Smartphone-based handheld Raman spectrometer and machine learning for essential oil quality evaluation

Lebanov, Leo; Paull, Brett

doi:10.1039/d1ay00886b

Cited by 6 publications

(10 citation statements)

References 33 publications

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“…A slight difference is noticeable only in intensity. This can be caused by the presence by the combination of vibrations from the C–H bond stretch mode with other vibration modes. , In addition, similarities are also seen in the presence of terpenoid group compounds. It can be seen that there are different peaks in the GBO, namely, at the wavenumber 1643 cm –1 in CRM, POO, and PGBO, while in the GBO, there is no peak absorption in the wavenumber.…”

Section: Resultsmentioning

confidence: 99%

“…These findings indicate the presence of the same compound in GBO and PO with differences in their ratio values. It is estimated that the compounds that affect vibration in 785 are C–H aromatic (aryl). , A very strong band on the essential oils of the Lamiaceae family showing a wide band at 1375 and 1450 cm –1 . In addition, it was a band at 842 cm –1 which was shifted to 885 cm –1 in the GBO.…”

Section: Resultsmentioning

confidence: 99%

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Partial Least Squares-Discriminant Analysis Classification for Patchouli Oil Adulteration Detection by Fourier Transform Infrared Spectroscopy in Combination with Chemometrics

et al. 2023

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This study aims to test chemometrically partial least squares-discriminant analysis (PLS-DA) classification models to detect the adulteration of patchouli oil (PO) with gurjun balsam oil (GBO) by utilization of Fourier transform infrared spectroscopy. Unsupervised analysis was tested using the pattern recognition method using the principal component analysis model against the original spectrum at wavenumbers 4000–500 cm–1 and at the fingerprint area (1800–600 cm–1). Model testing was also carried out on the spectrum that had been pre-processed using the standard normal variate, second derivative Savitzky–Golay, and normalization approaches. Variable Y samples used were certified reference material (CRM), PO, GBO, and PO forged with GBO (PGBO) with a counterfeiting ratio of 0.5 (v/v) to 10% (v/v) with an interval of 0.5%. The same treatment was carried out on testing of the PLS-DA model. In pattern recognition tests, the best separation of the original spectrum was obtained at wavenumbers 1800–600 cm–1. The model was further tested on PLS-DA by making assumptions or codes for CRM, PO, GBO, and PGBO as +2, +1, 0, and −1, respectively. The results of the model analysis showed that even at the lowest counterfeiting ratio (0.5%), the presence of counterfeiting material was detected by the PLS-DA model. The RMSEC value is close to zero with a value of 0.22, and the R square is close to 1, which is 0.954. This very significant separation is clearly illustrated in the loading plot and bi-plot due to the contribution of chemical compounds in the GBO that undergo vibration at wavenumbers 603, 786, and 1386 cm–1. Validation of the PLS-DA model was carried out strongly using the PLS model, and it showed that the difference between the calibration concentration and the prediction was very low (average 0.45) with an accuracy percent above 99%. The efficacy of the model is further substantiated by the consistent and precise values of sensitivity and selectivity, obtained from both the training set and test set.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Partial Least Squares-Discriminant Analysis Classification for Patchouli Oil Adulteration Detection by Fourier Transform Infrared Spectroscopy in Combination with Chemometrics

et al. 2023

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“…The recent technological advancement has realized portable and handheld versions of Raman spectrometers, facilitating their on-site and industrial applications. 33 − 36 …”

Section: Introductionmentioning

confidence: 99%

“…Several researchers have illustrated the potential of infrared (IR) and Raman spectroscopies for the rapid characterization of edible oils and quantitative estimation of cis–trans composition and unsaturation of fatty acids present. − At the same time, others have applied advanced spectroanalytical techniques such as multiple linear regression, genetic algorithm, principal component analysis, and partial least-squares regression for discriminating genuine samples from those adulterated and also for quantifying the extent of fraudulence. ,,− In particular, Raman spectroscopy is a noninvasive technique that allows rapid and nondestructive data acquisition from samples in their native state. The recent technological advancement has realized portable and handheld versions of Raman spectrometers, facilitating their on-site and industrial applications. − …”

