An overview of recent advances and applications of FT-IR spectroscopy for quality, authenticity, and adulteration detection in edible oils

Mousa, Magdi A. A.; Wang, Yangyang; Antora, Salma Akter; Al‐Qurashi, Adel D.; Ibrahim, Omer H. M.; He, Hong Ju; Liu, Shu; Kamruzzaman, Mohammed

doi:10.1080/10408398.2021.1922872

Cited by 36 publications

(13 citation statements)

References 117 publications

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“…The diverse compositions of various types of Daqu have different re ections on FT-IR (Fourier transform infrared) spectroscopy, which could show functional group information and ngerprint characteristic [19,20]. Through comparative analysis by means of chemometrics, discriminative models could be established to recognize unknown samples, especially those untypical samples.…”

Section: Cluster Analysis Of Different Types Of Daqu Via Midinfrared/...mentioning

confidence: 99%

Differentiation of three different types of high temperature Daqu based on melanoidin content and mid-infrared spectrum analysis

Shanqimuge¹,

Wang²,

Tang³

et al. 2022

Preprint

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Yellow Daqu, black Daqu and white Daqu are three common types of high-temperature Daqu, which are formed due to the difference of Daqu Culture microenvironment. However, the judgment of these types still depends on sensory evaluation, and no objective evaluation depends on data. In this study, three types of Daqu were differentiated based on melanoidin content and mid-infrared spectrum analysis for the first time. Mid-infrared spectroscopy technology was utilized for the rapid identification of different types of Daqu based on various functional group information and fingerprint characteristics. Furthermore, near-infrared spectroscopy technology provides us an effective and efficient quantitative approach of melanoidins. In addition, the difference in microbial community structure and metabolites including volatiles and non-volatiles were further analyzed through multivariate statistical analysis. Microbial functional potential prediction using PICRUSt2 shows that bacteria plays an important role in white and yellow Daqu, while fungi were main contributors in black and yellow Daqu in major metabolic pathway. Bacillales, Lactobacillales, Pseudonocardiales and Eurotiales, and Wallemiales were also selected as biomarker microbes in order’s level.

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Section: Cluster Analysis Of Different Types Of Daqu Via Midinfrared/...mentioning

confidence: 99%

Differentiation of three different types of high temperature Daqu based on melanoidin content and mid-infrared spectrum analysis

Shanqimuge¹,

Wang²,

Tang³

et al. 2022

Preprint

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“…Traditional methods for detecting adulteration in Camellia oil based on visual or odor assessments are often subjective, leading to unreliable results [ 7 ]. Recently, primary methods for detecting edible oil adulteration have included chromatography [ 8 ], nuclear magnetic resonance [ 9 ], electronic nose [ 10 ], and spectroscopy [ 11 ]. Among them, chromatography, nuclear magnetic resonance, and the electronic nose method have certain drawbacks.…”

Section: Introductionmentioning

confidence: 99%

Rapid and Low-Cost Quantification of Adulteration Content in Camellia Oil Utilizing UV-Vis-NIR Spectroscopy Combined with Feature Selection Methods

Liu

Gong

et al. 2023

Molecules

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This study aims to explore the potential use of low-cost ultraviolet-visible-near infrared (UV-Vis-NIR) spectroscopy to quantify adulteration content of soybean, rapeseed, corn and peanut oils in Camellia oil. To attain this aim, test oil samples were firstly prepared with different adulterant ratios ranging from 1% to 90% at varying intervals, and their spectra were collected by an in-house built experimental platform. Next, the spectra were preprocessed using Savitzky–Golay (SG)–Continuous Wavelet Transform (CWT) and the feature wavelengths were extracted using four different algorithms. Finally, Support Vector Regression (SVR) and Random Forest (RF) models were developed to rapidly predict adulteration content. The results indicated that SG–CWT with decomposition scale of 25 and the Iterative Variable Subset Optimization (IVSO) algorithm can effectively improve the accuracy of the models. Furthermore, the SVR model performed best for predicting adulteration of camellia oil with soybean oil, while the RF models were optimal for camellia oil adulterated with rapeseed, corn, or peanut oil. Additionally, we verified the models’ robustness by examining the correlation between the absorbance and adulteration content at certain feature wavelengths screened by IVSO. This study demonstrates the feasibility of using low-cost UV-Vis-NIR spectroscopy for the authentication of Camellia oil.

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“…Italy is one of the most important countries in the world in terms of olive oil supply and demand: the Italian olive heritage contains over 500 varieties (Piscopo et al ., 2021) amongst which Coratina , Leccino , Nocellara, Sinopolese (typical of South Italy) (Sicari et al ., 2021) or Leccino‐Frantoio , Carboncella, Itrana ( Lazio ), Moraiolo ( Toscana ), Coratina ( Puglia ). Previous works are devoted to study the EVOO for nutritional health purpose, diseases relapses, investigating cultivars and adulteration processes (Camposeo et al ., 2021; Drira et al ., 2021; Tomé‐Rodríguez et al ., 2021; Taiti et al ., 2022) by destructive procedures such as extraction, deuteration and transesterification (Gurdeniz et al ., 2007; Hirri et al ., 2015; Drira et al ., 2021; Mousa et al ., 2021; Stilo et al ., 2021). In this context, spectroscopic techniques combined with computational analyses are the new trend scenario (Mohamed et al ., 2018; Jamwal et al ., 2021; Zaroual et al ., 2021).…”

Section: Introductionmentioning

confidence: 99%

FTIR coupled with machine learning to unveil spectroscopic benchmarks in the Italian EVOO

Scatigno

Festa

2022

Int J of Food Sci Tech

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Non-destructive analytical analyses coupled with classification and regression algorithms are promising techniques for monitoring quality, traceability and safety assessment in food industry. To prevent food fraud, Italian Extra Virgin Olive Oil (EVOO) is particularly held in check. Here, attenuated total Reflectance-Fourier Transform Infrared spectroscopy (ATR-FTIR) with Machine Learning is carried out to study an Italian EVOO data set coming from 6 regions to verify the geographical traceability, the cultivar and the repeatability of the agronomical practices, till the adulterated EVOO from soy and corn. The present work is carried out without reagents or esterification processes and considering the entire frequency range without any spectral windows selection, drastically reducing time and costs. Toscana, Lazio, Puglia and Calabria result regions well reproducible in terms of geo-traceability, unlike the Sicilia and Umbria. The model extracts spectral benchmarks in EVOO in the following vibrational modes at 3004, 2952, 2922, 2852, 1742 and 1160 cm -1 .

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An overview of recent advances and applications of FT-IR spectroscopy for quality, authenticity, and adulteration detection in edible oils

Cited by 36 publications

References 117 publications

Differentiation of three different types of high temperature Daqu based on melanoidin content and mid-infrared spectrum analysis

Differentiation of three different types of high temperature Daqu based on melanoidin content and mid-infrared spectrum analysis

Rapid and Low-Cost Quantification of Adulteration Content in Camellia Oil Utilizing UV-Vis-NIR Spectroscopy Combined with Feature Selection Methods

FTIR coupled with machine learning to unveil spectroscopic benchmarks in the Italian EVOO

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