Chemometric applications to assess quality and critical parameters of virgin and extra-virgin olive oil. A review

Investigating the Quality and Purity Profiles of Olive Oils from Diverse Regions in Selçuk, İzmir

The Selçuk district of Izmir is one of the most essential regions in terms of olive oil production. In this study, 60 olive oil samples were obtained from five different locations (ES: Eski Şirince Yolu, KK: Kınalı Köprü, AU: Abu Hayat Üst, AA: Abu Hayat Alt, and DB: Değirmen Boğazı) in the Selçuk region of Izmir during two (2019-2020 and 2020-2021) consecutive cropping seasons. Quality indices (free acidity, peroxide value, p-Anisidine value, TOTOX, and spectral absorption at 232 and 270 nm) and the fatty acid, phenolic, and sterol profiles of the samples were determined to analyze the changes in the composition of Selcuk olive oils according to their growing areas. When the quality criteria were analyzed, it was observed that KK had the lowest FFA (0.11% oleic acid, PV (6.66 meq O2/kg), p-ANV (11.95 mmol/kg), TOTOX (25.28), and K232 (1.99) values and K270 had the highest value. During the assessment of phenolic profiles, the ES group exhibited the highest concentration of the phenolic compound p-HPEA-EDA (oleocanthal), with a content of 93.58 mg/kg, equivalent to tyrosol. Upon analyzing the fatty acid and sterol composition, it was noted that AU displayed the highest concentration of oleic acid (71.98%) and β-sitosterol (87.65%). PCA analysis illustrated the distinct separation of the samples, revealing significant variations in both sterol and fatty acid methyl ester distributions among oils from different regions. Consequently, it was determined that VOOs originating from the Selçuk region exhibit distinct characteristics based on their geographical locations. Hence, this study holds great promise for the region to realize geographically labeled VOOs.

Discrimination/Classification of Edible Vegetable Oils from Raman Spatially Solved Fingerprints Obtained on Portable Instrumentation

Currently, the combination of fingerprinting methodology and environmentally friendly and economical analytical instrumentation is becoming increasingly relevant in the food sector. In this study, a highly versatile portable analyser based on Spatially Offset Raman Spectroscopy (SORS) obtained fingerprints of edible vegetable oils (sunflower and olive oils), and the capability of such fingerprints (obtained quickly, reliably and without any sample treatment) to discriminate/classify the analysed samples was evaluated. After data treatment, not only unsupervised pattern recognition techniques (as HCA and PCA), but also supervised pattern recognition techniques (such as SVM, kNN and SIMCA), showed that the main effect on discrimination/classification was associated with those regions of the Raman fingerprint related to free fatty acid content, especially oleic and linoleic acid. These facts allowed the discernment of the original raw material used in the oil's production. In all the models established, reliable qualimetric parameters were obtained.

Development and Application of an Automated Raman Sensor for Bioprocess Monitoring: From the Laboratory to an Algae Production Platform

Microalgae provide valuable bio-components with economic and environmental benefits. The monitoring of microalgal production is mostly performed using different sensors and analytical methods that, although very powerful, are limited to qualified users. This study proposes an automated Raman spectroscopy-based sensor for the online monitoring of microalgal production. For this purpose, an in situ system with a sampling station was made of a light-tight optical chamber connected to a Raman probe. Microalgal cultures were routed to this chamber by pipes connected to pumps and valves controlled and programmed by a computer. The developed approach was evaluated on Parachlorella kessleri under different culture conditions at a laboratory and an industrial algal platform. As a result, more than 4000 Raman spectra were generated and analysed by statistical methods. These spectra reflected the physiological state of the cells and demonstrate the ability of the developed sensor to monitor the physiology of microalgal cells and their intracellular molecules of interest in a complex production environment.

Resonant Raman Spectroscopy of Carotenoids in Aging of Extra Virgin Olive Oil

This work uses resonant Raman spectroscopy (RRS) to investigate changes in carotenoid concentration in extra virgin olive oil (EVOO) as it oxidizes under accelerated thermal aging. Carotenoids are nutritious antioxidants and biomarkers that represent the general quality of olive oil. HPLC is the conventional method used to determine the concentration of carotenoids, but it is expensive, time-consuming, and requires sample handling. A simple optical technique for estimating carotenoid concentration in extra virgin olive oil is, therefore, desirable. This work shows that the normally weak carotenoid signal is strongly amplified when using the resonant Raman technique. The aging and oxidation of EVOO decreases the Raman intensities associated with carotenoids and increases the fluorescence and Raman intensities associated with fatty acids. From these quantities, two Raman intensity ratios are presented as indicators of the effects of aging.

