INTRODUCTION:

Pandanus conoideus Lam, commonly known as red fruit, is a fruit that is widely grown on the island of Papua. Red fruit has 600 – 700 clones spread across the island of Papua^1,2. Red fruit contains much oil and has nutritional content, namely protein, fat, carbohydrates, vitamin C, B1, calcium, iron, and phosphorus^3,4.

The active components contained therein are flavonoids⁴, phenols, tocopherols, α-tocopherol, γ-tocopherol^5,6, α-carotene, β-carotene, α-cryptoxanthin⁷, β-cryptoxanthin⁸, oleic acid, and palmitic acid¹. These various active components are proven to have beneficial properties, including fighting cervical cancer cells⁹, as antibacterial agents^10–12, have antioxidant activity^6,13–15, overcoming toxoplasmosis¹⁶, lowering LDL (low-density lipoprotein) and triglycerides¹⁷, reduce pain during menstruation¹⁸, have hypolipidemic effects¹⁹, help lower blood glucose levels²⁰, have hepatoprotective effects²¹, prevent preeclampsia symptoms²², reduce DNA oxidative damage caused by genotoxicants²³, and have cytotoxic effects on carcinoma cells²⁴.

Extraction of red fruit produces vegetable oil called red fruit oil (RFO)²⁵. Each time, the production of red fruit without pith as much as ±800 – 1000 kg produces ±100 L of red fruit oil²⁶. RFO prices in the retail market is more expensive than that of other vegetable oils because RFO has many health benefits. As a result, red fruit oil may become a target for adulteration by producers and traders.

Oil adulteration, especially of edible oils, is troubling in the food industry. This practice harms unaware consumers and can damage the reputation of honest and reliable producers. Adulteration of edible oils often involves blending genuine edible oils with cheaper edible oils or completely different types of oils. RFO can be targeted for adulteration by diluting RFO using alternative, more affordable vegetable oils like sesame oil, palm oil, corn oil, coconut oil, and others. Consequently, several analytical methods are offered to authenticate RFO. Several analytical methods for oil and fat authentication, such as FTIR spectroscopy^27,28, NMR spectroscopy^29,30, Raman spectroscopy³¹, gas chromatography³², high-performance liquid chromatography (HPLC)^33,34, and Vis/ NIR spectroscopy^35,36. This research focuses on analyzing the authenticity of RFO using Vis/NIR and FTIR spectroscopy methods. Both methods were chosen because they allow direct analysis of samples without the need for complicated sample processing. Additionally, they can provide fingerprinting and are accurate, fast, inexpensive, and non-destructive. Vis/NIR and FTIR spectroscopy methods will be combined with multivariate partial least squares (PLS) calibration for quantitative analysis of adulterant oil in RFOs. PLS is a multivariate calibration technique often used in food authentication research^35–43.

MATERIALS AND METHODS:

Materials:

The collection of red fruits from Papua, Indonesia. The Testing Laboratory - UPF Traditional Health Services Tawangmangu, Karanganyar, Central Java, has determined that the red fruit belongs to the Pandanaceae family and the species Pandanus conoideus Lam. The purchase of coconut oil took place at a retail market in Yogyakarta. Reagents used during FTIR and Vis/NIR spectroscopic analysis were hexane (Supelco, Germany) and acetone (Merck, Germany).

Methods:

Preparation of red fruit oil (RFO):

Use a knife to remove the drupa from the pedicel of the red fruits. Then, the drupa was transferred into a container and dried using a cabinet dryer at 40°C for 60 minutes. After the sample was dried, it was wrapped in filter cloth. The filter cloth used has pores to allow oil to escape and can hold the rest of the sample. Samples that have been wrapped well are put into a hydrophilic tool, namely a press cylinder that has holes on the sides. The cylinder was then attached to the hydraulic press and tightened by turning the lever at the top of the machine until it was locked properly. The tool was then set with a force of 100 kN for 10 minutes. Afterwards, the RFO was collected and centrifugated for 10 minutes at 3000 rpm⁴⁴. The resulting oil is then stored in a dark bottle.

