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.

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.

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

scholarly journals Evaluation of Visible-Near Infrared Reflectance Spectra of Avocado Leaves as a Non-destructive Sensing Tool for Detection of Laurel Wilt

Plant Disease ◽  
2012 ◽  
Vol 96 (11) ◽  
pp. 1683-1689 ◽  
Author(s):  
Sindhuja Sankaran ◽  
Reza Ehsani ◽  
Sharon A. Inch ◽  
Randy C. Ploetz
Keyword(s):  
Discriminant Analysis ◽  
Vegetation Index ◽  
Near Infrared ◽  
Principal Component ◽  
Persea Americana ◽  
Near Infrared Reflectance ◽  
Laurel Wilt ◽  
Linear Discriminant ◽  
Raffaelea Lauricola ◽  
Non Destructive

Laurel wilt, caused by the fungus Raffaelea lauricola, affects the growth, development, and productivity of avocado, Persea americana. This study evaluated the potential of visible-near infrared spectroscopy for non-destructive sensing of this disease. The symptoms of laurel wilt are visually similar to those caused by freeze damage (leaf necrosis). In this work, we performed classification studies with visible-near infrared spectra of asymptomatic and symptomatic leaves from infected plants, as well as leaves from freeze-damaged and healthy plants, both of which were non-infected. The principal component scores computed from principal component analysis were used as input features in four classifiers: linear discriminant analysis, quadratic discriminant analysis (QDA), Naïve-Bayes classifier, and bagged decision trees (BDT). Among the classifiers, QDA and BDT resulted in classification accuracies of higher than 94% when classifying asymptomatic leaves from infected plants. All of the classifiers were able to discriminate symptomatic-infected leaves from freeze-damaged leaves. However, the false negatives mainly resulted from asymptomatic-infected leaves being classified as healthy. Analyses of average vegetation indices of freeze-damaged, healthy (non-infected), asymptomatic-infected, and symptomatic-infected leaves indicated that the normalized difference vegetation index and the simple ratio index were statistically different.

scholarly journals Pengembangan Metode Pengujian Keaslian Beras Aceh Menggunakan Nirs Dengan Metode PCA

2019 ◽  
Vol 4 (1) ◽  
pp. 578-587
Author(s):  
Masyitah Masyitah ◽  
Syahrul Syahrul ◽  
Zulfahrizal Zulfahrizal
Keyword(s):  
Principal Component Analysis ◽  
Protein Content ◽  
Near Infrared ◽  
Scatter Correction ◽  
Principal Component ◽  
Reflectance Spectroscopy ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance Spectroscopy ◽  
Near Infrared Reflectance ◽  
Ft Ir

Abstrak. Tujuan dari penelitian ini adalah membangun model pendugaan untuk menilai keaslian beras Aceh berdasarkan spektrum NIRS yang dihasilkan. Pendeteksian keaslian beras Aceh secara cepat dan efesien dapat diwujudkan melalui pengembangan teknologi Near Infrared Reflectance Spectroscopy (NIRS). Penelitian ini menggunakan beras varietas Sigupai (Aceh Barat Daya), varietas  Sanbay (Simeulue) dan varietas Ciherang. Jumlah sampel yang digunakan pada penelitian ini adalah 45 sampel. Pengukuran spektrum beras menggunakan Self developed FT-IR IPTEK T-1516. Klasifikasi data spektrum beras menggunakan Principal Component Analysis (PCA) dengan dua  pretreatment yaitu De-trending dan Multiplicative Scatter Correction. Hasil penelitian ini diperoleh yaitu: Spektrum NIRS beras menunjukkan keberadaan kandungan lemak pada panjang gelombang 2355 nm - 2462 nm. Kandungan karbohidrat pada panjang gelombang 2256 nm - 2321 nm.  Kandungan protein pada panjang gelombang 2056 nm - 2166 nm. Kandungan kadar air pada panjang gelombang 1910 nm-1980 nm dan panjag gelombang 1411 nm - 1492 nm menunjukkan kandungan protein dan kadar air. NIRS dengan metode PCA mampu membedakan pencampuran beras Sigupai dengan beras Ciherang dimana pembedaan terbaik terjadi dalam bentuk dua macam pengelompokan yaitu beras  Sigupai ≥ 75 dan beras Sigupai ≤50 dan pretreatment de-trending merupakan pretreatment terbaik dalam mengklasifikasi beras Aceh (Sigupai dan Sanbay) dengan beras Nasional (Ciherang).Development of Methods for Testing the Authenticity of Aceh Rice Using NIRS with the PCA MethodAbstract. The purpose of this study is to develop a prediction model to assess the authenticity of Aceh rice based on the NIRS spectrum produced. The detection of the authenticity of Aceh rice quickly and efficiently can be realized through technological development Near Infrared Reflectance Spectroscopy (NIRS). This study uses Sigupai rice varieties (Aceh Barat Daya), Sanbay (Simeulue) and Ciherang. The number of samples used in this study was 45 samples. Measurement of rice spectrum  using Self developed FT-IR IPTEK T-1516. Rice spectrum data classification uses the Principal Component Analysis (PCA) with two pretreatments, namely De-trending and Multiplicative Scatter Correction. The results of this study were obtained: NIRS spectrum of rice showed the presence of fat content at a wavelength of 2355 nm - 2462 nm. Carbohydrate content at wavelength 2256 nm - 2321 nm. Protein content at wavelength 2056 nm - 2166 nm. The content of water content at a wavelength of 1910 nm-1980 nm and wave length of 1411 nm - 1492 nm shows the protein content and water content. NIRS with the PCA method was able to distinguish the mixing of Sigupai rice from Ciherang rice where the best differentiation occurred in the form of two types of grouping namely Sigupai rice ≥ 75 and Sigupai rice ≤ 50 and de-trending pretreatment was the best pretreatment in classifying Aceh rice (Sigupai and Sanbay) with National rice (Ciherang).

