Rapid Estimation of Xanthan and Biomass Concentration during Xanthan Production under Solid State Fermentation on Polyurethane Foam with Near–Infrared Spectroscopy

2012 ◽  
Vol 482-484 ◽  
pp. 1515-1519
Author(s):  
Zhi Guo Zhang ◽  
Hong Zhang Chen
Keyword(s):  
Infrared Spectroscopy ◽  
Solid State ◽  
Polyurethane Foam ◽  
Near Infrared Spectroscopy ◽  
Solid State Fermentation ◽  
Near Infrared ◽  
External Validation ◽  
Biomass Concentration ◽  
Inert Support ◽  
Xanthan Production

Recently, some solid state fermentation (SSF) processes of xanthan production were studied. However, quantitative analysis of the concentration of xanthan and biomass is more complicated than that of submerged fermentation. To facilitate the analysis of these components, near–infrared spectroscopy (NIRS) was used. A NIRS calibration models for rapidly estimating xanthan and biomass concentration in xanthan fermentation on inert support of polyurethane foam was established. The wavenumber and spectral pretreatment method were optimized. The data of cross validation and external validation shows that NIRS was suitable for rapid and accurate quantification of the concentration of xanthan and biomass in solid state fermentation on inert support. This method will provide much convenience for the research of solid state fermentation on inert support.

Quantitative Detection of Ethanol Solid-State Fermentation Process Parameters Based on Near Infrared Spectroscopy

2017 ◽  
Vol 54 (2) ◽  
pp. 023002
Author(s):  
张航 Zhang Hang ◽  
刘国海 Liu Guohai ◽  
江辉 Jiang Hui ◽  
梅从立 Mei Congli ◽  
黄永红 Huang Yonghong
Keyword(s):  
Infrared Spectroscopy ◽  
Solid State ◽  
Near Infrared Spectroscopy ◽  
Process Parameters ◽  
Solid State Fermentation ◽  
Fermentation Process ◽  
Near Infrared ◽  
Quantitative Detection

scholarly journals Near-Infrared Spectroscopy (NIRS) and Optical Sensors for Estimating Protein and Fiber in Dryland Mediterranean Pastures

2021 ◽  
Vol 3 (1) ◽  
pp. 73-91
Author(s):  
João Serrano ◽  
Shakib Shahidian ◽  
Ângelo Carapau ◽  
Ana Elisa Rato
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Optical Sensors ◽  
Near Infrared ◽  
Production Systems ◽  
External Validation ◽  
Neutral Detergent Fiber ◽  
Reference Laboratory ◽  
Pasture Quality ◽  
Animal Grazing

Dryland pastures provide the basis for animal sustenance in extensive production systems in Iberian Peninsula. These systems have temporal and spatial variability of pasture quality resulting from the diversity of soil fertility and pasture floristic composition, the interaction with trees, animal grazing, and a Mediterranean climate characterized by accentuated seasonality and interannual irregularity. Grazing management decisions are dependent on assessing pasture availability and quality. Conventional analytical determination of crude protein (CP) and fiber (neutral detergent fiber, NDF) by reference laboratory methods require laborious and expensive procedures and, thus, do not meet the needs of the current animal production systems. The aim of this study was to evaluate two alternative approaches to estimate pasture CP and NDF, namely one based on near-infrared spectroscopy (NIRS) combined with multivariate data analysis and the other based on the Normalized Difference Vegetation Index (NDVI) measured in the field by a proximal active optical sensor (AOS). A total of 232 pasture samples were collected from January to June 2020 in eight fields. Of these, 96 samples were processed in fresh form using NIRS. All 232 samples were dried and subjected to reference laboratory and NIRS analysis. For NIRS, fresh and dry samples were split in two sets: a calibration set with half of the samples and an external validation set with the remaining half of the samples. The results of this study showed significant correlation between NIRS calibration models and reference methods for quantifying pasture quality parameters, with greater accuracy in dry samples (R2 = 0.936 and RPD = 4.01 for CP and R2 = 0.914 and RPD = 3.48 for NDF) than fresh samples (R2 = 0.702 and RPD = 1.88 for CP and R2 = 0.720 and RPD = 2.38 for NDF). The NDVI measured by the AOS shows a similar coefficient of determination to the NIRS approach with pasture fresh samples (R2 = 0.707 for CP and R2 = 0.648 for NDF). The results demonstrate the potential of these technologies for estimating CP and NDF in pastures, which can facilitate the farm manager’s decision making in terms of the dynamic management of animal grazing and supplementation needs.