Sample preparation:

RFO (23 samples) mixed with coconut oil (CO) was prepared for the calibration model. RFO was mixed with CO with concentrations of 0.0%, 1.0%, 2.5%, 5.0%, 7.5 %, 10.0%, 12.5%, 15.0%, 17.5%, 20.0%, 22.5%, 25.0 %, 27.5%, 30.0%, 32.5%, 35.0%, 37.5%, 40.0%, 42.5 %, 45.0%, 47.5%, 50.0%, and 100.0% (v/v).

Instrumental Analysis:

Samples were measured using Nicolet iS10 FTIR-ATR spectrophotometry (Thermo Fisher Scientific Inc., USA) with 32 scan and 16 cm^-1 resolution in the 4000 – 600 cm^-1 wave number region. RFO samples were placed on a horizontally attenuated total reflectance (HATR) consisting of ZnSe crystals at a controlled temperature (20°C). Each of the sampels spectra was measured and then fitted to the background spectrum at each sample scan. Samples were also measured using Spectra Star™ XT-R Vis/NIR spectrophotometry (Unity Scientific, Australia) using the wave number range 680 – 2600nm, which is the near-infrared region with 12 scans, 1nm resolution, and 20 degrees/sec speed using a ring cup.

Chemometric Analysis:

The PLS calibration model was conducted utilizing TQ Analyst software (Thermo Fisher Scientific Inc., USA) to process FTIR spectra data at wave numbers 4000 – 600cm^-1 and SIMCA® version 14 (Umetrics®, Sweden) to process Vis/NIR spectra data at wavelengths 680 – 2600nm. Then, to measure prediction or validation samples, the predictive power of the PLS calibration model is utilized. The principal component analysis (PCA) of CO, RFO, and others was performed using Minitab software version 21 (Minitab Inc., USA).

RESULTS AND DISCUSSION:

Principal component analysis (PCA):

The research aims to show the existence of other oils mixed with RFO. Candidates for adulterant oils used in this research are avocado, canola, coconut, corn, extra-virgin olive, moringa, palm, refined olive pomace, olive pomace, pure mustard, refined olive pomaceblended with extra virgin olive, sesame, soybean, sunflower, sacha inchi, and unrefined sacha inchi. The FTIR spectrophotometer was used to analyze the RFOs containing the counterfeit oil candidates at wavelengths between 4000 – 600 cm^-1. Then, to identify oils with similar characteristics to RFO, principal component analysis (PCA) is used to sort and calcify 17 vegetable oils, including RFO and CO. This analysis utilizes absorption values at wave numbers ranging from 721 to 3004 cm^-1 as variables. PCA is a chemometric method that reduces multivariate data when there is a correlation between data. PCA is often referred to as a latent variable because of its ability to cluster^45–49. Similar sample profiles show similar first (PC1) and second (PC2) principal components. Therefore, the first and second principal component can be regarded as latent variables⁴. Figure 1 shows the principal component analysis score plot of 17 edible oils, including CO and RFO. According to the analysis of the first and second principal components, it can be determined that CO has a shorter distance than other oils to RFO based on PC1 and PC2. Therefore, we analyzed CO as a counterfeiter in RFO in this study.

Description: red fruit oil (RFO), avocado oil (AVO), canola oil (CNO), coconut oil (CO), corn oil (CRO), extra olijfolie van eerste persing (EOVEP), moringa oil (MO), palm oil (CPO), refined olive oil pomace (OORP), olive pomace oil (OPO), pure mustard oil (PMO), refined olive pomace oil blended with extra virgin olive oil (ROPO EVOO), sacha inchi oil (SIO), sesame oil (SSO), soybean oil (SBO), sunflower oil (SFO), and unrefined sacha inchi oil (USIO).

Figure 1. The principal component analysis score plot of 17 edible oils, including coconut oil and RFO.

Spectral Analysis:

The Vis/NIR spectra of CO and RFO obtained in the near-infrared range (680 – 2600nm) are shown in Figure 2. Different molecules absorb light, and the overlapping peaks produce a near-infrared spectrum. The tones and fusion of hydrogen bon vibrations in the mid-infrared range produce absorption bands that increase from low to high wavelength. Table 1 shows the functionality responsible for infrared absorption in the Vis/NIR spectrum and their vibration types. Vis/NIR spectra of RFO can be distinguished from CO in that the band intensities in the 1724, 1726, and 1760 frequency regions are slightly different. The 1724 band represents monounsaturated fatty acids, 1726 oils rich in saturated fatty acids, and 1760 trans-unsaturated triglycerides⁵⁰. It can be interpreted that the Vis/NIR spectra can identify the presence of monounsaturated fatty acids in RFO, saturated fatty acids in CO, and trans-unsaturated triglycerides in RFO and CO.

