scholarly journals Near infrared spectra features of cocoa pod husk used for feedstuff

2021 ◽  
Vol 922 (1) ◽  
pp. 012011
Author(s):  
Samadi ◽  
S Wajizah ◽  
Z Zulfahrizal
Keyword(s):  
Spectral Data ◽  
Near Infrared ◽  
Animal Feed ◽  
Raw Materials ◽  
Chemical Properties ◽  
Principal Component ◽  
Chemical Information ◽  
Crude Fibre ◽  
Aceh Province ◽  
Cocoa Pod Husk

Abstract This presented study aimed to study the near infrared spectroscopic features of cocoa pod husk samples used as raw materials for animal feedstuff. Spectral data of organic material samples contains chemical properties information that can be revealed through modelling, Thus, the study of this features is essential to assess and reveal buried respective information. Cocoa pod husk samples were obtained from several districts in Aceh Province, grinded and prepared as bulk samples. Diffuse reflectance spectral data for a total of 30 bulk cocoa pod husk samples were acquired and recorded in wavelength range from 1000 to 2500 nm. Spectral data were firstly projected onto principal component analysis to observe similarities among samples. Spectra correction, namely mean normalization was employed to enhance spectra features. The results showed that several chemical information related to cocoa properties can be revealed such as dry matter, crude protein, crude fibre, ether extract, nitrogen-free extract and ash content due to the second and third overtones pf combination bands O-H, C-O-H and N-H. Optimum wavelength for estimating cocoa pod husk attributes are in 1217, 1405-1474 nm, 1629 nm, 1906-1979 nm, and 2283 nm. Based on obtained study, it may conclude that several quality attributes of animal feed samples further can be determined by means of near infrared spectroscopy approach.

scholarly journals Hazardous Noxious Substance Detection Based on Ground Experiment and Hyperspectral Remote Sensing

2021 ◽  
Vol 13 (2) ◽  
pp. 318
Author(s):  
Jae-Jin Park ◽  
Kyung-Ae Park ◽  
Pierre-Yves Foucher ◽  
Philippe Deliot ◽  
Stephane Le Floch ◽  
...  
Keyword(s):  
Near Infrared ◽  
Spectral Characteristics ◽  
Chemical Properties ◽  
Principal Component ◽  
Component Analysis ◽  
Noise Removal ◽  
Vertex Component Analysis ◽  
Shortwave Infrared ◽  
Spectral Mixture ◽  
Physical And Chemical

With an increase in the overseas maritime transport of hazardous and noxious substances (HNSs), HNS-related spill accidents are on the rise. Thus, there is a need to completely understand the physical and chemical properties of HNSs. This can be achieved through establishing a library of spectral characteristics with respect to wavelengths from visible and near-infrared (VNIR) bands to shortwave infrared (SWIR) wavelengths. In this study, a ground HNS measurement experiment was conducted for artificially spilled HNS by using two hyperspectral cameras at VNIR and SWIR wavelengths. Representative HNSs such as styrene and toluene were spilled into an outdoor pool and their spectral characteristics were obtained. The relative ratio of HNS to seawater decreased and increased at 550 nm and showed different constant ratios at the SWIR wavelength. Noise removal and dimensional compression procedures were conducted by applying principal component analysis on HNS hyperspectral images. Pure HNS and seawater endmember spectra were extracted using four spectral mixture techniques—N-FINDR, pixel purity index (PPI), independent component analysis (ICA), and vertex component analysis (VCA). The accuracy of detection values of styrene and toluene through the comparison of the abundance fraction were 99.42% and 99.56%, respectively. The results of this study are useful for spectrum-based HNS detection in marine HNS accidents.

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.

