Determination of the acid values of edible oils via FTIR spectroscopy based on the OH stretching band

Jiang, Xiuming; Shen, Li; Xiang, Guoqiang; Li, Qiuhong; Fan, Li; He, Lijun; Gu, Kai

doi:10.1016/j.foodchem.2016.06.035

Cited by 88 publications

(34 citation statements)

References 34 publications

(34 reference statements)

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“…Findings from other studies (Mariutti & Bragagnolo, 2017;Zhang, Lin, Leng, Huang, & Zhou, 2013) also established that salt, as well as thermal processing influence the oxidative deterioration of polyunsaturated fatty acids. To determine the degree of lipid oxidation, researchers often measure oxidation attributes, including 2-thiobarbituric acid reactive substances (TBARS) (Kerth & Rowe, 2016;Wenjiao, Yongkui, Yunchuan, Junxiu, & Yuwen, 2014), acid value (Jiang et al, 2016;Song, Kim, Kim, & Lee, 2017;Zhang et al, 2015), and peroxide value (PV) (Cebi, Yilmaz, Sagdic, Yuce, & Yelboga, 2017;Yang, Li, Yu, Chen, & Wang, 2014). The analytical procedures for determining these oxidation markers are not only dependent on the use of harmful chemicals such as trichloroacetic acid and chloroform but are also time-consuming, prone to experimental bias coupled with low reproducibility.…”

Section: Introductionmentioning

confidence: 99%

Combination of spectra and image information of hyperspectral imaging data for fast prediction of lipid oxidation attributes in pork meat

Aheto

Huang

Tian

et al. 2019

J Food Process Engineering

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This study highlighted the potential of integrating texture and spectra information of hyperspectral imaging to predict the evolution of 2‐thiobarbituric acid reactive substances (TBARS) and peroxide value (PV) as lipid oxidation attributes in pork muscles under diverse processing conditions. Partial least square regression (PLSR) models were established on data fusion information (image and optimal spectra) and compared with other three PLSR models built on spectra (full range and optimal), and image information. Regarding TBARS measurement, model based on optimal spectra was superior with determination coefficient of prediction (Rp2) of .896, root mean square error of prediction (RMSEP) of 1.034, and residual predictive deviation (RPD) of 2.311 as compared to the data fusion model (Rp2 = .865, RMSEP = 0.994, RPD = 2.280). For PV, model on data fusion yielded the best (Rp2 = .899, RMSEP = 0.966, RPD = 2.314) followed by that on optimal spectra (Rp2 = .883, RMSEP = 1.073, RPD = 2.281). Then prediction maps were drawn to visualize the distribution of TBARS and PV in the samples using models built on optimal spectra. The maps revealed intrinsic differences in samples which are not readily perceptible by visual observation. Practical Applications Models based on spectra and spatial information of hyperspectral imaging have been widely used to predict and visualize food quality attributes. However, these are not enough to elucidate the impact of processing on the meat samples as it may not be able to directly measure attributes related to external properties such as texture properties. The present study investigated the feasibility of integrating texture features obtained from gray‐level co‐occurrence matrix and spectra data to predict quality changes associated with pork meat subjected to diverse processing conditions. The results obtained proved that the methods presented can be useful for designing future experiments in a bid to establishing robust models for food quality assessment.

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Section: Introductionmentioning

confidence: 99%

Combination of spectra and image information of hyperspectral imaging data for fast prediction of lipid oxidation attributes in pork meat

Aheto

Huang

Tian

et al. 2019

J Food Process Engineering

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“…The latent variables required were considerably reduced. The biPLS 34 model had the best performance with RMSECV ¼ 2.68, RMSEP ¼ 2.73, and RPD ¼ 5.01; when, the spectral set was divided into 34 intervals and the interval numbers 1,2,3,5,6,9,10,13,18,19,26, and 30 were used ( Table 4). The selected intervals included the region from 3500 to 3400/cm (intervals 5 and 6), which belongs to the ROO group.…”

Section: Bipls Modelmentioning

confidence: 99%

“…As a non-destructive analytical method, Fourier transform infrared spectroscopy (FTIR) has been applied in the analysis of several foods including edible fats and oils due to its high energy throughput, scan speed, multiplexing capability, and wavelength accuracy and stability. Specifically, FTIR has been applied in the determination of acid value [5], peroxide value [6], fatty acid composition [7], and adulteration identification [8][9][10]. However, few studies have used FTIR to determine IV.…”

