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Nondestructive Detection of Moisture Content in Palm Oil by Using Portable Vibrational Spectroscopy and Optimal Prediction Algorithms

Nondestructive Detection of Moisture Content in Palm Oil by Using Portable Vibrational... Hindawi Journal of Analytical Methods in Chemistry Volume 2023, Article ID 3364720, 8 pages https://doi.org/10.1155/2023/3364720 Research Article Nondestructive Detection of Moisture Content in Palm Oil by Using Portable Vibrational Spectroscopy and Optimal Prediction Algorithms 1 2 3 1 Ernest Teye , Charles L. Y. Amuah , Tai-Sheng Yeh , and Regina Nyorkeh Department of Agricultural Engineering, School of Agriculture, College of Agriculture and Natural Sciences, University of Cape Coast, Cape Coast, Ghana Department of Physics, Laser and Fibre Optics Centre, School of Physical Sciences, University of Cape Coast, Cape Coast, Ghana Department of Food Science and Nutrition, Meiho University, Neipu Township, Taiwan Correspondence should be addressed to Ernest Teye; teyernest@gmail.com Received 21 September 2022; Revised 11 November 2022; Accepted 13 January 2023; Published 31 January 2023 Academic Editor: Ricardo Jorgensen Cassella Copyright © 2023 Ernest Teye et al. Tis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Rapid and nondestructive measurement of moisture content in crude palm oil is essential for promoting the shelf-stability and quality. In this research, micro NIR spectrometer coupled with a multivariate calibration model was used to collect and analyse fngerprinted information from palm oil samples at diferent moisture contents. Several preprocessing methods such as standard normal variant (SNV), multiplicative scatter correction (MSC), Savitzky–Golay frst derivative (SGD1), Savitzky–Golay second derivative (SGD2) together with partial least square (PLS) regression techniques, full PLS, interval PLS (iPLS), synergy interval PLS (SiPLS), genetic algorithm PLS (GAPLS), and successive projection algorithm PLS (SPA-PLS) were comparatively employed to construct an optimum quantitative prediction model for moisture content in crude palm oil. Te models were evaluated according to the coefcient of determination and root mean square error in calibration (Rc and RMSEC) and prediction (Rp and RMSEC) set, respectively. Te model SGD1+SiPLS was the optimal novel algorithm obtained among the others with the performance of Rc �0.968 and RMSEC �0.468 in the calibration set and Rp �0.956 and RMSEP �0.361 in the prediction set. Te results showed that rapid and nondestructive determination of moisture content in palm oil is feasible and this would go a long way to facilitating quality control of crude palm oil. storage. Also, the moisture content is among the pa- 1. Introduction rameters that dictate the price. Research has shown that Palm oil is the most consumed edible vegetable oil in the the moisture content of palm oil increases water activity world with various applications in food products including and this further leads to high hydrolysis and is a possible the production of margarine, ice creams, crackers, choco- cause of a steady rise in free fatty acid values during lates, and fried foods, among others [1] Demand for palm oil storage [1]. Other studies have shown that moisture continues to grow steadily worldwide as global production content and amount of free fatty acid (FFA) are the falls short of supply at 70 million metric tonnes since 2017 important quality parameters of palm oil [3]. High [2]. Generally, palm oil is rich in carotenoids and other very moisture content causes rancidity, Aspergillus Niger, and important nutritional phytonutrients such as vitamin E Mucor species growth in edible oils with high moisture components (tocopherols and tocotrienols) and it is known content [4]. Recently, high moisture contents had been to provide health-benefcial properties. reported in palm oil and raised concerns for storage During extraction and processing of crude palm oil, stability and spoilage [5]. A cost-efective method for the moisture content is monitored until it gets to its fnal moisture determination of palm oil is therefore urgently required state as this determines the quality during needed. Terefore, measuring the moisture content of 2 Journal of Analytical Methods in Chemistry crude palm oil during processing and storage is vital to 2. Materials and Methods ensuring quality and maintaining storage life. 2.1. Sample Collection. Palm oil samples were collected from Various techniques had been employed previously to fve major palm oil-producing regions in Ghana at diferent detect moisture in edible oil. Traditionally, the moisture moisture contents; others were specifcally collected directly content is determined by the oven drying method; the Karl from factories in central and western regions of Ghana. All Fisher method has been employed frequently for the de- the palm oil samples were at diferent levels of moisture termination of moisture content, and this method uses content. A total of 150 samples were collected into smaller a complicated titrator, expensive chemicals, and time- 250ml bottles and transported into the laboratory in the consuming procedures. Also, others use diferent methods School of Agriculture Technology Centre. such as over dry methods as carried out by others [6], microwave six-port refectometer [7], and the pure micro- wave method and titration method among others. However, 2.2. Spectral Collections. In the laboratory, the samples were these methods are time-consuming, cumbersome, labour scanned individually and the spectrum was collected using intensive and require laboratory infrastructure, cannot be a small pocket-sized NIR spectrometer (SCIO ) in the range used on-site, and require skilled personnel. Furthermore, ™ of 740–1070nm in a 1-nm resolution for spectra data re- headspace GC had also been proposed for the determination cording assisted by a smartphone (Nokia 6). For each of moisture in edible oil, but the instrument demands higher sample, the palm oil was poured into a Petri dish and maintenance costs [8]. scanned three times after rotating the sample cup as carried Terefore, processors together with quality control of- out by others [3]. Te entire process was carried out at an fcers require rapid and nondestructive determination of