Industrial Applications of Near-Infrared Chemical Imaging Microscopy

2001 ◽  
Vol 7 (S2) ◽  
pp. 162-163
Author(s):  
EN Lewis ◽  
LH Kidder ◽  
KS Haber
Keyword(s):  
Spatial Distribution ◽  
Near Infrared ◽  
Imaging System ◽  
Raw Materials ◽  
Single Point ◽  
Nir Spectroscopy ◽  
Chemical Imaging ◽  
Industrial Applications ◽  
Chemical Information ◽  
Spatially Resolved

Single point near-infrared (NIR) spectroscopy is used extensively for characterizing raw materials and finished products in a wide variety of industries: polymers, paper, film, pharmaceuticals, paintings and coatings, food and beverages, agricultural products. As advanced industrial materials become more complex, their functionality is often determined by the spatial distribution of their discrete sample constituents. However, conventional single point NIR spectroscopy cannot adequately probe the interrelationship between the spatial distribution of sample components with the physical properties of the sample. to fully characterize these samples, it is necessary to probe simultaneously spatial and chemical heterogeneity and correlate these properties with sample characteristics.Recently, we have developed a novel NIR imaging spectrometer that can deliver spatially resolved chemical information very rapidly. in contrast to conventional, single point NIR spectrometers, the imaging system uses an infrared focal-plane array (FPA) to collect up to 76,800 complete spectra, one for each pixel on the array, in approximately one minute.

Identification Performance of Different Types of Handheld Near-Infrared (NIR) Spectrometers for the Recycling of Polymer Commodities

2018 ◽  
Vol 72 (9) ◽  
pp. 1362-1370 ◽  
Author(s):  
Hui Yan ◽  
Heinz W. Siesler
Keyword(s):  
Near Infrared ◽  
Raw Materials ◽  
Discrimination Performance ◽  
Principal Component ◽  
Identification Performance ◽  
Score Plot ◽  
Discrimination Capability ◽  
Standard Normal Variate ◽  
Correct Assignment ◽  
Standard Normal

For sustainable utilization of raw materials and environmental protection, the recycling of the most common polymers—polyethylene (PE), polypropylene (PP), polyethylene terephthalate (PET), polyvinyl chloride (PVC), and polystyrene (PS)—is an extremely important issue. In the present communication, the discrimination performance of the above polymer commodities based on their near-infrared (NIR) spectra measured with four real handheld (<200 g) spectrometers based on different monochromator principles were investigated. From a total of 43 polymer samples, the diffuse reflection spectra were measured with the handheld instruments. After the original spectra were pretreated by second derivative and standard normal variate (SNV), principal component analysis (PCA) was applied and unknown samples were tested by soft independent modeling of class analogies (SIMCA). The results show that the five polymer commodities cluster in the score plots of their first three principal components (PCs) and, furthermore, samples in calibration and test sets can be correctly identified by SICMA. Thus, it was concluded that on the basis of the NIR spectra measured with the handheld spectrometers the SIMCA analysis provides a suitable analytical tool for the correct assignment of the type of polymer. Because the mean distance between clusters in the score plot reflects the discrimination capability for each polymer pair the variation of this parameter for the spectra measured with the different handheld spectrometers was used to rank the identification performance of the five polymer commodities.

scholarly journals PENDUGAAN KANDUNGAN ENERGI BRUTO TEPUNG IKAN MENGGUNAKAN TEKNOLOGI Near Infrared (NIR) (Prediction of the Gross Energy for Fishmeal using Near Infrared Reflectant (NIR) Technology)

2016 ◽  
Vol 1 (1) ◽  
pp. 33
Author(s):  
Ati Atul Quddus
Keyword(s):  
Standard Error ◽  
Coefficient Of Variation ◽  
Near Infrared ◽  
Animal Feed ◽  
Principal Component Regression ◽  
Principal Component ◽  
Calibration Equation ◽  
Bomb Calorimeter ◽  
Gross Energy ◽  
Pcr Method

