Penerapan Teknologi Visible-Near Infrared Spectroscopy untuk Prediksi Cepat dan Simultan Kadar Air Buah Melon (Cucumis melo L.) Golden

Kadar air merupakan salah satu atribut kualitas yang penting pada komoditas hortikultura. Penetapan kadar air buah melon dengan metode konvensional memakan waktu yang lama dan perlu merusak sampel buah. Penelitian ini bertujuan untuk memprediksi kadar air buah melon golden menggunakan teknologi visible-near infrared spectroscopy (Vis-NIRS). Metode koreksi spektra orthogonal signal correction (OSC) diterapkan pada spektra original untuk meningkatkan kehandalan model kalibrasi. Partial least squares regression (PLSR) digunakan sebagai metode pendekatan regresi untuk mengekstraksi data spektra Vis-NIRS. Hasil penelitian membuktikan bahwa Vis-NIRS dapat diandalkan untuk memprediksi kadar air buah melon golden. Metode koreksi spektra OSC mampu memperkecil jumlah principal component (PC) pada spektra original. Linieritas pada model kalibrasi menggunakan spektra OSC tercatat memperoleh nilai tertinggi sebesar 0,92. Ratio of performance to deviation (RPD) pada spektra OSC menampilkan nilai tertinggi pula yaitu 3,63. Model kalibrasi yang diperoleh pada penelitian ini dapat ditransfer ke dalam spektrometer Vis-NIRS untuk prediksi kadar air melon golden secara cepat dan simultan

Near Infrared Technology As a Robust and Environmental Friendly Approach To Biofuel Analysis: Rapid Biodiesel Classification and Quality Prediction

Abstract. The use of ethanol and biodiesel, which are alternative fuels or biofuels, has increased in the last few years. Modern official standards list 25 parameters that must be determined to certify biodiesel quality. In order to determine biofuel quality, several methods were already widely used in which most of them were based on solvent extraction followed by other laboratory procedures. Yet, these methods are expensive, laborious and complicated processing for samples. Near infrared reflectance spectroscopy (NIRS) can be considered as a fast, pollution-free and non-destructive method in determining biofuel quality parameters. The objective of this study is to apply near infrared technology in classifying biodiesel based on KOH (0.3, 0.5 and 0.7) and to predict related biodiesel quality properties (water content, linolenic fatty acid, oleic acid,  and stearic acid) based on its infrared reflectance. Biodiesel infrared spectrum was acquired in wavelength range from 1000 to 2500 nm for different mentioned three KOH content. Principal component analysis (PCA) with non-iterative partial least square (NIPALS) was applied to analyze biodiesel spectral data. The result showed that two principal components (PC1=97% ad PC2 = 2%) based on infrared reflectance data were successfully able to recognize and classify biodiesel based on their used KOH. Moreover, the wavelength range of 1000 – 1140 were to be believed related to linolenic fatty acid whilst 1450 nm and 1930 nm were associated with water content. Stearic acid can be predicted in wavelength range of 1330 – 1380 nm and wavelength range of 1725 – 1790 nm were related to oleic acid of biodiesel. This may conclude that infrared technology was feasible to use as a rapid, effective and non-invasive method in biofuel classification and evaluation

The Application of Fourier Transform Infrared Photoacoustics Spectroscopy (FTIR-PAS) for Rapid Soil Quality Evaluation