Toward the Non-Targeted Detection of Adulterated Virgin Olive Oil with Edible Oils via FTIR Spectroscopy & Chemometrics: Research Methodology Trends, Gaps and Future Perspectives

Fourier-Transform mid-infrared (FTIR) spectroscopy offers a strong candidate screening tool for rapid, non-destructive and early detection of unauthorized virgin olive oil blends with other edible oils. Potential applications to the official anti-fraud control are supported by dozens of research articles with a "proof-of-concept" study approach through different chemometric workflows for comprehensive spectral analysis. It may also assist non-targeted authenticity testing, an emerging goal for modern food fraud inspection systems. Hence, FTIR-based methods need to be standardized and validated to be accepted by the olive industry and official regulators. Thus far, several literature reviews evaluated the competence of FTIR standalone or compared with other vibrational techniques only in view of the chemometric methodology, regardless of the inherent characteristics of the product spectra or the application scope. Regarding authenticity testing, every step of the methodology workflow, and not only the post-acquisition steps, need thorough validation. In this context, the present review investigates the progress in the research methodology on FTIR-based detection of virgin olive oil adulteration over a period of more than 25 years with the aim to capture the trends, identify gaps or misuses in the existing literature and highlight intriguing topics for future studies. An extensive search in Scopus, Web of Science and Google Scholar, combined with bibliometric analysis, helped to extract qualitative and quantitative information from publication sources. Our findings verified that intercomparison of literature results is often impossible; sampling design, FTIR spectral acquisition and performance evaluation are critical methodological issues that need more specific guidance and criteria for application to product authenticity testing.

How Chemometrics Can Fight Milk Adulteration

Adulteration and fraud are amongst the wrong practices followed nowadays due to the attitude of some people to gain more money or their tendency to mislead consumers. Obviously, the industry follows stringent controls and methodologies in order to protect consumers as well as the origin of the food products, and investment in these technologies is highly critical. In this context, chemometric techniques proved to be very efficient in detecting and even quantifying the number of substances used as adulterants. The extraction of relevant information from different kinds of data is a crucial feature to achieve this aim. However, these techniques are not always used properly. In fact, training is important along with investment in these technologies in order to cope effectively and not only reduce fraud but also advertise the geographical origin of the various food and drink products. The aim of this paper is to present an overview of the different chemometric techniques (from clustering to classification and regression applied to several analytical data) along with spectroscopy, chromatography, electrochemical sensors, and other on-site detection devices in the battle against milk adulteration. Moreover, the steps which should be followed to develop a chemometric model to face adulteration issues are carefully presented with the required critical discussion.

Typical Characterization of Commercial Camellia Oil Products Using Different Processing Techniques: Triacylglycerol Profile, Bioactive Compounds, Oxidative Stability, Antioxidant Activity and Volatile Compounds

The processing technique is one of the key factors affecting the quality of camellia oil. In this study, camellia oils were obtained using four different processing techniques (cold-pressed, roast-pressed, fresh-pressed, and refined), and their triacylglycerols (TAGs) profile, bioactive compound (tocopherols, sterols, squalene, and polyphenols) level, oxidative stability, and volatile compounds were analyzed and compared. To further identify characteristic components in four camellia oil products, the TAG profile was analyzed using UPLC-QTOF-MSE. Five characteristic markers were identified, including OOO (m/z 902.8151), POL (m/z 874.7850), SOO (m/z 904.8296), PPL (m/z 848.7693), PPS (m/z 852.7987). Regarding the bioactive compound level and antioxidant capacity, the fresh-pressed technique provided higher α-tocopherols (143.15 mg/kg), β-sitosterol (93.20 mg/kg), squalene (102.08 mg/kg), and polyphenols (35.38 mg/kg) and showed stronger overall oxidation stability and antioxidant capacity. Moreover, a total of 65 volatile compounds were detected and identified in four camellia oil products, namely esters (23), aldehydes (19), acids (8), hydrocarbons (3), ketones (3), and others (9), among which pressed oil was dominated by aldehydes, acid, and esters, while refined oil had few aroma components. This study provided a comprehensive comparative perspective for revealing the significant influence of the processing technique on the camellia oil quality and its significance for producing camellia oil of high quality and with high nutritional value.

