Empirical mode decomposition of near-infrared spectroscopy signals for predicting oil content in palm fruits

Novianty, Inna; Baskoro, Ringga Gilang; Nurulhaq, Muhammad Iqbal; Nanda, Muhammad Achirul

doi:10.1016/j.inpa.2022.02.004

Cited by 6 publications

(5 citation statements)

References 44 publications

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“…The performance of the model was analyzed based on the calibration and validation results according to the coefficient correlation of calibration (rc) and validation (rcv), the root means a square error of calibration (RMSE-C) and cross-validation (RMSE-CV) [18]- [21]. Furthermore, the prediction-to-deviation 150 ratio (RPD) was evaluated [22]- [24].…”

Section: Methodsmentioning

confidence: 99%

Prediction of mango firmness by near infrared spectroscopy tandem with machine learning

Agustina¹,

Bulan

Sitorus

2022

Comput. Sci. Inf. Technol.

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The firmness of the mango fruit is one of the internal physical properties that can show its quality. Unfortunately, non-destructive methods to measure this are not yet available. In the current study, we develop a calibration model using near infrared spectroscopy to predict the physical properties (firmness) of the mango cultivar Arumanis (Mangifera indica cv. Arumanis) via machine learning. Spectral data were acquired using the fourier transform near-infrared (FTNIR) benchtop with a wavelength range of 1000 to 2500 nm. Multivariate spectra analysis based on machine learning, including principal component regression (PCR), partial least squares regression (PLSR), and support vector machine regression (SVMR), was utilized and compared to estimate the firmness of fresh mangos. The results obtained show that the prediction of machine learning by PLSR is better than that of SVMR and PCR for the prediction of mango firmness. The coefficient correlation of calibration (rc) and validation (rcv), the root means square error of calibration (RMSE-C) and validation (RMSE-CV), and the ratio of prediction to deviation (RPD) were 0.941, 0.382 kgf, 0.920, 0.472 kgf, and 2.556, respectively. The general results satisfactorily indicate that near infrared spectroscopy technology integrated with an appropriate machine learning algorithm has optimistic results in determining the firmness of mango non-destructively.

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Section: Methodsmentioning

confidence: 99%

Prediction of mango firmness by near infrared spectroscopy tandem with machine learning

Agustina¹,

Bulan

Sitorus

2022

Comput. Sci. Inf. Technol.

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“…Typically, ripeness is predominantly carried out manually with human visual inspection, while the oil content and water content are analyzed in a analysis focuses on the wavelength range of 10,000-6,666.7 cm -1 or 1,000-1,500 nm. Based on preliminary research, the NIR signals with this wavelength range have minimal visual overlap compared to other regions and helps reduce the computational burden as the number of wavelengths has been reduced during the initial steps of signal processing (Novianty et al, 2022). The energy reflected by the palm oil fruit samples was captured by the detector as reflectance data or spectrum data, which was then recorded using the NIRFlex N-500 Fiber Optic Solids.…”

Section: Introductionmentioning

confidence: 99%

Non-Destructive Prediction of Chemical Content in Palm Oil Fruit Using Near-Infrared Spectroscopy and Artificial Neural Network

