Measurement of the Concentrations of Mannosyl Erythritol Lipid and Soybean Oil in the Glycolipid Fermentation Process Using near Infrared Spectroscopy

2002 ◽  
Vol 10 (1) ◽  
pp. 53-61 ◽  
Author(s):  
Kazuhiro Nakamichi ◽  
Ken-Ichiro Suehara ◽  
Yasuhisa Nakano ◽  
Koji Kakugawa ◽  
Masahiro Tamai ◽  
...  
Keyword(s):  
Soybean Oil ◽  
Ethyl Acetate ◽  
Spectral Data ◽  
Standard Error ◽  
Fermentation Process ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Raw Material ◽  
Second Derivative ◽  
Calibration Equation

In a glycolipid fermentation, mannosyl erythritol lipid (MEL) is produced from soybean oil added to a medium as a source of carbon. A measurement system for the concentrations of MEL and soybean oil in the fermentation process has been developed using near infrared (NIR) spectroscopy. MEL and soybean oil in the culture broth were extracted with ethyl acetate. NIR spectra of the ethyl acetate extract were measured in the wavelength range between 400 and 2500 nm at 2 nm intervals. The absorption caused by MEL was observed at 1436, 1920 and 2052 nm. To obtain a calibration equation, a multiple linear regression (MLR) was carried out between the second derivative NIR spectral data at 2040 and 1312 nm and MEL concentrations obtained using thin-layer chromatography with a flame-ionisation detector (TLC/FID) method. The values of the regression coefficient ( R) and the standard error of calibration ( SEC) were 0.994 and 0.48 g L−1, respectively. The absorption caused by soybean oil was observed at 1208, 1716, 1766, 2182 and 2302 nm. A calibration equation for soybean oil was formulated with the second derivative NIR spectral data at 2178 and 2090 nm. The values of R and SEC were 0.974 and 0.77 g L−1, respectively. After validation of the calibration equation, good agreement was observed between the results of the TLC/FID method and those of the NIR method for both constituents. The values of the correlation coefficient ( r) for MEL and the standard error of prediction ( SEP) were 0.994 and 0.45 g L−1, respectively. The values of r and SEP for soybean oil were 0.979 and 0.56 g L−1, respectively. The NIR method was applied to the measurement of the concentrations of MEL and soybean oil in an actual fermentation process and good results were obtained. The study indicates that NIR spectroscopy is a useful method for the measurement of the raw material and product in glycolipid fermentation.

Development of near Infrared Analysis of Faeces to Estimate Non-Grass Proportions in Diets Selected by Cattle Grazing Tropical Pastures

2008 ◽  
Vol 16 (5) ◽  
pp. 471-480 ◽  
Author(s):  
David B. Coates ◽  
Rob M. Dixon
Keyword(s):  
Standard Error ◽  
Nutritive Value ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Grass Species ◽  
Infrared Analysis ◽  
Tropical Pastures ◽  
Calibration Equation ◽  
Ruminant Diets ◽  
Modified Formula

Grass (monocots) and non-grass (dicots) proportions in ruminant diets are important nutritionally because the non-grasses are usually higher in nutritive value, particularly protein, than the grasses, especially in tropical pastures. For ruminants grazing tropical pastures where the grasses are C4 species and most non-grasses are C3 species, the ratio of 13C/12C in diet and faeces, measured as δ13C‰, is proportional to dietary non-grass%. This paper describes the development of a faecal near infrared (NIR) spectroscopy calibration equation for predicting faecal δ13C from which dietary grass and non-grass proportions can be calculated. Calibration development used cattle faeces derived from diets containing only C3 non-grass and C4 grass components, and a series of expansion and validation steps was employed to develop robustness and predictive reliability. The final calibration equation contained 1637 samples and faecal δ13C range (‰) of [12.27]–[27.65]. Calibration statistics were: standard error of calibration (SEC) of 0.78, standard error of cross-validation (SECV) of 0.80, standard deviation ( SD) of reference values of 3.11 and R2 of 0.94. Validation statistics for the final calibration equation applied to 60 samples were: standard error of prediction ( SEP) of 0.87, bias of −0.15, R2 of 0.92 and RPD of 3.16. The calibration equation was also tested on faeces from diets containing C4 non-grass species or temperate C3 grass species. Faecal δ13C predictions indicated that the spectral basis of the calibration was not related to 13C/12C ratios per se but to consistent differences between grasses and non-grasses in chemical composition and that the differences were modified by photosynthetic pathway. Thus, although the calibration equation could not be used to make valid faecal δ13C predictions when the diet contained either C3 grass or C4 non-grass, it could be used to make useful estimates of dietary non-grass proportions. It could also be used to make useful estimates of non-grass in mixed C3 grass/non-grass diets by applying a modified formula to calculate non-grass from predicted faecal δ13C. The development of a robust faecal-NIR calibration equation for estimating non-grass proportions in the diets of grazing cattle demonstrated a novel and useful application of NIR spectroscopy in agriculture.

