Development and Validation of an Analytical Method for Identification of Granulated Nicarbazin by near Infrared Reflectance Spectroscopy

1997 ◽  
Vol 5 (1) ◽  
pp. 41-65 ◽  
Author(s):  
K.B. Bradfield ◽  
R.A. Forbes
Keyword(s):  
Near Infrared ◽  
Reflectance Spectroscopy ◽  
Raw Material ◽  
Second Derivative ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance Spectroscopy ◽  
Near Infrared Reflectance ◽  
Distance Method ◽  
Designed Experiments ◽  
Development And Validation

A rapid method was developed for verification of the identity of granulated nicarbazin raw material by near infrared reflectance. A library was developed from 30 second derivative near infrared reflectance spectra obtained from 15 representative batches of nicarbazin. Following identification, new batches were qualified by conformance testing according to the wavelength distance method. The identification/qualification method was validated for selectivity and for ruggedness utilising statistically designed experiments.

scholarly journals Komparasi Metode Koreksi Spektrum NIRS (De-Trending dan Derivatif ke-2) untuk Penentuan Kadar Air Bubuk Biji kakao

2016 ◽  
Vol 1 (1) ◽  
pp. 1059-1068
Author(s):  
Masdar Masdar ◽  
Agus Arip Munawar ◽  
Zulfahrizal Zulfahrizal
Keyword(s):  
Least Squares ◽  
Water Content ◽  
Partial Least Squares ◽  
Near Infrared ◽  
Reflectance Spectroscopy ◽  
Second Derivative ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance Spectroscopy ◽  
Near Infrared Reflectance ◽  
Ft Ir

Rendahnya pengawasan mutu kakao menyebabkan harga jual di pasar dunia menurun akibat kurangnya pengawasan kadar air. Salah satu metode yang tepat dan cepat dalam penentuan kadar air adalah menggunakan atau Near Infrared Reflectance Spectroscopy (NIRS). Tujuan penelitian adalah melihat kemampuan NIRS dalam memprediksi kadar air bubuk biji kakao dengan menggunakan metode Partial Least Squares (PLS) serta membandingkan dua metode pretreatment De-trending dan Derivatif ke-2.Alat yang digunakan FT-IR IPTEK T-1516, dan pengolahan data dengan unscrambler software® X version 10. Hasil penelitian menunjukkan NIRS mampu menduga kadar air dalam jumlah 10 gram dengan selang kadar air 7.42 – 11.09 % menggunakan PLS secara non pretreatment maupun pretreatment. Panjang gelombang relevan dalam menduga kadar air bubuk biji kakao adalah  1400-1450 nm dan 1800-1950 nm. Peningkatkan kinerja PLS yang paling bagus menggunakan pretreatment derivative ke-2.Abstract The lowest quality of cocoa supervision cause the selling price descrease due to the lack of supervision on the water content. One of the exact method in determining the water content is Near Infrared Reflectance Spectroscopy (NIRS). The purpose of this study is to know the capability of NIRS in order to predict the water content of cocoa by using Partial Least Squares (PLS) method then compared the two pretreatment methods namely De-trending and second Derivative. The instrument used was FT-IR IPTEK T-1516, and the spectra data were analyzed by using unscrambler software® X version 10. The results showed that NIRS can be used to predict the water content in amount 10 grams in a range of water content 7:42 to 11:09% by using PLS non pretreatment and vice versa. The relevantwavelengthsused to predict water content of cocoa powder ware1400-1450 nm and 1800-1950 nm. The optimum best pretreatment method was found to be second Derivative.

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 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.

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