ambient temperature of 31 C with a steady state of humidity moisture content in palm oil. NIR spectroscopy ofers at the laboratory of the Food fraud and safety centre of the a possible solution for the rapid determination of palm oil School of Agriculture, University of Cape Coast. All the moisture content. Tis technique has been used for assessing samples were analyzed in triplicate and the spectra were the quality parameter of other edible oils [9, 10]. Tus, averaged to provide a mean spectrum as the original spectroscopic techniques have proven to turn out results spectrum of the sample used. quickly without using expensive chemicals. Moisture con- tent in edible oil had been determined by FTIR with transmission measurement through NaCl window [11], dry 2.3. Moisture Content Determination. Te moisture content solvent extraction [12–14], reaction method [15, 16], and by of all the samples was carried out using the standard method the application of infrared transparent PTFE membranes for according to the standard used by other authors [6]. Te transmission measurement [17]. Moisture in olive oil had moisture contents of the samples were carried out in trip- been determined by the NIR spectra method [18]. Also, the licate and average to represent one sample. univariate NIR method was used to improve the speed of measurement of moisture content with disposable glass tubes and PLS multivariate data analysis [19]. Te recent 2.4. Chemometric Analysis. To analyse the spectra fnger- miniaturization of the NIR spectrometer has been found print, the data recordings stored in the cloud were down- useful in diferent food analysis applications [20–22]. Other loaded onto the computer and imported into chemometric previous studies have been reported on the use of desktop software in MATLAB (2021a; MathWorks Inc., USA) using FTIR or NIR spectrometer for moisture content in edible oil. windows 10 Basic for all data processing. Te fngerprinted It will therefore be of great interest to see whether a portable information was modelled and compared using diferent NIR spectrometer could perform a similar task as the algorithms to determine the optimum technique for de- desktop version. Herein, the present study employs pocket- termining moisture content in palm oil. size NIR spectrometer for rapid determination of moisture content in palm oil. Up until now, little or no studies have investigated the use of portable NIR spectroscopy for on-site 2.5. Pretreatment Methods. In this study, several mathe- detection of palm oil moisture content in developing matical transformational techniques compared with raw (no countries. Furthermore, proper preprocessing of spectra treatment) were used to improve spectra fngerprinted data. data is known to have an impact on the multivariate data Tese techniques used include standard normal variant analysis [23–25]; hence, in this study, diferent signal pre- (SNV), multiplicative scatter correction (MSC), frst de- processing methods would be employed comparatively to rivative (D1), and second derivative (D2). Tese pre- develop a robust optimal method. Moreso, the variable processing treatments have their unique strengths and selection method would be an additional advantage of this weaknesses in spectra fngerprint; for more information on study because research in other felds has shown that, it their theoretical background, refer to other authors [29–31]. improves the performance of the regression model [26–28]. In this study, Savitzky–Golay smoothing was performed on Tus, the variable selection methods, such as interval PLS the derivative spectra treatments (frst and second; SGD1 (iPLS), synergy interval PLS (SiPLS), genetic algorithm PLS and SGD2) to eliminate noise which is known to be (GA-PLS), and successive projections algorithm (SPA-PLS), a drawback of derivative methods [31]. All these pre- were used and compared to fnd the best variable selection treatment techniques were carried out to improve the method for PLS. correlation between spectra fngerprint and chemical Journal of Analytical Methods in Chemistry 3 Table 1: Reference measurement of moisture content (Mc) and composition of interest as in the case of moisture content in statistic data. our study. Parameter Subset Number Max Min Mean Std Calibration 286 7.220 0.060 1.212 1.879 2.6. Full and Variable Selection Algorithms. Te study also Mc (%) Prediction 123 6.130 0.060 0.670 1.112 employed full and variable spectra selection quantitative pre- diction techniques by using partial least square regression (PLSR), interval PLS (iPLSR), synergy interval PLS (SiPLS), pretreatments. Te original spectral profle obtained con- genetic algorithm PLS (GaPLS), and successive projection al- tains information related to the chemical composition of the gorithm PLS (SPA-PLS). For more information on the theories samples, as well as irrelevant interference data such as of the regression methods used kindly refer to other authors baseline drift, sample physical properties, background, and [30, 32]. Te performances of the algorithms used were noise [34]. Tese weaknesses in the data directly afect the compared and evaluated in terms of correlation determination accuracy of the fnal outcome. Hence, to improve the of calibration (Rc), correlation of prediction determination modeling efciency of the moisture content, SNV, MSC, (Rp), root mean square error of calibration (RMSEC), and root SGD1, and SGD2 were used to preprocess the original mean squared error of prediction (RMSEP). spectral data. Te unique profle and results for each pre- processing treatment are shown in Table 2. From this table, it 3. Results and Discussion could be seen, all the pretreatment methods had a diferent impact on the fnal results. More importantly, Savitz- 3.1. Moisture Content in Palm Oil. Te 409 samples of palm ky–Golay smoothing frst derivative (SGD1) spectra pre- oil with unique moisture contents were used in this study, treatment performed better than all the others, with an and the values cover all the range of moisture content of increased performance of R �0.948 and RMSEP �0.586 in palm oil during processing in the factory as well as in the the prediction set. As seen in Figure 2, the residuals were various markets in Ghana as seen in Table 1. Te moisture randomly also scattered about their mean value. Also, the content of the various samples ranges from 0.060 to 7.220%. SGD1 preprocessing method made the best impact on the From the table, it could also be seen that the palm oil samples performance of the model in this study, with an improved used had a wide range of values to cover the entire moisture prediction efciency. However, PLS uses a full spectra range content levels observed during processing and storage. Tis that contains both useful and redundant information. is particularly useful as it makes the model robust. Terefore, the modeling was further optimized by employing other interval spectral selection algorithms in 3.2. Data Preprocessing and Splitting. To select a set of this study. representative objects for calibration/prediction set in the PLS models, the Kennard–Stone algorithm was employed [33]. 