Prediction of crude protein and neutral detergent fibre concentration in residues of in situ ruminal degradation of pasture samples by near-infrared spectroscopy (NIRS)

2016 ◽  
Vol 56 (9) ◽  
pp. 1504 ◽  
Author(s):  
J. P. Keim ◽  
H. Charles ◽  
D. Alomar
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Crude Protein ◽  
Near Infrared ◽  
External Validation ◽  
Neutral Detergent Fibre ◽  
Insufficient Amount ◽  
Ruminal Degradation ◽  
Animal Feedstuffs

An important constraint of in situ degradability studies is the need to analyse a high number of samples and often with insufficient amount of residue, especially after the longer incubations of high-quality forages, that impede the study of more than one nutritional component. Near-infrared spectroscopy (NIRS) has been established as a reliable method for predicting composition of many entities, including forages and other animal feedstuffs. The objective of this work was to evaluate the potential of NIRS for predicting the crude protein (CP) and neutral detergent fibre (NDF) concentration in rumen incubation residues of permanent and sown temperate pastures in a vegetative stage. In situ residues (n = 236) from four swards were scanned for their visible-NIR spectra and analysed for CP and NDF. Selected equations developed by partial least-squares multivariate regression presented high coefficients of determination (CP = 0.99, NDF = 0.95) and low standard errors (CP = 4.17 g/kg, NDF = 7.91 g/kg) in cross-validation. These errors compare favourably to the average concentrations of CP and NDF (146.5 and 711.2 g/kg, respectively) and represent a low fraction of their standard deviation (CP = 38.2 g/kg, NDF = 34.4 g/kg). An external validation was not as successful, with R2 of 0.83 and 0.82 and a standard error of prediction of 14.8 and 15.2 g/kg, for CP and NDF, respectively. It is concluded that NIRS has the potential to predict CP and NDF of in situ incubation residues of leafy pastures typical of humid temperate zones, but more robust calibrations should be developed.

Rapid identification of epoxy resin and phenolic resin using near infrared spectroscopy

2017 ◽  
Vol 25 (5) ◽  
pp. 324-329 ◽  
Author(s):  
Li Dan ◽  
Wu Yi-Hui
Keyword(s):  
Infrared Spectroscopy ◽  
Epoxy Resin ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Phenolic Resin ◽  
External Validation ◽  
Principal Component ◽  
Rapid Identification ◽  
Classical Analysis ◽  
Prediction Rate

The aim of this research was to investigate the feasibility of Fourier transform near infrared spectroscopy combined with chemometric analysis to develop a rapid method for identification of different resin types which had been deemed similar by a preliminary visual examination. Principal component analysis was applied on spectral data to classify two types of epoxy resin samples and three types of phenolic resin samples. In this case, a total of two hundred and fifteen samples were used for the evaluation and validation of two types of epoxy resin samples (SY1342 and SY1346) and three types of phenolic resin samples (Y3567, Y2705 and Y2137). All were correctly differentiated by their respective models. Moreover, in the external validation, the prediction rate of samples correctly classified was also 100%. Such classifications are very important for the detection of adulterated samples and for quality control. Near infrared spectroscopy was shown to be a very reliable, accurate and useful tool to classify resin samples in a fast, clean and inexpensive way compared to classical analysis, and it will enable copper clad laminate manufacturers to detect and take early corrective actions that will ultimately save time and money while establishing a uniform quality.

scholarly journals Near-Infrared Spectroscopy (NIRS) as a Method for Biological Sex Discrimination in the Endangered Houston Toad (Anaxyrus houstonensis)

2021 ◽  
Vol 5 (1) ◽  
pp. 4
Author(s):  
Li-Dunn Chen ◽  
Mariana Santos-Rivera ◽  
Isabella J. Burger ◽  
Andrew J. Kouba ◽  
Diane M. Barber ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
External Validation ◽  
Sex Discrimination ◽  
Amphibian Species ◽  
Linear Discriminant ◽  
Biological Sex ◽  
Non Invasive ◽  
Houston Toad

Biological sex is one of the more critically important physiological parameters needed for managing threatened animal species because it is crucial for informing several of the management decisions surrounding conservation breeding programs. Near-infrared spectroscopy (NIRS) is a non-invasive technology that has been recently applied in the field of wildlife science to evaluate various aspects of animal physiology and may have potential as an in vivo technique for determining biological sex in live amphibian species. This study investigated whether NIRS could be used as a rapid and non-invasive method for discriminating biological sex in the endangered Houston toad (Anaxyrus houstonensis). NIR spectra (N = 396) were collected from live A. houstonensis individuals (N = 132), and distinct spectral patterns between males and females were identified using chemometrics. Linear discriminant analysis (PCA-LDA) classified the spectra from each biological sex with accuracy ≥ 98% in the calibration and internal validation datasets and 94% in the external validation process. Through the use of NIRS, we have determined that unique spectral signatures can be holistically captured in the skin of male and female anurans, bringing to light the possibility of further application of this technique for juveniles and sexually monomorphic species, whose sex designation is important for breeding-related decisions.

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