scholarly journals The rapid determination of volatile fatty acid number in para rubber latex using fourier transform-near infrared spectroscopy based on quantification and discrimination model

2015 ◽  
Vol 08 (05) ◽  
pp. 1550042 ◽  
Author(s):  
Sureeporn Narongwongwattana ◽  
Ronnarit Rittiron ◽  
Chin Hock Lim
Keyword(s):  
Fatty Acid ◽  
Volatile Fatty Acid ◽  
Near Infrared ◽  
Rapid Determination ◽  
Nir Spectroscopy ◽  
Acid Number ◽  
Rubber Latex ◽  
Discriminant Model ◽  
Standard Normal Variate ◽  
Performance Check

Volatile Fatty Acid number (VFA no.) is one of the parameters indicating the state of quality of Para rubber latex at that particular time. Most factories analyze this parameter using standard analytical method as in ISO 506:1992(E). Nevertheless, this procedure is complicated, chemical and time consuming, as well as skilled analyst required. Therefore, near infrared (NIR) spectroscopy which is rapid, accurate and nonchemicals method was applied to determine the VFA no. in field latex and concentrated latex based on quantification and discriminant model. The best calibration equation was obtained from standard normal variate (SNV) spectra in the region of 6109.7–5770.3, 4613.1–4242.9 cm-1 with R = 0.832, SECV = 0.036 and no bias. From the performance check, statistically it was found that SECV and bias were low enough for practical acceptance and the predicted VFA no. was not different significantly from actual VFA no. at 95% confidence intervals. In addition, discriminant model was developed to separate good quality latex from the deteriorated latex using VFA no. at 0.06 as standard as in ISO 2004:2010(E). The discriminant model can be used to screen the latex with overall accuracy of 91.86% in validation set.

Near infrared spectroscopy for rapid determination of solids content of amino resins

2020 ◽  
Vol 28 (5-6) ◽  
pp. 344-350
Author(s):  
M Gonçalves ◽  
NT Paiva ◽  
JM Ferra ◽  
J Martins ◽  
F Magalhães ◽  
...  
Keyword(s):  
Near Infrared ◽  
Rapid Determination ◽  
Nir Spectroscopy ◽  
Coefficient Of Determination ◽  
Water Addition ◽  
Reliable Technique ◽  
Standard Normal Variate ◽  
And Performance ◽  
Solids Content

Near infrared (NIR) spectroscopy is a fast and reliable technique for assessing properties of amino resins. One important property that defines the cost and performance of these resins is the solids content (SC). This work studied the prediction of SC of amino resins by combining NIR spectroscopy with partial least squares (PLS) regression. A total of 990 industrial NIR spectra of amino resins were obtained and split randomly by a ratio of 2/3 for calibration and 1/3 for validation. The best model achieved a root mean-square error of prediction (RMSEP) of 0.32% (m/m) and a coefficient of determination of prediction ([Formula: see text]) of 81%. standard normal variate (SNV) was found to be the NIR pre-processing that provided the best results for model construction. Addition of water to two amino resins showed that the NIR model does not respond to the water addition, despite water making great contribution to the SC value. An inference that can be obtained from this is that the NIR model of amino resins uses NIR properties of amino resins that relate to the SC and from there predict the most probable SC, instead of looking at all the components that affect the SC of amino resins.

Prediction of Fatty Acid Content in Beef Neck Lean by near Infrared Reflectance Analysis

1998 ◽  
Vol 6 (1) ◽  
pp. 229-234 ◽  
Author(s):  
William R. Windham ◽  
W.H. Morrison
Keyword(s):  
Fatty Acids ◽  
Fatty Acid ◽  
Near Infrared ◽  
Fatty Acid Content ◽  
Reference Method ◽  
Nir Spectroscopy ◽  
Coefficient Of Determination ◽  
Longissimus Dorsi ◽  
Near Infrared Reflectance ◽  
Total Fatty Acids