3.5. Interval Selection PLS Regression Algorithms. Norgaard and other researchers proposed interval selection PLS (iPLS) and synergy interval selection PLS (SiPLS) to 3.3.SpectraExamination. Te spectra profles of the palm oil overcome the weaknesses and challenges of full PLS re- samples were mathematically pretreated by diferent tech- gression in spectra data analysis [35]. In this study, iPLS was niques and their unique fngerprints were observed in the attempted to optimize the results and further prove the study as shown in Figure 1(a). It is well known that each strength of interval spectra selection. From Table 3, it could pretreatment method showed unique properties that con- be observed that iPLS was optimized with 13 best intervals tribute to enhancing the performance of multivariate al- with a performance of Rc=0.944 and Rp=0.905. Tis gorithms. Among the diferent pretreatment used, SNV, outcome showed a slight similarity to the full PLS regression MSC, SGD1, and SGD2 spectra are not signifcantly diferent results in the calibration set but less in the prediction set. from each other. Major peaks were seen in the wavelength Tis could be explained that iPLS actually solved the range of 750–800nm, 850–900nm, 910–950nm, and weaknesses of full PLS by selecting only one maximum 1025–1050nm. Tus, these wavelengths could be responsible region that corresponded to moisture content to calibrate for O-H deformation and O-H stretching which corre- the PLS model. However, selecting only one wavelength sponds to water. Tese peaks vary from one pretreatment to interval could lead to leaving out other equally important the other. Most especially, the peaks were more pronounced spectra information; therefore, this could infuence the when Savitzky–Golay smoothing frst derivative spectra pre- performance of the model [36]. Also, as seen in Figure 3, the treatment (Figure 1(b)) was employed, and this is a typical residuals are distributed about the mean value which is good. characteristic of derivative pretreatments. Also, these spectra On the other hand, SiPLS which solves the shortcomings of wavelengths are made up of carbonyl group; C-H stretch and iPLS was also comparatively used. From Table 4, it could be C-H deformation correspond to phytochemicals in palm oil. observed that SiPLS showed its unique superiority with the model performance of Rc=0.968 and Rp=0.956 3.4. Efect of Pretreatment on PLS Regression. Te perfor- (Figure 4(a)). Te best optimal interval of 986–1002nm and 1003–1019 nm at 5 latent variables were selected. Tese mance of PLS regression to the determined moisture content in palm oil was modelled with the help of diferent optimal spectra intervals selected by SiPLS corresponded to 4 Journal of Analytical Methods in Chemistry -3 1.5 ×10 1.4 1.3 1.2 1.1 -2 0.9 -4 0.8 -6 0.7 -8 0.6 750 800 850 900 950 1000 1050 -10 Wavelength (nm) 750 800 850 900 950 1000 1050 Wavelength (nm) (a) (b) Figure 1: Raw (a) and SGD1 (b) preprocessed spectra of crude palm oil. Table 2: Efect of spectra pre-treatment on PLS regression. Pretreatment LV Rc RMSEC Rp RMSEP Raw 2 0.897 0.893 0.941 0.500 SNV 3 0.931 0.566 0.912 0.415 MSC 3 0.818 1.318 0.733 0.612 SGD1 3 0.948 0.586 0.938 0.574 SGD2 2 0.941 0.599 0.932 0.553 Rc = 0.948 RMSEC = 0.502 1.5 Rp = 0.938 RMSEP = 0.498 0.5 -0.5 -1 -1.5 -2 -2.5 -3 0 12345678 0 50 100 150 200 250 300 Measured moisture content Observations Calibration Calibration Prediction Prediction (a) (b) Figure 2: SGD1-PLS score plot of measured versus predicted moisture content (a) and residuals versus samples (b). various absorption bands that could be related to moisture 3.6. General Discussion. Te optimal performance of a mi- content and water activities in palm oil. As seen from cro-NIR spectrometer coupled with diferent multivariate Figure 4(b), the residual was randomly distributed about the regression models was comparatively studied. As seen in mean value and comparatively and satisfactorily close to Table 5, it was observed that diferent regression models zero (0) thus low bias [37]. performed diferently for the moisture content model in Predicted moisture content log (1/R) Residuals FD of log (1/R) Journal of Analytical Methods in Chemistry 5 Table 3: SGD1-iPLS result for diferent numbers of intervals. Number of Best interval Rc RMSEC Rp RMSEP intervals 10 7 0.909 0.700 0.844 0.634 11 7 0.937 0.650 0.901 0.527 12 8 0.943 0.584 0.899 0.513 13 9 0.944 0.614 0.905 0.516 14 9 0.945 0.610 0.879 0.581 15 10 0.930 0.643 0.840 0.620 16 11 0.926 0.708 0.838 0.669 17 10 0.925 0.706 0.856 0.646 18 12 0.921 0.674 0.855 0.658 19 13 0.929 0.688 0.864 0.638 20 13 0.914 0.760 0.827 0.705 Best results. Rc = 0.944 RMSEC = 0.613 Rp = 0.905 RMSEP = 0.516 -1 -2 -3 -4 1 234567 0 50 100 150 200 250 300 Measured moisture content Observations Calibration Calibration Prediction Prediction (a) (b) Figure 3: SGD1-iPLS score plot of measured versus predicted moisture content (a) and residuals versus samples (b). Table 4: SGD1-Si-PLS result for diferent number of intervals. Number of Best interval Rc RMSEC Rp RMSEP intervals 10 1 2 7 10 0.956 0.560 0.925 0.460 11 1 3 8 10 0.956 0.549 0.933 0.451 12 2 4 8 12 0.956 0.553 0.920 0.472 13 7 8 0.965 0.484 0.952 0.347 14 2 7 10 13 0.957 0.564 0.931 0.434 15 8 9 0.961 0.514 0.952 0.359 16 2 8 11 13 0.958 0.568 0.930 0.448 17 13 14 0.963 0.501 0.935 0.423 18 10 11 0.964 0.475 0.951 0.343 19 15 16 0.968 0.468 0.956 0.361 20 10 12 0.964 0.492 0.948 0.392 palm oil. At a full range of 740–1070nm, the PLS model could be explained that SiPLS selected