Determination of some edible oils adulteration with paraffin oil using infrared spectroscopy

The spectroscopy of molecular vibrations using mid-infrared or near-infrared techniques was used more and more to characterize different compounds, including edible oil, in order to monitor any changes and to detect fraudulent modifications. This article presents a new method for quantitative adulteration of extra virgin olive oil (EVOO) or corn germ oil (CGO) with a mineral oil, such as paraffin oil (PO). A Fourier transform infrared (FT-IR) spectrometric method, using ATR spectra, was developed for the rapid, direct measurement of edible oils adulteration. The results indicate the efficiency of the proposed method for the detection of paraffin oil in adulteration of EVOO and CGO with RSD (< 3.0%).

Graphical abstract:

Nuclear magnetic resonance spectroscopy in extra virgin olive oil authentication

Extra virgin olive oil (EVOO) is a high-quality product that has become one of the stars in the food fraud context in recent years. EVOO can encounter different types of fraud, from adulteration with cheaper oils to mislabeling, and for this reason, the assessment of its authenticity and traceability can be challenging. There are several officially recognized analytical methods for its authentication, but they are not able to unambiguously trace the geographical and botanical origin of EVOOs. The application of nuclear magnetic resonance (NMR) spectroscopy to EVOO is reviewed here as a reliable and rapid tool to verify different aspects of its adulteration, such as undeclared blends with cheaper oils and cultivar and geographical origin mislabeling. This technique makes it possible to use both targeted and untargeted approaches and to determine the olive oil metabolomic profile and the quantification of its constituents.

Quantitative analysis of blended corn-olive oil based on Raman spectroscopy and one-dimensional convolutional neural network

Blended vegetable oil is a vital product in the vegetable oil market, and quantifying high-value vegetable oil is of great significance to protect the rights and interests of consumers. In this study, we established a one-dimensional convolutional neural network (1D CNN) quantitative identification model based on Raman spectra to identify the amount of olive oil in a corn-olive oil blend. The results show that the 1D CNN model based on 315 extended average Raman spectra can quantitatively identify the content of olive oil, with R²p and RMSEP values of 0.9908 and 0.7183 respectively. Compared with partial least squares regression (PLSR) and support vector regression (SVR), although the index is not optimal, it provides a new analytical method for the quantitative identification of vegetable oil.

Rapid Identification of Adulteration in Extra Virgin Olive Oil via Dynamic Headspace Sampling and High-Pressure Photoionization Time-of-Flight Mass Spectrometry

High-pressure photoionization time-of-flight mass spectrometry (HPPI-TOFMS) combined with dynamic headspace sampling was developed for rapid identification of adulteration in extra virgin olive oil (EVOO). The volatile organic compound (VOC) fingerprints of EVOO, refined rapeseed oil (r-RO), peanut oil (PO), corn oil (CO), fragrant rapeseed oil (f-RO), and sunflower oil (SO) were obtained in just 1.5 min, which enabled satisfactory classification of different edible oils. 1,4-Bis(methylene)cyclohexane and dimethyl disulfide were unique VOCs in r-RO and f-RO, respectively, while 2,5-dimethylpyrazine and 2-methylpyrazine were distinctive VOCs in PO. Percentages as low as 3% r-RO, 1% PO, and 1% f-RO in r-RO-EVOO, PO-EVOO, and f-RO-EVOO mixtures, respectively, were successfully identified based on the characteristic VOCs. Linear regression equations of these VOCs were established and utilized for predicting the adulteration proportions. The good agreements between the actual adulteration proportions and the predicted ones demonstrated that HPPI-TOFMS was reliable for the quantification of EVOO adulteration.

Vibrational Spectroscopy Combined with Chemometrics in Authentication of Functional Foods

Many foods have both edible and medical importance and are appreciated as functional foods, preventing diseases. However, due to unscrupulous vendors and imperfect market supervision mechanisms, curative foods are prone to adulteration or some other events that harm the interests of consumers. However, traditional analytical methods are unsuitable and expensive for a broad and complex application. Therefore, people urgently need a fast, efficient, and accurate detection method to protect self-interests. Recently, the study of target samples by vibration spectrum shows strong qualitative and quantitative ability. The model established by platform technology combined with the stoichiometric analysis method can obtain better parameters, which it has good robustness and can detect functional food efficiently, quickly and nondestructive. The review compared and prospect five different vibrational spectroscopic techniques (near-infrared, Fourier transform infrared, Raman, hyperspectral imaging spectroscopy and Terahertz spectroscopy). In order to better solve some of the actual situations faced by certification, we explore and through relevant research and investigation to appropriately highlight the applicability and importance of technology combined with chemometrics in functional food authentication. There are four categories of authentication discussed: functional food authenticated in source, processing method, fraud and ingredient ratio. This paper provides an innovative process for the authentication of functional food, which has a meaningful reference value for future review or scientific research of relevant departments.

