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

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). 

Comparison of Multivariate Calibration and Discriminant Analysis in Evaluating NIR Spectroscopy for Determination of Meat Tenderness

1997 ◽  
Vol 51 (3) ◽  
pp. 350-357 ◽  
Author(s):  
Tormod Næs ◽  
Kjell Ivar Hildrum
Keyword(s):  
Discriminant Analysis ◽  
Reference Values ◽  
Near Infrared ◽  
Multivariate Calibration ◽  
Principal Component Regression ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Meat Tenderness ◽  
Intermediate Group ◽  
Calibration Methods

Often the primary goal of analytical measurement tasks is not to find good estimates of continuous reference values but rather to determine whether a sample belongs to one of a number of categories or subgroups. In this paper the potential of different statistical techniques in the classification of raw beef samples in tenderness subgroups was studied. The reference values were based on sensory analysis of beef tenderness of 90 samples from bovine M. longissimus dorsi muscles. The sample set was divided into three categories—very tough, intermediate, and very tender—according to degree of tenderness. A training set of samples was used to find the relationship between category and near-infrared (NIR) spectroscopic measurements. The study indicates that classical discriminant analysis has advantages in comparison to multivariate calibration methods [i.e., principal component regression (PCR)], in this application. One reason for this observation seems to be that PCR underestimates high measurement values and overestimates low values. In this way most samples are assigned to the intermediate group of samples, causing a small number of erroneous classifications for the intermediate subgroup, but a large number of errors for the two extreme groups. With the use of PCR the number of correct classifications in the extreme subgroups was as low as 23%, while the use of discriminate analysis increased this number to almost 60%. The number of classifications in correct or neighbor subgroup for the two extreme subgroups was equal to 97%. A “bias-correction” was also attempted for PCR, and this gave results comparable to the best results obtained by discriminant analysis methods. Test sets used NIR analysis of fresh, raw beef samples with different processing. While this spectroscopic approach had previously been shown to be useful with frozen products, it appears unsuitable at this time for fresh beef. However, its marginal analytical utility proved useful in evaluating the two classification approaches employed in this study.

scholarly journals Fast And Simultaneous Prediction Of Agricultural Soil Nutrients Content Using Infrared Spectroscopy

2019 ◽  
Vol 12 (1) ◽  
pp. 61-66
Author(s):  
Devianti Devianti ◽  
Zulfahrizal Zulfahrizal ◽  
Sufardi Sufardi ◽  
Agus Arip Munawar
Keyword(s):  
Agricultural Soil ◽  
Near Infrared ◽  
Prediction Models ◽  
Agricultural Soils ◽  
Principal Component Regression ◽  
Principal Component ◽  
Nutrient Content ◽  
Laboratory Method ◽  
Near Infrared Reflectance ◽  
Simultaneous Prediction

Abstract. The functions soil depends on the balances of its structure, nutrients composition as well as other chemical and physical properties. Conventional methods, used to determine nutrients content on agricultural soil were time consuming, complicated sample processing and destructive in nature. Near infrared reflectance spectroscopy (NIRS) has become one of the most promising and used non-destructive methods of analysis in many field areas including in soil science. The main aim of this present study is to apply NIRS in predicting nutrients content of soils in form of total nitrogen (N). Transmittance spectra data were obtained from a total of 18 soil samples from 8 different sites followed by N measurement using standard laboratory method. Principal component regression (PCR) with full cross validation were used to develop and validate N prediction models. The results showed that N content can be predicted very well even with raw spectra data with coefficient correlation (r) and residual predictive deviation index (RPD) were 0.95 and 3.35 respectively. Furthermore, spectra correction clearly enhances and improve prediction accuracy with r = 0.96 and RPD = 3.51. It may conclude that NIRS can be used as fast and simultaneous method in determining nutrient content of agricultural soils.

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.