Near infrared (NIR) spectroscopy in the prediction of individual and total fatty acids of bovine M. Longissimus dorsi neck muscles has been studied. Beef neck lean was collected from meat processing establishments using advanced meat recovery systems and hand-deboning. Samples ( n = 302) were analysed to determine fatty acid (FA) composition and scanned from 400 to 2498 nm. Total saturated and unsaturated FA values ranged from 43.2 to 62.0% and 38.3 to 56.2%, respectively. Results of partial least squares (PLS) modeling shown reasonably accurate models were attained for total saturate content [standard error of performance ( SEP = 1.10%); coefficient of determination on the validation set ( r2 = 0.77)], palmitic ( SEP = 0.94%; r2 = 0.69), unsaturate ( SEP = 1.13%; r2 = 0.77), and oleic ( SEP = 0.97; r2 = 0.78). Prediction of other individual saturated and unsaturated FAs was less accurate with an r2 range of 0.10 to 0.53. However, the sum of individual predicted saturated and unsaturated FA was acceptable compared with the reference method ( SEP = 1.10 and 1.12%, respectively). This study shows that NIR can be used to predict accurately total fatty acids in M. Longissimus dorsi muscle.

Near infrared spectroscopy as an alternative method for rapid determination of the solidification point of 2,4,6-trinitrotoluene in production

2019 ◽  
Vol 27 (4) ◽  
pp. 286-292
Author(s):  
Chongchong She ◽  
Min Li ◽  
Yunhui Hou ◽  
Lizhen Chen ◽  
Jianlong Wang ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Rapid Determination ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Quality Parameter ◽  
Solidification Point ◽  
Fast Analysis ◽  
Online Application

The solidification point is a key quality parameter for 2,4,6-trinitrotoluene (TNT). The traditional solidification point measurement method of TNT is complicated, dangerous, not environmentally friendly and time-consuming. Near infrared spectroscopy (NIR) analysis technology has been applied successfully in the chemical, petroleum, food, and agriculture sectors owing to its characteristics of fast analysis, no damage to the sample and online application. The purpose of this study was to study near infrared spectroscopy combined with chemometric methods to develop a fast and accurate quantitative analysis method for the solidification point of TNT. The model constructed using PLS regression was successful in predicting the solidification point of TNT ([Formula: see text] = 0.999, RMSECV = 0.19, RPDCa = 33.5, [Formula: see text] = 0.19, [Formula: see text] = 0.999). Principal component analysis shows that the model could identify samples from different reactors. The results clearly demonstrate that the solidification point can be measured in a short time by NIR spectroscopy without any pretreatment for the sample and skilled laboratory personnel.

scholarly journals Rapid Determination of Nutritional Parameters of Pasta/Sauce Blends by Handheld Near-Infrared Spectroscopy

Molecules ◽  
2019 ◽  
Vol 24 (11) ◽  
pp. 2029 ◽  
Author(s):  
Marina D. G. Neves ◽  
Ronei J. Poppi ◽  
Heinz W. Siesler
Keyword(s):  
Near Infrared ◽  
Rapid Determination ◽  
Analytical Data ◽  
Nir Spectroscopy ◽  
Nutritional Parameters ◽  
Direct To Consumer ◽  
Calibration Models ◽  
Food And Beverage ◽  
The Individual ◽  
Near Future

Nowadays, near infrared (NIR) spectroscopy has experienced a rapid progress in miniaturization (instruments < 100 g are presently available), and the price for handheld systems has reached the < $500 level for high lot sizes. Thus, the stage is set for NIR spectroscopy to become the technique of choice for food and beverage testing, not only in industry but also as a consumer application. However, contrary to the (in our opinion) exaggerated claims of some direct-to-consumer companies regarding the performance of their “food scanners” with “cloud evaluation of big data”, the present publication will demonstrate realistic analytical data derived from the development of partial least squares (PLS) calibration models for six different nutritional parameters (energy, protein, fat, carbohydrates, sugar, and fiber) based on the NIR spectra of a broad range of different pasta/sauce blends recorded with a handheld instrument. The prediction performance of the PLS calibration models for the individual parameters was double-checked by cross-validation (CV) and test-set validation. The results obtained suggest that in the near future consumers will be able to predict the nutritional parameters of their meals by using handheld NIR spectroscopy under every-day life conditions.

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 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.

