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A novel strategy of NIR spectra multivariate calibration in the presence both of small dataset and non-linearity: A comparative study
(2023-12-01)
Devianti
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Ismy, Adi Saputra
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Siahaan, Herbert Hasudungan
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Sitorus, Agustami
The presence of non-linearity and, at the same time, the small number of datasets are often the constraints that appear together in the case of NIR spectra (381–1065nm). This makes some chemometricians think again about presenting a reasonable and robust calibration model using the linear calibration method. On the other hand, even though obtaining a high and robust calibration model, the NIR spectra-based approach is still an alternative method that must still consider low cost and ease of getting it. This study introduces a novel strategy for developing robust calibration models from small and non-linearity NIR spectra datasets. The prediction performance of two groups of chemometric methods, linear (partial least squares regression, PLSR) and non-linear calibration techniques (k-nearest neighbor, k-NN; Ada boosting, AB; Bayesian ridge regression, BRR), were also compared and investigated in depth. A total of forty raw NIR spectral data was used to develop a calibration model to predict the content of B-pinene, D-limonene, and safrole from the nutmeg fruit. The first strategy, non-linearity due to the effect of light scattering on the NIR spectral data, will be handled directly by the non-linear calibration technique algorithm from machine learning to generate the non-linearity model without preprocessing techniques. The second strategy, the robustness of the model, is tested by performing random splitting of data several times without supervision and ending with a rigorous statistical procedure adopted to ensure reliable comparison. The results suggest that the non-linear calibration method is the most promising among the investigated methods. Furthermore, although none of the techniques is always the best to predict on all references, k-NN (for prediction of B-pinene and safrole) and BRR (for prediction of B-pinene and D-limonene), some of them are found to be the most promising in terms of low prediction error (the maximum R<inf>p</inf>2 is 81.6%, and RMSE is less than 1.139%). There are non-linear calibration techniques explored with limited success being achieved.
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A rapid method to predict type and adulteration of coconut milk by near-infrared spectroscopy combined with machine learning and chemometric tools
(2023-12-01)
Sitorus, Agustami
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Lapcharoensuk, Ravipat
Coconut milk is a soft target for adulterators owing to its simplicity of chemical composition. Professionals and consumers want to control the originalitas of coconut milk, while sellers can profit by mixing fresh coconut milk from low-cost products into high-value fresh coconut milk. Non-destructively and rapidly identifying coconut milk classification goods may be useful in quality assurance settings. However, no studies to date have investigated this topic. In this study, near-infrared spectra (NIRs) were collected from fresh coconut milk (FCM), instant coconut milk (ICM), and adulterated fresh coconut milk (A-FCM) in order to investigate the prospect of non-invasively discriminating coconut milk type and at the same time predicting the level of A-FCM. Partial least squares (PLS), linear discriminant analysis (LDA), support vector machine (SVM), and multilayer perceptron (MLP) were employed to establish classification and regression models using NIRs. Combining 18 preprocessing types and hyperparameter optimization of individual machine learning algorithms is carried out together and evaluated using 5-folds cross-validation. All algorithms in this study (LDA, SVM, MLP) obtained the same satisfactory results with all the precision, recall, F1-score, and perfect accuracy (100%) to distinguish FCM, ICM, and A-FCM in both calibration and prediction. Regression models using the SVM obtained acceptable results, with a determination coefficient of calibration and prediction all over 0.93, root mean square error of calibration and prediction all below 8.30%, and ratio of prediction to deviation over 3.80. Last but not least, this study would help apply NIRs to detect the originality of coconut milk in real-world conditions.