only relevant spectra performed at Rp �0.943 which is fairly good; however, an information and combined them as in the case of this study (986–1002nm, and 1003–1019nm) to calibrate the PLS attempt to improve the results by using other algorithms revealed that iPLS showed slightly similar performance to model so that much useful information that corresponded to PLS while SiPLS performed better than iPLS and full PLS. It the moisture content in the palm oil would be included in Predicted moisture content Residuals 6 Journal of Analytical Methods in Chemistry RMSEC = 0.468 Rc = 0.968 1.5 Rp = 0.956 RMSEP = 0.361 0.5 -0.5 -1 -1.5 -2 1 234567 0 50 100 150 200 250 300 Measured moisture content Observations Calibration Calibration Prediction Prediction (a) (b) Figure 4: SGD1-SiPLS score plot of measured versus predicted moisture content (a) and residuals versus samples (b). ascribed to the removal of uninformative variables from the Table 5: Optimum comparison of model performance for diferent modeling process [32]. Also, SPA is known to select variable selection techniques. a subset of variables with small multicollinearity and Selected wavelength Method Rc RMSEC Rp RMSEP suitable prediction power [32]. (nm) PLS 740–1070 0.943 0.582 0.938 0.474 4. Conclusion i-PLS 943–958 0.944 0.614 0.905 0.516 Si-PLS 986–1002, 1003–1019 0.968 0.468 0.956 0.361 Moisture content in palm oil has been determined by using GA-PLS 740–1070 0.952 0.553 0.950 0.387 a micro-NIR spectrometer together with multivariate algo- 768, 778, 816, 932, SPA-PLS 0.957 0.558 0.941 0.493 953, 1009 rithms. Generally, the fndings revealed that Savitzky–Golay frst derivative transformation techniques together with the partial least square regression (PLSR) model, specifcally the universal model. On the other hand, the comparatively synergy interval partial least square (Si-PLS), could be used to less performance of PLS, iPLS, and GAPLS than that of develop a prediction equation from the spectra data set to SiPLS could be a result of weaknesses in full PLS range quantify moisture content in palm oil samples at a favourable spectra where the entire spectra are mixed with useful and coefcient of prediction above 0.94. Among the models used redundant information that could have infuenced the (PLSR, i-PLSR, Si-PLSR, GA-PLSR, and SPA-PLSR), SGD1 results. For iPLS, the selection of only one interval could together with the Si-PLS model was superior to all with the result in leaving out other equally important spectra in- model performance of Rc �0.968 and RMSEC �0.468 in the formation that could have improved the results. While for calibration set and Rp �0.956 and RMSEP �0.361 in the GAPLS, the limitation was the fact that when spectra in- prediction set. Te results showed that rapid and non- tensities are measured at a very large number of wave- destructive determination of moisture content in palm oil is lengths, the search domain increases correspondingly, and feasible and this would go a long way to facilitating quality therefore, the detection of the relevant regions is much control of crude palm oil. Te study provides feasibility, and more difcult and hindered [38]. Also, the successive further work is needed to include wide samples from diferent projection algorithm (SPA) regression used, performed locations and factory settings to make the technique universal quite well; however, it showed overftting as the calibration and robust. Furthermore, this research provides the potential set had signifcantly lower than the prediction set. How- of incorporating portable NIR spectrometers into a smart- ever, the comparatively better prediction results may be phone for use by rural processors and quality control ofcers. Predicted moisture content Residuals Journal of Analytical Methods in Chemistry 7 a fve-port refectometer,” Sensors, vol. 11, no. 4, pp. 4073– Data Availability 4085, 2011. [8] W. Q. Xie, Y. X. Gong, and K. X. 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However, the with chemometrics for quality control and authentication of conventional analytical methods for the determination of edible fats and oils: a review,” Applied Spectroscopy Reviews, the moisture content of palm oil are tedious and time- vol. 52, no. 7, pp. 589–604, 2017. wasting. Tis research work provides a practical and efcient [11] Y. Che Man and M. Mirghani, “Rapid method for de- alternative method for rapid, accurate, and nondestructive termining moisture content in crude palm oil by Fourier transform infrared spectroscopy,” Journal of the American Oil determination of moisture content in palm oil using portable Chemists’ Society, vol. 77, no. 6, pp. 631–637, 2000. NIR spectroscopy combined with a regression algorithm. [12] A. Al-Alawi, F. R. Van De Voort, and J. Sedman, “A new Fourier transform infrared method for the determination of Conflicts of Interest moisture in edible oils,” Applied Spectroscopy, vol. 59, no. 10, pp. 1295–1299, 2005. Te authors declare that they have no conficts of interest. [13] X. Meng, J. Sedman, and F. Van De Voort, “Improving the determination of moisture in edible oils by FTIR spectroscopy Acknowledgments using acetonitrile extraction,” Food Chemistry, vol. 135, no. 2, pp. 722–729, 2012. Te authors would like to acknowledge the support provided [14] F. R. van de Voort, J. Sedman, and A. Ismail, “A rapid FTIR by the Directorate of Research, Innovation, and Consultancy quality-control method for determining fat and moisture in of the University of Cape Coast (RSG/INT/CANS/2021/101) high-fat products,” Food Chemistry, vol. 48, no. 2, pp. 213– and all the crude palm oil processors in Ghana that are 221, 1993. working to sustain the industry. Te assistance provided by [15] S. H. Saleh and C. P. 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Nondestructive Detection of Moisture Content in Palm Oil by Using Portable Vibrational Spectroscopy and Optimal Prediction Algorithms

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Hindawi Publishing Corporation
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2090-8865
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2090-8873
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10.1155/2023/3364720
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Abstract

