lab-stationary and portable NIR systems are reliable and fast techniques for detecting PA/PANO contamination in bee pollen.

Prediction of Soil TN and TC at a Farm-Scale Using VIS-NIR Spectroscopy

2011 ◽  
Vol 225-226 ◽  
pp. 1258-1261
Author(s):  
Hai Qing Yang ◽  
Bo Yan Kuang ◽  
Abdul M. Mouazen
Keyword(s):  
Near Infrared ◽  
Nir Spectroscopy ◽  
Cost Effective ◽  
Total Carbon ◽  
Least Squares Regression ◽  
Farm Scale ◽  
Validation Set ◽  
Effective Models ◽  
Leave One Out ◽  
Spectral Ranges

Building cost-effective models is of academic and practical value for fast measurement of soil properties, especially at a farm-scale. The aim of this study is to build quantitative models for soil total nitrogen (TN) and total carbon (TC) using visible and near infrared (VIS-NIR) spectroscopy. Dried samples (n=122) collected from an experimental farm, at Silsoe, Bedfordshire, United Kingdom, were scanned from 350 to 2500 nm at 1-nm intervals. Samples were divided into a calibration set (75%) and an independent validation set (25%). A partial least squares regression (PLSR) with leave-one-out cross validation was carried out based on different spectral ranges. Result shows that the best predictions (R2>0.90 and RPD>3.3) are achieved for TN using the VIS range (400-700nm) and for TC using the VIS-NIR range (400-2500nm). It is concluded that VIS-NIR spectroscopy coupled with PLSR can be adopted for the prediction of soil TN and TC at a farm-scale.

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