Review of NIR spectroscopy methods for nondestructive quality analysis of oilseeds and edible oils

Xue, Li; Zhang, Liangxiao; Zhang, Yong; Wang, Du; Wang, Xuefang; Yu, Li; Wen, Zhang; Li, Peiwu

doi:10.1016/j.tifs.2020.05.002

Cited by 107 publications

(55 citation statements)

References 104 publications

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“…Some studies carried out with these techniques can be observed in Table 1. In general, it has been described that these techniques present several advantages, like low running cost, rapidity, they are non-destructive and no or minimum sample preparation is needed [12,14,15]. In the following paragraph, the basis of each technique and also examples of their use for the determination of EVOO adulteration will be explained.…”

Section: Spectroscopic and Spectrometric Techniquesmentioning

confidence: 99%

See 1 more Smart Citation

State-of-the-Art of Analytical Techniques to Determine Food Fraud in Olive Oils

Pereira

Otero

Fraga-Corral

et al. 2021

Foods

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The benefits of the food industry compared to other sectors are much lower, which is why producers are tempted to commit fraud. Although it is a bad practice committed with a wide variety of foods, it is worth noting the case of olive oil because it is a product of great value and with a high percentage of fraud. It is for all these reasons that the authenticity of olive oil has become a major problem for producers, consumers, and legislators. To avoid such fraud, it is necessary to develop analytical techniques to detect them. In this review, we performed a complete analysis about the available instrumentation used in olive fraud which comprised spectroscopic and spectrometric methodology and analyte separation techniques such as liquid chromatography and gas chromatography. Additionally, other methodology including protein-based biomolecular techniques and analytical approaches like metabolomic, hhyperspectral imaging and chemometrics are discussed.

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Section: Spectroscopic and Spectrometric Techniquesmentioning

confidence: 99%

“…However, the technique has some drawbacks. For example, NIR spectrum may contain interferences from noise and overlapping bands [14], and the low signal sensitivity make the detection of low compound concentrations difficult [48].…”

Section: Fourier Transform Near-infrared Spectroscopymentioning

confidence: 99%

State-of-the-Art of Analytical Techniques to Determine Food Fraud in Olive Oils

Pereira

Otero

Fraga-Corral

et al. 2021

Foods

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“…Moreover, some vegetable oils contain certain molecules with biological properties. Some of them include anti-carcinogenic effects, reduction of cholesterol absorption, and decrease in blood levels of low-density lipoprotein (LDL) [207,208]. For these reasons, several scientific groups have focused on development of NIR spectroscopy methods for evaluation of quality, as well as determination of various components present in edible oils [194,[209][210][211][212][213].…”

Section: Edible Oilsmentioning

confidence: 99%

QCM Sensor Arrays, Electroanalytical Techniques and NIR Spectroscopy Coupled to Multivariate Analysis for Quality Assessment of Food Products, Raw Materials, Ingredients and Foodborne Pathogen Detection: Challenges and Breakthroughs

Bwambok

Siraj

Macchi

et al. 2020

Sensors

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Quality checks, assessments, and the assurance of food products, raw materials, and food ingredients is critically important to ensure the safeguard of foods of high quality for safety and public health. Nevertheless, quality checks, assessments, and the assurance of food products along distribution and supply chains is impacted by various challenges. For instance, the development of portable, sensitive, low-cost, and robust instrumentation that is capable of real-time, accurate, and sensitive analysis, quality checks, assessments, and the assurance of food products in the field and/or in the production line in a food manufacturing industry is a major technological and analytical challenge. Other significant challenges include analytical method development, method validation strategies, and the non-availability of reference materials and/or standards for emerging food contaminants. The simplicity, portability, non-invasive, non-destructive properties, and low-cost of NIR spectrometers, make them appealing and desirable instruments of choice for rapid quality checks, assessments and assurances of food products, raw materials, and ingredients. This review article surveys literature and examines current challenges and breakthroughs in quality checks and the assessment of a variety of food products, raw materials, and ingredients. Specifically, recent technological innovations and notable advances in quartz crystal microbalances (QCM), electroanalytical techniques, and near infrared (NIR) spectroscopic instrument development in the quality assessment of selected food products, and the analysis of food raw materials and ingredients for foodborne pathogen detection between January 2019 and July 2020 are highlighted. In addition, chemometric approaches and multivariate analyses of spectral data for NIR instrumental calibration and sample analyses for quality assessments and assurances of selected food products and electrochemical methods for foodborne pathogen detection are discussed. Moreover, this review provides insight into the future trajectory of innovative technological developments in QCM, electroanalytical techniques, NIR spectroscopy, and multivariate analyses relating to general applications for the quality assessment of food products.

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“…Jiang et al, applied a near-infrared spectroscopy system to determine acid values during edible oil stored procedures [15]. However, the near-infrared spectroscopy was generally effective for qualitative analysis, while quantitative analysis based on near-infrared spectroscopy was not ideal in some studies of edible oils [16]. The wavelengths in near-infrared spectrum range mainly reflect the molecular vibration characteristics of objects.…”

Section: Introductionmentioning

confidence: 99%

Rapid Detection of Fatty Acids in Edible Oils Using Vis-NIR Reflectance Spectroscopy with Multivariate Methods

Pan

Wang

et al. 2021

Biosensors

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The composition and content of fatty acids are critical indicators to identify the quality of edible oils. This study was undertaken to establish a rapid determination method for quality detection of edible oils based on quantitative analysis of palmitic acid, stearic acid, arachidic acid, and behenic acid. Seven kinds of oils were measured to obtain Vis-NIR spectra. Multivariate methods combined with pretreatment methods were adopted to establish quantitative analysis models for the four fatty acids. The model of support vector machine (SVM) with standard normal variate (SNV) pretreatment showed the best predictive performance for the four fatty acids. For the palmitic acid, the determination coefficient of prediction (RP2) was 0.9504 and the root mean square error of prediction (RMSEP) was 0.8181. For the stearic acid, RP2 and RMSEP were 0.9636 and 0.2965. In the prediction of arachidic acid, RP2 and RMSEP were 0.9576 and 0.0577. In the prediction of behenic acid, the RP2 and RMSEP were 0.9521 and 0.1486. Furthermore, the effective wavelengths selected by successive projections algorithm (SPA) were useful for establishing simplified prediction models. The results demonstrate that Vis-NIR spectroscopy combined with multivariate methods can provide a rapid and accurate approach for fatty acids detection of edible oils.

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Review of NIR spectroscopy methods for nondestructive quality analysis of oilseeds and edible oils

Cited by 107 publications

References 104 publications

State-of-the-Art of Analytical Techniques to Determine Food Fraud in Olive Oils

State-of-the-Art of Analytical Techniques to Determine Food Fraud in Olive Oils

QCM Sensor Arrays, Electroanalytical Techniques and NIR Spectroscopy Coupled to Multivariate Analysis for Quality Assessment of Food Products, Raw Materials, Ingredients and Foodborne Pathogen Detection: Challenges and Breakthroughs

Rapid Detection of Fatty Acids in Edible Oils Using Vis-NIR Reflectance Spectroscopy with Multivariate Methods

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