Estimation of soil organic matter using proximal and satellite sensors after a wildfire in Mediterranean Croatia

2021 ◽  
Author(s):  
Iva Hrelja ◽  
Ivana Šestak ◽  
Igor Bogunović
Keyword(s):  
Organic Matter ◽  
Soil Organic Matter ◽  
Spectral Data ◽  
Near Infrared ◽  
Mean Squared Error ◽  
Nir Spectroscopy ◽  
Soil Samples ◽  
Least Squares Regression ◽  
Macro Scale ◽  
Sentinel 2

<p>Spectral data obtained from optical spaceborne sensors are being recognized as a valuable source of data that show promising results in assessing soil properties on medium and macro scale. Combining this technique with laboratory Visible-Near Infrared (VIS-NIR) spectroscopy methods can be an effective approach to perform robust research on plot scale to determine wildfire impact on soil organic matter (SOM) immediately after the fire. Therefore, the objective of this study was to assess the ability of Sentinel-2 superspectral data in estimating post-fire SOM content and comparison with the results acquired with laboratory VIS-NIR spectroscopy.</p><p>The study is performed in Mediterranean Croatia (44° 05’ N; 15° 22’ E; 72 m a.s.l.), on approximately 15 ha of fire affected mixed <em>Quercus ssp.</em> and <em>Juniperus ssp.</em> forest on Cambisols. A total of 80 soil samples (0-5 cm depth) were collected and geolocated on August 22<sup>nd</sup> 2019, two days after a medium to high severity wildfire. The samples were taken to the laboratory where soil organic carbon (SOC) content was determined via dry combustion method with a CHNS analyzer. SOM was subsequently calculated by using a conversion factor of 1.724. Laboratory soil spectral measurements were carried out using a portable spectroradiometer (350-1050 nm) on all collected soil samples. Two Sentinel-2 images were downloaded from ESAs Scientific Open Access Hub according to the closest dates of field sampling, namely August 31<sup>st</sup> and September 5<sup>th </sup>2019, each containing eight VIS-NIR and two SWIR (Short-Wave Infrared) bands which were extracted from bare soil pixels using SNAP software. Partial least squares regression (PLSR) model based on the pre-processed spectral data was used for SOM estimation on both datasets. Spectral reflectance data were used as predictors and SOM content was used as a response variable. The accuracy of the models was determined via Root Mean Squared Error of Prediction (RMSE<sub>p</sub>) and Ratio of Performance to Deviation (RPD) after full cross-validation of the calibration datasets.</p><p>The average post-fire SOM content was 9.63%, ranging from 5.46% minimum to 23.89% maximum. Models obtained from both datasets showed low RMSE<sub>p </sub>(Spectroscopy dataset RMSE<sub>p</sub> = 1.91; Sentinel-2 dataset RMSE<sub>p</sub> = 0.99). RPD values indicated very good predictions for both datasets (Spectrospcopy dataset RPD = 2.72; Sentinel-2 dataset RPD = 2.22). Laboratory spectroscopy method with higher spectral resolution provided more accurate results. Nonetheless, spaceborne method also showed promising results in the analysis and monitoring of SOM in post-burn period.</p><p><strong>Keywords:</strong> remote sensing, soil spectroscopy, wildfires, soil organic matter</p><p><strong>Acknowledgment: </strong>This work was supported by the Croatian Science Foundation through the project "Soil erosion and degradation in Croatia" (UIP-2017-05-7834) (SEDCRO). Aleksandra Perčin is acknowledged for her cooperation during the laboratory work.</p>

scholarly journals Separation of Cultivars of Soybeans by Chemometric Methods Using Near Infrared Spectroscopy