Hindawi Journal of Analytical Methods in Chemistry Volume 2023, Article ID 3364720, 8 pages https://doi.org/10.1155/2023/3364720 Research Article Nondestructive Detection of Moisture Content in Palm Oil by Using Portable Vibrational Spectroscopy and Optimal Prediction Algorithms 1 2 3 1 Ernest Teye , Charles L. Y. Amuah , Tai-Sheng Yeh , and Regina Nyorkeh Department of Agricultural Engineering, School of Agriculture, College of Agriculture and Natural Sciences, University of Cape Coast, Cape Coast, Ghana Department of Physics, Laser and Fibre Optics Centre, School of Physical Sciences, University of Cape Coast, Cape Coast, Ghana Department of Food Science and Nutrition, Meiho University, Neipu Township, Taiwan Correspondence should be addressed to Ernest Teye; teyernest@gmail.com Received 21 September 2022; Revised 11 November 2022; Accepted 13 January 2023; Published 31 January 2023 Academic Editor: Ricardo Jorgensen Cassella Copyright © 2023 Ernest Teye et al. Tis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Rapid and nondestructive measurement of moisture content in crude palm oil is essential for promoting the shelf-stability and quality. In this research, micro NIR spectrometer coupled with a multivariate calibration model was used to collect and analyse fngerprinted information from palm oil samples at diferent moisture contents. Several preprocessing methods such as standard normal variant (SNV), multiplicative scatter correction (MSC), Savitzky–Golay frst derivative (SGD1), Savitzky–Golay second derivative (SGD2) together with partial least square (PLS) regression techniques, full PLS, interval PLS (iPLS), synergy interval PLS (SiPLS), genetic algorithm PLS (GAPLS), and successive projection algorithm PLS (SPA-PLS) were comparatively employed to construct an optimum quantitative prediction model for moisture content in crude palm oil. Te models were evaluated according to the coefcient of determination and root mean square error in calibration (Rc and RMSEC) and prediction (Rp and RMSEC) set, respectively. Te model SGD1+SiPLS was the optimal novel algorithm obtained among the others with the performance of Rc �0.968 and RMSEC �0.468 in the calibration set and Rp �0.956 and RMSEP �0.361 in the prediction set. Te results showed that rapid and nondestructive determination of moisture content in palm oil is feasible and this would go a long way to facilitating quality control of crude palm oil. storage. Also, the moisture content is among the pa- 1. Introduction rameters that dictate the price. Research has shown that Palm oil is the most consumed edible vegetable oil in the the moisture content of palm oil increases water activity world with various applications in food products including and this further leads to high hydrolysis and is a possible the production of margarine, ice creams, crackers, choco- cause of a steady rise in free fatty acid values during lates, and fried foods, among others [1] Demand for palm oil storage [1]. Other studies have shown that moisture continues to grow steadily worldwide as global production content and amount of free fatty acid (FFA) are the falls short of supply at 70 million metric tonnes since 2017 important quality parameters of palm oil [3]. High [2]. Generally, palm oil is rich in carotenoids and other very moisture content causes rancidity, Aspergillus Niger, and important nutritional phytonutrients such as vitamin E Mucor species growth in edible oils with high moisture components (tocopherols and tocotrienols) and it is known content [4]. Recently, high moisture contents had been to provide health-benefcial properties. reported in palm oil and raised concerns for storage During extraction and processing of crude palm oil, stability and spoilage [5]. A cost-efective method for the moisture content is monitored until it gets to its fnal moisture determination of palm oil is therefore urgently required state as this determines the quality during needed. Terefore, measuring the moisture content of 2 Journal of Analytical Methods in Chemistry crude palm oil during processing and storage is vital to 2. Materials and Methods ensuring quality and maintaining storage life. 2.1. Sample Collection. Palm oil samples were collected from Various techniques had been employed previously to fve major palm oil-producing regions in Ghana at diferent detect moisture in edible oil. Traditionally, the moisture moisture contents; others were specifcally collected directly content is determined by the oven drying method; the Karl from factories in central and western regions of Ghana. All Fisher method has been employed frequently for the de- the palm oil samples were at diferent levels of moisture termination of moisture content, and this method uses content. A total of 150 samples were collected into smaller a complicated titrator, expensive chemicals, and time- 250ml bottles and transported into the laboratory in the consuming procedures. Also, others use diferent methods School of Agriculture Technology Centre. such as over dry methods as carried out by others [6], microwave six-port refectometer [7], and the pure micro- wave method and titration method among others. However, 2.2. Spectral Collections. In the laboratory, the samples were these methods are time-consuming, cumbersome, labour scanned individually and the spectrum was collected using intensive and require laboratory infrastructure, cannot be a small pocket-sized NIR spectrometer (SCIO ) in the range used on-site, and require skilled personnel. Furthermore, ™ of 740–1070nm in a 1-nm resolution for spectra data re- headspace GC had also been proposed for the determination cording assisted by a smartphone (Nokia 6). For each of moisture in edible oil, but the instrument demands higher sample, the palm oil was poured into a Petri dish and maintenance costs [8]. scanned three times after rotating the sample cup as carried Terefore, processors together with quality control of- out by others [3]. Te entire process was carried out at an fcers require rapid and nondestructive determination of ambient temperature of 31 C with a steady state of humidity moisture content in palm oil. NIR spectroscopy ofers at the laboratory of the Food fraud and safety centre of the a possible solution for the rapid determination of palm oil School of Agriculture, University of Cape Coast. All the moisture content. Tis technique has been used for assessing samples