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). 

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.

scholarly journals Aplikasi Teknologi Near Infrared Reflectance Spectroscopy (NIRS) Untuk Membedakan Beras Apek dan Tidak Apek Menggunakan Metode Principal Component Analysis (PCA)

2019 ◽  
Vol 4 (2) ◽  
pp. 387-396
Author(s):  
Herlina Herlina ◽  
Susi Chairani ◽  
Zulfahrizal Zulfahrizal
Keyword(s):  
Principal Component Analysis ◽  
Near Infrared ◽  
Principal Component ◽  
Component Analysis ◽  
Reflectance Spectroscopy ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance Spectroscopy ◽  
Near Infrared Reflectance ◽  
Infrared Reflectance Spectroscopy ◽  
Ft Ir

Abstrak. Beras merupakan salah satu tanaman pangan utama hampir dari setengah populasi dunia. Beras sebagai menu pokok ini memiliki kandungan pati yang cukup besar. Selain itu, dalam beras juga mengandung vitamin, protein, mineral, dan air. Pendistribusian beras terkadang membuat beras rusak  yang disebabkan oleh beberapa faktor, seperti penyimpanan yang terlalu lama, dan suhu tempat penyimpanan beras. Beras yang terendam air juga bisa menyebabkan beras itu rusak, seperti beras yang ada dalam gudang yang terkena air hujan yang dapat menyebabkan beras tersebut  bau apek. Tujuan dari penelitian ini adalah membangun model pendugaan mutu beras berdasarkan sifat apek beras menggunakan metode Principal Component Analysis (PCA) dengan pretreatment De-Trending (DT). Penelitian ini menggunakan alat FT-IR IPTEK T-1516. Bahan yang digunakan adalah beras varietas Ciherang 20 g per sampel dengan total jumlah sebanyak 56 sampel. Untuk memperoleh beras apek dilakukan perendaman selama 2 jam dengan penyimpanan 2 hari, 4 hari dan 6 hari dan beras dikeringkan di bawah sinar matahari. Perlakuan terhadap bahan dibagi 2, pertama beras tanpa campuran dan kedua beras dengan campuran. Pencampuran beras bagus dengan beras apek dengan rasio 75% dan 25%. Akuisisi spectrum beras dilakukan dalam bentuk tumpukan. Masing-masing sampel yang telah dimasukkan ke dalam botol plastik akan dilakukan pengambilan spektrum dengan cara diletakkan masing-masing sampel tersebut pada lubang sinar. Untuk mengekplorasi kemiripan spectrum antar sampel dan untuk mencari outlier data dengan menggunakan metode Hotteling T2 ellipse. Hasil dari penelitian yang telah dilakukan diperoleh NIRS mampu menghasilkan klasifikasi beras bagus dan beras apek dengan tingkat keberhasilan di atas 80%. Pretreatment DT mampu menghasilkan model klasifikasi beras sehingga mencapai keberhasilan 83,33%. Technology Application Near Infrared Reflectance Spectroscopy (NIRS) To Distinguish The Rice Is Stale And Not Stale Using The Principal Component Analysis Method (PCA)Abstract. Rice is one of the main food crops of almost half the world's population. Rice as a staple menu has a considerable starch content. In addition, rice also contains vitamins, protein, minerals, and water. The distribution of rice sometimes destroys rice caused by several factors, such as too long storage, and the temperature where the rice is stored. Rice that is submerged in water can also cause the rice to be damaged, such as rice in a warehouse exposed to rain which can cause the rice to smell musty. The purpose of this study is to build a model for estimating the quality of rice based on the musty nature of rice using the Principal Component Analysis (PCA) method with pretreatment De-Trending (DT). This study used the FT-IR tool of Science and Technology T-1516. The material used was rice of Ciherang variety of 20 g per sample with a total amount of 56 samples. To obtain musty rice, soaking is carried out for 2 hours with storage of 2 days, 4 days and 6 days and the rice is dried in the sun. Treatment of ingredients is divided into 2, first rice without mixture and both rice with mixture. Mixing good rice with musty rice with a ratio of 75% and 25%. Acquisition of spectrum of rice is done in the form of piles. Each sample that has been inserted into a plastic bottle will be taken spectrum by placing each of these samples in a ray hole. To explore the similarity spectrum between samples and to find outliers of data using the T2 ellipse Hotteling method. The results of the research that has been done obtained by NIRS are able to produce a classification of good rice and musty rice with a success rate above 80%. DT pretreatment was able to produce a rice classification model so that it achieved 83.33% success.        

scholarly journals Predicting quality, texture and chemical content of yam (Dioscorea alata L.) tubers using near infrared spectroscopy

2021 ◽  
pp. 096703352110075
Author(s):  
Adou Emmanuel Ehounou ◽  
Denis Cornet ◽  
Lucienne Desfontaines ◽  
Carine Marie-Magdeleine ◽  
Erick Maledon ◽  
...  
Keyword(s):  
High Throughput ◽  
Dry Matter ◽  
Near Infrared ◽  
Reflectance Spectroscopy ◽  
Infrared Reflectance ◽  
Quality Traits ◽  
Near Infrared Reflectance Spectroscopy ◽  
Near Infrared Reflectance ◽  
Infrared Reflectance Spectroscopy ◽  
Texture Attributes

Despite the importance of yam ( Dioscorea spp.) tuber quality traits, and more precisely texture attributes, high-throughput screening methods for varietal selection are still lacking. This study sets out to define the profile of good quality pounded yam and provide screening tools based on predictive models using near infrared reflectance spectroscopy. Seventy-four out of 216 studied samples proved to be moldable, i.e. suitable for pounded yam. While samples with low dry matter (<25%), high sugar (>4%) and high protein (>6%) contents, low hardness (<5 N), high springiness (>0.5) and high cohesiveness (>0.5) grouped mostly non-moldable genotypes, the opposite was not true. This outline definition of a desirable chemotype may allow breeders to choose screening thresholds to support their choice. Moreover, traditional near infrared reflectance spectroscopy quantitative prediction models provided good prediction for chemical aspects (R2 > 0.85 for dry matter, starch, protein and sugar content), but not for texture attributes (R2 < 0.58). Conversely, convolutional neural network classification models enabled good qualitative prediction for all texture parameters but hardness (i.e. an accuracy of 80, 95, 100 and 55%, respectively, for moldability, cohesiveness, springiness and hardness). This study demonstrated the usefulness of near infrared reflectance spectroscopy as a high-throughput way of phenotyping pounded yam quality. Altogether, these results allow for an efficient screening toolbox for quality traits in yams.

Sign in / Sign up

Export Citation Format

Share Document