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An automatic generation of pre-processing strategy combined with machine learning multivariate analysis for NIR spectral data
(2023-09-01)
Arianti, Nunik Destria
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Saputra, Edo
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Sitorus, Agustami
Pre-processing near-infrared (NIR) spectral data is indispensable in multivariate analysis, since the measured spectra of complex samples are often subject to overwhelming background, light scattering, varying noises, and other unexpected factors. Various pre-processing methods have been developed to remove or reduce the interference of these effects. Until now, most applications of NIR spectra pre-processing in multivariate calibration have been trial-and-error, with selecting a proper method depending on the nature of the data, expertise, and practitioner experience. Thus, it is usually challenging to determine the best pre-processing method for a given data. In order to tackle these problems, this study proposes a new concept of data pre-processing, namely, automatically generating a pre-processing strategy (AGoES). This concept belongs to the ensemble pre-processing method, where machine learning algorithms (PLSR, SVM, k-NN, DT, AB, and GPR) built on differently preprocessed data are combined by 5-fold cross-validation and grid search optimization. To investigate our concept, a public NIR spectral dataset was used to predict three responses, including dry matter content (DM), organic matter content (OM) and ammonium nitrogen content (AN) from manure organic waste. The results show that SVM is the best algorithm combined with the AGoES pre-processing to predict DM and AN with a ratio of prediction to deviation (RPD) of 3.619 and 2.996, respectively. The AB tandem with AGoES pre-processing is the best strategy for predicting OM with an RPD of 3.185. Therefore, in the framework of the AGoES concept, it is unsupervised pre-processing, more simple, and feasible to apply multivariate analysis using machine learning algorithms.
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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
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Sufardi
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Bulan, Ramayanty
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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
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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 (R2<inf>c</inf>) and prediction (R2<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.
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A Vis-NIRs Calibration Model for the Prediction of Myristicin and Alpha-Pinene on Nutmeg: A Comparison Study of PLSR Algorithm and Machine Learning Algorithm
(2022-01-01)
Devianti, Devianti
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Sufardi, Sufardi
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Yusmanizar, Yusmanizar
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Siahaan, Herbert Hasudungan
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Sitorus, Agustami
Determination of the myristicin and alpha-pinene content of nutmeg is still constrained by the extended testing time in the laboratory, which is expensive and is carried out destructively. In addition, non-destructive testing using spectroscopy often faces problems in building models that only rely on algorithms that perform linearly, such as PCR and PLSR. Therefore, the present study studied Vis-NIR (381-1065 nm) as a fast, inexpensive, and non-destructive mechanism to determine the myristicin and alphapinene of nutmeg fruits from Aceh, Indonesia. Two algorithms commonly used in spectral data processing, partial least squares regression (PLSR) and machine learning represented by a support vector machine (SVM), were employed and compared to predict myristicin and alpha-pinene in nutmeg fruits. The chemical reference parameters (myristicin and alpha-pinene) were measured using gas chromatography mass spectrometry (GC-MS). Standard normal variate (SNV) and multiplicative scatter correction (MSC) preprocessing were involved as spectra enhancement before the prediction models outcome. The results show that the kernel of the radial basis function (RBF) kernel v-SVM algorithm is better than PLSR for myristicin prediction with gamma (γ), c, and nu (v) of 0.1, 1.0, and 0.99, respectively. Also, the ε-SVM algorithm by RBF kernel is better than PLSR for the prediction of alpha-pinene in nutmeg fruits with gamma (γ), c, and epsilon (ε) compositions of 0.01, 10, 0.1, respectively. The coefficient correlation of calibration (rc) and coefficient determination of prediction (R<inf>p</inf> 2), the root means square error of calibration (RMSEC) and prediction (RMSEP), and the ratio (RPD) for the prediction of myristicin were 0.992, 0.986, 0.941%, 1.325% and 8.348, respectively. The coefficient correlation of calibration (rc) and coefficient determination of prediction (R<inf>p</inf> 2), the root mean square error of calibration (RMSEC) and prediction (RMSEP), and the ratio of prediction to deviation ratio (RPD) for the prediction of alpha-pinene were 0.976, 0.979, 0.305%, 0.317% and 6.826, respectively. In general, the results satisfactorily indicate that Vis-NIRS, with the appropriate algorithm, has promising results in determining myristicin and alpha-pinene on nutmeg from Aceh, Indonesia, as nondestructive measurement.

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