were analyzed in triplicate and the spectra were the quality parameter of other edible oils [9, 10]. Tus, averaged to provide a mean spectrum as the original spectroscopic techniques have proven to turn out results spectrum of the sample used. quickly without using expensive chemicals. Moisture con- tent in edible oil had been determined by FTIR with transmission measurement through NaCl window [11], dry 2.3. Moisture Content Determination. Te moisture content solvent extraction [12–14], reaction method [15, 16], and by of all the samples was carried out using the standard method the application of infrared transparent PTFE membranes for according to the standard used by other authors [6]. Te transmission measurement [17]. Moisture in olive oil had moisture contents of the samples were carried out in trip- been determined by the NIR spectra method [18]. Also, the licate and average to represent one sample. univariate NIR method was used to improve the speed of measurement of moisture content with disposable glass tubes and PLS multivariate data analysis [19]. Te recent 2.4. Chemometric Analysis. To analyse the spectra fnger- miniaturization of the NIR spectrometer has been found print, the data recordings stored in the cloud were down- useful in diferent food analysis applications [20–22]. Other loaded onto the computer and imported into chemometric previous studies have been reported on the use of desktop software in MATLAB (2021a; MathWorks Inc., USA) using FTIR or NIR spectrometer for moisture content in edible oil. windows 10 Basic for all data processing. Te fngerprinted It will therefore be of great interest to see whether a portable information was modelled and compared using diferent NIR spectrometer could perform a similar task as the algorithms to determine the optimum technique for de- desktop version. Herein, the present study employs pocket- termining moisture content in palm oil. size NIR spectrometer for rapid determination of moisture content in palm oil. Up until now, little or no studies have investigated the use of portable NIR spectroscopy for on-site 2.5. Pretreatment Methods. In this study, several mathe- detection of palm oil moisture content in developing matical transformational techniques compared with raw (no countries. Furthermore, proper preprocessing of spectra treatment) were used to improve spectra fngerprinted data. data is known to have an impact on the multivariate data Tese techniques used include standard normal variant analysis [23–25]; hence, in this study, diferent signal pre- (SNV), multiplicative scatter correction (MSC), frst de- processing methods would be employed comparatively to rivative (D1), and second derivative (D2). Tese pre- develop a robust optimal method. Moreso, the variable processing treatments have their unique strengths and selection method would be an additional advantage of this weaknesses in spectra fngerprint; for more information on study because research in other felds has shown that, it their theoretical background, refer to other authors [29–31]. improves the performance of the regression model [26–28]. In this study, Savitzky–Golay smoothing was performed on Tus, the variable selection methods, such as interval PLS the derivative spectra treatments (frst and second; SGD1 (iPLS), synergy interval PLS (SiPLS), genetic algorithm PLS and SGD2) to eliminate noise which is known to be (GA-PLS), and successive projections algorithm (SPA-PLS), a drawback of derivative methods [31]. All these pre- were used and compared to fnd the best variable selection treatment techniques were carried out to improve the method for PLS. correlation between spectra fngerprint and chemical Journal of Analytical Methods in Chemistry 3 Table 1: Reference measurement of moisture content (Mc) and composition of interest as in the case of moisture content in statistic data. our study. Parameter Subset Number Max Min Mean Std Calibration 286 7.220 0.060 1.212 1.879 2.6. Full and Variable Selection Algorithms. Te study also Mc (%) Prediction 123 6.130 0.060 0.670 1.112 employed full and variable spectra selection quantitative pre- diction techniques by using partial least square regression (PLSR), interval PLS (iPLSR), synergy interval PLS (SiPLS), pretreatments. Te original spectral profle obtained con- genetic algorithm PLS (GaPLS), and successive projection al- tains information related to the chemical composition of the gorithm PLS (SPA-PLS). For more information on the theories samples, as well as irrelevant interference data such as of the regression methods used kindly refer to other authors baseline drift, sample physical properties, background, and [30, 32]. Te performances of the algorithms used were noise [34]. Tese weaknesses in the data directly afect the compared and evaluated in terms of correlation determination accuracy of the fnal outcome. Hence, to improve the of calibration (Rc), correlation of prediction determination modeling efciency of the moisture content, SNV, MSC, (Rp), root mean square error of calibration (RMSEC), and root SGD1, and SGD2 were used to preprocess the original mean squared error of prediction (RMSEP). spectral data. Te unique profle and results for each pre- processing treatment are shown in Table 2. From this table, it 3. Results and Discussion could be seen, all the pretreatment methods had a diferent impact on the fnal results. More importantly, Savitz- 3.1. Moisture Content in Palm Oil. Te 409 samples of palm ky–Golay smoothing frst derivative (SGD1) spectra pre- oil with unique moisture contents were used in this study, treatment performed better than all the others, with an and the values cover all the range of moisture content of increased performance of R �0.948 and RMSEP �0.586 in palm oil during processing in the factory as well as in the the prediction set. As seen in Figure 2, the residuals were various markets in Ghana as seen in Table 1. Te moisture randomly also scattered about their mean value. Also, the content of the various samples ranges from 0.060 to 7.220%. SGD1 preprocessing method made the best impact on the From the table, it could also be seen that the palm oil samples performance of the model in this study, with an improved used had a wide range of values to cover the entire moisture prediction efciency. However, PLS uses a full spectra range content levels observed during processing and storage. Tis that contains both useful and redundant information. is particularly useful as it makes the model robust. Terefore, the modeling was further optimized by employing other interval spectral selection algorithms in 3.2. Data Preprocessing and Splitting. To select a set of this study. representative objects for calibration/prediction set in the PLS models, the Kennard–Stone algorithm was employed [33]. 