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 Transformasi Attenuated Total Reflectance Untuk Prediksi Vitamin C Pada Buah Mangga Arumanis (Mangifera indica)

2019 ◽  
Vol 4 (3) ◽  
pp. 75-84
Author(s):  
Muslem Muslem ◽  
Sri Purnama Sari ◽  
Agus Arip Munawar
Keyword(s):  
Principal Component Analysis ◽  
Vitamin C ◽  
Near Infrared ◽  
Mangifera Indica ◽  
Principal Component Regression ◽  
Principal Component ◽  
Attenuated Total Reflectance ◽  
Total Reflectance ◽  
Ft Ir ◽  
Pcr Method

Abstrak, Parameter yang digunakan dalam penilaian mutu buah mangga antara lain ukuran atau berat, kekerasan, tingkat ketuaan serta bebas dari cacat. Kekerasan pada buah mangga merupakan fungsi dari tingkat kematangan, sedangkan kematangan berhubungan dengan tingkat ketuaan yang dapat diduga melalui penampilan visual. Vitamin C merupakan vitamin yang larut dalam air dan esensial untuk biosintesis kolagen.pengukuran vitamin C pada buah mangga menggunkan metode tetrasi, dan penggunaan gelombang elektromaknetik seperti Near Infrared. Penelitian ini bertujuan untuk memprediksi kadar vitamin C dalam buah mangga menggunakan metode Spektrofotometri UV-Vis dan Iodimetri, serta membandingkan hasil dari kedua metode tersebut. Sampel yang diidentifikasi yaitu buah mangga yang sudah matang dengan menggunakan model transformasi Attenuated Total Reflectance dan menggunakan metode Principal Component Analysis (PCA) dan menggunakan metode Principal Component Regression  (PCR). Penelitian ini menggunakan buah mangga jenis Arumanis, yang berjumlah 30 sampel. Prediksi vitamin C dengan NIRS menggunakan alat FT-IR IPTEK T-1516. Pengolahan data menggunakan Unscramble software® X versi 10.5. Hasil penelitian menunjukkan prediksi vitamin C mangga dengan metode Principal Component Regression (PCR) menghasilkan sufficient performance dengan nilai RPD yang didapat yaitu 2,0083 (r) sebesar 0,8638 , (R2 ) sebesar 0,7463 dan (RMSEC) sebesar 5,1854 Transformation Of Attenuated Total Reflectance (ATR) Near Infrared for prediction of Vitamin C In Arumanis Mangoes (Mangifera Indica)Abstract. Parameters used in assessing the quality of mangoes are size or weight, hardness, age level and free from defects. Hardness in mangoes is a function of maturity level, while the maturity is related to the level of aging that can be predicted through visual appearance. Vitamin C is a water-soluble vitamin which is essential for collagen biosynthesis. The measurement of vitamin C in mangoes use tetration methods, and the using of electromagnetic waves such as Near Infrared. This study aims to predict vitamin C contains in mango fruit using the UV-Vis and Iodymetry Spectrophotometry method, and comparing the results of the two methods. The samples identified were mature mangoes using the attenuated total reflectance transformation model and using the Principal Component Analysis (PCA) method also using the Principal Component Regression (PCR) method. This study used Arumanis mangoes, which amounted to 30 samples. Prediction of vitamin C with NIRS using the FT-IR IPTEK T-1516. Data processing use the Unscramble software® X 10.5 version. The results showed that the prediction of vitamin C mango using the Principal Component Regression (PCR) method resulted in sufficient performance with the obtained RPD value of  2,0083, (r) of 0,8638, (R2) of 0,7463 and (RMSEC) of 5,1854.

Nondestructive NIR and NIT Determination of Protein, Fat, and Water in Plastic-Wrapped, Homogenized Meat

1992 ◽  
Vol 46 (11) ◽  
pp. 1685-1694 ◽  
Author(s):  
Tomas Isaksson ◽  
Charles E. Miller ◽  
Tormod Næs
Keyword(s):  
Diffuse Reflectance ◽  
Near Infrared ◽  
Multivariate Calibration ◽  
Principal Component Regression ◽  
Principal Component ◽  
Prediction Errors ◽  
Optimal Test ◽  
Meat Samples ◽  
Water Contents