2018 ◽  
Vol 10 (4) ◽  
pp. 351
Author(s):  
João S. Panero ◽  
Henrique E. B. da Silva ◽  
Pedro S. Panero ◽  
Oscar J. Smiderle ◽  
Francisco S. Panero ◽  
...  
Keyword(s):  
Spectral Data ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Relevant Information ◽  
Whole Grains ◽  
Signal Correction ◽  
Different Types ◽  
Linear Fit

Near Infrared (NIR) Spectroscopy technique combined with chemometrics methods were used to group and identify samples of different soy cultivars. Spectral data, collected in the range of 714 to 2500 nm (14000 to 4000 cm-1), were obtained from whole grains of four different soybean cultivars and were submitted to different types of pre-treatments. Chemometrics algorithms were applied to extract relevant information from the spectral data, to remove the anomalous samples and to group the samples. The best results were obtained considering the spectral range from 1900.6 to 2187.7 nm (5261.4 cm-1 to 4570.9 cm-1) and with spectral treatment using Multiplicative Signal Correction (MSC) + Baseline Correct (linear fit), what made it possible to the exploratory techniques Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA) to separate the cultivars. Thus, the results demonstrate that NIR spectroscopy allied with de chemometrics techniques can provide a rapid, nondestructive and reliable method to distinguish different cultivars of soybeans.

Focusing near Infrared Spectroscopy on the Business Objectives of Modern Pharmaceutical Production

1998 ◽  
Vol 6 (A) ◽  
pp. A13-A19 ◽  
Author(s):  
T.G. Axon ◽  
R. Brown ◽  
S.V. Hammond ◽  
S.J. Maris ◽  
F. Ting
Keyword(s):  
High Performance ◽  
Near Infrared ◽  
Real Life ◽  
Nir Spectroscopy ◽  
Raw Material ◽  
Develop Model ◽  
Data Sets ◽  
Total Quality ◽  
Model Free ◽  
Pharmaceutical Production

The early use of near infrared (NIR) spectroscopy in the pharmaceutical industry was for raw material identification, later moving on to some conventional “calibrations” for various ingredients in a variety of sample types. The approach throughout this development process has always been “conventional” with one measurement by NIR directly replacing some other slower method, be it Mid-IR identification, or determinations by Karl Fischer, high performance liquid chromatography (HPLC)etc. A significant change in approach was demonstrated by Plugge and Van der Vlies1 in 1993, where a qualitative system was used to provide “quantitative like” answers for potency of a drug substance. Following on from that key paper, there has been a realisation that the qualitative analysis ability of NIR, has the potential to be a powerful tool for process investigation, control and validation. The final step has been to develop “model free” approaches, that consider individual data sets as unique systems, and present the opportunity for NIR to escape the shackles of “calibration” in one form or another. The use of qualitative, or model free, approaches to NIR spectroscopy provides an effective tool for satisfying many of the demands of modern pharmaceutical production. “Straight through production,” “right first time,” “short cycle time” and “total quality management” philosophies can be realised. Eventually the prospect of parametric release may be materialised with a strong contribution from NIR spectroscopy. This paper will illustrate the above points with some real life examles.

scholarly journals Analysis of the Acid Detergent Fibre Content in Turnip Greens and Turnip Tops (Brassica rapa L. Subsp. rapa) by Means of Near-Infrared Reflectance

Foods ◽  
2019 ◽  
Vol 8 (9) ◽  
pp. 364 ◽  
Author(s):  
Sara Obregón-Cano ◽  
Rafael Moreno-Rojas ◽  
Ana María Jurado-Millán ◽  
María Elena Cartea-González ◽  
Antonio De Haro-Bailón
Keyword(s):  
Standard Error ◽  
Near Infrared ◽  
Mathematical Treatment ◽  
Second Derivative ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance ◽  
First Derivative ◽  
Acid Detergent Fibre ◽  
Turnip Greens ◽  
Turnip Tops

