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Author: search.filters.author.Bulan, RamayantySubject: search.filters.subject.Chemometrics

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    Vis-NIR spectra combined with machine learning for predicting soil nutrients in cropland from Aceh Province, Indonesia
    (2022-12-01)
    Devianti
    ;
    Sufardi
    ;
    Bulan, Ramayanty
    ;
    Sitorus, Agustami
    Rapid analytical methods are needed to measure soil nutrient content in cropland, especially in Aceh Province, Indonesia. This is necessary for quick and accurate decision-making on the suitability of the land in terms of soil nutrients and the types of plants to be cultivated on its cropland. Visible near-infrared (Vis-NIR) spectroscopy with suitable chemometric methods through applied machine learning algorithms could be used to predict soil nutrients in the land of agriculture. The current study compared the implementations of machine learning algorithms (support vector machine for regression (SVR), partial least squares artificial neural network (PLS-ANN) and gradient-boosted tree regression (GBRT)) to predict soil nutrients (TN,TP, and TK content) in cropland in Aceh province (Indonesia). The approaches studied used three algorithms of machine learning with four preprocessing employed from spectral data. Samples (n = 102) of soil horizons (0–60 cm) were taken from ten regions in the province of Aceh (Indonesia) and the soil nutrient was measured, including the TN content by the Kjeldahl method and the TP and TK content by the Bray method. Their Vis-NIR spectra (400–2150 nm) were scanned after air drying and ground into powder. 71 examples were used to create the models, while the remaining 31 were used for validation. All of the machine learning algorithms tested as a chemometric approach yielded outstanding models for quantitative estimations of TN, TP, and TK content. Generally, the accuracy of the SVR models of the algorithm utilizing the full spectra was equivalent to that of the PLS-ANN models. Nevertheless, the ANN algorithm using reduced component spectral data (PLS-ANN) served more usefulness than the SVR algorithm depending on the preprocessing method. The most precise models for the content of TN, TP and TK were obtained using the GBRT algorithm (RPD = 2.64, 3.93 and 2.38 for the content of TN, TP and TK, respectively). The results demonstrate that Vis-NIR related to the machine learning algorithm is trustworthy to apply to measure the content of TN, TP, and TK in soil cropland.
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    Development of a screening method for adulteration detection in coconut milk via mid-infrared spectroscopy: A study of linear and nonlinear regression method
    (2022-12-01)
    Sitorus, Agustami
    ;
    Bulan, Ramayanty
    In the present study, we developed a screening method for detection of adulteration in coconut milk via mid-infrared spectroscopy. Linear and nonlinear regression methods (principal component regression (PCR), partial least squares regression (PLSR), and support vector machine regression (SVMR)) were employed and compared to achieve an optimal screening method. Spectral data were scanned using the FTIR benchtop with a wavelength range of 4000–16702 nm. The calibration models of the linear and nonlinear regression methods were developed using the leave-one-out cross-validation method before testing using predictive data that had been prepared. Furthermore, five spectral data treatment techniques were employed to improve the accuracy of the proposed calibration model. The results obtained show that the SVMR method is better than PCR and PLSR for the detection of adulteration in coconut milk by mid-infrared data spectroscopy. The coefficient of determination for calibration (R<sup>2</sup><inf>c</inf>) and prediction (R<sup>2</sup><inf>p</inf>), the root mean square error of calibration (RMSEC) and prediction (RMSEP) and the ratio of prediction to deviation (RPD) using the SVMR method were 100%, 0.81, 98.40%, 0.87 and 7.86, respectively. Furthermore, based on RPD analysis, it is known that the SVMR model can be used to perform excellent quality control of water-adulterated coconut milk.