Rapid Detection of Avocado Oil Adulteration Using Low-Field Nuclear Magnetic Resonance

Avocado oil (AO) has been found to be adulterated by low-price oil in the market, calling for an efficient method to detect the authenticity of AO. In this work, a rapid and nondestructive method was developed to detect adulterated AO based on low-field nuclear magnetic resonance (LF-NMR, 43 MHz) detection and chemometrics analysis. PCA analysis revealed that the relaxation components area (S23) and relative contribution (P22 and P23) were crucial LF-NMR parameters to distinguish AO from AO adulterated by soybean oil (SO), corn oil (CO) or rapeseed oil (RO). A Soft Independent Modelling of Class Analogy (SIMCA) model was established to identify the types of adulterated oils with a high calibration (0.98) and validation accuracy (0.93). Compared with partial least squares regression (PLSR) models, the support vector regression (SVR) model showed better prediction performance to calculate the adulteration levels when AO was adulterated by SO, CO and RO, with high square correlation coefficient of calibration (R2C > 0.98) and low root mean square error of calibration (RMSEC < 0.04) as well as root mean square error of prediction (RMSEP < 0.09) values. Compared with SO- and CO-adulterated AO, RO-adulterated AO was more difficult to detect due to the greatest similarity in fatty acids’ composition being between AO and RO, which is characterized by the high level of monounsaturated fatty acids and viscosity. This study could provide an effective method for detecting the authenticity of AO.

The Impact of Fermentation on Bee Pollen Polyphenolic Compounds Composition

Bee-collected pollen is one of the most valuable natural products. However, the pollen cell walls limit the digestibility and release of nutrients to the human body. Solid-state lactic acid fermentation can be used to ease the release of bioactive compounds from the pollen cell. The aim of this research was to determine the impact of a solid-state lactic acid fermentation process on biologically active compound composition and antioxidant activity of bee-collected pollen from various European regions (Italy, Netherlands, Lithuania, Poland, Sweden, Denmark, Malta, Slovakia, and Spain). Spontaneous fermentation and fermentation using an L. rhamnosus culture were performed. The total content of phenolic compounds, total content of flavonoids, and radical (DPPH) scavenging activity were measured by spectrophotometric tests, while UPLC was employed for quantification of phenolic compounds. The determined fermentation positive effects included an increase of total phenolic content by 1.4-2.3 times, total flavonoid content by 1.1-1.6 times, and radical scavenging activity by 1.4-2.3 times. Naringenin (21.09-135.03 µg/g), quercetin (6.62-78.86 µg/g), luteolin (29.41-88.90 µg/g), and rutin (21.40-89.93 µg/g) were the most abundant flavonoids in all samples; however, their variation level was both geographical in origin and fermentation-type dependent. Fermentation increased the content of phenolic acids with high antioxidant potentials such as ellagic, ferulic and caffeic, while reduction of chlorogenic acid was determined.

Phytochemical Profile of Eight Categories of Functional Edible Oils: A Metabolomic Approach Based on Chromatography Coupled with Mass Spectrometry

Functional vegetable oils are highly considered not only for their nutritional value, but also for their health benefits. The profile of phytochemicals responsible for their quality is useful also for the identification of possible mislabeling or adulteration. The comparative composition of eight categories (sunflower, pumpkin, hempseed, linseed, soybean, walnut, sea buckthorn and olive) of commercial vs. authentic oils was determined. Fatty acids, volatiles, carotenoids, tocopherols, and phenolic components were analyzed by gas- and liquid chromatography-based techniques coupled with diode array, mass spectrometry, or fluorescence detection. Classification models, commonly used in metabolomics, e.g., principal component analysis, partial least squares discriminant analysis, hierarchical clusters and heatmaps have been applied to discriminate each category and individual samples. Carotenoids, tocopherols, and phenolics contributed mostly, qualitatively, and quantitatively to the discrimination between the eight categories of oils, as well as between the authentic and the commercial ones. This metabolomic approach can be easily implemented and the heatmaps can be considered as “identity” cards of each oil category and the quality of commercial oils, comparative to the authentic ones of the same botanical and geographical origin.