Standard wet chemistry analytical techniques currently used to determine plant fibre constituents are costly, time-consuming and destructive. In this paper the potential of near-infrared reflectance spectroscopy (NIRS) to analyse the contents of acid detergent fibre (ADF) in turnip greens and turnip tops has been assessed. Three calibration equations were developed: in the equation without mathematical treatment the coefficient of determination (R2) was 0.91, in the first-derivative treatment equation R2 = 0.95 and in the second-derivative treatment R2 = 0.96. The estimation accuracy was based on RPD (the ratio between the standard deviation and the standard error of validation) and RER (the ratio between the range of ADF of the validation as a whole and the standard error of prediction) of the external validation. RPD and RER values were of 2.75 and 9.00 for the treatment without derivative, 3.41 and 11.79 with first-derivative, and 3.10 and 11.03 with second-derivative. With the acid detergent residue spectrum the wavelengths were identified and associated with the ADF contained in the sample. The results showed a great potential of NIRS for predicting ADF content in turnip greens and turnip tops.

Estimation of Eucalyptus delegatensis wood properties by near infrared spectroscopy

2001 ◽  
Vol 31 (10) ◽  
pp. 1671-1675 ◽  
Author(s):  
L R Schimleck ◽  
R Evans ◽  
J Ilic
Keyword(s):  
Large Scale ◽  
Near Infrared ◽  
Microfibril Angle ◽  
Nir Spectroscopy ◽  
Wood Properties ◽  
Raw Material ◽  
Modulus Of Rupture ◽  
Single Type ◽  
Wide Range ◽  
Eucalyptus Delegatensis

The use of calibrated near infrared (NIR) spectroscopy for the prediction of a range solid wood properties is described. The methods developed are applicable to large-scale nondestructive forest resource assessment and to tree breeding and silvicultural programs. A series of Eucalyptus delegatensis R.T. Baker (alpine ash) samples were characterized in terms of density, longitudinal modulus of elasticity (EL), microfibril angle (MFA), and modulus of rupture (MOR). NIR spectra were obtained from the radial–longitudinal face of each sample and used to generate calibrations for the measured physical properties. The relationships were good in all cases, with coefficients of determination ranging from 0.77 for MOR through 0.90 for EL to 0.93 for stick density. In view of the rapidly expanding range of applications for this technique, it is concluded that appropriately calibrated NIR spectroscopy could form the basis of a "universal" testing instrument capable of predicting a wide range of product properties from a single type of spectrum obtained from the product or from the raw material.

scholarly journals Use of Visible and Near-Infrared Reflectance Spectroscopy Models to Determine Soil Erodibility Factor (K) in an Ecologically Restored Watershed

2020 ◽  
Vol 12 (18) ◽  
pp. 3103
Author(s):  
Qinghu Jiang ◽  
Yiyun Chen ◽  
Jialiang Hu ◽  
Feng Liu
Keyword(s):  
Land Use ◽  
Spectral Data ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Soil Erodibility ◽  
Infrared Reflectance ◽  
Cultivated Land ◽  
Near Infrared Reflectance ◽  
Natural Land ◽  
Soil Erodibility Factor

This study aimed to assess the ability of using visible and near-infrared reflectance (Vis–NIR) spectroscopy to quantify soil erodibility factor (K) rapidly in an ecologically restored watershed. To achieve this goal, we explored the performance and transferability of the developed spectral models in multiple land-use types: woodland, shrubland, terrace, and slope farmland (the first two types are natural land and the latter two are cultivated land). Subsequently, we developed an improved approach by combining spectral data with related topographic variables (i.e., elevation, watershed location, slope height, and normalized height) to estimate K. The results indicate that the calibrated spectral model using total samples could estimate K factor effectively (R2CV = 0.71, RMSECV = 0.0030 Mg h Mj−1 mm−1, and RPDCV = 1.84). When predicting K in the new samples, models performed well in natural land soils (R2P = 0.74, RPDP = 1.93) but failed in cultivated land soils (R2P = 0.24, RPDP = 0.99). Furthermore, the developed models showed low transferability between the natural and cultivated land datasets. The results also indicate that the combination of spectral data with topographic variables could slightly increase the accuracies of K estimation in total and natural land datasets but did not work for cultivated land samples. This study demonstrated that the Vis–NIR spectroscopy could be used as an effective method in predicting K. However, the predictability and transferability of the calibrated models were land-use type dependent. Our study also revealed that the coupling of spectrum and environmental variable is an effective improvement of K estimation in natural landscape region.