Abstrak. Tanah merupakan media tumbuh tanaman dan berperan dalam menjaga keseimbangan alam. Evaluasi kualitas dan kesuburan tanah menjadi hal penting dan merupakan pekerjaan rutin pada crop management system. Untuk memonitor dan menentukan kualitas tanah, beberapa metode telah diterapkan. Akan tetapi, metode tersebut berbasis pengukuran laboratorium yang melibatkan bahan kimia, memerlukan waktu yang lama dan kurang efektif pada aplikasinya. Infrared spectroscopy muncul sebagai salah satu teknologi yang cepat, simultan dan ramah lingkungan untuk digunakan dalam evaluasi kualitas dan kesuburan tanah dengan memprediksi nutrisi tanah yang utama berupa C, N, P dan K. Spektrum transmisi infrared (IR) diakuisisi pada panjang gelombang 1000-2500 nm dengan menerapkan metode photo-acoustic spectroscopy (PAS). Pendekatan metode Least square-support vector machine regression (LS-SVM) digunakan untuk memprediksi parameter nutrisi tanah. Hasil studi menemukan bahwa parameter C dan N pada tanah dapat diprediksi dengan sempurna karena C-N mengalami stretching akibat serapan gelombang IR. Sedangkan unsur nutrisi lain seperti P, K, Mg, Ca, S dapat diprediksi dengan maksimum residual predictive deviation (RPD) index maksimum 1.9. Lebih lanjut, lempung tanah, air tanah, dan mikroba tanah kemungkinan dapat diklasifikasi dengan baik dengan metode IR-PAS dan bantuan metode klasifikasi least-square discriminant analysis (LS-DA) dan cluster analysis (CA). Berdasarkan hasil studi, dapat disimpulkan bahwa teknologi FTIR-PAS dapat digunakan untuk real-time monitoring kualitas dan kesuburan tanah. The Application of Fourier Transform Infrared Photoacoustics Spectroscopy (FTIR-PAS) for Rapid Soil Quality Evaluation Abstract. The major function of soil is to provide fundamental natural resources for survival of plants, animals, and the human race. Soil functions depend on the balances of its structure and composition, well as the chemical, biological, and physical properties. It is become one important key aspect and routine activity in crop management system. To monitor and determine soil quality properties, several methods were already widely used in which most of them are based on solvent extraction followed by other laboratory procedures. However, these methods often require laborious and complicated processing for samples. They are time consuming and destructive. In last few decades, the application of infrared spectroscopy as non-destructive technique in determining soil quality properties (C, N, P and K) rapidly and simultaneously. Fourier transform infrared spectrum (FTIR) were acquired in wavelength range from 1000 to 2500 nm with applying photo-acoustic spectroscopy (PAS). Least square-support vector machine regression (LS-SVM) approach was then applied to predict soil quality properties. The results showed that C and N can be predicted accurately using FTIR-PAS whilst other parameters (P, K, Mg, Ca, S) can be predicted with maximum RPD index is 1.9. Moreover, soil clay, moisture and soil microbes were feasible to be detected by using FTIR-PAS combining with discriminant analysis (LS-DA) or cluster analysis (CA). It may conclude that FTIR-PAS technology can be used as a real time method  in monitoring soil quality and fertility properties

Model Prediksi Kadar Air Buah Cabai Rawit Domba (Capsicum frutescens L.) Menggunakan Spektroskopi Ultraviolet Visible Near Infrared

Penelitian ini bertujuan untuk menduga kadar air buah cabai rawit domba (Capsicum frutescens L.) menggunakan spektroskopi UV-Vis-NIR. Total sampel yang digunakan yaitu 45 buah. Analisis dilakukan di Laboratorium Hortikultura, Fakultas Pertanian, Universitas Padjadjaran. Akuisisi data spektra dengan rentang panjang gelombang 300 – 1050 nm (Nirvana AG410). Spektra diperbaiki dengan metode multiplicative scatter correction (MSC), standard normal variate transformation (SNV), orthogonal signal correction (OSC), first derivative (dg1) dan second derivative (dg2). Analisis data dilakukan dengan menggunakan partial least squares regression (PLSR). Berdasarkan penelitian ini menunjukkan bahwa metode koreksi OSC menghasilkan model kalibrasi tertinggi dengan Rkal, RMSEC, Rval, RMSECV, RPD dan faktornya masing-masing yaitu 0.99, 0.31, 0.98, 0.68, 6.62 dan 4. Hal ini menunjukkan bahwa spektroskopi UV-Vis-NIR dapat digunakan untuk memprediksi kadar air pada buah cabai rawit domba

Rapid and non-destructive prediction of C-organic in agricultural soil using near infrared reflectance spectroscopy (NIRS)

Soil organic carbon (C-organic) is one of main of soil quality which affects the assortment of organic materials and mixtures properties of soils. This C-organic also have a practical value and importance in agriculture. To determine C-organic, normally, conventional and laborious procedures were employed. Yet, this method is expensive, time consuming, involve chemical materials and may cause pollution. Thus, alternative fast and environmental friendly method is required to determine C-organic in soil. The near infrared reflectance spectroscopy (NIRS) technique can be considered to be applied, since this method is fast, nondestructive, simple preparation and pollution free. Therefore, the main objective of this present study is apply NIRS technique in predicting C-organics and classifying soils based on geographical characteristics. Soil samples from 4 different site locations were taken spectra data of these samples were acquired in wavenumbers range of 4000-10 000 cm -1 . C-organic prediction model was developed using NIR spectra data and partial least square regression (PLS), while classification model was established using principal component analysis (PCA). The results showed that Soil characteristics from 4 different locations can be classified with total explained variance of PCA was 99% (PC1 = 88% and PC2 = 11%). Moreover, NIRS technique was able to predict C-organic with maximum correlation coefficient (r) was 0.93 and residual predictive deviation (RPD) index was 3.22 which categorized as excellent prediction model performance. It may conclude that NIRS technique can be applied as a rapid and non-destructive method in predicting C-organic and classifying soil characteristics