Mid-Infrared and Near-Infrared Spectral Properties of Mycorrhizal and Non-Mycorrhizal Root Cultures

2009 ◽  
Vol 63 (5) ◽  
pp. 494-500 ◽  
Author(s):  
Francisco J. Calderón ◽  
Veronica Acosta-Martinez ◽  
David D. Douds ◽  
James B. Reeves ◽  
Merle F. Vigil
Keyword(s):  
Fatty Acid ◽  
Glomus Intraradices ◽  
Near Infrared ◽  
Arbuscular Mycorrhizal ◽  
Am Fungi ◽  
Spectroscopic Properties ◽  
Good Resolution ◽  
Mid Infrared ◽  
Standard Normal Variate ◽  
Pure Cultures

We investigated the Fourier-transformed mid-infrared (MIR) and near-infrared (NIR) spectroscopic properties of mycorrhizal (M) and non-mycorrhizal (NM) carrot roots with the goal of finding infrared markers for colonization by arbuscular mycorrhizal (AM) fungi. The roots were cultured with or without the AM fungus Glomus intraradices under laboratory conditions. A total of 50 M and NM samples were produced after pooling subsamples. The roots were dried, ground, and scanned separately for the NIR and MIR analyses. The root samples were analyzed for fatty acid composition in order to confirm mycorrhizal infection and to determine the presence of fatty acid markers. Besides the roots, fatty acid standards, pure cultures of saprophytic fungi, and chitin were also scanned in order to identify spectral bands likely to be found in M samples. Principal components analysis (PCA) was used to illustrate spectral differences between the M and NM root samples. The NIR analysis achieved good resolution with the raw spectral data and no pretreatment was needed to obtain good resolution in the PCA analysis of the NIR data. Standard normal variate and detrending pretreatment improved the resolution between M and NM in the MIR range. The PCA loadings and/or the spectral subtraction of selected samples showed that M roots are characterized by absorbances at or close to 400 cm−1, 1100–1170 cm−1, 1690 cm−1, 2928 cm−1, and 5032 cm−1. The NM samples had characteristic absorbances at or near 1734 cm−1, 3500 cm−1, 4000 cm−1, 4389 cm−1, and 4730 cm−1. Some of the bands that differentiate M from NM roots are prominent in the spectra of pure fungal cultures, chitin, and fatty acids. Our results show that mycorrhizal and nonmycorrhizal root tissues can be differentiated via MIR and NIR spectra with the advantage that the same samples can then be used for other analyses.

scholarly journals Rapid Discrimination of the Geographical Origins of an Oolong Tea (Anxi-Tieguanyin) by Near-Infrared Spectroscopy and Partial Least Squares Discriminant Analysis

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Si-Min Yan ◽  
Jun-Ping Liu ◽  
Lu Xu ◽  
Xian-Shu Fu ◽  
Hai-Feng Cui ◽  
...  
Keyword(s):  
Discriminant Analysis ◽  
Least Squares ◽  
Partial Least Squares ◽  
Sensitivity And Specificity ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Classification Model ◽  
Standard Normal Variate ◽  
Statistical Model Selection ◽  
Rapid Discrimination

This paper focuses on a rapid and nondestructive way to discriminate the geographical origin of Anxi-Tieguanyin tea by near-infrared (NIR) spectroscopy and chemometrics. 450 representative samples were collected from Anxi County, the original producing area of Tieguanyin tea, and another 120 Tieguanyin samples with similar appearance were collected from unprotected producing areas in China. All these samples were measured by NIR. The Stahel-Donoho estimates (SDE) outlyingness diagnosis was used to remove the outliers. Partial least squares discriminant analysis (PLSDA) was performed to develop a classification model and predict the authenticity of unknown objects. To improve the sensitivity and specificity of classification, the raw data was preprocessed to reduce unwanted spectral variations by standard normal variate (SNV) transformation, taking second-order derivatives (D2) spectra, and smoothing. As the best model, the sensitivity and specificity reached 0.931 and 1.000 with SNV spectra. Combination of NIR spectrometry and statistical model selection can provide an effective and rapid method to discriminate the geographical producing area of Anxi-Tieguanyin.

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