Quantitative Detection of Extra Virgin Olive Oil Adulteration, as Opposed to Peanut and Soybean Oil, Employing LED-Induced Fluorescence Spectroscopy

As it is high in value, extra virgin olive oil (EVOO) is frequently blended with inferior vegetable oils. This study presents an optical method for determining the adulteration level of EVOO with soybean oil as well as peanut oil using LED-induced fluorescence spectroscopy. Eight LEDs with central wavelengths from ultra-violet (UV) to blue are tested to induce the fluorescence spectra of EVOO, peanut oil, and soybean oil, and the UV LED of 372 nm is selected for further detection. Samples are prepared by mixing olive oil with different volume fractions of peanut or soybean oil, and their fluorescence spectra are collected. Different pre-processing and regression methods are utilized to build the prediction model, and good linearity is obtained between the predicted and actual adulteration concentration. This result, accompanied by the non-destruction and no pre-treatment characteristics, proves that it is feasible to use LED-induced fluorescence spectroscopy as a way to investigate the EVOO adulteration level, and paves the way for building a hand-hold device that can be applied to real market conditions in the future.

Geographical Origin Assessment of Extra Virgin Olive Oil via NMR and MS Combined with Chemometrics as Analytical Approaches

Geographical origin assessment of extra virgin olive oil (EVOO) is recognised worldwide as raising consumers’ awareness of product authenticity and the need to protect top-quality products. The need for geographical origin assessment is also related to mandatory legislation and/or the obligations of true labelling in some countries. Nevertheless, official methods for such specific authentication of EVOOs are still missing. Among the analytical techniques useful for certification of geographical origin, nuclear magnetic resonance (NMR) and mass spectroscopy (MS), combined with chemometrics, have been widely used. This review considers published works describing the use of these analytical methods, supported by statistical protocols such as multivariate analysis (MVA), for EVOO origin assessment. The research has shown that some specific countries, generally corresponding to the main worldwide producers, are more interested than others in origin assessment and certification. Some specific producers such as Italian EVOO producers may have been focused on this area because of consumers’ interest and/or intrinsic economical value, as testified also by the national concern on the topic. Both NMR- and MS-based approaches represent a mature field where a general validation method for EVOOs geographic origin assessment could be established as a reference recognised procedure.

Composition Profiling and Authenticity Assessment of Camellia Oil Using High Field and Low Field 1H NMR

Camellia oil (CA), mainly produced in southern China, has always been called Oriental olive oil (OL) due to its similar physicochemical properties to OL. The high nutritional value and high selling price of CA make mixing it with other low-quality oils prevalent, in order to make huge profits. In this paper, the transverse relaxation time (T2) distribution of different brands of CA and OL, and the variation in transverse relaxation parameters when adulterated with corn oil (CO), were assessed via low field nuclear magnetic resonance (LF-NMR) imagery. The nutritional compositions of CA and OL and their quality indices were obtained via high field NMR (HF-NMR) spectroscopy. The results show that the fatty acid evaluation indices values, including for squalene, oleic acid, linolenic acid and iodine, were higher in CA than in OL, indicating the nutritional value of CA. The adulterated CA with a content of CO more than 20% can be correctly identified by principal component analysis or partial least squares discriminant analysis, and the blended oils could be successfully classified by orthogonal partial least squares discriminant analysis, with an accuracy of 100% when the adulteration ratio was above 30%. These results indicate the practicability of LF-NMR in the rapid screening of food authenticity.

Instrumental Techniques to Classify Olive Oils according to Their Quality

This review includes an update of the publications on quality classification of olive oils into extra, virgin or lampante olive oil categories. Nowadays, the official method to carry out this classification is time-consuming and, sometimes, it is not systematic and/or objective. It is based on conventional physicochemical analysis and on a sensorial tasting of olive oils carried out by a panel of experts. The aim of this review was to explore and give value to the alternative techniques reported in the bibliography to complement the current official methods established for that classification of olive oils. Specifically considered were non-separation and separation analytical techniques which could contribute to correctly classify olive oils according to their physicochemical and/or sensorial characteristics. An in-depth description has been written on the methods used to differentiate these three types of olive oils and the main advantages and disadvantages of the proposed procedures. The techniques here reviewed could be a real and fast option to complement or even substitute some of the analysis included in the official method. Finally, general trends and detected difficulties found to address this issue have been discussed throughout the article.