3.5. Interval Selection PLS Regression Algorithms. Norgaard and other researchers proposed interval selection PLS (iPLS) and synergy interval selection PLS (SiPLS) to 3.3.SpectraExamination. Te spectra profles of the palm oil overcome the weaknesses and challenges of full PLS re- samples were mathematically pretreated by diferent tech- gression in spectra data analysis [35]. In this study, iPLS was niques and their unique fngerprints were observed in the attempted to optimize the results and further prove the study as shown in Figure 1(a). It is well known that each strength of interval spectra selection. From Table 3, it could pretreatment method showed unique properties that con- be observed that iPLS was optimized with 13 best intervals tribute to enhancing the performance of multivariate al- with a performance of Rc=0.944 and Rp=0.905. Tis gorithms. Among the diferent pretreatment used, SNV, outcome showed a slight similarity to the full PLS regression MSC, SGD1, and SGD2 spectra are not signifcantly diferent results in the calibration set but less in the prediction set. from each other. Major peaks were seen in the wavelength Tis could be explained that iPLS actually solved the range of 750–800nm, 850–900nm, 910–950nm, and weaknesses of full PLS by selecting only one maximum 1025–1050nm. Tus, these wavelengths could be responsible region that corresponded to moisture content to calibrate for O-H deformation and O-H stretching which corre- the PLS model. However, selecting only one wavelength sponds to water. Tese peaks vary from one pretreatment to interval could lead to leaving out other equally important the other. Most especially, the peaks were more pronounced spectra information; therefore, this could infuence the when Savitzky–Golay smoothing frst derivative spectra pre- performance of the model [36]. Also, as seen in Figure 3, the treatment (Figure 1(b)) was employed, and this is a typical residuals are distributed about the mean value which is good. characteristic of derivative pretreatments. Also, these spectra On the other hand, SiPLS which solves the shortcomings of wavelengths are made up of carbonyl group; C-H stretch and iPLS was also comparatively used. From Table 4, it could be C-H deformation correspond to phytochemicals in palm oil. observed that SiPLS showed its unique superiority with the model performance of Rc=0.968 and Rp=0.956 3.4. Efect of Pretreatment on PLS Regression. Te perfor- (Figure 4(a)). Te best optimal interval of 986–1002nm and 1003–1019 nm at 5 latent variables were selected. Tese mance of PLS regression to the determined moisture content in palm oil was modelled with the help of diferent optimal spectra intervals selected by SiPLS corresponded to 4 Journal of Analytical Methods in Chemistry -3 1.5 ×10 1.4 1.3 1.2 1.1 -2 0.9 -4 0.8 -6 0.7 -8 0.6 750 800 850 900 950 1000 1050 -10 Wavelength (nm) 750 800 850 900 950 1000 1050 Wavelength (nm) (a) (b) Figure 1: Raw (a) and SGD1 (b) preprocessed spectra of crude palm oil. Table 2: Efect of spectra pre-treatment on PLS regression. Pretreatment LV Rc RMSEC Rp RMSEP Raw 2 0.897 0.893 0.941 0.500 SNV 3 0.931 0.566 0.912 0.415 MSC 3 0.818 1.318 0.733 0.612 SGD1 3 0.948 0.586 0.938 0.574 SGD2 2 0.941 0.599 0.932 0.553 Rc = 0.948 RMSEC = 0.502 1.5 Rp = 0.938 RMSEP = 0.498 0.5 -0.5 -1 -1.5 -2 -2.5 -3 0 12345678 0 50 100 150 200 250 300 Measured moisture content Observations Calibration Calibration Prediction Prediction (a) (b) Figure 2: SGD1-PLS score plot of measured versus predicted moisture content (a) and residuals versus samples (b). various absorption bands that could be related to moisture 3.6. General Discussion. Te optimal performance of a mi- content and water activities in palm oil. As seen from cro-NIR spectrometer coupled with diferent multivariate Figure 4(b), the residual was randomly distributed about the regression models was comparatively studied. As seen in mean value and comparatively and satisfactorily close to Table 5, it was observed that diferent regression models zero (0) thus low bias [37]. performed diferently for the moisture content model in Predicted moisture content log (1/R) Residuals FD of log (1/R) Journal of Analytical Methods in Chemistry 5 Table 3: SGD1-iPLS result for diferent numbers of intervals. Number of Best interval Rc RMSEC Rp RMSEP intervals 10 7 0.909 0.700 0.844 0.634 11 7 0.937 0.650 0.901 0.527 12 8 0.943 0.584 0.899 0.513 13 9 0.944 0.614 0.905 0.516 14 9 0.945 0.610 0.879 0.581 15 10 0.930 0.643 0.840 0.620 16 11 0.926 0.708 0.838 0.669 17 10 0.925 0.706 0.856 0.646 18 12 0.921 0.674 0.855 0.658 19 13 0.929 0.688 0.864 0.638 20 13 0.914 0.760 0.827 0.705 Best results. Rc = 0.944 RMSEC = 0.613 Rp = 0.905 RMSEP = 0.516 -1 -2 -3 -4 1 234567 0 50 100 150 200 250 300 Measured moisture content Observations Calibration Calibration Prediction Prediction (a) (b) Figure 3: SGD1-iPLS score plot of measured versus predicted moisture content (a) and residuals versus samples (b). Table 4: SGD1-Si-PLS result for diferent number of intervals. Number of Best interval Rc RMSEC Rp RMSEP intervals 10 1 2 7 10 0.956 0.560 0.925 0.460 11 1 3 8 10 0.956 0.549 0.933 0.451 12 2 4 8 12 0.956 0.553 0.920 0.472 13 7 8 0.965 0.484 0.952 0.347 14 2 7 10 13 0.957 0.564 0.931 0.434 15 8 9 0.961 0.514 0.952 0.359 16 2 8 11 13 0.958 0.568 0.930 0.448 17 13 14 0.963 0.501 0.935 0.423 18 10 11 0.964 0.475 0.951 0.343 19 15 16 0.968 0.468 0.956 0.361 20 10 12 0.964 0.492 0.948 0.392 palm oil. At a full range of 740–1070nm, the PLS model could be explained that SiPLS selected only relevant spectra performed at Rp �0.943 which is fairly good; however, an information and combined them as in the case of this study (986–1002nm, and 1003–1019nm) to calibrate the PLS attempt to improve the results by using other algorithms revealed that iPLS showed slightly similar performance to model so that much useful information that corresponded