Figure 2. Vis/ NIR spectra of coconut oil and RFO were scanned at near-infrared region (680 – 2600 nm).

Table 1. Functionality and vibration types from Vis/ NIR spectra for oil evaluation

Wavenumber (nm)	Functional group	Mode of Vibration
1209	-CH=CH-	Stretching C-H 2^nd overtone
1388, 1389	-CH₃-	Stretching C-H combination
1435, 1436, 1439	H₂O	Stretching O-H 1^st overtone
1724, 1726, 1760	-CH₂-	Stretching C-H 1^st overtone
1922, 1923, 1928	H₂O	Stretching O-H combination
2146, 2182	-CH=CH-	Stretching C-H combination
2305, 2345, 2379, 2380, 2382, 2383	-CH₂-	Stretching C-H combination

Sources: Hourant et al., 2000; Stuart, 2004; Liu et al., 2020^50–52.

The FTIR spectra of CO and RFO obtained in the mid-infrared range are shown in Figure 3. The characteristic absorption bands of vegetable oils can be seen in all FTIR spectra. The sample's FTIR spectrum is considered a fingerprint so that no oil will have the same FTIR spectrum with the same number of bands, intensity, and frequency of maximum absorption bands^4,53. Table 2 shows the functionality responsible for infrared absorption in the Vis/NIR spectrum and their vibration types. The FTIR spectrum of CO can be distinguished from RFO, where the impact of the bands in the frequency region 3004, 1108, 1090, and 1116 is slightly different. The band at 3004 corresponds to the degree of unsaturation of triacylglycerol (TAG), while frequencies 1108, 1090, and 1116 are the absorption of ester bonds from TAG⁴. FTIR spectra can determine the degree of unsaturation of TAG in RFO and the absorption of ester bonds from TAG in RFO and CO.

Figure 3. FTIR spectra of coconut oil and RFO scanned at the 4000 – 600 cm^-1 regions.

Table 1. Functionality and vibration types from FTIR spectra for oil evaluation.

Wavenumber (cm^-1)	Functional group	Mode of Vibration
3004	=C-H (cis)	Stretching
2921	-C-H (CH₂)	Stretching (asymmetric)
2853	-C-H (CH₂)	Stretching (symmetric)
1742, 1707	-C=O (acid)	Stretching
1461, 1459	-C-H (CH₂)	Bending (scissoring)
1413	=C-H (cis)	Bending (rocking)
1376, 1373	-C-H (CH₃)	Stretching (symmetric)
1283, 1245 1227	-C-O -CH₂-	Stretching Bending
1153	-CH₂-	Bending
1116, 1108, 1090	-C-O	Stretching
963, 934	-CH=CH- (trans)	Stretching
877	=CH₂	Wagging
722, 721	-CH=CH- (cis)	Bending (out of plane)

Sources: Lerma-García et al., 2010²⁷.

Quantitative Analysis:

The PLS multivariate calibration method helps in the quantitatively analyze of CO in RFO. The power to utilize spectral information over a wide region and establish the relevance of spectral changes resulting from changes in analyte (CO) levels is related to PLS calibration capabilities^4,54,55. The PLS calibration model was created using a calibration standard that consists of varying concentrations of analyte (CO) mixed into RFO with varied weights.

Optimization of Vis/NIR spectra for measuring CO level in RFO. The results of the PLS calibration model in analyzing CO in binary mixtures with RFO are seen from the RMSEP and R² values shown in Figure 3. The research results show that PLS analysis using the second derivation spectrum in the wave number range 680 – 2600 nm was chosen because it provides the lowest RMSEP and highest R² values. This value shows that the PLS calibration model can describe the relationship between predictor and response variables well for measuring CO in the RFO mixture⁵⁶. Figure 4 shows the correlation between predicted Vis/NIR values (x-axis) and actual values (y-axis) of CO at RFO in the region 680 – 2600 nm.