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.

scholarly journals Prediction of Total Phenolic Content in Extracts of Prunella Species from HPLC Profiles by Multivariate Calibration

2012 ◽  
Vol 2012 ◽  
pp. 1-7 ◽  
Author(s):  
Saliha Sahin ◽  
Esra Isik ◽  
Cevdet Demir
Keyword(s):  
High Performance ◽  
Multivariate Calibration ◽  
Principal Component Regression ◽  
Principal Component ◽  
Total Phenol ◽  
Total Phenolic ◽  
Total Phenol Content ◽  
Phenol Content ◽  
Calibration Methods ◽  
Hplc Profiles

The multivariate calibration methods—principal component regression (PCR) and partial least squares (PLSs)—were employed for the prediction of total phenol contents of four Prunella species. High performance liquid chromatography (HPLC) and spectrophotometric approaches were used to determine the total phenol content of the Prunella samples. Several preprocessing techniques such as smoothing, normalization, and column centering were employed to extract the chemically relevant information from the data after alignment with correlation optimized warping (COW). The importance of the preprocessing was investigated by calculating the root mean square error (RMSE) for the calibration set of the total phenol content of Prunella samples. The models developed based on the preprocessed data were able to predict the total phenol content with a precision comparable to that of the reference of the Folin-Ciocalteu method. PLS model seems preferable, because of its predictive and describing abilities and good interpretability of the contribution of compounds to the total phenol content. Multivariate calibration methods were constructed to model the total phenol content of the Prunella samples from the HPLC profiles and indicate peaks responsible for the total phenol content successfully.

scholarly journals Widely distributed exogenic materials of varying compositions and morphologies on asteroid (101955) Bennu

2021 ◽  
Author(s):  
Eri Tatsumi ◽  
Marcel Popescu ◽  
Humberto Campins ◽  
Julia de León ◽  
Juan Luis Rizos García ◽  
...  
Keyword(s):  
Near Infrared ◽  
Principal Component ◽  
Parent Body ◽  
Ordinary Chondrites ◽  
Near Infrared Reflectance ◽  
Spatially Resolved ◽  
Average Spectrum ◽  
Band Absorption ◽  
Rich Material ◽  
The One

Abstract Using the multiband imager MapCam onboard the OSIRIS-REx (Origins, Spectral Interpretation, Resource Identification, and Security–Regolith Explorer) spacecraft, we identified 77 instances of proposed exogenic materials distributed globally on the surface of the B-type asteroid (101955) Bennu. We identified materials as exogenic on the basis of an absorption near 1 µm that is indicative of anhydrous silicates. The exogenic materials are spatially resolved by the telescopic camera PolyCam. All such materials are brighter than their surroundings, and they are expressed in a variety of morphologies: homogeneous, breccia-like, inclusion-like, and others. Inclusion-like features are the most common. Visible spectrophotometry was obtained for 46 of the 77 locations from MapCam images. Principal component analysis indicates at least two trends: (i) mixing of Bennu's average spectrum with a strong 1-µm band absorption, possibly from pyroxene-rich material, and (ii) mixing with a weak 1-µm band absorption. The endmember with a strong 1-µm feature is consistent with Howardite-Eucrite-Diogenite (HED) meteorites, whereas the one showing a weak 1-µm feature may be consistent with HEDs, ordinary chondrites, or carbonaceous chondrites. The variation in the few available near-infrared reflectance spectra strongly suggests varying compositions among the exogenic materials. Thus, Bennu might record the remnants of multiple impacts with different compositions to its parent body, which could have happened in the very early history of the Solar System. Moreover, at least one of the exogenic objects is compositionally different from the exogenic materials found on the similar asteroid (162173) Ryugu, and they suggest different impact tracks.

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.

Analysis of artificial mixtures of pure chemicals by near-infrared reflectance

1990 ◽  
Vol 114 (3) ◽  
pp. 253-257 ◽  
Author(s):  
G. Z. Wetherill ◽  
I. Murray ◽  
C. A. Glasbey
Keyword(s):  
Near Infrared ◽  
Compositional Analysis ◽  
Principal Component ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance ◽  
Chemical Methods ◽  
Wet Chemical ◽  
Linear Regressions ◽  
Unexplained Variation ◽  
Artificial Mixtures

SUMMARYCompositional analysis of feeds and other materials by near-infrared reflectance (NIR) has been proposed as a cheap and rapid alternative to traditional wet chemical methods. A theoretical basis for NIR measurements is needed and may be obtained from the study of artificial mixtures of pure chemicals.Mixtures of lactose, casein and sodium oleate, in widely differing concentrations, were analysed by NIR. Principal component analysis was used to study the variations between spectra, and multiple linear regressions gave predictors of sample compositions from the spectra. Optical densities at most combinations of wavelengths gave good predictions of sample compositions because there was much less unexplained variation between NIR spectra than would occur between natural samples.

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