Adiarifia,

Budiastra,

Mardjan

2024

JTEP

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Oil and water content are an important quality criteria of crude palm oil (CPO) resulted from palm oil fruit processing. Those contents are usually determined using chemical method in the laboratory. This method is time consuming, long procedure, and destructive. Some efforts had been carried out to determine oil and water content of palm oil fruit non-destructively using some methods including Near-Infrared Spectroscopy (NIRS), but the results had not been satisfied. This research aims to assess Artificial Neural Network (ANN) and NIRS method to predict oil and water content of palm oil fruit’s non-destructively. The samples were palm oil fruits with ten maturity levels harvested from plantation in Bogor. Sample’s reflectance was measured with spectrometer NIR-Flex 500 at wavelength of 1000-2500 nm. After that, oil and water content were determined using chemical method. Some pre-treatments of NIR spectra namely normalization, savitzky-golay first derivative, their combinations, and standard normal variate were applied. Multivariate analysis such as PLS were carried out and the results of Factor Component (FC) were input for ANN model. The result showed the best method to predict oil content was combination savitzky-golay first derivative and normalization pre-treatment using PLS-ANN with 20 FC (R2=0.99; SEC=0,58%, RPD = 29.89; CV = 2.47%). For water content, the best prediction was standard normal variate pre-treatment using PLS-ANN with 20 FC (R2=0.99; SEC=1,07%, RPD=20.68; CV=1,73%). The result shows that developed ANN and NIRS can predict oil and water content of palm oil fruit non-destructively.

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“…Various methods for detecting the water content of oil palm fruit have been developed and tested, including based on: near-infrared (NIR) spectroscopy [3], inductive sensor [5], computer vision [12], image analysis [13], and electronic nose [14]. The NIR-based system is one of the popular and most promising methods to be used in predicting the water content of oil palm [15]. Spectroscopic analysis in both the visible and near-infrared ranges has been widely used by researchers for classification and assessing the quality and internal properties of fruit, such as analysis of vitamin C content in apples [16], chemical properties of mango [17], and internal quality of citrus [18].…”

Section: Introductionmentioning

confidence: 99%

“…Using a spectrometer and NIR spectroscopy, the chemical composition of oil palm fruit is assessed using a regression model. The biggest challenge in implementing NIR technology is managing the immense amount of data produced by each wavelength spectrum [15]. If this is applied directly to technological equipment, the computing burden will increase much further To create an accurate prediction, it is required to undertake an efficient and effective chemometric analysis.…”

Section: Introductionmentioning

confidence: 99%

“…If this is applied directly to technological equipment, the computing burden will increase much further To create an accurate prediction, it is required to undertake an efficient and effective chemometric analysis. Researchers have presented chemometric analysis strategies for evaluating the quality of oil palm, including: principal component analysis [3], artificial neural network (ANN) [10], empirical mode decomposition [15], partial least squares [19] and genetic algorithm [10]. Due to the nonlinear complexity of NIR signals, certain signal processing techniques are necessary to improve forecast accuracy.…”

Section: Introductionmentioning

confidence: 99%

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Shannon entropy on near-infrared spectroscopy for nondestructively determining water content in oil palm

Novianty¹,

Sholihah²,

Mindara³

et al. 2023

IJECE

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<span lang="EN-US">Indonesia is the world’s largest producer of palm oil. To preserve its competitive advantages, the Indonesian oil palm sector must expand high-quality palm oil output. In oil palm quality control, the water content is a crucial parameter as it can be used as a reference to determine the right harvest time. Thus, this study proposed a near-infrared (NIR) spectroscopy as a fast and non-destructive analysis to assess oil palm water content. NIR spectra were processed using Shannon entropy to describe the characteristics at each wavelength. In this study, oil palm fruit samples at various maturity levels were collected with eight different maturity fractions. Based on the analysis, the Shannon entropy value is closely related to any changes in the water content of palm oil. The entropy value has a decreasing trend as the water content increases. The proposed technique can predict the water content of an oil palm with satisfactory performance with values of 0.9746 of coefficient of determination (R<sup>2</sup>) and 2,487 of root mean square error (RMSE). Application of this model will lead to a fast and accurate prediction system related to oil palm water content.</span>

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Empirical mode decomposition of near-infrared spectroscopy signals for predicting oil content in palm fruits

Cited by 6 publications

References 44 publications

Prediction of mango firmness by near infrared spectroscopy tandem with machine learning

Prediction of mango firmness by near infrared spectroscopy tandem with machine learning

Non-Destructive Prediction of Chemical Content in Palm Oil Fruit Using Near-Infrared Spectroscopy and Artificial Neural Network

Shannon entropy on near-infrared spectroscopy for nondestructively determining water content in oil palm

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