Table 2. Results of the PLS calibration model using the FTIR spectra in the analysis of coconut oil in RFO.

Frequency Region (cm^-1)	Spectra	Calibration		Prediction
Frequency Region (cm^-1)	Spectra	RMSEC	R²	RMSEP	R²
4000 – 600	Normal	3.07	0.9903	0.0503	1.0000
	1^st Derivation	3.02	0.9906	0.794	1.0000
	2^nd Derivation	2.97	0.9909	0.782	1.0000
1780 – 1680	Normal	3.02	0.9906	0.440	1.0000
	1^st Derivation	2.95	0.9910	0.248	1.0000
	2^nd Derivation	2.85	0.9916	0.644	1.0000
1500 – 600	Normal	6.24	0.9591	2.89	1.0000
	1^st Derivation	3.55	0.9870	1.30	1.0000
	2^nd Derivation	3.63	0.9863	1.61	1.0000
1200 – 1000	Normal	3.81	0.9850	0.702	1.0000
	1^st Derivation	5.91	0.9634	2.21	1.0000
	2^nd Derivation	5.62	0.9670	1.86	1.0000
1780 – 1680 and 1200 – 1000	Normal	2.97	0.9909	1.00	1.0000
	1^st Derivation	3.21	0.9893	0.467	1.0000
	2^nd Derivation	2.91	0.9913	0.489	1.0000

Note: Those in bold represent the conditions selected for quantitative analysis.

Figure 5. Correlation between predicted values of FTIR spectroscopy and actual values of coconut oil in RFO obtained in the region 4000 – 600 cm^-1.

This study used FTIR and Vis/NIR spectroscopic methods, combined with chemometrics, to distinguish adulterated RFO. It can be shown that the normal spectrum combination with the PLS calibration technique in the 4000 – 600 cm^-1 (FTIR) frequency region and the second derivative spectrum combination with the PLS calibration technique in the 680 – 2600 nm (Vis/NIR) frequency region and is a good model. The PLS calibration model can describe the relationship between predictor and response variables well for measuring CO in the RFO mixture. The results indicated that both methods can successfully detect the adulteration of RFO, as demonstrated by the R² value of more than 0.98. The results of this research align with previous research where the combination of Vis/NIR and FTIR spectroscopy with chemometry was able to detect adulterated olive oil in soybean oil with 100% accuracy³⁵.

CONCLUSION:

This research concludes that the combination of Vis/NIR and FTIR spectra with PLS are a reliable method to verify the authenticity of RFO by quantitatively analyzing CO as an adulterant in RFO. The frequency regions of 680 – 2600nm (Vis/NIR) and 4000 – 600cm^-1 (FTIR) are preferred for quantitatively analyzing of CO in RFO. The combination of Vis/NIR dan FTIR spectroscopy is a reliable method for the authentication study of RFO providing fast sensitive, non-destructive to the sample, and free of chemical reagents.

FUNDING:

The Indonesian Food and Drug Authority (Badan POM) Foundation funded the research passes Badan POM Scholarships 2022/2024.

ACKNOWLEDGMENTS:

M.E. is grateful to the Indonesian Food and Drug Authority (Badan POM) Foundation for a Masters Studentship passes the Badan POM Scholarships 2022/2024.

CONFLICTS OF INTEREST:

All authors declare that there is no conflict of interest.

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Received on 23.04.2024 Revised on 13.08.2024

Accepted on 26.10.2024 Published on 27.03.2025

Available online from March 27, 2025

Research J. Pharmacy and Technology. 2025;18(3):1237-1243.

DOI: 10.52711/0974-360X.2025.00179

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. Creative Commons License.

Frequency Region (nm)	Spectra	Calibration		Prediction
Frequency Region (nm)	Spectra	RMSEC	R²	RMSEP	R²
680 – 2600	Normal	0.0339070	0.975	0.0340901	0.9768
	1^st Derivation	0.0334991	0,976	0.0333334	0.9774
	2^nd Derivation	0.0238714	0.984	0.0281795	0.9887
1100 – 2600	Normal	0.0346033	0.974	0.0340502	0.9758
	1^st Derivation	0.0324414	0.978	0.0319481	0.9788
	2^nd Derivation	0.0255107	0.984	0.0280664	0.9871