In this work, the abilities of near-infrared diffuse reflectance (NIR) and transmittance (NIT) spectroscopy to noninvasively determine the protein, fat, and water contents of plastic-wrapped homogenized meat are evaluated. One hundred homogenized beef samples, ranging from 1 to 23% fat, wrapped in polyamide/polyethylene laminates, were used. Results of multivariate calibration and prediction for protein, fat, and water contents are presented. The optimal test set prediction errors (root mean square error of prediction, RMSEP), obtained with the use of the principal component regression method with NIR data, were 0.45, 0.29 and 0.50 weight % for protein, fat, and water, respectively, for plastic-wrapped meat (compared to 0.40, 0.28 and 0.45 wt % for unwrapped meat). The optimal prediction errors for the NIT method were 0.31, 0.52 and 0.42 wt % for protein, fat, and water, respectively, for plastic-wrapped meat samples (compared to 0.27, 0.38, and 0.37 wt % for unwrapped meat). We can conclude that the addition of the laminate only slightly reduced the abilities of the NIR and NIT method to predict protein, fat, and water contents in homogenized meat.

The Effect of Multiplicative Scatter Correction (MSC) and Linearity Improvement in NIR Spectroscopy

1988 ◽  
Vol 42 (7) ◽  
pp. 1273-1284 ◽  
Author(s):  
Tomas Isaksson ◽  
Tormod Næs
Keyword(s):  
Near Infrared ◽  
Scatter Correction ◽  
Principal Component Regression ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Data Sets ◽  
Prediction Errors ◽  
Regression Methods ◽  
Multiplicative Scatter Correction ◽  
Linearity Improvement

Near-infrared (NIR) reflectance spectra of five different food products were measured. The spectra were transformed by multiplicative scatter correction (MSC). Principal component regression (PCR) was performed, on both scatter-corrected and uncorrected spectra. Calibration and prediction were performed for four food constituents: protein, fat, water, and carbohydrates. All regressions gave lower prediction errors (7–68% improvement) by the use of MSC spectra than by the use of uncorrected absorbance spectra. One of these data sets was studied in more detail to clarify the effects of the MSC, by using PCR score, residual, and leverage plots. The improvement by using nonlinear regression methods is indicated.

Analisis Kandungan Nutrisi Kulit Kopi (Coffea sp.) dengan Menggunakan Metode Near Infrared Reflectance Spectroscopy (NIRS)

2017 ◽  
Vol 2 (4) ◽  
Author(s):  
Andika Boy Yuliansyah ◽  
Sitti Wajizah ◽  
Samadi Samadi
Keyword(s):  
Near Infrared ◽  
Principal Component Regression ◽  
Principal Component ◽  
Coffea Canephora ◽  
Reflectance Spectroscopy ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance Spectroscopy ◽  
Near Infrared Reflectance ◽  
Coffee Pulp ◽  
Infrared Reflectance Spectroscopy