Section: Introductionmentioning

confidence: 99%

Rapid Iodine Value Estimation Using a Handheld Raman Spectrometer for On-Site, Reagent-Free Authentication of Edible Oils

et al. 2022

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Edible oil adulteration is a common and serious issue faced by human societies across the world. Iodine value (IV), the total unsaturation measure, is an authentication tool used by food safety officers and industries for edible oils. Current wet titrimetric methods (e.g., Wijs method) employed for IV estimation use dangerous chemicals and elaborate procedures for analysis. Alternate approaches for oil analysis require sophisticated and costly equipment such as gas chromatography (GC), liquid chromatography, high-performance liquid chromatography, mass spectrometry (MS), UV-Visible, and nuclear magnetic resonance spectroscopies. Mass screening of the samples from the market and industrial environment requires a greener, fast, and more robust technique and is an unmet need. Herein, we present a handheld Raman spectrometer-based methodology for fast IV estimation. We conducted a detailed Raman spectroscopic investigation of coconut oil, sunflower oil, and intentionally adulterated mixtures with a handheld device having a 785 nm excitation source. The obtained data were analyzed in conjunction with the GC–MS results and the conventional wet Wijs titrimetric estimated IVs. Based on these studies, a specific equation for IV estimation is derived from the intensity of identified Raman spectral bands. Further, an algorithm is designed to automate the signal processing and IV estimation, and a stand-alone graphical user interface is created in user-friendly LabVIEW software. The data acquisition and analysis require < 2 minutes, and the estimated statistical parameters such as the R 2 value (0.9), root-mean-square error of calibration (1.3), and root-mean-square error of prediction (0.9) indicate that the demonstrated method has a high precision level. Also, the limit of detection and the limit of quantification for IV estimation through the current approach is ∼1 and ∼3 gI 2 /100 g oil, respectively. The IVs of different oils, including hydrogenated vegetable oils, were evaluated, and the results show an excellent correlation between the estimated and reported ones.

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“…At present, some oil identification methods have been developed, including chromatography, mass spectrometry, spectroscopy, nuclear magnetic resonance (NMR), differential scanning calorimetry, and electronic nose. [ 5–14 ] Among them, fatty acid analysis based on gas chromatography‐mass spectrometry (GC‐MS) remains the most widely accepted method for its simplicity and convenience. [ 15 ] da Silva et al.…”

Section: Introductionmentioning

confidence: 99%

Identification of Adulterated Evening Primrose Oil Based on GC‐MS and FT‐IR Combined with Chemometrics

Pan

Yang

Chen

et al. 2022

Euro J Lipid Sci & Tech

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Evening primrose oil has a high market value due to its rich unsaturated fatty acids, so it is likely to be adulterated under the drive of economic interests. In this study, evening primrose oil's fatty acid and physicochemical properties are systematically determined, and the characteristic fatty acids are screened out. The feasibilities of GC‐MS and FT‐IR in identifying oil adulteration are also evaluated. Evening primrose oil's major fatty acids are linoleic acid (C18:2), γ‐linolenic acid (C18:3n6), palmitic acid (C16:0), oleic acid (C18:1), and stearic acid (C18:0). In addition, palmitoleic acid (C16:1), hexadecadienoic acid (C16:2), nonadecanoic acid (C19:0), eicosatrienoic acid (C20:3), erucic acid (C22:1), tricosanoic acid (C23:0) and lignoceric acid (C24:0) are rarely detected in other related studies on the evening primrose oil. This study uses fatty acids as indicators, hierarchical cluster analysis, and cosine similarity analysis to identify the evening primrose oil mixed with >5% peanut oil and >10% sunflower oil. Besides, the principal component analysis also distinguishes evening primrose oil blended with different proportions of peanut and sunflower oils. In summary, this study confirms that fatty acids can be characteristic indexes to identify adulterated evening primrose oil by GC‐MS and FT‐IR combined with chemometrics. Practical Applications: This study further clarifies the major and characteristic fatty acids of evening primrose oil. On this basis, the adulteration of evening primrose oil (taking peanut oil and sunflower oil as an example) is identified. Therefore, it is helpful to assess the quality and identify the authenticity of the evening primrose oil, which is vital for the stability of the evening primrose oil market and the interests of consumers. These methods combined with chemometrics can also be extended to ensure the certification of other oils and to classify oils into different classes. The improvements in vegetable oil quality will also benefit the vegetable oil industry and control bodies.

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Smartphone-based handheld Raman spectrometer and machine learning for essential oil quality evaluation

Abstract: We present a method, utilising a smartphone-based miniaturized Raman spectrometer and machine learning for the fast identification and discrimination of adulterated EOs. Firstly, the approach was evaluated for discrimination of...

Cited by 6 publications

References 33 publications

Partial Least Squares-Discriminant Analysis Classification for Patchouli Oil Adulteration Detection by Fourier Transform Infrared Spectroscopy in Combination with Chemometrics

Partial Least Squares-Discriminant Analysis Classification for Patchouli Oil Adulteration Detection by Fourier Transform Infrared Spectroscopy in Combination with Chemometrics

Rapid Iodine Value Estimation Using a Handheld Raman Spectrometer for On-Site, Reagent-Free Authentication of Edible Oils

Identification of Adulterated Evening Primrose Oil Based on GC‐MS and FT‐IR Combined with Chemometrics

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