Authentication of the Botanical and Geographical Origin and Detection of Adulteration of Olive Oil Using Gas Chromatography, Infrared and Raman Spectroscopy Techniques: A Review

Olive oil is among the most popular supplements of the Mediterranean diet due to its high nutritional value. However, at the same time, because of economical purposes, it is also one of the products most subjected to adulteration. As a result, authenticity is an important issue of concern among authorities. Many analytical techniques, able to detect adulteration of olive oil, to identify its geographical and botanical origin and consequently guarantee its quality and authenticity, have been developed. This review paper discusses the use of infrared and Raman spectroscopy as candidate tools to examine the authenticity of olive oils. It also considers the volatile fraction as a marker to distinguish between different varieties and adulterated olive oils, using SPME combined with gas chromatography technique.

Rapid detection of Chinese-specific peony seed oil by using confocal Raman spectroscopy and chemometrics

Peony seed oil (PSO) is a new woody nut oil which is unique to China. Its unsaturated fatty acids are over 90% and are rich in α - linolenic acid. Although the PSO industry is in its infancy, it is bound to become a top vegetable oil food material because of its own advantages. The potential high commercial profit of its adulteration with cheap vegetable oil will be an important factor hindering the healthy development of PSO industry. It is of great significance to study the adulteration of PSO for preventing large-scale adulteration. In this study, the qualitative and quantitative analysis of PSO was realised based on Raman spectroscopy combined with chemometrics analysis, and the fatty acid composition of PSO was analysed according to Raman characteristic peaks. The technology can be applied to routine analysis and quality control of PSO.

Fraud Detection of Herbal Medicines Based on Modern Analytical Technologies Combine with Chemometrics Approach: A Review

Fraud in herbal medicines (HMs), commonplace throughout human history, is significantly related to medicinal effects with sometimes lethal consequences. Major HMs fraud events seem to occur with a certain regularity, such as substitution by counterfeits, adulteration by addition of inferior production-own materials, adulteration by chemical compounds, and adulteration by addition of foreign matter. The assessment of HMs fraud is in urgent demand to guarantee consumer protection against the four fraudulent activities. In this review, three analysis platforms (targeted, non-targeted, and the combination of non-targeted and targeted analysis) were introduced and summarized. Furthermore, the integration of analysis technology and chemometrics method (e.g., class-modeling, discrimination, and regression method) have also been discussed. Each integration shows different applicability depending on their advantages, drawbacks, and some factors, such as the explicit objective analysis or the nature of four types of HMs fraud. In an attempt to better solve four typical HMs fraud, appropriate analytical strategies are advised and illustrated with several typical studies. The article provides a general workflow of analysis methods that have been used for detection of HMs fraud. All analysis technologies and chemometrics methods applied can conduce to excellent reference value for further exploration of analysis methods in HMs fraud.

Integrating metabolomic data with machine learning approach for discovery of Q-markers from Jinqi Jiangtang preparation against type 2 diabetes

Background

Jinqi Jiangtang (JQJT) has been widely used in clinical practice to prevent and treat type 2 diabetes. However, little research has been done to identify and classify its quality markers (Q-markers) associated with anti-diabetes bioactivity. In this study, a strategy combining mass spectrometry-based untargeted metabolomics with backpropagation artificial neural network (BP-ANN)-based machine learning approach was proposed to screen Q-markers from JQJT preparation.

Methods

This strategy mainly involved chemical profiling of herbal medicines, statistic processing of metabolomic datasets, detection of different anti-diabetes activities and establishment of BP-ANN model. The chemical features of seventy-eight batches of JQJT extracts were first profiled by using the untargeted UPLC-LTQ-Orbitrap metabolomic approach. The chemical features obtained which were associated with different anti-diabetes activities based on three modes of action were normalized, ranked, and then pre-selected by using ReliefF feature selection. BP-ANN model was then established and optimized to screen Q-markers based on mean impact value (MIV).

Results

Optimized BP-ANN architecture was established with high accuracy of R > 0.9983 and relative low error of MSE < 0.0014, which showed better performance than that of partial least square (PLS) model (R² < 0.5). Meanwhile, the BP-ANN model was subsequently applied to further screen potential bioactive components from the pre-selected chemical features by calculating their MIVs. With this machine learning model, 10 potential Q-markers with bioactivity were discovered from JQJT. The tested anti-diabetes bioactivities of 78 batches of JQJT could be accurately predicted.