Abstrak.     Tujuan penelitian ini adalah untuk mengevaluasi akurasi metode analisis pakan dengan metode (Near Infrared Reflectance Sectroscopy) NIRS dalam memprediksi kandungan nutrisi limbah kulit kopi serta mengetahui panjang gelombangnya.  Penelitian ini dilakukan di Laboratorium Ilmu Nutrisi dan Teknologi Pakan, Univeritas Syiah Kuala, dari Agustus hingga September 2017.  Penelitian ini menggunakan 30 sampel limbah kulit kopi yang terdiri dari 2 varietas kopi yaitu kopi arabika (Coffea arabica) dan kopi robusta (Coffea canephora). Spektrum diukur dengan menggunakan yaitu FT-IR IPTEK T-1516 pada rentang wavelengrh 1000-2500 nm dan di kalibrasi dan validasi dengan menggunakan software The Unscrambler X version 10.4.  Pretreatment yang digunakan yaitu Multiplicative scatter analysis (MSC) dan DeTrending (DT) dengan metode regresi Principal Component Regression (PCR). Parameter nutrisi yang dianalisis yaitu bahan kering (BK), protein kasar (PK) dan serat kasar (SK).  Hasil penelitian memperlihatkan bahwa NIRS dengan model yang telah dibangun tidak dapat menprediksi bahan kering dengan baik. Hal ini ditunjukkan dengan nilai r, R2 dan RPD yang rendah (0.58, 0.34 dan 3.06) serta RMSEC yang tinggi (3.06). Metode NIRS dapat memprediksi kandungan PK dan SK dengan baik pada penggunaan pretreatment MSC (PK= r: 0.87, R2: 0.76, RMSEC: 0.45 dan RPD: 2.07; SK= r: 0.87, R2: 0.75, RMSEC: 2.83 dan RPD: 2.03). Prediksi kasar untuk PK dan SK didapatkan dengan menggunakan pretreatment DT (PK= r: 0.75, R2: 0.57, RMSEC: 0.60 dan RPD: 1.55; SK= r: 0.84, R2: 0.71, RMSEC: 3.06 dan RPD: 1.88). Analysis of Coffee Pulp (Coffea sp.) Nutrition Content Using Near Infrared Reflectance Spectroscopy (NIRS) Method Abstract.   The aim of present study was to evaluate the accuration of feed analysis method of Near infrared reflectance spectroscopy (NIRS) in predicting nutritional content of Coffee pulp and to know its wavelength.  The study was conducted in  nutrition science and feed technology Laboratory,   Department of Animal Husbandry,  Faculty of Agriculture,  Syiah Kuala University,  august until september, 2017.   As many as 30 coffee pulps  were used in this study and seperated to 2 specieses of coffee, arabica coffee (Coffea arabica) and robusta coffee (Coffea canephora).  The spectrum was scanned using. FT-IR IPTEK T-1516 at 1000 to 2500 nm wavelength and calibrated and validated using The Unscrambler X version 10.4 software. Pretreatment used in this study was Multiplicative scatter analysis (MSC) dan DeTrending (DT) with Principal component regression (PCR) calibration method. Nutrition parameters analyzed were dry matter (DM), crude protein (CP) and dietary fiber (DF). The results of study showed that NIRS with prediction models that have been build cannot predicted DM content in coffee pulp. This was shown with low value of r, R2 dan RPD (0.58, 0.34 dan 3.06) and high value of RMSEC (3.60). NIRS method can predicted CP and DF content quite well using MSC pretreatment (CP= r: 0.87, R2: 0.76, RMSEC: 0.45 dan RPD: 2.07; DF= r: 0.87, R2: 0.75, RMSEC: 2.83 dan RPD: 2.03). Rough prediction for CP and DM content was obtained by using DT pretreatment (CP= r: 0.75, R2: 0.57, RMSEC: 0.60 dan RPD: 1.55; DF= r: 0.84, R2: 0.71, RMSEC: 3.06 dan RPD: 1.88). 

Use of residual maximum likelihood to evaluate accuracy of two NIRS calibration procedures, relative to Kjeldahl, for determining nitrogen concentration of soybeans

1991 ◽  
Vol 71 (2) ◽  
pp. 385-392 ◽  
Author(s):  
G. B. Schaalje ◽  
H. -H. Mündel
Keyword(s):  
Maximum Likelihood ◽  
Near Infrared ◽  
Nitrogen Concentration ◽  
Principal Component Regression ◽  
Principal Component ◽  
Field Trials ◽  
Near Infrared Reflectance ◽  
Residual Maximum Likelihood ◽  
Maximum Likelihood Procedure ◽  
Calibration Methods

The accuracy of estimates of plant properties based on near-infrared reflectance spectroscopy (NIRS) varies with many factors including the biological material in question and the method used to calibrate the NIRS instrument. This study investigated the accuracy, relative to Kjeldahl analysis, of NIRS analysis based on two calibration methods in estimating nitrogen concentration of four stages and/or parts of soybean (Glycine max (L.) Merr.) plants. Samples of whole top growth at anthesis, whole top growth at maturity, whole top growth at maturity excluding seeds, and seeds were obtained from two field trials and one phytotron experiment. Two Kjeldahl determinations of nitrogen concentration were obtained for each sample, as well as reflectance values at each of 19 infrared wavelengths, using a Technicon InfraAlyser 400R. Different subsets of the sample data were used for calibration and assessment of accuracy. The instrument was calibrated using stepwise multiple linear regression (SMLR) and principal component regression (PCR). The residual maximum likelihood procedure was useful in showing that NIRS estimates based on either SMLR or PCR were at least as accurate as Kjeldahl estimates for all stages and/or parts except whole top growth at maturity excluding seeds. Key words: Calibration, principal component regression, stepwise regression