to PLS while SiPLS performed better than iPLS and full PLS. It the moisture content in the palm oil would be included in Predicted moisture content Residuals 6 Journal of Analytical Methods in Chemistry RMSEC = 0.468 Rc = 0.968 1.5 Rp = 0.956 RMSEP = 0.361 0.5 -0.5 -1 -1.5 -2 1 234567 0 50 100 150 200 250 300 Measured moisture content Observations Calibration Calibration Prediction Prediction (a) (b) Figure 4: SGD1-SiPLS score plot of measured versus predicted moisture content (a) and residuals versus samples (b). ascribed to the removal of uninformative variables from the Table 5: Optimum comparison of model performance for diferent modeling process [32]. Also, SPA is known to select variable selection techniques. a subset of variables with small multicollinearity and Selected wavelength Method Rc RMSEC Rp RMSEP suitable prediction power [32]. (nm) PLS 740–1070 0.943 0.582 0.938 0.474 4. Conclusion i-PLS 943–958 0.944 0.614 0.905 0.516 Si-PLS 986–1002, 1003–1019 0.968 0.468 0.956 0.361 Moisture content in palm oil has been determined by using GA-PLS 740–1070 0.952 0.553 0.950 0.387 a micro-NIR spectrometer together with multivariate algo- 768, 778, 816, 932, SPA-PLS 0.957 0.558 0.941 0.493 953, 1009 rithms. Generally, the fndings revealed that Savitzky–Golay frst derivative transformation techniques together with the partial least square regression (PLSR) model, specifcally the universal model. On the other hand, the comparatively synergy interval partial least square (Si-PLS), could be used to less performance of PLS, iPLS, and GAPLS than that of develop a prediction equation from the spectra data set to SiPLS could be a result of weaknesses in full PLS range quantify moisture content in palm oil samples at a favourable spectra where the entire spectra are mixed with useful and coefcient of prediction above 0.94. Among the models used redundant information that could have infuenced the (PLSR, i-PLSR, Si-PLSR, GA-PLSR, and SPA-PLSR), SGD1 results. For iPLS, the selection of only one interval could together with the Si-PLS model was superior to all with the result in leaving out other equally important spectra in- model performance of Rc �0.968 and RMSEC �0.468 in the formation that could have improved the results. While for calibration set and Rp �0.956 and RMSEP �0.361 in the GAPLS, the limitation was the fact that when spectra in- prediction set. Te results showed that rapid and non- tensities are measured at a very large number of wave- destructive determination of moisture content in palm oil is lengths, the search domain increases correspondingly, and feasible and this would go a long way to facilitating quality therefore, the detection of the relevant regions is much control of crude palm oil. Te study provides feasibility, and more difcult and hindered [38]. Also, the successive further work is needed to include wide samples from diferent projection algorithm (SPA) regression used, performed locations and factory settings to make the technique universal quite well; however, it showed overftting as the calibration and robust. Furthermore, this research provides the potential set had signifcantly lower than the prediction set. How- of incorporating portable NIR spectrometers into a smart- ever, the comparatively better prediction results may be phone for use by rural processors and quality control ofcers. Predicted moisture content Residuals Journal of Analytical Methods in Chemistry 7 a fve-port refectometer,” Sensors, vol. 11, no. 4, pp. 4073– Data Availability 4085, 2011. [8] W. Q. Xie, Y. X. Gong, and K. X. Yu, “Efcient quantifcation Te data used to support the fndings of this study are of water content in edible oils by headspace gas chroma- available from the corresponding author upon request. tography with vapour phase calibration,” Journal of the Sci- ence of Food and Agriculture, vol. 98, no. 8, pp. 3208–3212, Additional Points [9] C. A. Nunes, “Vibrational spectroscopy and chemometrics to Determination of moisture content in palm oil is a vital assess authenticity, adulteration and intrinsic quality pa- activity that ensures the overall quality of the oil. Especially rameters of edible oils and fats,” Food Research International, during the extraction and processing of palm oil, rapid vol. 60, pp. 255–261, 2014. monitoring of moisture content until it gets to the fnal [10] A. Rohman, “Te use of infrared spectroscopy in combination required state is a key determinant of shelf-life. However, the with chemometrics for quality control and authentication of conventional analytical methods for the determination of edible fats and oils: a review,” Applied Spectroscopy Reviews, the moisture content of palm oil are tedious and time- vol. 52, no. 7, pp. 589–604, 2017. wasting. Tis research work provides a practical and efcient [11] Y. Che Man and M. Mirghani, “Rapid method for de- alternative method for rapid, accurate, and nondestructive termining moisture content in crude palm oil by Fourier transform infrared spectroscopy,” Journal of the American Oil determination of moisture content in palm oil using portable Chemists’ Society, vol. 77, no. 6, pp. 631–637, 2000. NIR spectroscopy combined with a regression algorithm. [12] A. Al-Alawi, F. R. Van De Voort, and J. Sedman, “A new Fourier transform infrared method for the determination of Conflicts of Interest moisture in edible oils,” Applied Spectroscopy, vol. 59, no. 10, pp. 1295–1299, 2005. Te authors declare that they have no conficts of interest. [13] X. Meng, J. Sedman, and F. Van De Voort, “Improving the determination of moisture in edible oils by FTIR spectroscopy Acknowledgments using acetonitrile extraction,” Food Chemistry, vol. 135, no. 2, pp. 722–729, 2012. Te authors would like to acknowledge the support provided [14] F. R. van de Voort, J. Sedman, and A. Ismail, “A rapid FTIR by the Directorate of Research, Innovation, and Consultancy quality-control method for determining fat and moisture in of the University of Cape Coast (RSG/INT/CANS/2021/101) high-fat products,” Food Chemistry, vol. 48, no. 2, pp. 213– and all the crude palm oil processors in Ghana that are 221, 1993. working to sustain the industry. Te assistance provided by [15] S. H. Saleh and C. P. 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Published: Jan 31, 2023

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