scholarly journals Feasibility of Determination of Foodborne Microbe Contamination of Fresh-Cut Shredded Cabbage Using SW-NIR

2019 ◽  
Vol 1 (2) ◽  
pp. 246-256
Author(s):  
Benjamaporn Matulaprungsan ◽  
Chalermchai Wongs-Aree ◽  
Pathompong Penchaiya ◽  
Phonkrit Maniwara ◽  
Sirichai Kanlayanarat ◽  
...  
Keyword(s):  
Quantitative Measurement ◽  
Partial Least Squares Regression ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Sample Type ◽  
Least Squares Regression ◽  
Calibration Equation ◽  
E Coli ◽  
Total Bacteria

Shredded cabbage is widely used in much ready-to-eat food. Therefore, rapid methods for detecting and monitoring the contamination of foodborne microbes is essential. Short wavelength near infrared (SW-NIR) spectroscopy was applied on two types of solutions, a drained solution from the outer surface of the shredded cabbage (SC) and a ground solution of shredded cabbage (GC) which were inoculated with a mixture of two bacterial suspensions, Escherichia coli and Salmonella typhimurium. NIR spectra of around 700 to 1100 nm were collected from the samples after 0, 4, and 8 h at 37 °C incubation, along with the growth of total bacteria, E. coli and S. typhimurium. The raw spectra were obtained from both sample types, clearly separated with the increase of incubation time. The first derivative, a Savitzky–Golay pretreatment, was applied on the GC spectra, while the second derivative was applied on the SC spectra before developing the calibration equation, using partial least squares regression (PLS). The obtained correlation (r) of the SC spectra was higher than the GC spectra, while the standard error of cross-validation (SECV) was lower. The ratio of prediction of deviation (RPD) of the SC spectra was higher than the GC spectra, especially in total bacteria, quite normal for the E. coli but relatively low for the S. typhimurium. The prediction results of microbial spoilage were more reliable on the SC than on the GC spectra. Total bacterial detection was best for quantitative measurement, as E. coli contamination could only be distinguished between high and low values. Conversely, S. typhimurium predictions were not optimal for either sample type. The SW-NIR shows the feasibility for detecting the existence of microbes in the solution obtained from SC, but for a more specific application for discrimination or quantitation is needed, proving further research in still required.

Evaluation of Soya Bean Quality for Tofu Processing by near Infrared Spectroscopy

1998 ◽  
Vol 6 (A) ◽  
pp. A325-A328
Author(s):  
T.L. Hong ◽  
Samson C.S. Tsou ◽  
S.-J. Tsai
Keyword(s):  
Water Absorption ◽  
Absorption Rate ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Soya Bean ◽  
Control Measures ◽  
Raw Material ◽  
Prolonged Storage ◽  
Tannin Content ◽  
Water Absorption Rate

Soya bean, as the raw material for tofu processing, is required to be of high quality. The variety characteristics, storage conditions and harvesting seasons of soya bean are the major contributors to soya bean quality. This study attempted to use near infrared (NIR) spectroscopy to evaluate the processing quality of soya bean. Evaluation models using NIR spectroscopy were developed for the analyses of tannin content, degrees of lipid oxidation, detection of harvest seasons and measurement of water absorption rate. Simulation experiments demonstrated that these models were not only able to analyse major compositions of soya bean, but also to sort out soya bean samples and their suitability for tofu making regardless of various defects, such as high tannin content, low water absorption rate, prolonged storage and unfavourable harvest seasons. Statistic analysis suggested that these models could be used as mass-screening techniques for breeding programmes and quality control measures in tofu-processing factories.

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.

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