Unlocking the Secrets of Terminalia Kernels Using Near-Infrared Spectroscopy

2021 ◽  
pp. 000370282199213
Author(s):  
Eshetu Bobasa ◽  
Michael Netzel ◽  
Anh Dao Thi Phan ◽  
Heather Smyth ◽  
Yasmina Sultanbawa ◽  
...  
Keyword(s):  
Near Infrared ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Near Infrared Reflectance ◽  
Classification Rate ◽  
Potential Health ◽  
Wide Range ◽  
Fruit Samples ◽  
The Fourier Transform ◽  
Kakadu Plum

In recent years, the native food industry in Australia has increased in both value and volume due to the discovery of a wide range of phytochemicals (e.g., vitamin C, polyphenols) that have potential health benefits. Thus, plant organs and tissues of these native plants are used in a wide range of applications. In particular, the kernel of a native plum, the Kakadu plum ( Terminalia ferdinandiana, Combretaceae) is considered to be rich in lipids and other phytochemical compounds. The aim of this study was to evaluate the use of NIR spectroscopy to analyze and characterize kernel samples and tissues of wild harvest fruit samples. The Fourier transform near-infrared reflectance spectra of cracked kernels, seeds cover tissues, and dry powder Kakadu plum kernels were acquired. Both principal component analysis and partial least squares discriminant analysis were used to analyze and interpret the spectral data. A correct classification rate of 93%, 86%, and 80% was achieved for the identification of kernel provenance using all tissues, seed coats, and the whole nuts, respectively. The results of this study reported for the first time the analysis of Kakadu plum kernels and their tissues using NIR spectroscopy.

scholarly journals Study on the Relations between Hyperspectral Images of Bananas (Musa spp.) from Different Countries, Their Compositional Traits and Growing Conditions

Sensors ◽  
2020 ◽  
Vol 20 (20) ◽  
pp. 5793
Author(s):  
Zhijun Wang ◽  
Sara Wilhelmina Erasmus ◽  
Xiaotong Liu ◽  
Saskia M. van Ruth
Keyword(s):  
Production Management ◽  
Near Infrared ◽  
Production Systems ◽  
Starch Content ◽  
Principal Component ◽  
Imaging Data ◽  
Carotene Content ◽  
Preliminary Separation ◽  
Growing Conditions ◽  
The One

Bananas are some of the most popular fruits around the world. However, there is limited research that explores hyperspectral imaging of bananas and its relationship with the chemical composition and growing conditions. In the study, the relations that exist between the visible near-infrared hyperspectral reflectance imaging data in the 400–1000 nm range of the bananas collected from different countries, the compositional traits and local growing conditions (altitude, temperature and rainfall) and production management (organic/conventional) were explored. The main compositional traits included moisture, starch, dietary fibre, protein, carotene content and the CIE L*a*b* colour values were also determined. The principal component analysis showed the preliminary separation of bananas from different geographical origins and production systems. The compositional and spectral data revealed positively and negatively moderate correlations (r around ±0.50, p < 0.05) between the carotene, starch content, and colour values (a*, b*) on the one hand and the wavelength ranges 405–525 nm, 615–645 nm, 885–985 nm on the other hand. Since the variation in composition and colour values were related to rainfall and temperature, the spectral information is likely also influenced by the growing conditions. The results could be useful to the industry for the improvement of banana quality and traceability.

scholarly journals Spectroscopic and Molecular Methods to Differentiate Gender in Immature Date Palm (Phoenix dactylifera L.)