Near Infrared Reflectance Spectroscopy: Prediksi Cepat dan Simultan Kadar Unsur Hara Makro pada Tanah Pertanian

Plants need an ideal and healthy soil condition for their growth and a sufficient amount of soil macronutrients. To determine soil nutrients, several methods have been widely employed. Yet, most of them are based on solvent extraction, which is normally time-consuming, requires complicated sample preparation, and sometimes involves chemical materials. Thus, a novel, fast and simultaneous method is required as an alternative method used to predict soil macronutrients in a short period and without involving chemical materials. Near infrared spectroscopy (NIRS) can be considered for this need, since this method is fast, environmentally friendly, and non-destructive. Therefore, the main objective of this study is to apply an NIRS method to predict soil macronutrients (N, P, and K). The diffuse reflectance spectrum was acquired for soil samples in a wavelength range from 1000–2500 nm. Spectra data were corrected using a smoothing method whilst prediction models were developed using principal component regression (PCR) and partial least square regression (PLSR). Prediction accuracy and robustness were evaluated using these following statistical indicators: correlation coefficient (r), root mean square error (RMSEC) and residual predictive deviation (RPD). The results showed that NIRS was able to predict soil macronutrients simultaneously with a maximum correlation coefficient r = 0.97 for N prediction, r = 0.99 for P prediction, and r = 0.95 for K prediction. Thus, it may be concluded that an NIRS method is feasible to be applied as a novel, reliable and fast method to predict soil macronutrients (N, P, and K) simultaneously

Effects of fermentation on protein profile of coffee by-products and its relationship with internal protein structure measured by vibrational spectroscopy

Objective To our knowledge, there are few studies on the correlation between internal structure of fermented products and nutrient delivery from by-products from coffee processing in the ruminant system. The objective of this project was to use advanced mid-infrared vibrational spectroscopic technique (ATR-FT/IR) to reveal interactive correlation between protein internal structure and ruminant-relevant protein and energy metabolic profiles of by-products from coffee processing affected by added-microorganism fermentation duration. Methods The by-products from coffee processing were fermented using commercial fermentation product, called Saus Burger Pakan, consisting of various microorganisms: cellulolytic, lactic acid, amylolytic, proteolytic, and xylanolytic microbes, for 0, 7, 14, 21, and 28 days. Protein chemical profiles, Cornell Net Carbohydrate and Protein System crude protein and CHO subfractions, and ruminal degradation and intestinal digestion of protein were evaluated. The attenuated total reflectance-Ft/IR (ATR-FTIR) spectroscopy was used to study protein structural features of spectra that were affected by added microorganism fermentation duration. The molecular spectral analyses were carried using OMNIC software. Molecular spectral analysis parameters in fermented and non-fermented by-products from coffee processing included: Amide I area (AIA), Amide II (AIIA) area, Amide I heigh (AIH), Amide II height (AIIH), α-helix height (αH), β-sheet height (βH), AIA to AIIA ratio, AIH to AIIH ratio, and αH to βH ratio. The relationship between protein structure spectral profiles of by-products from coffee processing and protein related metabolic features in ruminant were also investigated. Results Fermentation decreased rumen degradable protein and increased rumen undegradable protein of by-products from coffee processing (p<0.05), indicating more protein entering from rumen to the small intestine for animal use. The fermentation duration significantly impacted (p<0.05) protein structure spectral features. Fermentation tended to increase (p<0.10) AIA and AIH as well as β-sheet height which all are significantly related to the protein level. Conclusion Protein structure spectral profiles of by-product form coffee processing could be utilized as potential evaluators to estimate protein related chemical profile and protein metabolic characteristics in ruminant system

Non-destructive method for maturity assessment of Indonesian’s mangoes by NIRS spectroscopy

Rapid and non-destructive method to determine maturity quality of mangoes accurately has played an increasing role in the fruits supply chain involving automation. Diffuse reflectance spectra (R) and absorbance spectra (Log 1/R) in the spectral range from 900 to 1400 nm were measured using NIRS spectroscopy at three different points of 257 Indonesians mangoes cultivar arumanis, manalagi, gadong, gincu and golek of different ripeness indices. Through principle component analysis (PCA) and cluster analysis (CA) of the absorbance and reflectance spectra, cultivars can be differed with an accuracy of 99.8%. Multiple linear regression was applied to develop calibration models from mangoes with soluble solid content ranged between 5.5 and 13.5 % Brix and firmness ranged between 0.25 and 4.50 kgf. Log 1 /R calibration model could assess the soluble solid content and firmness of the mangoes with higher accuracy than R model with the coefficient of determination of 0.96 and 0.93, respectively. Results showed that NIRS spectroscopy has the feasibility to be employed in the maturity measures of Indonesian’s mangoes

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

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