Conclusions

This proposed artificial intelligence approach is desirable for quick and easy identification of Q-markers with bioactivity from JQJT preparation.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13020-021-00438-x.

Detection of camellia oil adulteration using chemometrics based on fatty acids GC fingerprints and phytosterols GC-MS fingerprints

The fatty acid, squalene, and phytosterols, coupled to chemometrics were utilized to detect the adulteration of camellia oil (CAO) with palm superolein (PAO), refined olive oil (ROO), high oleic- sunflower oil (HO-SUO), sunflower oil (SUO), corn oil (COO), rice bran oil (RBO), rice oil (RIO), peanut oil (PEO), sesame oil (SEO), soybean oil (SOO), and rapeseed oil (RAO). CAO was characterized with higher triterpene alcohols, thus differentiated from other vegetable oils in principle component analysis (PCA). Using partial least squares-discriminant analysis (PLS-DA), CAO adulterated with PAO, ROO, HO-SUO, SUO, COO, RBO, RIO, PEO, SEO, SOO, RAO (5%-100%, w/w), could be classified, especially higher than 92.31% of the total discrimination accuracy, at an adulterated ratio above 30%. With less than 22 potential key markers selected by the variable importance in projection (VIP), the optimized PLS models were confirmed to be accurate for the adulterated level prediction in CAO.

Review of some adulteration detection techniques of edible oils

Edible oils have economical and nutritional benefits. These oils offer nutrients that are essential to human health because they are the primary source of mono- and poly-unsaturated fats. Moreover, edible oils are used in home cooking and industrial food manufacturing. Therefore, edible oils have a considerable demand worldwide. However, some edible oils, such as olive oil, are more expensive than any other vegetable oils. Thus, oils such as olive oil are mixed with cheap edible oils as a result of the high price difference. Accordingly, adulteration in edible oils to obtain additional profit for the producer becomes a major issue of high concern for consumers. Moreover, adulteration in edible oils can cause several problems that affect consumer health. Therefore, the need for a sensitive, accurate and suitable method to detect the adulteration is highly considered. We provide a brief review of the different methods and techniques used to detect adulteration in edible oils, especially olive oil, with the aim of promoting consumer awareness of the authenticity of edible oils. © 2020 Society of Chemical Industry.

Advances in infrared spectroscopy combined with artificial neural network for the authentication and traceability of food

The authentication and traceability of food attract more attention due to the increasing consumer awareness regarding nutrition and health, being a new hotspot of food science. Infrared spectroscopy (IRS) combined with shallow neural network has been widely proven to be an effective food analysis technology. As an advanced deep learning technology, deep neural network has also been explored to analyze and solve food-related IRS problems in recent years. The present review begins with brief introductions to IRS and artificial neural network (ANN), including shallow neural network and deep neural network. More notably, it emphasizes the comprehensive overview of the advances of the technology combined IRS with ANN for the authentication and traceability of food, based on relevant literature from 2014 to early 2020. In detail, the types of IRS and ANN, modeling processes, experimental results, and model comparisons in related studies are described to set forth the usage and performance of the combined technology for food analysis. The combined technology shows excellent ability to authenticate food quality and safety, involving chemical components, freshness, microorganisms, damages, toxic substances, and adulteration. As well, it shows excellent performance in the traceability of food variety and origin. The advantages, current limitations, and future trends of the combined technology are further discussed to provide a thoughtful viewpoint on the challenges and expectations of online applications for the authentication and traceability of food.

Attenuated Total Reflectance-Fourier Transform Infrared Spectroscopy (ATR-FTIR) Coupled with Chemometrics, to Control the Botanical Authenticity and Quality of Cold-Pressed Functional Oils Commercialized in Romania