Plants ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 536
Author(s):  
Abdul Latif Khan ◽  
Ahmed Al-Harrasi ◽  
Muhammad Numan ◽  
Noor Mazin AbdulKareem ◽  
Fazal Mabood ◽  
...  
Keyword(s):  
Date Palm ◽  
Near Infrared ◽  
Sex Differentiation ◽  
Principal Component ◽  
Phoenix Dactylifera ◽  
Partial Least Square ◽  
Least Square ◽  
Transcript Accumulation ◽  
Near Infrared Reflectance ◽  
Phoenix Dactylifera L

Phoenix dactylifera (date palm) is a well-known nutritious and economically important fruit tree found in arid regions of the Middle East and North Africa. Being diploid, it has extremely high divergence in gender, where sex differentiation in immature date palms (Phoenix dactylifera L.) has remained an enigma in recent years. Herein, new robust infrared (near-infrared reflectance spectroscopy (NIRS) and Fourier transform infrared attenuated total reflectance (FTIR/ATR)) and nuclear magnetic resonance (NMR) spectroscopy methods coupled with extensive chemometric analysis were used to identify the sex differentiation in immature date palm leaves. NIRS/FTIR reflectance and 1H-NMR profiling suggested that the signals of monosaccharides (glucose and fructose) and/or disaccharides (maltose and sucrose) play key roles in sex differentiation. The three kinds of spectroscopic data were clearly differentiated among known and unknown male and female leaves via principal component and partial least square discriminant analyses. Furthermore, sex-specific genes and molecular markers obtained from the lower halves of LG12 chromosomes showed enhanced transcript accumulation of mPdIRDP52, mPdIRDP50, and PDK101 in females compared with in males. The phylogeny showed that the mPdIRD033, mPdIRD031, and mPdCIR032 markers formed distinctive clades with more than 70% similarity in gender differentiation. The three robust analyses provide an alternative tool to differentiate sex in date palm trees, which offers a solution to the long-standing challenge of dioecism and could enhance in situ tree propagation programs.

scholarly journals Klasifikasi Cepat dan Non-Destruktif Bubuk Biji Kakao Berdasarkan Tingkat Fermentasi Menggunakan Near Infrared Reflectance Spectroscopy

2016 ◽  
Vol 1 (1) ◽  
pp. 1046-1051
Author(s):  
Rita Zahara ◽  
Agus Arip Munawar ◽  
Zulfahrizal Zulfahrizal
Keyword(s):  
Principal Component Analysis ◽  
Method Development ◽  
Near Infrared ◽  
Principal Component ◽  
Component Analysis ◽  
Accurate Estimate ◽  
Peak Performance ◽  
Cocoa Beans ◽  
Near Infrared Reflectance ◽  
Ft Ir

Abstrak.  Kakao merupakan salah satu komoditas perkebunan andalan di Provinsi Aceh. Hampir keseluruhan areal perkebunan kakao adalah perkebunan rakyat. Biji kakao dari perkebunan rakyat cenderung masih bermutu rendah yang disebabkan oleh pengolahan pascapanen yang kurang baik seperti masalah fermentasi biji kakao. Penjaminan mutu biji kakao melalui pengembangan metode pendugaan mutu yang cepat dan akurat menjadi kata kunci, peningkatan daya saing ekspor biji kakao Indonesia ditingkat dunia. Sampel biji kakao mentah varietas lindak. Sampel dibuat dalam  bentuk bubuk sebanyak 44 sample (10 gr per sampel) dengan penggunaan alat NIRS FT-IR IPTEK T-1516. Klasifikasi data spektrum menggunakan Principal Component Analysis (PCA) dengan tiga  pretreatment spektrum yaitu: de-trending, mean normalization dan standart normal variate. Hasil penelitian diperoleh yaitu Panjang gelombang 1910-2170 nm merupakan, panjang gelombang yang relevan untuk menduga procyanidin pada bubuk biji kakao. Penambahan pretreatment mampu memperbaiki tampilan puncak penanda procyanidin pada spektrum bubuk biji kakao, PCA tanpa pretreatment tidak mampu mengklasifikasi bubuk biji kakao berdasarkan tingkat fermentasi sedangkan dengan bantuan pretreatment mampu mengklasifikasi dengan tingkat keberhasilan diatas 85%, Pretreatment terbaik dalam meningkatkan kinerja PCA dalam klasifikasi bubuk biji kakao berdasarkan tingkat fermentasi yaitu SNV dengan tingkat keberhasilan  97,72 %.Abstract. Cocoa is one Aceh’s most  samples were beans plantation commodities. Most of cocoa belong to the small holder estates. Unfortunately cocoa beans owned by the locals, tend to have low quality as a result of poor postharvest management, such as a cocoa beans fermentation related issue. The assurance of cocoa beans quality through a rapid and accurate estimate method development will be a key in the efforts to promote global export competitions of Indonesia’s cocoa beans. The following sample is raw cocoa beans of lindak variety. Samples were made in the form of cocoa powder with a total of 44 samples (10 gr per samples) using an instrument of NIRS FT-IR IPTEK T-1516. The spectrum data classification uses the Principal Component Analysis  (PCA) three spectrum pretreatment, namely de-trending , mean normalization and standard normal variate. The result show that wavelength range of1910-2170 nm were considered as relevant wavelengths  to predict procyanidin on cocoa seed powder. The addition of the pretreatment will fix procyanidin peak performance on the cocoa beans powder based on the fermentation level of success over 85%. The best pretreatment to increase the PCA permonce classifying the cocoa beans powder according to fermentation level is SNV and the level of success is 97,72%.Keywords: 

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