A Comparison of Techniques for Modelling Data with Non-Linear Structure

2003 ◽  
Vol 11 (1) ◽  
pp. 55-70 ◽  
Author(s):  
Laila Stordrange ◽  
Olav M. Kvalheim ◽  
Per A. Hassel ◽  
Dick Malthe-Sørenssen ◽  
Fred Olav Libnau
Keyword(s):  
Near Infrared ◽  
Principal Component Regression ◽  
Predictive Ability ◽  
Principal Component ◽  
Linear Structure ◽  
Data Set ◽  
Linear Pattern ◽  
Near Infrared Spectra ◽  
Non Linear ◽  
Regression Techniques

Partial least squares (PLS) is a powerful tool for multivariate linear regression. But what if the data show a non-linear structure? Near infrared spectra from a pharmaceutical process were used as a case study. An ANOVA test revealed that the data are well described by a 2nd order polynomial. This work investigates the application of regression techniques that account for slightly non-linear data. The regression techniques investigated are: linearising data by applying transformations, local PLS, i.e. splitting of data, and quadratic PLS. These models were compared with ordinary PLS and principal component regression (PCR). The predictive ability of the models was tested on an independent data set acquired a year later. Using the knowledge of non-linear pattern and important spectral regions, simpler models with better predictive ability can be obtained.

scholarly journals Agricultural products quality determination by means of near infrared spectroscopy

2022 ◽  
Vol 951 (1) ◽  
pp. 012112
Author(s):  
A A Munawar ◽  
Z Zulfahrizal ◽  
R Hayati ◽  
Syahrul
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Prediction Models ◽  
Principal Component Regression ◽  
Principal Component ◽  
Agricultural Products ◽  
Cocoa Bean ◽  
Cocoa Beans ◽  
Non Destructive

Abstract Cocoa is one of main agricultural products cultivated in many tropical countries and processed onto several derivative products. To determine cocoa beans qualities, laboratory procedures based on solvent extractions were mainly used, however most of them are destructive and may cause environmental pollutions. The main purpose of this present study is to employ near infrared spectroscopy (NIRS) for rapid and non-destructive assessment of cocoa beans in form of fat content. Near infrared spectral data of cocoa bean samples were measured as diffuse reflectance in wavelength range from 1000 to 2500 nm. Reference fat contents were measured using standard laboratory methods. Prediction models were developed using principal component regression with raw and baseline corrected spectra data. The results showed that fat contents of cocoa beans can be predicted and determined with maximum correlation coefficient (r) of 0.89 and ratio prediction to deviation (RPD) index of 2.87 for raw spectra and r of 0.91, RPD of 3.18 for baseline spectra correction. It may conclude that NIRS was feasible to be applied as a rapid and non-destructive method for cocoa bean quality assessment.

Beyond Prediction: Extracting Relevant Information from near Infrared Spectra

1996 ◽  
Vol 4 (1) ◽  
pp. 75-84 ◽  
Author(s):  
P. Robert ◽  
M.-F. Devaux ◽  
D. Bertrand
Keyword(s):  
Near Infrared ◽  
Principal Component Regression ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Relevant Information ◽  
Correlated Data ◽  
Spectral Interpretation ◽  
Highly Correlated ◽  
Prediction Techniques ◽  
Spectral Patterns

With the increase of near infrared (NIR) applications, numerous chemometric methods have been developed. Among the mathematical treatments available, principal comoponent analysis (PCA) is certainly the most well-known when considering highly correlated data. In the field of near infrared spectroscopy, it allows the study of spectra without deleting wavelengths and without making any preliminary assumptions on the data. One advantage of PCA lies in the graphical displays obtained and, more precisely, on the similarity maps and spectral patterns. While the maps reveal clusters of the samples, the spectral patterns make a spectral interpretation possible. The present paper reviews our contribution to the development and application of PCA to NIR spectroscopy. It shows that PCA is the core of various mathematical treatments such as principal component regression (PCR), factorial discriminant analysis (FDA) and canonical correlation analysis (CCA). One advantage of using PCA in the prediction techniques lies in the use of all the wavelengths in the predictive model. The extraction of relevant and comprehensive wavelengths can be guided by CCA which allows the description of the samples by taking both mid- and near infrared data into account. Besides a comprehensive presentation of the mathematical treatements, examples are given.

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