Section: Introductionmentioning

confidence: 99%

Iodine value determination of edible oils using ATR‐FTIR and chemometric methods

Meng

Nie

et al. 2017

Euro J Lipid Sci & Tech

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Iodine value (IV) was determined using Fourier transform mid infrared spectroscopy with attenuated total reflection and chemometric methods. A set of 245 oil samples were used for calibration purposes, and 76 samples were used for validation purposes. Models developed using full spectra partial least square (PLS), interval PLS (iPLS), forward interval PLS (fiPLS), and backward interval PLS (biPLS) algorithms were compared in terms of performance. Root‐mean‐square error of leave‐one‐out cross‐validation (RMSECV) of 2.68 and root‐mean‐square errors of prediction (RMSEP) of 2.73 were obtained using biPLS algorithm with spectra divided into 34 intervals and combinations of eight intervals (4000–3700, 3600–3400, 3200–3000, 2800–2700, 2300–2100, 1700–1600, 1500–1400, and 1100–1000/cm). This model had a correlation coefficient of 0.9885. In addition, sub‐models based on one oil type had higher performances (RMSECV = 0.24–0.76, RMSEP = 0.29–1.25) than the model based on all oil samples, which suggests that the IV model is oil‐type dependent. However, PLS could compensate for this error provided the calibration set covered enough oil types. The proposed method allowed a rapid determination of IV. Practical applications: IV determination is important for edible oils and oleochemicals. However, traditional wet chemical methods require toxic solvents and reagents. FTIR coupled with multivariate analysis can be used to measure IV. Most of the studies on IV determination using FTIR have focused on specific single oil types without interval selection. A general IV model that incorporates several oil types is expected to improve prediction performances. Therefore, the present method represents a rapid and accurate tool for measuring IV of edible fats and oils. An ATR‐FTIR method combined with interval optimization is described to determine the iodine value in edible oil. The RMSECV for the prediction of IV from isolated wavelength intervals are compared with full‐spectrum PLS model in GA. The result showed that interval optimization is effective in term of improved prediction performance of IV.

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“…Due to its property as fingerprint analytical techniques [13], FTIR spectroscopy in combination with chemometrics have been developed for analysis of acid value [14,15], the peroxide value of FO [16], iodine value [17], and anisidive value [18]. However, these oil parameters were determined individually, and there are limited reports regarding the determination of acid value (AV), peroxide value (PO) and iodine value (IV) simultaneously.…”

Section: Introductionmentioning

confidence: 99%

Monitoring Oxidative Levels of Frying Oils Using Ftir Spectroscopy and Multivariate Calibration

2018

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Objective: To develop a rapid reliable technique based on Fourier transform infrared-attenuated total reflectance (FTIR-ATR) spectroscopy in combination with multivariate calibrations for prediction of frying oil quality, namely acid value (AV), iodine value (IV) and peroxide value (PV).Methods: FTIR spectra were directly obtained and subjected to optimization and spectral treatments including a selection of wavenumbers region and spectral derivatization. The condition selected was based on its capability to provide the highest coefficient of determination (R 2 ) and the lowest root mean square error of calibration (RMSEC) and root mean square error of prediction (RMSEP) for the relationship between actual values of AV, IV, and PV as determined using standard titrimetric methods and predicted values as determined by FTIR spectroscopy aided with multivariate calibrations.Results: Using optimized condition, FTIR spectroscopy combined with multivariate calibrations could be successfully used for prediction of AV, and PV. Acid value (AV) could be determined using the first derivative spectra at wavenumbers of 1524-658 cm -1 . The R 2 of 0.973 (in calibration model) and 0.932 (in prediction model) with low RMSEC and RMSEP values was obtained. Iodine value (IV) was best predicted using principle component regression (PCR) with normal FTIR spectra at the combined wavenumbers region of 3076-2783 and 1811-656 cm -1 . PCR using normal spectra at combined wavenumbers region of 3076-2783and 1811-656 cm -1 was also selected for prediction of PV.Conclusion: FTIR spectroscopy in combination with multivariate calibration has been successfully used for prediction of acid value, iodine value and peroxide value in frying oils. The developed method could be an alternative technique for analysis of these values to perform quality assurance of frying oils. I In nt te er rn na at ti io on na al l J Jo ou ur rn na al l o of f A Ap pp pl li ie ed d P Ph ha ar rm ma ac ce eu ut ti ic cs s

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Determination of the acid values of edible oils via FTIR spectroscopy based on the OH stretching band

Cited by 88 publications

References 34 publications

Combination of spectra and image information of hyperspectral imaging data for fast prediction of lipid oxidation attributes in pork meat

Combination of spectra and image information of hyperspectral imaging data for fast prediction of lipid oxidation attributes in pork meat

Iodine value determination of edible oils using ATR‐FTIR and chemometric methods

Monitoring Oxidative Levels of Frying Oils Using Ftir Spectroscopy and Multivariate Calibration

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