PRINCIPAL COMPONENT REGRESSION OF NEAR?INFRARED REFLECTANCE SPECTRA FOR BEEF TENDERNESS PREDICTION

2001 ◽  
Vol 44 (3) ◽  
Author(s):  
B. Park ◽  
Y. R. Chen ◽  
W. R. Hruschka ◽  
S. D. Shackelford ◽  
M. Koohmaraie
Keyword(s):  
Near Infrared ◽  
Principal Component Regression ◽  
Principal Component ◽  
Reflectance Spectra ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance ◽  
Beef Tenderness

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). 

Principal Component Analysis of Diffuse Near-Infrared Reflectance Data from Paper Currency

1989 ◽  
Vol 43 (8) ◽  
pp. 1399-1405 ◽  
Author(s):  
John M. Dale ◽  
Leon N. Klatt
Keyword(s):  
Principal Component Analysis ◽  
Near Infrared ◽  
Principal Component ◽  
Component Analysis ◽  
Test System ◽  
Reflectance Spectra ◽  
Sample Population ◽  
Infrared Reflectance ◽  
K Nearest Neighbor ◽  
Near Infrared Reflectance

Product tampering and product counterfeiting are increasing the need for methods to quickly determine product authenticity. One of the concepts that we are investigating for the detection of counterfeit objects involves the use of pattern recognition techniques to analyze multivariant data acquired from properties intrinsic to the object. The near-infrared reflectance spectra of currency and other paper stock were used as a test system. The sample population consisted of authentic currency, circulated and uncirculated, and cotton and rag paper stock as stand-ins for counterfeit currency. Reflectance spectra were obtained from a spot that was essentially void of printing on both sides of the currency specimens. Although the reflectance spectra for all of the samples were very similar, principal component analysis separated the samples into distinct classes without there being any prior knowledge of their chemical or physical properties. Class separation was achieved even for currency bills that differed only in their past environment. Leave-One-Out procedures resulted in 100% correct classification of each member of the sample set. A K-Nearest-Neighbor test or a linear discriminate can be used to correctly classify unknown samples.

scholarly journals Prediksi Kadar Air Gabah Menggunakan Near Infrared Reflectance Spectroscopy dengan Metode Principal Component Regression (Pre-treatment Multiplicative Scatter Correction, Second Derivative dan De-trending)

2019 ◽  
Vol 4 (1) ◽  
pp. 568-577
Author(s):  
Marvika Sari ◽  
Indera Sakti Nasution ◽  
Zulfahrizal Zulfahrizal
Keyword(s):  
Near Infrared ◽  
Principal Component Regression ◽  
Principal Component ◽  
Reflectance Spectroscopy ◽  
Second Derivative ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance Spectroscopy ◽  
Near Infrared Reflectance ◽  
Infrared Reflectance Spectroscopy ◽  
Pre Treatment

Abstrak. Penelitian ini bertujuan untuk membangun model pendugaan kandungan kadar air pada gabah menggunakan Near Infrared Reflectance Spectroscopy (NIRS) dengan metode Principal Component Regression (PCR) sebagai metode regresi serta membandingkan antara pre-treatment Multiplicative Scatter Correction (MSC), Second Derivative (D2) dan De-trending sebagai metode koreksi. Penelitian ini dilakukan pada gabah kering simpan varietas Ciherang yang didapatkan di daerah Blang Bintang, Aceh Besar. Perlakuan yang diberikan pada sampel yaitu tanpa perendaman dan perendaman (10, 20 dan 30 menit). Pengujian kadar air di laboratorium menggunakan metode thermogravimetri dan akuisisi spektrum kadar air gabah menggunakan self developed FT-IR IPTEK T-1516. Pengolahan data menggunakan Unscramble software® X version 10.5. Hasil penelitian yang telah dilakukan  yaitu spektrum kadar air gabah yang telah diberikan pre-treatment menunjukkan adanya perubahan yang baik dimana spektrum tampak lebih tipis dan noise pada spektrum berkurang. Panjang gelombang optimum dapat dilihat melalui grafik loading plot dimana kandungan kadar air dengan struktur senyawa kimia H-O-H dapat dideteksi pada panjang gelombang 1869 – 2015 nm dan 1411 – 1493 nm. Model prediksi terbaik didapatkan dengan penggabungan antara PCR dan metode koreksi de-trending dengan nilai RPD sebesar 2,508, koefisien korelasi (r) sebesar 0,912, koefisien determinasi (R2) sebesar 0,832 dan RMSEC sebesar 0,883. Prediction of Grain Moisture Content Using Near Infrared Reflectance Spectroscopy With  Principal Component Regression Method (Pretreatment MSC, Second Derivative dan De-trending)Abstract. This study aims are to build a model for estimating water content in grain using Near Infrared Reflectance Spectroscopy (NIRS) with Principal Component Regression (PCR) as a regression method and comparing between pre-treatment Multiplicative Scatter Correction (MSC), Second Derivative (D2) and De-trending as a correction method. This research was carried out on Ciherang variety dry grain which was obtained in Blang Bintang, Aceh Besar. The treatment given to the sample is without soaking and soaking (10, 20 and 30 minutes). Testing the water content in the laboratory using thermogravimetric method and the acquisition of grain moisture content using the self-developed FT-IR IPTEK T-1516. Data processing using Unscramble software® X version 10.5. The results of the research that has been carried out show that spectrum of grain moisture content that has been given pre-treatment shows a good change in the spectrum which appears thinner and the noise in the spectrum is reduced. The optimum wavelength can be seen through the loading plot graph where the water content with the structure of the chemical compound H-O-H can be detected at a wavelength of 1869 - 2015 nm and 1411 - 1493 nm. The best prediction models in this study obtained by PCR and de-trending correction method with RPD value of 1.83, correlation coefficient (r) of 0.827, determination coefficient (R2) of 0.683 and RMSEC of 1.303.

Effects of Particle Size on the Near-Infrared Reflectance Spectra of Wheat and Rape Seed Meal Mixtures

1995 ◽  
Vol 49 (1) ◽  
pp. 84-91 ◽  
Author(s):  
Marie-Françoise Devaux ◽  
Nathalie Nathier-Dufour ◽  
Paul Robert ◽  
Dominique Bertrand
Keyword(s):  
Particle Size ◽  
Near Infrared ◽  
Reflectance Spectra ◽  
Seed Meal ◽  
Infrared Reflectance ◽  
Rape Seed ◽  
Near Infrared Reflectance

Northwest Africa 6232: Visible-near infrared reflectance spectra variability of an olivine diogenite

2018 ◽  
Vol 53 (10) ◽  
pp. 2228-2242 ◽  
Author(s):  
Cristian Carli ◽  
Giovanni Pratesi ◽  
Vanni Moggi-Cecchi ◽  
Francesca Zambon ◽  
Fabrizio Capaccioni ◽  
...  
Keyword(s):  
Near Infrared ◽  
Reflectance Spectra ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance ◽  
Northwest Africa
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