Abstrak Penelitian ini bertujuan untuk menduga kandungan energi bruto tepung ikan untuk bahan pakan ternak menggunakan teknologi Near Infrared (NIR). Tepung ikan yang digunakan dalam penelitian ini diperoleh dari poultry shop yang ada di beberapa daerah di Indonesia dan industri pakan ternak. Penelitian ini menggunakan 50 tepung ikan. Tiga puluh lima sampel digunakan untuk kalibrasi, sedangkan 15 sampel digunakan untuk validasi. Pengukuran NIR reflektan menggunakan sistem NIR. Energi bruto diukur menggunakan bomb calorimeter. Data dianalisis dengan menggunakan regresi linier berganda (RLB) dan Principal Component Regression (PCR). Persamaan kalibrasi dari reflektan dianalisis menggunakan 29 panjang gelombang untuk memprediksi energi bruto. Hasil dari validasi menunjukkan akurasi yang tinggi dengan standar eror dan koefisien variasi untuk energi bruto yaitu 6,6 Kkal/Kg dan 0,2%. Persamaan kalibrasi dari metode PCR menggunakan data absorban. Hasil dari validasinya menunjukkan kurang akurasi dengan nilai standar eror dan koefisien variasi yaitu 119,2 Kkal/kg dan 4,16%. Kata kunci : energi bruto, NIR, RLB, PCR Abstract This experiment was aimed to predict gross energy (GE) content of fishmeal by using Near Infrared (NIR) technology. Fishmeal that was used in this experiment was obtained from the poultry shop in several regions in Indonesia and from animal feed industries. This experiment was conducted by using 50 fishmeals. Thirty five samples out of 50 samples fishmeal was used to develop the NIR of calibration and the rest 15 samples was used to test the accuracy of the calibration. NIR reflectant was measured by NIR system. Gross energy was measured by bomb calorimeter. Collected data were analyzed by using multivariate linier regression (MLR) and principal component regression (PCR). Calibration equation of reflectant was analyzed by using 29 wavelengths for predicting GE. The results of the validation indicated high accuracy with standard error and coefficient of variation for GE: SEp = 6.6 Kkal/Kg, CV = 0.2 % . Calibration equation was obtained from PCR method by using absorbent data. The result of the validation indicated less accuracy with standard error and coefficient of variation for GE: SEp = 119.92 Kkal/Kg, CV = 4.16% . Keywords : Gross Energy, Near infrared Reflectant (NIR), fishmeal, Multivariate Linier Regression (MLR), Principal Component Regression (PCR)

scholarly journals A Near Infrared Spectroscopic Discrimination of Noodle Flours Using a Principal-Component Analysis Coupled with Chemical Information.

2002 ◽  
Vol 18 (10) ◽  
pp. 1145-1150 ◽  
Author(s):  
Masanori KUMAGAI ◽  
Kikuko KARUBE ◽  
Tomoaki SATO ◽  
Naganori OHISA ◽  
Toshio AMANO ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Near Infrared ◽  
Principal Component ◽  
Component Analysis ◽  
Chemical Information ◽  
Infrared Spectroscopic

scholarly journals Comparative study of physicochemical properties of Tithonia diversifolia and Sunflower seed oils

2020 ◽  
Vol 5 (2) ◽  
pp. 69-77
Author(s):  
Israel Olusegun Otemuyiwa ◽  
Abolanle Saheed Adekunle ◽  
Julianah Funmilayo Adegbite ◽  
Olumuyiwa Sunday Falade
Keyword(s):  
Sunflower Oil ◽  
Raw Materials ◽  
Chemical Properties ◽  
Smoke Point ◽  
Biodiesel Production ◽  
Total Phenol Content ◽  
Tithonia Diversifolia ◽  
Crude Fibre ◽  
Sunflower Oils ◽  
Conventional Oils