Attenuated total reflectance-Fourier transform infrared ppectroscopy (ATR-FTIR) proved to be a reliable, rapid, and easy-to-use technique to evaluate vegetable oils quality and authenticity. The spectral range of the middle infrared region (MIR) of FTIR spectra, from 4000 to 600 cm−1, has been commonly used to fingerprint specific functional groups of lipids and their modified forms induced by oxidation of thermal treatment. The applicability of FTIR-MIR spectroscopy in assessing oil fingerprinting and quality parameters is crucially dependent on the chemometric methods, including calibrations with authentic samples. We report here the evaluation of seven types of cold-pressed functional oils (sunflower, pumpkin, hempseed, soybean, walnut, linseed, sea buckthorn) produced in Romania, provided directly from small enterprises (as genuine, process-controlled authentic samples) comparative to commercialized samples. Concomitantly, olive oils of similar claimed quality were investigated. The ATR-FTIR-MIR data were complemented by UV–Vis spectral fingerprints and multivariate analysis using Unscrambler X.10.4 and Metaboanalyst 4.0 software (e.g., PCA, PLSDA, cluster analysis, heatmap, Random forest analysis) and ANOVA post-hoc analysis using Fischer’s least significant difference. The integration of spectral and chemometric analysis proved to offer valuable criteria for their botanical group recognition, individual authenticity, and quality, easy to be applied for large cohorts of commercialized oils.

Phenolic Compounds Obtained from Olea europaea By-Products and their Use to Improve the Quality and Shelf Life of Meat and Meat Products-A Review

Consumers are interested in consuming clean label foods. Replacing synthetic additives with natural alternatives (especially sources rich in polyphenols) is a valid solution to produce and also preserve foods, especially meat and meat products. Olea europaea leaves and olive pomace and wastewater contain polyphenols that can be explored in this context. In this review, we summarize the main aspects related to the phenolic composition, extraction conditions, antimicrobial potential, and antioxidant activity (in vitro and in vivo) of Olea europaea leaves, olive pomace and wastewater as well as their applications in the production of meat and meat products. This review found evidence that extracts and isolated polyphenols from the Olea europaea tree and olive processing by-products can be explored as natural antioxidant and antimicrobial additives to improve the preservation of meat and meat products. The polyphenols found in these residues (especially oleuropein, hydroxytyrosol and tyrosol) increased the redox state in the main meat-producing animals and, consequently, the oxidative stability of fresh meat obtained from these animals. Moreover, the extracts and isolated polyphenols also improved the shelf life of fresh meat and meat products (as additive and as active component in film) by delaying the growth of microorganisms and the progression of oxidative reactions during storage. The accumulated evidence supports further investigation as a natural additive to improve the preservation of reformulated muscle products and in the production of edible and sustainable films and coatings for fresh meat and meat products.

Olive Oil Quality and Authenticity Assessment Aspects Employing FIA-MRMS and LC-Orbitrap MS Metabolomic Approaches

Edible vegetable oils comprise integral components of humans' daily diet during the lifetime. Therefore, they constitute a central part of dietary-exposome, which among other factors regulates human health. In particular, the regular consumption of olive oil (OO) has been largely accepted as a healthy dietary pattern. Responsible for its recognition as a superior edible oil is its exceptional aroma and flavor. Its unique composition is characterized by high levels of monounsaturated fatty acids and the presence of minor constituents with important biological properties, such as the so-called OO polyphenols. Being a high added value product, OO suffers from extensive fraud and adulteration phenomena. However, its great chemical complexity, variability, and the plethora of parameters affecting OO composition hamper significantly the selection of the absolute criteria defining quality and authenticity, and a reliable and robust methodology is still unavailable. In the current study, Flow Injection Analysis-Magnetic Resonance Mass Spectrometry (FIA-MRMS) was investigated under a metabolic profiling concept for the analysis of Greek Extra Virgin Olive Oils (EVOO). More than 200 monovarietal (Koroneiki) EVOO samples were collected from the main Greek OO producing regions and investigated. Both intact oil and the corresponding polyphenols were analyzed in fast analysis time of 2 and 8 min, respectively. In parallel, an LC-Orbitrap MS platform was used to verify the efficiency of the method as well as a tool to increase the identification confidence of the proposed markers. Based on the results, with FIA-MRMS, comparable and improved projection and prediction models were generated in comparison to those of the more established LC-MS methodology. With FIA-MRMS more statistically significant compounds and chemical classes were identified as quality and authenticity markers, associated with specific parameters, i.e. geographical region, cultivation practice, and production procedure. Furthermore, it was possible to monitor both lipophilic and hydrophilic compounds with a single analysis. To our knowledge, this approach is among the few studies in which two FT-MS platforms combining LC and FIA methods were integrated to provide solutions to quality control aspects of OO. Moreover, both lipophilic and hydrophilic components are analyzed together, providing a holistic quality control workflow for OO.