Calibration models database of near infrared spectroscopy to predict agricultural soil fertility properties

Munawar, Agus Arip; Yunus, Yuswar; Devianti,; Satriyo, Purwana

doi:10.1016/j.dib.2020.105469

Cited by 28 publications

(33 citation statements)

References 15 publications

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“…Generally, several methods are widely employed to determine cocoa beans' inner quality parameters and other agricultural products. However, most of these methods are based on liquid and solvent extraction followed by other laboratory procedures, such as titration to determine the quantity of ascorbic acid and its separation using a centrifuge [3]. These methods are often laborious, time-consuming, destructive, and complicated [4].…”

Section: Introductionmentioning

confidence: 99%

Robust prediction performance of inner quality attributes in intact cocoa beans using near infrared spectroscopy and multivariate analysis

Hayati

Zulfahrizal

Munawar

2021

Heliyon

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Fast and simultaneous determination of inner quality parameters, such as fat and moisture contents, need to be predicted in cocoa products processing. This study aimed to employ the near-infrared reflectance spectroscopy (NIRS) in predicting the quality mentioned above parameters in intact cocoa beans. Near-infrared spectral data, in a wavelength ranging from 1000 to 2500 nm, were acquired for a total of 110 bulk cocoa bean samples. Actual fat and moisture contents were measured with standard laboratory procedures using the Soxhlet and Gravimetry methods, respectively. Two regression approaches, namely principal component regression (PCR) and partial least square regression (PLSR), were used to develop the prediction models. Furthermore, four different spectra correction methods, namely multiple scatter correction (MSC), de-trending (DT), standard normal variate (SNV), and orthogonal signal correction (OSC), were employed to enhance prediction accuracy and robustness. The results showed that PLSR was better than PCR for both quality parameters prediction. Spectra corrections improved prediction accuracy and robustness, while OSC was the best correction method for fat and moisture content prediction. The maximum correlation of determination (R 2 ) and residual predictive deviation (RPD) index for fat content were 0.86 and 3.16, while for moisture content prediction, the R 2 coefficient and RPD index were 0.92 and 3.43, respectively. Therefore, NIRS combined with proper spectra correction method can be used to rapidly and simultaneously predict inner quality parameters of intact cocoa beans.

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Section: Introductionmentioning

confidence: 99%

Robust prediction performance of inner quality attributes in intact cocoa beans using near infrared spectroscopy and multivariate analysis

Hayati

Zulfahrizal

Munawar

2021

Heliyon

Self Cite

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“…Pre-processing multiplicative scatters correction (MSC), and standard normal variate (SNV) is also the most common correcting spectra and enhancement method used for intact grape berries [5] ]. MSC is compensated for additive and multiplicative effects in the spectral data caused by physical effects [6] . The algorithm also tried to reduce scattering effects by linearising each spectrum to an optimal range of spectra data that fits the typical spectrum [7] , [8] , [9] .…”

Section: Experimental Design Materials and Methodsmentioning

confidence: 99%

Dataset of adulteration with water in coconut milk using FTIR spectroscopy

Sitorus

Muslih

Cebro³

et al. 2021

Data in Brief

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This paper presents the spectroscopic dataset, pre-processing, calibration, and predicted model database of Fourier transform infrared (FTIR) spectroscopy used to detect adulterated coconut milk with water. Absorbance spectral data were acquired and recorded in wavelength range from 2500 to 4000 nm for a total of 43 coconut milk samples. Coconut milk ware prepared in three forms of adulteration. Coconut milk comes from traditional markets and instant coconut milk in Indonesia. Spectra data may also be pre-processed to increase prediction accuracy, robustness performance using normalize, multiplicative scatter correction (MSC), standard normal variate (SNV), 1st derivative, 2nd derivative, and combination of 1st derivative and MSC. Calibration models and cross-validation to forecast those adulteration parameters use two regression algorithms, i.e., principal component regression (PCR) and partial least square regression (PLSR). By looking at its statistical metrics, prediction efficiency can be measured and justified (correlation coefficient (r), correlation of determination (R 2 ), and root mean square error (RMSE)). Obtained FTIR datasets and models can be used as a non-invasive method to predict and determine adulteration on coconut milk.

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“…After that, the compound was removed from the barrel and allowed to cool for 5 to 10 minutes. The compound was turned into pellets by using the crusher machine [10]. Next, the injection molding was set into the temperature of 190˚C and the pellets were passed through the machine.…”

Section: Manufacture Of Injection Moulded Wpc Test Samplesmentioning

confidence: 99%

High Density Polyethylene (HDPE) composite mixed with Azadirachta excelsa (Sentang) tree waste flour: Mechanical and physical properties

Zakaria

Jamaludin

Zakaria

et al. 2022

IOP Conf. Ser.: Earth Environ. Sci.

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This article presents the application of plantation waste materials (leaves, branches and trunks) of Azadirachta excelsa (Sentang) tree in order to evaluate and compare their suitability as reinforcement and filler for high density polyethylene (HDPE) thermoplastics. The aim of the study was to investigate the effect of different types of Azadirachta excelsa (Sentang) trunks flour, branches flour and leaves flour fillers on the mechanical and physical properties of HDPE composite. The composite samples were produced using 25%, 35% and 45% by weight of flour filler loading and 2% coupling agent (maleic anhydride) using a twin-screw extruder, followed by injection molding process. The flexural modulus and tensile strength of the composite filled with trunk flour were not significantly different with the composite-filled branch flour. However, there is a significant difference between composite-filled leaf flour when compared to both composite-filled trunk flour and composite-filled branch flours. Overall, composite samples with trunk flour show better mechanical properties, while composite samples with lower filler loadings of 25% exhibit better dimensional stability compared to the other such as 35% and 45% filler loadings. The study also indicated that composite filled with leaf, branch and trunk flours had better mechanical strength than virgin HDPE.

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Calibration models database of near infrared spectroscopy to predict agricultural soil fertility properties

Cited by 28 publications

References 15 publications

Robust prediction performance of inner quality attributes in intact cocoa beans using near infrared spectroscopy and multivariate analysis

Robust prediction performance of inner quality attributes in intact cocoa beans using near infrared spectroscopy and multivariate analysis

Dataset of adulteration with water in coconut milk using FTIR spectroscopy

High Density Polyethylene (HDPE) composite mixed with Azadirachta excelsa (Sentang) tree waste flour: Mechanical and physical properties

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