This study investigates the physico-chemical properties of Tithonia diversifolia seed and oil using standard analytical methods and then compared the results with Sunflower oil. The results showed that Tithonia diversifolia seed contained 5.80% moisture, 18.83% crude protein, 30.40% crude fat, 17.85% crude fibre, 4.30% ash and 22.82% carbohydrate. The content of magnesium, copper, iron, zinc and calcium were 3930, 168, 277, 2091 and 432 mg/kg, respectively. Iodine value for T. diversifolia and Sunflower oils, respectively were 109.00 and 145.67 g iodine/kg; saponification values, 212.61 and 188.63 mg KOH/g; ester values, 184.15 and 206.86 mg KOH/g; peroxide values 4.0 and 5.87 meq peroxide/kg; acid values, 5.76 and 4.48 mg KOH / g; % unsaponifiable matter, 0.83 and 1.22 %; and total phenol content, 118.63 and 108.75 µg/g. Others include, specific gravity, 0.937 and 0.920; surface tension 0.042 and 0.051 N/m; viscosity 42.50 and 30.50 cSt; and smoke point, 215 and 245 0C. The study revealed that Tithonia diversifolia seed oil content and physicochemical parameters are comparable with those of Sunflower oil. Hence the oil could be used as raw materials for industrial processes, biodiesel production and a good source of dietary antioxidant which could complement or replace some conventional oils.

scholarly journals A note on Mahalanobis and related distance measures in WinISI and The Unscrambler

2019 ◽  
Vol 27 (4) ◽  
pp. 253-258 ◽  
Author(s):  
A Garrido-Varo ◽  
J Garcia-Olmo ◽  
T Fearn
Keyword(s):  
Spectral Data ◽  
Outlier Detection ◽  
Mahalanobis Distance ◽  
Near Infrared ◽  
Distance Measure ◽  
Principal Component ◽  
Distance Measures ◽  
Total Variance ◽  
Software Packages ◽  
Component Scores

In identifying spectral outliers in near infrared calibration it is common to use a distance measure that is related to Mahalanobis distance. However, different software packages tend to use different variants, which lead to a translation problem if more than one package is used. Here the relationships between squared Mahalanobis distance D2, the GH distance of WinISI, and the T2 and leverage (L) statistics of The Unscrambler are established as D2 = T2 ≈ L × n ≈ GH × k, where n and k are the numbers of samples and variables, respectively, in the set of spectral data used to establish the distance measure. The implications for setting thresholds for outlier detection are discussed. On the way to this result the principal component scores from WinISI and The Unscrambler are compared. Both packages scale the scores for a component to have variances proportional to the contribution of that component to total variance, but the WinISI scores, unlike those from The Unscrambler, do not have mean zero.

A New Mathematical Procedure for NIR Analysis: The Lattice Technique. Application to the Prediction of Sugar Content of Apples

1989 ◽  
Vol 43 (6) ◽  
pp. 1045-1049 ◽  
Author(s):  
P. Robert ◽  
D. Bertrand ◽  
M. Crochon ◽  
J. Sabino
Keyword(s):  
Spectral Data ◽  
Near Infrared ◽  
Sugar Content ◽  
Principal Component ◽  
Base System ◽  
Mathematical Procedure ◽  
Analytical Applications ◽  
Spectral Libraries ◽  
Calibration Equations

Analytical applications of near-infrared spectroscopy require the determination of calibration equations linking chemical and spectral values. Such equations are difficult to update by including new calibration specimens. A new procedure for prediction which was not based on multiple linear regression has been investigated. This procedure could be included in a data base system. The proposed method consists of three steps: compression of the spectral data by applying principal component analysis, creation of a predictive lattice, and projection of the spectra of unknown specimens on to the predictive lattice. This enables the prediction of chemical data that are not perfectly linked to spectral data by a linear relationship. The procedure has been applied to the prediction of the refractive index of apples. A predictive lattice was designed with the use of 45 specimens of calibration. A prediction with 43 verification specimens gave a standard error of 0.8%, which appeared sufficient for grading apples in quality classes. Further studies are required in order to include the proposed method in spectral libraries specializing in analytical applications.

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